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The human has a very complex perception system, including vision, auditory, olfactory, touch, and gustation. This paper will introduce the recent studies about providing a technical solution for image recognition, by applying a algorithm called Convolutional Neural Network (CNN) which is inspired by animal visual system. Convolution serves as a perfect realization of an optic nerve cell which merely responds to its receptive field and it performs well in image feature extraction. Being highly-hierarchical networks, CNN is structured with a series of different functional layers. The function blocks are separated and described clearly by each layer in this paper. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.

We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a novel decorrelated score function to handle the impact of high dimensional nuisance parameters. We consider both hypothesis tests and confidence regions for generic penalized M-estimators. Unlike most existing inferential methods which are tailored for individual models, our method provides a general framework for high dimensional inference and is applicable to a wide variety of applications. In particular, we apply this general framework to study five illustrative examples: linear regression, logistic regression, Poisson regression, Gaussian graphical model and additive hazards model. For hypothesis testing, we develop general theorems to characterize the limiting distributions of the decorrelated score test statistic under both null hypothesis and local alternatives. These results provide asymptotic guarantees on the type I errors and local powers. For confidence region construction, we show that the decorrelated score function can be used to construct point estimators that are asymptotically normal and semiparametrically efficient. We further generalize this framework to handle the settings of misspecified models. Thorough numerical results are provided to back up the developed theory.

The novel coronavirus disease (COVID-19) has been rapidly transmitted in China, Macau, Hong Kong, and other Asian and European counterparts. This COVID-19 epidemic has aroused increasing attention nationwide. Patients, health professionals, and the general public are under insurmountable psychological pressure which may lead to various psychological problems, such as anxiety, fear, depression, and insomnia. Psychological crisis intervention plays a pivotal role in the overall deployment of the disease control. The National Health Commission of China has summoned a call for emergency psychological crisis intervention and thus, various mental health associations and organizations have established expert teams to compile guidelines and public health educational articles/videos for mental health professionals and the general public alongside with online mental health services. In addition, mental health professionals and expert groups are stationed in designated isolation hospitals to provide on-site services. Experts have reached a consensus on the admission of patients with severe mental illness during the COVID-19 outbreak in mental health institutions. Nevertheless, the rapid transmission of the COVID-19 has emerged to mount a serious challenge to the mental health service in China.

Despite the increasing resolution, forcing on the mean circulation by resolved waves in general circulation models is not yet converging. Parameterization of the forcing remains a major source of model uncertainty. This study examines the scale invariance of zonal spectra of momentum flux and wave forcing, and shows that it can be used to quantify the forcing by unresolved waves with knowledge of the resolved ones in global models. The result reveals the leading order importance of the small-scale wave forcing, which is in general agreement with that required for obtaining the zonal mean wind climatology. It is also found that wave and mean flow interaction is important in maintaining the robust spectral structure. This method may provide a strategy to design physically consistent and scale-aware parameterization schemes for scale invariant quantities, when a model has sufficient resolution to partially resolve their spectra. Parametrizations of unresolved small-scale atmospheric waves are an important source of uncertainty in climate models. Scale invariance is used to estimate the forcing of these small-scale waves and it is shown to have a leading order effect on the mean flow.

The best thermoelectric materials are believed to be heavily doped semiconductors. The presence of a band gap is assumed to be essential to achieve large thermoelectric power factor and figure of merit. In this work, we propose semi-metals with large asymmetry between conduction and valence bands as an alternative class of thermoelectric materials. To illustrate the idea, we study semi-metallic HgTe in details experimentally and theoretically. We employ ab initio calculations with hybrid exchange-correlation functional to accurately describe the electronic band structure in conjunction with the Boltzmann Transport theory to investigate the electronic transport properties. We calculate the lattice thermal conductivity using first principles calculations and evaluate the overall figure of merit. To validate our theoretical approach, we prepare semi-metallic HgTe samples and characterize their transport properties. Our first-principles calculations agree well with the experimental data. We show that intrinsic HgTe, a semimetal with large disparity in its electron and hole masses, has a high thermoelectric power factor that is comparable to the best known thermoelectric materials. Finally, we propose other possible materials with similar band structures as potential candidates for thermoelectric applications.

Recently, more than 300 Chinese patients with psychiatric disorders were diagnosed with the 2019 novel coronavirus disease (COVID-19). Possible reasons quoted in the report were the lack of caution regarding the COVID-19 outbreak in January and insufficient supplies of protective gear. We outlined major challenges for patients with psychiatric disorders and mental health professionals during the COVID-19 outbreak, and also discussed how to manage these challenges through further mental health service reform in China.

Mammalian cells synthesize and release heterogeneous extracellular vesicles (EVs) which can be generally recognized as subclasses including exosomes, microvesicles (MVs), and apoptotic bodies (ABs), each differing in their biogenesis, composition and biological functions from others. EVs can originate from normal or cancer cells, transfer bioactive cargoes to both adjacent and distant sites, and orchestrate multiple key pathophysiological events such as carcinogenesis and malignant progression. Emerging as key messengers that mediate intercellular communications, EVs are being paid substantial attention in various disciplines including but not limited to cancer biology and immunology. Increasing lines of research advances have revealed the critical role of EVs in the establishment and maintenance of the tumor microenvironment (TME), including sustaining cell proliferation, evading growth suppression, resisting cell death, acquiring genomic instability and reprogramming stromal cell lineages, together contributing to the generation of a functionally remodeled TME. In this article, we present updates on major topics that document how EVs are implicated in proliferative expansion of cancer cells, promotion of drug resistance, reprogramming of metabolic activity, enhancement of metastatic potential, induction of angiogenesis, and escape from immune surveillance. Appropriate and insightful understanding of EVs and their contribution to cancer progression can lead to new avenues in the prevention, diagnosis and treatment of human malignancies in future medicine.

