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Cooling outweighs warming across phenological transitions in the Northern Hemisphere
Vegetation phenology, i.e., seasonal biological events such as leaf-out and leaf-fall, regulates local climate through biophysical processes like evapotranspiration (ET) and albedo. However, the net surface temperature impact of these processes—whether ET cooling or albedo-induced warming predominates—and how the dominance changes across phenological transitions and regions remains poorly understood. Here, we investigated the effects of vegetation foliage on daytime land surface temperature (LST) following six phenological transitions, spanning from the start of season to end of season, in deciduous and mixed forests across the mid- to high-latitude Northern Hemisphere during 2013–2021 using multiple satellite products and ground observations. We quantified vegetation effect as the difference between observed LST and LST estimates from the Annual Temperature Cycle (ATC) model, representing a no-foliage scenario. We found that vegetation-induced cooling consistently outweighs warming following all phenological transitions except for the end of the season. Cooling intensity increased with vegetation greenness, ranging from 1.0 ± 0.5 °C (mean ± 0.15 SD) in 59% of forests after the start of the season (SOS) to 6.1 ± 0.8 °C in 89% of forests following the onset of maturity, before declining toward the end of the season. Over half of the regions experiencing cooling showed intensification of surface cooling with climate warming, suggesting an amplified vegetation-mediated cooling under future climate change. The findings provide a more precise understanding of the role of vegetation in modulating climate at the intraseasonal scale, highlighting the importance of integrating phenological impacts into climate adaptation strategies and Earth system modeling.
Prevalence and clustering of cardiovascular risk factors in a population aged 25–64 in Czechia: A cross-sectional study
Introduction Cardiovascular events are still the most common cause of death in the Czech Republic. The increasing prevalence of risk factors such as dyslipidaemia, hypertension, obesity, and diabetes individually and collectively contribute to cardiovascular events. The aim of this study was to determine their prevalence and interrelationships. Method The data for this epidemiological study were obtained from the Czech cross-sectional study EHES 2019 (European Health Examination Survey) with stratified random sampling. Firstly, individual risk factors (dyslipidaemia, hypertension, obesity, diabetes, and smoking) in population aged 25–64 years of age were monitored using questionnaires and physical and laboratory measurements; additionally, the cumulative effect of these risk factors in subjects was examined. Finally, cardiovascular risk in the age group of 40–64 years (767 out of 1057 participants) was estimated using the SCORE EU chart (for countries with high cardiovascular risk). Individual parameters were assessed according to standard criteria: dyslipidaemia = total cholesterol ≥5.0 mmol/l, and/or HDL-C < 1.0 mmol/l in men, or <1.2 mmol/l in women, and/or LDL-C ≥ 3.0 mmol/l and/or fasting TAG ≥ 1.7 mmol/l (or ≥2 mmol/l without fasting) and/or medication with lipid-lowering drugs; obesity = BMI > 30 kg/m2; hypertension = systolic blood pressure ≥ 140 mmHg, and/or diastolic blood pressure ≥ 90 mmHg, and/or antihypertensive treatment; diabetes mellitus = HbA1c ≥ 48 mmol/mol and/or on treatment. Data were analysed by descriptive statistics. Results Of the total number of 1057 study participants (426 men and 631 women) aged 25–64 years, roughly 84% presented with ≥1 cardiovascular disease risk factor. The most common risk factor was dyslipidaemia, which occurred in 71.7% of the subjects. The prevalence of hypertension was 36.3% (men 46.0%, women 26.3%). 29.7% of subjects were obese, diabetes mellitus occurred in 5.7% (men 7.6%, women 3.7%). 17.7% were regular smokers; another 6.6% reported occasional smoking. A combination of risk factors was common, e.g., 77.3% had dyslipidaemia and/or hypertension. There were 16.1% of subjects without monitored cardiovascular risk factors. After stratification of cardiovascular risk prediction according to SCORE chart, 49.7% of individuals fall into low risk, 28.6% into medium risk, and up to 11.3% into high and 10.4% into very high risk. Conclusion The most common risk factor is lipid spectrum disorders (71.7%). Combination of risk factors is common, which increases the risk of cardiovascular events in these individuals. In the 40–64 age group, 21.7% of the population is at >5% risk of a fatal cardiovascular event over the next 10 years.
