The bacteria in the water column and surface sediments are inherently intertwined and inseparable in aquatic ecosystems, yet little is known about the integrated spatiotemporal dynamics and driving mechanisms of both planktonic and sedimentary bacterial communities in reservoirs. By investigating the planktonic and sedimentary bacteria during four seasons from 88 samples of 11 representative sites across the Danjiangkou reservoir, we depicted an integrated biogeographic pattern of bacterial communities in the water source of the world's largest water diversion project. Our study revealed both planktonic (mantel r = 0.502, P = 0.001) and sedimentary (mantel r = 0.131, P = 0.009) bacterial communities were significantly correlated with environmental heterogeneity, but a weak disparity along spatial heterogeneity, and the significant seasonal dynamics of planktonic (mantel r = 0.499, P = 0.001) rather than sedimentary bacteria. Particularly, rare biosphere played a main role in determining the community succession in the reservoir. It not only exhibited a more striking environmental separation than abundant taxa but also was an essential part in mediating spatiotemporal shifts of planktonic bacteria and maintaining the stability of bacterial community. These rare bacteria were respectively mediated by stochastic (62.68%) and selective (79.60%) processes in water and sediments despite abundant taxa being largely determined by stochastic processes (86.88-93.96%). Overall, our study not only fills a gap in understanding the bacterial community dynamics and underlying drivers in source water reservoirs, but also highlights the particular importance of rare bacteria in mediating biogeochemical cycles in world's large reservoir ecosystems.
Summary A growing number of governments are pledging to achieve net-zero greenhouse gas emissions by mid-century. Despite such ambitions, realized emissions reductions continue to fall alarmingly short of modeled energy transition pathways for achieving net-zero. This gap is largely a result of the difficulty of realistically modeling all the techno-economic and sociopolitical capabilities that are required to deliver actual emissions reductions. This limitation of models suggests the need for an energy-systems analytical framework that goes well beyond energy-system modeling in order to close the gap between ambition and reality. Toward that end, we propose the Emissions-Sustainability-Governance-Operation (ESGO) framework for structured assessment and transparent communication of national capabilities and realization. We illustrate the critical role of energy modeling in ESGO using recent net-zero modeling studies for the world's two largest emitters, China and the United States. This illustration leads to recommendations for improvements to energy-system modeling to enable more productive ESGO implementation.
Abstract We present OH observations made in Amazonas, Brazil during the Green Ocean Amazon campaign (GoAmazon2014/5) from February to March of 2014. The average diurnal variation of OH peaked with a midday (10:00?15:00) average of 1.0 ? 106 (±0.6 ? 106) molecules cm?3. This was substantially lower than previously reported in other tropical forest photochemical environments (2?5 ? 106 molecules cm?3) while the simulated OH reactivity was lower. The observational data set was used to constrain a box model to examine how well current photochemical reaction mechanisms can simulate observed OH. We used one near-explicit mechanism (MCM v3.3.1) and four condensed mechanisms (i.e., RACM2, MOZART-T1, CB05, CB6r2) to simulate OH. A total of 14 days of analysis shows that all five chemical mechanisms were able to explain the measured OH within instrumental uncertainty of 40% during the campaign in the Amazonian rainforest environment. Future studies are required using more reliable NOx and VOC measurements to further investigate discrepancies in our understanding of the radical chemistry in the tropical rainforest.
Conventional digital cameras typically accumulate all the photons within an exposure period to form a snapshot image. It requires the scene to be quite still during the imaging time, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scenes. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic light intensity variation continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, which fires a spike and restarts the photon accumulation immediately once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from captured spike stream, this paper presents an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via adaptive temporal filtering. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with low model complexity. Experimental results demonstrate that the proposed scheme outperforms the existing schemes significantly, producing much better objective and subjective qualities for spike camera image reconstruction.
Soil microbes assemble in highly complex and diverse microbial communities, and microbial diversity patterns and their drivers have been studied extensively. However, diversity correlations and co-occurrence patterns between bacterial, fungal, and archaeal domains and between microbial functional groups in arid regions remain poorly understood. Here we assessed the relationships between the diversity and abundance of bacteria, fungi, and archaea and explored how environmental factors influence these relationships. We sampled soil along a 1500-km-long aridity gradient in temperate grasslands of Inner Mongolia (China) and sequenced the 16S rRNA gene of bacteria and archaea and the ITS2 gene of fungi. The diversity correlations and co-occurrence patterns between bacterial, fungal, and archaeal domains and between different microbial functional groups were evaluated using α-diversity and co-occurrence networks based on microbial abundance. Our results indicate insignificant correlations among the diversity patterns of bacterial, fungal, and archaeal domains using α-diversity but mostly positive correlations among diversity patterns of microbial functional groups based on α-diversity and co-occurrence networks along the aridity gradient. These results suggest that studying microbial diversity patterns from the perspective of functional groups and co-occurrence networks can provide additional insights on patterns that cannot be accessed using only overall microbial α-diversity. Increase in aridity weakens the diversity correlations between bacteria and fungi and between bacterial and archaeal functional groups, but strengthens the positive diversity correlations between bacterial functional groups and between fungal functional groups and the negative diversity correlations between bacterial and fungal functional groups. These variations of the diversity correlations are associated with the different responses of microbes to environmental factors, especially aridity. Our findings demonstrate the complex responses of microbial community structure to environmental conditions (especially aridity) and suggest that understanding diversity correlations and co-occurrence patterns between soil microbial groups is essential for predicting changes in microbial communities under future climate change in arid regions.
