Browse Articles

Scalable prediction of local road traffic volume via macro-level indicators and spatial heterogeneity modeling

Pd/Mo-Catalyzed <i>ortho</i> -Dihydroxylation of Aromatic Rings: Dual-Metal-Mediated C(sp2)–H Functionalization

Abstract We report a direct ortho-dihydroxylation protocol of benzoic acids using a Pd/Mo bimetallic catalyst and tert-butyl hydroperoxide (TBHP) as the oxidant. The Mo component significantly promotes the dihydroxylation reaction, especially the second hydroxylation step, by boosting the oxidative capacity of TBHP and presumably facilitating the oxidative addition process of Pd–Ar σ-intermediate. Mechanistic studies, including radical inhibition experiments, spectroscopic analyses, and substituent effect evaluations, reveal that Mo most likely facilitates the conventional Pd-catalyzed pathway by introducing a radical-generating step. Notably, this dual-metal-mediated method can be readily tuned to afford monohydroxylation products by simply reducing the amount of TBHP. Gram-scale reactions and subsequent derivatizations further confirm the practicality of this method. Since the directing carboxyl group can be easily removed via catalytic or thermal decarboxylation, this Pd/Mo bimetallic catalytic system offers a concise synthetic route to meta-diphenolic compounds, the vital fine chemicals conventionally synthesized via multistep industrial processes.

Benchmarking machine learning and deep learning models with a novel graph-attention SAGEConv-transformer for ECG arrhythmia detection

Accounting for spatial and environmental sampling bias in a species distribution model of Aedes vexans (Diptera: Culicidae)

Abstract This study evaluates the potential of species distribution models to predict habitat suitability for the floodplain mosquito Aedes vexans , with a particular focus on ephemeral breeding habitats such as flood-prone areas. These habitats are essential for oviposition, yet have not been explicitly incorporated as predictors in existing modelling approaches. Flood hazard was included as an environmental predictor to represent the species’ dependence on transient water bodies. However, initial model runs showed only a marginal contribution of this predictor to overall model performance. We demonstrate that this weak contribution was not due to the ecological irrelevance of ephemeral habitats, but instead resulted from strong sampling biases in the occurrence data, which were predominantly concentrated in urban environments. To address this issue, we applied spatial thinning and environmental filtering to reduce spatial clustering and the overrepresentation of environmentally similar regions. Using Maxent, we compared candidate models with and without bias correction across multiple environmentally stratified test datasets. Our results show that correcting for sampling bias improved model accuracy more than the inclusion of an additional habitat-relevant predictor alone. The final model produced more reliable suitability predictions, particularly in flood-prone and previously under-sampled areas.

An activity–resistance trade-off constrains enzyme evolution

The presence of self-resistance genes in antibiotic-producing organisms poses a paradox: How can resistance evolve before the antibiotic exists, and how can an antibiotic producer arise without first evolving resistance? Here, we examine the evolutionary origins of self-resistance to mycophenolic acid (MPA), an inhibitor of inosine monophosphate dehydrogenase (IMPDH). The MPA biosynthetic gene cluster (BGC) includes a resistant IMPDH-B. Homologs of IMPDH-B occur not only in MPA producers but also in many nonproducing fungi, where remnants of the MPA BGC remain detectable. The phylogeny of IMPDH-B is incongruent with the fungal species tree, consistent with multiple horizontal gene transfer events between Aspergillus and Sordariomycetes. We characterized eleven extant IMPDH-Bs, five from MPA producers and six from nonproducers, along with seven resurrected ancestral enzymes (Anc1–Anc7). MPA resistance appeared between Anc2 and Anc3 and coincided with a loss of catalytic efficiency. Across both ancestral and extant enzymes, MPA resistance correlated strongly with reduced activity, revealing a robust activity–resistance trade-off that has persisted for millions of years. Unexpectedly, both the IMPDH-Bs and ancestral enzymes Anc3–Anc7 were also resistant to ribavirin-5′-monophosphate (RVP), an IMP-competitive inhibitor. Because MPA and RVP bind to similar enzyme conformations, the activity–resistance trade-off may reflect a design constraint imposed by the need to maintain resistance to multiple inhibitors. Intriguingly, although Anc1 and Anc2 are equally sensitive to MPA, Anc2 shows reduced susceptibility to RVP. This pattern suggests that preexisting resistance to another IMPDH inhibitor may have created a permissive background for the later evolution of MPA biosynthesis.

