AIMC Topic: Animals

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Integrating large-scale single-cell RNA sequencing in central nervous system disease using self-supervised contrastive learning.

Communications biology
The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-...

Severe degranulation of mesenteric mast cells in an experimental rat mammary tumor model.

Turkish journal of medical sciences
BACKGROUND/AIM: Breast cancers are one of the most common cancers in women and are responsible for many deaths worldwide. Mast cells are inflammatory cells. Their role in cancers is controversial, and there is limited data on systemic mast cell activ...

Accelerating segmentation of fossil CT scans through Deep Learning.

Scientific reports
Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably ext...

Augmenting interpretation of vaginoscopy observations in cycling bitches with deep learning model.

BMC veterinary research
Successful identification of estrum or other stages in a cycling bitch often requires a combination of methods, including assessment of its behavior, exfoliative vaginal cytology, vaginoscopy, and hormonal assays. Vaginoscopy is a handy and inexpensi...

A Comparison of Three Automated Root-Knot Nematode Egg Counting Approaches Using Machine Learning, Image Analysis, and a Hybrid Model.

Plant disease
spp. (root-knot nematodes [RKNs]) are a major threat to a wide range of agricultural crops worldwide. Breeding crops for RKN resistance is an effective management strategy, yet assaying large numbers of breeding lines requires laborious bioassays th...

Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region.

Scientific reports
This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. T...

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Molecular neurobiology
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...

Development of a novel prognostic signature derived from super-enhancer-associated gene by machine learning in head and neck squamous cell carcinoma.

Oral oncology
Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and progression. SEs have been demonstrated as novel promising diagnostic/prognostic biomarkers and therapeutic targets across multiple human cancers. He...

Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.

The Journal of infection
OBJECTIVES: Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are commonly found in...

Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Sensors (Basel, Switzerland)
Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively...