Search Results (167)

Background: Group A Streptococcus (GAS) induces a wide spectrum of human diseases, ranging from superficial infections to life-threatening invasive conditions and post-infectious sequelae such as rheumatic heart disease, posing a heavy global health burden. Critically, there is still no licensed commercial vaccine against GAS, making the development of novel, effective vaccines against this pathogen an urgent and crucial unmet medical need. Methods: We developed a dual-antigen nanoconjugate vaccine against GAS. The Group A Carbohydrate polyrhamnose backbone (GAC^PR) and truncated SLO were site-specifically conjugated via Protein Glycan Coupling Technology (PGCT) in engineered E. coli, and then linked to ferritin nanoparticles using the SnoopTag/SnoopCatcher system. Safety, immunogenicity, and protective efficacy were evaluated in murine models. Results: The nanovaccine was successfully synthesized with high purity. It elicited robust GAC- and SLO-specific IgG/IgG1 responses, conferred 90% survival against lethal GAS challenge (vs. 0–50% in controls), reduced bacterial loads in organs, and lowered inflammatory cytokines. Passive immunization with vaccine-induced serum also achieved 90% survival. No abnormal biochemical indicators, inflammatory responses, or organ pathology were observed. Conclusions: This study successfully developed a bivalent nanoparticle vaccine against GAS. This novel nanovaccine exhibits excellent safety, strong immunogenicity, and effective protection against GAS, providing a promising vaccine candidate. Full article

Propose an enhanced LoRA (Low-Rank Adaptation) MoE (mixed expert) architecture, SLoRA (Enhanced LoRA MoE Architecture), aimed at addressing the key problem of efficient parameter fine-tuning in multitasking scenarios. Given the high cost of traditional full fine-tuning as the parameter size of visual language models increases, and the limitations of LoRA as a popular PEFT (parameter-efficient fine-tuning) method in multitasking, such as inadequate adaptability and difficulty in capturing complex task patterns, as well as the catastrophic forgetting and knowledge fragmentation challenges faced by existing research on integrating mixed expert (MoE) mechanisms into LoRA, SLoRA utilizes orthogonal constraint optimization to reduce disturbance to existing knowledge through constraint solution space initialization, alleviating catastrophic forgetting (old task accuracy retention rate reaches 92.4%, 16.1% higher than LoRA), and an optimized MoE structure that includes general experts (retaining pre-trained knowledge) and task-specific experts (dynamic routing adaptation tasks) to enhance multitask adaptability. Experimental results show that in commonsense reasoning tasks, SLoRA’s accuracy is 9.0% higher than LoRA and 3.7% higher than AdaLoRA on the WSC dataset, and its F1 score is 7.7% higher than LoRA and 2.9% higher than AdaLoRA on the CommonsenseQA dataset; in multimodal tasks, its average score is up to 15.3% higher than LoRA, demonstrating significant advantages over existing methods. Full article

Large language models in IoT–edge–cloud settings face bursty, heterogeneous requests that make pipeline-parallel inference prone to micro-batch imbalance and communication stalls, causing GPU idle time and SLO violations. We propose a runtime-adaptive scheduler that jointly tunes token budgets and micro-batch counts to balance prefill/decode workloads and minimize pipeline bubbles under changing compute and network conditions. On a four-node pipeline-parallel cluster across Llama-2-13b and Qwen2.5-14b at 100/1000 Mbps, our method outperforms vLLM and SGLang, reducing GPU idle time by up to 55% and improving throughput by up to 1.61 × while improving TTFT/ITL SLO satisfaction. These results show that dynamic scheduling is essential for scalable, latency-stable LLM inference in IoT–edge–cloud environments. Full article

