Browse Articles

Metabolic feedbacks drive population dynamics and can lead to oscillations among leaf bacteria

Abstract Metabolic interactions are fundamental to the assembly and function of microbiomes. Yet, our understanding of how specific interaction mechanisms can drive broader ecological outcomes and population dynamics remains limited. Here, we monitor interactions resulting from plant oligosaccharide degradation by leaf-associated bacteria using a microfluidic device that enables direct cell observation and quantitative metabolite detection. This approach enables the identification of key metabolic mediators, revealing recipient-specific patterns of carbon substrate and cofactor complementation. By linking these patterns to emergent dynamics observed between pairs of bacteria, we identify metabolically driven feedbacks that could lead to a variety of ecological outcomes – from outcompetition to coexistence characterized by oscillating population abundances. Investigating these observations with metabolic modeling allows us to systematically assess the impact of specific molecular mediators on population dynamics, yielding predictions of interaction outcomes that we validate experimentally. Our results provide a detailed mapping of metabolic mechanisms to emergent population trajectories among environmental microbes and help inform strategies for designing microbiomes with desired steady states.

Fluorescent carbon dot-based biosensor for the rapid and sensitive detection of Escherichia coli DNA

Spatiotemporal changes in inter-city sustainability impacts linked to emission challenges worldwide

Participation of neuropeptide Y and its receptors in leukotriene formation in the pig inflamed endometrium

Template-assisted [6+6] cycloaddition for the construction of photothermally responsive functional materials

Syndecan-4 in microglia mediates ischemic stroke-induced mitochondrial dysfunction and blood-brain barrier damage by interacting with Dishevelled

Abstract Ischemic stroke is a leading cause of disability and mortality, resulting in impaired mitochondrial function and disruption of the blood-brain barrier (BBB). Studies have demonstrated that Syndecan-4 (SDC4) influences BBB integrity and function, however, it remains unclear whether SDC4 influences mitochondrial function and BBB integrity following a stroke. We first obtained single-cell data from cortical tissue of transient middle cerebral artery occlusion (tMCAO) mice from public databases to identify changes in disease-associated cells and related molecular composition during disease progression. We then performed functional analyses to elucidate the functional characteristics of microglial subsets. Trajectory analysis was used to investigate cellular transition state signatures. Subsequently, we injected adeno-associated virus-sh_SDC4 (AAV-sh_SDC4) into the brain of C57BL/6J mice before tMCAO. Primary microglia were cultured and transfected with lentiviral-sh_SDC4 (LV-sh_SDC4) before oxygen-glucose deprivation/reoxygenation (OGD/R). Western blot, flow cytometry, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were used to investigate the expression, function, and mechanisms of SDC4. Molecular docking and molecular dynamics simulations were used to investigate binding. We identified 13 major cell populations, most of which underwent dynamic changes after MCAO. Microglia were the predominant cell population in all groups. Subsequent clustering analysis demonstrated that the relative abundances of repair- and anti-inflammatory as well as senescence-related microglial subpopulations were reduced following MCAO. Complementary trajectory inference modeling further uncovered a progressive upregulation of SDC4 expression in microglia throughout the course of disease progression. To validate these observations, we assessed SDC4 levels in experimental models of cerebral ischemia-reperfusion (I/R) injury and found elevated expression of this protein in both in vitro and in vivo settings. AAV-sh_SDC4 significantly improved I/R-induced motor impairment, restored mitochondrial morphology and function, increased occludin and claudin-5 expression, and protected BBB integrity. AAV-sh_SDC4 and LV-sh_SDC4 treatment enhanced Wnt/β-catenin signaling. XAV-939 reversed the protective effects of SDC4 interference. Mechanistically, SDC4 interacts with Dishevelled (Dvl) in microglia, physically sequestering Dvl and inhibiting Wnt/β-catenin signaling, ultimately leading to microglial mitochondrial dysfunction and associated BBB damage. The interaction between SDC4 and Dvl promotes mitochondrial dysfunction in microglia and is closely associated with the disruption of BBB integrity, thus offering a potential therapeutic strategy for the clinical treatment of cerebral ischemia.

