Atomic-number (Z)-correlated atomic sizes for deciphering electron microscopic molecular images

With the advent of atomic resolution transmission electron microscopy (AR-TEM) achieving sub-Ångstrom image resolution and submillisecond time resolution, an era of cinematic molecular science where chemists can visually study the time evolution of molecular motions and reactions at atomistic precision has arrived. However, the appearance of experimental TEM images often differs greatly from that of conventional molecular models, and the images are difficult to decipher unless we know in advance the structure of the specimen molecules. The difference arises from the fundamental design of the molecular models that represent atomic connectivity and/or the electronic properties of molecules rather than the nuclear charge of atoms and electrostatic potentials that are felt by the e-beam in TEM imaging. We found a good correlation between the atomic number (Z) and the atomic size seen in TEM images when we consider shot noise in digital images. We propose Z-correlated (ZC) atomic radii for modeling AR-TEM images of single molecules and ultrathin crystals with which we can develop a good estimate of the molecular structure from the TEM image much more easily than with conventional molecular models. Two parameter sets were developed for TEM images recorded under high-noise (ZC_HN) and low-noise (ZC_LN) conditions. The molecular models will stimulate the imaginations of chemists planning to use AR-TEM for their research.

The world of atoms and molecules invisible to human eyes has been illuminated by the development of three-dimensional models in which atoms represented by spheres are connected to each other with appropriate spatial disposition. Here, the choice of the radii of the spheres, in addition to coloring, is the most crucial visual identifier of atoms and atomic ions, and a variety of useful constructions of radii, either empirical or quantum mechanical (1, 2), have been proposed in accord with chemical intuition. Whereas wire and ball-and-stick models are primitive, showing only bond lengths, bond angles, and torsional angles, chemists have implemented further information into molecular models. Representing the van der Waals isosurface, a Corey–Pauling–Koltun (CPK) model was developed to illustrate the molecular surface for analysis of intermolecular interactions (3). Shannon’s ionic model (4) is useful for inorganic chemistry. The atomic radii chosen in these models reflect the electronic properties of atoms and molecules.With the advent of atomic-resolution transmission electron microscopy (AR-TEM) achieving sub-Ångstrom image resolution and submillisecond time resolution (5–8), an era of “cinematic molecular science” has arrived, where chemists can visually study the time evolution of molecular motions and reactions at atomistic precision (9–11). However, experimental TEM images often differ greatly from what chemists expect using their favorite molecular models (Fig. 1), and this discrepancy contributed to reducing the popularity of AR-TEM in molecular science. The difficulty is particularly important when we deal with unknown structures that are mobile and lack structural periodicity. For example, structure assignment of the AR-TEM images of a rotating zinc cluster (Fig. 1A) that forms in the synthesis of a crystal of a metal–organic framework (MOF; MOF-5) (12) was impossible without reducing the number of initial guess structures down to several hundred from a total of 4,096 isomers and their rotamers as to the 2-iodo-1,4-phenylene bridges (Fig. 1 D and E). Here, we found the CPK atomic radii (Fig. 1B) more confusing than revealing, and we became convinced of a need for new atomic radii that better represent the experimental images (compare Fig. 1 B and C). In doing so, we found that the atom size seen in TEM images shows a strong correlation to the atomic number (Z), particularly if we consider the shot noise in digital images. In a noise-free image, we find little correlation, as theory predicts (13). Our idea may be symbolically described as comparing the sizes of mountains by measuring the width of the lowest portion of each mountain that is visible above a sea of clouds. We report here a set of Z-correlated (ZC) atomic radii for deciphering AR-TEM images, with which we can develop a good estimate of the molecular structure from the TEM image much more easily than with conventional molecular models. Two parameter sets are described: one for TEM images under high-noise conditions (ZC_HN, Fig. 2C) and another under low-noise conditions (ZC_LN, Fig. 2D). The former is useful for single-molecule imaging and the latter for an ultrathin film such as UiO-66 MOF (Fig. 1F) (14, 15). The ZC radii reflect the relative sizes of various elements and help chemists develop an initial estimate of the molecular structures. Note, however, that we do not intend the ZC model to reproduce the TEM images exactly. No chemists expect the CPK model to represent the van der Waals properties of the molecule exactly, and molecular models are there to stimulate chemists’ imaginations.Open in a separate windowFig. 1.Experimental AR-TEM image and atomic-number-correlated molecular model (ZC model). (A) Time evolution of TEM image of a MOF-5 precursor, a cubic cluster made of Zn²⁺ and 2-iodoterephthalic acid captured on a carbon nanotube. The molecular images are blurred by rotation and vibration. Time is shown in seconds. Taken from ref. 12 (Copyright 2019, Nature Publishing Group). (B) A CPK model corresponding to the image at 10.0 s. (C) A ZC_HN model also for the image at 10.0 s. (D) Chemical structure of the cubic cluster. (E) Schematic illustration of isomers of the cubic MOF-5 precursor. Four structures out of 4096 possible isomers are shown. The structure seen in Fig. 1A is highlighted by a red square. (F) TEM image of an ultrathin crystal of UiO-66 (adapted from ref. 15). The benzene ring in a benzene dicarboxylate linker is indicated by an arrow (Copyright 2018, The American Association for the Advancement of Science). (G) A ZC_LN model corresponding to (F). (Scale bars: 1 nm.)Open in a separate windowFig. 2.Periodic table of atomic sizes used in molecular models. (A) CPK model. (B) Shannon’s ionic parameters. (C) ZC_HN model. (D) ZC_LN model. (Scale bar: 2 Å.) (E) List of ZC_HN radii for single-molecule imaging and ZC_LN radii for ultrathin crystals (in Å).In Fig. 2, we compare the atomic radii used in the CPK model, Shannon’s ionic model, and the ZC_LN and ZC_HN models. CPK radii are smaller for transition metals and larger for main group elements, and Shannon’s radii decrease with the increasing atomic number within the same period. These atomic radii are unsuitable for deciphering AR-TEM images, where the behavior of the e-beam in the electrostatic potential created by atomic nuclei plays a decisive role (16, 17). In the ZC model proposed here, the atomic radius shows element dependence, changing systematically as Z increases. The ZC_HN atomic radii (Fig. 2C) are smaller in signal size than the ZC_LN radii (Fig. 2D), show large Z-dependence, and are suitable for fast molecular imaging where the level of shot noise is large (Fig. 1 A–C) (18). The ZC_LN radii are suitable for images of ultrathin crystalline films where the level of shot noise is comparatively low. The atomic sizes for the graphical construction of the ZC models are listed in Fig. 2E. We anticipate that the molecular model will stimulate the imaginations of chemists planning to use AR-TEM for their research.     

Insect nicotinic receptor interactions in vivo with neonicotinoid,organophosphorus, and methylcarbamate insecticides and a synergist

The nicotinic acetylcholine (ACh) receptor (nAChR) is the principal insecticide target. Nearly half of the insecticides by number and world market value are neonicotinoids acting as nAChR agonists or organophosphorus (OP) and methylcarbamate (MC) acetylcholinesterase (AChE) inhibitors. There was no previous evidence for in vivo interactions of the nAChR agonists and AChE inhibitors. The nitromethyleneimidazole (NMI) analog of imidacloprid, a highly potent neonicotinoid, was used here as a radioligand, uniquely allowing for direct measurements of house fly (Musca domestica) head nAChR in vivo interactions with various nicotinic agents. Nine neonicotinoids inhibited house fly brain nAChR [³H]NMI binding in vivo, corresponding to their in vitro potency and the poisoning signs or toxicity they produced in intrathoracically treated house flies. Interestingly, nine topically applied OP or MC insecticides or analogs also gave similar results relative to in vivo nAChR binding inhibition and toxicity, but now also correlating with in vivo brain AChE inhibition, indicating that ACh is the ultimate OP- or MC-induced nAChR active agent. These findings on [³H]NMI binding in house fly brain membranes validate the nAChR in vivo target for the neonicotinoids, OPs and MCs. As an exception, the remarkably potent OP neonicotinoid synergist, O-propyl O-(2-propynyl) phenylphosphonate, inhibited nAChR in vivo without the corresponding AChE inhibition, possibly via a reactive ketene metabolite reacting with a critical nucleophile in the cytochrome P450 active site and the nAChR NMI binding site.The nicotinic nervous system has two principal sites of insecticide action, the nicotinic receptor (nAChR) activated by acetylcholine (ACh) and neonicotinoid agonists (1–6), and acetylcholinesterase (AChE) inhibited by organophosphorus (OP) and methylcarbamate (MC) compounds to generate and maintain localized toxic ACh levels (Fig. 1) (7). The nAChR and AChE targets have been identified in insects by multiple techniques but not by direct assays of the ACh binding site in the brain of poisoned insects. Here we use the outstanding insecticidal potency of the nitromethyleneimidazole (NMI) analog of imidacloprid (IMI) (8) as a radioligand (9), designated [³H]NMI, to directly measure the house fly (Musca domestica) nAChR not only in vitro but also in vivo, allowing us to validate by a previously undescribed method the neonicotinoid direct and OP/MC indirect nAChR targets (Fig. 2). This approach also helped solve the intriguing mechanism by which an O-(2-propynyl) phosphorus compound strongly synergizes neonicotinoid insecticidal activity (10) by dual inhibition of cytochrome P450 (CYP) (11–13) and the nAChR agonist site (described herein). Insecticide disruption at the insect nAChR can now be readily studied in vitro and in vivo with a single radioligand allowing better understanding of the action of several principal insecticide chemotypes (Fig. 3).Open in a separate windowFig. 1.The insect nicotinic receptor is the direct or indirect target for neonicotinoids, organophosphorus compounds and methylcarbamates, which make up about 45% of the insecticides by number and world market value (2, 7).Open in a separate windowFig. 2.In this study, Musca nicotinic receptor in vivo interactions with major insecticide chemotypes are revealed by a [³H]NMI radioligand reporter assay. *Position of tritium label.Open in a separate windowFig. 3.Two neonicotinoid nicotinic agonists and two anticholinesterase insecticides.     

Stereospecific cis- and trans-ring fusions arising from common intermediates

Highly concise and stereospecific routes to cis and trans fusion, carrying various functionality at one of the bridgehead carbons, have been accomplished.Our group has been studying the synthetic utility of the Diels–Alder (DA) reaction of the parent cyclobutenone, 2 (1). Recently reported results demonstrate that 2 is a highly reactive, endo-selective dienophile (Fig. 1, Eq. 1) (2). We have also developed a series of intramolecular Diels–Alder (IMDA) reactions, wherein the cyclobutenone component is tethered to various conjugated dienes (compare 4); cycloaddition of these substrates delivers adducts of the type 5, which can be readily converted to trans-fused systems bearing iso-DA patterns (Fig. 1, Eq. 2, 5→6) (3). Additionally, we have described the synthesis and DA cycloaddition of an even more powerful dienophile, 2-bromocyclobutenone (Fig. 1, Eq. 3, 7) (4). The direct adducts of this [4+2] reaction (compare 8) are readily converted to norcarane carboxylic acids (9) through exposure to hydroxide base. The research described herein was initially focused on efforts to add carbon-based nucleophiles to DA cycloadducts of the type 8. It might well have been expected that such reactions would give rise to products such as 10, wherein a ketone is appended to the junction of the norcarane system (Fig. 1, Eq. 4).Open in a separate windowFig. 1.Expanding the scope of the Diels–Alder reaction.     

Groundwater sapping as the cause of irreversible desertification of Hunshandake Sandy Lands,Inner Mongolia,northern China

In the middle-to-late Holocene, Earth’s monsoonal regions experienced catastrophic precipitation decreases that produced green to desert state shifts. Resulting hydrologic regime change negatively impacted water availability and Neolithic cultures. Whereas mid-Holocene drying is commonly attributed to slow insolation reduction and subsequent nonlinear vegetation–atmosphere feedbacks that produce threshold conditions, evidence of trigger events initiating state switching has remained elusive. Here we document a threshold event ca. 4,200 years ago in the Hunshandake Sandy Lands of Inner Mongolia, northern China, associated with groundwater capture by the Xilamulun River. This process initiated a sudden and irreversible region-wide hydrologic event that exacerbated the desertification of the Hunshandake, resulting in post-Humid Period mass migration of northern China’s Neolithic cultures. The Hunshandake remains arid and is unlikely, even with massive rehabilitation efforts, to revert back to green conditions.Earth’s climate is subject to abrupt, severe, and widespread change, with nonlinear vegetation–atmosphere feedbacks that produced extensive and catastrophic ecosystem shifts and subsequent cultural disruption and dispersion during the Holocene (1–7). In the early and middle Holocene, northern China’s eastern deserts, including much of the currently sparsely vegetated and semistabilized Hunshandake (Figs. 1 and ​and2),2), were covered by forests (8), reflecting significantly wetter climate associated with intensification of monsoon precipitation by up to 50% (6).Open in a separate windowFig. 1.Geographical location of the Hunshandake Sandy Lands (A) and its area (encircled by red line in B). The black rectangle in B marks the location of the enlarged maps C and D on the Right, and the green rectangle shows the location of Fig. 2. Map C shows the localities of water samples, and map D shows the localities of sections with stratigraphy presented in Fig. 3. The sand–paleosol section P (Fig. 3) is on the southern margin, and the site Bayanchagan marks the coring site of ref. 8. Rivers with headwaters in the Hunshandake likely formed by groundwater sapping are marked in blue. Drainages to the southwest and west are currently undergoing groundwater sapping, with substantial spring-driven flow found at the current river base level.Open in a separate windowFig. 2.(Left) Holocene lakes and channels in the Hunshandake and lake extent at selected epochs. Upper, middle, and lower lakes are indicated by points A, B, and C, respectively. Xilamulun River (point D) drains to the east. Groundwater-sapping headcuts at the upper reaches of incised canyons (point E) suggest a mid-Holocene interval of easterly surface flow, followed by groundwater drainage beginning at the ca. 4.2 ka event. Northern and central channels at point E are currently abandoned, and groundwater sapping has migrated to the southerly of the three channels shown. (Right) Cross-sections of the predrainage shift, northerly drainage into Dali Lake (Localities shown on the Left), showing the increase in widths of channels downstream (Vertical exaggeration ∼30:1).Monsoonal weakening, in response to middle-to-late Holocene insolation decrease, reduced precipitation, leading to a green/sandy shift and desertification across Inner Mongolia between ca. 5,000 and 3,000 y (years) ago (6). However, variations in the timing of this transition (9, 10) suggest local/regional thresholds or possibly environmental tipping by stochastic fluctuations. The impacts of this wet-to-dry shift in the Hunshandake, expressed as variations in surface and subsurface hydrology coincident with the termination of the formation of thick and spatially extensive paleosols, and the impacts of a ca. 4.2 ka (1 ka = 1,000 years) mid-Holocene desiccation of the Hunshandake on the development of early Chinese culture remain poorly understood and controversial (6, 11). Here we report for the first time to our knowledge on variability in a large early-to-middle Holocene freshwater lake system in China’s Hunshandake Sandy Lands and associated vegetation change, which demonstrates a model of abrupt green/desert switching. We document a possible hydrologic trigger event for this switching and discuss associated vegetation and hydrologic disruptions that significantly impacted human activities in the region.     

