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1.
Sarah K. Wexler Christine L. Goodale Kevin J. McGuire Scott W. Bailey Peter M. Groffman 《Proceedings of the National Academy of Sciences of the United States of America》2014,111(46):16413-16418
Despite decades of measurements, the nitrogen balance of temperate forest catchments remains poorly understood. Atmospheric nitrogen deposition often greatly exceeds streamwater nitrogen losses; the fate of the remaining nitrogen is highly uncertain. Gaseous losses of nitrogen to denitrification are especially poorly documented and are often ignored. Here, we provide isotopic evidence (δ15NNO3 and δ18ONO3) from shallow groundwater at the Hubbard Brook Experimental Forest indicating extensive denitrification during midsummer, when transient, perched patches of saturation developed in hillslopes, with poor hydrological connectivity to the stream, while streamwater showed no isotopic evidence of denitrification. During small rain events, precipitation directly contributed up to 34% of streamwater nitrate, which was otherwise produced by nitrification. Together, these measurements reveal the importance of denitrification in hydrologically disconnected patches of shallow groundwater during midsummer as largely overlooked control points for nitrogen loss from temperate forest catchments.Many forested catchments export far less nitrogen (N) in streamwater than they receive in atmospheric deposition (1, 2). The rest of the deposited N may accumulate in vegetation or soil organic matter, or be lost in gaseous form. Losses of N to denitrification, the microbial reduction of aqueous nitrate (NO3−) to nitrous oxide (N2O, a greenhouse gas) and N2 gas, are extremely difficult to measure due to the difficulty in directly measuring N2 fluxes and due to the high degree of spatiotemporal variability in redox conditions and substrate sources (3). Many past studies using a range of measurements (streamwater nitrate isotopic composition, the acetylene block technique, N2O emissions, and mass balance calculations) have concluded that denitrification in temperate forests is highly uncertain or generally unimportant (e.g., refs. 4–8).Nitrogen budgets are particularly perplexing in the northern hardwood forests at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire, USA, where atmospheric deposition has supplied 6–8 kg N ha−1⋅yr−1 for half a century, a rate ∼5–10 times preindustrial levels (7–10). Accumulation of N in plant biomass ceased in the early 1990s (10, 11), while streamwater inorganic N export from catchments across the HBEF and nearby streams decreased to <1 kg N ha−1⋅yr−1, for reasons that remain elusive (9, 10, 12). These N flux measurements imply increasingly important roles for N gas loss or storage in soil organic matter. However, both processes are so difficult to quantify that the fate, drivers, and consequences of the “missing” N remain unknown, at the HBEF and elsewhere (8–10, 12).Measurement of the dual isotopic composition of NO3− (δ15NNO3 and δ18ONO3) provides a powerful tool to identify NO3− sources and to infer its loss to denitrification (13–16). Values of δ18ONO3 differ greatly between NO3− in precipitation and NO3− produced by nitrification (refs. 13–16, Table S1), which is the microbial oxidation of NH4+ to NO3−. Measurements of δ18ONO3 have enabled detection of direct contributions of precipitation NO3− to streamwater (Table S1), especially during snowmelt, when catchments often release large quantities of NO3− (7, 8). However, past δ18ONO3 measurements show that nitrification—not precipitation—supplies the vast majority of NO3− in streamwater at the HBEF (10, 17, 18) and other forested catchments (refs. 14 and 19, Table S1).Nitrate isotopic composition reflects not only NO3− sources but also fractionation from a range of processes (14, 20). During denitrification, heterotrophic microbes consume organic carbon using NO3− as an electron acceptor under low-oxygen conditions, in a 5:4 molar ratio of carbon:NO3−. If NO3− is not replenished or consumed