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Therapeutic peptides represent a rapidly expanding class of drug candidates due to their diverse biological activities and high specificity. However, accurately predicting peptide functions directly from sequence information remains a major challenge in computational peptidomics. Current tools, typically standalone applications or functionally constrained web servers, lack the flexibility and scalability essential for modern peptide discovery workflows. Therefore, it is necessary to develop a cloud-based, no-code platform that enables customizable modeling and high-throughput functional screening of therapeutic peptides. PepPharmaHub provides a cloud-based, end-to-end platform that integrates advanced sequence-based language modeling with curated benchmark datasets and interactive visualization modules. The platform features a high-throughput screening module powered by a diverse set of 24 models targeting 20 therapeutic properties, alongside a customizable model training pipeline for user-defined screening tasks. Comprehensive benchmarking on 24 public datasets demonstrates that PepPharmaHub matches or surpasses state-of-the-art predictors, significantly improving the efficiency of large-scale peptide screening. Compared with existing public web servers, PepPharmaHub attains a higher, more tightly distributed accuracy on 3475 newly reported bioactive peptides from 1 January 2023 to 1 June 2025 (20 independent tasks), indicating stronger generalization and practical utility. PepPharmaHub enables accurate, high-throughput prediction of peptide functions through customizable deep learning models and a no-code interface. By outperforming existing tools across multiple benchmarks and supporting interpretable sequence analysis, the platform offers a practical solution for accelerating peptide-based drug discovery.

Jupyter Notebooks have transformed the way we conduct computational research thanks to a versatile platform that combines code, annotations, plots, and a lightweight format that enables versioning. However, steps for installing dependencies and downloading data for a specific notebook may require significant time and failure to establish identical environments can compromise reproducibility of environments. Deploying a JupyterHub server not only provides a containerized environment for each notebook to ensure fast execution and exact reproducibility but also provides a computational engine that makes these applications available from any device. By providing all the steps for deploying a JupyterHub server, and providing our experience deploying and maintaining Netbooks, a server for academic investigations in network biology, we want to encourage other groups to deploy JupyterHub servers for their own computational work and to recognize their value in improving reproducibility, accelerating manuscript review, and supporting education. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Setting up a JupyterHub server Support Protocol 1: Compiling a collection of Jupyter Notebooks Support Protocol 2: Securing HTTPS access Support Protocol 3: Adding parameters for JupyterHub Support Protocol 4: Data management for JupyterHub Support Protocol 5: Customizing the website homepage Support Protocol 6: Importing/Exporting conda environments Alternate Protocol: Running a JupyterHub server as a virtual machine.

Background and objectives Hypothyroidism is the most common thyroid disorder during pregnancy and, if not managed adequately, increases the risk of adverse foeto-maternal outcomes. The present systematic review and meta-analysis was conducted to assess the prevalence of hypothyroidism among Indian pregnant women and related foeto-maternal outcomes. Methods A systematic search was conducted across PubMed, Google Scholar, and preprint servers to identify observational studies reporting the prevalence of hypothyroidism and associated foeto-maternal outcomes among Indian pregnant women. A random-effects model was utilised to pool effect sizes, and heterogeneity was assessed using I2 statistic. Funnel plots, along with Begg's and Egger's tests, were used to assess publication bias. Data were analysed using STATA version 17. Results A total of 60 studies were included. The pooled prevalence of hypothyroidism among pregnant women was 17% [95% confidence interval (CI): 14%, 19%] with subclinical hypothyroidism at 15% (95% CI: 12%, 18%) and overt hypothyroidism at 3% (95% CI: 3%, 4%). In women with subclinical hypothyroidism, the pooled prevalence of adverse maternal outcomes was 9% (95% CI: 6%, 11%), while the prevalence of adverse foetal outcomes was 11% (95% CI: 9%, 14%). The pooled prevalence was 18% for preterm birth (95% CI: 11%, 25%), 17% for low birth weight (95% CI: 10%, 25%), 7% for intrauterine death (95% CI: 2, 14%), and 2% for stillbirth (95% CI: 0, 4%). Among women with overt hypothyroidism, the prevalence of adverse maternal and foetal outcomes was 12% (95% CI: 10%, 15%) and 14% (95% CI: 11%, 17%), respectively. The pooled prevalence was 22% for low birth weight (95% CI: 13%, 31%), 16% for preterm birth (95% CI: 9%, 24%), 16% for intrauterine death (95% CI: 7%, 27%), and 6% for stillbirth (95% CI: 1%, 13%). Most studies used trimester-specific TSH cut-offs based on the American Thyroid Association guidelines. One fourth (n=15) of the 60 studies applied alternative thresholds, with upper limits for normal TSH varying from 4.0-10.0 mIU/L. Interpretation and conclusions The rising burden and adverse consequences of hypothyroidism in pregnancy demand urgent attention. Uniform, evidence-based screening and management practices must be implemented at all levels of care. There is a pressing need for India-specific diagnostic cut-offs and large-scale prospective studies to inform treatment thresholds and long-term outcomes.

