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Abstract The precise forecasting of air quality is of great significance as an integral component of early warning systems. This remains a formidable challenge owing to the limited information of emission source and the considerable uncertainties inherent in dynamic processes. To improve the accuracy of air quality forecasting, this work proposes a new spatiotemporal hybrid deep learning model based on variational mode decomposition (VMD), graph attention networks (GAT) and bi-directional long short-term memory (BiLSTM), referred to as VMD–GAT–BiLSTM, for air quality forecasting. The proposed model initially employ a VMD to decompose original PM2.5 data into a series of relatively stable sub-sequences, thus reducing the influence of unknown factors on model prediction capabilities. For each sub-sequence, a GAT is then designed to explore deep spatial relationships among different monitoring stations. Next, a BiLSTM is utilized to learn the temporal features of each decomposed sub-sequence. Finally, the forecasting results of each decomposed sub-sequence are aggregated and summed as the final air quality prediction results. Experiment results on the collected Beijing air quality dataset show that the proposed model presents superior performance to other used methods on both short-term and long-term air quality forecasting tasks.

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed.

Abstract Norcoclaurine synthase from Thalictrum flavum (TfNCS) demonstrated high stereospecificity and yield in catalyzing the Pictet-Spengler reaction of dopamine with chiral aldehydes, achieving kinetic resolution of aldehydes. However, the mechanism and the factors contributing to the stereoselectivity remain unclear. Herein, by using quantum chemical calculations, the mechanisms of TfNCS-catalyzed reactions of dopamine with both enantiomers of α-methyl-phenylacetaldehyde are studied. The calculations reveal a mechanism mirroring the reaction of natural substrates, for which the deprotonation of the C5−H of the cyclized intermediate is rate-limiting. The calculated overall barriers are 20.1 kcal mol-1 and 21.6 kcal mol-1 for the reactions of (R)- and (S)-α-methyl-phenylacetaldehyde, respectively. The M97 and L72 residues are proposed to be the key residues contributing to the stereospecificity. The obtained detailed information is helpful for designing new variants of TfNCS with extended substrate scope, and also advancing our understanding of TfNCS reactions for potential applications.

IntroductionAs the COVID-19 outbreak escalated into a global pandemic, there was a significant surge in mental health issues worldwide. Over the past three decades, traditional Chinese exercises (TCEs) has gained increasing recognition for its ability to regulate mental well-being. The aim of this study (PROSPERO CRD42024516002) was to comprehensively assess and carry out a meta-analysis on the impact of traditional Chinese exercise on personal mental well-being amidst the COVID-19 pandemic.MethodsLiterature with publication dates from 2020 to 2023 was searched in four databases, including CNKI, Wanfang, Pubmed and Web of science. The literature was selected layer by layer according to the PRIMA guidelines, and then the quality of the included literature was assessed using the Cochrane Risk assessment tool.ResultsA total of 174 articles were screened, and 10 studies met the criteria and were included in the study. The results showed that TCEs had a positive effect on anxiety symptoms [SMD = −1.68, I2 = 98.40, 95%CI = (−2.80, −0.56), p = 0.00] and depressive symptoms [SMD = -1.23, I2 = 89.23, 95%CI = (−1.87, −0.58), p = 0.00]. The data of subgroup analysis showed that Baduanjin exercise had the best effect on reducing anxiety [SMD = −2.29, I2 = 98.3, 95%CI = (−3.69, −0.89), p

Abstract As the population ages, the worldwide prevalence of Alzheimer's disease (AD) as the most common dementia in the elderly is increasing dramatically. However, a long‐term challenge is to achieve rapid and accurate early diagnosis of AD by detecting hallmarks such as amyloid beta (Aβ42). Here, a multi‐channel microfluidic‐based plasmonic fiber‐optic biosensing platform is established for simultaneous detection and differentiation of multiple AD biomarkers. The platform is based on a gold‐coated, highly‐tilted fiber Bragg grating (TFBG) and a custom‐developed microfluidics. TFBG excites a high‐density, narrow‐cladding‐mode spectral comb that overlaps with the broad absorption of surface plasmons for high‐precision interrogation, enabling ultrasensitive monitoring of analytes. In situ detection and in‐parallel discrimination of different forms of Aβ42 in cerebrospinal fluid (CSF) are successfully demonstrated with a detection of limit in the range of ≈30–170 pg mL−1, which is one order of magnitude below the clinical cut‐off level in AD onset, providing high detection sensitivity for early diagnosis of AD. The integration of the TFBG sensor with multi‐channel microfluidics enables simultaneous detection of multiple biomarkers using sub‐µL sample volumes, as well as combining initial binding rate and real‐time response time to differentiate between multiple biomarkers in terms of binding kinetics. With the advantages of multi‐parameter, low consumption, and highly sensitive detection, the sensor represents an urgently needed potentials for large‐scale diagnosis of diseases at early stage.

Through the effective application of Essential Principles of Safety and Performance of Medical Devices and IVD Medical Devices (EP), to continuously improve the corresponding management tools to ensure the safety and effectiveness of medical device in the quality management system, risk management system, evaluation of safety and effectiveness for the supervision departments and manufacturers. The current status of the application of EP and the application issues are analyzed in the study. Take artificial joint products for example, the idea of using EP in quality management system, risk management system and evaluation of safety and effectiveness is investigated, and several thoughts are proposed. Supervision departments should strengthen the unified understanding of EP, develop requirements according to the classification of medical device，and refine specific execution requirements.

In this paper, we apply the classical FKKM lemma to obtain the Ky Fan minimax inequality defined on nonempty non-compact convex subsets in reflexive Banach spaces, and then we apply it to game theory and obtain Nash’s existence theorem for non-compact strategy sets, which can be regarded as a new, simple but interesting application of the FKKM lemma and the Ky Fan minimax inequality, and we can also present another proof about the famous John von Neumann’s existence theorem in two-player zero-sum games. Due to the results of Li, Shi and Chang, the coerciveness in the conclusion can be replaced with the P.S. or G.P.S. conditions.

A marine-derived fungal strain, Aspergillus sp. ITBBc1, was isolated from coral collected from the South China Sea in Hainan province. Intensive chemical investigation of the fermentation extract of this strain afforded four new secondary metabolites (1–4), named megastigmanones A–C and prenylterphenyllin H, along with four known compounds (5–8). Their structures were elucidated by extensive spectroscopic analysis including one-and two-dimensional (1D and 2D) NMR spectroscopy and high-resolution electrospray ionization mass spectrometry (HR-ESI-MS). The modified Mosher’s method was undertaken to determine the absolute configurations of new compounds. The phytotoxic activity test showed that compounds 6–8 exhibited significant antagonistic activity against the germination of Triticum aestivum L. and Oryza sativa L. seeds with a dose-dependent relationship.

