Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.

Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture.

Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior.

Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in adult zebrafish. Using a dataset comprising video recordings of unstressed and pre-stressed zebrafish, we extracted features such as total inactivity duration/immobility, time spent at the bottom, time spent at the top and turn angles (large and small).

Bioinspired Hydrophobicity via Temperature-Induced Phase Separation of Beeswax: A Pathway for Developing Cellulose Nanofiber-Based Adsorbents for the Removal of Conventional Tetracycline Tablets.

Cellulose nanofiber-based aerogels (CNFAs) hold immense promise across diverse fields, but their innate hydrophilicity and structural fragility in water have constrained their utility in water purification. This study introduces a green approach to induce hydrophobicity into CNFAs via thermally induced phase separation (TIPS) of beeswax, which was adhered to the nanofiber by hydrogen bonding and hydrophobic-hydrophobic interactions. The fabricated aerogel was characterized by using FTIR, SEM, XRD, TGA, contact angle, BET, and compression test.

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis.

Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in particular, serve as a valuable model for studying anxiety-like behavior, typically measured through the novel tank diving (NTD) assay.