Digital transformation of European healthcare is progressing rapidly, yet implementation in outpatient specialist care remains uneven. In Germany, office-based urologists face distinct structural, regulatory and economic challenges. We assessed digital infrastructure, tool utilization, perceived barriers and investment behaviour, including age-related differences. We conducted a nationwide cross-sectional survey (April-July 2024) among private-practice urologists in Germany using a 35-item questionnaire structured along three dimensions of digital adoption (infrastructure, functional use, physician attitudes). Items were adapted from two previously published, peer-reviewed German urology surveys and pretested with 12 urologists prior to launch. 189 valid responses (response rate 17.4%) were analysed using descriptive statistics, chi-squared tests and pre-specified multivariable logistic regression adjusting for age, gender, practice size, years in practice and region. Self-reported digital familiarity was high (66.1% familiar or very familiar), yet infrastructure remained traditional: 92.6% used on-site servers and only 7.4% any cloud component. Telemedicine was rarely used (61.9% never), while 49.2% used health apps or DiGAs. Adjusted analyses confirmed that age <55 years (aOR 2.93, 95% CI 1.56-5.52, p<0.001) and shorter time in practice (aOR 0.78 per +5 years, 95% CI 0.65-0.94, p=0.009) independently predicted DiGA use and revealed that telemedicine adoption was driven primarily by practice size (aOR 1.16 per additional full-time equivalent, 95% CI 1.05-1.29, p=0.005) rather than age. Most practices planned to invest less than &#x20ac;30,000 over five years. Digitalization in outpatient urology remains primarily administrative. High familiarity does not translate into clinical transformation, and adoption is constrained by structural rather than generational barriers. Targeted funding, interoperable systems, and clear reimbursement frameworks are needed to enable clinically meaningful digital integration.

Gargle sampling emerged as a novel method for diagnostic testing during the coronavirus disease 2019 (COVID-19) pandemic, yet uncertainty remained about its performance when compared with conventional sampling methods. To evaluate the performance of self-collected gargle samples compared to traditional healthcare worker (HCW)-collected upper respiratory tract swabs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection with nucleic acid amplification testing (NAAT). We conducted a systematic review (PROSPERO registration: CRD42022312628) to (1) estimate sensitivity of gargle sampling, (2) estimate the difference in sensitivity between gargle and swab methods, and (3) understand how various testing contexts may impact gargle sensitivity. MEDLINE, EMBASE, Web of Science, Global Index Medicus, and preprint servers were searched. Studies reporting primary data and investigating COVID-19 diagnostic performance of self-collected gargle samples compared to HCW-collected swabs tested using NAAT were included. Quality assessment was performed, and random effects meta-analysis was conducted to estimate the pooled gargle sensitivity and mean difference in sensitivity between gargle and swab methods. Searches identified 1453 results with 32 studies included. Meta-analysis pooled 34 gargle-swab comparisons. Gargle sensitivity was estimated to be 92.2% (95% confidence interval: 89.6% to 94.2%), and 3.3% (0.4% to 6.3%) less sensitive than swab collection. Gargle sensitivity was greater than 87.0% across diverse patient characteristics, settings, type or volume of gargle liquid, length of gargling time, wait time prior to gargling, and reference swab type used. Greatest sensitivities were observed when gargle sampling for 30&#xa0;s or greater using 5-9 mL of saline. Gargle sensitivity of 93.0% (87.8% to 96.1%) was observed when there was no required wait time, and 90.1% (87.0% to 92.5%) when compared to combined nasopharyngeal and oropharyngeal swabs. Gargle sampling may be a sensitive, alternative method for SARS-CoV-2 detection across various testing contexts, and its implementation has potential to reduce barriers associated with HCW-collected swabs, that may be challenging to collect.

