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The experiment was evaluated with thirty two okra genotypes in Randomized Block Design (RBD) with two replications. Thirteen characters were measured for randomly selected plants for path analysis. Path analysis studies revealed high direct effect of days to initiation for first flowering, number of branches per plant, fruit length, weight of fruit and number of fruits per plant also recorded desirable direction with yield. Hence, the genotypes which exhibited better performance for these characters can be used in further improvement of okra.

A field experiment was conducted at Precision Farming Development Center, college farm, college of agriculture, Rajendranagar, Hyderabad during kharif, The experiment was laid out in split plot design with three drip irrigation levels as main plot treatments (viz., I1- Drip irrigation at 1.0 E pan, I2- Drip irrigation 0.8 E pan, I3- Drip irrigation 0.6E pan) and four mulches (viz., M1-No mulch, M2- Paddy straw, M3- Black plastic mulch, M4- Dried leaves) as sub treatments and were replicated thrice. The experimental soil was sandy clay loam in texture with low in available nitrogen and high in available phosphorus and potassium. Moisture content was estimated under all three main plot treatments and four subplot treatments. Higher moisture was observed below black plastic mulch and lowest moisture observed below the no mulch condition. Drip irrigation scheduled at 0.6 E pan recorded higher water use efficiency (111.09 kg ha-1 mm-1). The least WUE was observed in drip irrigation at 1.0 E pan. On the other hand maximum WUE was recorded with application of polythene mulch (113.5 kg ha-1 mm-1), While the least was recorded with no mulch (90.43 kg ha-1 mm-1). The results of soil moisture content in the present study indicated that, application of black plastic mulch would reduce the soil moisture evaporation which will enhance the uniform distribution availability of soil moisture for longer days, compare to other mulches namely no mulch, paddy straw and dried leaves.

Rare diseases (RDs) are diseases that occur infrequently and affect a small fraction of the population. Although these diseases individually affect small number of people, together they affect 400 million people globally at any given time. In India, where resources are scarce, healthcare infrastructure and policy framework are focused on mitigating diseases that affect many people. Further, the level of RD awareness among healthcare professionals, researchers, and general public is considerably low. As a result, many cases of RDs remain unreported, undiagnosed, and untreated. To frame policies regarding RDs, it is crucial to understand the current level of RD awareness among healthcare professional and researchers, as they are key stakeholders in diagnosis, treatment, policy making, and drug development. We conducted an exploratory survey to understand the current level of RD awareness among healthcare professionals and researchers based on identification of an RD, time for diagnosis, treatment options, and relationship with family history and geographic location. We noted that our respondents have considerably low level of RD awareness. They correctly identified the importance of family history but failed to realize the association with geographic location. After presenting the survey findings, we have made recommendation to improve RD awareness in India. Our findings will be helpful to design awareness campaigns and frame relevant policies.

The objective of the study was to evaluate genetic divergence among okra genotypes based on yield and yield contributing traits. The experiment comprised of 32 genotypes of okra, which were sown in Randomized Block Design with two replications at College of Horticulture, Sri Konda Laxman Telangana State Horticultural University, Rajendranagar, Hyderabad during the period Kharif, 2016. Analysis of variance indicated existence of large variability among the genotypes for all the traits. On the basis of D2 analysis, the 32 genotypes could be grouped into 6 clusters. Cluster I had the highest number of genotypes (15) followed cluster II (11) and cluster III and cluster V (2 genotypes in each group) and remaining clusters, i.e., IV and VI were mono genotypic. Plant height had the highest contribution towards the total genetic divergence (42.94 %) followed by overall YVMV PDI percent (16.73 ). The highest intra-cluster distances were recorded in cluster II (73.17) followed by cluster V (66.30). The maximum inter-cluster distance was recorded between cluster III and cluster IV (775.06) followed by cluster IV and cluster VI (723.37). The genotypes which were in the cluster II, III and VI also exhibited significant performance for fruit yield per plant. Based on per se performance and genetic investigation methods superior genotypes RHBG-1, RHBG-7, VRO-6, IC-42490, IC-10265, IC-111515, Arka Abhay, Pusa A-4, IC-29119, RHBG-12, IC-90219. The identified genotypes are promising for hybrid breeding as parents for yileld and YVMV resistance.IC-04328 and EC-755647 were identified for yield and yield attributing traits and YVMV resistance in okra. The identified genotypes are promising for hybrid breeding as parents for yileld and YVMV resistance.