Taxodione and arenarone inhibit farnesyl diphosphate synthase by binding to the isopentenyl diphosphate site

We used in silico methods to screen a library of 1,013 compounds for possible binding to the allosteric site in farnesyl diphosphate synthase (FPPS). Two of the 50 predicted hits had activity against either human FPPS (HsFPPS) or Trypanosoma brucei FPPS (TbFPPS), the most active being the quinone methide celastrol (IC₅₀ versus TbFPPS ∼20 µM). Two rounds of similarity searching and activity testing then resulted in three leads that were active against HsFPPS with IC₅₀ values in the range of ∼1–3 µM (as compared with ∼0.5 µM for the bisphosphonate inhibitor, zoledronate). The three leads were the quinone methides taxodone and taxodione and the quinone arenarone, compounds with known antibacterial and/or antitumor activity. We then obtained X-ray crystal structures of HsFPPS with taxodione+zoledronate, arenarone+zoledronate, and taxodione alone. In the zoledronate-containing structures, taxodione and arenarone bound solely to the homoallylic (isopentenyl diphosphate, IPP) site, not to the allosteric site, whereas zoledronate bound via Mg²⁺ to the same site as seen in other bisphosphonate-containing structures. In the taxodione-alone structure, one taxodione bound to the same site as seen in the taxodione+zoledronate structure, but the second located to a more surface-exposed site. In differential scanning calorimetry experiments, taxodione and arenarone broadened the native-to-unfolded thermal transition (T_m), quite different to the large increases in ΔT_m seen with biphosphonate inhibitors. The results identify new classes of FPPS inhibitors, diterpenoids and sesquiterpenoids, that bind to the IPP site and may be of interest as anticancer and antiinfective drug leads.Farnesyl diphosphate synthase (FPPS) catalyzes the condensation of isopentenyl diphosphate (IPP; compound 1 in Fig. 1) with dimethylallyl diphosphate (DMAPP; compound 2 in Fig. 1) to form the C₁₀ isoprenoid geranyl diphosphate (GPP; compound 3 in Fig. 1), which then condenses with a second IPP to form the C₁₅ isoprenoid, farnesyl diphosphate (FPP; compound 4 in Fig. 1). FPP then is used in a wide range of reactions including the formation of geranylgeranyl diphosphate (GGPP) (1), squalene (involved in cholesterol and ergosterol biosynthesis), dehydrosqualene (used in formation of the Staphylococcus aureus virulence factor staphyloxanthin) (2), undecaprenyl diphosphate (used in bacterial cell wall biosynthesis), and quinone and in heme a/o biosynthesis. FPP and GGPP also are used in protein (e.g., Ras, Rho, Rac) prenylation, and FPPS is an important target for the bisphosphonate class of drugs (used to treat bone resorption diseases) such as zoledronate (compound 5 in Fig. 1) (3). Bisphosphonates targeting FPPS have activity as antiparasitics (4), act as immunomodulators (activating γδ T cells containing the Vγ2Vδ2 T-cell receptor) (5), and switch macrophages from an M2 (tumor-promoting) to an M1 (tumor-killing) phenotype (6). They also kill tumor cells (7) and inhibit angiogenesis (8). However, the bisphosphonates in clinical use (zoledronate, alendronate, risedronate, ibandronate, etidronate, and clodronate) are very hydrophilic and bind avidly to bone mineral (9). Therefore, there is interest in developing less hydrophilic species (10) that might have better activity against tumors in soft tissues and better antibacterial (11) and antiparasitic activity.Open in a separate windowFig. 1.Chemical structures of FPPS substrates, products, and inhibitors.The structure of FPPS (from chickens) was first reported by Tarshis et al. (12) and revealed a highly α-helical fold. The structures of bacterial and Homo sapiens FPPS (HsFPPS) are very similar; HsFPPS structure (13, 14) is shown in Fig. 2A. There are two substrate-binding sites, called here “S1” and “S2.” S1 is the allylic (DMAPP, GPP) binding site to which bisphosphonates such as zoledronate bind via a [Mg²⁺]₃ cluster (15) (Fig. 2B). S2 is the homoallylic site to which IPP binds, Fig. 2B. Recently, Jahnke et al. (10) and Salcius et al. (16) discovered a third ligand-binding site called the “allosteric site” (hereafter the “A site”). A representative zoledronate+A-site inhibitor structure [Protein Data Bank (PDB) ID code 3N46] (Nov_980; compound 6 in Fig. 1) showing zoledronate in S1 and Nov_980 (compound 6) in the A site is shown in a stereo close-up view in Fig. 2B, superimposed on a zoledronate+IPP structure (PDB ID code 2F8Z) in S2. Whether the allosteric site serves a biological function (e.g., in feedback regulation) has not been reported. Nevertheless, highly potent inhibitors (IC₅₀ ∼80 nM) have been developed (10), and the best of these newly developed inhibitors are far more hydrophobic than are typical bisphosphonates (∼2.4–3.3 for cLogP vs. ∼−3.3 for zoledronate) and are expected to have better direct antitumor effects in soft tissues (10).Open in a separate windowFig. 2.Structures of human FPPS. (A) Structure of HsFPPS showing zoledronate (compound 5) and IPP (compound 1) bound to the S1 (allylic) and S2 (homoallylic) ligand-binding sites (PDB ID code 2F8Z). (B) Superposition of the IPP-zoledronate structure (PDB ID code 2F8Z) on the zoledronate-Nov_980 A-site inhibitor structure (PDB ID code 3N46). Zoledronate binds to the allylic site S1, IPP binds to the homoallylic site S2, and the allosteric site inhibitor binds to the A site. Active-site “DDXXD” residues are indicated, as are Mg²⁺ molecules (green and yellow spheres, respectively). The views are in stereo.In our group we also have developed more lipophilic compounds (e.g., compound 7 in Fig. 1) (17, 18) as antiparasitic (19) and anticancer drug leads (18) and, using computational methods, have discovered other novel nonbisphosphonate FPPS inhibitors (e.g., compound 8 in Fig. 1) that have micromolar activity against FPPS (20). In this study, we extended our computational work and tried to discover other FPPS inhibitors that target the A site. Such compounds would be of interest because they might potentiate the effects of zoledronate and other bisphosphonates, as reported for other FPPS inhibitors (21), and have better tissue distribution properties in general.     

A 2,300-year-old architectural and astronomical complex in the Chincha Valley,Peru

Recent archaeological research on the south coast of Peru discovered a Late Paracas (ca. 400–100 BCE) mound and geoglyph complex in the middle Chincha Valley. This complex consists of linear geoglyphs, circular rock features, ceremonial mounds, and settlements spread over a 40-km² area. A striking feature of this culturally modified landscape is that the geoglyph lines converge on mounds and habitation sites to form discrete clusters. Likewise, these clusters contain a number of paired line segments and at least two U-shaped structures that marked the setting sun of the June solstice in antiquity. Excavations in three mounds confirm that they were built in Late Paracas times. The Chincha complex therefore predates the better-known Nasca lines to the south by several centuries and provides insight into the development and use of geoglyphs and platform mounds in Paracas society. The data presented here indicate that Paracas peoples engineered a carefully structured, ritualized landscape to demarcate areas and times for key ritual and social activities.The Chincha Valley, located 200 km south of Lima, was one of the largest and most productive regions of southern coastal Peru (Fig. 1). Previous research identified a rich prehispanic history in the valley, beginning at least in the early first millennium BCE and continuing through the Inca period in the 16th century CE (1–3). The earliest settled villages were part of the Paracas culture, a widespread political and social entity that began around 800 BCE and continued up to around 100 BCE. Previous field surveys identified at least 30 major Paracas period sites in the valley (1, 3, 4), making Chincha one of the main centers of development for this early Andean civilization (5). As such, it is an ideal area to test models of social evolution in general and to define the strategies that early peoples used to construct complex social organizations within the opportunities and constraints provided by their environments.Open in a separate windowFig. 1.Map showing location of the Chincha Valley, southern coastal Peru.Previous research demonstrated a dense Paracas settlement in the lower valley that focused on large platform mound complexes (Fig. 2) (4, 6). Three seasons of systematic, intensive survey and excavations by our team confirm the existence of a rich and complex Paracas occupation in the midvalley area as well, including both mound clusters and associated geoglyph features. In short, our data indicate that (i): the Chincha geoglyphs predate the better-known Nasca drainage ones by at least three centuries; (ii) Paracas period peoples created a complex landscape by constructing linear geoglyphs that converge on key settlements; and (iii) solstice marking was one component underlying the logic of geoglyph and platform mound construction and use in the Chincha Valley during the Paracas period.Open in a separate windowFig. 2.Distribution of archaeological sites linked to Paracas period settlement in Chincha, Peru. Redrawn from Canziani (4).     