Background: Smith–Lemli–Opitz syndrome (SLOS) is an inborn error of cholesterol biosynthesis, caused by biallelic mutations in the DHCR7 gene. Genotype–phenotype correlations regarding DHCR7 variants could explain the variation in severity, ranging from in utero demise or severe SLOS to a mild phenotype. Clinical recognition can be challenging. This study aimed to determine the frequency of SLOS carriers in the Central European population, as well as the mutational spectrum of DHCR7 in these carriers. Methods: A retrospective analysis of DHCR7 variants was conducted using next-generation sequencing data from 55,289 individuals in Czech and Hungarian genetic laboratories. Results: The SLOS carrier frequency and the mutational spectrum of the DHCR7 gene in its carriers were established in the Czech and Hungarian sub-cohorts. In the combined dataset, we identified causative DHCR7 variants on 1567 alleles among 55,289 tested individuals, contributing to an SLOS carrier frequency of 2.83%. Of the 31 DHCR7 variants detected, the c.452G>A variant was the most prevalent, accounting for 1.8% of all detected alleles in our cohorts. In contrast, the c.964-1G>C variant was more frequent in non-Finnish Europeans, as indicated by the gnomAD 4.1.0 database. The DHCR7 mutational spectra of patients and carriers were comparable in terms of the most common variants. Conclusions: The high SLOS carrier frequency (2.83%) underscores the importance of SLOS carrier screening in Central European populations. The prevalent DHCR7 null mutations and their potential combinations may explain the lower-than-expected prevalence of SLOS, whilst Central and Eastern European populations remain likely underrepresented in the current gnomAD database. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

Data-driven quality control (QC) systems for the hot forming of steel parts increasingly rely on deep learning models deployed at the network edge, making multivariate sensor time series a critical asset for both local decisions and management information system (MIS) reporting. However, these models are vulnerable to adversarial perturbations and realistic signal disturbances, which can induce misclassification and distort key performance indicators (KPIs) such as first-pass yield (FPY), scrap-related losses, and latency service-level objectives (SLOs). To address this risk, this study introduces a Digital-Twin-Conditioned Diffusion Purification (DTCDP) framework that constrains latent diffusion-based denoising using process states from a lightweight digital twin of the hot-forming line. At each reverse-denoising step, the twin provides physics residuals that are converted into a scalar penalty, and the diffusion latent is updated with a guidance term. This directly bends the sampling trajectory toward reconstructions that adhere to process constraints while removing adversarial perturbations. DTCDP operates as an edge-side preprocessing module that purifies sensor sequences before they are consumed by existing long short-term memory (LSTM)-based QC models, while exposing purification metadata and physics-guidance diagnostics to the plant MIS. In a four-week production dataset comprising more than 40,000 bars, with white-box ℓ∞ attacks crafted on multivariate sensor time series using Fast Gradient Sign Method and Projected Gradient Descent at perturbation budgets of 1–3% of the physical range, combined with additional realistic disturbances, DTCDP improves the robust classification performance of an LSTM-based QC model from 61.0% to 81.5% robust accuracy, while keeping clean accuracy (≈93%) and FPY on clean data (≈97%) essentially unchanged. These results indicate that physics-aware, digital-twin-guided diffusion purification can enhance the adversarial robustness of edge QC in hot forming without compromising operational KPIs. Full article

Background/Objectives: Smith–Lemli–Opitz syndrome (SLOS) is a genetic metabolic disorder characterized by impaired cholesterol synthesis and a wide range of developmental anomalies. This article presents a case of a girl with SLOS, diagnosed with two pathogenic variants of the DHCR7 gene. The objective is to evaluate the impact of early, multidisciplinary therapeutic interventions on the patient’s development. Methods: Following diagnosis, a comprehensive metabolic therapy was initiated, including cholesterol and cholic acid supplementation. An interdisciplinary therapeutic approach was employed, involving physical therapy, speech therapy, and sensory integration, aimed at addressing various developmental challenges faced by the patient. Results: The therapy led to gradual improvements in the patient’s psychomotor development, although the cholesterol levels were only partially improved and the accumulation of sterol precursors (7-DHC and 8-DHC) persisted. The coordinated care model facilitated better outcomes compared to less integrated efforts. Conclusions: The results highlight the importance of early diagnosis and integrated care in optimizing developmental outcomes for children with SLOS. A multidisciplinary approach is essential for addressing the complexities of the syndrome and promoting overall development. Full article