A field investigation was undertaken during Kharif (June–September) 2019 at Agriculture Research Institute, Professor Jayashankar Telangana State Agriculture University, Rajendranagar, Hyderabad, Telengana State, India with an object to evaluate the CROPGRO-Tomato model under different dates of planting and cultivars.The experiment was carried out with dates of planting (02nd Jul, 12th Jul, 22nd Jul, 02nd Aug, 11th Aug, 23rd Aug, 03rd Sep and 13th Sep) as main plot treatments and cultivars US 440 and TO-3251 (Saaho) as sub-plot treatments. The CROPGRO-Tomato model performed well in the simulation of phenology, biomass, fruit yield and N uptake during calibration for US 440 and TO-3251.Calibration results revealed that the model perfectly predicted days to anthesis with no difference between simulated and observed data for both cultivars with RMSE of 0 days, the further model simulated the days to last picking,biomassat maturity, fruit yield and nitrogen uptake with RMSE of 0.9 and 0.7 day, 285 and 435 kg ha-1, 545 kg ha-1 and 389 kg ha-1 (dry weight), 6 and 5 kg ha-1 for US 440 and TO-3251 cultivars respectively. The calibrated model was used to further validate the experimental data and found that, simulation of days to anthesisand days to the last picking was excellent with NRMSE value of less than 10% for both cultivars,fair with dry matter production with NRMSE value of 25% for both cultivars and was poor with total fruit yield greater than 30% and N uptake for both cultivars under study was poor with NRMSE value more than 30%.

A field experiment was conducted during kharif season 2019to study and identify the impact of weather on growth and fruit yield of tomato under open field Crop was planted on eight different dates viz., 02 Jul, 12 Jul, 22 Jul, 02 Aug, 11 Aug, 23 Aug, 03 Sep and 13 Sep as main plots and two cultivars viz., US 440 and TO-3251 (Saaho) as sub-plots in split plot design and replicated thrice. Result revealed that, significantly more yield attributes and fruit yield of tomato was recorded with maximum temperature range of 30.7 to 32.8oC during vegetative phase, morning Relative humidity (RH) of 88 to 92% during fruit development phase, Vapour Pressure Defficient (VPD) of 0.6 to 0.7 kPa and 0.4 to 0.6kPa during fruit development and harvest phase. Further correlation studies revealed that the most critical weather parameter from fruit initiation to first picking stage was morning RH as this was negatively correlated with drymatter production at fruit development (-0.93**), harvest (-0.95**) and total fruit yield (-0.91**) of tomato, which accounted for 86%, 89% and 83% variation in drymatter production during fruit development, harvest phase and total fruit yield respectively.

