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Leveraging large language models and embedding representations for enhanced word similarity computation
Inflammatory cytokines promote interferon regulatory factor (IRF) transcriptional activity in human pulmonary epithelial cells through the induction of IRF1 by nuclear factor-κB
Interferon regulatory factors (IRFs) play key roles during viral and bacterial infections. However, their regulation by inflammatory cytokines, including interleukin (IL)-1β and tumor necrosis factor (TNF) α, remains underexplored. As airway epithelial cells (AECs) modulate lung inflammation, IRF expression was characterized in pulmonary A549 and bronchial BEAS-2B epithelial cells along with primary AECs grown in submersion, or air-liquid interface, culture. While, IRF6 mRNA was only highly expressed in primary cells, IRF4 and IRF8 mRNAs were consistently low across the models. All the other IRF mRNAs were expressed in each model. IRF3 and IRF9 mRNAs were highly expressed, but their proteins remained primarily cytoplasmic post-IL-1β treatment in A549 cells. IRF2 showed moderate/high mRNA expression and was constitutively nuclear. However, RNA silencing did not support roles for IRF2 or IRF3, with only a modest role for IRF9, in the IL-1β-induced activation of an IRF reporter. IRF1 mRNA was highly induced by IL-1β in A549 and primary cells. Similarly, IRF1 protein was increased by IL-1β and TNFα in A549 cells, and by TNFα in BEAS-2B cells. In A549 cells, IL-1β-induced IRF1 protein localized to the nucleus and since IRF1 silencing prevented IRF reporter activity, a major transcriptional role was indicated. Mechanistically, the inflammatory transcription factor, nuclear factor (NF)-κB, was necessary for IL-1β- and TNFα-induced IRF1 expression. Further, four novel enhancer regions 5′ to IRF1 bound the NF-κB subunit, p65, and their IL-1β/TNFα-induced reporter activity required consensus NF-κB motifs. Three such regions recruited RNA polymerase-2 and were flanked by the active chromatin mark, histone 3 lysine 27 acetylation, supporting enhancer involvement in IRF1 transcription. Finally, IRF1 expression, transcription rate, and enhancer activity induced by IL-1β, or TNFα, were relatively unaffected by glucocorticoid. IRF1-dependent gene expression may therefore show insensitivity to glucocorticoid and could contribute to glucocorticoid-resistance in diseases that include severe asthma.
Meta-optics redefines microdisplay: monolithic color LCoS without polarization dependency
Abstract Liquid crystal on silicon (LCoS) panels are pivotal to high-resolution optical projection and imaging displays, yet their inherent polarization sensitivity and reliance on multi-chip architectures for color reproduction constrain the upper limit of light utilization, increase system complexity and restrict broader applicability. Here, we demonstrate a monolithic color meta-LCoS prototype that integrates dual-layer metasurfaces to achieve polarization-insensitive, full-color amplitude modulation on a single chip. Polarization sensitivity is eliminated via a synergistic design combining metasurface-enabled polarization conversion and voltage-controlled liquid crystal phase modulation, achieving a high-contrast, polarization-insensitive optical switch. By embedding red, green, and blue metasurface subpixels and meticulously designed off-axis angles, enabling direct color synthesis through a unified device. We showcase a 64-pixel monochrome and a 9-pixel color prototype capable of dynamically projecting diverse patterns under unpolarized illumination. Fully compatible with existing LCoS fabrication processes, our device significantly reduces system complexity and cost, offering transformative applications in next-generation projectors and AR/VR displays.