Student learning outcome (SLO) tracing aims to monitor students’ learning progress by predicting their likelihood of passing or failing courses using Deep Knowledge Tracing (DKT). However, conventional DKT models often lack interpretability, limiting their adoption in educational settings that require transparent decision-making. To address this challenge, this quantitative study proposes an interpretable ensemble framework that integrates Item Response Theory (IRT) with DKT. Specifically, multiple IRT-based DKT models are developed to capture student ability and item characteristics, and these models are combined using a bagging strategy to enhance predictive performance and robustness. The framework is evaluated on an SLO tracing dataset from Prairie View A&M University (PVAMU), a historically Black college and university (HBCU). Result analysis includes comparisons of evaluation metrics such as Area Under the Curve (AUC), accuracy (ACC), and precision across individual and ensemble models, as well as visualizations of student ability, item difficulty, and predicted probabilities to assess interpretability. Experimental results demonstrate that the ensemble approach consistently outperforms single models while providing clear, interpretable insights into student learning dynamics. These findings suggest that integrating ensemble methods with IRT can simultaneously improve prediction accuracy and transparency in SLO tracing. Full article

Inference now dominates the lifecycle footprint of large language models, yet published estimates often use inconsistent boundaries and optimize carbon while ignoring water. We present a provider-agnostic framework that unifies scope-transparent measurement with time-resolved, SLO-aware orchestration and jointly optimizes carbon and consumptive water. Measurement reports daily medians at a comprehensive serving boundary that includes accelerators, host CPU/DRAM, provisioned idle, and PUE uplift, and provides accelerator-only whiskers for reconciliation. Optimization uses a mixed-integer linear program solved over five-minute windows; it selects region, batch size, and phase-aware hardware for prefill and decode while enforcing $p95$ TTFT and TPOT as well as capacity constraints. Applied to four representative models, a single SLO-aware policy reduces comprehensive-boundary medians by 57 to 59 percent for energy, 59 to 60 percent for water, and 78 to 80 percent for location-based CO₂, with SLOs met in every window. For a day with 500 million queries on GPT-4o, totals fall from 0.344 to 0.145 GWh, 1.196 to 0.490 ML, and 121 to 25 t CO₂ (location-based). The framework offers a deployable template for carbon- and water-aware LLM serving with auditable and scope-transparent reporting. Full article

Oil and its derivatives affect marine ecosystems due to pollution. Analytical methods for detecting oils and greases in saline water can identify oil-derived pollutants in seas and oceans, supporting the preservation and recovery of water quality. This study describes a methodology based on Raman spectroscopy to quantify oil in saline water. Specific seriate volumes of synthetic lubricating oil (SLO) and diesel fuel oil (DFO) were added to a beaker containing 1000 mL of saline water. A magnetic stirrer was used to create vortex, where the added oil dispersed uniformly over the surface and created a thin film. Raman spectra of the surface’s film were obtained by a spectrometer (830 nm, 350 mW) at a fixed position with reference to the beaker border, in triplicate. Two spectral models were developed; one based on the intensity of the peak at ~1400–1500 cm⁻¹ and another based on partial least squares regression (PLSR). Both spectral models enabled the quantification of SLO and DFO at concentrations ranging from 25.6 to 307 mg/L, and from 16.8 to 205 mg/L, respectively, with correlation coefficients as high as r = 0.99. The results highlight the potential of using Raman spectroscopy for analyzing oil in environmental water samples. Full article