Secure outsourced computation (SOC) provides secure computing services by taking advantage of the computation power of cloud computing and the technology of privacy computing (e.g., homomorphic encryption). Expanding computational operations on encrypted data (e.g., enabling complex calculations directly over ciphertexts) and broadening the applicability of SOC across diverse use cases remain critical yet challenging research topics in the field. Nevertheless, previous SOC solutions frequently lack the computational efficiency and adaptability required to fully meet evolving demands. To this end, in this paper, we propose a toolkit for TEE-assisted (Trusted Execution Environment) SOC over integers, named TRUST. In terms of system architecture, TRUST falls in a single TEE-equipped cloud server only through seamlessly integrating the computation of REE (Rich Execution Environment) and TEE. In consideration of TEE being difficult to permanently store data and being vulnerable to attacks, we introduce a (2, 2)-threshold homomorphic cryptosystem to fit the hybrid computation between REE and TEE. Additionally, we carefully design a suite of SOC protocols supporting unary, binary and ternary operations. To achieve applications, we present SEAT, secure data trading based on TRUST. Security analysis demonstrates that TRUST enables SOC, avoids collusion attacks among multiple cloud servers, and mitigates potential secret leakage risks within TEE (e.g., from side-channel attacks). Experimental evaluations indicate that TRUST outperforms the state-of-the-art and requires no alignment of data as well as any network communications. Furthermore, SEAT is as effective as the Baseline without any data protection.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personalized cancer care. This systematic review and meta-analysis aim to evaluate the predictive performance, methodological quality, and clinical implementation of deep learning models for cancer treatment outcomes. A comprehensive search across ten databases and preprint servers identified 158 eligible studies, with 89 included in the quantitative synthesis. Results revealed pooled AUCs of 0.823 (internal validation) and 0.787 (external validation), with superior performance observed in multimodal and Transformer-based models. However, given the substantial heterogeneity (I2&#xa0;>&#xa0;70&#xa0;%) across included studies, these pooled estimates should be interpreted as broad indicators of methodological feasibility rather than definitive performance benchmarks. Methodological inconsistencies, high risk of bias, and limited external validation were common, and only 9&#xa0;% of models had been implemented clinically. This study contributes to the literature by providing the first cross-cancer meta-analytic synthesis of deep learning in treatment prediction across cancer types and model architectures. Findings highlight both the promise and the current limitations of AI integration in oncology and emphasize the need for rigorous validation, transparent reporting, and translational research. The review encompassed studies on both solid tumors (breast, lung, colorectal, prostate, and others) and various treatment modalities including chemotherapy, immunotherapy, radiation therapy, targeted therapy, and surgical interventions. Outcome measures included treatment response prediction (measured via AUC), overall survival and progression-free survival (evaluated using C-index and hazard ratios), and clinical utility (assessed through net benefit and decision curve analyses).

Sixth-generation (6G) networks are likely to support advanced Internet of Vehicles (IoV) applications that have rigid latency, reliability, and computation demands. Nevertheless, efficient task offloading is a challenging problem because vehicle environments are characterized by mobility, changing channels, varying task requirements, constrained edge resources, and growing energy demands. To address these issues, this study presents an Optimized Multi-Tier Task Offloading Strategy (OMTOS) for sustainable IoV systems. The proposed framework comprises a four-tier computing architecture comprising vehicles, roadside units (RSUs), mobile edge computing (MEC) servers, and cloud infrastructure. The generalized latency-energy optimization problem is formulated to allocate tasks across these levels, accounting for task due dates, resource capacity, communication delay, computation delay, and energy consumption. To address dynamic offloading, OMTOS employs a centralized training and decentralized execution (CTDE) based multi-agent Soft Actor-Critic (SAC) method, where the vehicle agents can make decentralized offloading decisions with centralized critics guiding the coordinated learning process during training. It is tested against rule-based and heuristic as well as deep reinforcement learning and various multi-agent reinforcement learning baselines, including LE, EO, RO, GO, DQN, DDPG, SAC, MADDPG, and MAPPO. The aforementioned results reveal that OMTOS achieves low average delay, low energy consumption, a high task success rate, and high convergence compared to the competing methods. Sensitivity analysis also indicates that the latency and energy weightings can be changed to suit various IoV service requirements, including delay-critical safety services, and energy-conscious delay-tolerant services. These results show that OMTOS offers an adaptive and sustainable task-offloading tool in 6G-enabled IoV environments.