Research on evaluation indicator and method of stand quality
Mapping the evidence for patient and public involvement and engagement in statistical methodology research: A scoping review protocol
Objective To identify and map the existing literature on the conduct of patient and public involvement and engagement (PPIE) for statistical methodology. Introduction PPIE refers to the consideration of patient and public perspectives into research development, conduct and dissemination and, while commonly integrated in applied healthcare research, it currently remains underutilised in statistical methodology research. Many statistical methodologists lack confidence in conducting PPIE citing barriers such as insufficient training, unclear tasks, and concerns about impact. Inclusion criteria This review will examine literature on PPIE in the context of statistical methodology aiming to inform the design or analysis of healthcare research studies and related methodological research domains such as trials methodology and health data science. Methods A three-step search strategy, developed with an information specialist and based on JBI guidelines, will identify published and unpublished PPIE literature. This will include database searches, hand-searching key journals, and grey literature searches via search engines. The authors will contact their professional networks to identify additional material. The literature will be screened, selected and extracted by two independent researchers. Results will be presented in an evidence map and using a qualitative, thematic analysis.
Score matching the descriptor density of states for model-agnostic free energy estimation
Measuring disaster resilience in MENA countries and its impact on disaster losses
Forecasting China’s shipping indices based on modal decomposition and optimized deep learning integrated model
This study proposes an innovative hybrid forecasting model, VMD-CPSO-BiLSTM, which significantly enhances the prediction accuracy of shipping indices in China’s maritime sector. The model employs a sophisticated three-phase methodology: (1) decomposition through Variational Mode Decomposition (VMD) to extract multiple intrinsic mode functions (IMFs) from the original time series, effectively capturing its nonlinear and complex patterns; (2) optimization using a Chaotic Particle Swarm Optimization (CPSO) algorithm to fine-tune the Bi-directional Long Short-Term Memory (BiLSTM) network parameters, thereby improving both predictive accuracy and model stability; and (3) integration of predictions from both high-frequency and low-frequency components to generate comprehensive final forecasts. Through extensive empirical validation using key Chinese shipping indices, our proposed model demonstrates superior performance compared to conventional single deep learning models and other hybrid approaches. The results indicate that VMD-CPSO-BiLSTM effectively addresses critical challenges in time series forecasting, including nonlinearity, non-stationarity, and multi-scale characteristics. The developed model offers substantial practical value as a reliable forecasting tool for shipping market trends, providing industry stakeholders with enhanced decision-making support for strategic planning and operational management. Its robust performance and methodological innovation contribute significantly to the field of maritime economics and financial time series analysis.
Generalized Probabilistic Approximate Optimization Algorithm
Abstract We introduce the generalized Probabilistic Approximate Optimization Algorithm (PAOA), a classical variational Monte Carlo framework that extends and formalizes the recently introduced PAOA, enabling parameterized and fast sampling on present-day Ising machines and probabilistic computers. PAOA operates by iteratively modifying the couplings of a network of binary stochastic units, guided by cost evaluations from independent samples. We establish a direct correspondence between derivative-free updates and the gradient of the full Markov flow over the exponentially large state space, showing that PAOA admits a principled variational formulation. Simulated annealing emerges as a limiting case under constrained parameterizations, and we implement this regime on an FPGA-based probabilistic computer with on-chip annealing to solve large 3D spin-glass problems. Benchmarking PAOA against QAOA on the canonical 26-spin Sherrington–Kirkpatrick model with matched parameters reveals superior performance for PAOA. We show that PAOA naturally extends simulated annealing by optimizing multiple temperature profiles, leading to improved performance over SA on heavy-tailed problems such as SK–Lévy.
Modified classification system of high-riding vertebral artery for the C2 screw placement strategy: a large-scale, cross-sectional study
Two‐Stage Catalytic Conversion of Carbon Dioxide Into Aromatics Via Methane
Abstract In the refinery of the future, the input shifts from crude oil to biomass, plastic, and CO 2 . Therefore, we need to find alternative routes to produce chemical building blocks, such as aromatics, which are used in products like, for example, fuels. In this study, we investigated a two‐stage route to produce benzene from CO 2 . In two sequential reactions, CO 2 is first converted into methane over a Ni/TiO 2 catalyst, and methane is further reacted to yield benzene using a Mo/ZSM‐5 catalyst via the methane dehydroaromatization (MDA) reaction. Through a combination of thermodynamic calculations and experiments, we found the goldilocks conditions for performing this two‐stage process. The unreacted CO 2 and H 2 from the first reaction extended the benzene production in the second reaction. Using a reaction mixture of CO 2 , H 2 , and CH 4 resulted in benzene production of at least 72 h, by suppressing carbon growth on the catalyst surface. However, the concentration range in which CO 2 and H 2 can be added to the feed without losing benzene production is narrow, as we show with H 2 fluctuation experiments. We demonstrate that the combination of CO 2 methanation and MDA allows us to catalytically convert CO 2 into benzene with an overall yield of 5%.