Abstract In recent years, panoptic segmentation has drawn increasing amounts of attention, leading to the rapid emergence of numerous related algorithms. A variety of deep neural networks have been used more frequently for panoptic segmentation, which is motivated by the significant success of deep learning methods in other tasks. This article presents a comprehensive exploration of panoptic segmentation, focusing on the analysis and understanding of RGB image data. Initially, the authors introduce the background of panoptic segmentation, including deep learning models and image segmentation. Then, the authors thoroughly cover a variety of panoptic segmentation‐related topics, such as datasets connected to the field, evaluation metrics, panoptic segmentation models, and derived subfields based on panoptic segmentation. Finally, the authors examine the difficulties and possibilities in this area and identify its future paths.

The NLRP3 inflammasome is a supramolecular complex that is linked to sterile and pathogen-dependent inflammation, and its excessive activation underlies many diseases. Ion flux disturbance and cell volume regulation are both reported to mediate NLRP3 inflammasome activation, but the underlying orchestrating signaling remains not fully elucidated. The volume-regulated anion channel (VRAC), formed by LRRC8 proteins, is an important constituent that controls cell volume by permeating chloride and organic osmolytes in response to cell swelling. We now demonstrate that Lrrc8a, the essential component of VRAC, plays a central and specific role in canonical NLRP3 inflammasome activation. Moreover, VRAC acts downstream of K+ efflux for NLRP3 stimuli that require K+ efflux. Mechanically, our data demonstrate that VRAC modulates itaconate efflux and damaged mitochondria production for NLRP3 inflammasome activation. Further in vivo experiments show mice with Lrrc8a deficiency in myeloid cells were protected from lipopolysaccharides (LPS)-induced endotoxic shock. Taken together, this work identifies VRAC as a key regulator of NLRP3 inflammasome and innate immunity by regulating mitochondrial adaption for macrophage activation and highlights VRAC as a prospective drug target for the treatment of NLRP3 inflammasome and itaconate related diseases.

Introduction To explore the epidemiological characteristics of ankylosing spondylitis (AS) in Guangxi Province of China through a large sample survey of more than 50 million aboriginal aboriginal population. Material and methods A systematic search was conducted using the International Classification of Diseases 10 (ICD-10) codes M45.x00(AS), M45.x03+(AS with iridocyclitis), and M40.101(AS with kyphosis) to search the database in the National Health Statistics Network Direct Reporting System (NHSNDRS). 14004 patients were eventually included in the study. The parameters analyzed included the number of patients, gender, marriage, blood type, occupation, age at diagnosis, and location of household registration data each year, and statistical analysis was performed. Results AS incidence rates increased from 1.30 (95% CI: 1.20–1.40) per 100,000 person-years in 2014 to 5.71 (95% CI: 5.50–5.92) in 2020 in Guangxi Province, and decreased slightly in 2021. Males have a higher incidence than females; the ratio was 5.61 : 1. The mean age of diagnosis in male patients was 45.4 (95% CI: 45.1–45.7) years, in females 47.6 (95% CI: 46.8–48.4) years. The most frequent blood type was O, and the most frequent occupation was farmer. The AS incidence rate was disparate in different cities. Liuzhou city had the highest eight-year average AS incidence rates from 2014 to 2021, and Chongzuo city had the lowest (p 0.05). Conclusions The AS person-years incidence rate was increasing in Guangxi province of China from 2014 to 2020, which had obvious gender and regional differences, showing the characteristics of local area aggregation.

Cardiac-resident macrophages (CRMs) play important roles in homeostasis, cardiac function, and remodeling. Although CRMs play critical roles in cardiac regeneration of neonatal mice, their roles are yet to be fully elucidated. Therefore, this study aimed to investigate the dynamic changes of CRMs during cardiac ontogeny and analyze the phenotypic and functional properties of CRMs in the promotion of cardiac regeneration. During mouse cardiac ontogeny, four CRM subsets exist successively: CX3CR1+CCR2–Ly6C–MHCII– (MP1), CX3CR1lowCCR2lowLy6C–MHCII– (MP2), CX3CR1–CCR2+Ly6C+MHCII– (MP3), and CX3CR1+CCR2–Ly6C–MHCII+ (MP4). MP1 cluster has different derivations (yolk sac, fetal liver, and bone marrow) and multiple functions population. Embryonic and neonatal-derived-MP1 directly promoted cardiomyocyte proliferation through Jagged-1–Notch1 axis and significantly ameliorated cardiac injury following myocardial infarction. MP2/3 subsets could survive throughout adulthood. MP4, the main population in adult mouse hearts, contributed to inflammation. During ontogeny, MP1 can convert into MP4 triggered by changes in the cellular redox state. These findings delineate the evolutionary dynamics of CRMs under physiological conditions and found direct evidence that embryonic and neonatal-derived CRMs regulate cardiomyocyte proliferation. Our findings also shed light on cardiac repair following injury.

The ever-increasing energy demand around the world has attracted research efforts to transform renewable energy sources efficiently. This study reports the design parameters of the parabolic solar dish Stirling (PSDS) system, and the applications of PSDS systems have been discussed. In order to find the optimized design choices for the implementation of the PSDS plants, the system performance and key technical features of the parabolic dish concentrator, thermal receiver, and Stirling engine (SE) are demonstrated. Existing literature depicts significant differences in scope and frontiers of PSDS systems and their primary design characteristics such as the concentrators reflecting material, the concentrator and receiver shape, the dish diameter, aperture area sizing of the dish, the focal length of the dish, rim angle, the geometric concentration ratio, receiver material, and receiver diameter. The data coincide within published articles have been gathered and disaggregated regarding the optimal performance that influences the dish and diameter of the receiver, rim angle, and the geometric concentration ratio. The overall maximum theoretical efficiency of a PSDS system is 23.05% whereas an experimental study of power generation through PSDS system stated 22.75% overall efficiency with levelized cost of energy is 0.2565 $/kWh. This scientific literature review imparts a knowledge gap of recent development and data inventory in PSDS technology. Besides, this study develops grounds for several research areas for the scientific community yet to be explored to attain the optimal performance of the parabolic dish system.

The segmentation of pepper leaves from pepper images is of great significance for the accurate control of pepper leaf diseases. To address the issue, we propose a bidirectional attention fusion network combing the convolution neural network (CNN) and Swin Transformer, called BAF-Net, to segment the pepper leaf image. Specially, BAF-Net first uses a multi-scale fusion feature (MSFF) branch to extract the long-range dependencies by constructing the cascaded Swin Transformer-based and CNN-based block, which is based on the U-shape architecture. Then, it uses a full-scale feature fusion (FSFF) branch to enhance the boundary information and attain the detailed information. Finally, an adaptive bidirectional attention module is designed to bridge the relation of the MSFF and FSFF features. The results on four pepper leaf datasets demonstrated that our model obtains F1 scores of 96.75%, 91.10%, 97.34% and 94.42%, and IoU of 95.68%, 86.76%, 96.12% and 91.44%, respectively. Compared to the state-of-the-art models, the proposed model achieves better segmentation performance. The code will be available at the website: https://github.com/fangchj2002/BAF-Net.

