Animals in human care are affected by stressors that can ultimately reduce fitness. When reproduction is affected, endangered species’ conservation programmes can be severely compromised. Thus, understanding factors related to stress and reproduction, and measures of related hormones, are important to ensure captive breeding success. Red pandas are endangered and populations in the wild are threatened with extinction. A global captive breeding programme has been launched to conserve the species with the goal of reintroduction. However there is little informaiton on how stressors impact reproductive aspects of the species. This study measured fecal glucocorticoid (fGCM), fecal progestagen (fPM) and fecal androgen (fAM) metabolite concentrations in 12 female and 8 male red pandas (Ailurus fulgens fulgens) at three zoos in northeastern India to determine predictors of adrenal and gonadal steroid activity and the influence of fGCM on reproduction. Results indicated that fGCM concentrations were higher in males than females, and positively correlated with number of visitors, while negatively related to frequency of feedings and enclosure area. Sex, visitor number, frequency of feeding, and enclosure area explained 67% of the variations in fGCM concentrations in the study population. Concentrations of fPM were positively associated with tree density in the enclosure, explaining 47% of the variation among females. For fAM, positive associations were found with frequency of feeding, but concentrations were negatively related to age and number of visitors; these three covariates explained 45% of the variation in fAM concentration among males. Comparison of fGCM with fPM showed a negative trend, indicating increasing adrenal hormones may decrease reproductive function among female red pandas. The study thus suggests that zoo management should consider increasing feeding frequency, providing larger enclosures with more trees, and regulating visitor numbers to reduce stress and increase reproductive fitness among red pandas.

The Pygmy hog (Porcula Salvania), till recently, classified as a critically endangered suid, is facing the threat of extinction globally due to habitat degradation. Efforts are being made to protect the pygmy hogs from extinction and breed them in captivity under Pygmy Hog Conservation Pro-gramme (PHCP). However, very little information is available on the reproductive physiology of pygmy hogs. Therefore, the present study aimed to standardize enzyme immunoassays (EIAs) for monitoring pregnancy and reproductive status using progesterone and testosterone metabolites. A total of 785 faecal samples were collected from five females and two males over a period of one year from PHCP Research and Breeding Centre, Guwahati, Assam. High-pressure liquid chro-matography (HPLC) analysis revealed the presence of immunoreactive progesterone and testos-terone metabolites in faeces. Mating was observed in all the five females and four of them gave birth successfully. We were able to detect pregnancy using faecal progesterone metabolites. Based on mating and parturition, the mean gestation period was estimated to be 153.25 days from four females. The breeding centre recorded 172 births between 1996 and 2000 and found strong sea-sonality in births and most of the births were between May and June. Faecal testosterone metabo-lites were significantly higher in the breeding season than the non-breeding season. This is the first study and will help in future breeding programs in other captive breeding centres and reproduc-tive monitoring of reintroduced populations.

Blood-brain-barrier is a major obstacle in treating brain-related disorders as it does not allow to deliver drugs in the brain. In order to facilitate delivery of drugs in brain, we developed a method for predicting blood-brain-barrier penetrating peptides. These blood-brain barriers penetrating peptides (B3PPs) can act as therapeutic as well as drug delivery agents. We trained, tested, and evaluated our models on blood-brain-barrier peptides obtained from the B3Pdb database. First, we compute a wide range of peptide features then we select relevant peptide features. Finally, we developed numerous machine learning-based models for predicting blood-brain-barrier peptides using selected features. Our model based on random forest performed best on the top 80 selected features and achieved a maximum 85.08% accuracy with 0.93 AUROC. We also developed a web server, B3pred that implements our best models. It has three major modules that allow users to; i) predict B3PPs, ii) scanning B3PPs in a protein sequence, and iii) designing B3PPs using analogs. Our web server and standalone software is freely available at https://webs.iiitd.edu.in/raghava/b3pred/.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020-21, 2021-22, and 2022-23), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K SNP Infinium iSelect SNP assay to obtain genome-wide SNP marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 MTAs located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p≤0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020-21 and 2021-22 environments. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host-pathogen interactions related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins, were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.