Cooperative mixing through hydrodynamic interactions in <i>Stylonychia lemnae</i>
Aquatic microorganisms typically inhabit a heterogeneous resource landscape, composed of localized and transient patches. To effectively exploit these resources, they have evolved a wide range of feeding strategies that combine chemotactic motility with active feeding flows. However, there is a notable lack of experimental studies that examine how these active flows shape resource fields to optimize feeding. In particular, the suspected cooperative hydrodynamics provided by groups of cells remains largely unexplored due to the difficulties in visualizing these dynamic three-dimensional flows. Here, we experimentally investigate how Stylonychia lemnae ciliates form feeding clusters of independent cells around food patches. Individual feeding flows interact hydrodynamically to create a chaotic collective flow at the population scale. Using a combination of experimental and numerical techniques, we measure and predict the entire collective flow, enabling us to assess its remarkable mixing and dispersion properties. We show that the active spreading of the food patch accelerates its detection by starving cells. As many fitness advantages provided by collective flows can be envisioned, we propose that this feeding cluster represents a form of intraspecific by-product cooperative behavior.
Scalable and sustainable process of spike spherical Mg(OH)2 adsorbent from magnesite by ammonia-cycle method for dye removal
This study presents an innovative, and environmentally friendly synthesis process for spike spherical magnesium hydroxide (SSMH) using magnesite and an ammonia-cycle method, which eliminates waste liquid, gas, and chemical reagent pollution. The process involves calcining magnesite to obtain calcined magnesite, which reacts with (NH4)2SO4 to produce magnesium sulfate and ammonia. Subsequently, magnesium sulfate reacts with ammonia water to generate SSMH. The optimized conditions for the extraction of magnesium ion (Mg²⁺) are as follows: (NH4)2SO4 concentration of 2.4 mol/L, 4 g of calcined magnesite powder, and a reaction time of 1.5 h, resulting in an extraction rate of Mg²⁺ of 93.4%. The optimized conditions for the precipitation of Mg² ⁺ are as follows: Mg² ⁺ concentration of 0.7–1.3 mol/L, NH₃H₂O/Mg² ⁺ molar ratio of 9, reaction temperature of 60°C, and reaction time of 1 h, with a precipitation rate of Mg²⁺ of about 85%. After five ammonia cycles, the precipitation rate of Mg² ⁺ stabilizes at 85%. Scanning Electron Microscopy (SEM) confirms the spike spherical structure of the SSMH, with uniform particle diameters and a particle size of 2 μm. Adsorption studies indicate a maximum adsorption capacity (Qm) of 150.4944 mg/g for Reactive Red X-3B (RRX) dye at 25°C, fitting well with the Langmuir and pseudo-second-order kinetic model. The adsorption is primarily chemisorption-driven. In conclusion, the ammonia-cycle method for SSMH from magnesite is an environmentally friendly and sustainable approach. SSMH with its high adsorption capacity and spike spherical structure, is effective for treating RRX aqueous solution and has potential for broader applications in removing heavy metals, persistent organic compounds, nitrogen, and phosphorus from various polluted sources.
Distinct prelimbic cortex ensembles encode response execution and inhibition
Learning when to initiate or withhold actions is essential for survival, requiring the integration of past experiences with new information to adapt to changing environments. The prelimbic cortex (PL) plays a central role in this process, with a stable PL neuronal population (ensemble) recruited during operant reward learning to encode response execution. However, it is unknown how this established reward-learning ensemble adapts to changing reward contingencies, such as reward omission during extinction. Specifically, does the same ensemble adjust its activity to support behavior suppression, or is a distinct ensemble recruited for this new learning? Our data reveal that operant extinction learning recruits a distinct PL Extinction ensemble to support response inhibition, and concerted engagement of both ensembles encodes both ongoing and subsequent context-specific behavior. Using single-cell calcium imaging, we longitudinally tracked PL neurons in rats as they pressed a lever for food rewards (Training), learned to suppress responding upon reward omission (Extinction), and reinstated responding following a noncontingent “priming” pellet (Reinstatement). We trained decoders on individual rats’ PL activity patterns to predict trial-wise responses and used an in silico deletion approach to identify separate PL Training and Extinction ensembles associated with response execution and inhibition, respectively. Critically, both ensembles were reengaged and maintained their distinct roles during Reinstatement. These findings highlight ensemble-based encoding of multiple, even opposing, learned associations within the same region, demonstrating how selective ensemble recruitment enables behavioral flexibility under changing contingencies.
