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Accurate prediction of drug–target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal information, and modeling fine-grained drug–protein interactions. To address these challenges, We propose Gated-Attention Dual-Fusion Drug–Target Interaction (GADFDTI), whose core contribution is a fusion module that constructs an explicit atom–residue similarity field, refines it with a lightweight 2D neighborhood operator, and performs gated bidirectional aggregation to obtain interaction-aware representations. To provide strong and width-aligned unimodal inputs to this fusion module, we integrate a compact multi-scale dense GCN for drug graphs and a masked multi-scale self-attention protein encoder augmented by a narrow 1D-CNN branch for local motif aggregation. Experiments on two benchmarks, Human and C. elegans, show that GADFDTI consistently outperforms several recently proposed DTI models, achieving AUC values of 0.986 and 0.996, respectively, with corresponding gains in precision and recall. A SARS-CoV-2 case study further demonstrates that GADFDTI can reliably prioritize clinically supported antiviral agents while suppressing inactive compounds, indicating its potential as an efficient in silico prescreening tool for lead-target discovery.

Efficacious interventions are urgently needed for the treatment of COVID-19. Here, we report a monoclonal antibody (mAb), MW05, with SARS-CoV-2 neutralizing activity by disrupting the interaction of receptor binding domain (RBD) with angiotensin-converting enzyme 2 (ACE2) receptor. Crosslinking of Fc with FcγRIIB mediates antibody-dependent enhancement (ADE) activity by MW05. This activity is eliminated by introducing the LALA mutation to the Fc region (MW05/LALA). Potent prophylactic and therapeutic effects against SARS-CoV-2 are observed in rhesus monkeys. A single dose of MW05/LALA blocks infection of SARS-CoV-2 in prophylactic treatment and clears SARS-CoV-2 in three days in a therapeutic treatment setting. These results pave the way for the development of MW05/LALA as an antiviral strategy for COVID-19. Here the authors characterize a monoclonal antibody from a COVID-19 convalescent patient that interferes with SARS-CoV-2 spike binding to ACE2 and has prophylactic and therapeutic activity in non-human primates. Antibody-dependent enhancement of infection is prevented by mutating the Fc region of the antibody.

Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword \"anxiety disorder\" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R =0.6512; P<.001 to R =0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R =0.1782; P=.003), perceptual process (R =0.1435; P=.008), biological process (R =0.3225; P<.001), and assent words (R =0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R =0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area \"discrimination and stigma\" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area \"family and life\" (R =0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

Unbiased Finite Impulse Response (UFIR) filters are widely used in engineering applications, such as vehicle attitude estimation, due to their advantages, including independence from initial conditions and insensitivity to noise. However, the performance of the UFIR filter heavily relies on the estimation horizon N, and different states within the system may exhibit an inverse correlation with respect to N, affecting the estimation results. To address this issue, this paper proposes an adaptive state-separated UFIR (ASSUFIR) filtering algorithm based on the properties of quaternions. By leveraging the relationship between quaternions and attitude angles, the algorithm reduces the computational burden of the batch UFIR filter estimation system, allowing different horizon lengths to be applied to different states. To mitigate the computational efficiency loss caused by disrupting the original UFIR filter structure, QR decomposition is introduced. The algorithm is first validated using simulated data and then compared with classical methods using real vehicle data. Experimental results demonstrate the practical applicability of the proposed method in engineering applications.

High entropy oxides are novel materials with diverse compositions that have received significant interest for their mechanical and photocatalytic properties. In this study, we enhanced the mechanical and photocatalytic performance of solid state fabricated high entropy oxide (La0.2Nd0.2Sm0.2Gd0.2Y0.2)2Zr2O7 through Eu doping. Samples doped with different Eu ion concentrations demonstrated good thermal stability under high temperature without phase transformation after sintering at 1500 °C. We performed high pressure X-ray diffraction measurements and found that bulk modulus and critical pressure were improved by approximately 30 GPa and 5 GPa, respectively. This significant compression difference confirmed that Eu doping enhanced structural stability under high pressure. (La0.2Nd0.2Sm0.2Gd0.2Y0.2)1.98Eu0.02Zr2O7 demonstrated the best photocatalytic performance, with a kinetic constant about 1.5 times that of undoped samples. The appropriate doping concentration, smaller grain size and wider light absorption range contribute to the better photocatalytic performance irradiated under UV light. This work provides valuable insights for the design and improvement of high entropy oxide mechanical and photocatalytic performance.

This paper investigates the issue of rating prediction for neighborhood-based collaborative filtering in recommendation systems. A novel rating prediction algorithm, called iterative rating prediction (IRP), is proposed for neighborhood-based collaborative filtering. The main idea behind IRP is neighborhood propagation. To predict ratings of items for target users, IRP relies on not only the rating information of direct neighbors but also that of indirect neighbors with different propagation depth. To implement the idea, IRP iteratively updates the ratings of items for users. The efficiency of the proposed method is examined through extensive experiments. Experimental results demonstrate the superior performance of our method, especially on small-scaled and sparse datasets.

