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An alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using Genetic Programming.
Environ Technol
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Centre for Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, India.
Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates.
View Article and Find Full Text PDFChromosome length genome assembly of the stone marten (Martes foina, Mustelidae): a new view on one of the cornerstones in carnivore cytogenetics.
J Hered
January 2025
Center for Evolutionary Hologenomics, The Globe Institute, The University of Copenhagen, 5A, Oester Farimagsgade, Copenhagen, 1353, Denmark.
The stone marten (Martes foina) is an important species for cytogenetic studies in the order Carnivora. ZooFISH probes created from its chromosomes provided a strong and clean signal in chromosome painting experiments and were valuable for studying the evolution of carnivoran genome architecture. The research revealed that the stone marten chromosome set is similar to the presumed ancestral karyotype of the Carnivora, which added an additional value for the species.
View Article and Find Full Text PDFClassification-based pathway analysis using GPNet with novel P-value computation.
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Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFInferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.
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Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM.
View Article and Find Full Text PDFParasitoid wasp venoms degrade imaginal discs for successful parasitism.
Sci Adv
January 2025
Life Science Center for Survival Dynamics, Tsukuba Advanced Research Alliance (TARA), University of Tsukuba, Tsukuba 305-8577, Japan.
Article Synopsis
- Parasitoid wasps, a highly diverse group of animals, use their venoms to manipulate the physiology of host larvae for their benefit.
- Researchers discovered that a specific wasp can cause the death and dysfunction of its host's tissue precursors, a process called imaginal disc degradation (IDD).
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