Impact of self-directed e-learning on nurses’ competency in arrhythmia interpretation in cardiology
Accurate electrocardiogram (ECG) interpretation is an essential competency for nurses, particularly in cardiology, where the timely identification of arrhythmias can be lifesaving and significantly impact patient outcomes. Nurses often serve as the first line of clinical observation, making their ability to interpret ECGs critical for early intervention and safe patient care. However, numerous studies have highlighted persistent gaps in ECG interpretation skills among nursing staff, emphasizing the urgent need for effective, accessible educational strategies. This study aimed to assess the effectiveness of a self-directed e-learning (SDL) package in improving nurses’ knowledge and competency in arrhythmia interpretation within the cardiology department of Rashid Hospital, Dubai. A quasi-experimental, one-group pre-test/post-test design was utilized with a sample of 50 nurses working in Coronary Care Units. Data were collected using a validated, structured questionnaire that included demographic data and ECG interpretation tests. The SDL package covered foundational ECG knowledge, rhythm analysis, and arrhythmia management. Results showed a statistically significant 15.92% improvement in knowledge following the intervention (t = −6.668, p < .001). A notable correlation was observed with years of experience; nurses with 1–5 years of experience demonstrated the highest improvement (p = .020). No significant differences were found based on gender (p = .234) or area of practice (p = .139). This study highlights the critical need to strengthen nurses’ arrhythmia interpretation skills and demonstrates that SDL is an effective, flexible, and scalable approach to bridging competency gaps in high-acuity clinical areas such as cardiology.
Muricholic acid mediates puberty initiation via the hypothalamic TGR5 signaling pathway
The onset of puberty is increasingly observed at earlier ages in children, especially in girls with obesity, a trend that predisposes them to long-term metabolic and reproductive disorders in adulthood. Bile acids have emerged as pivotal signaling molecules in both metabolic and reproductive disorders, but remain unexplored in the early onset of puberty in children. Herein, we find elevated levels of muricholic acid (MCA) species in the serum of girls with central precocious puberty, which strongly correlate with indices of hypothalamic–pituitary–gonadal axis activation and can reach peak levels during puberty among healthy children. Intriguingly, reduction of MCA species can lead to decreased expression of gonadotropin-releasing hormone (GnRH) and delay the early onset of puberty, while elevated MCA levels induced premature sexual development in female mice. Mechanistically, we demonstrated that MCA had strong activation effects on Takeda G-protein-coupled receptor 5 (TGR5), and MCA enhanced GnRH expression in GnRH neurons through activation of the TGR5-PI3K/Akt-mTOR signaling pathway. Our findings reveal a link between metabolic status and reproductive maturation, highlighting MCA as a potential therapeutic target for managing early puberty initiation.
Exploring the impact of visual function degradation on manual prehension movements in normal-sighted individuals
Impairments of visual function abilities, such as visual acuity and contrast sensitivity, can negatively impact our ability to perform manual prehension tasks. Despite the clear link between visual input and motor output, there is still limited understanding of how visual function deficits affect hand motor behavior. This study aimed to explore the impact of different levels of visual function degradation, specifically in terms of visual acuity and contrast sensitivity, on the reach and grasp components of manual prehension. To this end, visual function degradation was induced in young participants with normal vision using five different densities of Bangerter occlusion foils. Participants were instructed to perform a natural and accurate reach-to-grasp task towards a cylindrical object with two different diameters (3.5 or 7 cm) and positioned at two distances (25 or 50 cm). The effects of visual function degradation, object size, and distance were evaluated by recording the position and trajectory of the right hand using an optoelectronic motion capture system. Three-dimensional kinematic analysis revealed that visual function degradation in normal-sighted individuals directly altered the reach and grasp components of prehension movements. These alterations included longer movement durations, lower velocity and acceleration profiles, slower deceleration phases, over-scaled hand grip apertures, and greater trajectory deviations. The effects were dependent on the level of visual degradation induced and the intrinsic (size) and extrinsic (distance) object properties. Reductions exceeding 70% in visual acuity and 55% in CS had the most pronounced impact on prehension components. However, subtle reductions greater than 30% in visual acuity and 15% in contrast sensitivity were sufficient to trigger compensatory mechanisms. These findings provide further understanding of how visual function degradation affects prehension movement strategies, highlighting the crucial relationship between visual feedback quality and object properties in the motor online control of the transport, manipulation and spatial components. Our results offer new insights into the implications of visual impairments on manual prehension movements.
