Missing genotypes in DNA sequence data are an issue in many evolutionary genomic studies, especially of non-model organisms. It can be addressed using genotype imputation. However, algorithms that do not require additional genotype data as reference for imputation, which is often not available for non-model taxa, and are able to work with large whole-genome data sets are scarce. Therefore, we developed a new algorithm called GenoPop-Impute, which imputes the whole genome in separate batches and employs a random forest algorithm for imputation of correlated data sets. The batch-wise approach utilizes linkage disequilibrium to increase imputation accuracy and allows computational parallelization and thus efficiency. Tests on simulated data demonstrate that linkage disequilibrium between SNPs has a positive effect on imputation accuracy, due to correlation that originated in a shared evolutionary history. In comparison to two alternative algorithms, GenoPop-Impute is more accurate and is the only one computationally applicable to data sets of whole genomes. In addition, we found that GenoPop-Impute also increases the accuracy of commonly estimated population genomic metrics and mitigates biases due to missing data in demographic modeling experiments. We conclude that genotype imputation can be a valuable tool for evolutionary genomic studies of non-model taxa and that GenoPop-Impute is a highly suitable algorithm for this.

Trait-based approaches offer valuable perspectives for vegetation classification, but functional traits struggle to capture resource allocation among competing plants, showing limitations across scales. This study aimed to introduce plant architecture to enhance trait-based vegetation classification. From 2021 to 2023, 32 plots of Coastal Dwarf Forests (CDF) and Coastal Normal Forests (NCDF) along China’s eastern coast were surveyed. Their community characteristics were quantified, and classification and clustering models assessed the advantages of plant architecture in distinguishing these communities. The results indicated plant architecture traits are more critical for distinguishing different community types than leaf-based functional traits. Additionally, plant architecture traits are effective in clustering plant associations within the same community type. This is because plant architecture traits are closely linked to habitat, phylogeny, and community structure, providing a comprehensive description of vegetation, while functional traits reflect only partial habitat information related to soil nutrients. Our findings underscore the importance of plant architecture in optimizing trait-based vegetation classification and suggest that variations in the plasticity of plant architecture traits may support the classification of CDF as a distinct vegetation unit.

In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analyzing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions, and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher-order interactions---that is, the interactions involving more than two brain regions or neurons. While methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework.

A coupled medium-fidelity drivetrain model is developed and implemented in OpenFAST for a 10-MW land-based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost, useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is extended to a monopile offshore wind turbine and used to investigate sensitivity of predicted main bearing rolling contact fatigue to different synthetic turbulence models. Large eddy simulations (LES) intentionally targeting stable, neutral, and unstable atmospheric conditions at below-, near- and above-rated wind speeds, were used as a reference. The turbulence models recommended by IEC, the Mann spectral tensor model and the Kaimal spectral model with exponential coherence, were fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb, based on LES data, was applied in the aero-hydro-servo-elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10 and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue with up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

Trophic interactions are crucial for maintaining ecological balance. In cities, avian predation has been shown to be particularly relevant due to its effects on community structure, pest control, and nutrient cycling. As humanity relies upon ecosystem services for survival and 70% of the human population is projected to live in cities by 2050, understanding the effects of urbanization on avian predation is imperative. This study investigates the effects of urban microclimates, impacted by impervious surface and green/blue infrastructure, and human-induced disturbances, on avian predation in urban areas. Plasticine caterpillars were placed in Quercus robur trees to assess the avian predation rate. The analyses evaluated the impact of artificial lighting at night, human population density, noise pollution, vegetation, and water bodies on predation rates during two months in the city of Amsterdam. Results indicated a substantial increase in predation during the second month, likely caused by an increase in naïve fledglings or elevated ambient temperatures. Noise pollution consistently emerged as the most reliable predictor of predation, consistently leading to a reduction in predation rates, possibly due to avoidance behaviour. Other predictors exhibited substantial temporal and spatial variability. Artificial lighting at night increased predation in the first month, suggesting that insectivorous birds predate near illuminated areas. The diminished effect of artificial lighting in the second month may be attributed to the increased daylength. During the second month, all predictors negatively affected predation, supporting the increasing disturbance hypothesis. These findings underscore the complex relationship between urban factors and avian predation, highlighting the need for mitigation efforts in urban planning.