Emerging single-phase state in small manganite nanodisks

In complex oxides systems such as manganites, electronic phase separation (EPS), a consequence of strong electronic correlations, dictates the exotic electrical and magnetic properties of these materials. A fundamental yet unresolved issue is how EPS responds to spatial confinement; will EPS just scale with size of an object, or will the one of the phases be pinned? Understanding this behavior is critical for future oxides electronics and spintronics because scaling down of the system is unavoidable for these applications. In this work, we use La_0.325Pr_0.3Ca_0.375MnO₃ (LPCMO) single crystalline disks to study the effect of spatial confinement on EPS. The EPS state featuring coexistence of ferromagnetic metallic and charge order insulating phases appears to be the low-temperature ground state in bulk, thin films, and large disks, a previously unidentified ground state (i.e., a single ferromagnetic phase state emerges in smaller disks). The critical size is between 500 nm and 800 nm, which is similar to the characteristic length scale of EPS in the LPCMO system. The ability to create a pure ferromagnetic phase in manganite nanodisks is highly desirable for spintronic applications.Owing to strong coupling between spin, charge, orbital, and lattice (1, 2), different electronic phases often coexist spatially in strongly correlated materials known as electronic phase separation (EPS) (3, 4). For colossal magnetoresistance (CMR) manganites, EPS has been observed to have strong influence on the global magnetic and transport properties (5, 6). Regarding the physical origin of EPS, it has been shown theoretically that quenched disorder can lead to inhomogeneous states in manganites (1, 3, 7). Once long-range effects such as coulombic forces (8), cooperative oxygen octahedral distortions (9), or strain effects (10) are included, calculations show infinitesimal disorder (8, 11) or even no explicit disorder (10) may lead to EPS. Within a phenomenological Ginzburg–Landau theory, it has been shown that EPS is intrinsic in complex systems as a thermodynamic equilibrium state (12).Although the details of the origin of the EPS remain as a matter of dispute, its very existence as a new form of electronic state has been well accepted. The length scale of the EPS has been observed to vary widely from nanometers to micrometers depending on many parameters that can affect the competition between different electronic phases (13–20). It is thus of great interest to examine whether the EPS state still exists as the system is scaled down, especially when the spatial dimension of the system is smaller than the length scale of the EPS domains.In this work, we use La_0.325Pr_0.3Ca_0.375MnO₃ (LPCMO) as a prototype system to show a spatial confinement-induced transition from the EPS state to a single ferromagnetic phase state. The LPCMO system is chosen because of its well-known large length scale of EPS domains (approximately a micrometer) (21), which allows us to conveniently fabricate LPCMO epitaxial thin films into disks with diameters that are smaller than the EPS domain size. In LPCMO bulk (21) and thin films (6, 22), the EPS state was observed to be the low-temperature ground state. Using magnetic force microscope, we observe that the EPS state remains to be the ground state in disks with the size of 800 nm in diameter or larger but vanishes in the 500-nm-diameter disks whose size is distinctly smaller than the characteristic length scale of the EPS domains. In the 500-nm disks, only the ferromagnetic phase can be observed at all temperatures below Curie temperature T_c, indicating that the system is in a single-phase state rather than a EPS state. Our results further indicate that the large length scale EPS in the LPCMO system does not cost extra Coulomb energy, which otherwise should lead to a scaling down of EPS with decreasing size of the LPCMO disks (23, 24).LPCMO films with 60-nm thickness were epitaxially grown on SrTiO₃(001) substrates by pulsed-laser deposition. The substrates were kept at 780 °C in oxygen atmosphere of 5 × 10⁻³ millibars during growth. Unit cell by unit cell growth was achieved as indicated by oscillations of intensity of reflection high-energy electron diffraction (RHEED). The films were postannealed to 950 °C for 3 h in flowing oxygen to reduce oxygen vacancy and make sure that the films have the same magnetic properties as the bulk. The LPCMO disks with diameters from 500 nm to 20 μm were fabricated from the epitaxial thin films by electron beam lithography with a negative tone resist (for details, see the sample fabrication method and Fig. S1 in the Supporting Information). Magnetic properties of the LPCMO disk arrays were carried out using superconducting quantum interference device (SQUID) and magnetic force microscope (MFM) measurements.Open in a separate windowFig. S1.(A and B) Schematics of LPCMO disks samples for magnetic property measurement (A) and MFM mapping (B). (C) Optical microscopic image of LPCMO disk array with specific diameter. (D) SEM image of LPCMO disk sample for MFM imaging.A distinct signature of the EPS state in the LPCMO system is the thermal hysteresis for temperature-dependent magnetic and transport properties. Fig. 1 A–D shows temperature dependent magnetic properties of LPCMO disks with different diameters. To enhance the measuring signal for SQUID, we fabricate disk arrays for each selected diameter (the optical microscopic image shown in Fig. 1B, Inset for the 1-μm disk array). Fig. 1 A–D shows temperature-dependent magnetization measured under 1,000 Oe in-plane field for 7-μm, 1-μm, 800-nm, and 500-nm disk arrays, respectively. Thermal hysteresis can be observed for disk arrays with size down to 800 nm, reflecting the fact that ferromagnetic metallic (FMM) and charge order insulating (COI) phases coexist during the first-order phase transition (7, 25). For the 500-nm disk array, however, no thermal hysteresis can be observed. This observation implies that the EPS state may no longer exist in the system (7, 26).Open in a separate windowFig. 1.Temperature dependence of magnetization (black lines for cooling and red lines for warming) under 1,000 Oe (A–D) and initial magnetization (red lines) and hysteresis loop (black lines) (E–H) at 5 K of arrays of LPCMO disks with sizes of 7 μm (A and E), 1 μm (B and F), 800 nm (C and G), and 500 nm (D and H) in diameter and an area of 3 mm × 3 mm. (B, Inset) The optical microscopic image of d = 1 μm array. (C and D, Insets) Zoomed-in M vs. T loop around the thermal hysteresis region.The lack of EPS state in the 500-nm disk array is supported by the field-dependent magnetization measurements. Fig. 1 E–H shows in-plane initial magnetization curves and magnetic hysteresis loops (M-H loops) for the disk arrays measured at 5 K after zero-field cooling. For 800-nm or larger disk arrays, there is a clear difference between the initial magnetization curves and the corresponding M-H loops due to the coexistence of FMM and COI phases. When the magnetic field is applied from the initial state, the magnetization of the FMM phase first quickly aligns along the field direction, leading to the low field fast rise of the initial magnetization curve. With increasing field, the COI phase is melted and transits into the FMM phase. Once transited, the FMM phase will mostly stay even if the field is reduced, giving rise to the difference between initial magnetization curve and the M-H loop. The difference, however, becomes smaller with decreasing size. For the 500-nm disk array, the initial magnetization curve and the M-H loop virtually superimpose each other, indicating no melting of COI phase occurs. Both the temperature- and field-dependent magnetization measurements show a transition from the EPS state to a single FMM state with decreasing size of the disk, and the critical size should be between 500 nm and 800 nm.The transition from the EPS state to a single FMM state can be seen in MFM images shown in Fig. 2 (for MFM imaging details, see micromagnetic mapping method in Supporting Information). Fig. 2A shows morphological appearance of LPCMO disks with different sizes acquired by atomic force microscope (AFM). Fig. 2 B–D shows the corresponding MFM images of the LPCMO disks acquired at different temperatures under a perpendicular magnetic field of 1T. Here, the perpendicular magnetic field is applied to yield some perpendicular magnetization components for MFM imaging because the easy magnetization axis is in the plane. In the present color scale, the contrast below zero (red or black) represents FMM phase, whereas the contrast above zero (green or blue) represents nonferromagnetic phase [i.e., COI phase based on previous knowledge of the LPCMO system (21, 22)]. Apparently, except the 500-nm disk, all other disks show distinct features of the EPS state (i.e., the coexistence of the FMM and COI phases). Although the portion of FMM phase increases noticeably with decreasing temperature, the system stays in the EPS state even at 10 K. The typical length scale of the EPS domains is around a micrometer, which is consistent with previous reports (21, 27).Open in a separate windowFig. 2.(A) AFM images of LPCMO disks with sizes of 500 nm, 1 μm, 2 μm, 3.8 μm, 5 μm, and 7 μm in diameter. (B–D) The MFM images of LPCMO disks under 1T field (external magnetic field direction is pointing perpendicularly to the sample surface plane) taken at 10 K (B), 100 K (C), and 180 K (D). The sizes of disks in MFM images are adjusted and corrected to have same scales for each size with the help of scanning electron microscope (SEM) images (shown in Fig. S2) and dash lines show the approximate physical boundary of disks. The negative value in MFM image indicates attractive force and positive value indicates repulsive force.In stark contrast to the larger disks, the 500-nm disk does not exhibit any features of EPS in Fig. 2. Instead, the whole disk is in a ferromagnetic phase with a magnetization profile peaking in the center. To ensure that the EPS state is not diminished by the magnetic field applied during MFM imaging, we took MFM images of the 500-nm disk at 10 K under different perpendicular magnetic fields from 0T to 1T, as shown in Fig. 3A. At 0T, signals with opposite sign can only be seen on two sides of the disk along the marked line (MFM images of 4 disks shown in Fig. S3). This pattern is a typical MFM image for an in-plane ferromagnetic single domain, because only the two ends of an in-plane magnetic dipole yield perpendicular field gradient (with opposite signs) for the MFM tip to detect. Once a perpendicular field of 0.15T is applied, the in-plane magnetization is driven out of plane, leading to a center peaked MFM contour. The MFM signal increases with increasing field, as shown in Fig. 3B by the marked line profiles extracted from Fig. 3A.Open in a separate windowFig. 3.(A) MFM images of 500-nm disks at 10 K under different magnetic field. The lines show the path the line profile extracted. (B) Line profiles extracted from MFM images partly shown in Fig. 3A. (C) Simulated results corresponding to line-profiles in B. (D and E) The simulated Z-component of stray field 100 nm above sample disks and magnetic structure of 500-nm disks under a different magnetic field. In E, the direction and size of arrows show the direction and relative value of in-plane component of magnetization, and the color of disk indicates the value of Z-component of magnetization presented by ratio of Z-component to total magnetization, as shown in the color bar.Open in a separate windowFig. S3.MFM images of 500-nm disks taken at 10 K under zero field after zero-field cooling.The field-dependent behavior of the MFM contrast of the 500-nm disk is in qualitative agreement with micromagnetic simulations. Based on the MFM observation, the 500-nm disk is in an in-plane, single-domain state. Using this model as input, we performed micromagnetic simulation and obtained the Z-component of magnetic stray field distribution at 100 nm above sample surface (Fig. 3D; for details, see the micromagnetic simulation method in the Supporting Information), which is virtually the signal detected by MFM tip. The corresponding magnetic structures under different magnetic fields are shown in Fig. 3E. The marked line profiles extracted from simulation (Fig. 3D) are shown in Fig. 3C alongside with the experimental MFM line profiles (Fig. 3B). The subtle differences between Fig. 3B and Fig. 3C are likely caused by the fact that experimental MFM images are convoluted from signals of both the LPCMO disks and the MFM tips (∼100 nm in size). The consistency of MFM images and simulation confirms that the 500-nm disk is in a ferromagnetic single-domain state with an in-plane easy magnetization axis.Finally, we show that the 500-nm disk is in a single FMM state at all temperatures. Fig. 4 shows MFM images of the 500-nm disk acquired every 20 K, from 20 K to 200 K under 1,000 Oe. Other than the center-peaked FMM phase, no traces of COI phase can be observed. The MFM signal decreases with increasing temperature, which is consistent with the behavior of the temperature-dependent magnetization shown in Fig. 1. Considering the fact that we have never observed pure COI phase in the 500-nm disks, we believe this phenomenon may be caused by the existence of the ferromagnetic metallic edge state in the LPCMO system (22), which assists the 500-nm disk to be in pure ferromagnetic state when a single state is energetically preferred in the 500-nm disk.Open in a separate windowFig. 4.MFM images of 500-nm disks taken every 20 K, from 20 K to 200 K.In summary, we discovered a spatial confinement-induced transition from a EPS state featuring coexistence of FMM and COI phases to a single FMM state in the LPCMO system. The critical size for the transition is between 500 nm and 800 nm, which is similar to the characteristic length scale of the EPS state in the LPCMO system. Combining the MFM data and the micromagnetic simulation, we conclude that the 500-nm LPCMO disk is in a single-domain ferromagnetic state at all temperatures below T_c. A similar conclusion can be reached for 300-nm LPCMO disks (shown in Fig. S4), although it needs to be studied further whether a new state would emerge if the disk size becomes a few tens of nanometers or smaller. Our work opens a way to control EPS without external field or introducing strain and disorder, which is potentially useful to design electronic and spintronic devices in complex oxides systems.Open in a separate windowFig. S4.MFM images of 300-nm disks taken at different temperatures and magnetic fields.     

Little evidence for enhanced phenotypic evolution in early teleosts relative to their living fossil sister group

Since Darwin, biologists have been struck by the extraordinary diversity of teleost fishes, particularly in contrast to their closest “living fossil” holostean relatives. Hypothesized drivers of teleost success include innovations in jaw mechanics, reproductive biology and, particularly at present, genomic architecture, yet all scenarios presuppose enhanced phenotypic diversification in teleosts. We test this key assumption by quantifying evolutionary rate and capacity for innovation in size and shape for the first 160 million y (Permian–Early Cretaceous) of evolution in neopterygian fishes (the more extensive clade containing teleosts and holosteans). We find that early teleosts do not show enhanced phenotypic evolution relative to holosteans. Instead, holostean rates and innovation often match or can even exceed those of stem-, crown-, and total-group teleosts, belying the living fossil reputation of their extant representatives. In addition, we find some evidence for heterogeneity within the teleost lineage. Although stem teleosts excel at discovering new body shapes, early crown-group taxa commonly display higher rates of shape evolution. However, the latter reflects low rates of shape evolution in stem teleosts relative to all other neopterygian taxa, rather than an exceptional feature of early crown teleosts. These results complement those emerging from studies of both extant teleosts as a whole and their sublineages, which generally fail to detect an association between genome duplication and significant shifts in rates of lineage diversification.Numbering ∼29,000 species, teleost fishes account for half of modern vertebrate richness. In contrast, their holostean sister group, consisting of gars and the bowfin, represents a mere eight species restricted to the freshwaters of eastern North America (1). This stark contrast between teleosts and Darwin''s original “living fossils” (2) provides the basis for assertions of teleost evolutionary superiority that are central to textbook scenarios (3, 4). Classic explanations for teleost success include key innovations in feeding (3, 5) (e.g., protrusible jaws and pharyngeal jaws) and reproduction (6, 7). More recent work implicates the duplicate genomes of teleosts (8–10) as the driver of their prolific phenotypic diversification (8, 11–13), concordant with the more general hypothesis that increased morphological complexity and innovation is an expected consequence of genome duplication (14, 15).Most arguments for enhanced phenotypic evolution in teleosts have been asserted rather than demonstrated (8, 11, 12, 15, 16; but see ref. 17), and draw heavily on the snapshot of taxonomic and phenotypic imbalance apparent between living holosteans and teleosts. The fossil record challenges this neontological narrative by revealing the remarkable taxonomic richness and morphological diversity of extinct holosteans (Fig. 1) (18, 19) and highlights geological intervals when holostean taxonomic richness exceeded that of teleosts (20). This paleontological view has an extensive pedigree. Darwin (2) invoked a long interval of cryptic teleost evolution preceding the late Mesozoic diversification of the modern radiation, a view subsequently supported by the implicit (18) or explicit (19) association of Triassic–Jurassic species previously recognized as “holostean ganoids” with the base of teleost phylogeny. This perspective became enshrined in mid-20th century treatments of actinopterygian evolution, which recognized an early-mid Mesozoic phase dominated by holosteans sensu lato and a later interval, extending to the modern day, dominated by teleosts (4, 20, 21). Contemporary paleontological accounts echo the classic interpretation of modest teleost origins (22–24), despite a systematic framework that substantially revises the classifications upon which older scenarios were based (22–25). Identification of explosive lineage diversification in nested teleost subclades like otophysans and percomorphs, rather than across the group as a whole, provides some circumstantial neontological support for this narrative (26).Open in a separate windowFig. 1.Phenotypic variation in early crown neopterygians. (A) Total-group holosteans. (B) Stem-group teleosts. (C) Crown-group teleosts. Taxa illustrated to scale.In contrast to quantified taxonomic patterns (20, 23, 24, 27), phenotypic evolution in early neopterygians has only been discussed in qualitative terms. The implicit paleontological model of morphological conservatism among early teleosts contrasts with the observation that clades aligned with the teleost stem lineage include some of the most divergent early neopterygians in terms of both size and shape (Fig. 1) (see, for example, refs. 28 and 29). These discrepancies point to considerable ambiguity in initial patterns of phenotypic diversification that lead to a striking contrast in the vertebrate tree of life, and underpins one of the most successful radiations of backboned animals.Here we tackle this uncertainty by quantifying rates of phenotypic evolution and capacity for evolutionary innovation for the first 160 million y of the crown neopterygian radiation. This late Permian (Wuchiapingian, ca. 260 Ma) to Cretaceous (Albian, ca. 100 Ma) sampling interval permits incorporation of diverse fossil holosteans and stem teleosts alongside early diverging crown teleost taxa (Figs. 1 and ​and2A2A and Figs. S1 and ​andS2),S2), resulting in a dataset of 483 nominal species-level lineages roughly divided between the holostean and teleost total groups (Fig. 2B and Fig. S2). Although genera are widely used as the currency in paleobiological studies of fossil fishes (30; but see ref. 31), we sampled at the species level to circumvent problems associated with representing geological age and morphology for multiple congeneric lineages. We gathered size [both log-transformed standard length (SL) and centroid size (CS); results from both are highly comparable (Figs. S3 and ​andS4);S4); SL results are reported in the main text] and shape data (the first three morphospace axes arising from a geometric morphometric analysis) (Fig. 2A and Figs. S1) from species where possible. To place these data within a phylogenetic context, we assembled a supertree based on published hypotheses of relationships. We assigned branch durations to a collection of trees under two scenarios for the timescale of neopterygian diversification based on molecular clock and paleontological estimates. Together, these scenarios bracket a range of plausible evolutionary timelines for this radiation (Fig. 2B). We used the samples of trees in conjunction with our morphological datasets to test for contrasts in rates of, and capacity for, phenotypic change between different partitions of the neopterygian Tree of Life (crown-, total-, and stem-group teleosts, total-group holosteans, and neopterygians minus crown-group teleosts), and the sensitivity of these conclusions to uncertainty in both relationships and evolutionary timescale. Critically, these include comparisons of phenotypic evolution in early crown-group teleosts—those species that are known with certainty to possess duplicate genomes—with rates in taxa characterized largely (neopterygians minus crown teleosts) or exclusively (holosteans) by unduplicated genomes. By restricting our scope to early diverging crown teleost lineages, we avoid potentially confounding signals from highly nested radiations that substantially postdate both genome duplication and the origin of crown teleosts (26, 32). This approach provides a test of widely held assumptions about the nature of morphological evolution in teleosts and their holostean sister lineage.Open in a separate windowFig. 2.(A) Morphospace of Permian–Early Cretaceous crown Neopterygii. (B) One supertree subjected to our paleontological (Upper) and molecular (Lower) timescaling procedures to illustrate contrasts in the range of evolutionary timescales considered. Colors of points (A) and branches (B) indicate membership in major partitions of neopterygian phylogeny. Topologies are given in Datasets S4 and S5. See Dataset S6 for source trees.Open in a separate windowFig. S1.Morphospace of 398 Permian–Early Cretaceous Neopterygii. Three major axes of shape variation are presented. Silhouettes and accompanying arrows illustrate the main anatomical correlates of these principal axes, as described in Open in a separate windowFig. S2.Morphospace of 398 Permian–Early Cretaceous Neopterygii, illustrating the major clades of (A) teleosts and (B) holosteans.Open in a separate windowFig. S3.Comparisons of size rates between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Identical taxon sampling leads the CS and pruned SL datasets to yield near identical results. Although the larger SL dataset results often differ slightly, the overall conclusion from each pairwise comparison (i.e., which outcome is the most likely in an overall majority of trees) is identical in all but one comparison (E, under molecular timescales).Open in a separate windowFig. S4.Comparisons of size innovation between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Comparisons of size innovation are presented for K value distributions of the three datasets resemble each other closely.     