Quantifying phage infectivity from characteristics of bacterial population dynamics
A frequent goal of phage biology is to quantify how well a phage kills a population of host bacteria. Unfortunately, traditional methods to quantify phage success can be time-consuming, limiting the throughput of experiments. Here, we use theory to show how the effects of phages on their hosts can be quantified using bacterial population dynamics measured in a high-throughput microplate reader (automated spectrophotometer). We use mathematical models to simulate bacterial population dynamics where specific phage and bacterial traits are known a priori. We then test common metrics of those dynamics (e.g., growth rate, time and height of peak bacterial density, death rate, extinction time, area under the curve) to determine which best predict: 1) infectivity over the short-term, and 2) phage suppression over the long term. We find that many metrics predict infectivity and are strongly correlated with one another. We also find that metrics can predict phage growth rate, providing an effective way to quantify the combined effects of multiple phage traits. Finally, we show that peak density, time of peak density, and extinction time are the best metrics when comparing across different bacterial hosts or over longer timescales where plasticity or evolution may play a role. In all, we establish a foundation for using bacterial population dynamics to quantify the effects of phages on their bacterial hosts, supporting the design of in vitro empirical experiments using microplate readers.
Research on robot positioning error compensation algorithm based on the Dog Leg and PSONN algorithm
The absolute positioning accuracy of industrial robots is much lower than that of repetitive. In this paper, an error compensation algorithm for industrial robots is proposed, which included the kinematic parameter calibration based on the enhanced Dog Leg algorithm, the odd point error prediction based on the Particle Swarm Optimization Neural Network (PSONN) algorithm, and the positioning error calculation based on the Spatial Grid Multipoint Interpolation (SGMI) algorithm. The proposed algorithm reduce the robot localization error in three progressive steps, which combines the interpretability of traditional algorithms and the nonlinear effect of neural networks, avoiding the low accuracy of traditional algorithms and the local optimal phenomenon of neural networks. The robot end positioning error model developed in this paper, mainly includes kinematic parameters, return angle, deceleration ratio relative error coefficients, joint angle coupling coefficients, and base coordinate system error. The experimental results demonstrate that, after calibrating kinematic parameter, the positioning error is reduced from 3.158 mm to 0.406 mm, the uncertainty is reduced from 1.726 mm to 0.160 mm. After compensating by the SGMI algorithm, the positioning error is reduced from 0.406 mm to 0.0685 mm. The results also demonstrate that the proposed SGMI algorithm calibrate the kinematic parameter effectively and reduced the positioning error of the industrial robot significantly.
Dynamic and precise electromagnetic levitation of single cells
The biophysical properties of single cells are crucial for understanding cellular function and behavior in biology and medicine. However, precise manipulation of cells in 3-D microfluidic environments remains challenging, particularly for heterogeneous populations. Here, we present “Electro-LEV,” a unique platform integrating electromagnetic and magnetic levitation principles for dynamic 3-D control of cell position during separation. We demonstrated that small current adjustments in electromagnets significantly alter the levitation heights of diverse particles and cell types. By periodically modulating and tracking cell positions along the z-axis, Electro-LEV identified distinct levitation behaviors between single cells and cell clusters, with clusters responding more rapidly to magnetic field changes. Furthermore, we demonstrated that Electro-LEV significantly enhances the purity and efficiency of levitational sorting, achieving 10-fold enrichment of live cells from 50% starting viability samples and 18.8-fold enrichment from 10% starting viability samples. These results establish Electro-LEV as a powerful tool for investigating cellular heterogeneity, differentiating cell sizes and types, and improving cell sorting efficiency. Thus, Electro-LEV is broadly applicable, offering different possibilities for high-resolution cell analysis and label-free cell sorting in various biomedical fields, including but not limited to single-cell sequencing and drug screening.
