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China's growth factor China, since mid-2010 the world's second biggest economy and tipped to become the biggest in a few decades, has tremendous climatic and ecological diversity. The likely impact of China's economic expansion on the global climate has been extensively studied but little is known about the reverse case — the susceptibility of natural and managed systems in China to climate change. In a Review, Shilong Piao et al . assess the impacts of historical and future climate change on water resources and agriculture in China. They find that in spite of clear trends in climate (especially temperature), overall impacts are overshadowed by natural variability and uncertainties in crop responses and projected climate, especially precipitation. In a best-case scenario, crop production is constant, whereas the worst-case scenario suggests that production could fall by about 20% by 2050. China has tremendous climatic and ecological diversity, so the impacts of climate change on natural and managed systems might likewise be expected to be diverse. Yet so far systematic studies have been rare. Here, the impacts of historical and future climate change on water resources and agriculture in China are assessed. Despite clear trends in climate, the overall impacts are overshadowed by natural variability and uncertainties in crop responses and projected climate, especially precipitation. China is the world’s most populous country and a major emitter of greenhouse gases. Consequently, much research has focused on China’s influence on climate change but somewhat less has been written about the impact of climate change on China. China experienced explosive economic growth in recent decades, but with only 7% of the world’s arable land available to feed 22% of the world’s population, China's economy may be vulnerable to climate change itself. We find, however, that notwithstanding the clear warming that has occurred in China in recent decades, current understanding does not allow a clear assessment of the impact of anthropogenic climate change on China’s water resources and agriculture and therefore China’s ability to feed its people. To reach a more definitive conclusion, future work must improve regional climate simulations—especially of precipitation—and develop a better understanding of the managed and unmanaged responses of crops to changes in climate, diseases, pests and atmospheric constituents.

With the increase of coal seam mining intensity in the western mining area, which has the typical mining geological characteristics of shallow burial depth, thin bedrock and thick loose layer, the structure of overburden rock has also changed greatly. When there is only one key layer structure in the thick bedrock and it is close to the coal seam, the strata pressure and surface crack development law of the working face are different from those of previous research results. Based on the research background of 52,307 large mining height working face in Daliuta Coal Mine, this paper adopts similar simulation and numerical simulation techniques to systematically study the strata pressure and surface crack development laws of the working face under the roof condition of such main key layer. The results show that: There is only one key layer in the roof overburden of the working face and it is only about 4 m away from the coal seam, and the key layer is broken in layering during the mining process, the lower part of the key layer collapses by itself under the influence of mining and falls into the collapse zone, and the upper part of the key layer forms a “face contact block” structure, which was unstable and broke to form a “surface contact block arch” structure, and caused the periodic pressure of the working face. The fracture and migration of key layers affects the movement of the overlying rock layers. When the key layer is broken and unstable, the overburden rock moves as a whole, forming a tensile effect on the surface and producing surface cracks. When the key layer of the working face is broken by the “fixed support beam” for the first time, ground cracks appear on the surface in a short time, and the surface cracks have a certain hysteresis, but the lag distance is short. In the process of pressure in each cycle, with the fracture and instability of the key layer, the overlying rock layer sinks and deforms, and different forms of surface cracks occur in the surface soil layer due to the increase of tensile stress and horizontal deformation force, and the cracks generally lag behind the working face.

Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

The mechanisms for El Niño–Southern Oscillation (ENSO) amplitude change under global warming are investigated through quantitative assessment of air–sea feedback processes in present-day and future climate simulations of four models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Two models (MPI-ESM-MR and MRI-CGCM3) project strengthened ENSO amplitude, whereas the other two models (CCSM4 and FGOALS-g2) project weakened ENSO amplitude. A mixed layer heat budget diagnosis shows that the major cause of the projected ENSO amplitude difference between the two groups is attributed to the changes of the thermocline and zonal advective feedbacks. A weaker (stronger) equatorial thermocline response to a unit anomalous zonal wind stress forcing in the Niño-4 region is found in CCSM4 and FGOALS-g2 (MPI-ESM-MR and MRI-CGCM3). The cause of the different response arises from the change in the meridional scale of ENSO. A narrower (wider) meridional width of sea surface temperature (SST) and zonal wind stress anomalies causes a strengthening (weakening) of the equatorial thermocline response and thus stronger Bjerknes and zonal advective feedbacks, as the subsurface temperature and zonal current anomalies depend on the thermocline response; consequently, the ENSO amplitude increases (decreases). The change of ENSO meridional width is caused by the change in mean meridional overturning circulation in the equatorial Pacific Ocean, which depends on change of mean wind stress and SST warming patterns under global warming.

•Both depleted and enriched gut microbes were found in CSVD patients.•The differential microbes related to metabolites enriched for Aminoacyl-tRNA biosynthesis pathway.•The affected metabolites related to multi-modal neuroimaging measures in association with cognition and emotion in CSVD patients.•We demonstrated a gut microbiome-metabolome-brain-behavior pathway through which gut microbiota dysbiosis was linked to CSVD. Prior efforts have revealed changes in gut microbiome, circulating metabolome, and multimodal neuroimaging features in cerebral small vessel disease (CSVD). However, there is a paucity of research integrating the multi-omic information to characterize the role of the microbiota-gut-brain axis in CSVD. We collected gut microbiome, fecal and blood metabolome, multimodal magnetic resonance imaging data from 37 CSVD patients with white matter hyperintensities and 46 healthy controls. Between-group comparison was performed to identify the differential gut microbial taxa, followed by performance of multi-stage microbiome-metabolome-neuroimaging-neuropsychology correlation analyses in CSVD patients. Our data showed both depleted and enriched gut microbes in CSVD patients. Among the differential microbes, Haemophilus and Akkermansia were associated with a range of metabolites enriched for Aminoacyl-tRNA biosynthesis pathway. Furthermore, the affected metabolites were associated with neuroimaging measures involving gray matter morphology, spontaneous intrinsic brain activity, white matter integrity, and global structural network topology, which were in turn related to cognition and emotion in CSVD patients. Our findings provide an integrative framework to understand the pathophysiological mechanisms underlying the interplay between gut microbiota dysbiosis and CSVD, highlighting the potential of targeting the microbiota-gut-brain axis as a therapeutic strategy in CSVD patients.