Objectives: The aim of this study was to determine which color imaging facilitated easier detection of the persistent avascular retina (PAR) in ultra-widefield (UWF) fundus imaging in children undergoing retinopathy of prematurity (ROP). Methods: A total of 20 eyes of 10 children aged between 6 and 9 who underwent diagnostic and therapeutic procedures for ROP were included. Fundus images were obtained using Optos confocal scanning laser ophthalmoscopy (cSLO; Optos PLC, Daytona, Dunfermline, UK). The images were divided and recorded into three groups as original imaging (composite), red reflectance imaging, and green reflectance imaging. These images were prepared as a slideshow for 10 ophthalmology specialists and they were surveyed to determine in which color imaging the peripheral avascular area was more easily detected. The results were evaluated. Results: The rate of detecting the PAR in green reflectance imaging by the participants included in the study was found to be statistically higher compared to other colors of imaging (composite 0.63 ± 0.09 (0.5–0.8), red 0.12 ± 0.05 (0.05–0.2), and green 0.94 ± 0.06 (0.85–1), p < 0.0001). All respondents reported that the boundaries of the peripheral avascular area were more clearly defined in the UWF (Optos PLC, Daytona, Dunfermline, UK) green reflectance imaging. Conclusions: Each color imaging used in UWF fundus imaging helps to visualize different layers of the retina. Our study showed that retinal vascular endings appear more distinct due to the lower penetration of the green laser into the choroidal vessels. Based on these findings, we believe that UWF fundus green reflectance imaging is more useful for detecting and monitoring PAR. Full article

This study examines Panama’s 2023 mining restrictions to illuminate persistent legitimacy crises in extractive governance. Employing a qualitative case study, it draws on 25 semi-structured interviews with government officials, industry representatives, Indigenous leaders, local communities, mining critics and other civil society actors, alongside policy and document analysis. Findings suggest that legitimacy reconstruction relies on four interdependent conditions: procedural justice, institutional trust, epistemic legitimacy, and relational governance. Stakeholders consistently emphasized transparency, capacity building, and inclusive engagement as essential for future mining activity, underscoring that technical standards alone are insufficient without credible institutions. Building on—but extending beyond—frameworks such as Social License to Operate (SLO) and Free, Prior and Informed Consent (FPIC), this paper offers Social Legitimacy for Mining (SLM) as a provisional, co-produced framework. Developed through literature synthesis and refined by diverse stakeholder perspectives, SLM is applied in Panama as an illustrative proof of concept that may inform further research and practice, while recognizing the need for additional adaptation across jurisdictions. Full article

Cotton is increasingly important in global development. The exploration of drivers of spatiotemporal patterns for cotton planting, considering spatial heterogeneity, is essential for optimizing its distribution and supporting sustainable production. This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted regression (GWR) to investigate the factors shaping cotton-planting patterns in the northern slope of the Tianshan Mountains (NSTM), China, from 2000 to 2020. Cotton distribution was derived from long-term Landsat image series, and its expansion showed an average annual growth rate of 2.10 × 10³ km², with intensive cultivation primarily distributed across the central and western counties. The dominant drivers of cotton distribution were elevation (ELE), sunshine duration (SD), slope (SLO), temperature (TEM), runoff (RO), and gross domestic product (GDP). ELE explained about 40% of the spatial heterogeneity. SD showed a declining influence, SLO remained stable, TEM increased in importance, and GDP exhibited a progressive upward trend, although weaker. Moreover, nonlinear weakening interactions, especially between ELE and other factors, as well as between socio-economic and climatic variables, substantially enhanced explanatory power. These findings highlight the significance of accounting for spatial heterogeneity and factor interactions in guiding the spatial optimization and sustainable management of cotton cultivation. Full article