Pronated foot and reactive balance: A preliminary comparative study of older women
Background Older adults with pronated foot may face greater challenges in maintaining balance, which increases their risk of falling. Reactive balance, which refers to the ability to restore stability following an unforeseen disturbance, is a key component in evaluating fall-related postural control. Evaluating reactive balance can provide insights about balance capabilities and potential fall risks in this population. Objective This study compared the reactive balance (center of mass displacement and reaction time) between older women with and without pronated foot. Methods Thirty-two older women comprising 16 with bilateral pronated foot and 16 without pronated foot participated in the study. To assess reactive balance, a three-dimensional motion analysis was conducted. Each participant was equipped with 29 retroreflective markers and compensatory stepping corrections were performed in the forward direction. Independent t-test was used to compare the center of mass displacement and reaction time between the two groups. Results The older women with pronated foot exhibited significantly slower reaction times than those without pronated foot (p = 0.017). However, no significant difference was determined for the center of mass displacement between the two groups (p = 0.367). Conclusion This study indicated that older women with pronated foot had significantly prolonged reaction times, suggesting an impairment in reactive response. However, the lack of significant differences in the center of mass displacement between those with and without pronated foot suggest that although older women with pronated foot maintain balance similar to those without pronated foot, their delayed reaction times may hinder the ability to make quick, involuntary stepping adjustments, potentially increasing fall risks.
Computational modeling of residual stress in welded high-strength steel box sections
This study investigates the residual stress patterns of welded box-section members constructed from high-strength steel (HSS). A finite element method (FEM) model developed in ANSYS is validated using experimental data from previous studies. Additionally, experimental data are directly utilized in the analysis to reinforce and contextualize numerical outcomes. A comprehensive parametric analysis explores the impact of plate thickness, width-to-thickness ratio, steel strength, welding sequence, and welding conditions on residual stress distributions. The results reveal that tensile residual stresses near weld regions consistently reach 82.6–97.8% of the yield strength and primarily depend on steel strength, with minimal sensitivity to section dimensions. In contrast, compressive residual stresses in mid-panel regions decrease by up to 72.2% with an increase in width-to-thickness ratio from 3.0 to 23.0, and the reduction rate is influenced by plate thickness. Additionally, welding sequences significantly affect residual stress magnitudes without altering their general distribution patterns. Diagonal welding method in the same direction effectively reduces mid-panel compressive stresses by up to 17.0%, and butt welds generate approximately 48.3% lower residual stresses than fillet welds. A residual stress distribution model for HSS welded box sections is developed. The model shows good agreement with experimental data with average deviation within 9.5% and can serve as a simplified yet reliable input for structural design, safety assessment, and advanced finite element modeling of welded steel members.
Single-cell transcriptome combined with genetic tracing reveals a roadmap of fibrosis formation during proliferative vitreoretinopathy
Ocular fibrosis, a severe consequence of excessive retinal wound healing, can lead to vision loss following retinal injury. Proliferative vitreoretinopathy (PVR), a common form of ocular fibrosis, is a major cause of blindness, characterized by the formation of extensive fibrous proliferative membranes. Understanding the cellular origins of PVR-associated fibroblasts (PAFs) is essential to decipher the mechanisms of ocular wound healing. In this study, we combined single-cell transcriptomics with genetic lineage tracing to map the contributions of retinal pigment epithelial (RPE) cells, immune cells, and Müller cells to disease progression. RPE cells were found to constitute the largest fraction of cells within PVR lesions, transitioning through metabolic, proliferative, and epithelial-to-mesenchymal transition stages during their conversion to PAFs. These cells exhibited remarkable plasticity and heterogeneity. Notably, Pdgfrb + RPE cells demonstrated significant morphological plasticity, transitioning toward a fibroblast-like phenotype, while macrophage-like RPE cells acquired inflammation-related functions post-PVR. Cell communication network analysis identified Thbs1 (encoding TSP-1) as a key hub gene driving RPE cell fate transitions during PVR. Importantly, therapeutic antibodies targeting TSP-1 significantly mitigated PVR progression. This study provides a detailed roadmap of fibrosis formation during ocular wound healing and highlights the therapeutic potential of targeting TSP-1 in the management of PVR.
