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Modified porous ceramic catalysts derived from titanium-bearing blast furnace slag for efficient toluene oxidation
Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients
Abstract Emerging evidence links metabolic dysfunction-associated fatty liver disease (MAFLD) with increased all-cause and circulatory system disease (CSD) mortality in adults, yet survival machine learning studies are limited. This study analyzed 4415 NHANES participants with MAFLD to predict mortality using five survival models, and further, the optimal models were selected to identify the most significant predictors of mortality. Machine learning models proved highly effective in prediction. The Gradient Boosted Survival (GBS) model performed best for all-cause mortality, while Extra Survival Trees (EST) excelled for CSD mortality. The Shapley Additive Explanations (SHAP) analyses revealed that the five clinical factors most strongly associated with all-cause mortality were age, gender, platelet count, high-density lipoprotein cholesterol, and smoking status. For CSD mortality, the key factors associated with increased risk were age, blood urea nitrogen, systolic blood pressure, history of heart attack, and gender. Subgroup analyses confirmed GBS and Cox proportional hazard (CoxPH) were optimal for middle-aged and older all-cause mortality, whereas Elastic Net-regularized Cox proportional hazard (CoxNet) was best for older CSD mortality. The findings demonstrate that survival machine learning models effectively predict mortality risk in MAFLD patients. Integrating these models with permutation importance and SHAP provides transparent insights into individual risk profiles, enabling clinicians to clearly interpret how key variables contribute to predictions and improve risk stratification.
Inverse design of stochastic, voxelated thermo-viscoelastic digital materials
Abstract Polymer material jetting enables the fabrication of voxelated, multi-material structures with material control at the microscale. However, current work often neglects viscoelastic effects and designing voxelated digital materials remains challenging due to the complexity of the vast design freedom and intractability of efficiently modeling macroscale voxel structures. We present an efficient representation of stochastically mixed, voxelated digital materials and develop a generalized viscoelastic temperature-dependent material model to design and simulate digital materials mixed from two constituent polymers. The material model is based on an extended percolation theory considering frequency and temperature. An artificial neural network is trained on the material model to directly estimate target material behavior given arbitrary non-linear, user requirements. The approach is validated using two case studies requiring tailored, non-linear material behavior: a personalized wrist orthosis and a machine damper. These show the newly unlocked possibilities for the design and fabrication of tuned, stochastic digital materials.
A national model for estimating United States public land visitation
Abstract Public land management relies on accurate visitor counts in order to understand and mitigate environmental impacts and to quantify the value of ecosystem services provided by natural areas. We build and test predictive visitation models suitable for publicly-managed parks, open space, and other protected lands based on multiple sources of digital mobility data including geotagged posts to social media platforms, community science observations, and a mobile device location dataset from a commercial vendor. Using observational visitation data series from the United States’ National Park Service, Forest Service, and Fish and Wildlife Service, we quantify the accuracy of statistical models to predict on-the-ground visitation using individual and combined sources of mobility data alongside other covariates. We find the predictive models performed best in settings where some on-site visitation data can be integrated into the models. On-site visitation data helps to account for meaningful differences in modeled relationships both within and across the three agencies considered. We find variation in the usefulness of the digital mobility data sources, with models combining multiple data sources outperforming those using a single source, including those based solely on mobile device locations. We discuss the practical implications of these findings as well as paths forward to improve visitation estimation on public lands.
Computational screening and in vitro evaluation of sphingosine-1-phosphate analogues as therapeutics for Non-Hodgkin’s lymphoma
Abstract Non-Hodgkin’s lymphoma (NHL) is a prevalent hematological malignancy that includes a variety of B-cell and T-cell proliferations. The S1P (sphingosine-1-phosphate) pathway, involved in cell survival, proliferation, and migration, plays a critical role in NHL pathogenesis. Targeting S1PR1, the receptor for S1P, may provide a therapeutic strategy for NHL. The primary objective of this study was to identify and evaluate the efficacy of S1P analogues against the S1PR1 receptor through computational methods and experimental validation. The crystal structure of S1PR1 was obtained from the Protein Data Bank, and computational methods, including molecular docking, QSAR modeling, and machine learning techniques, were employed to screen 779 S1P analogues. The most promising compounds were further analyzed through molecular dynamics simulations. In vitro, Raji cells were treated with the potent analogue CHEMBL1540377. MTT assay and colony formation assays were used to evaluate cell viability and proliferation. Additionally, apoptosis and necrosis were assessed by AO/EB staining. Computational studies identified several analogues with high binding affinities to S1PR1, including CHEMBL1540377. Molecular dynamics simulations confirmed the stability and binding of CHEMBL1540377 with S1PR1. In vitro assays demonstrated that CHEMBL1540377 significantly reduced cell viability and inhibited colony formation in Raji cells. AO/EB staining revealed that the compound induced both apoptosis and necrosis in the treated cells. This study identifies CHEMBL1540377 as a potent analogue targeting S1PR1 for NHL therapy. The combination of computational and experimental findings provides a strong foundation for future research and potential clinical application of S1P analogues in treating NHL.