Atoms of recognition in human and computer vision

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.The human visual system makes highly effective use of limited information (1, 2). As shown below (Fig. 1 and Figs. S1 and ​andS2),S2), it can recognize consistently subconfigurations that are severely reduced in size or resolution. Effective recognition of reduced configurations is desirable for dealing with image variability: Images of a given category are highly variable, making recognition difficult, but this variability is reduced at the level of recognizable but minimal subconfigurations (Fig. 1B). Minimal recognizable configurations (MIRCs) are useful for effective recognition, but, as shown below, they also are computationally challenging because each MIRC is nonredundant and therefore requires the effective use of all available information. We use them here as sensitive tools to identify fundamental limitations of existing models of visual recognition and directions for essential extensions.Open in a separate windowFig. 1.Reduced configurations. (A) Configurations that are reduced in size (Left) or resolution (Right) can often be recognized on their own. (B) The full images (Upper Row) are highly variable. Recognition of the common action can be obtained from local configurations (Lower Row), in which variability is reduced.Open in a separate windowFig. S1.MIRCs. Discovered MIRCs for each of the 10 original images (10 object classes) are ordered from large to small image coverage within each class. Below each MIRC are the recognition rate (Left) and size in image samples (Right).Open in a separate windowFig. S2.MIRCs coverage. Each colored frame outlines a MIRC (which may be at a reduced resolution). Together, they provide a redundant representation because recognition can be obtained from a single MIRC. Warmer colors of the MIRC frame outline areas of larger coverage.A MIRC is defined as an image patch that can be reliably recognized by human observers and which is minimal in that further reduction in either size or resolution makes the patch unrecognizable (below criterion) (Methods). To discover MIRCs, we conducted a large-scale psychophysical experiment for classification. We started from 10 greyscale images, each showing an object from a different class (Fig. S3), and tested a large hierarchy of patches at different positions and decreasing size and resolution. Each patch in this hierarchy has five descendants, obtained by either cropping the image or reducing its resolution (Fig. 2). If an image patch was recognizable, we continued to test the recognition of its descendants by additional observers. A recognizable patch in this hierarchy is identified as a MIRC if none of its five descendants reaches a recognition criterion (50% recognition; results are insensitive to criterion) (Methods and Fig. S4). Each human subject viewed a single patch from each image with unlimited viewing time and was not tested again. Testing was conducted online using the Amazon Mechanical Turk (MTurk) (3, 4) with about 14,000 subjects viewing 3,553 different patches combined with controls for consistency and presentation size (Methods). The size of the patches was measured in image samples, i.e., the number of samples required to represent the image without redundancy [twice the image frequency cutoff (5)]. For presentation to subjects, all patches were scaled to 100 × 100 pixels by standard interpolation; this scaling increases the size of the presented image smoothly without adding or losing information.Open in a separate windowFig. 2.MIRCs discovery. If an image patch was recognized by human subjects, five descendants were presented to additional observers: Four were obtained by cropping 20% of the image (Bottom Row) and one by 20% reduced resolution (Middle Row, Right). The process was repeated on all descendants until none of the descendants reached recognition criterion (50%). Detailed examples are shown in Fig. S4. The numbers next to each image indicate the fraction of subjects that correctly recognized the image.Open in a separate windowFig. S3.Original images used in the human study. The image stimuli in the human study were extracted from these 10 original images (10 object categories). In the experiment, the size of each original image was 50 × 50 image samples, or a cutoff spatial frequency of 25 cycles per image.Open in a separate windowFig. S4.MIRC hierarchical trees. Examples of MIRCs (in red boxes) and their hierarchical trees, including subimage descendants (sub-MIRCs) and superimage ancestors (super-MIRCs). At the top of each tree is a depiction of the MIRC’s position in the original image marked in a red-bordered box. The human recognition rate is shown below the image patches.     

Continental crust beneath southeast Iceland

The magmatic activity (0–16 Ma) in Iceland is linked to a deep mantle plume that has been active for the past 62 My. Icelandic and northeast Atlantic basalts contain variable proportions of two enriched components, interpreted as recycled oceanic crust supplied by the plume, and subcontinental lithospheric mantle derived from the nearby continental margins. A restricted area in southeast Iceland—and especially the Öræfajökull volcano—is characterized by a unique enriched-mantle component (EM2-like) with elevated ⁸⁷Sr/⁸⁶Sr and ²⁰⁷Pb/²⁰⁴Pb. Here, we demonstrate through modeling of Sr–Nd–Pb abundances and isotope ratios that the primitive Öræfajökull melts could have assimilated 2–6% of underlying continental crust before differentiating to more evolved melts. From inversion of gravity anomaly data (crustal thickness), analysis of regional magnetic data, and plate reconstructions, we propose that continental crust beneath southeast Iceland is part of ∼350-km-long and 70-km-wide extension of the Jan Mayen Microcontinent (JMM). The extended JMM was marginal to East Greenland but detached in the Early Eocene (between 52 and 47 Mya); by the Oligocene (27 Mya), all parts of the JMM permanently became part of the Eurasian plate following a westward ridge jump in the direction of the Iceland plume.The North Atlantic Igneous Province covers vast areas in Baffin Island, Greenland, United Kingdom, Ireland, the Faroe Islands, and offshore regions (Fig. 1). Volcanic activity commenced ∼62 Mya (1) and is attributed to the impingement of a mantle plume head on the lithosphere (2). Enriched and depleted geochemical signatures in the Paleogene to Recent basalts from Iceland reflect a combination of plume-derived and shallow asthenospheric material, representing a classic case of plume–ridge interaction. The volcanic products of the Iceland plume have signatures of recycled oceanic crust and primordial-like material with high ³He/⁴He ratios (up to 50 R_A, where R_A is the ³He/⁴He ratio of air), spanning a range of refractory to fertile compositions (3, 4). Additional shallow-level contributions include the depleted mid-ocean ridge basalt–type asthenosphere, variably mixed with subcontinental lithospheric mantle material (5–8). The proximity of Iceland to the Jan Mayen Microcontinent (JMM) (Fig. 1) raises the question of whether Iceland includes underlying fragments of continental crust (9, 10). There are unconfirmed reports of both Precambrian and Mesozoic zircons in young basalts from Iceland, but only in abstract form (9, 11). The recovery of a 1.8-Ga-old grain from the most primitive Öræfajökull basalts (9) in southeast Iceland (Figs. 1 and ​and2A)2A) is potentially very interesting, although two Jurassic zircon grains (160 Ma) uncovered in the same separation are now suspected to be due to laboratory contamination.Open in a separate windowFig. 1.Crustal thickness map based on gravity inversion and revised location of the Iceland plume (white star symbols in 10-My intervals) relative to Greenland back to 60 Ma (see Fig. 6A for details). Transition between continental and oceanic crust (COB, black lines), plate boundaries (blue lines), site locations for dated North Atlantic Igneous Province magmatism (yellow circles with black fill), and the estimated size of the “classic” Jan Mayen Microcontinent (JMM) and the extended JMM (JMM-E) are also shown. The classic JMM is ∼500 km long (200 km at its widest and shown with four continental basement ridges), and crustal thicknesses are about 18–20 km (scale at Upper Left), which suggests considerable continental stretching before drifting off Greenland. We extend JMM 350 km southwestward (∼70 km wide) beneath southeast Iceland where we calculate maximum crustal thicknesses of ∼32 km (Fig. 2B). The size of JMM-E is a conservative estimate and could be as large as the white-stippled area. Öræfajökull location is shown as a yellow star.Open in a separate windowFig. 2.(A) Simplified geological map of Iceland outlining the major rift zones (RZ), rift zone central volcanoes (black outline), flank zone central volcanoes (colored outlines), the location of the Öræfajökull central volcano, and the position of the current plume axis of Shorttle et al. (25). Yellow areas are fissure swarms. WRZ, ERZ, NRZ, and MIB: Western, Eastern, and Northern Rift Zone and Mid-Iceland Belt, respectively. The stippled west-northwest–trending line through the plume axis depicts the position of the cross sections in Fig. 9. (B) Enlarged crustal thickness map (Fig. 1) with superimposed earthquake locations (earthquake.usgs.gov/earthquakes) and contour intervals (in meters) showing that the anomalously thick crust under southeast Iceland extends offshore to the northeast suggesting that it is a southerly fragment of the Jan Mayen Microcontinent (JMM) rather than an extension of the southeast-northwest–orientated Iceland-Faroes Ridge.     

Myriad manifestations of Williams syndrome

4 months male child presented with failure to thrive. On general examination child had normal O2 saturation with characterstic elfin facies. Further evaluation of the patient showed major manifestations of Williams syndrome in form of supravalvar aortic stenosis, branched pulmonary artery stenosis along with cardiomyopathy. Although the entity is known, this article shows comprehensive diagnostic workup with the aid of multimodality imaging techniques. The genetic diagnosis of Williams syndrome was confirmed using fluroscent in situ hybridisation techniques (FISH). In this patient most of the manifestations of elastin vasculopathy were noted in the form of involvement of ascending aorta, pulmonary arteries and myocardium. We also want to emphasis the importance of echocardiography in newborn patients with dysmorphic facies as Williams syndrome can be easily missed in neonatal period.A 4 month old male child presented with symptoms of heart failure and poor weight gain. On examination, O₂ saturation in both limbs was 99% and there was no significant blood pressure difference in all four limbs. He had a characterstic ‘elfin facies’ with a sunken nasal bridge, a long philtrum, wide mouth, prominent lower lip, small chin and low set ears (Fig. 1). 2D echocardiography showed situs solitus, concentric left ventricular hypertrophy with mild narrowing in the supravalvular region (Fig. 2) with systolic gradients of 26 mm Hg across the segment (Fig. 3) (Online Video 1). The right pulmonary artery (RPA) after its origin showed significant short segment stenosis with peak systolic gradients of 32 mmHg. The left pulmonary artery (LPA) after its origin showed mild narrowing (Figs. 4 and 5) (Online Video 2). Cardiac CT demonstrated supravalvular aortic stenosis (SVAS) with hour glass appearance of the aorta (Figs. 6 and 7) with bilateral pulmonary artery stenosis (PAS) involving RPA more than LPA (Figs. 8 and 9). Fluorescent in-situ hybridization (FISH) studies (Fig. 10) showed heterozygous deletion of elastin gene (Chromosome 7q11.23) and confirmed the diagnosis of Williams syndrome (WS).Open in a separate windowFig. 1Characterstic elfin facies.Open in a separate windowFig. 22D echocardiography showing mild narrowing in the supravalvular region.Open in a separate windowFig. 3Doppler showing gradients of 26 mm Hg across narrowed supravalvular region.Open in a separate windowFig. 42D echocardiography with color Doppler showing bilateral PAS.Open in a separate windowFig. 5Doppler investigation showing peak systolic gradients of 32 mmHg across the narrowed segment of RPA.Open in a separate windowFig. 6Cardiac CT with 3D reconstruction showing supravalvular aortic stenosis (SVAS).Open in a separate windowFig. 7Cardiac CT (Coronal section) showing SVAS.Open in a separate windowFig. 8Cardiac CT (Transverse section) showing bilateral PAS.Open in a separate windowFig. 9Cardiac CT with 3D reconstruction showing bilateral pulmonary artery stenosis (PAS) involving RPA more than LPA.Open in a separate windowFig. 10Fluorescent in-situ hybridization studies showing deletion of elastin gene (Chromosome 7q11.23).Supplementary data related to this article can be found online at http://dx.doi.org/10.1016/j.ihj.2015.02.026.The following are the Supplementary data related to this article:Video 1: Subcostal view showing turbulence in supravalvar region of aorta and the branched pulmonary arteries.Click here to view.^{(1.3M, mp4)}Video 2: High parasternal view showing confluent pulmonary arteries with turbulence across the branched pulmonary arteries.Click here to view.^{(853K, mp4)}WS is a genetic disorder occurring with a frequency of 1 in 20,000–50,000 live births. Manifestations of WS include congenital heart disease, hypertension, dysmorphic facial features, infantile hypercalcaemia and mental retardation. Apart from supravalvular AS and branched PA stenosis other cardiac abnormalities observed are bicuspid aortic valve, mitral valve regurgitation, coarctation of the aorta, ventricular or atrial septal defects. In neonates, cardiovascular symptoms were evident in 47% of WS children.1 PA stenosis often tends to regress spontaneously and SVAS tends to progress with time. In this patient most of the manifestations of elastin vasculopathy were noted in the form of involvement of ascending aorta and pulmonary arteries. The concentric left ventricular hypertrophy observed in our patient may be an expression of hypertrophic cardiomyopathy which is known to be associated with WS.1 In neonatal period all newborn patients with dysmorphic facies should be evaluated with echocardiography so that the cardiac abnormalities are not missed.     