RETRACTED: In silico investigation of novel Plasmodium Falciparum glycogen synthase kinase (pfGSk3β) inhibitors for the treatment of malaria infection
Malaria, a parasitic disease, remains a major global health concern, with over 260 million cases reported worldwide in 2023. As resistance to current antimalarial drugs increases, the demand for ongoing research into new therapeutic targets and strategies grows. Glycogen synthase kinase (pfGSK3β) is a crucial enzyme involved in metabolic processes of the malaria parasite. In this research, an in silico study was conducted to explore this enzyme as a potential target for drug repurposing. A Python program was used to mine and extract data from the CHEMBL database, which yielded 53 potential GSK-3β inhibitors. Subsequent in silico studies included molecular docking, molecular dynamics simulations (MD, run at 100 ns on GROMACS 2023 1), and molecular mechanics Poisson-Boltzmann surface area (MMPBSA). In silico data analysis identified three potential drug molecules: S20-CHEMBLID 1910196 (4-[5-(6- hydroxy- 1H-indol-2- yl)pyridin-3- yl]benzonitrile), S39-CHEMBL ID 2321945 (2-(7- bromo- 2- hydroxy- 1H-indol-3-yl)-3- oxoindole- 6- carboxylic acid), and S56-CHEMBL ID 2321951 (methyl 2-(2- hydroxy-1H-indol-3-yl)-3-nitroso-1H-indole-5-carboxylate),which could inhibit pfgsk 3β. Compound S56 demonstrated better in silico performance than S1 – (3,6- diamino- 4-(2- chlorophenyl)thieno[2, 3- b] pyridine- 2, 5-,5-dicarbonitrile), the co-crystallised ligand in pfgsk 3 β used as a control. The binding affinities of S1 and S56 are- 7.1157074 (10 ligand interactions) and – 5.64057302 (12 ligand interactions), respectively. The MD runs yielded average root-mean-square deviations (RMSDs) of 4.5 nm for S1 and 1.0 nm for S56. Furthermore, the root mean square fluctuation (RMSF) of S1 showed greater fluctuation between 0–1000 atoms compared to S56. MMPBSA analysis revealed comparable total energies: S56 was −14.45 kj/mol and S1 was −13.03 kj/mol. An in silico toxicity study using Protox III indicated the possible toxicity of the repurposed compounds. In conclusion, we propose that molecules S39, S20, and S56 could be repurposed as potential anti-malaria drugs.
