Search Results (536)

Patients with cancer are at increased risk of severe outcomes from COVID-19. Yet, the immunological determinants underlying this vulnerability remain incompletely understood, particularly in low- and middle-income settings. Moreover, the impact of the severe viral disease surge and its compensatory mechanisms, such as stressed myelopoiesis, on this population needs further elucidation. This study aims to characterize the cytokine and lymphocyte profiles of cancer patients with COVID-19, correlate these profiles with disease severity, and compare them to those of non-cancer patients with COVID-19. Plasma cytokine, chemokine, and growth factor levels were quantified using Luminex technology, and immune cell subsets were characterized by flow cytometry. A total of 67 patients were analyzed: 40 with cancer (26 mild cases and 14 severe cases) and 27 without cancer (12 mild cases and 15 severe cases). Clinical outcomes showed an 86% mortality rate in cancer patients due to severe COVID-19. This contrasted with a 3.8% mortality rate in cancer patients with mild COVID-19, all unrelated to the infection. Our findings revealed elevated CXCL10 (IP-10) and reduced MIF levels in cancer patients with COVID-19, distinguished by disease severity. Compared with that in cancer patients with mild COVID-19, the level of CXCL10 in cancer patients with severe COVID-19 was further elevated. Additionally, cancer patients with COVID-19 presented reduced CD3⁺ T lymphocytes, expansion of CD4⁺CD25⁺FoxP3⁺ regulatory cells and CD56^BRIGHT NK cells, a shift from effector memory to central memory T-cells, and increased numbers of exhausted (PD-1⁺) T lymphocytes. In conclusion, our data suggest a distinct immunological profile observed in cancer patients with COVID-19. Especially in severe cases, viral surge-related suppressor cells and proinflammatory cytokines were accompanied by a compensatory immunosuppressive state, with decreased effector function and increased exhaustion. This may negatively impact clinical outcomes and highlight potential implications for the management of cancer patients. Full article

3D magnetotelluric (MT) inversion based on the Gauss–Newton (GN) framework plays an important role in deep mineral exploration by imaging subsurface electrical conductivity structures. However, large-scale 3D MT inversion remains computationally expensive due to the high cost of sensitivity-matrix construction. To address this challenge, we develop a stochastic Gauss–Newton (SGN) framework that reduces computational cost through random data subsampling while preserving the practical convergence behavior of GN inversion. In the proposed framework, only a randomly selected subset of data is used to approximate the GN search direction. By exploiting a key property of MT forward modelling, namely that responses at all receivers are obtained simultaneously for each frequency, the line search is performed using the full dataset, ensuring stable convergence of the inversion process. The SGN framework is validated using both a synthetic multiblock model and a field dataset from the Akebasitao area in Xinjiang, China. The recovered models remain highly consistent with those obtained using conventional full-data Gauss–Newton inversion across a wide range of sampling ratios. For the synthetic example, reducing the sampling ratio from 100% to 10% decreases peak memory consumption from approximately 433 GB to 242 GB and reduces runtime from 86.8 h to 23.9 h while maintaining comparable inversion quality. Similar computational savings are achieved for the field-data inversion. The field application successfully recovers the major conductive structures along the margins of the intrusion that are associated with hydrothermal alteration and fluid activity, highlighting the capability of SGN to delineate geologically meaningful targets relevant to deep mineral exploration. These results demonstrate that SGN provides an efficient and scalable approach for large-scale 3D MT inversion. Full article

