Browse Articles

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.

Feasibility of the Italian version of the Teen Online Problem Solving Program (I-TOPS) in a sample of adolescents with acquired brain injury: Results from a randomized controlled trial

Numerous neurocognitive and behavioral deficits in adolescents with acquired brain injury have been associated with impairments in executive functions. Nevertheless, many adolescents do not receive targeted rehabilitation. One of the most validated interventions addressing executive dysfunction in this population is the web-based Teen Online Problem Solving (TOPS) program, developed in the United States in the early 2000s. This study aimed to evaluate the feasibility of the Italian version of TOPS (I-TOPS) in its first implementation in Italy, compared with an active control wellness intervention in adolescents aged 11–19 years. Data on the feasibility of both the training and the study design and procedures are reported from the randomized controlled trial conducted to assess feasibility prior to a full evaluation of efficacy. Forty-two adolescents participated, 21 allocated to I-TOPS and 21 to the wellness intervention. Overall willingness to participate in the study, assessed before randomization across the full sample, was moderate (61.73%). The I-TOPS group, but not the wellness group, demonstrated adequate training adherence, training satisfaction and participation rates. No technological issues hindering program completion, accessibility difficulties with the web-based platform or significant loss to follow-up among participants who completed the training were reported in either group. However, challenges in collecting outcome measures on parent functioning due to refusal were observed in both groups. Adherence to the planned assessment timeline was satisfactory but not fully met. In sum, the findings suggest that I-TOPS represents a feasible approach that is subjectively perceived as useful and engaging by adolescents with acquired brain injury and their families. Nevertheless, the moderate acceptance rate in the study and the potential stigma associated with reporting mental health concerns among some parents may limit the generalizability of the findings. These factors should be taken into account when planning efficacy studies. This study was registered at ClinicalTrials.gov (NCT05169788).

GATA2-LHCGR regulatory network promotes follicle development by enhancing granulosa cell proliferation and ovarian steroidogenesis in Wanxi white geese

Peroxisomal ether lipid synthesis regulates cortical neurogenesis and maintains mitochondrial energy homeostasis in radial glial cells

Neurogenesis is characterized by dynamic structural changes and functional remodeling of multiple organelles, which interact to form an intricate network that precisely modulates processes including neural progenitor cell self-renewal, neurogenesis, and terminal neuronal development. However, the spatiotemporal dynamics of peroxisomes and their functional contributions within this regulatory network remain incompletely defined during mammalian cortical development. Here, we found that radial glial cells (RGCs) exhibit enriched peroxisome abundance, whereas neural differentiation is associated with reduced peroxisome numbers and increased pexophagy, accompanied by the remodeling of lipid metabolic programs. Acute disruption of peroxisomes by PLAAT3-PEX11 impaired neural differentiation in the embryonic mouse cortex, while PEX7 knockout compromised neurogenic progression in human cortical organoids, supporting a conserved requirement for peroxisomal function during cortical development. Lipidomic and imaging analyses revealed that peroxisome-derived ether lipids were essential for driving neural differentiation and were specifically enriched in mitochondria. Consistently, knockdown of Gnpat , which catalyzes the initial step of ether lipid biosynthesis, reduced neural differentiation, and disrupted mitochondrial structure and function, while batyl alcohol supplementation partially restored these defects. Mechanistically, the ether lipids maintain the structural integrity of mitochondrial cristae and thereby support respiratory chain activity, which in turn promotes oxidative phosphorylation and activates the NAD + associated signaling. Collectively, this work highlights the precise spatiotemporal regulation of neurogenesis through peroxisomal dynamics and interorganelle crosstalk and identifies ether lipids as a potential therapeutic target for neurodevelopmental disorders.

TranExamic Atomized for Pediatric post-Operative Tonsillectomy hemorrhage (TEAPOT): Study protocol for a pilot randomized controlled trial