From the Cover: Montsechia,an ancient aquatic angiosperm

The early diversification of angiosperms in diverse ecological niches is poorly understood. Some have proposed an origin in a darkened forest habitat and others an open aquatic or near aquatic habitat. The research presented here centers on Montsechia vidalii, first recovered from lithographic limestone deposits in the Pyrenees of Spain more than 100 y ago. This fossil material has been poorly understood and misinterpreted in the past. Now, based upon the study of more than 1,000 carefully prepared specimens, a detailed analysis of Montsechia is presented. The morphology and anatomy of the plant, including aspects of its reproduction, suggest that Montsechia is sister to Ceratophyllum (whenever cladistic analyses are made with or without a backbone). Montsechia was an aquatic angiosperm living and reproducing below the surface of the water, similar to Ceratophyllum. Montsechia is Barremian in age, raising questions about the very early divergence of the Ceratophyllum clade compared with its position as sister to eudicots in many cladistic analyses. Lower Cretaceous aquatic angiosperms, such as Archaefructus and Montsechia, open the possibility that aquatic plants were locally common at a very early stage of angiosperm evolution and that aquatic habitats may have played a major role in the diversification of some early angiosperm lineages.When did early angiosperms begin to diversify ecologically? This question is currently unanswered. Age estimates of the divergence of crown-group angiosperms using molecular clock data vary considerably, although it is in the range of (max. 210–) often accepted, 150–140 (min. 130) million years (1–7). Parsimony reconstruction of early angiosperm habit suggests that they may have been shrubs living in “damp, dark, and disturbed” habitats (8). In contrast, many living aquatic angiosperms are basal in angiosperm phylogenies [e.g., Nymphaeales in Amborella, Nymphaeales and Illiciales, Trimeniaceae-Austrobaileya (ANITA) or Ceratophyllales with the eudicots as commonly understood]. In the fossil record, we have found an aquatic angiosperm, Montsechia vidalii (Zeiller) Teixeira, which is an atypical plant fossil found in the Barremian (130–125 million years ago) freshwater limestone in the Pyrenees and Iberian Range in Spain. Montsechia (Fig. 1) lacks roots (no proximal or adventitious roots were found in more than 1,000 shoots examined) and shows flexible axes and two types of phyllotaxy and leaf morphology. The cuticle is very thin with rare stomata. The fruit is closed with a pore near the distal tip, indehiscent, and contains one unitegmic seed developed from an orthotropous and pendent ovule (Figs. 2 and ​and3).3). Cladistic analysis of these characters places Montsechia on the stem lineage basal to extant Ceratophyllum or a clade formed by Ceratophyllum and Chloranthaceae (Fig. 4) suggesting that mesangiosperms (non-ANITA angiosperms) existed 125 million years ago, as indicated by the tricolpate pollen record. Montsechia is well-adapted to a submerged aquatic habit. Montsechia is contemporaneous with another aquatic plant fossil, Archaefructus, indicating that some of the earliest angiosperms were fully aquatic very early in their ecological diversification.Open in a separate windowFig. 1.Long- and short-leaved forms of Montsechia vidalii. (A) The long-leaved specimen shows very flexuous branches and opposite, long leaves. LH02556. (Scale bar, 10 mm.) (B) The short-leaved specimen shows regularly developed lateral branches and tiny leaf rosettes. LH07198. (Scale bar, 10 mm.)Open in a separate windowFig. 2.Fruit and seed of Montsechia vidalii. The fruit shows a small apical pore (po). The funicle (f) of the single, upside-down seed (orthotropous pendent) is attached from the hilum (h) to the placenta (pl). (Scale bar, 500 µm.)Open in a separate windowFig. 3.Reconstructions of Montsechia vidalii. (A) The long-leaved form shows the opposite leaves and branches. (B) The short-leaved form shows the alternate phyllotaxy of leaves and branches bearing pairs of ascidiate, nonornamented fruits. (C and D) The fruit shows a small apical pore and a single seed developed from an orthotropous pendent ovule. The funicle arises from the placenta (near the micropyle) to the hilum (near the pollination pore). (C) Lateral view. (D) Front view. Diagram by O. Sanisidro, B.G., and V.D.-G.Open in a separate windowFig. 4.Most parsimonious position of Montsechia in a simplified tree derived from the matrix by Endress and Doyle (26) using the J & M backbone. Taxa in blue are considered ancestrally water-related (27). Diagram by C.C. and B.G.     

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing ‟grind and find” model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.Notwithstanding the historical importance of natural products in drug discovery (1) the field continues to face a number of challenges that affect the relevance of natural products research in modern biomedical science (2). Among these are the increasing rates of rediscovery of known classes of natural products (3–6) and the high rates of attrition of bioactive natural products in secondary assays due to limited information about compound modes of action in primary whole-cell assays (7). Although pharmaceutical companies recognize that natural products are an important component of drug discovery programs because of the different pharmacologies of natural products and synthetic compounds (8), there is a reluctance to return to “grind and find” discovery methods (9). Therefore, there is a strong need for technologies that address these issues and provide new strategies for the prioritization of lead compounds with unique structural and/or biological properties (10).Natural product drug discovery is challenging in any assay system because extract libraries are typically complex mixtures of small molecules in varying titers, making it difficult to distinguish biological outcomes (11). This is compounded by issues of additive effects of multiple bioactive compounds and the presence of nuisance compounds that cause false positives in assay systems (12). To address these issues, our laboratory has recently developed several image-based screening platforms that are optimized for natural product discovery (13–16). The cytological profiling platform optimized by Schulze and coworkers characterizes the biological activities of extracts using untargeted phenotypic profiling. These phenotypic profiles are compared with natural products extracts and a training set of compounds with known modes of action to characterize the bioactivity landscape of the screening library (17, 18). This cytological profiling tool forms the basis of the biological characterization component of the Compound Activity Mapping platform, as described below.In the area of chemical characterization of natural product libraries, untargeted metabolomics is gaining attention as a method for evaluating chemical constitution (3, 19–22). Modern “genes-to-molecules” and untargeted metabolomics approaches taking advantage of principal component analysis and MS² spectral comparisons have also been developed to quickly dereplicate complex extracts and distinguish noise and nuisance compounds from new molecules (23–27). Unfortunately, although these techniques are well suited to the discovery of new chemical scaffolds, they are unable to describe the function or biological activities of the compounds they identify. Therefore, there is still a need for new approaches to systematically identify novel bioactive scaffolds from complex mixtures.To overcome some of these outstanding challenges we have developed the Compound Activity Mapping platform to integrate phenotypic screening information from the cytological profiling assay with untargeted metabolomics data from the extract library (Fig. 1). By correlating individual mass signals with specific phenotypes from the high-content cell-based screen (Fig. 2), Compound Activity Mapping allows the prediction of the identities and modes of action of biologically active molecules directly from complex mixtures, providing a mechanism for rational lead selection based on desirable biological and/or chemical properties. To evaluate this platform for natural products discovery we examined a 234-member extract library, from which we derived 58,032 biological measurements (Fig. 1C) and 10,977 mass spectral features (Fig. 1A). By integrating and visualizing these data we created a Compound Activity Map for this library composed of 13 clusters containing 16 compounds from 11 compound classes (Fig. 3). This integrated data network enabled the discovery of four new compounds, quinocinnolinomycins A–D (1–4, Fig. 4), which are the first examples to our knowledge of microbial natural products containing the unusual cinnoline core (Fig. 5). Clustering the cytological profiles of the quinocinnolinomycins with those of the Enzo library training set suggests that these compounds induce endoplasmic reticulum (ER) stress and the protein unfolding response.Open in a separate windowFig. 1.Overview of Compound Activity Mapping. (A) Representation of the chemical space in the tested extract library. The network displays extracts (light blue) connected by edges to all m/z features (red) observed from the metabolomics analysis, illustrating the chemical complexity of even small natural product libraries. (B) Histograms of activity and cluster scores for all m/z features with cutoffs indicated as red lines (for full-size histograms see SI Appendix, Fig. S5). (C) Compound Activity Map, with the network displaying only the m/z features predicted to be associated with consistent bioactivity, and their connectivity to extracts within the library. (D) Expansion of the staurosporine cluster (dotted box in C) with extract numbers and relevant m/z features labeled.Open in a separate windowFig. 2.Determination of synthetic fingerprints and cluster and activity scores. (A) Table of Pearson correlations for the cytological profiles between all extracts containing a specific m/z feature (m/z of 489.1896, rt of 1.59). In each cytological profile, yellow stripes correspond to positive perturbations in the observed cytological attribute and blue stripes correspond to negatively perturbed attributes. The cluster score is determined by calculating the average of the Pearson correlation scores for all relevant extracts. (B) Calculated synthetic fingerprint and activity score for feature (m/z of 489.1896, rt of 1.59). Synthetic fingerprints are calculated as the averages of the values for each cytological attribute to give a predicted cytological profile for each bioactive m/z feature in the screening set.Open in a separate windowFig. 3.Annotated Compound Activity Map. An expanded view of the Compound Activity Map from Fig. 1C, with the extracts and m/z features separated into subclusters and colored coded using the Gephi modularity function. Each bioactive subcluster is composed of extracts containing a family of compounds with a defined biological activity. The Compound Activity Map is annotated with a representative molecule from each of the families of compounds that have been independently confirmed by purification and chemical analysis.Open in a separate windowFig. 4.The prioritization, isolation, and confirmation of the quinocinnolinomycins A–D (1–4). (A) Bioactive m/z features plotted on a graph of activity score vs. cluster score. The color of the dot corresponds to the retention time of the m/z feature with the color bar and scale below in minutes. (B) Isolated cluster from Fig. 1C and Fig. 3 containing both the relevant extracts (blue) and bioactive m/z features (red). (C) HPLC trace of extract RLPA-2003E and the isolation of quinocinnolinomycins A–D (highlighted with blue boxes on HPLC trace). (D) Cell images of pure compounds screened as a twofold dilution series for quinocinnolinomycins A and B in both stain sets compared with images of vehicle (DMSO) wells. (E) Comparison of the synthetic and actual cytological fingerprints of the pure compounds is presented below the relevant images, demonstrating the relationship between experimental and calculated cytological profiles for these two metabolites.Open in a separate windowFig. 5.Structure elucidation of quinocinnolinomycins A–D (1–4). (A) Structures of quinocinnolinomycins A–D. (B) Key NMR correlations used in the structure elucidation of quinocinnolinomycin A. COSY correlations are indicated by bold lines. Heteronuclear multiple-bond correlations are indicated by curved arrows. (C) ∆δ^SR values for the Mosher’s α-methoxy-α-trifluoromethylphenylacetic acid (MTPA) ester analysis of the secondary alcohol in quinocinnolinomycin A (1) to assign the absolute configuration at position C11.     

