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Viral tolerance enabled by a bat-specific genomic tweak
Home cage-based insights into motor learning and strategy adaptation in a Huntington disease mouse model
Huntington disease (HD) is a genetic neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and neuropsychiatric symptoms. Assessing early motor skill deficits in HD mouse models is challenging with traditional behavioral tasks. This study uses a home cage-based lever-pulling task, PiPaw2.0, to evaluate motor learning in 6–7 months-old zQ175 knock-in HD mice in a more naturalistic environment. In this task, mice learn to pull a lever for a water reward, with the requirement to hold the lever within a specific goal range for a required hold time. As the mice improved, the required hold time increased, thereby gradually increasing the task demands. Both wild type (WT) and zQ175 mice initially showed similar task engagement, but zQ175 mice had significant deficits in adapting to increasing hold time. The WT mice refined their strategies over time, shifting from random to more precise lever pulls, while zQ175 mice failed to make this adjustment, maintaining erratic performance. Additionally, in group-housing WT mouse lever performance benefited from peer interactions, an effect absent in zQ175 mice. Post-task neural assessments revealed that WT mice developed experience-mediated synaptic plasticity in the left striatum (contralateral to lever-pulling paw), while zQ175 mice showed no significant changes, consistent with known corticostriatal plasticity impairments in HD mouse models. In conclusion, our findings demonstrate the effectiveness of group-housed, home cage-based assessments for evaluating motor learning and adaptation in HD mouse models. This study provides insights into the motor control and adaptive learning deficits in HD, emphasizing the value of automated home cage systems in advancing neurodegenerative disease research and highlighting the importance of peer influences on performance.
The Rise of Private Equity in Health Care — Not a Uniquely American Phenomenon
The composite governance mechanisms and sustainable economic performance of Pakistan’s textile industry
This study examines the influence of corporate governance mechanisms on the sustainable economic performance of the textile industry in Pakistan. Performance is measured by using a global reporting initiative (GRI 201–1), an economic performance approach. The composite corporate governance mechanism index (CGMI) construction consists of five sub-indices: board of directors, disclosure and transparency, audit committee, shareholder rights, and remuneration committee. The empirical analysis demonstrated the consistency of the fixed effect model through the Hausman model specification test for data from textile firms listed on the Pakistan Stock Exchange from 2008 to 2023. However, the investigation has invoked the Generalized Method of Moments (GMM) estimation model to check its robustness. The findings indicated the positive empirical relationship between the composite CGMI and sustainable economic performance, supported by modified theoretical logics: agency theory, stewardship theory, stakeholder theory, resource-based theory, and transaction-cost theory. The findings support policymakers, regulators, and managers in executing appropriate governance mechanisms with reduced agency cost and transaction costs and improve sustainable performance.
Effects of Food and Nutrition Insecurity on Global Health
A Nitrogen budget for Norway analysis of Nitrogen flows from societal and natural sources (1961–2020)
Nitrogen is a chemical element that is cycled through Earth’s lithosphere, hydrosphere, cryosphere and atmosphere, as well as a crucial component of the molecular machinery of life. Being an intrinsic part of the biosphere, the flow of nitrogen in a country can provide unique insights into the sustainability of a society. This study investigates how nitrogen is used in Norway between 1961 and 2020. Nitrogen inputs from atmospheric deposition, biological nitrogen fixation and from weathering, as well as from synthetic fertilizers are addressed. These sources of nitrogen are used by plants that form the basis of agriculture and forestry. Nitrogen, in the form of protein, is imported to sustain the Norwegian population and the production of animals in agriculture and fisheries. Agricultural livestock is used almost exclusively for domestic consumption, while fish captured and raised in aquaculture are the main sources of exported food. Even though petroleum and wood contain only a small proportion of nitrogen, due to the traded volume, a large amount of nitrogen contained in these goods is exported. Nitrogen is lost through sewage and manure leaching, as well as atmospheric emissions. These emissions are mostly in the form of nitrogen oxides and ammonia, which are released by burning fossil fuels and metabolizing animals, respectively.