Association between ABO and Rh blood groups and subtypes of age-related macular degeneration: A cross-sectional study

Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment and is driven by complex genetic, inflammatory, and vascular mechanisms. ABO and Rh blood group antigens, which are expressed on vascular endothelium and involved in immune and hemostatic pathways, have been implicated in various systemic diseases; however, their potential association with AMD phenotypic variation remains unclear. In this cross-sectional study, 488 patients with clinically confirmed AMD from a tertiary referral center were included. AMD was classified as neovascular AMD (wet AMD) or nonneovascular AMD (dry AMD) based on multimodal imaging and standardized diagnostic criteria. ABO and Rh blood group data were obtained from medical records, and individuals with major known AMD risk factors, including smoking, alcohol consumption, and obesity, were excluded to reduce confounding. Associations between AMD subtypes and ABO blood groups and Rh factor were evaluated using the Pearson chi-square test, and crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. The mean age of participants was 66.9 ± 4.7 years, and 273 patients (55.9%) had neovascular AMD. A statistically significant association was observed between ABO blood group distribution and AMD subtypes (χ² = 8.31, p = 0.040). A higher proportion of patients with neovascular AMD had blood group A, whereas blood group B was relatively more common among patients with nonneovascular AMD. However, individual pairwise crude odds ratio analyses did not demonstrate statistically significant associations for specific ABO blood groups. No association was observed for Rh status. These findings suggest a possible modest association between overall ABO blood group distribution and AMD phenotype; however, the clinical significance of this relationship remains uncertain, particularly in the absence of multivariable adjustment. Larger multicenter studies with comprehensive multivariable analyses are warranted.

Microfluidic platform for rapid passive preparation of complex biological matrices in point-of-care testing

Sentinel plants enable quantitative monitoring of bioavailable nitrate in soils and microbial environments

Microbial transformations of nitrogen in soils strongly influence plant nutrition and ecosystem function, yet monitoring these processes remains challenging. Existing approaches rely largely on extraction-based laboratory assays, limiting the ability to track nitrogen dynamics in situ. Here, we engineer “sentinel plants,” genetically encoded plant biosensors that convert nitrate perception into a quantitative signal reporting plant-accessible nitrate. The sensor uses a synthetic nitrate-responsive promoter to drive a ratiometric luciferase reporter, enabling high-dynamic-range measurements. Sentinel plants exhibited a dose-dependent, reversible nitrate response with high specificity over alternative nitrogen sources. In agricultural soils from multiple California field sites, sensor output tracked analytically measured nitrate levels and resolved incremental nitrate amendments, reporting plant-accessible nitrate in complex soil matrices. Beyond environmental sensing, sentinel plants detected microbially generated nitrate in both liquid culture and a model soil. Using this platform, we characterized a minimal three-member microbial consortium that converted atmospheric nitrogen into plant-available nitrate via sequential nitrogen fixation and nitrification. This consortium increased tissue nitrate accumulation and plant fresh weight, demonstrating that sentinel plants can both monitor nitrate availability and characterize microbial communities that enhance plant growth.

Stability and interpretability of penalized logistic regression models for breast cancer risk prediction

Penalized logistic regression is widely used in biomedical classification to address multicollinearity and improve predictive performance, yet the stability and reproducibility of selected predictors are often overlooked. This study evaluates feature stability and interpretability in ridge, lasso, and elastic-net logistic regression for breast cancer diagnosis using the Wisconsin Diagnostic Breast Cancer dataset. Models were trained with cross-validated tuning and evaluated on an independent test set using discrimination, classification, and calibration metrics. Feature stability was quantified through bootstrap selection frequencies. All penalized models achieved near-perfect discrimination and improved calibration compared with unpenalized logistic regression. However, substantial differences emerged in stability and sparsity. Ridge regression exhibited maximal stability but retained all predictors, limiting interpretability. Lasso regression produced highly sparse models but showed greater selection variability. Elastic-net regression balanced sparsity and stability, consistently retaining correlated predictors linked to tumor morphology. These findings demonstrate that stability assessment provides critical information beyond predictive accuracy and supports stability-aware penalized modeling for interpretable and reproducible biomedical risk prediction.