by other processes, denitrification progressively enriches both 18O and 15N in the residual NO3−, with an O:N fractionation ratio of 0.4–0.7 in the field (14, 20, 21) and up to 1.0 in laboratory studies (22). The fractionation ratio is the slope of the relationship between δ18ONO3 and δ15NNO3. Dual isotopic enrichment and these enrichment ratios provide evidence of denitrification. Dual isotope analysis of NO3− has provided evidence for denitrification in large aquifers (e.g., ref. 20) and in drainage waters receiving heavy agricultural N loads (e.g., refs. 21 and 23), whereas recent catchment studies in a subtropical forest (15) and a warm Mediterranean grassland (24) have reported isotopic evidence of denitrification in soil or groundwater. However, dozens of past studies of stream δ18ONO3 and δ15NNO3 in naturally vegetated temperate and boreal catchments (refs. 14 and 19, Table S1), including the HBEF (10, 17), have revealed little if any isotopic evidence of denitrification.To investigate the role of denitrification at the HBEF, we measured NO3− isotopic composition throughout watershed 3 (WS3), a hydrologic reference catchment drained by Paradise Brook within the HBEF (Fig. 1), during the first two weeks of July 2011, close to the warmest part of the year (Fig. S1). Sampling encompassed nine shallow groundwater wells, a seep, and 19 stream sites along Paradise Brook and its tributaries. The shallow groundwater wells accessed water from saturated soil within the solum above the C horizon at depths between 30 and 115 cm. Three of the nine wells were close to (<2 m) or within the perennial stream channel; the other six were more distal (≥4 m) and upgradient from the perennial channel. Four rain events occurred during the sampling period (0.8–12.3 mm, 26.8 mm total); all contained NO3− and NH4+ concentrations that exceeded those in streamwater and groundwater by an order of magnitude (Fig. 2 A and B). Nitrogen export over the study period amounted to 0.006 ± 0.003 kg N ha−1, consisting of 24% NO3−, 13% NH4+, and 63% dissolved organic nitrogen (DON). Streamflow over the sampling period (5.3 mm) exported less than 2% of rainfall N input (0.335 ± 0.087 kg N ha−1). The remaining 98% was retained within the catchment or lost via denitrification. If this 98% were denitrified in soil or shallow groundwater, it gives a maximum denitrification rate of 5.0 (3.6–6.4) kg N ha−1 if extrapolated over the growing season. Although this figure represents the maximum loss of N to denitrification, recent extrapolations of N2 and N2O flux measurements from soil cores from the HBEF found denitrification rates during the growing season higher than previous estimates and equal to or higher than atmospheric deposition while follow-up measurements found rates ranging from 4–10 kg N ha−1⋅y−1 at HBEF (25).Open in a separate windowFig. 1.Location of HBEF in the northeast United States (Top Right), showing HBEF watersheds 1–9 (Bottom Right) and watershed 3 (WS3; Left). WS3 shows drainage network comprising Paradise Brook and tributary channels, with sampling locations from the weir (triangle), streams (filled circles), wells (empty circles; including wells ≥4 m from surface streamflow in July 2011 (JD05, JD17, JD18, JD19, JD29, JD30) and those <2 m (R12, east bank; R13 in-stream; R14, west bank) and a seep (dash). Contour interval is 3 m; elevation range is 537–732 m.
Open in a separate windowMean ±1 SD are shown. Values are combined by sample type across sites and dates, except as noted for 3 July.Open in a separate windowFig. 2.Temporal pattern of nitrate concentration (A and B), δ15NNO3 (C and D), and δ18ONO3 (E and F) for rainfall and streams (A, C, and E) and for wells (≥4 m from surface flow) and the seep (B, D, and F). Symbols are denoted in A and B for each site type. The asterisk in E denotes an estimated rainfall δ18ONO3 value, as the average of adjacent dates. Isotopic values are expressed per mil (‰) relative to established standards, Vienna Standard Mean Ocean Water (VSMOW) for δ18O and air for δ15N. 相似文献
Table 1.