Flow cytometry is an essential diagnostic method in hematology, and one of its main applications is the assessment of the clonality of mature B cells. We present a case report of a patient referred for the investigation of absolute lymphocytosis. The flow cytometry study revealed an increased percentage of B cells, but it could not establish B-cell clonality, based on the study of surface light chains in combination with the pattern of expression of mature B-cell markers. The diagnosis of Persistent Polyclonal B-cell Lymphocytosis (PPBL) was considered in the differential diagnosis as the mature B cells were found to be immunophenotypically memory B cells. However, due to the markedly elevated count of B cells, molecular testing with Polymerase Chain Reaction (PCR) for B-cell clonality based on IGH (Immunoglobulin Heavy Chain) gene rearrangements was performed, and it revealed the presence of two clones of B cells. Approximately one year later, the same work-up was repeated in the patient’s bone marrow aspirate. By flow cytometry, a distinct clonal B-cell population was isolated, while the molecular testing with PCR for B cell clonality based on IGH heavy-chain gene rearrangements revealed the presence of three clones of B cells. In addition, evaluation of the sample with high-dimensional mass cytometry showed the presence of four major immunophenotypically abnormal B-cell subsets. Full article

Despite recent advances, medical question answering systems still struggle with domain-specific reasoning and data efficiency. This paper presents Med-LLaMA3, a family of medical large language models developed by parameter-efficient fine-tuning of the LLaMA-3.1 (8 billion) and LLaMA-3.2 (1 and 3 billion) architectures using quantized low-rank adaptation (QLoRA) and low-rank adaptation (LoRA) with 4-bit quantization. Beyond model training, this work contributes the following: (1) a formalized dataset curation taxonomy (source type × clinical granularity × task format) with a source-category ablation confirming that the multi-source combination drives benchmark gains beyond any single category; (2) a systematic characterization of low-rank-adaptation rank-scaling behavior for the LLaMA-3 family in the medical domain (monotonic improvement up to rank 128, with no observed plateau); and (3) statistically validated comparisons using McNemar’s test and 95% bootstrap confidence intervals. We curated a medical instruction dataset of over 1.5 million samples spanning medical examinations, clinical dialogues, and biomedical literature. Our approach trains only ∼4% of the base model’s parameters and, consistent with prior studies of parameter-efficient methods in the medical domain, achieves performance comparable to full fine-tuning at a fraction of the memory footprint. Evaluated with five in-context examples per prompt, the 8-billion-parameter model attains a mean accuracy of 75.71% across the eight medical-domain subsets of the Massive Multitask Language Understanding benchmark; improvements over the unmodified LLaMA-3.1-8B-Instruct baseline are statistically significant on the medical multiple-choice benchmark MedMCQA and, after Bonferroni correction across the eight subsets, on three subsets (Clinical Knowledge, Medical Genetics, and Nutrition), with two further subsets being significant only before correction. A structured named-entity-recognition evaluation on 100 hospital discharge summaries (macro-averaged F1 0.94; dual-annotator agreement $\kappa =0.87$) provides complementary evidence of clinical-text utility. A safety mitigation pilot shows that context-disambiguation preprocessing reduces the highest-severity abbreviation-ambiguity error rate from 30% to 10% on a 30-case held-out set. These results show that parameter-efficient fine-tuning can deliver high-performance medical large language models while training only ∼4% of the model’s parameters and reducing memory use by roughly 75%, enabling development on low-cost consumer-grade hardware. Full article

Aero-engine predictive maintenance is challenged by heterogeneous operating conditions, complex degradation patterns, and the need for interpretable maintenance alerts rather than solely numerical life estimates. This study investigates a condition-aware data-driven framework for jet engine health assessment using the NASA C-MAPSS dataset, which contains four benchmark subsets (FD001–FD004) with different operating conditions and fault modes. Instead of formulating the task as conventional remaining useful life regression, this study reformulates degradation assessment as a three-class health state classification problem, including Normal, Warning, and Fault. A unified preprocessing pipeline is developed, incorporating condition-wise normalization, first-order differential feature construction, and per-unit sliding window segmentation to reduce operating-condition bias, capture degradation dynamics, and prevent data leakage. Five representative models are evaluated under the same framework, including XGBoost, LightGBM, Random Forest, a context-aware multi-scale temporal attention convolutional neural network, and a bidirectional long short-term memory network. The results show that the proposed framework achieves consistently high classification accuracy across all four subsets, with the best results of 0.9841 on FD001, 0.9764 on FD002, 0.9891 on FD003, and 0.9832 on FD004. In addition, Bi-LSTM outperforms MSTA-CNN on all subsets, for example improving accuracy from 0.9614 to 0.9747 on FD002 and from 0.9773 to 0.9806 on FD004, which is consistent with the importance of long-term temporal dependency modeling for this task. These findings suggest that the proposed framework provides an effective and maintenance-decision-aligned solution for C-MAPSS-based health monitoring, where the three-class alert output offers clearer operational meaning than a single numerical life estimate. Full article