The common mycotoxins in polluted grains are aflatoxin B1(AFB1), zearalenone (ZEN) and deoxynivalenol (DON). Because of the potential threat to humans and animals, it is necessary to detect mycotoxin contaminants rapidly. At present, later flow immunoassay (LFIA) is one of the most frequently used methods for rapid analysis. However, multistep sample pretreatment processes and organic solvents are also required to extract mycotoxins from grains. In this study, we developed a one-step and “green” sample pretreatment method without using organic solvents. By combining with LFIA test strips and a handheld detection device, an on-site method for the rapid detection of AFB1, ZEN and DON was developed. The LODs for AFB1, ZEN and DON in corn are 0.90 μg/kg, 7.11 μg/kg and 10.6 μg/kg, respectively, and the working ranges are from 1.25 μg/kg to 40 μg/kg, 20 μg/kg to 2000 μg/kg and 35 μg/kg to 1500 μg/kg, respectively. This method has been successfully applied to the detection of AFB1, ZEN and DON in corn, rice and peanut, with recoveries of 89 ± 3%–106 ± 3%, 86 ± 2%–108 ± 7% and 90 ± 2%–106 ± 10%, respectively. The detection results for the AFB1, ZEN and DON residues in certified reference materials by this method were in good agreement with their certificate values.

Change in pH in growth conditions is the primary stress for most neutralophilic bacteria, including model microorganism Escherichia coli. However, different survival capacities under acid stress in different bacteria are ubiquitous. Research on different acid-tolerance mechanisms in microorganisms is important for the field of combating harmful gut bacteria and promoting fermentation performance of industrial strains. Therefore, this study aimed to carry out a narrative review of acid-stress response mechanism of E. coli discovered so far, including six AR systems, cell membrane protection, and macromolecular repair. In addition, the application of acid-tolerant E. coli in industry was illustrated, such as production of industrial organic acid and developing bioprocessing for industrial wastes. Identifying these aspects will open the opportunity for discussing development aspects for subsequent research of acid-tolerant mechanisms and application in E. coli.

Day-length changes represent an important cue for modulating flowering time. In Arabidopsis, the expression of the florigen gene FLOWERING LOCUS T (FT) exhibits a 24-h circadian rhythm under long-day (LD) conditions. Here we focus on the chromatin-based mechanism regarding the control of FT expression. We conducted co-immunoprecipitation assays along with LC-MS/MS analysis and identified HD2C histone deacetylase as the binding protein of the H3K4/H3K36 methylation reader MRG2. HD2C and MRG1/2 regulate flowering time under LD conditions, but not under short-day conditions. Moreover, HD2C functions as an effective deacetylase in planta, mainly targeting H3K9ac, H3K23ac and H3K27ac. At dusk, HD2C is recruited to FT to deacetylate histones and repress transcription in an MRG1/2-dependent manner. More importantly, HD2C competes with CO for the binding of MRG2, and the accumulation of HD2C at the FT locus occurs at the end of the day. Our findings not only reveal a histone deacetylation mechanism contributing to prevent FT overexpression and precocious flowering, but also support the model in which the histone methylation readers MRG1/2 provide a platform on chromatin for connecting regulatory factors involved in activating FT expression in response to daylight and decreasing FT expression around dusk under long days.

Osteoporosis is a progressive bone disease caused by impaired function of endogenous bone marrow-derived mesenchymal stem cells (BMSCs). Herein, we investigated the mechanism of lncRNA SNHG14 in osteoporosis progression. BMSCs were isolated from BALB/c mice. The osteogenic ability of BMSCs was assessed by Alkaline phosphatase (ALP) and Alizarin Red S Staining (ARS) staining. The interaction between miR-493-5p and SNHG14 or myocyte enhancer factor 2 C (Mef2c) was confirmed by dual-luciferase reporter assay. Bone histomorphometry changes were evaluated to analyze SNHG14’roles in osteoporosis in vivo. Our results illustrated SNHG14 and Mef2c levels were increased in a time-dependent manner in BMSCs, and miR-493-5p expression was decreased. SNHG14 knockdown inhibited osteogenic differentiation of BMSCs, and SNHG14 upregulation had the opposite effect. SNHG14 overexpression elevated bone mineral density and bone trabecular number, and alleviated osteoporosis progression in vivo. Mechanically, miR-493-5p was a target of SNHG14 , and miR-493-5p targeted the Mef2c gene directly. SNHG14 overexpression reversed the inhibition of miR-493-5p on the osteogenic ability of BMSCs, and miR-493-5p silencing accelerated BMSCs osteogenesis by activating Mef2c-mediated autophagy to accelerate BMSCs osteogenesis. In short, SNHG14 activated autophagy via regulating miR-493-5p / Mef2c axis to alleviate osteoporosis progression, which might provide a new molecular target for osteoporosis treatment. SNHG14 regulates the expression of Mef2c and activates autophagy through miR-493-5p, promoting osteogenic differentiation of BMSCs and consequently alleviating osteoporosis development.