Concentrations of NO3−, NH4+, DON, and DOC (μM), and δ15NNO3 and δ18ONO3 of water samples from watershed 3, Hubbard Brook Experimental Forest, New Hampshire July 2011Concentration | Isotopic composition, ‰ | ||||||||
Sample type | N | NO3−, μM | NH4+, μM | DON, μM | DOC, μM | DOC:DON, molar ratio | DOC:NO3−, molar ratio | δ15NNO3 | δ18ONO3 |
Rainfall | 4 | 27.3 ± 12.4 | 54.7 ± 12.0 | 2.4 ± 0.8 | N/A | N/A | N/A | −5.1 ± 3.0 | 59.2 ± 9.7 |
Seep | 6 | 1.6 ± 0.6 | 0.8 ± 0.5 | 11.3 ± 3.0 | 303 ± 56 | 26 ± 6 | 218 ± 103 | −1.9 ± 1.0 | −2.4 ± 3.3 |
Wells ≥4 m from stream | 21 | 2.3 ± 2.4 | 2.3 ± 1.7 | 8.7 ± 1.8 | 364 ± 105 | 41 ± 11 | 353 ± 271 | 10.9 ± 5.4 | 16.7 ± 4.6 |
Wells <2 m from stream | 28 | 3.4 ± 2.2 | 1.5 ± 1.0 | 7.1 ± 2.5 | 210 ± 59 | 32 ± 11 | 92 ± 79 | 3.3 ± 4.0 | 2.3 ± 8.4 |
Streams, 3 July | 7 | 2.4 ± 1.3 | 0.8 ± 0.2 | 8.1 ± 3.4 | 247 ± 43 | 28 ± 9 | 129 ± 69 | −3.3 ± 3.0 | 15.9 ± 3.6 |
Streams, 4–14 July | 48 | 1.8 ± 1.2 | 1.2 ± 0.8 | 5.9 ± 2.7 | 236 ± 96 | 42 ± 15 | 197 ± 162 | −0.9 ± 2.1 | −1.6 ± 2.7 |
Weir, 4–14 July | 8 | 2.0 ± 1.1 | 1.0 ± 0.6 | 5.3 ± 2.4 | 219 ± 88 | 43 ± 16 | 137 ± 83 | −1.4 ± 1.8 | 0.4 ± 3.7 |
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3.
J. M. Kale Sniderman Jon D. Woodhead John Hellstrom Gregory J. Jordan Russell N. Drysdale Jonathan J. Tyler Nicholas Porch 《Proceedings of the National Academy of Sciences of the United States of America》2016,113(8):1999-2004
The Pliocene epoch (5.3–2.6 Ma) represents the most recent geological interval in which global temperatures were several degrees warmer than today and is therefore considered our best analog for a future anthropogenic greenhouse world. However, our understanding of Pliocene climates is limited by poor age control on existing terrestrial climate archives, especially in the Southern Hemisphere, and by persistent disagreement between paleo-data and models concerning the magnitude of regional warming and/or wetting that occurred in response to increased greenhouse forcing. To address these problems, here we document the evolution of Southern Hemisphere hydroclimate from the latest Miocene to the middle Pliocene using radiometrically-dated fossil pollen records preserved in speleothems from semiarid southern Australia. These data reveal an abrupt onset of warm and wet climates early within the Pliocene, driving complete biome turnover. Pliocene warmth thus clearly represents a discrete interval which reversed a long-term trend of late Neogene cooling and aridification, rather than being simply the most recent period of greater-than-modern warmth within a continuously cooling trajectory. These findings demonstrate the importance of high-resolution chronologies to accompany paleoclimate data and also highlight the question of what initiated the sustained interval of Pliocene warmth.Our knowledge of Pliocene climates is based predominantly on the rich marine sediment record (1), but understanding of Pliocene climates on land remains limited because the few existing terrestrial archives tend to have poor age control and are of short duration. This deficiency is no more evident than in paleovegetation records, which are integral to modeling Pliocene climates because vegetation is both a critical indicator of regional precipitation and also makes a large contribution to planetary albedo (2, 3). Several syntheses of Pliocene vegetation have been compiled (3, 4), but their value is hampered by substantial uncertainty surrounding the synchroneity of the records. For example, vegetation syntheses have focused on the Late Pliocene (the Piacenzian Age, 3.6–2.6 Ma) because this period is considered likely to be a closer biological and geological analog for future warming than the Early Pliocene (1, 5). However, only 6 of 32 Southern Hemisphere records interpreted by Salzmann