The integration of big data into nephrology research has opened new avenues for analyzing and understanding complex biological datasets, driving advancements in personalized management of cardiovascular and kidney diseases. This paper explores the multifaceted challenges and opportunities presented by big data in nephrology, emphasizing the importance of data standardization, sophisticated storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight the potential of predictive and prescriptive analytics, as well as the application of large language models (LLMs), in improving clinical decision-making and enhancing the accuracy of disease predictions. The use of high-performance computing (HPC) is also examined, showcasing its critical role in processing large-scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research.

Understanding population demography of threatened species and how they vary in relation to natural and anthropogenic stressors is essential for effective conservation. We used a long-term photographic capture-recapture dataset (1993 – 2020) of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the highly urbanised Adelaide Dolphin Sanctuary (ADS), South Australia, to estimate key demographic parameters and their variability over time. These parameters were analysed in relation to environmental variables used as indicators of local and large-scale climatic events. Our findings indicate that apparent survival was high (0.98-0.99) and did not vary seasonally. Estimates of abundance were not directly related to environmental variables but were linked to seasonal temporary emigration. Abundance peaked in summer with an average of 85.37 dolphins (SD = 30.23), and was lowest in winter, with 68.57 (SD = 24.70) individuals. Site fidelity at the population level was low, but lagged identification rates revealed a resident population of approximately 28 individuals. Trend analysis suggests high levels of dolphin abundance and persistence of the population over decades despite significant urbanisation, but numbers have declined in recent years. Further research is needed to understand the cumulative impacts leading to this population decline and to assess its future viability under different management scenarios. Conservation strategies aimed at increasing reproductive rates and promoting connectivity to adjacent waters are likely to be more effective in reversing population declines.

not-yet-known not-yet-known not-yet-known unknown Abstract Few studies have investigated blood glucose levels and complication management in elderly patients with type 2 diabetes (T2D) at community hospitals in China. The objective of this study was to investigate the factors influencing blood glucose control in elderly patients with T2D and assess the adherence of doctors in community hospitals to the latest diabetes guidelines regarding the use of glucose-lowering medications. This study involved 1150 elderly patients (age≥65 years) with diabetes to assess blood glucose control, complications management, and adherence of medication according to the guidelines of American Diabetes Association. To evaluate blood glucose control, different glycated haemoglobin targets were assigned according to patient characteristics and health status (including comorbidities and cognitive status). Univariate and multivariate logistic regression analyses were used to investigate the factors affecting glucose control. Among the 1150 participants, 351 (30.52%) had poor glucose control. Frailty (odds ratio [OR]:0.393; 95% confidence interval [CI]:0.195-0.789; P=0.009), male sex (OR:1.472; 95% CI:1.131-1.915, P=0.004), and insulin treatment (OR:4.364; 95% CI:3.151-6.042; P<0.001) were significantly associated with poor blood glucose control in patients treated with glucose-lowering medications. The proportion of patients without frailty with poor control was higher than those with frailty (31.28% vs. 17.46%, respectively). In conclusion, blood glucose control in elderly Chinese patients with T2D is poor and influenced by frailty, sex, and insulin treatment. Hence, it is crucial to enhance the implementation of sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists in Chinese community hospitals and strengthen the differentiation of updated guidelines within these healthcare settings.

Company earnings calls are crucial events that provide transparency into a company’s financial health and prospects. Large language models (LLMs) offer a promising approach to automatically generate the first draft of earnings call scripts from financial data and past examples. We evaluate two methods: 1) Few-shot prompt engineering with a state-of-the-art model, and 2) Fine-tuning a language model on earnings call transcript data. Our results indicate both approaches can produce coherent scripts covering key metrics, updates, and guidance. However, there are trade-offs in comprehensiveness, hallucinations, writing style, ease of use, and cost. We discuss the pros and cons of each method to guide practitioners on effectively leveraging large language models for this financial communication task.

How to treat recurrent electrical storm after failed acute myocardial

Key Clinical MessageA 42-year-old female with left nasal blockage, facial numbness, upper palate pain, and headache was diagnosed with fungal ball sinusitis via CT scan. Refusing surgery, she opted for Ayurveda. Comprehensive treatment led to complete symptom resolution and a clear CT scan at three months.

