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In late November 2022, most regions in China were hit by a strong northwest-path cold wave, bringing a high-hazard extreme low-temperature event (ELTE). Several parts of Northwest China experienced extremely low temperatures and record-breaking snow depths. A stratosphere–troposphere synergetic effect was suggested to be closely related to the ELTE according to a diagnostic analysis. On the one hand, the concurrent establishment of two blockings in Europe–Northeast Atlantic and North Pacific led to a polar vortex split at the tropopause, and the Arctic Oscillation phase subsequently turned negative. An airflow with high potential vorticity (PV) was squeezed out of the Arctic. Meanwhile, a high-PV air that originated from the lower stratospheric Arctic was conveyed southwards to the western Siberian Plain along the sloping isentropic surface. This condition triggered a tropospheric response in which the East Asia trough deepened due to the intensified cyclonic circulation induced by the high-PV intrusion. On the other hand, downward propagation of stratospheric anomalies accompanied by stratospheric polar vortex displacement and split was observed in mid- and late November, respectively. Changes in stratospheric circulation contributed to enhanced blockings over Europe–Northeast Atlantic in the lower stratosphere or upper troposphere. As a result, the inverted omega-shaped circulation pattern was formed in the middle to upper troposphere, and it consisted of the intensified East Asia trough and two blockings in the upstream and downstream regions. The high-PV air upstream of the East Asia trough was advected to China, which directly led to the outbreak of the ELTE. The establishment of double blockings and the displacement or split of the stratospheric polar vortex can be efficient signals for cold-event prediction in China. This study provides novel insights into the cause of ELTEs under warming climates in the future.

Abstract This study classifies the spatial distribution of heavy precipitation in summer (June–August) from 1979 to 2021 in the three provinces of Northeast China (TPNC) into two patterns by using the self‐organizing maps (SOM) neural network, and then quantitatively analyzes their moisture transport channels and sources using the Lagrangian model. The results show that the summer heavy precipitation in TPNC can be divided into the northern and southern patterns according to the distribution of the heavy precipitation. Both patterns of heavy precipitation are affected by the low‐level vortex west of TPNC, but the strength and shape of the low vortex are different. The northern pattern is mainly influenced by the westerly flow in the vortex in the mid‐high latitudes, which transports moisture from the upstream westerly region into TPNC. The southern pattern is mainly affected by the southerly jet stream southeast of TPNC, which conveys a large amount of moisture from the East Asian summer monsoon region into TPNC. In terms of the summer climatological mean, the northern pattern has a higher precipitation recycling rate, while the southern pattern has a lower recycling rate.

Abstract Background Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Results Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Å root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. Conclusions The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format for downstream geometric deep learning applications.

Abstract This study investigated the impacts of Tibetan Plateau vertical heating (TPVH) on the Asian summer monsoon (ASM) on the interdecadal scale since the beginning of the 20th century. The ASM, corresponding to TPVH, generally enhances on the interdecadal scale. In the upper troposphere, the upper‐level westerly and easterly jet streams shift northwards in Asia, with two meridional “southern low and northern high” wave‐like patterns dominating in East Asia and India, respectively. In the lower troposphere, the summer monsoonal airflow and convergence zone migrate northwards in Asia, and two meridional “southern high and northern low” patterns appear in East Asia and India, respectively. Thus, TPVH can strengthen the baroclinicity over northern East Asia and northern India, but weaken it over southern East Asia and southern India. Moreover, two anomalous meridional circulation cells are identified in East Asia and India, with ascending branches in their northern parts and descending branches in their southern parts. Eventually, the ASM strengthens, leading to a southern dry and northern wet pattern in East Asia and India.

While early genetic and low-resolution structural observations suggested that extracellular conductive filaments on metal-reducing organisms such as Geobacter were composed of type IV pili, it has now been established that bacterial c-type cytochromes can polymerize to form extracellular filaments capable of long-range electron transport. Atomic structures exist for two such cytochrome filaments, formed from the hexaheme cytochrome OmcS and the tetraheme cytochrome OmcE. Due to the highly conserved heme packing within the central OmcS and OmcE cores, and shared pattern of heme coordination between subunits, it has been suggested that these polymers have a common origin. We have now used cryo-electron microscopy (cryo-EM) to determine the structure of a third extracellular filament, formed from the Geobacter sulfurreducens octaheme cytochrome, OmcZ. In contrast to the linear heme chains in OmcS and OmcE from the same organism, the packing of hemes, heme:heme angles, and between-subunit heme coordination is quite different in OmcZ. A branched heme arrangement within OmcZ leads to a highly surface exposed heme in every subunit, which may account for the formation of conductive biofilm networks, and explain the higher measured conductivity of OmcZ filaments. This new structural evidence suggests that conductive cytochrome polymers arose independently on more than one occasion from different ancestral multiheme proteins.