Background: Production of biofuels from lignocellulosic crop biomass is an alternative to reduce greenhouse gas emissions. The biofuel production involves collecting biomass, breaking down cell wall components followed by the conversion of sugars to ethanol. The lingo-cellulosic biomass comprises 40-50% cellulose, 20-30% hemicellulose, and 10-25% lignin. Sorghum is a widely adapted energy crop for biofuel production. Biomass with low lignin, high cellulose, and high hemicellulose contents are exploited to attain maximum biofuel production efficiency. Resistance to lodging, pest, disease, and abiotic stresses related to cell wall components is well documented, and quantitative trait loci were identified to understand these traits' genetic correlation. Selection for reduced lignin and increased cellulose content in stover can increase the ethanol yield. The Genome-Wide Association Studies (GWAS) is a complementary approach to evaluating the marker and phenotype associations among large diversity panels. Single nucleotide polymorphisms were scanned to identify loci associated with the traits of interest. In this study, the GWAS was performed on 245 sorghum minicore genotypes to analyze agronomic traits (days to 500lowering, fresh biomass yield, dry biomass yield) and cell wall components (cellulose, hemicellulose, and lignin). Further, in-silico validation of the candidate genes was performed in a global gene expression data from large-scale RNA sequencing studies in sorghum available in the NCBI GEO database was used. Objective: The objectives of this study are to evaluate native variations in biofuel related agronomic traits and stalk cell wall components and to identify significant SNPs or loci related to the cell wall components. Methods: In this article, an association mapping panel, comprising of 245 sorghum minicore germplasm accessions, was evaluated during two post rainy seasons of 2013 and 2014, and observations were recorded on the whole plot- for days to 50% flowering, fresh biomass yield (tha-1), and dry biomass yield (tha-1). The biomass of sun-dried plants from both seasons was collected separately, chopped, dried, and ground to powder. The cellulose, hemicellulose, and lignin contents were determined in the powdered. The content of each of these three components in sorghum was expressed in percent of dry matter. The data on agronomic traits and composition analysis was subjected to Analysis of Variance. For the current study, we remapped the raw GBS data with the sorghum assembly version v3.1. A total of 27,589 SNPs were obtained with a minor allele frequency (MAF)>1% and missing data Results: High broad-sense heritability was exhibited for all the traits in individual seasons along with significant genotype × environment interaction across seasons except lignin. Association mapping with a P-4 revealed genomic regions associated with the- (i) agronomic traits (days to 50% flowering, fresh and dry biomass), and (ii) biochemical traits (cellulose, hemicellulose, and lignin) associated with biofuels production, in individual seasons. Twelve significant SNPs for flowering time, 30 fresh biomass yields, and 24 for dry biomass yield, 25 for cellulose, 7 for hemicellulose, and 21 for lignin were identified. CIRCOS plot was constructed to identify and analyze similarities and differences while comparing the sorghum genome with different crops. For cellulose high similarity of >80% was observed for all sorghum gene sequences with the maize homologs. The overall similarity of sorghum homologs with foxtail millet was >65%, for Arabidopsis from 30.6% to 48.6%, and rice from 28.2% to 92.8%. SNPs for hemicellulose displayed maximum similarity to foxtail millet followed by maize. The sequence similarity of lignin SNPs in sorghum was highest with the maize genome followed by Arabidopsis. Both rice and foxtail millet showed >55% similarity to the sorghum genome. Conclusion: This study reports large variability for agronomic and biofuel traits in the sorghum minicore collection with high heritability. The genetic architecture of cell wall components using the GWAS approach was studied and candidate genes for each component were annotated. These results give a better understanding of the genetic basis of the sorghum cell wall composition. The association analysis identified regions of the genome that could be targeted to enhance the quality of biomass and yield along with the desired composition promoting breeding efficiency for enhanced biofuel yield.

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Graphical Abstract Impact of various data learning approaches on NIR spectral data

Aim: In spine surgery, it is common practice to thoroughly exhaust all conservative measures before surgery. With the increasing popularity of injection procedures, significant number may still require surgery post epidural steroid infiltration (ESI). We aim to study the incidence of such patients who eventually go in for surgery after a failed ESI. We also studied and compared the functional outcomes of transforaminal (TF) versus interlaminar (IL) procedures in intervertebral disc prolapse (IVDP) patients. Materials and Methods: 150 patients with lumbar disc prolapsed were subjected to epidural steroid infiltration after 6 weeks of failed conservative management, of which 101 underwent IL and 49 TF procedure according to surgeon's preference, and followed up for 6 weeks. Those with persisting (or worsening) Visual analogue scale (VAS), Modified Oswestry diability index (ODI) and with progressive neurological deficits, underwent lumbar discectomy and all were followed up for 6 months.Results: Out of 150 patients, 45 patients underwent discectomy (30 %) after total 6 months follow up, and out of the remaining 105 patients, 40 (81.6%) from TF group and 65 (64.4%) from IL group improved. In total, 62% of females went to surgery, Minimal disability in ODI score was seen in 77.8% in surgery group and 98.1% in epidural group at final follow up.Conclusion: Our study observed a much smaller crossover rate (30%) to surgery, and a significant difference in outcomes of the two injection methods and concludes that TF is a better procedure than IL.