Azadirachta indica (Neem) is well-known for its therapeutic potential against bacteria, while Staphylococcus saprophyticus and Citrobacter koseri are significant public health threats due to their potential to cause skin infections. This study aims to identify phytocompounds from the methanolic extract of A. indica leaves and evaluate their efficacy to inhibit those bacteria through in vitro and computational approaches. The bacteria were isolated from boil-derived pus, and the extract's growth-inhibitory effect was examined by the disc diffusion method. Additionally, GC-MS identified phytochemicals remaining in the extract. The pharmacokinetic and ADMET properties of those phytochemicals were assessed via PKCSM and SwissADME servers. Screened phytocompounds were docked with functional proteins using AutoDock Vina and GLIDE. Following that, molecular dynamics simulation in GROMACS, post-simulation, and DFT analysis were conducted. Five of the sixteen phytocompounds with favorable pharmacokinetics and toxicity profiles were docked with Peptidoglycan D, D-transpeptidase MrdA of Citrobacter koseri, and D-alanine--D-alanine ligase of Staphylococcus saprophyticus. Among them, 2-(Acetoxymethyl)-3-(methoxycarbonyl)biphenylene, and Cyclopropane-1-carboxamide, 2-butyl-N-(5,6,7,8-tetrahydro-7,7-dimethyl-5-oxoquinazolin-2-yl)- demonstrated higher binding affinity, greater stability, and smaller energy gap in docking, simulation, and DFT analysis, respectively. This study highlights the two compounds as promising lead phytocompounds to inhibit those bacteria. Further in vivo investigations are essential to validate their therapeutic effectiveness.

Galaxy (https://galaxyproject.org), now in its third decade, is a globally accessible, community-driven platform for reproducible and collaborative data analysis. With over 650&#x2009;000 registered users, major public servers handle ~2&#x2009;000&#x2009;000 monthly analysis jobs from ~20&#x2009;000 users. Recent, massive modernization has overhauled the user interface and workflow infrastructure to improve usability and scalability. Key updates include a redesigned history interface, enhanced tool discovery, a centralized dataset view, and a new visualization framework. Workflow editing is now substantially enhanced with search, undo/redo, and new invocation graph views. Data management is more flexible with intelligent sample sheet handling, expanded collection support, redesigned file source interfaces, and the introduction of "scratch" storage. User-defined repositories like Google Drive, Dropbox, and Zenodo are now enabled by default. Coupled with the growth of the Galaxy Training Network, these advances solidify Galaxy as a robust, widely adopted AI-ready ecosystem for open science.

Conducting dynamic exploration in complex and unpredictable environments, particularly in space exploration, reveals the great potential of systems based on tensegrity structures. However, the implementation of such systems faces a series of intelligent challenges, including the reliability of wireless monitoring, the efficiency of human-computer interaction, and the optimization of intelligent analysis and recommendation capabilities. In this study, we introduce a 6-bar tensegrity system equipped with 24 flexible sensors, leveraging fine-tuned multimodal large language model to enable autonomous state cognition system including self-shape recognition, alarm system, as well as fault diagnosis. Supported by long and short-term memory models, the tensegrity is able to reconstruct its own shape via conductive flexible tendons without relying on external sensors. By combining the flask server with the fine-tuned large language model, the tensegrity automatically transmits data to the iPhone for wireless monitoring. Finally, we developed the fine-tuned LLM and employed it to facilitate fault diagnosis and human interaction, enabling users to effectively obtain the requisite information through natural language processing techniques. This autonomous state cognition system relying on tensioning bars shows great potential for future exploration and becomes a powerful tool for multifunctional applications in the real world.

Stromal progenitor cells of bone marrow origin are non-hematopoietic cells that give rise to osteoblasts and adipocytes in the postnatal organism. Marrow stromal cells (also known as mesenchymal stem cells - MSCs) are currently being employed in a large number of clinical trials for regenerative purposes post in vitro expansion. However, the clinical outcome has been variable, which might in part be due to the heterogeneity of the cells and the lack of a defined cell product with a molecular signature that favors tissue regeneration. In this study, we determined the cellular heterogeneity of primary stromal cultures and examined how inter-donor variation in subpopulation composition contributes to the differentiation potential of primary cultures. We profiled 136 014 stromal progenitors from 26 donors and identified 5 subpopulations that were linked to distinct bone-related pathways and genetic traits of bone mineral density and morphology. Abundance of one cluster characterized by high expression of ITGA11 (integrin alpha-11) and genes related to matrix function, collagen organization, and elevated expression upon lineage commitment was positively correlated with osteoblastic differentiation capacity in vitro. In addition, ITGA11 protein expression in progenitor cells was a predictive marker for matrix mineralization in vitro and ectopic bone formation in vivo. Sorting stromal progenitors into ITGA11high and ITGA11low cells established cultures with high and low osteoblastic differentiation potential and revealed transcriptional differences reflective of the subpopulation-specific signature, which was not affected by siRNA-mediated knockdown of ITGA11 expression. Our findings corroborate the presence of an extensive donor-dependent cellular heterogeneity that persists in cultured stromal cells, and that ITGA11 can be employed as a marker for isolating cells with high bone-forming potential, a feature likely to benefit clinical trials of bone regeneration.