et al. (4) as documenting the nature of Late Pliocene vegetation are both based on plant fossils and can confidently be assigned to the Late Pliocene; the remaining records have such poor chronologies that their possible ages range from Late Miocene to Early Pleistocene, or the records only infer the nature of vegetation indirectly from geomorphology or vertebrate fossils (SI Appendix, Table S1). As a result, current understanding of the response of Southern Hemisphere vegetation to Late Pliocene climates (2–4) may conflate the climate and vegetation history of ≥5 My of the late Cenozoic (Fig. 1), an interval that is characterized by global cooling (6) and, in subtropical- to midlatitudes, increasing aridity (7–10). This uncertainty is problematic for two reasons. First, it has become clear that the peak of Pliocene warmth globally was not generally within the Late Pliocene but occurred earlier, within the Early Pliocene (11). Second, largely because of a lack of continuous and well-dated records, the relationship between the longer-term cooling/aridification trend and Pliocene warmth remains unclear. Thus, was Pliocene warmth a discrete interruption of late Cenozoic cooling/aridification trends (6), or was the Pliocene merely an interval immediately preceding the abrupt steepening of these trends associated with the onset of extensive Northern Hemisphere glaciation (12, 13)?Open in a separate windowFig. 1.Conservative estimates of the age ranges of Southern Hemisphere vegetation records accepted by Salzmann et al. (3) as indicative of Late Pliocene (Piacenzian, 2.6–3.6 Ma) terrestrial vegetation. Sample code numbers are those used by Salzmann et al. Colors represent record type (pollen, wood, vertebrate, sediment). Bold colors indicate records clearly falling within the Late Pliocene, and faint colors indicate records either falling outside of the Late Pliocene or with broader age ranges. Of the 32 records, only 6 based on plant fossil data can be confidently assigned a Late Pliocene age. For Makapan, two age estimates are provided, reflecting uncertainty whether the record can be attributed to the Pliocene as a whole or to the Late Pliocene.To place the temporal evolution of Southern Hemisphere Pliocene vegetation and climate on a firmer chronological footing, we generated fossil pollen records from radiometrically dated speleothems (secondary cave carbonate deposits such as stalagmites and flowstones) from southern Australia. Pollen assemblages were extracted from samples collected in five caves on the Nullarbor Plain (Fig. 2 and SI Appendix, Fig. S1), a large (200,000 km2) karst province uplifted above sea level during the late Miocene (14). Consistent with its position in Southern Hemisphere subtropical latitudes, the Nullarbor Plain is currently semiarid, receiving mean annual precipitation (MAP) of ca. 180–270 mm (SI Appendix, Fig. S1), with a weak winter-maximum. Mean annual temperature is ∼18 °C. The vegetation is largely treeless chenopod shrubland, grading coastward into sparse, low open woodlands dominated by Acacia (Mimosaceae) or Eucalyptus (Myrtaceae). Nullarbor speleothems grew during the late Neogene (15) but negligible calcite speleothem growth occurs today. Recent development and refinement of the uranium-lead (U-Pb) chronometer has allowed high-precision geochronology of such ancient speleothem samples (15–18). Our fossil pollen record is composed of 13 polleniferous samples (out of 81 explored for pollen), the oldest dated at 5.59 ± 0.15 Ma, in the latest Miocene, and the youngest 0.41 ± 0.07 Ma, in the Middle Pleistocene (Open in a separate windowFig. 2.Locality map showing Nullarbor caves in southern Australia and sites mentioned in the text. The map was produced using Ocean Data View (odv.awi.de).
Open in a separate window 相似文献
Table 1.
