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ELife Jun 2024Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and...
Animals can use a repertoire of strategies to navigate in an environment, and it remains an intriguing question how these strategies are selected based on the nature and familiarity of environments. To investigate this question, we developed a fully automated variant of the Barnes maze, characterized by 24 vestibules distributed along the periphery of a circular arena, and monitored the trajectories of mice over 15 days as they learned to navigate towards a goal vestibule from a random start vestibule. We show that the patterns of vestibule visits can be reproduced by the combination of three stochastic processes reminiscent of random, serial, and spatial strategies. The processes randomly selected vestibules based on either uniform (random) or biased (serial and spatial) probability distributions. They closely matched experimental data across a range of statistical distributions characterizing the length, distribution, step size, direction, and stereotypy of vestibule sequences, revealing a shift from random to spatial and serial strategies over time, with a strategy switch occurring approximately every six vestibule visits. Our study provides a novel apparatus and analysis toolset for tracking the repertoire of navigation strategies and demonstrates that a set of stochastic processes can largely account for exploration patterns in the Barnes maze.
Topics: Animals; Maze Learning; Mice; Stochastic Processes; Spatial Navigation; Mice, Inbred C57BL; Male
PubMed: 38899521
DOI: 10.7554/eLife.88648 -
ELife Jun 2024Comprehensive biodiversity data is crucial for ecosystem protection. The mobile app, launched in Japan, efficiently gathers species observations from the public using...
Comprehensive biodiversity data is crucial for ecosystem protection. The mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million observations since 2019. Nonetheless, community-sourced data may exhibit spatial and taxonomic biases. Species distribution models (SDMs) estimate species distribution while accommodating such bias. Here, we investigated the quality of data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2000 records for accurate models (Boyce index ≥ 0.9), while blending the two data sources reduced this to around 300. The uniform coverage of urban-natural gradients by data, compared to traditional data biased towards natural areas, may explain this improvement. Combining multiple data sources better estimates species distributions, aiding in protected area designation and ecosystem service assessment. Establishing a platform for accumulating community-sourced distribution data will contribute to conserving and monitoring natural ecosystems.
Topics: Biodiversity; Animals; Smartphone; Japan; Conservation of Natural Resources; Mobile Applications; Ecosystem; Plants
PubMed: 38899444
DOI: 10.7554/eLife.93694 -
Kidney International Reports Jun 2024Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician's knowledge of etiologic associations existing... (Review)
Review
Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician's knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the clinician's knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (perspective #1), because the cause-specific hazards form the "building blocks" of CIFs. In addition, though we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, "what is the conditional probability of the patient receiving a DDKT, given that he or she would not previously experience one of the competing events (known as the cause-specific conditional failure probability)," only an appropriate estimator of this conditional type of cumulative incidence should be used (perspective #2). One suggested estimator, the well-known "one minus Kaplan-Meier" approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used).
PubMed: 38899174
DOI: 10.1016/j.ekir.2024.01.050 -
International Journal of... 2024Hepatocellular carcinoma (HCC) is the most common and fatal primary liver cancer. Genetic variants of DNA repair systems can reduce DNA repair capability and increase...
Hepatocellular carcinoma (HCC) is the most common and fatal primary liver cancer. Genetic variants of DNA repair systems can reduce DNA repair capability and increase HCC risk. This study aimed to examine, in Egyptian hepatitis C virus (HCV) patients, the relationship between the X-ray repair cross-complementing group 1 (XRCC1) rs1799782 single nucleotide polymorphism (SNP) and HCC susceptibility. We included 100 adult HCV-positive patients with HCC and 100 adult HCV-positive patients with liver cirrhosis as pathological controls. XRCC1 rs1799782 SNP genotyping was done in both groups using quantitative real-time PCR (qPCR). The distribution of genotypes in patients and controls was compared using several inheritance models. We found that the CT genotype, when analyzed under both the co-dominant (OR (95 % CI): 2.147 (1.184-3.893), = .012) and the over-dominant (OR (95 % CI): 2.055 (1.153-3.660), = .015) models, as well as the combined CT and TT genotypes under the dominant model (OR (95 % CI) of 1.991 (1.133-3.497), = .017), were associated with increased susceptibility to HCC. The frequency of the T allele was higher among HCC participants (32%) compared to those with cirrhosis (23.5%) and carrying the T allele increased the risk of HCC by 1.532 times, however, these associations did not reach statistical significance (-values >0.05). Moreover, the variant T allele was associated with worse clinical manifestations and laboratory results among the HCC group, but AFP levels were not affected significantly. Egyptians with XRCC1 rs1799782 SNP may have a higher risk of HCV-related HCC. More extensive multi-center prospective investigations must confirm this association.