Elimination of a neutrophil Pad4 byproduct restores stem cell–mediated bone regeneration in hyperglycemia
Agri-food wastes as substrates for oyster mushroom (Pleurotus ostreatus) cultivation and their agricultural potential
Abstract Fungi of the Pleurotus genus , are known for their health-promoting properties. They are grown on a wide range of lignocellulosic substrates , including i.e. straw , rice hulls and corn residues. There are numerous literature reports proving that the composition of the culture substrate affects the growth rate and yield of oyster mushrooms. The aim of the present study was to optimize the mixture of the culture medium , composed of agro-industrial waste , for the cultivation of Pleurotus ostreatus in order to achieve maximum yield and the fastest mycelial growth. In addition , the chemical composition of selected wastes was determined and the possibility of using spent P. ostreatus substrate as a biostimulant for plant growth was assessed. The best results in terms of yield , mycelial growth rate , and time required for substrate colonization , primordia formation , and first harvest , were obtained for straw with spent brewery grains and for straw with wheat bran in a proportion of 70/30% , while the mixture of wheat straw , wheat bran and sugar beet pulp in a proportion of 50/25/25% gave slightly poorer results. The maximum yield of 51.7 g/bag and the fastest growth was obtained for the substrate with the addition of spent brewery grains. Substrate formula 70% wheat straw , with 30% addition of spent brewery grains , gave the fastest time for substrate colonization , primordia formation and the first harvest (16 days , 20 days , 28 days respectively). The study carried out by ICP-Spectrometry technique confirmed that the Pleurotus spent substrate is a source of valuable elements (Ca , Cu , Fe , K , P , Mg , Mn , S) , indicating its biostimulatory potential in agriculture. Enzyme activity associated with substrate (carbohydrate) degradation , performed using a commercial API ZYM assay increased significantly in straw with spent brewery grains and straw with wheat bran mixture. The highest total levels of degradation products were examined by photometric method using commercially available Hach Lange cuvette tests. The highest total content of Pleurotus substrate degradation products , including organic acids , orthophosphate , ammonia nitrogen , nitrate , and nitrite , was found in the substrate supplemented with brewery grains. Their levels increased significantly compared to the control , indicating enhanced degradation activity in this substrate. Substrate after cultivation of higher fungi (Spent Mushroom Substrate-SMS) contains large amounts of valuable elements and can affect the growth and yield of crops. Their amount varies depending on the substrate used and depends on the availability of sources of C and N. Studies of the growth rate of P. ostreatus on a wide range of lignocellulosic substrates , i.e. straw , corn residues , rice husks , among others , are known. The authors of the present study extended these studies to evaluate the biodegradation of waste materials that have not yet been used in the culture of higher fungi (brewery spent grain , wheat bran , beet pulp) , making a significant contribution to the discipline. This study supports the implementation of the Sustainable Development Goals , by promoting the valorization of agro-industrial waste through sustainable biotechnological processes.
One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section
Benchmarking DNA foundation models for genomic and genetic tasks
Abstract The rapid evolution of DNA foundation models promises to revolutionize genomics, yet comprehensive evaluations are lacking. Here, we present a comprehensive, unbiased benchmark of five models (DNABERT-2, Nucleotide Transformer V2, HyenaDNA, Caduceus-Ph, and GROVER) across diverse genomic and genetic tasks including sequence classification, gene expression prediction, variant effect quantification, and topologically associating domain (TAD) region recognition, using zero-shot embeddings. Our analysis reveals that mean token embedding consistently and significantly improves sequence classification performance, outperforming other pooling strategies. Model performance varies among tasks and datasets; while general purpose DNA foundation models showed competitive performance in pathogenic variant identification, they were less effective in predicting gene expression and identifying putative causal QTLs compared to specialized models. Our findings offer a framework for model selection, highlighting the impact of architecture, pre-training data, and embedding strategies on performance in genomic and genetic tasks.