Application-layer Distributed Denial of Service (App-DDoS) attacks are an ongoing issue in the cyber security world. The attack constructs request headers and uses a large number of channels to disrupt targeted services, such as an automated attack tool. A variety of approaches have been attempted, but the detection of attacks through the identification of forged request headers is a significant gap in the research. Signature detection, which shows a strong ability to accurately identify attacks with low false positive and false negative rates, can be used to address this challenge. The paper introduces a new detection method to categorize traffic as malicious or legitimate by analyzing the request headers of the traffic. To address the concerns of various researchers regarding outdated attack patterns and the lack of datasets available for public research, the dataset used in this research is recent and representative of real-world App DDoS attack patterns. The signature detection demonstrates promising performance in detecting the attack with the latest data set, which has strong implications for adaptation to real-world applications. The key contributions of this study are the proposed detection algorithms that can detect forged request headers at the initial stage, prior to their processing by the web server, and the practical analysis, which showed that the attack strategy approach relies on the manipulation of request headers. The hybrid feature selection method employed in this research was proven to be workable and successfully identified the features which contribute significantly to the detection performance with an accuracy of 96.93%, a precision of 99.11%, a recall of 97.55%, and an F1-score of 98.32%. These results demonstrate that the signature-based detection is effective and appropriate for detecting the attack. Although Machine Learning (ML) is gaining traction, signature-based detection can still be effective for checking signatories generated by the attack in the request headers.

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH3), Acetaldehyde (CH3CHO), Acetone ((CH3)2CO), Ethylene (C2H4), Ethanol (C2H5OH), Toluene (C6H5CH3). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD-DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 &#xb1; 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD-DM-20 achieved 47.91 &#xb1; 18.79% mean accuracy over Batches 2-10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework-accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference-is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work.

Predicting blood-brain barrier (BBB)-penetrating peptides remains critical for peptide-based central nervous system drug delivery, yet model performance depends strongly on data curation and feature representation. In this study, we constructed a benchmark dataset from publicly available resources by merging peptide records and removing duplicate sequences, resulting in 426 positive and 6,865 negative samples. Each peptide was encoded using fused representations that combine protein language model embeddings with physicochemical descriptors, yielding a 2,121-dimensional feature space. After variance filtering, standardization, and mutual-information-based feature selection, the top 700 features were retained for classification. To address class imbalance, the majority class in the training set was randomly undersampled to achieve a 1:5 positive-to-negative ratio. A foundation model for tabular classification, termed B3BPFN, was then trained on the processed feature matrix and evaluated on an independent balanced test set comprising 20% of the positive samples and an equal number of negative samples. The final model achieved a sensitivity of 0.9294, specificity of 0.8824, accuracy of 0.9059, Matthews correlation coefficient (MCC) of 0.8127, and area under the receiver operating characteristic curve (AUROC) of 0.9460. SHAP analysis further revealed that composition-transition-distribution (CTDD) descriptors serve as important features for BBB-penetrating peptide prediction. A user-friendly web server is freely available at https://ycclab.cuhk.edu.cn/b3bpfn to facilitate community use.

Current software packages to mine key transcription factors (TFs) regulating the differentially expressed genes (DEGs) have not been satisfactorily utilized. Here, we present TransPilot, a web server that identifies key TFs from transcriptome data via weighted Kendall's tau rank correlation of ranked and directed TF-target sets against DEG lists. TransPilot employed an artificial neural network (ANN), which was trained using features derived from a log-likelihood ratio (LLR) array model, to identify transcription factor binding sites (TFBSs). The TF-target sets were ranked based on the ANN scores, which represent the binding potentials of the TFBSs. An imputation method was introduced to fill in the LLRs for genes that lack TFBS in their promoters. Most importantly, the direction for each ranked TF-target pair was annotated as positively or negatively regulated based on the correlation coefficient of their expression profiles in a reference transcriptome dataset. The key TFs were identified from transcriptome data by testing the correlation between the order of genes in the TF-target sets and the corresponding order in the DEG list. The analysis emphasizes end-ranked genes with large weights and de-emphasizes middle-ranked genes with small weights. The server was benchmarked on a transcriptome dataset derived from a macrophage polarization experiment. The data, code, and results utilized in this study can be accessed at http://www.thua45.cn/transpilot.