Behavioral determinants and constraints of social media use for agro-advisory services among farming community in India

The quantum ensemble variational optimization algorithm: Applications to molecular inverse design

Designing molecules with optimized properties remains a fundamental challenge due to the intricate relationship between molecular structure and properties. Traditional computational approaches that address the combinatorial number of possible molecular designs become unfeasible as the molecular size increases, suffering from the so-called “curse of dimensionality” problem. Recent advances in quantum computing hardware present new opportunities to address this problem. Here, we introduce the quantum ensemble variational optimization (QEVO) method for near-term and early fault-tolerant quantum computing platforms. QEVO efficiently maps molecular structures onto an orthonormal basis of binary strings and samples from a superposition state generated by a variational ansatz. The ansatz is iteratively optimized to identify molecular candidates with the desired property. Our numerical simulations demonstrate the potential of QEVO to design drug-like molecules with anticancer properties, operating in combinatorial spaces composed of up to 2 160 solutions, while employing a shallow quantum circuit that requires only a modest number of qubits. We envision that QEVO could be applied to a wide range of complex problems, offering practical solutions to problems with combinatorial complexity.

OphthoEvidence report: Comparative effects of prophylactic strategies for post-intravitreal injection endophthalmitis: Protocol for a systematic review and network meta-analysis

Background Endophthalmitis is a rare but serious complication of intravitreal injections (IVI). To mitigate the risk of post-injection endophthalmitis (PIE), retinal specialists use different strategies, combining various prophylactic measures. However, existing systematic reviews have evaluated the effects of these measures in isolation, which does not consider their concurrent use in practice. A trustworthy systematic review with network meta-analysis (NMA) by providing a framework for simultaneous comparisons of different prophylactic combinations is essential to guide decision-making. Objectives To present a protocol for addressing the comparative effects of different strategies for the prevention of PIE. Methods We will search Medline, EMBASE, Cochrane CENTRAL, Web of Science, and ClinicalTrials.gov from inception for randomized controlled trials and observational studies comparing any strategy for preventing PIE with an alternative strategy, or placebo, or no prophylactic strategy in patients aged 18 years or older who received IVI of any pharmacological agent for any indication except treatment of bacterial endophthalmitis. Paired reviewers will independently screen studies, extract the data and assess risk of bias for each outcome that includes infectious endophthalmitis, confirmed endophthalmitis, any endophthalmitis, need for surgical intervention for management of PIE, and best-corrected visual acuity. For each outcome, when possible, we will conduct frequentist random-effects NMAs. We will assess the certainty of evidence and interpret findings using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Discussion This systematic review and NMA will provide a comprehensive and up-to-date summary of the evidence addressing the effects of all available strategies for the prevention of PIE. Registration Open Science Framework ( https://doi.org/10.17605/OSF.IO/SJC4U ).

Seasonal dynamics of soil CO2 efflux across land use systems and implications for mitigation under a changing climate

Abstract Carbon dioxide (CO 2 ) is a major greenhouse gas driving climate change. In Ghana, the Agriculture, Forestry, and Other Land Use (AFOLU) sector remains a significant source of CO 2 emissions, largely due to land use change and degradation. This study assessed seasonal dynamics of soil respiration rates (SRR) across four land-use types, forest, fallow, maize, and rice fields, within the semi-deciduous forest zone of Ghana. The aim was to provide baseline data and identify key soil and environmental factors influencing SRR across these systems. SRR was measured twice monthly over 13 months using a closed-chamber system, with concurrent measurements of soil moisture and temperature, while baseline soil properties (organic matter, pH, and texture) were determined from initial soil sampling. Correlation and stepwise regression analyses were performed to determine the variables most strongly associated with SRR. Results revealed clear temporal and land-use differences, although seasonal patterns were not uniform across sites. Fallow land and croplands (maize and rice fields) recorded the highest SRR values within the study area, whereas forest plots consistently showed the lowest efflux, largely due to persistent moisture limitation rather than temperature or substrate availability. Soil OM, pH, moisture, and silt content were the most influential predictors of SRR, with the final regression model explaining 58% of the observed variability. These findings highlight the importance of forest conservation and sustainable land management in mitigating CO 2 emissions in tropical regions. To further reduce emissions in the AFOLU sector, policies should also support reforestation, agroforestry, and reduced soil disturbance, which enhance soil carbon storage and promote sustainable land use.