Comprehensive profiling of smoke-induced T cells in mice implicates clonal γδT17 cells as a hallmark of COPD
Analysis of risk factors, clinical data, treatment outcomes for cats with feline infectious peritonitis using GS-441524 (2020–2024)
Abstract Feline infectious peritonitis (FIP) is a lethal, immune-mediated disease caused by feline coronavirus (FCoV). FIP was considered untreatable; however, GS-441524, a nucleoside analog, has become a hopeful antiviral treatment. Despite its effectiveness, survival outcomes depend on several prognostic factors, especially the type of disease and clinical presentation. This research aimed to assess the effectiveness of GS-441524 in a large population of cats with FIP in Iran, examine survival rates, and identify crucial prognostic factors affecting treatment outcomes. Additionally, it evaluates alterations in clinical, laboratory, and imaging outcomes during treatment, proposing a secure treatment protocol for veterinarians utilizing GS-441524 for FIP. This retrospective study analyzed 629 cats diagnosed with or highly suspected of having FIP in Iran between December 2020 and March 2024. Diagnosis was based on clinical signs, laboratory findings, ultrasonographic features, and therapeutic responses. Cats received GS-441524 via subcutaneous injection and/or oral administration for a minimum of 12 weeks. Dosages were adjusted according to the FIP form, and clinical, laboratory, and imaging data were collected before, during, and at the end of treatment with GS-441524. Dosages were further adjusted based on FIP form, weight gain, clinical improvement, and laboratory or imaging results. Statistical analyses comprised ANOVA, t-tests, chi-square tests, and non-parametric techniques to pinpoint important prognostic indicators and treatment effects. The survival rate reached 94.12%, with a relapse rate of 0.63%. Most reported type was Effusive forms accounted for 54.84% of the cats. Key prognostic factors associated with reduced survival included being male, over 6 years old, having neurological or mixed forms of FIP, and exhibiting fever, icterus, anemia, and thrombocytopenia ( p < 0.05). Significant improvements were observed in parameters such as the A: G ratio, albumin, globulin, bilirubin levels, and changing in imaging findings. The study also assessed dosage modifications during treatment. The average starting dose for effusive forms was 6.9 mg/kg, later increasing to 10.11 mg/kg, while neurological and mixed forms initially required 9.65 mg/kg, which was then raised to 12.7 mg/kg. Treatment duration extended beyond 84 days for 17.32% of cats (109 cats) due to showing abnormalities. Imaging studies confirmed a gradual resolution of abdominal and pleural effusions, reduced lymph node size (both abdominal and mediastinal), decreased kidney size in cats of renomegaly, and regression of gallbladder edema. This research features the largest group of FIP-treated cats in Iran and ranks among the largest worldwide, showcasing impressive survival rates with GS-441524 treatment. Positive results rely on timely intervention, suitable dosage modifications, and extended treatment periods, particularly for cats showing neurological and ocular signs. Major risk factors affecting lower survival rates were fever, icterus, anemia, and low platelet count. These findings provide vital clinical insights for veterinarians, underscoring the need for customized treatment approaches and continued research to enhance FIP outcomes therapy and long-term care.
Longitudinal biomarker progression and validation for predicting operational tolerance in a prospective multicenter liver transplantation immunosuppression withdrawal trial
Liver transplantation (LT) is a life-saving treatment for end-stage liver disease, but long-term immunosuppression (IS) is associated with significant side effects. Achieving operational tolerance (OT), where the graft is accepted without IS, remains a critical goal. Biomarkers play a pivotal role in understanding the complex mechanisms of OT, enabling personalized treatment strategies and improving patient outcomes. Additionally, machine learning techniques offer powerful tools for identifying predictive biomarkers and optimizing IS withdrawal protocols. This multicenter trial aimed to investigate the longitudinal evolution of genetic biomarkers during IS withdrawal and validate their predictive value for OT in LT recipients. A prospective, multicenter IS withdrawal trial was conducted with 91 LT patients. Tolerant (TOL) and non-tolerant (non-TOL) patients were compared, and longitudinal blood and liver samples were collected to analyze biomarkers. Generalized Additive Mixed Models (GAMMs) and logistic algorithms were employed to assess biomarker associations and predict OT. Of the 45 patients who completed the trial, 17 (37.8%) achieved OT. Molecular biomarker analysis revealed significant differences between TOL and non-TOL groups. Non-TOL patients exhibited higher baseline methylation of the FOXP3 regulatory T cell-specific demethylated region (TSDR) in whole blood. Longitudinal analysis showed distinct patterns in FOXP3, SENP6, miR31, and miR95 expression between groups. Notably, FOXP3 expression followed a U-shaped trajectory in TOL patients, decreasing during IS withdrawal and increasing post-withdrawal. Machine learning identified several key predictive biomarkers for OT. This study confirms the association between FOXP3 TSDR methylation and OT in LT patients and identifies FEM1C, miR31 and TFRC as promising predictive biomarkers. These findings highlight the potential for personalized IS withdrawal strategies, though further validation in larger cohorts is needed before clinical application.
