Isolation of sixteen microsatellite loci for Rhoadsia altipinna (Characiformes: Characidae) from an impacted river basin in western Ecuador

Sixteen polymorphic, dinucleotide microsatellite loci were developed for Rhoadsia altipinna, a small characid fish from impacted rivers in south western Ecuador. None of the loci were in linkage disequilibrium or deviated significantly from Hardy–Weinberg equilibrium after sequential Bonferroni correction. Variability was relatively high with allelic richness ranging between 2 and 22 alleles per locus (average = 9.125), observed heterozygosity ranging between 0.125 and 0.958 (average = 0.695), and expected heterozygosity ranging between 0.120 and 0.952 (average = 0.736).     

Is fluorine-18-fluorodeoxyglucose positron emission tomography useful in monitoring the response to treatment in patients with multiple myeloma?

Fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) is a useful diagnostic tool for the staging of patients with multiple myeloma (MM). The aim of this study is to perform a systematic review of the usefulness of FDG-PET or PET/CT in monitoring response to treatment in patients with MM. A comprehensive computer literature search of the PubMed/MEDLINE, Scopus and Embase databases was carried out to identify relevant peer-reviewed articles on the use of FDG-PET or PET/CT in monitoring the response to treatment in patients with MM. Ten studies described investigations of the role of FDG-PET or PET/CT in monitoring the response to treatment in 690 patients with MM or solitary plasmacytoma: six of these were conducted prospectively, while four studies were retrospective. These articles were retrieved in full-text version and analyzed. Based on these findings from the literature, FDG-PET or PET/CT appear to be useful in the assessment of treatment response in patients with MM.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Sensing surface morphology of biofibers by decorating spider silk and cellulosic filaments with nematic microdroplets

Probing the surface morphology of microthin fibers such as naturally occurring biofibers is essential for understanding their structural properties, biological function, and mechanical performance. The state-of-the-art methods for studying the surfaces of biofibers are atomic force microscopy imaging and scanning electron microscopy, which well characterize surface geometry of the fibers but provide little information on the local interaction potential of the fibers with the surrounding material. In contrast, complex nematic fluids respond very well to external fields and change their optical properties upon such stimuli. Here we demonstrate that liquid crystal droplets deposited on microthin biofibers—including spider silk and cellulosic fibers—reveal characteristics of the fibers’ surface, performing as simple but sensitive surface sensors. By combining experiments and numerical modeling, different types of fibers are identified through the fiber-to-nematic droplet interactions, including perpendicular and axial or helicoidal planar molecular alignment. Spider silks align nematic molecules parallel to fibers or perpendicular to them, whereas cellulose aligns the molecules unidirectionally or helicoidally along the fibers, indicating notably different surface interactions. The nematic droplets as sensors thus directly reveal chirality of cellulosic fibers. Different fiber entanglements can be identified by depositing droplets exactly at the fiber crossings. More generally, the presented method can be used as a simple but powerful approach for probing the surface properties of small-size bioobjects, opening a route to their precise characterization.Natural microfilaments produced by plants, insects, or spiders are fascinating materials not just because of their specific properties such as wear resistance, elasticity, tensile strength, and toughness (1–5) but also because of their microorganization (6–9). Their macroscopic properties can match properties of materials like kevlar but are at the same time biocompatible and biodegradable (10). These fascinating macroscopic properties actually originate from bulk and surface properties of the fibers (1). The chemical composition of the threads combined with their morphology determines the final properties of the material (11–13). The mechanical properties of the spider fibers are determined by the existence of a lyotropic liquid crystalline phase, from which the threads are drawn (14). Such silks are known to include nanoscale networks of defects and cavities that yield surface structures notably dependent on the spider species (3). These differences do not affect much the mechanical performance of the fibers (1, 3, 5). From a technological perspective, many attempts have been made to reproduce these natural bionetworks (15–17). In fact cellulose-based fibers with few micrometers of diameter, produced by electrospinning, can also acquire different morphologies depending upon the processing conditions, giving diverse features of the final threads and mats (18). Therefore, probing the surface structure of the microfibers is crucial for a complete understanding of their individual and interthreaded properties.From another perspective, nematic complex fluids are materials which are inherently responsive to diverse external stimuli, notably including diverse surface interactions which in the literature are known as the surface anchoring (19). Being effectively elastic materials, the orientational order of nematics responds on long, typically micrometer scales (20–22), which results in a spatially varying birefringence that can be optically detected (23). Recently, it was demonstrated that glass fibers induce numerous defects in a well-aligned nematic liquid crystal cell and thus provide a simple illustration of topological phenomena (24). It is also known that liquid crystal droplets can considerably change their structure by the action of otherwise imperceptibly small external stimuli (21). Pierced nematic and chiral nematic droplets develop defects that can be controlled by the liquid crystal elasticity, chirality, and surface boundary conditions (25, 26) indicating exceptional sensitivity. Therefore, to generalize, putting nematics into contact with diverse surfaces (18, 27) can be used as a simple but very powerful technique to detect the surface properties of microobjects such as biological fibers.In this paper we demonstrate the surface morphology sensing of biorelevant fibers, including spider silk and cellulosic microfibers, by nematic droplets that are sprayed onto the fibers. Specifically, we explore the chiral and achiral nature of the fiber’s surface and the in-plane or perpendicular alignment fields the fibers impose on the nematic. Droplets with degenerate in-plane and perpendicular alignment of the nematic at their free surfaces are explored, combining experiments and numerical modeling, to allow for tuning of the sensing precision. Further, the entanglement sites of the fiber webs are explored, with the droplets deposited at the sites clearly revealing contact, noncontact, and entangled morphologies.