Classification of Behaviour in Conventional and Slow-Growing Strains of Broiler Chickens Using Tri-Axial Accelerometers.

Behavioural states such as walking, sitting and standing are important in indicating welfare, including lameness in broiler chickens. However, manual behavioural observations of individuals are often limited by time constraints and small sample sizes. Three-dimensional accelerometers have the potential to collect information on animal behaviour. We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25 to 49 days old) were used to train and test the algorithm, and unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains, the random forest model classified behaviours with very good accuracy (92%) and specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain, the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%). We therefore successfully used a random forest model to automatically detect three broiler behaviours across four different strains and different ages using accelerometers. These findings demonstrated that accelerometers can be used to automatically record behaviours to supplement biomechanical and behavioural research and support in the reduction principle of the 3Rs.

G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand-receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

Dominant role of the non-forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery.

The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low-frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre-El Niño levels by the end of 2020, resulting in an AGC sink of 0.18 0.14 0.26 $$ {0.18}_{0.14}^{0.26} $$ Pg C year-1 during 2014-2020. This sink was dominated by strong AGC increases ( 0.61 0.49 0.84 $$ {0.61}_{0.49}^{0.84} $$ Pg C year-1) in non-forest woody vegetation during 2016-2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non-forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

Urban forest species selection for improvement of ecological benefits in Polish cities - The actual and forecast potential.

Trees in cities perform important environmental functions: they produce oxygen, filter pollutants, provide habitat for wildlife, mitigate stormwater runoff, and reduce the effects of climate change, especially in terms of lowering temperatures and converting carbon dioxide from the atmosphere into stored carbon. Generally, to increase the environmental benefits of urban forests, the number of trees is increased, directly influencing the canopy coverage. However, little is known about potential of modifying the species composition of urban tree communities in order to increase ecological benefits. Planting and managing trees to increase canopy is particularly challenging in city centres, where the dense, often historic infrastructure of buildings and roads do not allow for a significant increase in greenspace. Estimations of canopy cover obtained through i-Tree Canopy analysis unveiled significant potential to increase canopy cover in historical urban areas in Polish cities from 15-34% to 31-51%. This study models the ecological benefits of urban forests in Polish cities, focusing on how different species compositions can enhance environmental functions such as carbon sequestration and pollution filtration. Two main scenarios were analyzed: one involving the addition of trees based on the most common species currently planted ("standard option" SO), and another incorporating changes to the species composition to enhance ecological benefits ("city specific option" SCO). Acer platanoides (14.5%) and Tilia cordata (11.45%) were the most frequently species of Polish cities. Betula pendula, Quercus robur, Robinia pseudoacacia, Fraxinus excelsior, Acer pseudoplatanus, Aesculus hippocastanum and Acer campestre were also common species in urban forest communities (up to 5%). The diverse range of tree species in Polish cities contributes significantly to the overall carbon sequestration potential. The results suggest that modifying species composition could significantly increase carbon sequestration rates by 47.8%-114% annually, with the city specific option (SCO) being the most effective in enhancing carbon sequestration potential. This highlights the importance of strategic species selection in urban forestry practices to maximize environmental benefits and mitigate climate change effects.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Dynamic UAV Deployment Scheme Based on Edge Computing for Forest Fire Scenarios.

This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.

Using Machine Learning for Non-Invasive Detection of Kidney Stones Based on Laboratory Test Results: A Case Study from a Saudi Arabian Hospital.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

A BERT-GNN Approach for Metastatic Breast Cancer Prediction Using Histopathology Reports.

Metastatic breast cancer (MBC) continues to be a leading cause of cancer-related deaths among women. This work introduces an innovative non-invasive breast cancer classification model designed to improve the identification of cancer metastases. While this study marks the initial exploration into predicting MBC, additional investigations are essential to validate the occurrence of MBC. Our approach combines the strengths of large language models (LLMs), specifically the bidirectional encoder representations from transformers (BERT) model, with the powerful capabilities of graph neural networks (GNNs) to predict MBC patients based on their histopathology reports. This paper introduces a BERT-GNN approach for metastatic breast cancer prediction (BG-MBC) that integrates graph information derived from the BERT model. In this model, nodes are constructed from patient medical records, while BERT embeddings are employed to vectorise representations of the words in histopathology reports, thereby capturing semantic information crucial for classification by employing three distinct approaches (namely univariate selection, extra trees classifier for feature importance, and Shapley values to identify the features that have the most significant impact). Identifying the most crucial 30 features out of 676 generated as embeddings during model training, our model further enhances its predictive capabilities. The BG-MBC model achieves outstanding accuracy, with a detection rate of 0.98 and an area under curve (AUC) of 0.98, in identifying MBC patients. This remarkable performance is credited to the model's utilisation of attention scores generated by the LLM from histopathology reports, effectively capturing pertinent features for classification.

Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach.

BACKGROUND: Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis. METHODS: A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment. RESULTS: The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676-0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors. CONCLUSIONS: The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.

Managing ecosystem services in oleaginous forests for bioenergy provision and climate change mitigation.

Oleaginous forests provide diverse ecosystem services, including timber, seed yield (a vital feedstock for biodiesel production), and substantial carbon savings. These carbon savings encompass carbon sequestration related to timber growth and carbon savings resulting from substituting fossil fuel with biodiesel. However, oleaginous forests are vulnerable to seed wasp attacks (disservice), which significantly threaten both seed yield and carbon savings. Using an integrated ecological-economic model that includes Faustmann's Land Expectation Value model and a pest damage control model, we aim to understand the intricate relationship among multiple ecosystem services and disservices of oleaginous forests. The results reveal four distinct phases contingent on varying pesticide application rates: the pesticide under-use phase, substitution phase, complementary phase, and over-use phase. Notably, a potential avenue to minimize pest damage is identified during the complementary phase by reducing the optimal rotation age at the expense of decreased carbon sequestration and bioenergy provision, posing a challenge to climate change mitigation. These findings have implications for formulating policies to manage conflicting ecosystem services of energy forests, offering valuable insights into the intersection of sustainable forest management and climate policy.

Can forests realize the carbon neutrality dream? Evidence from a global sample.

The enlarge in economic activities and the urban population at the global level has brought about an increase in the demand for energy, food, and natural resources, as well as an exacerbation in global climate change concerns. In this respect, it is important to ensure the balance between global climate change and global economic activities. Therefore, a wide literature has emerged that searches for alternative solutions to improve climate change and carbon dioxide (CO2) emissions. The majority of existing studies emphasize the importance of renewable energy sources in environmental improvement efforts. Few studies highlight the importance of forestation in environmental improvement efforts, highlighting the non-linear effects of forestation. To fill this gap, this study uses panel data from 181 countries between 1990 and 2022 and evaluates the non-linear impact of economic growth, forest extent, energy efficiency, and urban growth on per capita CO2 emissions using a dynamic panel threshold and dynamic panel quantile threshold methods. Furthermore, we extend the model and conduct robustness tests examining the non-linear threshold effects of renewable and non-renewable energy consumption on per capita CO2 emissions. Our findings provide pieces of evidence that forest extents are an alternative solution to renewable energy use and energy efficiency in environmental improvement efforts.

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty.

BACKGROUND: In total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty. METHODS: We conducted this prospective observational study using convenience sampling and collected data from 327 total joint arthroplasty patients in a tertiary hospital from March 4, 2023 to September 11, 2023. Of those, 229 patients were assigned to the training and 98 to the testing sets. The Chi-square, Mann-Whitney U, and t-tests were used for baseline analyses. The feature variables selection used the RF algorithms, and the decision tree model was trained on 299 examples and validated on 98. The sensitivity, specificity, recall, F1 score, and area under the curve (AUC) were used to test the model's performance. RESULTS: The RF algorithms identified the preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation as risk factors for IOH. The decision tree was grown to five levels with nine terminal nodes. The overall incidence of IOH was 42.13%. The sensitivity, specificity, recall, F1 score, and AUC were 0.651, 0.907, 0.916, 0.761, and 0.810, respectively. The model indicated strong internal consistency and predictive ability. CONCLUSIONS: The preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation could accurately predict IOH in total joint arthroplasty patients. By monitoring these factors, the clinical staff could achieve early detection and intervention of IOH in total joint arthroplasty patients.

A decision methodology for site-level ecosystem accounting.

The United Nations System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) framework is the international standard for ecosystem accounting. To date, application of SEEA EA has been predominantly at large scales, usually at landscape and national levels. However, many environmental management decisions are taken locally, in site-specific contexts. While the use of SEEA EA continues to develop at all scales, there is currently no widely endorsed methodology for employing SEEA EA at local scales, such as the site level. We present a methodology for developing site-level ecosystem accounts, describing the important decisions at each step of the process. We also provide two case studies that demonstrate the context-dependent nature of the decision-making process of ecosystem accounting at small scales. The two major challenges for site-level accounting are stakeholder engagement and data availability. As the use of SEEA EA continues to increase in policy and decision-making processes worldwide, there is a need for local-scale case studies that adapt this methodology across a broad range of contexts. Our case studies provide some of the first published examples of the application of SEEA EA at the site level and are intended to promote consistent implementation of ecosystem accounting across scales.