Probing large conformational rearrangements in wild-type and mutant spectrin using structural mass spectrometry

Conformational changes of macromolecular complexes play key mechanistic roles in many biological processes, but large, highly flexible proteins and protein complexes usually cannot be analyzed by crystallography or NMR. Here, structures and conformational changes of the highly flexible, dynamic red cell spectrin and effects of a common mutation that disrupts red cell membranes were elucidated using chemical cross-linking coupled with mass spectrometry. Interconversion of spectrin between closed dimers, open dimers, and tetramers plays a key role in maintaining red cell shape and membrane integrity, and spectrins in other cell types serve these as well as more diverse functions. Using a minispectrin construct, experimentally verified structures of closed dimers and tetramers were determined by combining distance constraints from zero-length cross-links with molecular models and biophysical data. Subsequent biophysical and structural mass spectrometry characterization of a common hereditary elliptocytosis-related mutation of α-spectrin, L207P, showed that cell membranes were destabilized by a shift of the dimer–tetramer equilibrium toward closed dimers. The structure of αL207P mutant closed dimers provided previously unidentified mechanistic insight into how this mutation, which is located a large distance from the tetramerization site, destabilizes spectrin tetramers and cell membrane integrity.Solving static structures of protein complexes and probing dynamic conformational rearrangements have frequently provided mechanistic insights into macromolecular functions as well as effects of disease-related mutations. However, high-resolution structural techniques such as X-ray crystallography and NMR usually cannot be applied to large proteins that are highly flexible, intrinsically disordered, or undergo large conformational changes. Chemical cross-linking coupled with mass spectrometry (CX-MS) is a powerful tool that identifies proximal amino acid residues of proteins in solution. These spatial constraints can greatly enhance and experimentally validate molecular modeling to result in reliable medium-resolution structures. This approach has been effectively used in numerous studies (1–5), although homobifunctional lysine-specific cross-linkers with relatively long spacer arms were mostly used. Such reagents have been preferred because they enable introduction of isotope labels and other functional sites that facilitate cross-linked peptide identifications (6–9). In contrast, zero-length cross-linkers such as 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide form a covalent bond between reactive amines (N-terminal amine or lysine side chain) and carboxyls (C-terminal carboxyl or aspartic or glutamic acid side chains) without inserting extra atoms (Fig. 1). Hence, reactive groups have to be within salt bridge distances to react. This results in tighter distance constraints that outperform those from longer cross-links when these data are used to refine structural models, and a much lower density of cross-links is needed to achieve high-quality structures (7). In this study we used a recently developed strategy for in-depth identification of zero-length cross-links, as summarized in Fig. 1, to probe structures and conformational changes in spectrin, a highly flexible, dynamic protein with multiple functions that is typically associated with cell membranes. In red cells, spectrin is the central component of a highly specialized, 2D, net-like submembraneous complex that confers both structural integrity and elasticity to the cell membrane. The 1,052-kDa spectrin tetramer is a long, highly flexible, worm-like protein composed primarily of many tandem, homologous, “spectrin-type” domains (Fig. 2A). In all reported crystal structures, these domains are approximately 50-Å-long three-helix bundles with helical connectors (Fig. 2 B and C). To date, crystallization of more than four spectrin-type domains has not been feasible, presumably owing to spectrin’s highly flexible nature. In the membrane skeleton, spectrin tetramers bridge short actin oligomers, and membrane stability is highly dependent upon the dimer–tetramer equilibrium because conversion to dimers breaks the spectrin bridges between actin oligomers (10, 11).Open in a separate windowFig. 1.Schematic for the zero-length CX-MS data acquisition and data analysis pipeline.Open in a separate windowFig. 2.Spectrin topography and minispectrin tetramer structure. (A) Schematic showing spectrin domains, the dimer–tetramer equilibria, and minispectrin. The spectrin-type domains that constitute most of the molecule are represented as rounded rectangles. Red asterisks in the minispectrin cartoon indicate the approximate location of the αL207P mutation. (B) Superimposition of the four crystal structures of spectrin-type domains [Protein Data Bank (PBD) ID: 1CUN, 1U5P, 3FB2, and 1S35] used as template building blocks for homology modeling. (C) Crystal structure for the spectrin tetramerization interface (PDB ID: 3LBX). (D) Locations of interdomain cross-links used to model minispectrin tetramer; blue lines, cross-links identified previously (21); red lines, previously unidentified cross-links; dashed lines, the same cross-links repeated in the second half of the tetramer. (E) Superimposition of present and previous tetramer structures. (F) Space-filling representations of tetramer models. β-Spectrin domains are colored in bright or pale cyan, and α-spectrin domains are colored in bright or pale orange to distinguish the two strands.The spectrin dimer–tetramer equilibrium actually involves three states, including closed dimers, open dimers, and bivalent head-to-head tetramers (Fig. 2A). Conversion from open to closed dimers involves a large conformational rearrangement of the longer α-subunit, where it folds back upon itself and forms a head-to-head association analogous to the head-to-head associations in tetramers (Fig. 2A). Closed dimers play a critical but poorly understood role in this equilibrium and are responsible for a high-energy threshold that regulates kinetics of the dimer–tetramer equilibrium (12). Furthermore, hereditary elliptocytosis (HE) and hereditary pyropoikilocytosis (HPP) are common human clinical disorders characterized by reduced spectrin tetramerization resulting in abnormal red cell shape, increased membrane fragility, and in some cases, severe anemia that is transfusion dependent (13, 14). Many HE and HPP mutations are located within the tetramerization site (13), although a number of interesting mutations are located large distances from this binding domain and destabilize tetramer formation through unknown mechanisms (15–19). For example, the very common αL207P mutation is located in the middle of the α2 domain, which is ∼75 Å from the tetramerization site. However, it seemed likely that this region could undergo important conformational changes during the closed–open dimer transition (Fig. 2A). We previously developed a 90 kDa fused minispectrin dimer (Fig. 2A) to further study the dimer–tetramer equilibrium (20) and used it here for the CX-MS experiments because it is substantially simpler than the 526-kDa full-length spectrin dimer and retains physiological tetramer binding properties.     

North American tree migration paced by climate in the West,lagging in the East

Tree fecundity and recruitment have not yet been quantified at scales needed to anticipate biogeographic shifts in response to climate change. By separating their responses, this study shows coherence across species and communities, offering the strongest support to date that migration is in progress with regional limitations on rates. The southeastern continent emerges as a fecundity hotspot, but it is situated south of population centers where high seed production could contribute to poleward population spread. By contrast, seedling success is highest in the West and North, serving to partially offset limited seed production near poleward frontiers. The evidence of fecundity and recruitment control on tree migration can inform conservation planning for the expected long-term disequilibrium between climate and forest distribution.

Effective planning for the redistribution of habitats from climate change will depend on understanding demographic rates that control population spread at continental scales. Mobile species are moving, some migrating poleward (1, 2) and/or upward in elevation (3, 4). Species redistribution is also predicted for sessile, long-lived trees that provide the resource and structural foundation for global forest biodiversity (5–7), but their movement is harder to study. Contemporary range shifts are recognized primarily where contractions have followed extensive die-backs (8) or where local changes occur along compact climate gradients in steep terrain (9, 10). Whether migration capacity can pace habitat shifts of hundreds of kilometers on decade time scales depends on seed production and juvenile recruitment (Fig. 1A), which have not been fitted to data in ways that can be incorporated in models to anticipate biogeographic change (11–13). For example, do the regions of rapid warming coincide with locations where species can produce abundant seed (Fig. 1B)? If so, does seed production translate to juvenile recruitment? Here, we combine continent-wide fecundity estimates from the Masting Inference and Forecasting (MASTIF) network (13) with tree inventories to identify North American hotspots for recruitment and find that species are well-positioned to track warming in the West and North, but not in parts of the East.Open in a separate windowFig. 1.Transitions, hypothesized effects on spread, and sites. (A) Population spread from trees (BA) to new recruits is controlled by fecundity (seed mass per BA) followed by recruitment (recruits per seed mass). (B) The CTH that warming has stimulated fecundity ahead of the center of adult distributions, which reflect climate changes of recent decades. Arrows indicate how centroids from trees to fecundity to recruitment could be displaced poleward with warming climate. (C) The RSH that cold-sensitive fecundity is optimal where minimum temperatures are warmer than for adult trees and, thus, may slow northward migration. The two hypotheses are not mutually exclusive. B and C refer to the probability densities of the different life stages. (D) MASTIF sites are summarized in SI Appendix, Table S2.2 by eco-regions: mixed forest (greens), montane (blues), grass/shrub/desert (browns), and taiga (blue-green).Suitable habitats for many species are projected to shift hundreds of kilometers in a matter of decades (14, 15). While climate effects on tree mortality are increasingly apparent (16–19), advances into new habitats are not (20–23). For example, natural populations of Pinus taeda may be sustained only if the Northeast can be occupied as habitats are lost in the South (Fig. 2). Current estimates of tree migration inferred from geographic comparisons of juvenile and adult trees have been inconclusive (2, 7, 21, 24, 25). Ambiguous results are to be expected if fecundity and juvenile success do not respond to change in the same ways (20, 26–29). Moreover, seedling abundances (7, 30) do not provide estimates of recruitment rates because seedlings may reside in seedling banks for decades, or they may turn over annually (31–33). Another method based on geographic shifts in population centers calculated from tree inventories (3, 34) does not separate the effects of mortality from recruitment, i.e., the balance of losses in some regions against gains in others. The example in Fig. 2 is consistent with an emerging consensus that suitable habitats are moving fast (2, 14, 15), even if population frontiers are not, highlighting the need for methods that can identify recruitment limitation on population spread. Management for forest products and conservation goals under transient conditions can benefit from an understanding of recruitment limitation that comes from seed supply, as opposed to seedling survival (35).Open in a separate windowFig. 2.Suitable habitats redistribute with decade-scale climate change for P. taeda (BA units m² /ha). (Suitability is not a prediction of abundance, but rather, it is defined for climate and habitat variables included in a model, to be modified by management and disturbance [e.g., fire]. By providing habitat suitability in units of BA, it can be related it to the observation scale for the data.) Predictive distributions for suitability under current (A) and change expected from mid-21^st-century climate scenario Representative Concentration Pathway 4.5 (B) showing habitat declines in the Southwest and East. Specific climate changes important for this example include net increases in aridity in the southeast (especially summer) and western frontier and warming to the North. Occupation of improving habitats depends on fecundity in northern parts of the range and how it is responding. Obtained with Generalized Joint Attribute Modeling (see Materials and Methods for more information).We hypothesized two ways in which fecundity and recruitment could slow or accelerate population spread. Contemporary forests were established under climates that prevailed decades to centuries ago. These climate changes combine with habitat variables to affect seeds, seedlings, and adults in different ways (36, 37). The “climate-tracking hypothesis” (CTH) proposes that, after decades of warming and changing moisture availability (Fig. 3 A and B), seed production for many species has shifted toward the northern frontiers of the range, thus primed for poleward spread. “Fecundity,” the transition from tree basal area (BA) to seed density on the landscape (Fig. 1A), is taken on a mass basis (kg/m² BA) as a more accurate index of reproductive effort than seed number (38, 39). “Recruitment,” the transition from seed density to recruit density (recruits per kg seed), may have also shifted poleward, amplifying the impact of poleward shifts in fecundity on the capacity for poleward spread (Fig. 1B). Under CTH, the centers for adult abundance, fecundity, and recruitment are ordered from south to north in Fig. 1B as might be expected if each life-history stage leads the previous stage in a poleward migration.Open in a separate windowFig. 3.Climate change and tracking. (A) Mean annual temperatures since 1990 have increased rapidly in the Southwest and much of the North. (Zero-change contour line is in red.) (B) Moisture deficit index (monthly potential evapotranspiration minus P summed over 12 mo) has increased in much of the West. (Climate sources are listed in SI Appendix.) (C) Fecundity (kg seed per BA summed over species) is high in the Southeast. (D) Recruits per kg seed (square-root transformed) is highest in the Northeast. (E and F) Geographic displacement of 81 species show transitions in Fig. 1A, as arrows from centroids for adult BA to fecundity (E) and from fecundity to recruitment (F). Blue arrows point north; red arrows point south. Consistent with the RSH (Fig. 1B), most species centered in the East and Northwest have fecundity centroids south of adult distributions (red arrows in E). Consistent with the CTH, species of the interior West have fecundity centroids northwest of adults (blue arrows). Recruitment is shifted north of fecundity for most species (blue arrows in F). SI Appendix, Fig. S2 shows that uncertainty in vectors is low.The “reproductive-sensitivity hypothesis” (RSH) proposes that recruitment may limit population growth in cold parts of the range (Fig. 1C), where fecundity and/or seedling survival is already low. Cold-sensitive reproduction in plants includes late frost that can disrupt flowering, pollination, and/or seed development, suggesting that poleward population frontiers tend to be seed-limited (40–44). While climate warming could reduce the negative impacts of low temperatures, especially at northern frontiers, these regions still experience the lowest temperatures. The view of cold-sensitive fecundity as a continuing rate-limiting step, i.e., that has not responded to warming in Fig. 1C, is intended to contrast with the case where warming has alleviated temperature limitation in Fig. 1B. Lags can result if cold-sensitive recruitment naturally limits growth at high-latitude/high-altitude population frontiers (Fig. 1C). In this case, reproductive sensitivity may delay the pace of migration to an extent that depends on fecundity, recruitment, or both at poleward frontiers. The arrows in Fig. 1C depict a case where optimal fecundity is equator-ward of optimal growth and recruitment. The precise location of recruitment relative to fecundity in Fig. 1C will depend on all of the direct and indirect effects of climate, including through seed and seedling predators and disturbances like fire. Fig. 1C depicts one of many hypothetical examples to show that climate variables might have opposing effects on fecundity and recruitment.Both CTH and RSH can apply to both temperature and moisture; the latter is here quantified as cumulative moisture deficit between potential evapotranspiration and precipitation, D=∑m=112(PETm−Pm) for month m, derived from the widely used Standardized Precipitation Evapotranspiration Index (45). Whereas latitude dominates temperature gradients and longitude is important for moisture in the East, gradients are complicated by steep terrain in the West, with temperature tending to decline and moisture increase with elevation.We quantified the transitions that control population spread, from adult trees (BA) to fecundity (seeds per BA) to recruitment (recruits per kg seed) (Fig. 1A–C). Fecundity observations are needed to establish the link between trees and recruits in the migration process. They must be available at the tree scale across the continent because seed production depends on tree species and size, local habitat, and climate for all of the dominant species and size classes (13, 46). These estimates are not sufficient in themselves, because migration depends on seed production per area, not per tree. The per-area estimates come from individual seed production and dispersal from trees on inventory plots that monitor all trees that occupy a fixed sample area. Fecundity estimates were obtained in the MASTIF project (13) from 211,000 (211K) individual trees and 2.5 million (2.5M) tree-years from 81 species. We used a model that accommodates individual tree size, species, and environment and the codependence between trees and over time (Fig. 1C). In other words, it allows valid inference on fecundity, the quasisynchronous, quasiperiodic seed production typical of many species (47). The fitted model was then used to generate a predictive distribution of fecundity for each of 7.6M trees on 170K forest inventory plots across the United States and Canada. Because trees are modeled together, we obtain fecundity estimates per plot and, thus, per area. BA (m² /ha) of adult trees and new recruits into the smallest diameter class allowed us to determine fecundity as kg seed per m² BA and recruitment per kg seed, i.e., each of the transitions in Fig. 1A.Recruitment rates, rather than juvenile abundances, come from the transitions from seedlings to sapling stages. The lag between seed production and recruitment does not allow for comparisons on an annual basis; again, residence times in a seedling bank can span decades. Instead, we focus on geographic variation in mean rates of fecundity and recruitment.We summarized the geographic distributions for each transition as 1) the mean transition rates across all species and 2) the geographic centroids (central tendency) for each species as weighted-average locations, where weights are the demographic transitions (BA to fecundity, fecundity to recruitment, and BA to recruitment). We analyzed central tendency, or centroids (e.g., refs. 3 and 34) because range limits cannot be accurately identified on the basis of small inventory plots (21). If fecundity is not limiting poleward spread (CTH of Fig. 1B), then fecundity centroids are expected to be displaced poleward from the adult population. If reproductive sensitivity dominates population spread (RSH of Fig. 1C), then fecundity and/or recruitment centroids will be displaced equator-ward from adult BA. The same comparisons between fecundity and recruitment determine the contribution of recruitment to spread.     