Topics: Humans; Carcinoma, Hepatocellular; X-ray Repair Cross Complementing Protein 1; Liver Neoplasms; Male; Case-Control Studies; Egypt; Female; Polymorphism, Single Nucleotide; Genetic Predisposition to Disease; Middle Aged; Pilot Projects; Adult; Hepatitis C; Risk Factors; Genotype
PubMed: 38898405
DOI: 10.1177/03946320241265263 -
Scientific Reports Jun 2024High-resolution digital elevation models are commonly utilized for detecting and classifying landslides. In this study, we aim to refine landslide detection and...
High-resolution digital elevation models are commonly utilized for detecting and classifying landslides. In this study, we aim to refine landslide detection and classification by analyzing the geometry of landslides using slope and aspect, coupled with descriptive statistics up to the fourth central moment (kurtosis). Employing the Monte Carlo method for creating terrain topography probability distributions and ANOVA tests for statistical validation, we analyzed 364 landslides in Gorce National Park, Poland, revealing significant kurtosis differences across landslide types and lithologies. This methodology offers a novel approach to landslide classification based on surface geometry, with implications for enhancing scientific research and improving landslide risk management strategies.
PubMed: 38898211
DOI: 10.1038/s41598-024-65026-z -
Communications Biology Jun 2024Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene...
Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene sets within a list of gene effect sizes that represent experimental outcomes is an everyday task in life science that crucially depends on robust and sensitive statistical tools. We here present GOAT, a parameter-free algorithm for gene set enrichment analysis of preranked gene lists. The algorithm can precompute null distributions from standardized gene scores, enabling enrichment testing of the GO database in one second. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene list length and gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT identifies more significant GO terms as compared to current methods. GOAT is freely available as an R package and user-friendly online tool for gene set enrichment analyses that includes interactive data visualizations: https://ftwkoopmans.github.io/goat .
Topics: Algorithms; Gene Ontology; Humans; Gene Expression Profiling; Animals; Computational Biology; Software; Proteomics; Databases, Genetic
PubMed: 38898151
DOI: 10.1038/s42003-024-06454-5 -
Scientific Reports Jun 2024Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and...
Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.
Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.
PubMed: 38898067
DOI: 10.1038/s41598-024-64667-4 -
CoDAS 2024To describe and analyze auditory and academic complaints of students and employees of a federal public university.
OBJECTIVE
To describe and analyze auditory and academic complaints of students and employees of a federal public university.
METHODS
The study was carried out using a non-probabilistic. The EAPAC Scale with adaptations was used to fulfill the research objectives. It has 14 questions about complaints related to listening skills and 12 questions related to the academic environment. Descriptive data analysis was performed through the frequency distribution of categorical variables and Pearson's chi-square test was used for association analyses.
RESULTS
646 individuals aged between 17 and 67 years old participated in the research. The most prevalent complaints were academic difficulty related to memory, concentration, and planning, hearing and understanding speech in noise, and memorization of tasks that were only heard. There was an association with bidirectional statistical significance between academic and auditory complaints.
CONCLUSION
It was possible to observe that there is an association between auditory and academic complaints in adults, marked by the relationship between cognitive and auditory aspects. It is relevant that these factors are considered when performing assessments of Central Auditory Processing when intervening in patients with auditory complaints, and in student life.
Topics: Humans; Adult; Adolescent; Male; Female; Young Adult; Middle Aged; Aged; Auditory Perception; Self Concept; Students; Brazil; Surveys and Questionnaires; Universities; Cross-Sectional Studies
PubMed: 38896744
DOI: 10.1590/2317-1782/20242023098pt -
BioRxiv : the Preprint Server For... Jun 2024Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have...
Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (, Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor () was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
PubMed: 38895364
DOI: 10.1101/2024.06.01.596951 -
BioRxiv : the Preprint Server For... Jun 2024Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there...
MOTIVATION
Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants.
RESULTS
We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest.
AVAILABILITY
ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.
PubMed: 38895293
DOI: 10.1101/2024.05.31.596816