In the face of global climatic changes, exploring how plant adaptation and responses to drought vary among different regions is crucial to understand and predict its geographic distribution. In this study, to explore the drought adaptation and responses of the dominant species in the semi-arid Eurasian Steppes and their differences among the species distribution regions in terms of growth, physiology and RNA-seq transcriptome, Stipa krylovii was chosen as the study object and a seed source (3 regions: eastern, middle and western regions) × soil moisture treatment (3 treatments: control, light drought and heavy drought) two-factor experiment was conducted. (1) Four growth traits for individuals from the western region were significantly lower than those from the other two regions. By Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis on gene expressions of individuals from each treatment, unique enriched pathways were found mainly under heavy drought. (2) The decrease in the number of tillers with the increasing drought was much lower for individuals from the western region than those from the other two regions. By KEGG functional enrichment analysis on differentially expressed genes (DEGs) between heavy drought treatment vs control, enrichment pathways of individuals from the eastern and middle regions mainly regulated energy metabolism and metabolism of other amino acids; and those from the western region regulated biosynthesis of other secondary metabolites and carbohydrate metabolism. (3) Clustering analysis based on gene expressions separated the western region from the other two regions under the same soil moisture treatment. This study indicates that drought adaptation and response mechanisms of S. krylovii vary among different regions, especially between individuals from the western region and the other two regions. These findings are essential for us to understand the adaptive evolution of population and germplasm resource protection for this important species in semi-arid grasslands.

Environmental and individual experiences can result in immediate and persistent changes in behaviour. Often, such effects are also sex-dependent. Interspecific interactions can be one of the most important environments an individual faces. Such social interactions are expected to affect a suite of behavioural traits and their correlations. Here, we used Drosophila melanogaster and high-throughput automated behavioural phenotyping to determine how social environment (group mixed sex, group single sex, and social isolation) and sex interact to affect basic behaviours (exploration, movement within a y-maze, and habituation to a startle) that likely underlie more complex behaviours such as mate searching and foraging. We show that such behaviours and some behavioural correlations are indeed context- and sex-dependent. Males tended to show greater exploration, while females were more likely to show a habituation response to startle. Males and females from the mixed sex and isolated treatments showed opposite exploratory behaviour in the Y-maze, and social treatment interacted with sex to affect the rate of habituation to a startle. Females also tended to have slightly stronger trait correlations compared to males. These results show that social environment and sex can play a significant role in shaping behaviour in Drosophila melanogaster. Our study provides insights into how the type of social stimulation and sex can interact to affect behaviours that are important in forming critical behaviours related to foraging and mate searching.

Objective: Studies on lung metastases of extrahepatic cholangiocarcinoma (ECC) are rare. This study aims to fill this gap by analyzing the influencing factors, prognosis, and treatment effects of ECC lung metastases, and to provide clinical guidance for their diagnosis and treatment. Methods: We retrieved data from the Surveillance, Epidemiology and End Results (SEER) database for patients with metastatic ECC (stage M1) from 2018 to 2021. The study analysed these characteristics using descriptive statistics. To calculate hazard ratios, multivariate COX regression analyses were performed. Overall survival (OS) was estimated using the Kaplan-Meyer method, and the survival of patients between groups was compared using the log-rank test. Results: A total of 762 people participated in the study, 50.4 per cent of whom were men. At the time of diagnosis, 17.8% of patients had pulmonary metastases. 52.5% received chemotherapy. Multivariate COX analysis identified lung metastases as a significant risk factor for death from metastatic ECC (HR 1.64, CI 1.32-2.03, P<0.001). Chemotherapy (HR 0.20, CI 0.17-0.25, P<0.001) and female (HR 0.80, CI 0.67-0.94, P=0.008) were associated with a better prognosis. Therefore, we further compared the prognosis and chemotherapy outcomes of male and female patients with ECC lung metastases. The median survival of male patients with and without lung metastases was 2 and 5 months, respectively (P=0.016), whereas there was no significant difference in female patients (P=0.19). Regardless of gender, patients with lung metastases had significantly worse OS even after receiving chemotherapy (P=0.0065 in the male group and P=0.0075 in the female group). Regardless of gender, patients with lung metastases who did not receive chemotherapy had significantly shorter overall survival than those who received chemotherapy. Not receiving chemotherapy vs receiving chemotherapy (male: 1 month vs 5 months, P<0.0001; female: 2 months vs 9 months, P<0.0001). Conclusion: Pulmonary metastases are an important prognostic factor in ECC and are associated with poorer survival, especially in male patients. In all patients with metastatic ECC, chemotherapy is associated with prolonged survival. Therefore, preventive measures and effective control of pulmonary metastases (such as chemotherapy), especially in male patients, may improve survival in patients with ECC.