A cost-effective diffuse optical tomographic system for imaging absorbing and fluorescent targets in a scattering medium
The bacterial spectrum of spinal infections based on blood culture, tissue culture, and molecular methods: a systematic review and meta-analysis
Abstract Spinal infections (SI) are on the rise due to an aging population and the prevalence of more invasive procedures. This study aims to systematically review the microbiological spectrum of SI to enhance diagnostic accuracy and inform effective antibiotic treatment strategies. The last search was conducted on May 9th, 2024, from databases including EMBASE, PubMed, and Web of Science. The outcome variable is infection rate, and the detection method used should be blood culture, tissue culture, or molecular biology method. Two researchers independently extracted research data and evaluated its quality using the JBI Critical Appraisal Tools. Out of 14,639 identified records, 156 studies (encompassing 13,539 patients) were included. Staphylococcus aureus was identified as the most prevalent pathogen, with pooled infection rates of 17.6% (95%CI: 12.8–22.9%; I 2 =93%) in blood culture, 16.8% (95%CI: 14.0–19.8%; I 2 =96%) in tissue culture, and 12.0% (95%CI: 9.3–15.0%; I 2 =35%) in molecular methods. The bacterial spectrum also featured Staphylococcus epidermidis , Escherichia coli , and Mycobacterium tuberculosis (MTB). Molecular methods, particularly metagenomic next-generation sequencing (mNGS), demonstrated markedly superior sensitivity for MTB detection, with a pooled rate of 9.7% (95%CI: 4.6–16.3%; I 2 =90%) compared to 1.3% (95%CI: 0.6–2.1%; I 2 =86%) by tissue culture. The odds ratio for MTB detection with mNGS versus conventional culture was 4.24 (95%CI: 1.68–10.73). This review confirms that a core group of pathogens, including Staphylococcus aureus, Staphylococcus epidermidis , MTB, and Escherichia coli . Our findings underscore that tissue culture is fundamental for common pyogenic bacteria, while metagenomic next-generation sequencing is indispensable for detecting fastidious organisms like MTB. Trial registration : The protocol was registered with PROSPERO (No. CRD42023427429). Registered on May 28, 2023
Microbial-vulcanized organic-inorganic dual-modulated cobalt hydroxide for oxygen evolution reaction
Understanding family perspectives: knowledge, attitudes, and practices regarding pediatric lymphoma in China
The role of community pharmacy professionals in combating Counterfeit and substandard drugs in the Amhara regional state, Ethiopia
Polynomial-time quantum Gibbs sampling for the weak and strong coupling regime of the Fermi-Hubbard model at any temperature
Abstract Quantum computers hold the potential to revolutionise the simulation of quantum many-body systems, with profound implications for fundamental physics and applications like molecular and material design. However, demonstrating quantum advantage in simulating quantum systems of practical relevance remains a significant challenge. In this work, we introduce a quantum algorithm for preparing Gibbs states of interacting fermions on a lattice with provable polynomial resource requirements. Our approach builds on recent progress in theoretical computer science that extends classical Markov chain Monte Carlo methods to the quantum domain. We derive a bound on the mixing time for quantum Gibbs state preparation by showing that the generator of the quantum Markovian evolution is gapped at any temperature up to a maximal interaction strength. This enables the efficient preparation of low-temperature states of weakly interacting fermions and the calculation of their free energy. We present exact numerical simulations for small system sizes that support our results and identify well-suited algorithmic choices for simulating the Fermi-Hubbard model beyond our rigorous guarantees.
Genetic spectrum among 2009 Iranian individuals with neuromuscular disorders using next generation sequencing and multiple ligation dependent probe amplification methods
The offonome reveals on and off states of gene expression near the detection limit of RNA-seq
Abstract RNA-seq, widely used for gene expression profiling, provides nucleotide level genome coverage and summary gene expression values. Generally, low-expressed genes are ignored due to their unfavorable signal-to-noise ratio, however, these genes may offer crucial information, such as detecting rare cells in bulk tissues. In this study, we applied an approach that transforms the expression levels of low-expressed genes into a robust dichotomized on / off state by leveraging similarities in transcript coverage shape. Applied to three human cancer cohorts from the Cancer Genome Atlas (TCGA), chosen based on tissue morphology and anatomic site, we identified genes, the “offonome” near the detection limit, consistently or occasionally off across samples. Genes in the offonome spectrum proved useful for supervised and unsupervised applications, including characterizing oncogenic pathways, and identifying rare populations of cells in bulk tissue. Interrogating the offonome is relevant to bulk tumor analyses like TCGA, potentially expediting gene investigation in low-input situations like single cell RNA-seq.
Unveiling the global urban virome through wastewater metagenomics
Low mood, not anxiety, connected with micro facial expression recognition
Abstract Previous research has demonstrated that facial expression recognition, an invaluable social skill, may be impaired amongst people suffering from anxiety. Research surrounding this relationship is equivocal and little attention has been given to the effects of anxiety on the recognition of micro expressions. Thus, the present study investigated this relationship. Based on previous research, we expected that participants with high trait anxiety will show (a) poorer overall micro expression recognition and (b) better angry face recognition. 401 participants completed measures of trait and state anxiety, depression, micro facial expression recognition and indicated demographic information. The results of the study supported neither of the two hypotheses. Combined with previous findings, these results indicate that trait anxiety does not have a robust effect on either general emotion recognition or anger recognition. Previous positive findings may potentially be a consequence of unaccounted effects of low mood or age. On the other hand, the results did show effects of low mood on improved overall micro expression recognition scores and sad face recognition, and a bias towards recognizing neutral faces as sad. These findings may be attributed to biases arising from the effects of mood congruence.