Capybara: Efficient estimation of generalized linear models with high-dimensional fixed effects
This paper introduces capybara, an R package implementing computationally efficient algorithms for estimating generalized linear models (GLMs) with high-dimensional fixed effects. Building on Stammann (2018), we combine the Frisch-Waugh-Lovell (FWL) theorem with alternating projections to achieve memory-efficient estimation. Our benchmarks demonstrate that capybara reduces computation time by 95-99% compared to traditional dummy variable approaches while maintaining numerical accuracy to 5 decimal places. For a complex gravity model with 28,000 observations and 3,200 fixed effects, capybara completes estimation in just 6 seconds using 33 MB of memory, compared to 11 minutes and 12 GB with base R. The package is particularly valuable for trade economics, labor economics, and other applications requiring multiple high-dimensional fixed effects to control for unobserved heterogeneity, making previously infeasible models computationally tractable on standard hardware.
Ecosystem consequences of a nitrogen-fixing proto-organelle
Microscale symbioses can be critical to ecosystem functions, but the mechanisms of these interactions in nature are often cryptic. Here, we use a combination of stable isotope imaging and tracing to reveal carbon (C) and nitrogen (N) exchanges among three symbiotic primary producers that fuel a salmon-bearing river food web. Bulk isotope analysis, nanoSIMS (secondary ion mass spectrometry) isotope imaging, and density centrifugation for quantitative stable isotope probing enabled quantification of organism-specific C- and N-fixation rates from the subcellular scale to the ecosystem. After winters with riverbed-scouring floods, the macroalga Cladophora glomerata uses nutrients in spring runoff to grow streamers up to 10 m long. During summer flow recession, riverine N concentrations wane and Cladophora becomes densely epiphytized by three species of Epithemia , diatoms with N-fixing endosymbionts (proto-organelles) descended from a free-living Crocosphaera cyanobacterium. Over summertime epiphyte succession on Cladophora , N-fixation rates increased as Epithemia spp. became dominant, Cladophora C-fixation declined to near zero, and Epithemia C-fixation increased. Carbon transfer to caddisflies grazing on Cladophora with high densities of Epithemia was 10-fold higher than C transfer to caddisflies grazing Cladophora with low Epithemia loads. In response to demand for N, Epithemia allocates high levels of newly fixed C to its endosymbiont. Consequently, these endosymbionts have the highest rates of C and N accumulation of any taxon in this tripartite symbiosis during the biologically productive season and can produce one of the highest areal rates of N-fixation reported in any river ecosystem.
The influence of investor sentiment on the Chinese stock market amid COVID-19: An event study analysis
This study investigates the influence of investor sentiment on the Chinese stock market during the COVID-19 pandemic, using an event study analysis to examine data from December 2019 to December 2022. It aims to explore how investor sentiment, driven by news, social media, and economic uncertainties, has affected stock market performance during the pandemic. Data from 2005 to 2022 have been used to analyze abnormal and cumulative returns across key pandemic-related events, such as government interventions, lockdowns, and vaccine rollouts. The results show significant fluctuations in market returns driven by changes in sentiment. Positive sentiment, linked to government stimulus measures and vaccine announcements, led to positive market reactions, while negative sentiment, stemming from pandemic uncertainty, triggered market downturns. The study contributes to understanding the role of sentiment in market volatility, particularly in an emerging market like China, during periods of crisis. Accordingly, the study suggests multiple policy implications for policy makers.