In this study, we investigate a new neural network method to solve Volterra and Fredholm integral equations based on the sine-cosine basis function and extreme learning machine (ELM) algorithm. Considering the ELM algorithm, sine-cosine basis functions, and several classes of integral equations, the improved model is designed. The novel neural network model consists of an input layer, a hidden layer, and an output layer, in which the hidden layer is eliminated by utilizing the sine-cosine basis function. Meanwhile, by using the characteristics of the ELM algorithm that the hidden layer biases and the input weights of the input and hidden layers are fully automatically implemented without iterative tuning, we can greatly reduce the model complexity and improve the calculation speed. Furthermore, the problem of finding network parameters is converted into solving a set of linear equations. One advantage of this method is that not only we can obtain good numerical solutions for the first- and second-kind Volterra integral equations but also we can obtain acceptable solutions for the first- and second-kind Fredholm integral equations and Volterra–Fredholm integral equations. Another advantage is that the improved algorithm provides the approximate solution of several kinds of linear integral equations in closed form (i.e., continuous and differentiable). Thus, we can obtain the solution at any point. Several numerical experiments are performed to solve various types of integral equations for illustrating the reliability and efficiency of the proposed method. Experimental results verify that the proposed method can achieve a very high accuracy and strong generalization ability.

Permeable asphalt pavement refers to an asphalt mixture layer with an air void content of more than 18% and internal water permeability and drainage capabilities that can quickly drain away water on the road surface, improve rainy day travel safety, and improve ride comfort. This paper aims to explore the optimal asphalt mixture design for long longitudinal slope pavement (referred to as the FAM mixture). By using CT scanning technology to analyze the air void content of different rotated and compacted asphalt mixture specimens and extensively testing and evaluating the performance of permeable pavement mixtures, the following conclusions are drawn: Based on the research philosophy of functional integration, a new asphalt mixture gradation suitable for long longitudinal slope roads is proposed, with the optimal key factor composition being: 0.075 mm passing rate of 7%, 2.36 mm passing rate of 20%, 9.5 mm passing rate of 55%, and an oil-stone ratio of 4.8%. The FAM mixture was divided into three parts for air void analysis, with the upper part having a slightly higher air void content than the lower part. The air void distribution diagram of the FAM mixture is concave, with higher air void rate curves on both sides and a lower middle curve. Through dynamic modulus testing, the strength requirement for the road asphalt mixture in the pavement structure design was evaluated. It was found that at high temperature conditions (50 °C), the minimum dynamic modulus value of the FAM mixture was 323 MPa, with a peak value of 22,746 MPa at a temperature of −10 °C and a frequency of 25 HZ. The dynamic modulus value at high temperature conditions is lower than at low temperature conditions, while the dynamic modulus value at high frequency conditions is higher than at low frequency conditions. This study provides useful information and experimental data for the design of new asphalt mixtures for long longitudinal slope roads and has conducted in-depth research on the air void distribution and performance of the mixture, providing strong support for related research fields and practical applications.

Abstract High‐fat diet (HFD) has been recognized as a primary factor in the risk of chronic disease. Obesity, diabetes, gastrointestinal diseases, neurodegenerative diseases, and cardiovascular diseases have long been known as chronic diseases with high worldwide incidence. In this review, the influences of gut microbiota and their corresponding bacterial metabolites on the mechanisms of HFD‐induced chronic diseases are systematically summarized. Gut microbiota imbalance is also known to increase susceptibility to diseases. Several studies have proven that HFD has a negative impact on gut microbiota, also exacerbating the course of many chronic diseases through increased populations of Erysipelotrichaceae, facultative anaerobic bacteria, and opportunistic pathogens. Since bile acids, lipopolysaccharide, short‐chain fatty acids, and trimethylamine N‐oxide have long been known as common features of bacterial metabolites, we will explore the possibility of synergistic mechanisms among those metabolites and gut microbiota in the context of HFD‐induced chronic diseases. Recent literature concerning the mechanistic actions of HFD‐mediated gut microbiota have been collected from PubMed, Google Scholar, and Scopus. The aim of this review is to provide new insights into those mechanisms and to point out the potential biomarkers of HFD‐mediated gut microbiota.

A coupled-inductor-based high step-up high step-down DC-DC converter with bidirectional capability suitable for electric vehicle (EV) applications is proposed in this paper. The voltage gain in both step-up and step-down operations is increased by a two-winding coupled inductor. The current ripple in the low-voltage port is low making it suitable for renewable energy sources. In addition, the low number of components causes the efficiency of the proposed converter to be competitive with similar topologies in the literature. Due to the use of only three bidirectional switches with low voltage stress, the cost and volume of the converter are reduced. To demonstrate the performance of the proposed bidirectional converter, an analysis of the operation modes in the steady state is presented. Also, a comparison between the suggested converter and other similar converters in the literature is provided. The results of these comparisons show that the proposed converter has a lower number of components and higher efficiency than the others. In addition, lower voltage stress and noticeable voltage gain in comparison with others are further results of these comparisons. Eventually, the experimental results in step-up and step-down operations are presented. In step-up operation, the experimental prototype is tested under fs = 50 kHz, VLow = 20 V, VHigh = 110 V, rated power = 120 W, with 94.60% efficiency. Furthermore, VHigh is considered 100 V with VLow = 33 V and 94.43% efficiency at 50 W rated power in step-down mode operation.

Marine natural products have been recognized as the most promising source of bioactive substances for drug discovery research. This review illustrates the diversity of culturable actinobacteria associated with marine algae, their bioactivity and metabolites, and approaches to their isolation and determination of their biological properties. Furthermore, actinobacteria associated with marine algae are presented as a new subject for an extensive investigation to find novel and active natural products, which make them a potentially rich and innovative source for new drug development deserving more attention and exploration.

In this study, an innovative and sophisticated graphical tuning approach is postulated, aimed at the design of tilt-integral-derivative (TID) controllers that are specifically customized for fractional-order interval plants, whose numerators and denominators consist of fractional-order polynomials that are subjected to parametric uncertainties. By leveraging the powerful value set concept and the advanced D-composition technique, a comprehensive set of stabilizing TID controllers is obtained. The validity and effectiveness of the proposed methodology are demonstrated by some examples, which vividly illustrate its remarkable performance and potential.