Background: Sorghum, the C4 dry-land cereal, important for food, fodder, feed and fuel, is a model crop for abiotic stress tolerance with smaller genome size, genetic diversity, and bioenergy traits. The heat shock proteins/chaperonin 60s (HSP60/Cpn60s) assist the plastid proteins, and participate in the folding and aggregation of proteins. However, the functions of HSP60s in abiotic stress tolerance in Sorghum remain unclear. Methods: Genome-wide screening and in silico characterization of SbHSP60s were carried out along with tissue and stress-specific expression analysis. Results: A total of 36 HSP60 genes were identified in Sorghum bicolor. They were subdivided into 2 groups, the HSP60 and HSP10 co-chaperonins encoded by 30 and 6 genes, respectively. The genes are distributed on all the chromosomes, chromosome 1 being the hot spot with 9 genes. All the HSP60s were found hydrophilic and highly unstable. The HSP60 genes showed a large number of introns, the majority of them with more than 10. Among the 12 paralogs, only 1 was tandem and the remaining 11 segmental, indicating their role in the expansion of SbHSP60s. Majority of the SbHSP60 genes expressed uniformly in leaf while a moderate expression was observed in the root tissues, with the highest expression displayed by SbHSP60-1. From expression analysis, SbHSP60- 3 for drought, SbHSP60-9 for salt, SbHSP60-9 and 24 for heat and SbHSP60-3, 9 and SbHSP10- 2 have been found implicated for cold stress tolerance and appeared as the key regulatory genes. Conclusion: This work paves the way for the utilization of chaperonin family genes for achieving abiotic stress tolerance in plants.

This study investigates the effect of packaging materials on physical parameters of sapota Cv. kalipatti. Ten different treatments involving five packaging materials, out of which four packaging materials were of 100 guage each with 1% and 2% ventilations, one gunny bag and control( no packaging) were tried in completely randomized design with three replications. Fruits packed with LDPE 200 guage with 2% ventilation (T7) recorded minimum physiological loss in weight (6.97%), maximum number of days for ripening (8.75 days), firmness (1.65 kg/cm2). While maximum ripening percentage (73.89%) was observed in gunny bag (T1).

Background Each year 3–6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed. Methods We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were “locked” prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD. Results We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2–100) sensitivity and 79.7% (95% CI 75.5–83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7–25.6) and 99.0% (95% CI 97.7–100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3–94.1) sensitivity and 39.7% (95% CI 34.7–44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression. Conclusions The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.

Background: Capsicum (Sweet pepper or Bell pepper), Capsicum annuum L. also popularly called as Simla mirch in India is one of the leading vegetables grown in open conditions as well as under protected conditions. It is attacked by various insect and mite pests from seedling to fruiting stage. The damage caused by Spodoptera litura (Fabricius) during flowering and fruit formation is the most concern and farmers everywhere feel the need to protect such high value crops from any type of damage caused by insect pests. Methods: The trial was conducted under poly house condition during kharif 2019 at horticulture garden, College of Agriculture, Rajendranagar, Hyderabad. The experiment was laid out in randomized block design (RBD) with eight treatments replicated thrice in a 7.5 m ´ 1 m plot size with a spacing of 45 cm ´ 40 cm. The capsicum hybrid WS 234 was raised as per the recommended package of practices except plant protection measures. The observations on pest incidence were recorded one day before treatment imposition as pre-treatment count. Post treatment counts were recorded at one, three, five and seven days after each chemigation/spray. Result: The present study indicated that treatment of cyantraniliprole 10.26% OD @ 90 g a.i. ha-1 applied through drip chemigation was effective by recording minimum larval population with lowest fruit damage over all the other treatments. The next best treatment was cyantraniliprole 10.26% OD @ 60 g a.i. ha-1 applied as foliar spray. The order of moderate effective treatments were drip applications of cyantraniliprole 10.26% OD @ 60 g a.i. ha-1 greater than cyantraniliprole + thiamethoxam 19.8+19.8 w/w @ 64 g a.i. ha-1 greater than cyantraniliprole + thiamethoxam 19.8+19.8 w/w @ 32 g a.i. ha-1 greater than imidacloprid 70 WG @ 50 g a.i. ha-1 greater than imidacloprid 70 WG @ 25 g a.i. ha-1.