Microglia-to-neuron signaling links <i>APOE4</i> and inflammation to enhanced neuronal lipid metabolism and network activity
Microglia regulate neuronal circuit plasticity. Disrupting their homeostatic function has detrimental effects on neuronal circuit health. Neuroinflammation contributes to the onset and progression of neurodegenerative diseases, including Alzheimer’s disease (AD), with several microglial activation genes linked to increased risk for these conditions. Inflammatory microglia alter neuronal excitability, inducing metabolic strain. Interestingly, expression of APOE4 , the strongest genetic risk factor for AD, affects both microglial activation and neuronal excitability, highlighting the interplay between lipid metabolism, inflammation, and neuronal function. It remains unclear how microglial inflammatory state is conveyed to neurons to affect circuit function and whether APOE4 expression alters this intercellular communication. Here, we use a reductionist model of human iPSC-derived microglial and neuronal monocultures to dissect how the APOE genotype in each cell type independently contributes to microglial regulation of neuronal activity during inflammation. Conditioned media (CM) from LPS-stimulated microglia increased neuronal network activity, assessed by calcium imaging, with APOE4 microglial CM driving greater neuronal activity than APOE3 CM. Both APOE3 and APOE4 neurons increase network activity in response to CM treatments, while APOE4 neurons uniquely increase presynaptic puncta in response to APOE4 microglial CM. CM-derived exosomes from LPS-stimulated microglia can mediate increases to network activity. Finally, increased network activity is accompanied by increased lipid droplet (LD) metabolism, and blocking LD metabolism abolishes network activity. These findings illuminate how microglia-to-neuron communication drives inflammation-induced changes in neuronal circuit function, demonstrate a role for neuronal LDs in network activity, and support a potential mechanism through which APOE4 increases neuronal excitability.
Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes
Background Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine learning (ML) and motor protein-related genes (MPRGs). Methods The prognosis relevance of the MPRGs in prostate cancer was analyzed by univariate Cox regression. Feature selection and model construction were performed using combinations of multiple machine learning algorithms. Model performance was assessed using receiver operating characteristic curve and C-index. Patients were stratified into high- and low-risk groups based on the risk signature, and comparisons of BCR incidence, gene expression profiles, immune cell infiltration patterns, and drug sensitivity were conducted between these groups. The gene expression of MPRGs were validated in vitro. Results Among 120 MPRGs, 17 were differentially expressed, of which 8 were significantly associated with BCR. A novel risk scoring system using a StepCox[forward] + Ridge model based on these 8 MPRGs effectively stratified patients into two different risk groups, and patients with high riskscores had significantly higher BCR rates than those with lower riskscores. Enrichment analysis revealed upregulation of inflammation response, EMT, hypoxia, and estrogen response pathways in the high-risk category, while mitotic spindle, G2M checkpoint, and E2F targets were downregulated. The MPRG-derived risk score correlated positively with M2 macrophage infiltration and ngatively correlated with CD4 T cells and mast cells, and the high-risk category showed higher sensitivity to drugs like cisplatin and bicalutamide. The final nomogram based on MPRGs-derived signature and T stage provided an excellent tool for predicting BCR. In vitro experiments further validated that the expression trends of MPRGs in the risk signature were consistent with the bioinformatics analysis results. Conclusion This study developed a novel MPRG-derived risk signature that effectively predicts BCR in prostate cancer, offering valuable insights for clinical management and personalized treatment strategies.
Wild bats hunt insects faster under lit conditions by integrating acoustic and visual information
Animals can improve their decision-making abilities by integrating information from multiple senses, which is especially beneficial when living in fluctuating environments. However, understanding how wild predators may use multimodal sensing when hunting prey in split-second interactions remains largely unexplored. As nocturnal hunters, bats rely on echolocation to navigate and to locate evasive prey, yet they have retained functional vision, despite the associated costs. We therefore hypothesized that bats use vision to enhance sensory redundancy when commuting and tracking small insects. To test this, we equipped 21 wild common noctule bats ( Nyctalus noctula ) with high-resolution light, sound, and motion sensor loggers and measured their echolocation and movements while commuting and foraging in both dark and lit environments. When commuting, the bats maintained consistent echolocation sampling across light levels. However, when tracking prey in illuminated environments, the bats emitted calls with half the rate and with 7 dB higher call levels compared to in dark conditions, but at much faster approach speeds (from 5.2 in darkness to 7.9 m/s in lit conditions). This suggests that, in illuminated environments, hunting bats integrate acoustic and visual information, resulting in more efficient approaches to prey. Our findings demonstrate how a wild sensory specialist predator uses multimodal sensing to hunt efficiently in highly dynamic resource landscapes.