Large stocks of permafrost soil organic carbon and nitrogen in Arctic river deltas

Investigating active dynamics of contractile actomyosin gels with micro particle image velocimetry (micro-PIV) analysis

Genomic insights into the improvement of Chinese fir from ancient domestication continuum to modern breeding

Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques

Sterically-extended asymmetric conjugated hole-selective layer for perovskite/silicon tandem solar cells

Machine learning based detection of subacute ruminal acidosis in early lactation dairy cows using multi-sensor behavioral, physiological, and milk production data

Noisy quantum learning theory

Abstract While known quantum learning speedups operate in idealized noiseless regimes, coupling to uncharacterized systems is a noisy process even given fault-tolerant devices. Here we show that noise can eliminate exponential quantum advantages of unphysical, noiseless learners, while demonstrating more nuanced directions towards meaningful quantum speedups in noisy experiments. We introduce the complexity class $${\mathsf{NBQP}}$$ NBQP ("noisy BQP”), modeling noisy fault-tolerant quantum computers that cannot generally error-correct the oracle systems they query. We prove that while natural $${\mathsf{NBQP}}$$ NBQP learners may be exponentially weaker than their idealized counterparts, a superpolynomial gap remains between $${\mathsf{NISQ}}$$ NISQ and fault-tolerant devices. Turning to canonical learning tasks, we find that the exponential advantage for purity testing collapses under local depolarizing noise. We then analyze noisy Pauli tomography, deriving lower bounds characterizing how instance size, quantum memory and noise jointly control sample complexity. We further study noise-dependent limitations on Heisenberg-limited metrology. Nevertheless, we identify a setting in which physical structure restores the purity testing speedup and highlight a noise-dependent polynomial speedup for Pauli tomography. Our results demonstrate that the primitives underlying quantum-enhanced experiments are fundamentally fragile to noise, and that realizing meaningful quantum advantages in future experiments will require interfacing noise-robust physics with available algorithmic techniques.

Integrated approach to model distribution and assess habitat suitability of killifish species in Oman’s local streams (wadis) under current and future climate conditions

Freshwater ecosystems in arid regions possess extraordinary levels of biodiversity, yet they are subject to unprecedented pressures of climate change and anthropogenic activities. We employed an integrated approach of incorporating species distribution modeling (MaxEnt), habitat suitability modeling, and protected area analysis to assess conservation requirements for two endemic/native killifish ( Aphaniops kruppi and A. stoliczkanus ) in Oman’s freshwater ecosystems. Using MaxEnt with CHELSA bioclimatic variables and topographic indices, we modelled climate change impacts under three shared socio-economic pathways (SSP1–2.6, SSP3–7.0, and SSP5–8.5) spanning 2011–2100. Predictive models demonstrated remarkable accuracy (AUC: 0.974 for A. kruppi , 0.950 for A. stoliczkanus ) revealing unique biogeographical patterns. A. kruppi showed restricted southern distribution dependent on monsoon moisture levels, with mean monthly climate moisture index (Cmi_m; 39.9%), mean diurnal range (Bio2; 18.3%), and sediment transport index (STI; 8.4%) as key variables. The distribution of A. stoliczkanus exhibited a more expansive northern range influenced by winter precipitation patterns, with precipitation of the coldest quarter (Bio19; 31%), the sediment transport index (STI; 20.2%), and the stream power index (SPI; 13.3%) as key drivers. Climate projections revealed high extrapolation risk (85–95%) with anticipated habitat reductions. Habitat suitability assessment of 12 stream sites (Boyce Index: 0.894) revealed unexpected specialization-dominance trade-off, where optimal Aphaniops conditions led to competitive exclusion, resulting in negative correlations between habitat suitability and aquatic biodiversity (Shannon diversity: r = −0.577, p = 0.049). Dissolved oxygen emerged as most critical parameter (mean suitability: 0.771 ± 0.308), with only 25% of sites demonstrating a Highly Suitable status. Spatial analysis revealed significant protection gaps: only 0.31–1.34% of high-suitability habitats and 2.6% of high-density wadis currently protected, requiring 6–24-fold increases to meet conservation targets. Species hybridization necessitates landscape-level conservation maintaining connectivity for gene flow. Results demonstrate that desert aquatic fauna conservation requires integrated strategies addressing climate dependencies, multi-habitat corridor protection, tiered water quality standards, and adaptive management accounting for hybridization zones, providing a replicable model for conservation in water-limited environments.