Towards accurate monitoring of water content in woody tissue across tropical forests and other biomes.

Forest ecosystems face increasing drought exposure due to climate change, necessitating accurate measurements of vegetation water content to assess drought stress and tree mortality risks. While Frequency Domain Reflectometry offers a viable method for monitoring stem water content by measuring dielectric permittivity, challenges arise from uncertainties in sensor calibration linked to wood properties and species variability, impeding its wider usage. We sampled tropical forest trees and palms in eastern Amazônia, to evaluate how sensor output differences are controlled by wood density, temperature and taxonomic identity. Three individuals per species were felled and cut into segments (total n = 262), within a diverse dataset comprising five dicotyledonous tree-and three monocotyledonous palm species on a wide range of wood densities. Water content was estimated gravimetrically for each segment using a temporally explicit wet-up/dry-down approach, and the relationship with the dielectric permittivity was examined. Woody tissue density had no significant impact on the calibration, but species identity and temperature significantly affected sensor readings. The temperature artefact was quantitatively important at large temperature differences which may have led to significant bias of daily and seasonal water content dynamics in previous studies. We established the first tropical tree and palm calibration equation that performed well for estimating water content. Notably, we demonstrated that the sensitivity remained consistent across species, enabling the creation of a simplified one-slope calibration for accurate, species-independent measurements of relative water content. Our one-slope calibration serves as a general, and species-independent standard calibration for assessing relative water content in woody tissue, offering a valuable tool for quantifying drought responses and stress in trees and forest ecosystems.

Clustering analysis for classifying fake real estate listings.

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

In situ forest with lycopsid trees bearing lobed rhizomorphs from the Upper Devonian of Lincheng, China.

The earliest forests in the Devonian were reported from only four localities worldwide. The tree lycopsids, sometimes as the primary elements of Devonian forests, had evolved several types of rooting systems. In recent years, we found and excavated a Late Devonian (Famennian, 374-359 Ma) lycopsid forest from Zhejiang Province, China. The fossil forest occurs at seven locations of Lincheng Town of Changxing County and mainly consists of in situ small tree lycopsid (Heliodendron longshanense gen. et sp. nov.) stems usually connected to lobed cormose rhizomorphs. The four short lobes of each rhizomorph often branch once and bear roots arranged radially. Allometry is observed between the trunk diameter of Heliodendron and the length of its rhizomorphic lobes, indicating that the trunk develops later than the rhizomorph in tree lycopsid plants. The Devonian witnessed the transformation from clastic nonlycopsid dominated forests to Carboniferous swampy forests dominated by giant lycopsid trees. These trees form a multigenerational community, as shown by the in situ preserved stems at various levels within the same area due to frequent sedimentation events.

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.

Objective: This study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model. Methods: We conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA). Results: Logistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit. Conclusion: This study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

A wingless fly on a winged mammal: host-parasite dynamics between Basilia travassosi (Diptera: Nycteribiidae) and Myotis lavali (Chiroptera: Vespertilionidae).

Nycteribiidae encompasses a specialized group of wingless blood-sucking flies that parasitize bats worldwide. Such relationships are frequently species- or genus-specific, indicating unique eco-evolutionary processes. However, despite this significance, comprehensive studies on the relationships of these flies with their hosts, particularly in the New World, have been scarce. Here, we provide a detailed description of the parasitological patterns of nycteribiid flies infesting a population of Myotis lavali bats in the Atlantic Forest of northeastern Brazil, considering the potential influence of biotic and abiotic factors on the establishment of nycteribiids on bat hosts. From July 2014 to June 2015, we captured 165 M. lavali bats and collected 390 Basilia travassosi flies. Notably, B. travassosi displayed a high prevalence and was the exclusive fly species parasitizing M. lavali in the surveyed area. Moreover, there was a significant predominance of female flies, indicating a female-biased pattern. The distribution pattern of the flies was aggregated; most hosts exhibited minimal or no parasitism, while a minority displayed heavy infestation. Sexually active male bats exhibited greater susceptibility to parasitism compared to their inactive counterparts, possibly due to behavioral changes during the peak reproductive period. We observed a greater prevalence and abundance of flies during the rainy season, coinciding with the peak reproductive phase of the host species. No obvious correlation was observed between the parasite load and bat body mass. Our findings shed light on the intricate dynamics of nycteribiid-bat interactions and emphasize the importance of considering various factors when exploring bat-parasite associations.