A surgical technique aimed at rerouting aqueous humor (AH) outflow from the anterior chamber to the suprachoroidal space (SCS) without performing surgical cyclodialysis was developed to decrease intraocular pressure (IOP) in glaucoma patients. The surgical outcomes of the technique were retrospectively analyzed in fifty-eight patients. At 6, 12, and 24 months, the mean IOP decreased from 27.8±8.3 to 14.95.0 mmHg, median 15.0 (25th percentile (p25 )13.0; 75th percentile (p75) 18.0), and 15.23.3 mmHg; hypotensive medication use reduced from median (p25; p75) 3 (2; 3) to 0 (0; 2), 0 (0; 2), and 0 (0; 1.5), and complete success was achieved in 62.8%, 51.2%, and 32.5% cases, with partial success in 28.4%, 44.2%, and 62.5% cases, respectively. Eight cases (13.8%) presented unsatisfactory outcomes. Intra- and postoperative complications were few and manageable. No bleb formation was observed in any of the cases. In 50% of cases, conjunctival lymphatic vessels (CLVs) developed following surgery in superior quadrants. The technique is safe and effective in providing a long-term decrease in IOP and hypotensive medication use. It was hypothesized in this study that the IOP decrease occurred as a result of enhanced AH outflow from the SCS to CLVs through intrascleral microchannels connecting the SCS to CLVs.

Two putative boar pheromones (Androstenone and Androstenol) were discovered in endangered mouse deer during captive breeding program. This study further examined the molecular characteristics, pheromone synthesis pathway, and the functional role of these pheromones in reproduction of mouse deer. CYP17A1 and CYB5 genes were cloned and expressed in HEK-293, COS-7 cell lines and gonads of mouse deer to investigate CYP17A1 gene’s andien-β-synthase activity towards synthesis of sex pheromones in mouse deer. An enzyme immunoassay was also developed and standardized to measure the fecal androstenone during reproductive cycles of mouse deer. Results showed that mouse deer’s CYP17A1 gene possesses andien-β-synthase activity and could transform pregnenolone into 5,16-androstadien-3β-ol. The expression of CYP17A1 gene upregulated in the testis and ovary, compared to other tissues in mouse deer. Significantly elevated pheromones and estrogens were recorded prior to delivery and postpartum estrus / mating in mouse deer. Further, there were weak correlations between fecal pheromones and estrogens/ androgens in mouse deer during breeding season. The findings suggest that the boar pheromones might play a direct role in the reproductive activities of mouse deer which might be used for breeding of mouse deer elsewhere.

Development of high nutritious bakery products with optimum utilization of food waste is a major challenge for food industries. Food waste is major challenge in an each country. The optimum utilization of food waste for sustainable development goal of country is important ant for growth of the nation. The aim of the present work is to prepare value-added composite flour mixed bread from wastes of fruit and vegetables. The composite flour was prepared in four formulations of Peel and Pomace Wheat Flour (PPWF), as PPWF1, PPWF2, PPWF3 and PPWF4. Compositing flour was blended by a mix of vegetable and fruits pomace powders and whole wheat flour. Indian gooseberry pomace powder, apple pomace powder, bottle gourd peel powder, and potato peel powder were used with whole wheat flour to make pomace and whole wheat flour compositions as PPWF1, PPWF2, PPWF3 and PPWF4. Broadly this work was divided into three phases, first phase has conducted nutritional and functional analysis on peel and pomace powders individually, which shows the significance of using them for our research work. The second phase works in the creation of composite flours with different composition ratios as discussed above as PPWF. This phase shows the nutritional and functional analysis of four composite flours. Out of these four flours, PPWF3 contained a good amount of fiber 8.16%, crude protein 3.18%, total phenolic content 14.48%, moisture 9.5%, vitamin C 13.64mg/100g, and total phenolic compound 14.48(mg/GAE/g) which are maximum and acceptable range values as compared to other three composite flours and the wheat flour-control group flour. Then in the third phase, the PPWF3 is used as a partial replacement ratio of wheat flour in wheat flour due to its high phenolic content, Vitamin C, and the richness of fibers. This composite flour is used to make bread dough, two samples G1 and G2 are made; out of which G2 offers better nutritional, functional, and sensory evaluations in comparison with refined wheat four bread is taken as a control group. Thus, such utilization of food wastes in bread making can generate a value of wastes and can improve nutritional attributes of bread, which may improve an individual’s health.

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground based hyperspectral canopy spectral reflectance measurements was recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., USA; spectral range: 350-2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, eight were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, elongation stage was the most accurately estimated using vegetation indices that exhibited significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.