Age and pollen yield of Nullarbor speleothemsSample | Age, Ma (±2σ) | Mass dissolved, g | Pollen count | Pollen grains g−1 |
2121-1 | 0.41 ± 0.07 | 215.48 | 401 | 1.9 |
645-15 | 3.47 ± 0.13 | 139.06 | 547 | 3.9 |
370-3 | 3.62 ± 0.14 | 311.68 | 105 | 0.3 |
370-1 | 3.63 ± 0.17 | 221.69 | 35 | 0.2 |
370-5 | 3.76 ± 0.12 | 198.75 | 221 | 1.1 |
645-13 | 4.14 ± 0.11 | 202.77 | 166 | 0.8 |
370-11 | 4.15 ± 0.12 | 302.09 | 256 | 0.8 |
2200-12.4 | 4.16 ± 0.12 | 57.8 | 389 | 6.7 |
2200-2 | 4.20 ± 0.14 | 68.47 | 279 | 4.1 |
483-9 | 4.89 ± 0.12 | 181.89 | 91 | 0.5 |
370-16 | 4.97 ± 0.12 | 760.1 | 60 | 0.1 |
370-17 | 5.34 ± 0.12 | 247.49 | 113 | 0.5 |
370-19 | 5.59 ± 0.15 | 240.83 | 152 | 0.6 |
∑ = 3,148 | ∑ = 2,815 | = 1.7 |
4.
《Proceedings of the National Academy of Sciences of the United States of America》2015,112(32):E4354-E4363
Recent advances in biosensing technologies present great potential for medical diagnostics, thus improving clinical decisions. However, creating a label-free general sensing platform capable of detecting multiple biotargets in various clinical specimens over a wide dynamic range, without lengthy sample-processing steps, remains a considerable challenge. In practice, these barriers prevent broad applications in clinics and at patients’ homes. Here, we demonstrate the nanoplasmonic electrical field-enhanced resonating device (NE2RD), which addresses all these impediments on a single platform. The NE2RD employs an immunodetection assay to capture biotargets, and precisely measures spectral color changes by their wavelength and extinction intensity shifts in nanoparticles without prior sample labeling or preprocessing. We present through multiple examples, a label-free, quantitative, portable, multitarget platform by rapidly detecting various protein biomarkers, drugs, protein allergens, bacteria, eukaryotic cells, and distinct viruses. The linear dynamic range of NE2RD is five orders of magnitude broader than ELISA, with a sensitivity down to 400 fg/mL This range and sensitivity are achieved by self-assembling gold nanoparticles to generate hot spots on a 3D-oriented substrate for ultrasensitive measurements. We demonstrate that this precise platform handles multiple clinical samples such as whole blood, serum, and saliva without sample preprocessing under diverse conditions of temperature, pH, and ionic strength. The NE2RD’s broad dynamic range, detection limit, and portability integrated with a disposable fluidic chip have broad applications, potentially enabling the transition toward precision medicine at the point-of-care or primary care settings and at patients’ homes.Biosensing platforms have enabled various applications in different fields of clinical medicine such as biomarker/drug discovery and initiation and monitoring of therapy (1–3). However, material cost, accessibility, ease of operation, lack of portability, and complexity in readout remain major challenges for developing robust diagnostic assays (SI Appendix, Table S1). Recent advances in nanotechnology and biosensing have created new avenues to address these issues (4–9). Technically, they have provided integration of high-throughput sampling with readout systems for quantitative detection of disease-specific biotargets. Therefore, they have demonstrated great potential to revolutionize medical diagnostics. However, from a clinical and technological perspective, existing platforms still face several challenges. First, lengthy assay time hinders physicians from making early clinical decisions. Second, examining clinical samples with diverse pH range, ionic content, and ionic strength requires extensive sample preprocessing steps to avoid signal fluctuations and inaccuracies, and this requirement increases the complexity of current systems. Third, temperature is an extrinsic factor that needs to be tightly controlled for reliable measurements, thus resulting in additional cost for developing diagnostic technologies. Fourth, a wide dynamic