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678 papers
The developed system successfully performs all major functions, including managing drug, category, and supplier data; recording purchase and sales transactions; automatically updating inventory; providing notifications for minimum stock levels and medicines approaching their expiration dates; and printing transaction reports.
Phishing conducted in Swahili has become a persistent threat to the millions of Tanzanians who depend on mobile-money services, yet the detection tools in common use are built for English and transfer poorly to a language whose morphology, register, and transactional vocabulary differ sharply from it. This study makes three contributions. It establishes that classical machine learning, given features tuned to Tanzanian Swahili, separates phishing from legitimate messages at near-ceiling accuracy, and that a deep-learning comparator adds nothing of operational consequence. It documents the compact lexical signature on which that separation rests, built from direct imperatives, money terms, and mobile-operator names. And it shows that lexical urgency, treated as a hallmark of phishing throughout the English-language literature, carries almost no discriminating signal in this language, a caution against porting feature assumptions across languages unexamined. The evidence comes from a corpus of 2,408 Tanzanian short messages, 1,377 of them real SMS drawn from the BongoSCAM collection, on which three classical classifiers and a convolutional neural network were compared under five-fold stratified cross-validation and four ablation experiments. The linear support vector machine and the random forest each returned a mean F1-score of 0.9983 (± 0.0016) and the convolutional network 0.9989 (± 0.0014), a difference smaller than one standard deviation. Performance held across every ablation, indicating that the signal is linguistic rather than an artefact of data construction. Classical models therefore offer an accurate and computationally frugal basis for protecting Swahili-speaking users, provided the gap between balanced-corpus evaluation and the low phishing prevalence of live traffic is managed by pairing the classifier with human review.
A multimodal long-term forecasting framework that integrates frequency-aware signal decomposition with semantic-enhanced representation learning and provides a modular and interpretable architecture combining frequency-aware and semantic-aware processing for intelligent system management is proposed.
Learning to read is a process and milestone with far-reaching implications for education, vocation, and health. Most models and empirical studies of reading development, however, focus on school entry and often limit potential influences to children's oral language and print-based skills. In contrast, a smaller corpus of behavioral, genetic, environmental, and neuroimaging research strongly suggests that reading development begins far earlier, in utero. This article first reviews major theoretical frameworks of reading development and synthesizes studies demonstrating that lower-order oral language and cognitive skills necessary for higher-order reading skills (e.g., reading comprehension) begin emerging during the perinatal period. It then characterizes the development of the reading brain, starting with regions involved in proficient reading and proceeding to neuroimaging work suggesting that the perinatal brain may already be equipped with a neural scaffold that supports reading development. Although brain scans are not suitable for identifying individual children at risk, they can inform accurate developmental, multifactorial models of reading that, in turn, can better guide preventative educational practices.
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This work constructs KorMMP, a benchmark keeping real regulator-sourced scam audio and real benign speech but decorrelating transcript from label, and MiLTL, an on-device detector built on affective and neutrosophic channels, including a cross-modal contradiction signal formulated to weigh lexical warmth against vocal coldness.
Averaging repeated answers removes only the smaller, random component; the larger, systematic one persists and requires calibration against reference data before clinical use can be considered.
Experimental results validate the feasibility of transferring knowledge from large models under low-computational-power constraints and provide a new technical pathway and engineering reference for recognising anomalies at the edge of intelligent connected vehicles.
Fluorogenic probes that report enzyme activity are essential for studying biological functions. However, designing them for targets with low catalytic turnover and narrow substrate specificity remains a significant challenge. Here, we present a precision design framework that separates the requirements for sensitivity and selectivity by integrating molecular docking, quantum chemical modeling of fluorogenic mechanisms, and targeted fine-tuning of the probe structures. As a proof of concept, we developed A5, a fluorogenic substrate for aldehyde dehydrogenase 2 (ALDH2) that exhibits high isoform selectivity and a >240-fold signal enhancement over the standard NADH assay. A5 enables quantitative imaging of ALDH2 activity across multiple biological scales─in blood samples, live cells, and intact mouse brains─and supports the identification of small-molecule activators with therapeutic potential in an Alzheimer's disease model. This work establishes a modular strategy for creating activity-based probes tailored to challenging enzymatic targets, with broad applications in precision imaging, drug discovery, and mechanistic biochemistry.
Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, while computationally efficient, frequently suffer from lack of precision and generalizability. Recent a number of machine learning-based models have been developed for atomic charge predictions, but they struggle with accurately representing molecular structures and capturing the chemical environments affecting atomic charges, thus limiting their generalization and accuracy. To overcome these limitations, we propose LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions. In constructing this model, we employ a strategy that integrates both high- and low-order information, enhancing its geometric spatial perception capability, which is currently underexplored in the field. Benchmark evaluations demonstrate that LumiCharge outperforms state-of-the-art (SOTA) models by 30%-60% across diverse data sets. Additionally, in cross-scale experiments, LumiCharge demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitations imposed by molecular sizes. On an external halogen-containing test set, LumiCharge achieves an RMSE of 0.055e, meeting practical application requirements. Finally, a case study of virtual screening for the androgen receptor (AR) target further validates its outstanding accuracy compared to the OPLS3e force field and other deep learning (DL)-based baseline models, highlighting its exceptional generalization capacity and practical utility in real-world scenarios.