Long-term reliability of the Athabasca River (Alberta,Canada) as the water source for oil sands mining

Exploitation of the Alberta oil sands, the world’s third-largest crude oil reserve, requires fresh water from the Athabasca River, an allocation of 4.4% of the mean annual flow. This allocation takes into account seasonal fluctuations but not long-term climatic variability and change. This paper examines the decadal-scale variability in river discharge in the Athabasca River Basin (ARB) with (i) a generalized least-squares (GLS) regression analysis of the trend and variability in gauged flow and (ii) a 900-y tree-ring reconstruction of the water-year flow of the Athabasca River at Athabasca, Alberta. The GLS analysis removes confounding transient trends related to the Pacific Decadal Oscillation (PDO) and Pacific North American mode (PNA). It shows long-term declining flows throughout the ARB. The tree-ring record reveals a larger range of flows and severity of hydrologic deficits than those captured by the instrumental records that are the basis for surface water allocation. It includes periods of sustained low flow of multiple decades in duration, suggesting the influence of the PDO and PNA teleconnections. These results together demonstrate that low-frequency variability must be considered in ARB water allocation, which has not been the case. We show that the current and projected surface water allocations from the Athabasca River for the exploitation of the Alberta oil sands are based on an untenable assumption of the representativeness of the short instrumental record.Over the past several decades, the province of Alberta has had Canada’s fastest growing economy, driven largely by the production of fossil fuels. Climatic change, periodic drought, and expanding human activities impact the province’s water resources, creating the potential for an impending water crisis (1). The Athabasca River (Fig. 1) is the only major river in Alberta with completely unregulated flows. It is the source of surface water for the exploitation of the Alberta oil sands, the world’s third-largest proven crude oil reserve at roughly 168 billion barrels. The oil and gas industry accounted for 74.5% of total surface water allocations in the Athabasca River Basin (ARB) in 2010 (Fig. 2) (2). An almost doubling of ARB water allocations since 2000, or 13 times the provincial average, is attributable to expanding oil sands production, which began in 1967 (Fig. 2).Open in a separate windowFig. 1.Alberta’s seven major drainage basins. The numbers refer to hydrometric gauges identified in 6). Reproduced with permission from the Government of Alberta.Open in a separate windowFig. 2.Licensed water allocations by sector and decades to 2000 and selected subsequent years in the Athabasca River Basin, 1900–2010. Reproduced with permission from the Government of Alberta.According to the Canadian Association of Petroleum Producers, in 2012, surface mining of the oil sands and in situ extraction (drilling) required 3.1 and 0.4 barrels of fresh water, respectively, to produce a barrel of crude oil (3). This amounted to 187 million cubic meters of fresh water use in 2012, or the equivalent of the residential water use of 1.7 million Canadians (4). Within the next decade, cumulative water use for oil sands production is projected to peak at about 505 million cubic meters per year or a rate of 16 m³⋅s⁻¹ (5). The current (2010 data) total water allocation represents only 4.4% of the mean annual Athabasca River flow (2) (Fig. 3); however, allocation and use as a proportion of average water levels does not account for the large variability in flow between seasons and years (interannual coefficient of variation = 22%). During 1952–2013, the Athabasca River gauge at Athabasca recorded mean seasonal flows of 100 m³⋅s⁻¹ in winter (December−February) and 911 m³⋅s⁻¹ in summer (June−August), with extreme monthly flows of 48 m³⋅s⁻¹ in December 2000 and 2,280 m³⋅s⁻¹ in June 1954. The mean, maximum, and minimum annual flows were 422 m³⋅s⁻¹, 702 m³⋅s⁻¹, and 245 m³⋅s⁻¹, respectively (Water Survey of Canada, wateroffice.ec.gc.ca/search/search_e.html?sType=h2oArc).Open in a separate windowFig. 3.Mean annual flow of the Athabasca River at Athabasca (gauge 07BE001, Water Survey of Canada) for the water year (October 1 to September 30, 1913–2013).Assessing the sustainability of ARB surface water availability is difficult because most regional streamflow gauges have been operational for a few years to a few decades, with long intervals of missing values, including during the 1930s and 1940s when drought was prevalent throughout the North American western interior. Various critics (e.g., ref. 4) emphasize the intraannual variability and express concern about water withdrawals in the low-flow winter season. Less attention has been given to flow variability at interannual to decadal scales associated with climate oscillations such as the Pacific Decadal Oscillation (PDO) and Pacific North American mode (PNA), which are known to have significant impacts on runoff from the Rocky Mountains, including in the ARB (6–9), and the potential consequences of a long period of predominantly low flow. We investigate the response of ARB flows to the low-frequency components of the PDO and PNA. This paper is the first, to our knowledge, to explicitly model the variability linked to the PDO and PNA, when testing for trends in discharge, thereby removing the tendencies associated with these climatic oscillations, which can confound trend detection in the relatively short instrumental records (6, 7). However, the available instrumental hydrologic records provide a limited sample of these low-frequency fluctuations; exploring longer records of this variability is critical. Therefore, we also reconstructed the Athabasca River annual flow from the growth rings in moisture-sensitive conifers at a network of sites in the upper reaches of the ARB and in an adjacent watershed (Fig. S1). We compare 900 y of inferred proxy flows to the streamflow variability recorded in recent decades, identifying extended periods of paleo low flow that exceeded the worst-case scenario in the instrumental records. We consider (i) the extent to which gauged flows (e.g., Fig. 3), which are the basis for surface water allocation, fail to capture the full range of hydroclimatic variability and extremes evident in a longer proxy record and (ii) the temporal evolution of low-frequency Athabasca River variability, related to the PDO and PNA, with its consequences for ARB surface water availability.Open in a separate windowFig. S1.The tree-ring sampling sites within and near the Athabasca River Basin in north-central Alberta. The colored triangles represent four coniferous tree species.     

Transketolase reaction under credible prebiotic conditions

A transketolase reaction was catalyzed by cyanide ion under prebiotic conditions instead of its modern catalyst, thiamine pyrophosphate (TPP). Cyanide ion converted fructose plus glyceraldehyde to erythrose plus xylulose, the same products as are formed in modern biochemistry (but without the phosphate groups on the sugars). Cyanide was actually a better catalyst than was TPP in simple solution, where there is a negligible concentration of the C-2 anion of TPP, but of course not with an enzyme in modern biology. The cyanide ion was probably not toxic on prebiotic earth, but only when the oxygen atmosphere developed and iron porphyrin species were needed, which cyanide poisons. Thus, catalyses by TPP that are so important in modern biochemistry in the Calvin cycle for photosynthesis and the gluconic acid pathway for glucose oxidation, among other processes, were probably initially performed instead by cyanide ion until its toxicity with metalloproteins became a problem and primitive enzymes were present to work with TPP, or most likely its primitive precursors.In modern biochemistry, transketolase reactions (1) play key roles in the Calvin cycle for photosynthesis and in the gluconic acid pathway for glucose conversion to carbon dioxide with ATP formation. The reactions are catalyzed by enzymes using thiamine pyrophosphate 1 (TPP) as the coenzyme. In a typical example (Fig. 1), the top two carbons of a ketosugar such as fructose-6-phosphate 2 (with an S stereocenter at carbon 3) are removed when the C-2 anion 3 of TPP adds to the carbonyl group of the ketosugar, allowing it to be removed as a TPP derivative of the hydroxyacetyl anion. This stays bound to the enzyme as the remaining erythrose-4-phosphate fragment 4 dissociates. Then a new aldosugar such as glyceraldehyde-3-phosphate 5 is bound to the enzyme and the two-carbon piece on TPP is added to the aldehyde group, in this case forming xylulose-5-phosphate 6 (with a new 3-S stereocenter) after the TPP anion 3 is expelled. The S stereochemistry is normally assumed to reflect enzyme selectivity. In photosynthesis, xylulose then isomerizes to ribulose with a 3-R configuration.Open in a separate windowFig. 1.A transketolase reaction catalyzed by the enzyme with TPP as the coenzyme.If such reactions were occurring in the prebiotic world, it is unlikely that a molecule as complex as TPP could have been the catalyst. Thus, we propose that the TPP anion 3 now used in biochemistry was instead a simple cyanide anion under prebiotic conditions. It would not be unacceptably toxic until iron compounds such as heme were developed to handle the oxygen atmosphere that developed after photosynthesis had begun. To test this idea, we have performed a transketolase reaction between D-fructose 7 and D-glyceraldehyde 8 with catalysis by cyanide ion (Fig. 2). We found that the major products were erythrose 9 and xylulose 10, parallel to the modern biochemical result (which involves sugar phosphates, not simple sugars). Strikingly, the new chiral center that is formed when a two-carbon piece is added to D-glyceraldehyde has mainly the S configuration of xylulose 10, not ribulose 11, as with the modern enzyme.Open in a separate windowFig. 2.A nonenzymatic transketolase reaction catalyzed by cyanide ion under credible prebiotic conditions.     