High-dimensional feature selection aims to identify compact and discriminative feature subsets from large feature spaces. In multi-objective feature selection (MOFS), this task remains challenging because the search space grows exponentially with dimensionality, and conventional binary evolutionary operators may generate ineffective perturbations in sparse high-dimensional spaces. To address these issues, this paper proposes CORAL, a rank-memory search framework for MOFS. CORAL uses a joint continuous score–cardinality representation to model feature priorities and subset sizes and applies Top-K decoding to obtain binary feature subsets. A rank-memory mechanism is introduced to extract feature occurrence information from elite solutions and guide score-space variation. In addition, elite local refinement and feature-number-stratified environmental selection are used to refine candidate subsets and maintain solutions across different sparsity regions. Experiments on 18 benchmark classification datasets show that CORAL achieves balanced performance in terms of solution-set quality, test classification performance, feature compactness, and computational efficiency. Ablation results further demonstrate the complementary roles of rank memory, elite local refinement, and stratified environmental selection. Full article

We show that a single Hodgkin–Huxley (HH) neuron with Pyragas-type delayed feedback control (DFC) can store multiple symbols as stable periodic orbits, where the specific orbit is selected by tuning the DFC gain K and time delay $\tau$. Sweeping the $(K,\tau )$ parameter plane at fixed bias current $I_{\mathrm{bias}}$ = 10.0 μA/cm² reveals 207 orbit types across 12 topological categories, with inter-spike interval (ISI) means from 5.9 to 56.9 ms. We establish: (i) a write protocol that reliably locks orbits with 13.9 ms median settling time; (ii) a novel Pattern-Oriented Limit-cycle Decoder (POLD) that reads orbits at 100% accuracy from only five observed ISIs (1200 trials across 12 orbits; Wilson 95% CI: 99.7–100%); (iii) a complete single-symbol write–read–erase (W–R–E) cycle with 100% read accuracy, 92% erase verification, and no decay over hold durations up to 50 s; and (iv) a fully validated 12-symbol memory capacity with a read-discriminable upper bound of 67 symbols (11.2× over rate coding; write viability confirmed only for the conservative 12-symbol subset). Reliable orbit addressing needs delay precision of ±2%, which constitutes a write-precision specification and not a fundamental capacity limit. These findings show that parametric delayed feedback is a viable mechanism for limit-cycle-based information storage in conductance-based spiking neurons. The biological interpretation is analogical, not direct: the ±2% delay-precision requirement exceeds what has been demonstrated for biological autaptic variability, and the orbit-coded memory framing is best understood as a computational proof-of-principle aimed at neuromorphic engineering, not as a claim about biological working memory. Full article