Hazard curvature makes within-host variability costly for survival

Tumors, pathogens, and immune responses are increasingly modeled as ecological and evolutionary systems inside hosts. Yet the clinically meaningful endpoint is usually not the trajectory of those internal populations but the fate of the host. In this article a framework is developed that links mechanistic within-host dynamics to outcomes through a hazard map. The framework gives rise to a curvature principle: If the instantaneous hazard is a convex function of a harmful host state, variability in that state is costly: That is, among trajectories with the same temporal mean, the path with least variance minimizes cumulative hazard, and any mean-preserving spread increases it. A local expansion shows that the penalty is set by hazard curvature and temporal variance. We extend the framework to proportional-hazards joint models, where the standard exponential link is convex by construction. This yields a curvature penalty, ½γ 2 ·Var w (z), such that for sinusoidal trajectories under a constant baseline hazard, an exact Bessel-function expression, I 0 (γA), for cumulative-hazard inflation is described. Both quantities can be derived from fitted joint models and quantify when the variability in the trajectory is likely to matter. The same framework highlights an endpoint mismatch: Strategies that improve burden-based control metrics, such as time to threshold, can worsen survival by repeatedly visiting high-risk states. The theory is illustrated with competitive tumor dynamics, a pathogen–immune–damage model and an example using parameters from a published joint model of SARS-CoV-2 viral kinetics and mortality. Hazard curvature links within-host dynamics, time-to-event outcomes, and treatment design.

Constant-time hardware implementation of Modular Inversion with Kaliski’s Algorithm for ECC

This paper introduces a hardware architecture designed for constant-time modular inversion over prime fields, a critical function in Elliptic Curve Cryptography (ECC). We use Kaliski’s Almost Inversion Algorithm to develop an efficient FPGA-based solution. The proposed design guarantees constant-time execution, optimizing modular inverse computations for ECC applications. When synthesized on a Xilinx Kintex-7 FPGA, the accelerator reaches a frequency of 222.9 MHz, occupying 1.7k Slices without using any DSP blocks. This work focuses on improving the speed and resource usage of modular inversion units, specifically for resource-constrained digital environments.

System-dependent performance analysis of metaheuristic optimization algorithms for directional overcurrent relay coordination in distribution networks and microgrids

Matrix effects reshape organic aerosol volatility and atmospheric persistence

The volatility of individual species is a fundamental property governing the gas-particle partitioning of organic aerosols. However, in complex organic mixtures, a compound’s apparent volatility may differ from its intrinsic volatility, depending on the matrix’s chemical composition. Herein, we systematically investigate component-resolved, mixture-specific volatility for more than 1,500 individual species across 33 proxies and chemically complex mixtures representative of selected organic aerosol types. The results show that species in simplified proxies and reference mixtures with limited components follow a higher-volatility trend that approaches their intrinsic values, whereas species present in ambient and biomass-burning organic aerosols exhibit the opposite behavior, with systematically reduced apparent volatility attributable to matrix effects. Using levoglucosan (LG), a representative biomass-burning tracer, as an illustrative example, we find that its volatility in complex organic mixtures is reduced by 1 to 4 orders of magnitude relative to its intrinsic volatility in pure LG. This pronounced reduction underscores strong matrix effects that substantially suppress the apparent volatility of individual species in mixed systems. Machine-learning analysis further indicates that mixture-dependent molecular metrics are more predictive of apparent volatility than compound-specific molecular properties that define intrinsic volatility. Collectively, these findings highlight the critical role of intermolecular interactions in governing gas-particle partitioning in multicomponent systems. This study provides strong evidence that matrix effects significantly influence the apparent volatility of individual species in aerosols and other environmental organic mixtures and should be explicitly considered in volatility prediction frameworks and aerosol transport models.