ANKLE1 processes chromatin bridges by cleaving mechanically stressed DNA
Effects of neuromuscular versus plyometric training on physical fitness and mental well-being in male pubertal soccer players
Abstract Neuromuscular training (NMT) and plyometric training (PT) are commonly used during long-term athlete development, yet their relative effects on physical fitness and mental well-being are not fully understood. This study compared 8 weeks of NMT versus PT on physical fitness, mental well-being, emotional intelligence, and attention in pubertal male soccer players and explored associations between training-induced changes in these domains. Twenty-four male soccer players (12.3–12.5 years, circa-peak height velocity: − 0.7 to − 0.8) were randomly assigned to NMT or PT. Both groups trained twice weekly in addition to regular soccer practice. NMT included balance, strength, plyometric, change-of-direction, and agility exercises, while PT focused on bilateral and unilateral jump-landing drills. Training volumes were matched. Physical fitness tests included the five-time jump test, 20-m sprint, and 15-m change-of-direction speed test. Mental well-being outcomes included cognitive and somatic anxiety, self-confidence, attention, and emotional intelligence. PT led to larger improvements in jump, sprint, and change-of-direction speed performances, whereas NMT produced greater gains in self-confidence, anxiety regulation, attention, and emotional intelligence. A graphical summary illustrates the distinct physical and psychological adaptations to PT and NMT, highlighting their complementary nature. The observed fitness improvements significantly correlated with changes in psychological outcomes. These findings suggest that strength and conditioning professionals should prioritize PT when aiming to enhance physical fitness and NMT when targeting psychological well-being, supporting a holistic approach to athletic development in pubertal soccer players.
Comparative analysis of AI on human nutrition knowledge: Evaluating large language model-based conversational agents against dietetics students and the general population
Understanding the core principles of nutrition is essential in the contemporary context of abundant and often contradictory dietary advice, to empower individuals to make informed dietary choices and manage diet-related non-communicable diseases. The role of Artificial Intelligence (AI) in providing nutritional information is increasingly prominent, but its reliability in this domain is not well-established yet. This study compares the nutrition knowledge of state-of-the-art Large Language Model (LLM)-based conversational agents and chatbots with that of human subjects having different levels of nutrition knowledge. The “General Nutrition Knowledge Questionnaire–Revised” (GNKQ-R) was administered to four LLMs (ChatGPT-3.5, ChatGPT-4, Google Bard, currently known as Google Gemini, and Microsoft Copilot), using zero-shot prompts. Responses were scored in accordance with the GNKQ-R’s guidelines. The average performance of AI systems across all LLMs was 77.3 ± 5.1 out of 88, comparable to that of dietetics students and significantly higher than English students. ChatGPT-4 scored highest among the LLMs (82/88), surpassing both groups of students (dietetics: 79.3/88, English: 67.7/88) as well as all other demographic groups. In “Dietary Recommendations”, ChatGPT-3.5 and ChatGPT-4 demonstrated comparable performance to dietetics students. ChatGPT-4 excelled in “Food Groups”, outperforming all human groups. In “Healthy Food Choices”, ChatGPT-4 achieved a perfect score, indicating a deep understanding of this subject. ChatGPT-3.5 excelled in “Diet, Disease and Weight Management”. Variations in the performances of the LLMs across different sections were observed, suggesting knowledge gaps in certain areas. Some of the tested LLMs, particularly ChatGPT-3.5 and ChatGPT-4, showed proficiency in nutrition knowledge, rivaling or even surpassing dietetics students in certain sections. This indicates their potential utility in nutritional guidance. However, this study also identified nuances and specific details where LLMs lack compared to specialized human education. The study highlights the potential of AI in public health and educational settings. However, LLMs may be limited in their capacity to generate personalized dietary advice that accounts for clinical complexity and individual variability, reinforcing the indispensable role of expert human judgment.