Accurate prediction of the peptide-protein interaction (PepPI) is crucial for developing peptide-based therapeutics and vaccines. However, this computational task has traditionally faced significant challenges, such as the scarcity of structure data along with the corresponding label of the binding affinity for bound complexes. To address these challenges, we introduce PepBAN, a deep learning framework for modeling PepPI predictions. PepBAN incorporates two technical advancements: (1) adopting the protein language model ESM-2 to characterize proteins and ESM-2 or a graph-based foundation model for peptides without structure data and (2) leveraging the conditional domain adversarial learning to enhance generalization across a broad range of protein targets, especially when there are limited binding data. At the core of PepBAN is a bilinear attention network (BAN) that effectively learns the pattern of pairwise local interactions, enables the identification of key residues participating in the peptide-protein interactions, and offers an intuitive approach to interpret the underlying mechanisms of PepPIs via analyzing attention weights. Our numerical experiments demonstrated that PepBAN outperformed the previous state-of-the-art models across several well-established benchmark studies. Furthermore, we evaluated PepBAN's applicability in predicting cyclic peptide-protein interactions, a task that poses significant challenges due to the presence of noncanonical amino acids. These nonstandard residues require specialized handling, which most existing sequence-based PepPI prediction models did not adequately address, and we adopt an atom-resolved molecular graph approach to process cyclic peptides. Despite this complexity, PepBAN demonstrated a clear advantage by achieving a superior prediction performance and offering a distinct edge in tackling the emerging chemical space of cyclic peptides, which has great potential for novel therapeutic development. In summary, PepBAN serves as a valuable tool for advancing peptide-based drug and therapeutic development.
Resistance-conferring mutations in the androgen receptor (AR) ligand-binding pocket (LBP) compromise the effectiveness of clinically approved orthosteric AR antagonists. Targeting the dimerization interface pocket (DIP) of AR presents a promising therapeutic approach. In this study, we report the design and optimization of N-(thiazol-2-yl) furanamide derivatives as novel AR DIP antagonists, among which C13 was the most promising candidate. C13 exhibited excellent AR antagonistic activity (IC50 = 0.010 μM), effectively blocked AR dimerization and nuclear translocation, and demonstrated potent efficacy in several castration-resistant prostate cancer (CRPC) cells. Notably, C13 showed superior efficacy against variant drug-resistant AR mutants, along with favorable metabolic stability, excellent pharmacokinetic properties, and low brain distribution. Furthermore, oral administration of C13 achieved 123.4% tumor growth inhibition in an LNCaP xenograft model without apparent toxicity. As a noncompetitive binder, C13 complements current LBP-targeting AR inhibitors and represents a promising therapy for drug-resistant PCa.
The androgen receptor (AR) represents a pivotal therapeutic target for prostate cancer. However, existing orthosteric ligand-binding pocket (LBP) antagonists [e.g., enzalutamide (ENZ)] encounter significant obstacles due to resistance-conferring mutations in the LBP. Allosteric antagonists targeting the BF3 site exhibit great potential in overcoming such resistance but have low inhibitory efficacy. In our study, we employed an integrated computational modeling strategy, including Gaussian-accelerated molecular dynamics (GaMD), MM/GBSA free-energy calculations, and elastic network model (ENM)-based signaling communication pathway analyses. This approach is used to probe the cooperativity of allosteric BF3 antagonists [e.g., VPC-13808 (VPC)] with diverse orthosteric LBP ligands [e.g., ENZ and testosterone (TES)] in suppressing AR activity. Herein, four types of AR systems were examined: AR bound to LBP agonist (AR·TES), LBP antagonists (e.g., AR·ENZ), and combinations of LBP agonist/antagonist with BF3 antagonist (e.g., AR·TES·VPC and AR·ENZ·VPC). Results indicate that BF3 antagonists can synergize with the LBP antagonist to amplify conformational flexibility in H12 and induce anticorrelated dynamics of H12 with H3 and H4. This induces the downward movement of H12 and its displacement away from H3/H4, triggering the wide opening of the AF2 binding cleft and substantially reducing the coactivator recruitment. Furthermore, the BF3 antagonist can interact with specific residues (e.g., F673, F826, L830, and Y834) and cooperate with the LBP agonist or antagonist to allosterically perturb the AF2 conformation. Multiple short- and/or long-range BF3→AF2 and LBP→AF2 signaling transition pathways are involved, such as F673→Y834→L722→L812→L744→V746→L873→ENZ→L880/V889/V891. These mechanistic insights establish the foundation for developing novel AR BF3 antagonist and LBP-BF3 combination therapies, suggesting a promising avenue for enhancing the efficacy and overcoming the resistance in castration-resistant prostate cancer treatment.
Data Management
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
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