This article observes fish acoustic telemetry RMS acceleration data as stochastic process system. The system is tested for stationarity and differentiability. In order to access continuation of the stationary stochastic system, it is considered Lagrange mean theorem and Wiener-Levy process. The continuation is tested on transition matrix of probability distributions between cause and product state levels of acceleration. The Wiener-Levy process estimates the diffusion system as the transition distributions with finer resolution, while preserving the mean value and standard deviation of the original distribution. The approach may help to simulate and understate the behavior of fish in captive.
This study evaluates the capabilities of artificial intelligence (AI) in scientific research, focusing on the prediction of breast cancer recurrence using MRI data. Here, ChatGPT 4o was employed to propose complementary analytical approaches, demonstrating potential in generating valid statistical methods such as PCA, K-means, Random Forest, and Cox models. However, significant limitations were observed in its ability to perform numerical calculations accurately. For instance, discrepancies in silhouette coefficients and variable importance rankings highlighted the AI’s unreliability in executing complex analyses without supervision. While ChatGPT 4o proved useful for hypothesis generation and experimental design, its lack of transparency and consistency underscores the need for human oversight. These findings suggest that AI can improve scientific research but is not yet ready to replace human expertise in rigorous, unsupervised analysis. The study calls for a balanced integration of AI tools, emphasizing ethical and methodological considerations in the evolving landscape of automated research.
Scientific research and practice of sports selection confirm the importance of peak height velocity (PHV) and maturity offset (MO) for the development of physical fitness and training programs for young athletes. This requires a method for assessing these indicators that combines maximum validity, ease of use, and confirmed results of testing in wide groups of athletes. In this paper, we aimed to test the interchangeability of two empirical equations for assessing maturity: Mirwald’s equation, created in 2002 and requiring measurements of standing height, body weight, sitting height, and leg length, and Moore’s equation, introduced in 2015 and requiring only standing height to be measured. The study involved 314 healthy non-athletic boys aged 13 to 17 years and 403 football players aged 8 to 18 years. Athletes were divided into 3 age groups: school childhood, puberty, and adolescence. All volunteers had their standing height, sitting height, leg length, and body weight measured. Based on the obtained data, peak height velocity (PHV) and maturity offset were calculated using Mirwald’s equation and Moore’s equation. Bland-Altman analysis was used to assess the consistency of these maturation parameters. The use of the simpler Moore’s equation leads to underestimation of MO and overestimation of age of PHV by an average of 2 years in subjects from all groups. The maximum error (3 years) of this method was found in adolescents aged 15 to 17 years. Replacing Mirwald’s equation with Moore’s equation for the purpose of simplification may lead to false positive predictions regarding age of PHV and overestimation of physical capabilities in a given athlete.
Brain gliomas, including astrocytomas, oligodendrogliomas, and mixed gliomas, present diagnostic and prognosis challenges. This study leverages RNA-Seq data obtained from TCGA database and machine learning approaches to distinguish between these glioma subtypes and tumor grades. By combining differential expression analysis with feature selection methods such as minimum Redundancy Maximum Relevance (mRMR) and Random Forest, implemented via the KnowSeq package, we identified compact and informative gene expression signatures. These signatures demonstrated strong potential for improving the distinction between glioma types and grades, yielding promising results. The best binary classification model for glioma subtype achieved an average accuracy of 84.15
Metagenomics has revolutionized the study of microbial communities by facilitating direct genetic analysis of environmental samples. This article proposes a novel metagenomic pipeline that integrates alignment-free k-mer screening using Mash Screen with precise approximate mapping via MashMap. The pipeline dynamically constructs targeted reference databases, reducing computational overhead while enhancing representativeness. Taxonomic assignments are strengthened by a weighted strategy based on alignment coverage and confidence scoring. Results demonstrate excellent F1 scores, particularly for prokaryotes and viruses, with scores exceeding 0.95 across all taxonomic levels. The pipeline processes most datasets in under 60 min with a memory footprint of approximately 5.82 GB, making it suitable for high-throughput analyses. By addressing the limitations of existing tools, this pipeline offers a scalable, accurate and user-friendly solution for the analysis of microbial communities, advancing our understanding of complex ecosystems and their functional potential.