Dengue fever is a mosquito-borne viral disease that annually affects 100–400 million people worldwide. Early detection of dengue enables easy treatment planning and helps reduce mortality rates. This study proposes three Swarm-based Metaheuristic Algorithms, Golden Jackal Optimization, Fox Optimizer, and Sea Lion Optimization, for feature selection and hyperparameter tuning, and an Extreme Gradient Boost classifier to forecast dengue fever using the Predictive Clinical Dengue dataset. Several existing models have been proposed for dengue fever classification, with some achieving high predictive performance. However, most of these studies have overlooked the importance of feature reduction, which is crucial to building efficient and interpretable models. Furthermore, prior research has lacked in-depth analysis of model behavior, particularly regarding the underlying causes of misclassification. Addressing these limitations, this study achieved a 10-fold cross-validation mean accuracy of 99.89%, an F-score of 99.92%, a precision of 99.84%, and a perfect recall of 100% by using only two features: WBC Count and Platelet Count. Notably, FOX-XGBoost and SLO-XGBoost achieved the same performance while utilizing only four and three features, respectively, demonstrating the effectiveness of feature reduction without compromising accuracy. Among these, GJO-XGBoost demonstrated the most efficient feature utilization while maintaining superior performance, emphasizing its potential for practical deployment in dengue fever diagnosis. SHAP analysis identified WBC Count as the most influential feature driving model predictions. Furthermore, DiCE explanations support this finding by showing that lower WBC Counts are associated with dengue-positive cases, whereas higher WBC Counts are indicative of dengue-negative individuals. SHAP interpreted the reasons behind misclassifications, while DiCE provided a correction mechanism by suggesting the minimal changes needed to convert incorrect predictions into correct ones. Full article

Homeostasis must be maintained for the healthy living of an organism. In addition to physiological and anatomical homeostasis, the maintenance of the immune system, called immune homeostasis or immunohomeostasis, is critical for overall well-being and general homeostasis. CD8⁺ cytotoxic T cells/lymphocytes (CTLs) are crucial components of the adaptive immune systems of all vertebrates with a thymus. Hence, the thymus is an essential primary lymphoid organ (PLO) for developing T cell-mediated immunity (TCMI) that comprises CD4⁺ helper T cells (Th) cells and their subtypes, such as Th0 (naïve helper T cells), Th1 (pro-inflammatory Th cells that secrete IFN-γ), Th2 (secrete type 2 cytokines, such as IL-4, IL-5, IL-6, IL-10, and IL-13), Th9 (secrete IL-9), Th17 (secrete IL-17), Th22 (secrete IL-22), follicular Th cells (T_fhs, secrete IL-21), regulatory T cells (T_regs), and CD8⁺CTLs. The current article explores the critical role of CD8⁺CTLs in the maintenance of immune homeostasis. The role of the thymus (PLO) in generating and regulating CD8⁺CTLs, as well as mobilizing them to distant lymph nodes (LNs) and the spleen, which are referred to as secondary lymphoid organs (SLOs) and target organs, is discussed in section two of the article. The subsequent third section discusses the role of CD8⁺CTLs’ cytotoxic and immunoregulatory action to maintain immune homeostasis during infection and other inflammatory conditions. Moreover, they mask themselves to different cell types, like Th cells, such as Tc2s, Tc9s, Tc17s, and Tc22s, to maintain immune homeostasis. CD8⁺CTLs also behave as T_regs to exert their immunoregulatory functions. In addition to conventional CD8⁺CTLs, granzyme K (GzmK)⁺CD8⁺CTLs and CD4⁺CTLs with their cytotoxic action to maintain immune homeostasis have also been discussed. The next section discusses cell–cell (APC–CD8⁺CTL) interactions that not only increase the cytotoxic functions of CD8⁺CTLs but also program APCs to support their cytotoxic functions. These CD8⁺CTLs secrete different cytokines (IFN-γ and IL-10) and cytotoxic molecules (perforin and Gzms), which exert immunoregulatory actions to maintain immune homeostasis. The article concludes with a future perspective and a conclusion section, highlighting the critical need to understand CD8⁺CTLs’ cytotoxic and immunoregulatory functions in maintaining immune homeostasis across various diseases, including those with newly identified roles for CD8⁺CTLs. Full article