Case 5-2025: A 30-Year-Old Woman with Headache and Dysesthesia
Effects of exogenous EBR on the physiology of cold resistance and the expression of the VcCBF3 gene in blueberries during low-temperature stress
The northern highbush blueberry variety ‘Duke’ was used as the test material, and different concentrations of 2,4-Epibrassinolide (EBR) (0, 0.2, 0.4, 0.6, and 0.8 mg·L-1) were applied during the bud expansion stage, with a second application administered at one-day intervals following the first. Samples were collected at the bud, flower, and fruit stages and subsequently treated with artificial low temperatures (2°C) after sampling. The effects of various concentrations of exogenous EBR on the physiological indices of cold resistance and the expression of the cold resistance gene VcCBF3 in blueberry buds, flowers, and young fruits were investigated through comprehensive evaluation and correlation analysis. The objective was to identify the optimal concentration of EBR to enhance the cold resistance of blueberries. The results indicate that: (1) Under low temperature stress, the contents of soluble sugar, soluble protein and proline increased, along with the activities of superoxide dismutase, peroxidase, and catalase. The expression of the VcCBF3 gene expression and the ascorbate-glutathione cycling system were up-regulated, and with the increase of EBR concentration, the expression of the VcCBF3 gene initially rose and then declined. The content of malondialdehyde and the production rate of superoxide anion radicals decreased, and with the increase of EBR concentration, the content of malondialdehyde first decreased and then increased. (2) Overall low temperature resistance, flowers > buds > young fruits. (3) Appropriate concentrations of exogenous EBR can effectively mitigate freezing damage in blueberries caused by low temperatures. A comprehensive evaluation and correlation analysis of each cold tolerance index and the expression of the VcCBF3 gene revealed that a treatment concentration of 0.4 mg·L-1 had the most significant mitigating effect among the sprayed EBR concentrations of 0, 0.2, 0.4, 0.6, and 0.8 mg·L-1.
CAR+ T-Cell Lymphoma after Cilta-cel Therapy for Relapsed or Refractory Myeloma
Learning based lower limb joint kinematic estimation using open source IMU data
Four-Electron-Transferred Pyrene-4,5,9,10-tetraone Derivatives Enabled High-Energy-Density Aqueous Organic Flow Batteries
Lower cardiorespiratory fitness is associated with an altered gut microbiome. The Study of Health in Pomerania (SHIP)
Abstract Sedentarism is characterized by low levels of physical activity, a risk factor for obesity and cardio-metabolic diseases. It can also adversely affect the composition and diversity of the gut microbiome which may result in harmful consequences for human health. While cardiorespiratory fitness (CRF) is inversely and independently associated with cardiovascular risk factors and diseases and all-cause mortality, the relationship between low CRF and the gut microbiome is not well known. A total of 3,616 individuals from two independent population-based cohorts of the Study of Health in Pomerania (SHIP-START and SHIP-TREND) performed standardized, symptom-limited cardiopulmonary exercise testing (CPET) and had faecal samples collected to determine gut microbiota profiles (16S rRNA gene sequencing). We analysed cross-sectional associations of CRF with the gut microbiome composition controlling for confounding factors. Lower CRF was associated with reduced microbial diversity, loss of beneficial short-chain fatty acid producing bacteria (i.e. Butyricoccus, Coprococcus, unclassified Ruminococcaceae or Lachnospiraceae) and an increase in opportunistic pathogens such as Escherichia/Shigella, or Citrobacter. Decreased cardiorespiratory performance was associated with a gut microbiota pattern that has been previously related to a proinflammatory state. These associations were independent of body weight or glycemic control.
DNL-17: A Small-Pore Aluminophosphate in ABC-6 Family with 24 Stacking Layers Unraveled by Three-Dimensional Electron Diffraction
A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and imbalanced gene microarray data
Highly Water-Soluble Polyether-Based Viologen Negolytes for Aqueous Redox-Flow Batteries
A dynamic cobweb resistance network solution based on a structured zeroing neural network and its applications
Connectomic analysis of taste circuits in Drosophila
Abstract Our sense of taste is critical for regulating food consumption. The fruit fly Drosophila represents a highly tractable model to investigate mechanisms of taste processing, but taste circuits beyond sensory neurons are largely unidentified. Here, we use a whole-brain connectome to investigate the organization of Drosophila taste circuits. We trace pathways from four populations of sensory neurons that detect different taste modalities and project to the subesophageal zone (SEZ), the primary taste region of the fly brain. We find that second-order taste neurons are primarily located within the SEZ and largely segregated by taste modality, whereas third-order neurons have more projections outside the SEZ and more overlap between modalities. Taste projections out of the SEZ innervate regions implicated in feeding, olfactory processing, and learning. We analyze interconnections within and between taste pathways, characterize modality-dependent differences in taste neuron properties, identify other types of inputs onto taste pathways, and use computational simulations to relate neuronal connectivity to predicted activity. These studies provide insight into the architecture of Drosophila taste circuits.
N-Protonated Acridinium Catalyst Enables Anti-Markovnikov Hydration of Unconjugated Tri- and Disubstituted Olefins
Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework
Abstract Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.