Abstract During January 21–24, 2016, most land areas in the Northern Hemisphere experienced extreme low temperatures. In North America, a historic snowstorm hit the northern and eastern United States. In East Asia, an unprecedented cold wave occurred and led to record‐breaking low temperatures in many regions. In this study, observational analyses revealed that both extreme events were triggered by a remarkable change in atmospheric circulation in the Arctic region in early January 2016, which switched from a concentric ring pattern to a dipole pattern. The dipole pattern resulted in two inverted Ω‐shaped circulation patterns that dominated the North America and East Asia. The inverted Ω‐shaped circulation patterns induced remarkable tropopause folding, which conveyed high‐potential‐vorticity cold air downwards from the lower stratosphere of Arctic to the middle and lower troposphere of North America, which increased cyclonic vorticity and negative height perturbations, and converged with moist air from the western North Atlantic and Gulf Stream, resulting in a severe snowstorm in the northern and eastern United States from 22 to 24 January. In East Asia, the tropopause folding transported high‐potential‐vorticity cold air downwards to the middle and lower troposphere of East Asia, resulting in the outbreak of a severe cold wave in East Asia from 21 to 24 January.

This paper investigates the decode-and-forward (DF) full-duplex (FD) relaying system under the presence of an eavesdropper. Moreover, the relay node is able to harvest energy from a transmitter, and then it uses the harvested energy for conveying information to the receiver. Besides, both two-hop and direct relaying links are taking into consideration. In the mathematical analysis, we derived the exact expressions for intercept probability and outage probability (OP) by applying maximal ratio combining (MRC) and selection combining (SC) techniques at the receiver. Next, the Monte Carlo simulation is performed to validate the mathematical analysis. The results show that the simulation curves match the mathematic expressions, which confirms the analysis section.

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.

Cryo-electron microscopy (cryo-EM) is becoming the imaging method of choice for determining protein structures. Many atomic structures have been resolved based on an exponentially growing number of published three-dimensional (3D) high resolution cryo-EM density maps. However, the resolution value claimed for the reconstructed 3D density map has been the topic of scientific debate for many years. The Fourier Shell Correlation (FSC) is the currently accepted cryo-EM resolution measure, but it can be subjective, manipulated, and has its own limitations. In this study, we first propose supervised deep learning methods to extract representative 3D features at high, medium and low resolutions from simulated protein density maps and build classification models that objectively validate resolutions of experimental 3D cryo-EM maps. Specifically, we build classification models based on dense artificial neural network (DNN) and 3D convolutional neural network (3D CNN) architectures. The trained models can classify a given 3D cryo-EM density map into one of three resolution levels: high, medium, low. The preliminary DNN and 3D CNN models achieved 92.73% accuracy and 99.75% accuracy on simulated test maps, respectively. Applying the DNN and 3D CNN models to thirty experimental cryo-EM maps achieved an agreement of 60.0% and 56.7%, respectively, with the author published resolution value of the density maps. We further augment these previous techniques and present preliminary results of a 3D U-Net model for local resolution classification. The model was trained to perform voxel-wise classification of 3D cryo-EM density maps into one of ten resolution classes, instead of a single global resolution value. The U-Net model achieved 88.3% and 94.7% accuracy when evaluated on experimental maps with local resolutions determined by MonoRes and ResMap methods, respectively. Our results suggest deep learning can potentially improve the resolution evaluation process of experimental cryo-EM maps.

Based on the simulations of 22 CMIP5 models in combination with socio-economic data and terrain elevation data, the spatial distribution of risk levels of flood disaster and the vulnerability to flood hazards in China are projected under the RCP8.5 for the near term period (2016–2035), medium term period (2046–2065) and long term period (2080–2099), respectively. The results show that regions with high flood hazard levels are mainly located in Southeast China, while the vulnerability to flood hazards is high in eastern China. Under the RCP8.5 greenhouse gas emissions scenario, future high flood risk levels will mainly appear in the eastern part of Sichuan, in major part of East China, and in the provinces of Hebei, Beijing, and Tianjin. The major cities in Northeast China, some areas in Shaanxi and Shanxi, as well as the coastal areas in southeastern China will also encounter high flood risks. Compared with the baseline period, the regional flood risk levels will increase towards the end of the 21st century, although the occurrences of floods change little. Due to the coarse resolution of the climate models and the indistinct methodology for determining the weight coefficients, large uncertainty still exists in the projection of flood risks. Citation: Xu, Y., Zhang, B., Zhou, B.-T., et al., 2014. Projected flood risks in China based on CMIP5. Adv. Clim. Change Res. 5(2), doi: 10.3724/SP.J.1248.2014.057.