Tonsillectomy is the second most common performed surgical procedure in children in the United States. Unfortunately, up to five percent of children return to emergency departments (ED) for post-tonsillectomy hemorrhage (PTH). To control PTH, most pediatric otolaryngologists return to the operating room (OR) for cauterization of the bleeding source. Retrospective studies suggest that nebulized tranexamic acid (TXA), an antifibrinolytic agent, can reduce the severity of post-tonsillectomy hemorrhage (PTH), however, its efficacy and safety have not yet been tested in randomized controlled trials. We designed the TranExamic Atomized for Pediatric post-Operative Tonsillectomy hemorrhage (TEAPOT) trial protocol to evaluate the feasibility of conducting a confirmatory clinical trial to evaluate the effects of TXA in children with PTH. We will randomize children presenting to the ED with secondary PTH to receive three 5-mL nebulized doses of either TXA (TXA 100 mg/ml) or placebo (normal saline). The following outcomes will be measured: feasibility (number of enrolled patients per site per year); protocol adherence (receipt of at least two nebulization doses); return to the operating room; pain reported on the FACES scale; parent and child anxiety assessed by the Patient-Reported Outcomes Measurement Information System (PROMIS) Anxiety measure; systemic and local (pharmacokinetic model derived) concentrations of nebulized TXA; coagulation biomarkers; and adverse events, including bleeding, thrombosis, and seizures. Our multicenter trial will provide important preliminary data on the feasibility and pharmacokinetics of nebulized TXA in preparation for a definitive clinical trial of children with secondary post-tonsillectomy hemorrhage. Registered at ClinicalTrials.gov ID# (NCT07565753) on May 4 th , 2026, https://clinicaltrials.gov/study/NCT07565753?term=teapot&viewType=Card&rank=1

Rockburst mechanism and control of staggered roadways beneath upper-slice residual coal pillars in extra-thick coal seams: a case study

Abstract Upper-slice residual coal pillars in extra-thick coal seams can form local high-stress structures and strongly affect lower-slice staggered roadways. To clarify the rockburst mechanism beneath such a pillar, this study investigated the 250101-2 lower-slice working face by integrating field damage investigation, microseismic monitoring, coal-pillar mechanical analysis, FLAC3D simulation, and destressing verification. During retreat, 5737 microseismic events (MS) were recorded, including 56 high-energy events with energy not lower than $$\:1.0\times\:{10}^{4}$$ J. These events were mainly distributed within approximately 200 m ahead of the working face and concentrated on the haulage roadway side. The 20 m residual-pillar zone accounted for 41% of the high-energy event frequency and 37% of the total high-energy release, consistent with repeated field manifestations such as rib deformation, support damage, mesh failure, coal-rock collapse, and floor heave. Mechanical analysis shows that the residual pillar may behave as a narrow pillar with peak-stress superposition or evolve into a wider composite bearing structure after goaf recompaction. Numerical simulation further indicates that, during lower-slice mining, the original 20 m pillar and adjacent compacted coal-rock mass formed an effective bearing zone of approximately 54 m, enhancing stress accumulation and downward transfer. The LW250101-2 haulage roadway was located near the boundary between the high-stress zone and the stress-relieved zone, where a sharp stress gradient promoted asymmetric deformation and dynamic instability. Coordinated destressing by roof deep-hole blasting, rib deep-hole blasting, and ultra-deep large-diameter boreholes transformed microseismic activity from concentrated high-energy release to more dispersed low-energy release, reducing rockburst risk in the residual-pillar-affected roadway.

Wireless, skin-attachable patch with 3D liquid metal electrodes for vagus nerve stimulation in depression

Current nonsurgical vagus nerve stimulation (VNS) approaches often suffer from broad current spread, high electrode-skin impedance, and unintended stimulation of surrounding tissues, which can reduce efficacy and cause side effects. Here, we present a wirelessly operable, skin-attachable VNS patch with three-dimensional liquid-metal electrodes that mimic the low modulus of biological tissues, developed as a minimally invasive platform for investigating the biological mechanisms underlying VNS in a mouse model of depression. The soft, low-modulus three-dimensional liquid metal electrodes gently penetrate the skin, bypass the stratum corneum, and improve electrode–tissue coupling while minimizing tissue damage compared with conventional planar or rigid electrodes. To comprehensively assess therapeutic effects, we conducted behavioral tests, neural probe-based brain signal analysis, dendritic spine density measurements, and blood and inguinal lymph node profiling in depression model mice. VNS increased plasma serotonin levels and increased dendritic spine density, indicating improved neuroplasticity. Corticosterone-induced depressive mice exhibited elevated CD4+ and B220+ cells, which normalized with VNS. These results establish an animal-level proof-of-concept that minimally invasive VNS can engage key neural and immunological pathways relevant to depression, providing a foundation for future neuromodulation strategies.