The gut microbiome plays a pivotal role in maintaining the health of the hosts; however, there is accumulating evidence that certain bacteria in the host, termed pathobionts, play roles in the progression of diseases. Although antibiotics can be used to eradicate unwanted bacteria, the side effects of antibiotic treatment lead to a great need for more targeted antimicrobial agents as tools to modulate the microbiome more precisely. Herein, we reviewed narrow-spectrum antibiotics naturally made by plants and microorganisms, followed by more targeted antibiotic agents including synthetic peptides, phage, and targeted drug delivery systems, from the perspective of using them as potential tools for modulating the gut microbiome for favorable effects on the health of the host. Given the emerging discoveries on pathobionts and the increasing knowledge on targeted antimicrobial agents reviewed in this article, we anticipate targeted antimicrobial agents will emerge as a new generation of a drug to treat microbiome-involved diseases.

Air quality PM2.5 prediction is an effective approach for providing early warning of air pollution. This paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality PM2.5prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. Experiment results on two collected real-world datasets in China, such as Beijing and Taizhou PM2.5 datasets, show that the proposed method outperforms other used methods on both short-term and long-term air quality PM2.5 prediction tasks.

Background: Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders. Enhancing hippocampal neurogenesis by promoting proliferation and differentiation of neural stem cells (NSCs) is a promising therapeutic strategy for AD. 20(S)-protopanaxadiol (PPD) and oleanolic acid (OA) are small, bioactive compounds found in ginseng that can promote NSC proliferation and neural differentiation in vitro. However, it is currently unknown whether PPD or OA can attenuate cognitive deficits by enhancing hippocampal neurogenesis in vivo in a transgenic APP/PS1 AD mouse model. Here, we administered PPD or OA to APP/PS1 mice and monitored the effects on cognition and hippocampal neurogenesis. Methods: We used the Morris water maze, Y maze, and open field tests to compare the cognitive capacities of treated and untreated APP/PS1 mice. We investigated hippocampal neurogenesis using Nissl staining and BrdU/NeuN double labeling. NSC proliferation was quantified by Sox2 labeling of the hippocampal dentate gyrus. We used western blotting to determine the effects of PPD and OA on Wnt/GSK3β/β-catenin pathway activation in the hippocampus. Results: Both PPD and OA significantly ameliorated the cognitive impairments observed in untreated APP/PS1 mice. Furthermore, PPD and OA significantly promoted hippocampal neurogenesis and NSC proliferation. At the mechanistic level, PPD and OA treatments resulted in Wnt/GSK-3β/β-catenin pathway activation in the hippocampus. Conclusion: PPD and OA ameliorate cognitive deficits in APP/PS1 mice by enhancing hippocampal neurogenesis, achieved by stimulating the Wnt/GSK-3β/β-catenin pathway. As such, PPD and OA are promising novel therapeutic agents for the treatment of AD and other neurodegenerative diseases.

Mangrove-associated fungi are important sources for the discovery of new bioactive natural products. Three new isocoumarins (1–3) and one new pyrone derivative (4) were isolated from the ethyl acetate extract of the fermentation broth of the mangrove endophytic fungus Phomopsis sp. DHS-11. Nuclear magnetic resonance (NMR) spectroscopy (one-dimensional and two-dimensional) and mass spectrometry were used to determine the structures of these new compounds. The absolute configurations for the new isocoumarins 1–3 were determined by comparing their experimental and calculated electronic circular dichroism (ECD) spectra, while the configuration for the new pyrone-derivative 4 was tentatively solved by comparison of its 13C NMR data with reported data. In the biological activity test, compounds 1 and 3 showed cytotoxic activity against HeLa cells with IC50 values of 11.49 ± 1.64 µM and 8.70 ± 0.94 µM, respectively. The initial structure and activity relationship (SAR) analysis revealed that the length of the side chain at C-3 for isocoumarin-type compounds 1–3 could affect the cytotoxicity against HeLa cells. Compound 4 exhibited cytotoxic activities against human hepatoma cells HepG2 with an IC50 value of 34.10 ± 2.92 µM. All compounds have no immunosuppressive activity.

Here, (-)-Tetrahydroalstonine (THA) was isolated from Alstonia scholaris and investigated for its neuroprotective effect towards oxygen–glucose deprivation/re-oxygenation (OGD/R)-induced neuronal damage. In this study, primary cortical neurons were pre-treated with THA and then subjected to OGD/R induction. The cell viability was tested by the MTT assay, and the states of the autophagy–lysosomal pathway and Akt/mTOR pathway were monitored by Western blot analysis. The findings suggested that THA administration increased the cell viability of OGD/R-induced cortical neurons. Autophagic activity and lysosomal dysfunction were found at the early stage of OGD/R, which were significantly ameliorated by THA treatment. Meanwhile, the protective effect of THA was significantly reversed by the lysosome inhibitor. Additionally, THA significantly activated the Akt/mTOR pathway, which was suppressed after OGD/R induction. In summary, THA exhibited promising protective effects against OGD/R-induced neuronal injury by autophagy regulation through the Akt/mTOR pathway.

Global warming and depletion of fossil fuels have urged the world to control their impacts and find clean and green energy sources. Due to this paradigm shift, renewable energy resources (RESs) have received tremendous attention around the globe and amongst other RESs, wind energy has been proving itself a promising renewable resource. However, still, the proper selection of wind turbines (WTs) and their installation is considered a major challenge. Presently, due to the economic boom of Pakistan as a regional power under the China Pakistan Economic Corridor (CPEC) and increasing energy demand, it needs to meet the energy shortage. This paper aims to conduct a pre-feasibility ainvestigation of wind farm exploitation at Gwadar and Quetta, major cities of Baluchistan province in Pakistan by harnessing the wind energy potential and electricity generation cost through different WT models. The wind speed (WS) data is recorded by a professional wind mast at different measurement heights during F.Y 2016 and analyzed using Five approaches, Modified Maximum Likelihood Method (MMLM), Graphical Method (GM), Empirical Method of Lysen (EML), Empirical Method of Jestus (EMJ) and Energy Pattern Factor Method (EPF) at the proposed site and EPF shows the best performance than other approaches. Different WTs models are also evaluated and out of them, the GW121 WT gives the lowest cost in dollars and lowest payback period at each site so it can be recommended as one of the most feasible WT for electricity generation for powering local communities at the proposed sites.