The escalating energy demand in today’s world underscores the need for a consistent and continuous power supply. There is a push towards promoting renewable energy sources to address this demand because of their positive impact on the environment and their widespread accessibility. Particularly in Rwanda, for the radial distribution network, the reliability improvement in electricity supply requires rectifications of power system infrastructure, including transmission, distribution system, and an increase in energy generation capacity. To evaluate the influence of renewable energy sources (RES) on the reliability of Rwanda’s power grid, Solar Photovoltaic (PV) systems combined with Battery Energy Storage Systems (BESS) are introduced into the Gatumba and Ntongwe feeders. For reliability enhancement of the grid to the feeders where solar PV system and BESS are connected, protection switches are configured so that the PV systems and BESS supply the load demand in the absence of the grid, thereby increasing the generation capacity and reducing interruption frequency and duration. Simulation results from DigSILENT and PV*SOL simulation tools showed that by integrating solar PV systems with BESS to Gatumba and Ntongwe feeders, the system reliability ameliorates by 71.6% and 95.5% for system average interruption frequency and duration, respectively.

Influenza viruses continue to cause severe morbidity and remain a continuous pandemic threat. Virus challenge studies in animal models are performed during preclinical vaccine development. Improved methods are needed to improve data quality in animal models to reduce both animal stress and the animal numbers. By evaluating automated activity monitors as a tool for Influenza vaccine development, we demonstrate that activity in mice declines after viral challenge and that this reduction in activity can be prevented by vaccination. Notably, the decline in activity precedes weight loss and could thus provide an objective and early endpoint in Influenza virus challenge studies.

In this study, we will investigate how expectations regarding the relationship between positivity and negativity may be informing election choices of leftist, left-wing, progressive, Democratic, and liberal-identifying individuals. We also aim to refine understanding of decision-making processes by collecting information about participants’ social identities (e.g., race, class, gender), experiences (e.g., activism participation), depression symptoms, and beliefs (e.g., political cynicism, social justice orientation). We hypothesize that participants likely to devalue reward will (I) report having been less likely and happy to vote for Biden-Harris, but more likely and happy to vote for Harris and Harris-Walz; (II) report lower felt negativity while imagining Trump-Vance winning; (III) report having been more likely to vote third-party before Biden stepped down; and (IV) that political cynicism will moderate reward devaluation. We outline our plans to assess our hypotheses via moderation analyses and nonparametric correlations, as well as propose exploratory analyses.

The loss and fragmentation of habitats caused by anthropogenic activities in the last decades has affected foraging habitat quality and, therefore, foraging success (food quantity, quality and foraging range) of many animals, including many pollinators. Honeybees (Apis mellifera) are important pollinators of many plant species, and habitat change has also affected their ability to collect the resources they need to maintain the colony. Two important biological traits that might be affected by land use are body size and fat stores, which have the potential to affect body condition, and therefore success, and colony health. However, few studies have investigated these traits in different landscapes and different times of year. We surveyed 47 sites in three different landscape types (agriculture, urban and mixed habitats) in south-western Germany. We measured honeybee body size, wing wear and analysed body fat quantity and composition using GC-MS in the spring, summer and autumn. We found that summer honeybees were smaller in urban and mixed habitats, they showed the greatest wing wear, but they had 18.4-21.3% larger fat stores compared to agricultural sites. Bees in agricultural habitats experienced a drop in fat stores in summer, while body size remained unaffected. In autumn, just before honeybees enter the inactive winter period, bees in urban and mixed areas experienced a drop in fat stores. Wing length decreased from spring to autumn irrespective of habitat type. Our findings indicate that bees in agricultural settings experience physiological challenges in a central European region in summer, possibly because urban and mixed habitats provide better nutritional conditions during summer. Our findings, thus, confirm that honeybees undergo morphological and physiological changes in response to land use and season, which could impact their physiological condition and winter survival.