Effect of foot orthotics on running kinetics in adults with anterior cruciate ligament reconstruction: A controlled laboratory study

ToxiTaRGET: a multi-omics database for toxicant-responsive molecular targets

Protocol for the development of a procedure guide on Laparoscopic Cholecystectomy: Beyond bile duct injury prevention

Background Laparoscopic cholecystectomy is one of the most frequently performed operations worldwide and the standard treatment for benign gallbladder disease. Although several “safe cholecystectomy” initiatives aim to prevent bile duct injury, current guidelines do not comprehensively address the intraoperative technical conduct of the procedure. Key elements—including exposure, dissection techniques, technical sequencing, and intraoperative decision-making—remain inconsistently defined. This protocol describes the development of an evidence-based, globally applicable clinical procedure guideline intended to standardize technical performance, enhance patient safety, and support surgical training and quality improvement. Methods The guideline will be developed following AGREE II standards to ensure methodological rigor, transparency, and stakeholder involvement. The Guideline Development Group comprises coordinators, a steering committee, and multidisciplinary experts from surgical societies and academic institutions. Clinical questions will be generated using the PICO framework and refined through a Delphi consensus process following the ACCORD guideline. For each question, systematic literature searches will be conducted in accordance with PRISMA standards. When feasible, evidence will be synthesized using pairwise or network meta-analysis (PRISMA-NMA, PRISMA-Search); when quantitative synthesis is not possible, findings will be summarized narratively using SWiM guidance. Study selection, data extraction, and risk-of-bias assessment will be performed independently using validated tools (ROB 2.0, ROBINS-I, AMSTAR-2), with search management in Rayyan®. Recommendations will be developed using the GRADE approach, with evidence profiles created in GRADEpro GDT. A second Delphi process will be conducted to reach consensus on each recommendation, applying predefined thresholds for participation and agreement. Discussion This guideline seeks to fill a critical gap in the technical standardization of laparoscopic cholecystectomy by providing a comprehensive, evidence-based framework that extends beyond bile duct injury prevention. Existing guidelines lack methodological rigor and fail to address key intraoperative elements such as exposure, dissection strategies, and decision-making. Through systematic evidence synthesis and consensus processes, this project aims to harmonize global surgical practice and establish a benchmark for training, safety, and quality improvement. Registration This protocol was prospectively registered in the Open Science Framework on December 18, 2025 ( https://doi.org/10.17605/OSF.IO/78QSE ).

Negative automatic thoughts mediate the effects of emotion regulation on distress in women with breast and gynecological cancer

Abstract Cancer’s emotionally taxing nature offers a compelling real-world model for examining psychological distress. Despite its relevance, empirical research remains limited on the cognitive mechanisms, particularly self-referent negative automatic thoughts (NATs) and emotion regulation (ER), sustaining distress in chronic illness. In this cross-sectional study women ( N  = 230) diagnosed with breast or gynecological cancer completed self-report measures of distress, depressive and anxiety symptoms, cognitive emotion regulation and negative automatic thoughts, along with sociodemographic and clinical data. Structural equation modeling was used for analysis. Mediation analyses revealed that maladaptive emotion regulation was positively associated with psychological distress ( β  = 0.405), depression ( β  = 0.631), and anxiety ( β  = 0.660), with NATs partially mediating these effects. Adaptive ER showed weaker total effects, but significant indirect effects via reduced NATs. A latent distress or general psychological distress (PD) model demonstrated excellent fit and confirmed partial mediation. NATs uniquely predicted depressive symptoms beyond general distress, underscoring their central role. The findings highlight the central role of maladaptive ER and NATs in psychological distress among cancer patients. The results support cognitive models suggesting that targeting maladaptive cognitive patterns may yield improvements in emotional well-being. The PD model offers a parsimonious, transdiagnostic framework for conceptualizing shared vulnerability across symptom domains.

Atom camera: super-resolution scanning microscope of a light pattern with a single ultracold atom

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.