Colorectal cancer (CRC) is an aggressive cancer. Isoalantolactone (IATL) has been reported to exert cytotoxicity against various cancer cells, but not CRC. In this study, we explored the anti-CRC effects and mechanism of action of IATL in vitro and in vivo. Our results demonstrated that IATL inhibited proliferation by inducing G0/G1 phase cell cycle arrest, apoptosis and autophagy in CRC cells. Repression of autophagy with autophagy inhibitors chloroquine (CQ) and Bafilomycin A1 (Baf-A1) enhanced the anti-CRC effects of IATL, suggesting that IATL induces cytoprotective autophagy in CRC cells. Mechanistic studies revealed that IATL lowered protein levels of phospho-AKT (Ser473), phospho-mTOR (Ser2448), phospho-70S6K (Thr421/Ser424) in CRC cells. Inhibition of AKT and mTOR activities using LY294002 and rapamycin, respectively, potentiated the inductive effects of IATL on autophagy and cell death. In vivo studies showed that IATL suppressed HCT116 tumor growth without affecting the body weight of mice. In consistent with the in vitro results, IATL lowered protein levels of Bcl-2, Bcl-XL, phospho-AKT (Ser473), phospho-mTOR (Ser2448), and phsopho-70S6K (Thr421/Ser424), whereas upregulated protein levels of cleaved-PARP and LC3B-II in HCT116 tumors. Collectively, our results demonstrated that in addition to inhibiting proliferation, inducing G0/G1-phase cell cycle arrest and apoptosis, IATL initiates cytoprotective autophagy in CRC cells by inhibiting the AKT/mTOR signaling pathway. These findings provide an experimental basis for the evaluation of IATL as a novel medication for CRC treatment.

Intercropping is widely used in agricultural production due to its capability of raising land productivity and providing an opportunity to achieve sustainable intensification of agriculture. In this study, soil samples from 10 to 20 cm depth of intercropping Pinto peanut in litchi orchard and litchi monoculture mode were established to determine soil attributes, enzyme activities, as well as the effect on soil bacterial diversity. On this basis, 16S rRNA V4-V5 region of soil bacterial communities in litchi/Pinto peanut intercropping (LP) mode and litchi monoculture mode (CK) was detected by the Illumina MiSeq sequencing platform. The results showed that the content of available potassium (AK) in LP was significantly higher than that in CK by 138.9%, and the content of available nitrogen (AN) in LP was significantly lower than that in CK by 19.6%. The soil enzyme activities were higher in LP as a whole, especially sucrase (SC) and acid protease (PT) were significantly higher by 154.4 and 76.5%, respectively. The absolute abundance and alpha diversity of soil microbiota were significantly higher in the intercropping group. Most importantly, endemic species with a significant difference in LP was higher by ~60 times compared to CK treatment. In the aspect of soil bacterial community structure, the dominant phyla of the two groups were Acidobacteria, Proteobacteria, Chloroflexi, and Actinobacteria. At the genus level, the absolute abundance of Flavobacterium and Nitrososphaera was significantly higher by 79.20 and 72.93%, respectively, while that of Candidatus_Koribacter was significantly lower with an amplitude of 62.24% in LP than in CK. Furthermore, the redundancy analysis (RDA) suggested that AK, which was highly associated with the dominant genera and phyla, is the vitally dominating environmental factors in LP groups, while in CK groups, it is AN and pH. In addition, PICRUSt2 analysis indicated that intercropping improved the metabolic activity of bacteria which can be correlated to the resistance of litchi root systems to soil-borne diseases. Overall, this study is expected to provide a theoretical basis and technical support for the healthy intercropping cultivation of litchi.

Automatic personality trait recognition has attracted increasing interest in psychology, neuropsychology, and computer science, etc. Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality trait recognition. This paper systematically presents a comprehensive survey on existing personality trait recognition methods from a computational perspective. Initially, we provide available personality trait data sets in the literature. Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Next, we describe the details of state-of-the-art personality trait recognition methods with specific focus on hand-crafted and deep learning-based feature extraction. These methods are analyzed and summarized in both single modality and multiple modalities, such as audio, visual, text, and physiological signals. Finally, we analyze the challenges and opportunities in this field and point out its future directions.

Introduction: Type 2 diabetes mellitus erectile dysfunction (T2DMED) is one of the common complications of type 2 diabetes mellitus (T2DM). Icariside II (ICA II), a flavonoid derived from Epimedium, has been shown to improve erectile function in T2DMED rats. Aim: To investigate the effect of ICA II and metformin (MET) on penile erectile function, mitochondrial autophagy, glucose-lipid metabolism in rats with T2DMED. Methods: In the control and T2DMED groups, rats were administered normal saline. In the MET group, rats were administered MET for 0.2 g/kg/day. In the ICA II+MET group, rats were administered ICA II for 10 mg/kg/day and MET for 0.2 g/kg/day. Results: The number of mating rats, number of erectile rats, erection rate, erection frequency, intracorneal pressure, and intracorneal pressure/mean arterial pressure in the ICA II+MET group and control group were significantly higher than corresponding values in than T2DMED group. The absolute values of fasting plasma glucose, glycated haemoglobin in the ICA II+MET group, MET group, and control group were significantly lower than in the T2DMED group. The advanced glycation end product (AGE) values in the ICA II+MET group and the MET group were lower than in the T2DMED group. The receptors for the AGE values and angiotensin II values in the ICA II+MET group were lower than in the T2DMED and MET groups. The high-density lipoprotein values, testosterone values, nitric oxide synthase activity, and cyclic guanosine monophosphate content in the ICA II+MET and control groups were higher than in the T2DMED group. The low-density lipoprotein values, triglyceride values, estradiol values, and total cholesterol values in the ICA II+MET and control groups were lower than in the T2DMED group. Conclusion: ICA II could increase erectile function and smooth muscle cell/collagen fibril proportions, decreased mitochondrial autophagy, and AGE concentrations and improve lipid metabolism, nitric oxide synthase activity, cyclic guanosine monophosphate content, testosterone, estradiol, and Ang II in rat with T2DMED.Zhang J, Li S, Zhang S, et al. Effect of Icariside II and Metformin on Penile Erectile Function, Histological Structure, Mitochondrial Autophagy, Glucose-Lipid Metabolism, Angiotensin II and Sex Hormone in Type 2 Diabetic Rats With Erectile Dysfunction. J Sex Med 2020;8:168–177.

Background: Active natural ingredients, especially small molecules, have recently received wide attention as modifiers used to treat neurodegenerative disease by promoting neurogenic regeneration of neural stem cell (NSC) in situ. 20(S)-protopanaxadiol (PPD), one of the bioactive ingredients in ginseng, possesses neuroprotective properties. However, the effect of PPD on NSC proliferation and differentiation and its mechanism of action are incompletely understood. Methods: In this study, we investigated the impact of PPD on NSC proliferation and neuronal lineage differentiation through activation of the Wnt/glycogen synthase kinase (GSK)-3β/β-catenin pathway. NSC migration and proliferation were investigated by neurosphere assay, Cell Counting Kit-8 assay, and EdU assay. NSC differentiation was analyzed by Western blot and immunofluorescence staining. Involvement of the Wnt/GSK3β/β-catenin pathway was examined by molecular simulation and Western blot and verified using gene transfection. Results: PPD significantly promoted neural migration and induced a significant increase in NSC proliferation in a time- and dose-dependent manner. Furthermore, a remarkable increase in antimicrotubule-associated protein 2 expression and decrease in nestin protein expression were induced by PPD. During the differentiation process, PPD targeted and stimulated the phosphorylation of GSK-3β at Ser9 and the active forms of β-catenin, resulting in activation of the Wnt/GSK-3β/β-catenin pathway. Transfection of NSCs with a constitutively active GSK-3β mutant at S9A significantly hampered the proliferation and neural differentiation mediated by PPD. Conclusion: PPD promotes NSC proliferation and neural differentiation in vitro via activation of the Wnt/GSK-3β/β-catenin pathway by targeting GSK-3β, potentially having great significance for the treatment of neurodegenerative diseases. Keywords: 20(S)-protopanaxadiol, Neural differentiation, Neural stem cell, Proliferation, Wnt/GSK-3β/β-catenin pathway