Medical education in India is undergoing a landmark transformation under the National Medical Commission implementing competency-based medical education (CBME). The CBME approach intends to ensure that medical graduates acquire the competencies needed to fulfill the health needs of the patient and society. This outcome-based approach shifts the focus from the traditional knowledge-based training to skill-based training valued on attitude, ethics, and communication (AETCOM) competencies. CBME thus aims to create medical professionals capable of providing holistic care with compassion and excellence embracing the global trends. The opportunity posed by the CBME should be utilized to sensitize and create interest among the learners about the science and scope of psychiatry. However, there are many challenges in the successful implementation of CBME, which have to be identified and addressed on time for serving the purpose. A basic tenet in CBME is to continue training until the desired competencies are achieved; in other words, to de-emphasize time-based learning. Moreover, the current COVID 19 pandemic is posing a significant influence on the execution of CBME implemented in August 2019. Online platforms could have several advantages in assisting the implementation of CBME; they provide an alternative to continue teaching-learning and assessment during these times and allow learners with the flexibility to learn at their own pace. In this article, we discuss the opportunities, including digital platforms and challenges to be overcame as well as the need for training the faculty toward assimilating the curriculum in the undergraduate psychiatric training.

The small heat shock proteins (sHsps/Hsp20s) are the molecular chaperones that maintain proper folding, trafficking and disaggregation of proteins under diverse abiotic stress conditions. In the present investigation, a genome-wide scan revealed the presence of a total of 47 sHsps in Sorghum bicolor (SbsHsps), distributed across 10 subfamilies, the major subfamily being P (plastid) group with 17 genes. Chromosomes 1 and 3 appear as the hot spot regions for SbsHsps, and majority of them were found acidic, hydrophilic, unstable and intron less. Interestingly, promoter analysis indicated that they are associated with both biotic and abiotic stresses, as well as plant development. Sorghum sHsps exhibited 15 paralogous and 20 orthologous duplications. Expression analysis of 15 genes selected from different subfamilies showed high transcript levels in roots and leaves implying that they are likely to participate in the developmental processes. SbsHsp genes were highly induced by diverse abiotic stresses inferring their critical role in mediating the environmental stress responses. Gene expression data revealed that SbsHsp-02 is a candidate gene expressed in all the tissues under varied stress conditions tested. Our results contribute to the understanding of the complexity of SbsHsp genes and help to analyse them further for functional validation.

Objectives: To assess the psychological impact of COVID-19 lockdown among healthy individuals in India Methods: A self-assessed cross-sectional web-based study was conducted between 3rd April and 4th May 2020 Centre for Epidemiological Study for Depression questionnaire was administered for assessing the depressive symptoms among general public during the COVID-19 lockdown Using the google forms the questionnaire was circulated among the public through social media Descriptive analysis was performed to represent the study characteristics, Chi-square test for assessing the associations of depressive symptoms among the study variables, and the logistic regression analysis for identifying the factors influencing the depressive symptoms Results: A total of 588 participants with a mean (SD) age of 27 59 (8 72) years were responded to the questionnaire The prevalence depressive symptoms were higher among males (54%) compared to females (36%) It was identified that the participants with educational background of pre-university education/diploma (58 22%) and middle school (71 42%), and occupation as semiskilled (60%) and unskilled workers (70%), social history of consumption alcohol (56 52%) and smoking (75%), and belong to joint family (53 10%) were the predictors for depressive symptoms Subgroup analysis identified that these factors influence depressive symptoms especially among male population Conclusions: It is an alarming situation that the depressive symptoms found in approximately 45% of healthy individuals during the COVID-19 lockdown Educational interventions by the government and non-government organizations are very much required to combat the progression of depressive symptoms into disorder