Most machine learning algorithms only have a good recognition rate on balanced datasets. However, in the field of malicious traffic identification, benign traffic on the network is far greater than malicious traffic, and the network traffic dataset is imbalanced, which makes the algorithm have a low identification rate for small categories of malicious traffic samples. This paper presents a traffic sample synthesizing model named Conditional Tabular Traffic Generative Adversarial Network (CTTGAN), which uses a Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to expand the small category traffic samples and balance the dataset in order to improve the malicious traffic identification rate. The CTTGAN model expands and recognizes feature data, which meets the requirements of a machine learning algorithm for training and prediction data. The contributions of this paper are as follows: first, the small category samples are expanded and the traffic dataset is balanced; second, the storage cost and computational complexity are reduced compared to models using image data; third, discrete variables and continuous variables in traffic feature data are processed at the same time, and the data distribution is described well. The experimental results show that the recognition rate of the expanded samples is more than 0.99 in MLP, KNN and SVM algorithms. In addition, the recognition rate of the proposed CTTGAN model is better than the oversampling and undersampling schemes.

•Three enterotypes showed differences in cortical thickness of the prefrontal cortex.•Three enterotypes differed in mean diffusivity of the cerebral peduncle and cingulum.•Prefrontal cortical thickness mediated the enterotype-working memory association. Enterotype analysis classifies individuals based on gut microbial community composition using clustering techniques. Despite evidence suggesting the important role of enterotype in affecting brain function and working memory, little is known about the brain structural substrates. We collected fecal samples and utilized 16S rDNA amplicon sequencing to identify three enterotypes (Bacteroides, Prevotella, and Ruminococcaceae) among 511 healthy young adults through unsupervised clustering. Structural and diffusion MRI techniques were adopted to assess gray matter morphology and white matter integrity. Inter-enterotype differences in brain structure were tested, followed by correlation and mediation analyses to investigate the potential relationships among enterotype, brain structure, and working memory. The three enterotypes exhibited significant differences in cortical thickness of the prefrontal cortex and mean diffusivity of the cerebral peduncle and cingulum. Moreover, prefrontal cortical thickness was correlated with working memory and further acted as a significant mediator of the association between enterotype and working memory. Our findings may contribute to the growing literature on the microbiota-brain-cognition relationship, setting the stage for future longitudinal and interventional research.

Previous evaluations of model simulations of the cloud and water vapor feedbacks in response to El Niño warming have singled out two common biases in models from phase 3 of the Coupled Model Intercomparison Project (CMIP3): an underestimate of the negative feedback from the shortwave cloud radiative forcing (SWCRF) and an overestimate of the positive feedback from the greenhouse effect of water vapor. Here, the authors check whether these two biases are alleviated in the CMIP5 models. While encouraging improvements are found, particularly in the simulation of the negative SWCRF feedback, the biases in the simulation of these two feedbacks remain prevalent and significant. It is shown that bias in the SWCRF feedback correlates well with biases in the corresponding feedbacks from precipitation, large-scale circulation, and longwave radiative forcing of clouds (LWCRF). By dividing CMIP5 models into two categories—high score models (HSM) and low score models (LSM)—based on their individual skills of simulating the SWCRF feedback, the authors further find that ocean–atmosphere coupling generally lowers the score of the simulated feedbacks of water vapor and clouds but that the LSM is more affected by the coupling than the HSM. They also find that the SWCRF feedback is simulated better in the models that have a more realistic zonal extent of the equatorial cold tongue, suggesting that the continuing existence of an excessive cold tongue is a key factor behind the persistence of the feedback biases in models.

The El Niño-Southern Oscillation (ENSO) is the greatest climate variability on interannual time scale, yet what controls ENSO amplitude changes under global warming (GW) is uncertain. Here we show that the fundamental factor that controls the divergent projections of ENSO amplitude change within 20 coupled general circulation models that participated in the Coupled Model Intercomparison Project phase-5 is the change of climatologic mean Pacific subtropical cell (STC), whose strength determines the meridional structure of ENSO perturbations and thus the anomalous thermocline response to the wind forcing. The change of the thermocline response is a key factor regulating the strength of Bjerknes thermocline and zonal advective feedbacks, which ultimately lead to the divergent changes in ENSO amplitude. Furthermore, by forcing an ocean general circulation mode with the change of zonal mean zonal wind stress estimated by a simple theoretical model, a weakening of the STC in future is obtained. Such a change implies that ENSO variability might strengthen under GW, which could have a profound socio-economic consequence.

The occurrence of depression is higher in patients with chronic liver disease (CLD) than that in the general population. The mechanism described in previous studies mainly focused on inflammation and stress, which not only exists in CLD, but also emerges in common chronic diseases, leaving the specific mechanism unknown. This review was to summarize the prevalence and risk factors of depression in CLD including chronic hepatitis B, chronic hepatitis, alcoholic liver disease, and non-alcoholic fatty liver disease, and to point out the possible underlying mechanism of this potential link. Clarifying the origins of this common comorbidity (depression and CLD) may provide more information to understand both diseases.