Artificial intelligence in spine care: A scoping review of diagnostic applications

Background Artificial intelligence (AI) is increasingly used to enhance diagnostic accuracy, automate image interpretation, and support clinical decision-making. In the field of spine care, applications include MRI and CT-based detection of lumbar disc degeneration, spinal stenosis, vertebral fractures, and axial spondyloarthritis, as well as emerging symptom-based and multimodal diagnostic tools. However, evidence remains dispersed across modalities and conditions, and the quality and clinical readiness of AI systems vary. This scoping review maps current AI applications for diagnosing spinal disorders and identifies gaps for future research and clinical translation. Methods This review followed Joanna Briggs Institute (JBI) and PRISMA-ScR guidelines. Ovid MEDLINE, AMED, Embase, Cochrane CENTRAL, Web of Science, and Scopus were searched from January 2019 to December 2024. Eligible studies were mapped according to AI methodology, diagnostic target, data source, and validation approach, and were required to involve human participants, include sufficient methodological detail, and published in English peer-reviewed journals. No geographic restrictions were applied. Data was extracted on study design, AI methodology, diagnostic target, validation approach, and usability. Methodological quality was assessed using a 19-point scoring system covering study design, reporting clarity, data validation, and feature selection. Results Forty-six studies met the inclusion criteria, conducted primarily in Asia and Europe, with two studies from North America and one from South America. Most investigations were retrospective, imaging-based deep learning models applied to MRI or CT for detecting disc herniation, lumbar spinal stenosis, modic changes, vertebral fractures, and sacroiliitis. Several studies used prospective designs or external validation. Diagnostic performance was generally high across imaging models, with many studies describing accuracy that approached or matched clinician benchmarks, particularly in sacroiliitis classification, disc disease detection, and stenosis grading. Methodological scores ranged from 7.5 to 17.5 out of 19, with recurrent weaknesses in handling missing data, feature selection, and data element validation. Conclusion This review maps a growing body of literature on AI applications for diagnosing spinal disorders, with studies most frequently reporting favorable performance for MRI- and CT-based detection of degenerative and inflammatory conditions. Evidence remains preliminary and heterogeneous.

Investigation of electrolyte-plasma treatment for high-precision surface shaping of rotary parts

PIP4K attenuates PIP5K lipid kinase activity by disrupting membrane-mediated dimerization

The phosphatidylinositol 4-phosphate 5-kinase (PIP5K) family of enzymes generate most of the phosphatidylinositol-4,5-bisphosphate [PI(4,5)P 2 ] lipids in eukaryotes. In solution, PIP5K exists in a weak monomer-dimer equilibrium but undergoes membrane-mediated dimerization, which potentiates lipid kinase activity. We hypothesized that mechanisms that regulate PIP5K dimerization could function to buffer lipid kinase activity, thus providing a mechanism for maintaining relatively constant PI(4,5)P 2 levels at the plasma membrane. Due to the transient nature and density dependence of PIP5K dimerization, deciphering how other proteins modulate PIP5K dimerization has not been feasible. To address this limitation, we established a single molecule Förster resonance energy transfer (FRET) assay to visualize membrane-mediated homodimerization and heterodimerization of PIP5K paralogs on supported lipid bilayers using Total Internal Reflection Fluorescence Microscopy. Using this approach, we find that PIP4K attenuates PIP5K lipid kinase activity by disrupting membrane-mediated dimerization. Guided by structure prediction, we generated PIP4K mutants that are unable to disrupt PIP5K membrane-mediated dimerization thus preventing the attenuation of lipid kinase activity. In vivo, mutations that disrupt the PIP4K–PIP5K interaction similarly prevent PIP4K-mediated inhibition of the PIP5K activity. Overall, this work reveals the molecular basis of the PIP4K-mediated inhibition of PIP5K, which has been shown to regulate PI(4,5)P 2 lipid homeostasis. Creation of this PIP5K dimerization FRET biosensor also establishes a tool for deciphering how proteins modulate membrane-mediated dimerization of PIP5K in the future.