Due to the indispensable use of the internet, malicious actors have exploited URLs as a threat source of network information security and integrity. URL detection based on traditional methods has become inefficient against the uncontrolled increase of URLs, especially when facing dynamic and large-scale threats. To address the limitations of traditional methods and to provide intelligent and scalable detection of malicious URLs, this study proposes the hybrid framework (NetGuard) by integrating probabilistic data structures (PDSs) with machine learning (ML) capabilities. The proposed NetGuard utilizes PDSs to develop a Hybrid Scalable Detection Filter (HSDF), which combines the strengths of counting Bloom filters (CBFs) (deletion capability) and Scalable Bloom filters (SBFs). The proposed HSDF provides efficient membership queries under bounded false-positive rates (approximately 0.01) and ensures efficient data management and low-latency lookups on a scale of 10⁻⁵ s. On the other hand, NetGuard leverages the ML classifier capabilities to train and package a learned classifier for detecting malicious URLs. The proposed framework utilizes Decision Trees (DTs) and Random Forest (RF) classifiers. The proposed classifiers are trained by a novel SupURLsIdDs dataset which includes fifteen distinctive lexical and structural URL features extracted from four URL classes: benign, defacement, malware, and phishing URLs. The experimental results indicated the effectiveness of the HSDF in insertion and deletion operations, with minimal memory consumption (approximately 2.7 MB for 222,000 URLs) while maintaining a controlled false-positive rate (approximately 0.01 on Real-only subset up to 0.12 with synthetic data). The HSDF memory footprint represents a 99.88% enhancement compared to the RF model (which demands 2253.17 MB); thus, the HSDF complements RF as an ultra-lightweight first line of defense. The ML classifiers showed the superiority of RF, which achieved an overall classification accuracy of approximately 96% on large-scale URL data. These experiments are conducted using benchmark datasets constructed from aggregated real and synthetic data to demonstrate the scalability, adaptability, and resource efficiency of the first phase of NetGuard as a practical foundation for real-time web threat detection. The real-time integration and dynamic updates are presented as a deployment architecture and constitute future work. Full article

Epcoritamab, a subcutaneous CD3×CD20 bispecific antibody, has shown substantial activity in relapsed or refractory (R/R) B-cell lymphomas, but the immunological correlates of durable remission and treatment discontinuation remain unclear. We retrospectively analyzed 21 consecutive patients who initiated epcoritamab at our institution between 1 December 2023 and 31 December 2025, including 17 with R/R large B-cell lymphoma (LBCL) and 4 with R/R follicular lymphoma (FL). Clinical follow-up was updated through 18 May 2026. Serial cytotoxic T lymphocyte (CTL) subset and T-cell receptor (TCR) Vβ repertoire analyses were performed in selected cases. Among response-evaluable patients, the overall response rate was 9/14 in LBCL and 4/4 in FL. Median overall survival was 431 days in LBCL and 431.5 days in FL. Progression-free survival was analyzed descriptively because of the small sample size and substantial censoring. A patient with clinically and radiologically suspected central nervous system relapse of LBCL achieved radiological complete remission after epcoritamab treatment. In two LBCL and one FL case in whom epcoritamab was electively discontinued after complete remission, Vβ-skewed CTL populations were observed, and total memory CTLs exceeded total effector CTLs at discontinuation. These exploratory findings suggest that epcoritamab treatment may be associated with longitudinal remodeling of CTL subsets and Vβ-skewed CTL populations in selected responders. The potential relevance of these immunological patterns to durable response and treatment discontinuation should be validated in larger prospective cohorts with functional and sequence-based T-cell analyses. Full article

Developing high-quality and reliable solver code for the job shop scheduling problem (JSSP) remains a challenging and expertise-intensive task because generated code must stay executable, produce feasible schedules, and achieve strong scheduling results. This paper proposes a large language model (LLM)-driven multi-agent evolution framework for scheduling solver code generation, where LLMs act as hyper-heuristics for program-space search under external evaluation. The framework forms a closed-loop process with three collaborating agents. A seed heuristic generation agent uses a structured constraint template and a shared solver skeleton to synthesize, screen, and diversify seed programs to construct a competitive initial code pool. An evolutionary operator agent updates the pool through program-space crossover and best-so-far mutation. A code reflection agent analyzes solver code and maintains trajectory-aware reflective memory to generate structured guidance for later revision. Experiments on standard JSSP benchmarks show that the framework outperforms representative metaheuristics across heterogeneous instance families and scales while reaching best-known reference quality on a subset of instances. Ablation results further confirm the contributions of the initialization design and the reflection-guided revision mechanism. More broadly, the proposed framework helps reduce manual heuristic design effort and offers a practical approach to production scheduling optimization in intelligent manufacturing environments. Full article