Sinuous flow in metals

Annealed metals are surprisingly difficult to cut, involving high forces and an unusually thick “chip.” This anomaly has long been explained, based on ex situ observations, using a model of smooth plastic flow with uniform shear to describe material removal by chip formation. Here we show that this phenomenon is actually the result of a fundamentally different collective deformation mode—sinuous flow. Using in situ imaging, we find that chip formation occurs via large-amplitude folding, triggered by surface undulations of a characteristic size. The resulting fold patterns resemble those observed in geophysics and complex fluids. Our observations establish sinuous flow as another mesoscopic deformation mode, alongside mechanisms such as kinking and shear banding. Additionally, by suppressing the triggering surface undulations, sinuous flow can be eliminated, resulting in a drastic reduction of cutting forces. We demonstrate this suppression quite simply by the application of common marking ink on the free surface of the workpiece material before the cutting. Alternatively, prehardening a thin surface layer of the workpiece material shows similar results. Besides obvious implications to industrial machining and surface generation processes, our results also help unify a number of disparate observations in the cutting of metals, including the so-called Rehbinder effect.A typical cutting process involves removal of material in the form of a continuous chip in plane–strain (2D) (Fig. S1). When the material being cut (workpiece) is in an initially annealed state, an unusually thick chip results and the forces involved in the process are very large. This difficulty in cutting, well known in industrial practice (1, 2), has hitherto eluded fundamental explanation. At the mesoscale ( ～ 100 μm), the structure of the chip is assumed homogeneous, resulting from laminar plastic flow (3, 4). Using such a framework, augmented by ex situ observations (5), the high forces are attributed to the thick chip developed in the process, without an explanation of the cause of such anomalous chip formation. In this work, we revisit this long-held hypothesis using in situ observations and analysis, in the process unearthing a collective mode of plastic deformation.Open in a separate windowFig. S1.Schematic of idealized plane–strain cutting showing chip formation by smooth laminar flow, with simple shear. The deformation zone is highlighted in blue. Initial chip thickness h₀ is measured from free surface to the surface of material separation. The workpiece material undergoes plastic shape transformation to form a chip with final thickness h_c. The velocity of bulk material flow against the tool is V₀.Our model system consists of an annealed oxygen-free high-conductivity (OFHC) copper workpiece (grain size  ～ 500 μm) cut by a hard steel wedge (tool) at a velocity of V₀ = 0.42 mm/s. Temperature effects are negligible at this low velocity. Part of the workpiece material undergoes plastic (irreversible) shape transformation to form the chip (Fig. S1). The tool face is fixed to be normal to V₀, whereas the cutting depth (initial chip thickness) is maintained at h₀ = 50 μm. This model system mimics many natural (6) and industrial cutting (2) processes. The flow of metal against the tool face is observed in situ and photographed using a high-speed camera. The images are postprocessed using particle image velocimetry (PIV) to obtain a comprehensive record of velocity, strain rate, and strain field histories. This enables quantitative characterization of material flow past the tool edge. Additional experimental details are provided in Materials and Methods.Much to our surprise, the flow responsible for the shape transformation of the workpiece material into the chip, as revealed by the streakline pattern, bore little resemblance to any reported in classical plasticity. Fig. 1A, derived from a high-speed image sequence, shows streaklines that reveal a highly unsteady, sinuous flow with significant vorticity. The streaklines are extensively folded over in the chip, with peak-to-peak amplitudes in a single fold being as much as two-thirds of the chip thickness. Small surface protuberances, which form in the compressive field just ahead of the tool face, appear to trigger the folding; one such bump is bounded by two arrows (1 and 2) in Fig. 1. These arrows demarcate pinning points which are central to fold growth. The entire chip thus forms by repeated folding of the incoming material, i.e., sinuous flow—a collective plastic deformation mode, in the same genre as kinking (7) and shear banding (8, 9). In addition to Cu, sinuous flow was observed in other systems such as α-brass and commercially pure aluminum, showing that it is a truly mesoscopic mode, independent of the material’s crystal structure. Note that sinuous flow is not to be confused with the transition between laminar and rotational dislocation motion (10), which occurs at a much smaller scale.Open in a separate windowFig. 1.Sinuous flow mode of deformation in annealed copper. (A) Streakline pattern illustrating the underlying folding. Points 1 and 2 (red arrows) are pinning locations—the material between them forms a surface bulge while continuously rotating due to differential displacements. This is seen by the inclination of the fold peak with respect to the horizontal. (B) Optical micrograph of the chip. The back surface of the chip shows regular mushroom-like corrugations, separated by gaps. These are often misinterpreted as surface cracks. (C) Strain distribution in the chip, with mean ??? = 5.62. The highly inhomogeneous strain distribution inside the chip is clearly reflective of multiple folds during deformation. The figures in A–C correspond to different instants of time.The occurrence of sinuous flow cannot be inferred purely from postmortem structural observations in the chip or force measurements. As an illustration, an optical micrograph of the removed chip is shown in Fig. 1B. The surface of the chip shows repeated mushroom-like formations, with gaps in between. This structure has been (erroneously) described as resulting from homogeneous flow, supplemented by cracking on the chip free surface (1). In situ analysis reveals that the strain field in the chip is actually highly nonhomogeneous (Fig. 1C). Interestingly, this inhomogeneity is not reflected in the measured forces; no detectable oscillations occur at the fold frequency of around 1.7 Hz (Fig. S2).Open in a separate windowFig. S2.Energies and force in cutting annealed copper. A comparison between the cutting energy computed from force (E_force) measurements and PIV (E_PIV) analysis is shown. E_PIV = E_sin + E_sub, is obtained by integrating the stress along pathlines in the PIV flow field. E_sin and E_sub are the energies dissipated in the chip and the subsurface, respectively. The specific energy U_sin for sinuous flow (see main text) is E_sin per unit volume. The cutting force F_C is also shown. Interestingly, no fluctuations are observed in the force trace at the fold formation frequency  ～ 1.7 Hz.The kinematics of fold nucleation and development is clear from the motion of the initial bump and its boundaries (compare Fig. 1A). The set of four frames in Fig. 2 shows the evolution of such a bump into a fold. The two points P₁ and P₂, bounding the initial bump, move along with the material in each frame. A white dotted line—the bump axis—joins the peaks of adjacent streaklines, indicating the orientation of the impending fold. The color scheme depicts the underlying strain rate field, from PIV calculations. The development of multiple such folds in sinuous flow, along with superimposed streaklines and strain field, is also shown in Movie S1.Open in a separate windowFig. 2.Sequence of images with superimposed streaklines showing development of folds. The underlying strain rate field (with color bar inset) captures regions of local deformation. (A) P₁ and P₂ (marked by white arrows) are material locations that delimit the initial bump. These provide the pinning points for the surface bulge in B. As shear occurs closer to the tool face (C), this bulge rotates and is stretched diagonally, before finally forming an impending fold in D. Local shear is evident from the underlying strain rate field as well as the change in orientation of the bump axis (dashed white line) between B, C, and D.In Fig. 2A, P₁ and P₂, likely grain boundaries, delimit the initial bump and act as the local pinning points. They force the bump to deform plastically, resulting in a pronounced bulge on the free surface in Fig. 2B. The underlying strain rate field reflects this deformation in the two local colored zones surrounding the initial bump (Fig. 2 A and B). The bump axis is nearly parallel in both frames. Simultaneously with surface bulging, the workpiece material is also constantly forced against the vertical tool face. This constraint imparts a vertical velocity to each material point. The bulge in Fig. 2B is hence sheared, causing the axis to rotate in a counterclockwise direction (Fig. 2C). The magnitude of shear increases as the material nears the tool face; see the strain rate field in Fig. 2C. The bulge is amplified while also reducing its original width (Fig. 2D), with the material between P₁ and P₂ now constituting a single impending fold. Folding is complete once the original bump axis is rotated by nearly 90°, at which time another bulge is initiated ahead of the tool face and the process repeats.The chip hence comprises a series of folds, developed one after another in the manner above. Corresponding folds in the streakline pattern provide quantitative geometric fold characteristics as well as variations along the chip thickness. The results of this analysis are summarized in Fig. 3 (see SI Materials and Methods for details).Open in a separate windowFig. 3.Geometric parameters characterizing the observed folds. (A) Two adjacent streaklines (blue, red) are shown, demarcating a single fold. P is the fold peak (curve maximum), M₁, M₂ are fold troughs (curve minima), M is the midpoint of M₁M₂. P^′ is the maximum of the second streakline, the axial line PP^′ and the line PM subtend angles ϕ and θ with M₁M₂. The fold amplitude A and width W are the lengths of PM and M₁M₂, respectively. (B) Scatter plot of ϕ and θ. Data shown for the first (○), second (?), and third (□) streaklines from the free surface. Marker color indicates fold width W: red (cyan) corresponding to large (small) W. The values fall around the 45° line, deviation from which implies nonuniform streakline spacing. All of the wide folds undergo large shear whereas smaller ones remain upright (θ, ? ～ 90°) (C) Histogram of fold widths W. The mean width (dashed line) W_m ? 50 μm and one SD (dotted lines) are also shown. The mean wavelength of the folds is  ～ 200 μm at the point of formation.Inhomogeneous shear in the material can easily be seen in a plot of ϕ vs. θ, as shown in Fig. 3B. For symmetrically sheared folds, maxima of adjacent streaklines are expected to lie on the line PM, corresponding to the 45° line (dashed) in Fig. 3B. However, local shear results in varying distance between adjacent streaklines, as indicated in Fig. 3A. Additionally, both ϕ- and θ-values are clustered near 0° and 180°, which indicate large shear. Geometrically, this brings fold peaks P closer to the extrapolated minima line M₁M₂. Most wide folds undergo large shear, whereas a minor fraction (small folds) remain upright (θ, ? ? 90°) and form over existing larger folds.The distribution for W, including the mean W_m ? 50 μm and one SD, is shown in Fig. 3C. The wider folds (W ≥ 150 μm) occur near the beginning of the streaklines, getting progressively narrower as material flows past the tool. Subsequently, the small folds, constituting  ～ 10% of the total, are developed. The mean and maximum fold widths are smaller than the initial grain size in the material ( ～ 500 μm). The average fold wavelength is  ～  200 μm at the point of formation.The immediate consequence of this sinuous flow mechanism is that the resulting chip is quite thick—its final thickness h_c being 14× the initial thickness h₀. However, this significant thickening is not a priori indicative of the actual unsteady folding phenomenon, for such a shape change can also be envisaged in the framework of ideal smooth laminar flow (Fig. S1). Characteristically, however, the sinuous flow also produces a highly nonuniform strain field in the chip, fluctuating between 4 and 8, that reflects the underlying fold pattern (Fig. 1C). It is interesting to note that the representative (volume-weighted) strain for sinuous flow, ? ～ 5.6, is actually much lower than for an equivalent shape change by laminar flow, where ? ～ 8.1. This latter value is obtained if only the chip thickness ratio (h_c/h₀) is considered, without accounting for the actual flow process (2).Like the strain, the specific energy U (energy per unit volume) for chip formation, i.e., shape transformation, is also significantly smaller for the sinuous flow. By the usual integration of stress and strain along path lines in the sinuous flow field, U_sin is obtained as 2.9 J/mm³ (SI Materials and Methods and Fig. S2). In comparison, the corresponding value for an equivalent laminar flow (with ? = 8.1) is U_lam = 4.2 J/mm³, which is 45% greater than U_sin.Based on the strain and specific energy, the shape transformation into a chip is thus much more efficiently achieved by sinuous flow than by laminar flow. This is counterintuitive because, at first sight, the highly folded, sinuous flow appears quite inefficient, involving extensive redundant deformation. But, because selection of collective deformation modes is in general governed by their relative stability, the material’s preference for sinuous flow is likely the result of a flow instability in smooth laminar flow. Whereas there are other instances where large-strain plastic deformation occurs via nonhomogeneous modes, e.g., shear banding, kinking, and buckling, we are not aware of any prior observations of large shape changes being effected by the sinuous flow mode demonstrated here.Our observations have uncovered a previously unidentified mesoscopic flow mode in the plastic deformation of ductile metals, in the process also explaining a longstanding problem in cutting. The mechanism of fold formation appears to be strongly tied in with the large grain size and ductility common to such annealed metals and is driven primarily by the ability of the material to undergo large plastic deformation. Microscopically, each grain roughly constitutes a single fold, consistent with both the formation mechanism (Fig. 2) and fold width distributions (Fig. 3C). In this respect, it seems to have a similar origin as the folding mechanism reported in sliding wear (11), although the resulting flow here is significantly different—there is actual material removal, and folds of very large amplitude occur. Fold patterns observed in the sinuous flow show remarkable resemblance with other well-studied folding phenomena in geophysics (12), thin films (13), fluid flow (14, 15), and non-Newtonian plastics (16).The mechanism of fold development suggests that the difficulty in cutting annealed metals can be resolved if the sinuous flow mode is suppressed or eliminated altogether. Prestraining a thin surface layer (thickness  ～ h₀) of the annealed material to strains ? ≥ 1 causes refinement of the grain size and a reduction in the ductility. Doing so removes the two main triggers for sinuous flow, viz. bulge formation and the establishment of pinning points. Thus, prestraining should suppress the sinuous flow, a fact confirmed experimentally (Fig. 4A). The streakline pattern in the figure shows that the folding is eliminated, with an almost 70% reduction in both the cutting force and strain. Additionally, the flow is completely laminar, with a sharp, well-defined deformation zone and thin chip, as in the classical plasticity model. This surface prestraining can be accomplished by a suitable surface deformation process (17, 18).Open in a separate windowFig. 4.Suppression of sinuous flow. (A) Strain rate field with superimposed streaklines when cutting hardened copper. A sharply defined narrow shear zone is seen, as assumed in conventional plasticity models. The flow is laminar with insignificant bump formation ahead of the tool–chip interface. (B) Comparison of cutting forces (force in the direction of V₀) for different surface conditions. An annealed Cu sample (Inset, brown) is surface-coated over half its length with marking ink (Inset, blue). The cutting force in the uncoated region is very large (brown). A drastic reduction ( > 50%) is seen when cutting the material coated with ink (blue), due to sinuous flow suppression. Cutting a Cu sample with a thin prestrained (hardened) surface layer (Inset, red) results in low cutting force (red), reflective of laminar flow. The application of ink on the free surface of such a hardened layer shows no measurable effect on forces, consistent with the explanation based on sinuous flow.In light of the above observations, an intriguing possibility now presents itself—can sinuous flow be suppressed also by the simple application of a thin coating to the workpiece surface? Presumably, such a coating could be expected to modify the surface mechanical state (ductility, stiffness), thereby preventing the triggers for sinuous flow. To test this hypothesis, a layer of ink (Dykem) was painted onto the free surface of the annealed workpiece (Fig. 4B, Inset, and Fig. S1), before the cutting. Dykem ink, consisting of colored pigments in an alcohol (propanol + diacetone alcohol) medium, is commonly used to mark metals. Note that the surface of ink application is above the surface along which material separation occurs (Fig. S1), and away from the tool–chip contact. Interestingly, the bump formation ahead of the tool was much reduced by this ink application, with concomitant suppression of the folding and significantly lower forces resulting. Fig. 4B shows the large force drop observed when cutting an annealed Cu sample with the ink coated along half its length. When cutting the annealed workpiece region with no surface application (brown), sinuous flow occurs as expected, and the cutting force is very large. The force decreases by more than 50% when cutting over the workpiece region with the ink coating (blue), owing to sinuous flow suppression. This effect of the ink is very similar to that due to the prestrained surface layer, also shown for comparison (red). Application of a variety of other substances such as resins and nail polish, and even marking the surface with a sketch pen, was found to suppress the sinuous flow to various degrees. Such surface layer applications, however, did not have any noticeable influence on the forces and flow when cutting prestrained Cu, where the flow is intrinsically laminar. We hope to further explore this suppression hypothesis via additional controlled experiments and modeling.These results also offer a coherent picture of the Rehbinder effect (19) in the cutting of metals. This phenomenon concerns the small reduction ( < 10%) in cutting forces upon application of a suitable volatile fluid (e.g., CCl₄) on the workpiece free surface (20, 21). The effect has traditionally been attributed to either “microcracks” on the workpiece surface promoting a physicochemical effect (21, 22) or a fundamental change in the dislocation structure near the surface (20). Besides the speculative nature of these explanations, the reports of the force reductions have been inconsistent (23, 24). Sinuous flow provides a natural explanation for this effect—any surface application, including volatile CCl₄, will modify the surface mechanical state of the workpiece and inhibit initial bump formation ahead of the tool. Consequently, the folding will be diminished, resulting in lower forces. The inconsistent force reductions observed are hence most certainly due to the large variability in the initial state (annealed, partially/fully hardened) of the workpiece arising from the specific preparation procedures.Two important and contrasting implications of our observations involve the utility and suppression of sinuous flow. The highly “redundant” nature of the flow pattern suggests its use in designing new materials for energy absorption applications. This can be viewed as being complementary to materials with a tendency to form shear bands, such as metallic glasses (25). Chip structures, similar to those in Fig. 1B, have been reported in other metallic systems such as low carbon steels and pure iron (1, 5, 21), and microtomy of metals (26). This, together with our in situ observations in Cu, brass, and Al, points to the widespread occurrence of sinuous flow in annealed metals. Hence, this flow mode will also play an important role in micromachining (27) and surface deformation (17, 18) processes. On the other hand, as we have shown, a suitable surface treatment can suppress sinuous flow, thereby enabling easier processing of annealed metals. The large reduction in forces translates directly into an equivalent energy reduction. Furthermore, the reduced forces and energy dissipation should have favorable consequences for industrial machining, e.g., avoiding chatter-vibration instability across a broader range of process conditions, improved component surface quality, and enhanced tool life.