Abstract Climate change will likely increase the total streamflow in most headwaters on the Tibetan Plateau in the next decades, yet the response of runoff components to climate change and permafrost thaw remain largely uncertain. Here, we investigate the changes in runoff components under a changing climate, based on a high-resolution cryosphere-hydrology model (Spatial Processes in Hydrology model, SPHY) and multi-decadal streamflow observations at the upstream (Jimai) and downstream stations (Maqu and Tangnaihai) in the source-region of the Yellow River (SYR). We find that rainfall flow dominates the runoff regime in SYR (contributions of 48%?56%), followed by snowmelt flow (contributions of 26%/23% at Maqu/Tangnaihai). Baseflow is more important at Jimai (32%) than at the the downstream stations (21%?23%). Glacier meltwater from the Anyê Maqên and Bayankala Mountains contributes negligibly to the downstream total runoff. With increasing temperature and precipitation, the increase in total runoff is smaller in the warm and wet downstream stations than in the cold and dry upstream station. This is because of a higher increase in evapotranspiration and a larger reduction in snowmelt flow in the downstream region in response to a warming climate. With temperature increase, there is less increase in rainfall flow in the downstream region due to increased water loss through evapotranspiration. Meanwhile, the decline in snowmelt flow is larger further downstream, which can negatively impact the spring irrigation for the whole Yellow River basin that supports the livelihoods of 140 million people. Importantly, we find that baseflow plays an increasingly important role in the permafrost-dominated upstream region with atmospheric warming and permafrost thaw, accompanied by decreased surface flow. These findings improve our current understanding of how different hydrological processes respond to climate change and provide insights for optimizing hydropower and irrigation systems in the entire Yellow River basin under a rapidly changing climate.
Performance and molecular changes of an aerobic denitrifying phosphorus accumulating bacteria Pseudomonas psychrophila HA-2 have been investigated under different temperatures and ZnO nanoparticles (NPs) exposures. Strain HA-2 removed 95.7% of total nitrogen (TN) and 24.6% of phosphorus at 10°C, which was attributed to the joint up-regulation of intracellular energy metabolism and ribosome. Moreover, with the increase of ZnO NPs from 0 to 100mg/L, TN and phosphurs removal efficiencies decreased from 95.7% to 44.5% and 24.6% to 6.8% at 10°C, respectively, whereas phosphorus removal rate increased from 10.5% to 24.5% at 20°C. Further transcriptomics and proteomics revealed that significant down-regulation of purine and amino acid metabolisms was the main reason for the inhibitory effect at 10°C, while the up-regulation of antioxidant pathways and functional genes expressions was responsible for the promoted phosphorus accumulation at 20°C. This study provides a potential solution for improving biological nutrients removal processes in winter months.
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.
Glyoxal and methylglyoxal are vital carbonyl compounds in the atmosphere and play substantial roles in radical cycling and ozone formation. The partitioning process of glyoxal and methylglyoxal between the gas and particle phase via reversible and irreversible pathways could efficiently contribute to secondary organic aerosol (SOA) formation. However, the relative importance of two partitioning pathways still remains elusive, especially in the real atmosphere. In this study, we launched five field observations in different seasons and simultaneously measured glyoxal and methylglyoxal in the gas and particle phase. The field-measured gas-particle partitioning coefficients were 5–7 magnitudes higher than the theoretical ones, indicating the significant roles of reversible and irreversible pathways in the partitioning process. The particulate concentration of dicarbonyls and product distribution via the two pathways were further investigated using a box model coupled with the corresponding kinetic mechanisms. We recommended the irreversible reactive uptake coefficient γ for glyoxal and methylglyoxal in different seasons in the real atmosphere, and the average value of 8.0×10-3 for glyoxal and 2.0×10-3 for methylglyoxal best represented the loss of gaseous dicarbonyls by irreversible gas-particle partitioning processes. Compared to the reversible pathways, the irreversible pathways played a dominant role, with a proportion of more than 90% in the gas-particle partitioning process in the real atmosphere and the proportion was significantly influenced by relative humidity and inorganic components in aerosols. However, the reversible pathways were also substantial, especially in winter, with a proportion of more than 10%. The partitioning processes of dicarbonyls in reversible and irreversible pathways jointly contributed to more than 25% of SOA formation in the real atmosphere. To our knowledge, this study is the first to systemically examine both reversible and irreversible pathways in the ambient atmosphere, strives to narrow the gap between model simulations and field-measured gas-particle partitioning coefficients, and reveals the importance of gas-particle processes for dicarbonyls in SOA formation.