The convergence of the Internet of Things (IoT), edge computing, and artificial intelligence (AI) is reshaping cyber defense in distributed cyber–physical environments. IoT-edge systems expose heterogeneous, resource-constrained, and intermittently connected devices to threats that unfold close to sensing and control processes, making purely signature-based or rule-based defenses increasingly insufficient. This article presents a structured review of AI for cybersecurity in IoT-edge systems from a systems-oriented perspective. Rather than surveying AI for IoT security in general, it organizes the literature around four practical lenses: AI methods, datasets and benchmarks, evaluation practice, and deployment constraints. The review reconstructs a workspace-verifiable corpus of 96 references, emphasizes literature published between January 2023 and April 2026 while retaining foundational benchmark papers, and uses a conservative 26-paper empirical subset for paper-level gap coding. Because this subset was purposively sampled and the original retrieval logs were not preserved, coded counts are interpreted as recoverable reporting signals and comparability indicators rather than field-level prevalence estimates. The revised synthesis further stratifies the coded evidence by task, model family, dataset, application scenario, metric type, and deployment signal, and translates deployment feasibility into a minimum reporting checklist and edge-hardware decision matrix. Within this evidence boundary, recent work remains dominated by intrusion and anomaly detection, with continued use of traditional machine learning, deep learning, federated learning, explainable AI, and graph-based approaches. However, experimentation remains concentrated around a small set of public benchmarks, while latency, memory, energy, communication overhead, operational robustness, and reproducibility are reported inconsistently. The field is therefore constrained less by classifier novelty than by benchmark concentration, weak deployment reporting, limited response-and-mitigation analysis, undercoverage of authentication, access-control, and trust-management tasks, and limited reproducible edge-aware evaluation. Full article

RGB–3D industrial anomaly detection seeks to jointly exploit texture and geometric cues for robust defect inspection. However, existing multimodal fusion methods still face two practical limitations: modality-specific anomaly evidence is often weakened after direct fusion, and image-level decisions remain unstable on difficult categories. To address these issues, this study develops a reliability-aware scoring enhancement on top of the released Hybrid Fusion/M3DM memory-bank pipeline. The method constructs a disagreement cue from RGB and point-cloud anomaly responses to enhance suspicious local regions and introduces a dual-branch image-level score calibration that combines a sensitive fusion branch with a robust statistical branch. Evaluated on MVTec 3D-AD under the official released-code full setting, the proposed method achieves 0.800 image-level ROCAUC, 0.980 pixel-level ROCAUC, and 0.926 AU-PRO, compared with 0.779, 0.975, and 0.915 for the corresponding released-code baseline in our environment. Additional evaluation on Eyecandies improves pixel-level ROCAUC and AU-PRO, while showing that image-level calibration remains dataset-sensitive. On a supplementary three-category Real-IAD D3 subset, the mean image-level ROCAUC, pixel-level ROCAUC, and AU-PRO improve from 0.963, 0.979, and 0.921 to 0.980, 0.988, and 0.941, respectively. These results indicate that explicit cross-modal disagreement modeling improves localization consistency, while image-level score calibration provides dataset-dependent gains rather than a uniform cross-dataset guarantee. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a systemic disorder driven by genetic predisposition, epigenetic programming, metabolic rewiring, and immune dysregulation. Although population genetics and single-cell transcriptomics have advanced our understanding, the multi-omic causal architecture of MASLD at cellular resolution remains poorly defined. This study aimed to establish an integrative framework linking genetic causality to cell-type-specific tissue dysfunction. Methods: Multi-layered Mendelian randomization (MR) and summary-data-based MR (SMR) across large-scale eQTL and pQTL datasets were applied to prioritize causal genes. Single-cell eQTL-based MR across 14 immune lineages generated cell-type-specific causal hypotheses, which were validated using human hepatic single-cell RNA-sequencing data (GSE136103). Two-step mediation MR quantified upstream epigenetic and downstream metabolic mechanisms. A high-fat diet (HFD)-induced murine model provided organismal validation. Results: Multi-layered MR nominated PLXND1 as a robust causal driver of MASLD. Single-cell eQTL-based MR revealed a functional dichotomy: PLXND1 upregulation in CD8⁺ effector memory T-cells decreased MASLD risk (OR = 0.486, 95% CI: 0.290–0.813, p = 0.006), whereas upregulation in natural killer cells (OR = 1.567, 95% CI: 1.337–1.837, p < 0.001), non-classical monocytes, and dendritic cells increased risk. Human hepatic single-cell transcriptomics confirmed that PLXND1 marks an anti-fibrotic, IFNG-high CD8⁺ T subset and a pro-inflammatory lipid-associated macrophage (LAM) population. Mediation MR identified DNA methylation at cg26767922 and cg08471739 as protective mediators acting predominantly via PLXND1 downregulation (92.39% and 64.50% mediation, respectively), and linked PLXND1 to six circulating metabolites. HFD mice showed significant hepatic PLXND1 upregulation. Conclusions:PLXND1 functions as a lineage-dependent molecular switch in MASLD, validated across genetic, epigenetic, metabolic, and single-cell dimensions. These findings caution against systemic PLXND1 blockade and support precision therapeutic strategies targeting hepatic innate immune cells. Full article