linear range is needed to distinguish concentrations of biotargets reliably. Fifth, sensitivity is hindered by the complex composition of biological specimens, thus requiring labeling (10, 11), which further increases assay cost significantly. Sixth, since a panel of parameters is monitored for complex diseases, detection of multiple biotargets is critically needed. Seventh, portability is another important parameter to deploy a biosensing platform at point-of-care (POC) settings. These practical barriers seriously reduce the broad clinical applicability of current platforms for diagnostic implementations.The development of various sensitive detection modalities has addressed some of these requirements. The mass-sensitive piezoelectric and microcantilever-based systems that use surface oscillations or changes in surface stress for molecular interactions and mass input (12, 13) have simplified sample-preparation steps. Likewise, electrical detection systems such as electrochemical sensors and interdigitated electrodes sense changes in molecular charges (14–17) and can provide affordable, simple, and high-throughput measurements. Further, plasmonic-based platforms that use an electrical field around metal surfaces/nanoparticles coupled with light (5, 18–22) have minimized readout complexity and demonstrated quantitative and sensitive measurements (23–26). In particular, surface plasmons on 3D-oriented surfaces generate highly sensitive spots that provide a sevenfold stronger electrical field than 2D-oriented plasmonic sensors (27–29). However, these advances have only reduced the effort required for sample preparation and the readout complexity, and some critical challenges remain unaddressed as summarized in Parameters Magneto-nanosensor chip (51) SMC (Singulex) (52) Simoa (Quanterix) (52) Bio-barcode assay (53) ChIP-NMR biosensor (54) Advanced SPR biosensor (55) Plasmonic ELISA (56) Plasmonic gold chip (36) NE2RD Target Lactoferrin, survivin, CEA, VEGF, EpCAM, G-CSF, TNF-α, and eotaxin cTnI, cytokines, amyloid-β, IL-22 PSA, TNF-α, Tau PSA S. aureus, mouse macrophages, breast cancer cells, and multiple protein biomarkers E. coli O157:H7 PSA and HIV-1 capsid antigen p24 Insulin Ab, GAD65 Ab, and IA2 Ab IFN-γ, carbamazepine, casein, E. coli, human lung adenocarcinoma epithelial cells, HBV, DENV-1, DENV-2, TVV-1, KSHV and HIV-1 Assay time >2 h 20 min Four 96-well plates per day (for single assay, not multiplex) Five 96-well plates per day (for single assay, not multiplex) >3 h 30 min 30 min for incubation and 10 min for analysis 30 min >5 h 30 min <2 h 1 h for incubation and 10 min for analysis Multiple biotarget detection Only protein biomarkers Not available 10-plex Not available Bacteria, mammalian cells, and protein biomarkers Not available Only two protein biomarkers Only autoimmune antibodies Protein biomarkers, protein allergens, drugs, bacteria, eukaryotic cells, and viruses Readout Magnetic field-based detection Fluorescent-based system (digital and analog at high concentrations) Fluorescent-based system (digital and analog at high concentrations) Scanometric detection for DNA NMR signal Long-range surface plasmons enhanced by magnetic nanoparticles Optical-based detection NIR-FE Plasmonic signal on a 3D-oriented substrate Limit of detection 1 pg/mL or 5 fM (with mono labeling) fg–pg/mL fg–pg/mL 330 fg/mL 5 pg/mL (∼1 ng in 5 µL) 50 cfu/mL 1 ag/mL 0.1 kU/mL 400 fg/mL and ∼100 copies/mL 10 fg/mL or 50 aM (with dual labeling) Linear dynamic range Six orders of magnitude Four orders of magnitude Four orders of magnitude Two to three orders of magnitude Four orders of magnitude Three to four orders of magnitude Three to four orders of magnitude Four to five orders of magnitude Eight orders of magnitude Sample type PBS, serum, urine, and saliva Plasma, serum, CSF, cell lysate, urine, human brain tissue homogenate Serum PBS and serum PBS and serum PBS PBS Whole serum or blood PBS, whole saliva, serum, and whole blood