Abstract Background Ubiquitin-specific protease 7 (USP7) is a de-ubiquitin enzyme that plays an essential role in multiple cancers and becomes a target for treatment. However, the role of USP7 and its therapeutic value for HCC remains unclear. Methods USP7 expression was examined in HCC tissues by western blot and immunohistochemistry. The correlation of USP7 and HCC prognosis was analyzed by Kaplan–Meier survival method. Mass spectrometry was determined and cell proliferation and tumorigenicity assays were conducted in vitro and in vivo treated by P22077 and sgRNA-USP7. Results USP7 expression was significantly increased in HCC and associated with its progression. Interestingly, many HCC cells are sensitive to USP7 inhibition by using P22077. P22077 treatment not only induced cell death but also inhibited cell proliferation and migration in Huh7 and SK-Hep1 cells. In a xenograft model, P22077 efficiently inhibited tumor growth. In chemo-resistant HCC cells, P22077 decreased cell sensitivity to chemotherapy. In addition, mass spectrometry reveals 224 of significantly changed proteins upon P22077 treatment. Conclusions We demonstrate a critical role of USP7 in HCC devolvement and chemoresistance. Disruption of USP7 function results in dis-regulated several key biological processes and subsequently activates BAX. USP7 might be a novel and drug-able target in HCC.

Spontaneous speech emotion recognition is a new and challenging research topic. In this paper, we propose a new method of spontaneous speech emotion recognition on the basis of binaural representations and deep convolutional neural networks (CNNs). The proposed method initially employs multiple CNNs to learn deep segment-level binaural representations such as Left-Right and Mid-Side pairs from the extracted image-like Mel-spectrograms. These CNNs are fine-tuned on target emotional speech datasets from a pre-trained image CNN model. Then, a new feature pooling strategy, called block-based temporal feature pooling, is proposed to aggregate the learned segment-level features for producing fixed-length utterance-level features. Based on the utterance-level features, linear support vector machines (SVM) is adopted for emotion classification. Finally, a two-stage score-level fusion strategy is used to integrate the obtained results from Left-Right and Mid-Side pairs. Extensive experiments on two challenging spontaneous emotional speech datasets, including the AFEW5.0 and BAUM-1s databases, demonstrate the effectiveness of our proposed method.

In the domain of speech emotion recognition (SER), generally there is an unbalanced data distribution of emotional samples in existing emotional datasets. Moreover, different fragment areas in an utterance contribute diversely to SER. To address these two issues, this paper proposes a new SER method by combining a unified first-order attention network with data balance. The proposed method firstly utilizes the strategy of data balance to augment and balance the training data. Then, a pre-trained convolutional neural network (CNN) model (i.e., VGGish) is fine-tuned on target emotional datasets to learn segment-level speech features from the extracted Log Mel-spectrograms. Next, the unified first-order attention mechanism, including different feature-pooling strategies such as sum, min, max, mean, and standard deviation (std), is embedded into the output of a bi-directional long short-term memory (Bi-LSTM) network. This is used for learning high-level discriminative segment-level features, and simultaneously aggregating the learned segment-level features into fixed-length utterance-level features for SER. Finally, based on utterance-level features, the softmax layer in a Bi-LSTM network is adopted to conduct final emotion classification task. Extensive experiments are implemented on three public datasets such as BAUM-1s, AFEW5.0, and CHEAVD2.0, demonstrate the advantage of the proposed method.

The concept of fuzzy theory was developed in 1965 and becomes an acknowledged research subject in both pure and applied mathematics and statistics, showing how this theory is highly applicable and productive in many applications. In the present study, we introduced the definition of fuzzy interval valued strongly h-convex function and investigated some of its properties. We established Hermite-Hadamard and Schur-type inequalities for the class of fuzzy interval valued strongly h-convex function.

Backgrounds: Advanced schistosomiasis is the late stage of schistosomiasis, seriously jeopardizing the quality of life or lifetime of infected people. This study aimed to develop a nomogram for predicting mortality of patients with advanced schistosomiasis japonica, taking Dongzhi County of China as a case study. Method: Data of patients with advanced schistosomiasis japonica were collected from Dongzhi Schistosomiasis Hospital from January 2019 to July 2022. Data of patients were randomly divided into a training set and validation set with a ratio of 7:3. Candidate variables, including survival outcomes, demographics, clinical features, laboratory examinations, and ultrasound examinations, were analyzed and selected by LASSO logistic regression for the nomogram. The performance of the nomogram was assessed by concordance index (C-index), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The calibration of the nomogram was evaluated by the calibration plots, while clinical benefit was evaluated by decision curve and clinical impact curve analysis. Results: A total of 628 patients were included in the final analysis. Atrophy of the right liver, creatinine, ascites level III, N-terminal procollagen III peptide, and high-density lipoprotein were selected as parameters for the nomogram model. The C-index, sensitivity, specificity, PPV, and NPV of the nomogram were 0.97 (95% [CI]: [0.95–0.99]), 0.78 (95% [CI]: [0.64–0.87]), 0.97 (95% [CI]: [0.94–0.98]), 0.78 (95% [CI]: [0.64–0.87]), 0.97 (95% [CI]: [0.94–0.98]) in the training set; and 0.98 (95% [CI]: [0.94–0.99]), 0.86 (95% [CI]: [0.64–0.96]), 0.97 (95% [CI]: [0.93–0.99]), 0.79 (95% [CI]: [0.57–0.92]), 0.98 (95% [CI]: [0.94–0.99]) in the validation set, respectively. The calibration curves showed that the model fitted well between the prediction and actual observation in both the training set and validation set. The decision and the clinical impact curves showed that the nomogram had good clinical use for discriminating patients with high risk of death. Conclusions: A nomogram was developed to predict prognosis of advanced schistosomiasis. It could guide clinical staff or policy makers to formulate intervention strategies or efficiently allocate resources against advanced schistosomiasis.