In a computer-based writing platform, keystroke log data can document a student's writing process (e.g., typing behaviors). A set of tools has been developed to collect the resulting log data. However, these tools originate from decades ago, and none of them has been adapted for use with cloud servers. In this research, we describe a cloud-based writing platform that we have developed, Clourite. It offers a series of benefits including improved scalability and automatic data collection. It is easy to use and requires only a few mouse clicks without installing any software. Moreover, with a sample of 309 college students, we collected empirical keystroke log data through Clourite and extracted writing process features using a Bayesian hierarchical mixture model. The non-hierarchical mixture model may enlarge the standard error when the number of pause events in that component is small, as shown in our previous work. In contrast, the proposed hierarchical model in a fully Bayesian framework has the advantage that essays are considered similar data units, meaning the model can borrow strength from longer essays to enhance the efficiency of estimation for shorter essays. We identified a distinct writing process pattern differentiating stronger and weaker writers, along with the association between process features and writing quality measures (i.e., human scores). These findings underscore the potential of keystroke logging analysis for characterizing student writing processes.

Bacterial vaginosis (BV), a prevalent form of vaginal dysbiosis, is primarily caused by Gardnerella vaginalis. The increasing prevalence of BV has raised global health concerns, prompting the need for a vaccine that can enhance human immunity and mitigate BV transmission. The study developed a multi-epitope vaccine for BV infection using immunoinformatic methodologies. B-cell and T-cell epitopes were identified using the IEDB recommended server and NetCTL. Specifically, HTL epitopes were predicted against HLA-DR alleles (including DRB101:01, DRB103:05, and DRB104:04), while CTL epitopes were predicted against HLA class I supertypes (including HLA-A02:01 and HLA-A*01:01). The selected epitopes were fused using adjuvants and linkers. The peptide sequence MSPSVRHSPSVRH, derived from the heat shock protein 60 (HSP60) of Mycobacterium tuberculosis, was incorporated as an adjuvant to activate innate immunity via TLR2/4 and enhance dendritic cell maturation. The B-cell and (CTL or HTL) epitopes were connected using GGGS linkers, whereas the CTL&#x2009;+&#x2009;HTL epitopes were connected using HEYGAEALERAG linkers. Additional epitopes were chosen based on antigenicity, allergenicity, and immunological features. TLR2 recognizes bacterial lipoproteins and peptidoglycan. TLR2 and TLR-4 showed robust interactions in molecular docking. The results of the present study demonstrate that the produced vaccine displayed stability, shown by a molecular weight of 49924.79 Da and an antigenicity value of 1.37. The Vaccine Construct demonstrated stability and basicity, shown by an instability score of 32.65 and a projected isoelectric point (pI) of 5.09. The expected secondary structure of the vaccine construct consisted of 94.57% random coil, and 5.43% extended strand. The suggested vaccine demonstrated efficient binding to its TLR2 and TLR4 receptors, yielding the maximum Van der Waals energy of (-97.2 +/- 5.9) and (-67.6 +/- 7.3) kcal/mol, respectively. The polypeptide vaccine's stability and compactness were assessed using molecular dynamics simulations. The vaccine showed favorable stability, expression, immunostimulatory properties, and solubility.

Molecular docking is widely used in drug design, particularly for large compound libraries. We previously developed EasyDock, an automated docking pipeline with multiserver task distribution to support such large-scale campaigns, and present here its extended version. Supported docking engines now include Vina-family CPU- and GPU-based variants (QVina2, Vina-GPU, etc.) and deep-learning-based engines (CarsiDock, SurfDock) via a built-in client-server architecture. Ligand preparation was enriched with salt stripping, stereoisomer enumeration, and conformational sampling of saturated ring systems. Integration of open-source protonation tools (pkasolver, MolGpKa, Uni-pKa) replaces previously required commercial software, making the pipeline fully open source. Postdocking analysis now includes protein-ligand interaction fingerprint (PLIF) computation and pose quality assessment via PoseBusters. We provide Apptainer/Docker containers for the docking engines and protonation tools, simplifying installation and HPC deployment. The source code is available at https://github.com/ci-lab-cz/easydock.