Imaging technologies for non-invasive detection of insect pest infestations in stored food grains: a review - B. Keerthana; G. Preetha; Umapathi Krishnamoorthy; V. R. Saminathan; T. Eevera; N. Anandaraja; J. Malathi (Journal of Plant Diseases and Protection, 2026)

Gonadal Stage Identification and Oocyte Size Measurement Using DL Integrated Ultrasonography in Asian Sea Bass Lates calcarifer (Bloch, 1790) - Thakkarajan Senthil Murugan; Dani Thomas; Aritra Bera; Marappan Makesh; R. Annie Uthra; J. Preetha Roselyn; Samiappan Dhanalakshmi; Saad Yunus Sait; Sani Meenakshi; Selvam Thamizhazhagan; Govindarajan Thiagarajan; Muniyandi Kailasam; et al. (New Zealand Journal of Marine and Freshwater Research, 2026)
This study investigated gonadal stages in Asian sea bass ( Lates calcarifer ) using portable ultrasonography (Sonosite Edge II, 6–13 MHz), with histological validation of female (primary oocyte, early vitellogenic, ripe) and male (ripe) stages. Distinct echogenicity patterns differentiated sexes, and ultrasound‐based gonad measurements showed strong correlations with actual dimensions (ovary length R 2 = 0.9942, ovary width R 2 = 0.9886, testis length R 2 = 0.9857, testis width R 2 = 0.9793). Deep learning models achieved 99.9% accuracy for sexual maturity prediction (DenseNet‐161) and 99.8% for sex classification (VGG16). CNN regression with L1 and L2 regularization was used to predict oocyte diameters. The MAE (mean absolute error) was 6.31 µm for minimum and 5.20 µm for maximum oocyte diameter, which is less than 1.82% of the actual oocyte diameter (256–544 µm). These results, along with averaging over 10‐fold and small confidence intervals, indicate the robustness of the classification and regression methods. This raises the possibility of automating the classification of the fish reproductive stage by combining deep learning with ultrasound technologies and further developing contactless underwater ultrasonographic assessment of gonadal maturation in commercially important fin fish species. It also reduces the dependency on experts to infer sexual maturation details from ultrasound scans. The developed machine learning model is deployed in real‐time processor, Nvidia Orin, through which real‐time testing is carried out and the results are validated.

Acoustic detection of Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae) in green gram and cowpea using MEMS microphone system - B. Keerthana; G. Preetha; K. Umapathi; V. R. Saminathan; T. Eevera; J. Malathi; S. Santhi (Journal of Plant Diseases and Protection, 2025)

Report from a symposium on catalyzing primary and secondary prevention of cancer in India - Suneeta Krishnan; Preet K. Dhillon; Afsan Bhadelia; Anna Schurmann; Partha Basu; Neerja Bhatla; Praveen Birur; Rajeev Colaco; Subhojit Dey; Surbhi Grover; Harmala Gupta; Rakesh Gupta; et al. (Cancer Causes & Control, 2015)

Joint effects between five identified risk variants, allergy, and autoimmune conditions on glioma risk - Mahboobeh Safaeian; Preetha Rajaraman; Patricia Hartge; Meredith Yeager; Martha Linet; Mary Ann Butler; Avima M. Ruder; Mark P. Purdue; Ann Hsing; Laura Beane-Freeman; Jane A. Hoppin; Demetrius Albanes; et al. (Cancer Causes & Control, 2013)

Genome-wide association study of glioma and meta-analysis - Preetha Rajaraman; Beatrice S. Melin; Zhaoming Wang; Roberta McKean-Cowdin; Dominique S. Michaud; Sophia S. Wang; Melissa Bondy; Richard Houlston; Robert B. Jenkins; Margaret Wrensch; Meredith Yeager; Anders Ahlbom; et al. (Human Genetics, 2012)

Insight in glioma susceptibility through an analysis of 6p22.3, 12p13.33-12.1, 17q22-23.2 and 18q23 SNP genotypes in familial and non-familial glioma - Yanhong Liu; Beatrice S. Melin; Preetha Rajaraman; Zhaoming Wang; Martha Linet; Sanjay Shete; Christopher I. Amos; Ching C. Lau; Michael E. Scheurer; Spiridon Tsavachidis; Georgina N. Armstrong; Richard S. Houlston; et al. (Human Genetics, 2012)

The impact of delayed blood centrifuging, choice of collection tube, and type of assay on 25-hydroxyvitamin D concentrations - Chu-Ling Yu; Roni T. Falk; Michael G. Kimlin; Preetha Rajaraman; Alice J. Sigurdson; Ronald L. Horst; Louis M. Cosentino; Martha S. Linet; D. Michal Freedman (Cancer Causes & Control, 2010)

Toxicity of selected insecticides to Trichogramma chilonis: Assessing their safety in the rice ecosystem - G. Preetha; J. Stanley; S. Suresh; S. Kuttalam; R. Samiyappan (Phytoparasitica, 2009)

Impact of Tropical Ecosystem on the Migrational Behavior of K-40, Cs-137, Th-232 U-238 in Perennial Plants - Ajay Kumar; R. K. Singhal; J. Preetha; K. Rupali; U. Narayanan; Sughandhi Suresh; Manish K. Mishra; A. K. Ranade (Water, Air, & Soil Pollution, 2008)

Determination of Ultra Trace Level of Uranium in Ground Water of Different Geo-Chemical Environment by Adsorptive Stripping Voltammetry - R. K. Singhal; V. M. Joshi; J. Preetha; Rupali Karpe; Ajay Kumar; A. G. Hegde (Water, Air, & Soil Pollution, 2007)