The prevalence of multiple neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), has been dramatically increasing, particularly in the aging population. However, the currently available therapies merely alleviate the symptoms of these diseases and are unable to retard disease progression significantly. Traditional Chinese medicine (TCM) has been used in clinical practice for thousands of years for ameliorating symptoms or interfering with the pathogenesis of aging- associated diseases. Modern pharmacological studies have proved that TCM imparts disease-modifying therapeutic effects against these diseases, such as protection of neurons, clearance of protein aggregates, and regulation of neuroinflammation. This review summarizes the evidence from recent studies on AD and PD therapies regarding the neuroprotective activities and molecular mechanisms of a series of TCM formulations comprising herbs and their active ingredients. The findings of this review support the use of TCM as an alternative source of therapy for the treatment of neurodegenerative diseases.

We, the Editors and Publisher of the journal Artificial Cells, Nanomedicine, and Biotechnology, have retracted the following article:Genggang Cui, Mingming Shao, Xingzhou Gu, Hongbo Guo, Shiqing Zhang, Jianlei Lu & Hongbin Ma (2019) The value of FGF9 as a novel biomarker in the diagnosis of prostate cancer. Artificial Cells, Nanomedicine, and Biotechnology, 47:1, 2241–2245, DOI: 10.1080/21691401.2019.1620250It has come to our attention that the full authorship list and affiliations for this manuscript, including the study site and ethics committee, were changed after the article was submitted. We have contacted the authors for an explanation, but we have not received a response within the requested timeframe. As determining authorship and the location of where the research was conducted is core to the integrity of published work, we are therefore retracting the article. The authors listed in this publication have been sent notification.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Air pollution is a serious global environmental problem, especially in developing countries. PM2.5 is a major air pollutant that poses critical risks in urban areas. In this study, we identified the influence of comprehensive urbanization on regional PM2.5 in the Yangtze River Delta (YRD), the largest metropolitan region in China, from 1998 to 2015. The impacts of four urbanization subsystems (Economic, Spatial, Demographic, and Social) on the spatiotemporal evolution of PM2.5 were investigated at the city level using a linear mixed effect model (LME). The annual average concentration of PM2.5 over the YRD increased during the study period, with rapid growth in the northern plains and slow growth in the mountainous south. The LME model showed good performance between the predicted and observed PM2.5, with an R2 value for 10-fold cross-validation reaching 0.87 and a regression coefficient of 0.88. Urban population, GDP ratio of secondary industry, built-up area, total road area, number of students in colleges and universities, and total retail sales of consumer goods all had positive associations with PM2.5 concentration, while the proportion of tertiary industry employment, GDP ratio of primary industry, forest area, and number of hospital beds all had negative associations. Economic had the strongest effect (44%) on PM2.5 early on (1998–2003), but over time the contribution of other subsystems gradually increased. The overall contributions of urbanization type to PM2.5 in the YRD were Spatial (33.10%) > Economic (26.76%) > Demographic (16.85%) > Social (12.72%). Our results help deepen the understanding of regional air pollution in China as well as its correlation with urbanization and its subsystems.

Automatic speech emotion recognition (SER) is a challenging component of human-computer interaction (HCI). Existing literatures mainly focus on evaluating the SER performance by means of training and testing on a single corpus with a single language setting. However, in many practical applications, there are great differences between the training corpus and testing corpus. Due to the diversity of different speech emotional corpus or languages, most previous SER methods do not perform well when applied in real-world cross-corpus or cross-language scenarios. Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have increasingly been adopted for cross-corpus SER. This paper aims to provide an up-to-date and comprehensive survey of cross-corpus SER, especially for various deep learning techniques associated with supervised, unsupervised and semi-supervised learning in this area. In addition, this paper also highlights different challenges and opportunities on cross-corpus SER tasks, and points out its future trends.

Fuzzy information granulation transfers the time series analysis from the numerical platform to the granular platform, which enables us to study the time series at a different granularity. In previous studies, each fuzzy information granule in a granular time series can reflect the average, range, and linear trend characteristics of the data in the corresponding time window. In order to get a more general information granule, this paper proposes polynomial fuzzy information granules, each of which can reflect both the linear trend and the nonlinear trend of the data in a time window. The distance metric of the proposed information granules is given theoretically. After studying the distance measure of the polynomial fuzzy information granule and its geometric interpretation, we design a time series prediction method based on the polynomial fuzzy information granules and fuzzy inference system. The experimental results show that the proposed prediction method can achieve a good long-term prediction.

Chemical investigation of the fermentation extract of the coral-associated fungus Aspergillus sp. ITBBc1 led to the discovery of five unreported p-terphenyl derivatives, sanshamycins A–E (1–5), together with five previously described analogues, terphenyllin (6), 3-hydroxyterphenyllin (7), candidusin A (8), 4,5-dimethoxycandidusin A (9), and candidusin C (10). Their structures were elucidated by HRESIMS data and NMR spectroscopic analysis. Compound 1 represents the first example of p-terphenyls with an aldehyde substitution on the benzene ring. Compounds 2–4 feature varying methoxyl and isopentenyl substitutions, while compound 5 features a five-membered lactone linked to a biphenyl. These findings expand the chemical diversity of the family of p-terphenyl natural products. Compounds 1–6 and 9 were evaluated for their inhibitory activity against type 4 phosphodiesterase (PDE4), which is a fascinating drug target for treatment of inflammatory, respiratory, and neurological diseases. Compound 3 was the most potent and exhibited PDE4D inhibitory activity with an IC50 value of 5.543 µM.

The secondary metabolites of the phytopathogenic fungus Corynespora cassiicola CC01 from Hevea brasiliensis were investigated. As a result, two new compounds, 5-acetyl-7-hydroxy-6- methoxybenzofuran-2(3H)-one (1) and (S)-2-(2,3-dihydrofuro [3,2-c]pyridin-2-yl)propan-2-ol (2), together with seven known compounds, 4,6,8-trihydroxy-3,4-dihydronaphthalen-1(2H)-one (3), 3,6,8-trihydroxy-3,4-dihydronaphthalen-1(2H)-one (4), curvulin acid (5), 2-methyl-5-carboxymethyl- 7-hydroxychromone (6), tyrosol (7), p-hydroxybenzoic acid (8) and cerevisterol (9), were isolated from the fermentation extract by comprehensive silica gel, reverse phase silica gel, Sephadex-LH20 column chromatography and high-performance liquid chromatography (HPLC). The structures of these compounds were identified by using high-resolution electrospray mass spectrometry (HRESIMS), nuclear magnetic resonance spectroscopy (NMR), optical rotation, ultraviolet and infrared spectroscopy techniques and a comparison of NMR data with those reported in the literature. Compounds 1 and 2 were new compounds, and compounds 3–9 were discovered from this phytopathogenic fungus for the first time. Compounds 1–9 were tested for phytotoxicity against the fresh tender leaf of Hevea brasiliensis, and the results show that none of them were phytotoxic. Additionally, these compounds were subjected to an antimicrobial assay against three bacteria (E. coli, methicillin-resistant Staphylococcus aureus and Micrococcus luteus), but they showed no activity.

Abstract In this paper, we prove a new quantitative deformation lemma, and then gain a new mountain pass theorem in Hilbert spaces. By using the new mountain pass theorem, we obtain the new existence of two nontrivial periodic solutions for a class of nonlinear second-order discrete systems, which greatly improves the result in (Zhou et al. in Proc. R. Soc. Edinb. A 134:1013–1022, 2004).

This paper summarizes the factors affecting schistosomiasis transmission in the middle and lower reaches of the Yangtze River before and after the completion of the Three Gorges Dam and analyzes schistosomiasis prevalence trends to provide a basis for the application of the Three Gorges project methodologies in other areas. The Three Gorges Dam has demonstrated a positive effect on schistosomiasis control. Hubei, Hunan, Jiangxi, Anhui, and Jiangsu in the middle and lower reaches of the Yangtze River are currently regions with a high prevalence of schistosomiasis. These five provinces contained 97.62% of known snail areas and 93.66% of calculated schistosomiasis cases with 90.07% of counties (cities, districts) not yet meeting the criteria for schistosomiasis transmission interruption by the end of 2016. After the Three Gorges Dam was built, the prevalence of schistosomiasis in the middle and lower reaches of the Yangtze River decreased. By the end of 2016, the estimated number of cases decreased by 92.75% and the snail habitat areas decreased by 6.56% compared to 2004. No acute schistosomiasis cases have occurred for two consecutive years since 2015.

One key challenging issues of facial expression recognition (FER) in video sequences is to extract discriminative spatiotemporal video features from facial expression images in video sequences. In this paper, we propose a new method of FER in video sequences via a hybrid deep learning model. The proposed method first employs two individual deep convolutional neural networks (CNNs), including a spatial CNN processing static facial images and a temporal CN network processing optical flow images, to separately learn high-level spatial and temporal features on the divided video segments. These two CNNs are fine-tuned on target video facial expression datasets from a pre-trained CNN model. Then, the obtained segment-level spatial and temporal features are integrated into a deep fusion network built with a deep belief network (DBN) model. This deep fusion network is used to jointly learn discriminative spatiotemporal features. Finally, an average pooling is performed on the learned DBN segment-level features in a video sequence, to produce a fixed-length global video feature representation. Based on the global video feature representations, a linear support vector machine (SVM) is employed for facial expression classification tasks. The extensive experiments on three public video-based facial expression datasets, i.e., BAUM-1s, RML, and MMI, show the effectiveness of our proposed method, outperforming the state-of-the-arts.

An-Gong-Niu-Huang Wan (AGNHW), a famous formula in traditional Chinese medicine, has been clinically used for centuries for treating cerebral diseases, but the protective effects of pre-treatment with AGNHW on cerebral ischemia have not yet been reported. The present study aimed to test such protective effects and elucidate the underlying mechanisms on cerebral ischemia in rats by phenotypic approaches (i.e. including the neurological functional score, cerebral infarct area, neuron apoptosis, and brain oxidative stress status) and target-based approaches (i.e. involving the GSK-3β/HO-1 pathway). AGNHW was administered orally at the doses of 386.26, 772.52, and 1545.04 mg/kg respectively for 7 days to male Sprague-Dawley rats and then cerebral ischemia was induced by middle cerebral artery occlusion (MCAO) for 1.5 h. Pre-treatment with AGNHW significantly ameliorated ischemic damage to the brain in a dose-dependent manner, including reduction of the neurological deficit score and infarct area. AGNHW pre-treatment increased the number of Nissl+ cells, NeuN+ and DCX+ cells, and decreased the number of Tunel+ cells. Moreover, AGNHW reversed the up-regulation of ROS and MDA induced by cerebral ischemia. AGNHW pre-treatment increased the expression of p-GSK-3β(Ser9)/GSK-3β (glycogen synthase kinase-3β) ratio and heme oxygenase-1 (HO-1). These results firstly revealed that short-term pre-treatment of AGNHW could significantly protect the rats from injury caused by cerebral ischemia-reperfusion, which support further clinical studies for disease prevention. The in vivo protective effect of AGNWH pre-treatment could be associated with its antioxidant properties by the activation of GSK-3β-mediated HO-1 pathway.

In order to explore the scavenging ability of free radicals in polymers and the derivation mechanism with the different influence of the composition and various structure of nanomaterials, two-dimensional graphene oxide, one-dimensional carbon nanotubes and zero-dimensional carbon black in carbon nanomaterials, and halloysite nanotubes in silicate nanomaterials were selected to carry out a task. The results manifest that the content of free radicals produced by γ-ray irradiation in epoxy resin reduced because of the introduction nanoparticle. Among them, the epoxy resin reinforced by graphene oxide has the least free radical, followed by carbon nanotubes, halloysite nanotubes and carbon black, respectively. The analysis shows that the free radical scavenging capability was significantly different in variety types of nanomaterials. The free radical scavenging ability of carbon nanomaterials is ascribed to its well-organized honeycomb lattice, which is different from the fact that silicate nanomaterials is attributed to its electron acceptor sites. Meanwhile, the stable electron cloud was formed in the carbon nanomaterials, which is more conducive to eliminate the free radicals. The addition of nanomaterials decreases the number of free radicals in the resin, and correspondingly weakens the damage of free radicals for the resin, resulting in improving the thermal stability and mechanical properties of the epoxy resin.

Abstract COVID‐19 is spreading rapidly worldwide, posing great threats to public health and economy. This study aims to examine how the transmission of COVID‐19 is modulated by climate conditions, which is of great importance for better understanding of the seasonal feature of COVID‐19. Constrained by the accurate observations we can make, the basic reproduction numbers (R0) for each country were inferred and linked to temperature and relative humidity (RH) with statistical analysis. Using R0 as the measure of COVID‐19 transmission potential, we find stronger transmission of COVID‐19 under mildly warm (0°C < T 60%) climate conditions, while extremely low (T 20°C) temperature or a dry climate (RH

Ovonic threshold switch (OTS) has received great attention in neuromorphic computing due to its support for high-density synapse array as a selector and leaky-integration-firing functions Hodgkin-Huxley neurons. However, there is no simple and complete model for device simulation and integrated circuit design, which hindered application until now. In this work, we developed a compact physical model of OTS based on the Poole-Frenkel effect accompanied by the thermal dissipation effect for the first time. The thermal dissipation effect describes the energy flow between the device and the environment so that the model is more practical. Compared with previous experiments, the numerical results fairly fitted the electrical characteristics, demonstrating the model validity. In addition, the relation of the device performance with material and structure was deduced, which can facilitate optimizing the OTS device. The model will be useful for device design and implemented with high speed for simplicity.