Background: The immunological factors associated with long-term hepatitis C virus (HCV)-specific humoral immunity after cure remain uncharacterized, particularly in people with HIV (PWH). This study investigated T-cell immunophenotypes and plasma biomarkers associated with anti-E2 binding (HCV-E2Abs) and neutralizing antibody (HCV-nAbs) titers 5 years after achieving sustained virologic response (SVR). Methods: This cross-sectional study analyzed 64 PWH with cured HCV and prior advanced fibrosis. We quantified plasma antibody titers against 5 HCV genotypes, T-cell phenotypes (n = 58), and plasma biomarkers (n = 50). Associations were assessed using Generalized Linear Models (gamma distribution, log-link function) adjusted for clinical confounders, reporting adjusted Arithmetic Mean Ratios (aAMRs) and false discovery rate (FDR)-corrected q-values. Results: Higher frequencies of CD4⁺ T-cell activation (CD38⁺; aAMR = 1.58; q = 0.028) and soluble CD27 levels (aAMR = 1.46; q = 0.038) were associated with higher HCV-E2Abs titers. In contrast, memory T-cell activation across CD4⁺ and CD8⁺ compartments (HLA-DR⁺ and CD38+; all q < 0.10) and elevated soluble immune checkpoints (sCD28, sPD-L2, sLAG-3, sCTLA-4; all q < 0.10) were associated with preserved HCV-nAbs titers. Conversely, a higher frequency of naïve CD8⁺ T-cells was associated with lower neutralization capacity (aAMR = 0.41; q = 0.042). Regarding inflammatory markers, soluble TNF-RI was positively associated with neutralizing titers (aAMR = 1.44; q = 0.019), whereas IL-18 was inversely associated (aAMR = 0.53; q = 0.019). Conclusions: Specific activated T-cell subsets, checkpoint shedding, and selective inflammatory signals were associated with higher long-term HCV-nAbs titers in PWH. In contrast, higher frequencies of naïve CD8⁺ T-cells and elevated IL-18 levels were associated with reduced neutralizing capacity. Full article

This paper presents the design, implementation, and controlled evaluation of a proof-of-concept ear-level wearable system that integrates local artificial intelligence for real-time physiological monitoring in construction safety applications. The proposed architecture combines photoplethysmography (PPG), non-contact infrared thermometry, and nine-axis inertial sensing on a Raspberry Pi Pico microcontroller, enabling local inference that reduces dependence on cloud processing. A lightweight logistic regression model with three binary outputs, trained on a subset of the publicly available WESAD dataset (subjects S2–S4), classifies three physiological states relevant to worker safety—elevated PPG variability, drowsiness, and fatigue—directly from the device’s 2 MB flash memory. The principal contribution is demonstrating that ear-level multi-sensor fusion combined with on-device machine learning achieves high agreement with clustering-derived proxy labels under controlled conditions (average F1-score: 97.80% on an unseen test subject) while sustaining sub-second inference latency (<0.5 s). These results support timely supervisor alerting and motivate subsequent field validation in operational construction environments. Full article