Key developments have been made to the NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM‐X). Among them, the most important are the self‐consistent solution of global electrodynamics, and transport of O+ in the F‐region. Other ionosphere developments include time‐dependent solution of electron/ion temperatures, metastable O+ chemistry, and high‐cadence solar EUV capability. Additional developments of the thermospheric components are improvements to the momentum and energy equation solvers to account for variable mean molecular mass and specific heat, a new divergence damping scheme, and cooling by O(3P) fine structure. Simulations using this new version of WACCM‐X (2.0) have been carried out for solar maximum and minimum conditions. Thermospheric composition, density, and temperatures are in general agreement with measurements and empirical models, including the equatorial mass density anomaly and the midnight density maximum. The amplitudes and seasonal variations of atmospheric tides in the mesosphere and lower thermosphere are in good agreement with observations. Although global mean thermospheric densities are comparable with observations of the annual variation, they lack a clear semiannual variation. In the ionosphere, the low‐latitude E × B drifts agree well with observations in their magnitudes, local time dependence, seasonal, and solar activity variations. The prereversal enhancement in the equatorial region, which is associated with ionospheric irregularities, displays patterns of longitudinal and seasonal variation that are similar to observations. Ionospheric density from the model simulations reproduces the equatorial ionosphere anomaly structures and is in general agreement with observations. The model simulations also capture important ionospheric features during storms. Plain Language Summary A comprehensive numerical model, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM‐X), has been improved, in order to simulate the entire atmosphere and ionosphere, from the Earth's surface to ∼700 km altitude. This new version (v. 2.0) adds the capability to calculate the motions and temperatures of ions and electrons in the ionosphere. The model results compare well with available ground‐based and satellite observations, under both quiet and disturbed space weather conditions. Even with constant solar forcing, the model displays large day‐to‐day weather changes in the upper atmosphere and ionosphere, with basic patterns that agree with observations. This demonstrates the model ability to describe the connections between weather near the surface and weather in space. Key Points The Whole Atmosphere Community Climate Model has been extended to include ionospheric electrodynamics WACCM‐X simulates the interaction of lower atmosphere and solar influences in the ionosphere Preliminary validation demonstrates agreement with observations

Depressive disorders represent a major contributor to the global burden of disease, with persistently rising prevalence rates posing significant challenges to individual quality of life and public health systems. Existing first-line medications such as selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) typically require 2-4 weeks to take effect, with complete remission rates below 60%. Approximately one-third of patients discontinue treatment within 90 days due to adverse reactions including gastrointestinal discomfort, weight changes, or sexual dysfunction. Consequently, exploring interventions with faster onset and improved tolerability holds significant clinical importance. A systematic search of seven databases-PubMed, Embase, Web of Science, Cochrane CENTRAL, CNKI, AMED, and Scopus-identified randomised controlled trials (RCTs) and mechanism studies published between 2010 and 2025. A qualitative synthesis method analysed clinical efficacy and adverse reactions, integrating evidence from metabolomics, epigenetics, and network pharmacology. Botanical drug identification was performed in accordance with ConPhYMP guidelines, with all species names validated taxonomically against the Medicinal Plant Names Services (MPNS) and Plants of the World Online (POWO) databases. Twenty-one RCTs (n = 2,766) and three mechanistic studies were included. Findings indicated that Xiaoyao Formula exert earlier effects than SSRIs (descriptive trend: 1-2 weeks vs. 2-4 weeks), with comparable or superior remission rates and lower residual symptom incidence. Adverse reactions, particularly gastrointestinal discomfort and sleep disturbances, were significantly reduced. No serious adverse events, hepatotoxicity, or clinically significant drug interactions were reported across the evidence base, although systematic adverse event reporting was incomplete in earlier trials. Mechanistic studies a hypothetical sequential pathway-'inflammation precedes neuroplasticity recovery'-involving downregulation of IL-6 and NLRP3 inflammasome, reduced methylation of the BDNF promoter, and activation of the PI3K-Akt pathway. A three-dimensional Q-marker system (based on UPLC-HRMS) comprising baicalin, ferulic acid, and glycyrrhizic acid provides a preliminary framework for quantifying the metabolite-mechanism-efficacy relationship and may serve as a candidate criterion for cross-centre consistency testing. Existing evidence preliminarily supports potential advantages of Xiaoyao Formula in treating depressive disorders, including possibly earlier onset of action, good tolerability, and potential additional benefits in female subgroups. However, given limitations such as small sample sizes, short intervention durations (6-12 weeks), and predominantly combination therapy rather than monotherapy comparisons, these conclusions should be regarded as suggestive or indicative findings rather than definitive efficacy. Long-term efficacy and generalisability across populations require further validation. Future studies should conduct multicentre, large-sample clinical trials with ≥24-week follow-up, incorporating wearable digital phenotyping technologies to confirm its application value in precision psychiatry.