Improvement of little millet (Panicum sumatrense) using novel omics platform and genetic resource integration.

MAIN CONCLUSION: This article explores possible future initiatives, such as the development of targeted breeding and integrated omics approach to boost little millet production, nutritional value, and environmental adaptation. Little millet (P. sumatrense) is a staple grain in many parts of Asia and Africa owing to its abundance in vitamins and minerals and its ability to withstand harsh agro-ecological conditions. Enhancing little millet using natural resources and novel crop improvement strategy is an effective way of boosting nutritional and food security. To understand the genetic makeup of the crop and figure out important characteristics linked to nutritional value, biotic and abiotic resistance, and production, researchers in this field are currently resorting on genomic technology. These realizations have expedited the crop's response to shifting environmental conditions by enabling the production of superior cultivars through targeted breeding. Going forward, further improvements in breeding techniques and genetics may boost the resilience, nutritional content, and production of little millet, which would benefit growers and consumers alike. The research and development on little millet improvement using novel omics platform and the integration of genetic resources are summarized in this review paper. Improved cultivars of little millet that satisfy changing farmer and consumer demands have already been developed through the use of these novel breeding strategies. This article also explores possible future initiatives, such as the development of targeted breeding, genomics, and sustainable agriculture methods. The potential for these measures to boost little millet's overall production, nutritional value, and climate adaptation will be extremely helpful in addressing nutritional security.

Synergistic cobalt oxide/reduced graphene oxide/biochar nano-composite catalyst: harnessing the power of the catalyst for sustainable remediation of organic dyes and chromium(vi).

The exponential growth of industrial activities has led to a significant rise in the release of organic effluents, containing hazardous heavy metals and dyes, into the environment. These pollutants exhibit resistance to conventional biodegradation processes and are associated with carcinogenic properties, posing a severe threat to living organisms. In this context, the present research endeavours to address this environmental challenge through the development of an affordable and efficient photocatalyst, the Co3O4/reduced graphene oxide/biochar (CBG-10) heterostructure. The structural analysis of CBG-10, conducted through various techniques such as XRD, XPS, SEM, and optical property measurements, demonstrates its potential as a highly effective and easily recoverable catalyst for the mineralization of persistent pollutants like methylene blue, malachite green, and hexavalent Cr(vi). The recyclability of CBG-10 was confirmed through XRD studies, highlighting its stability and practical usability in wastewater purification. The photocatalytic behaviour of the catalyst was attributed to the generation of hydroxyl (˙OH) and superoxide radicals (˙O2-) during visible light illumination, as revealed by quenching experiments. The cost-effectiveness and stability of CBG-10 position it as a promising solution for addressing the challenges associated with the removal of stubborn organic contaminants from wastewater, thereby contributing to environmental protection and public health.

A Comprehensive Molecular, Biochemical, Histochemical, and Spectroscopic Characterization of Early and Medium Duration Rice Genotypes Investigating Dry Matter Accumulation Efficiencies.

Investigation on accumulation of cell wall components over critical growth stages will surely provide a new insight into dry matter accumulation studies in rice. An elevated biomass production provides an alternative strategy of yield improvement, which in turn maneuvers the species concerned as potential dual-purpose crop. On that note, present study was carried on 33 early and 39 medium duration rice genotypes. The average cellulose accumulation was 6.51% and 8.17% in early and medium duration genotypes, respectively, at flowering stage, which later on dipped to 1.43% and 3.46%, respectively, at physiological maturity. The gene specific marker MDgsp-5.a exhibited highest estimate of polymorphic information content (PIC), i.e., 0.685, closely followed by MDgsp-6.a with polymorphic information content (PIC) of 0.683. The control genotypes, i.e., Pratap and Mandakini, are grouped under the same cluster, i.e., Cluster-I.A, indicating their inherent genetic divergence from that of potential accumulators pertaining to cellulose accumulation. Pratap and Mandakini failed to produce peaks of conspicuous form at 3342 cm-1 and 1635 cm-1, bearing out by their low performance pertaining to cellulose and lignin accumulation at the later stages of development, respectively. From histochemistry studies, it was observed that the cell walls of sclerenchyma, peripheral vascular bundles, and parenchyma of the culm transections in control genotypes stained lightly than that of prolific accumulator cell walls, thus corroborating the findings of compositional analysis. The variation in cell wall thickening is primarily accounted due to altered carbohydrate accumulation across the growth stages as explored under scanning electron micrograph.

A comprehensive review on genetic modification of plant cell wall for improved saccharification efficiency.

The focus is now on harnessing energy from green sources through sustainable technology to minimize environmental pollution. Several crop residues including rice and wheat straw are having enormous potential to be used as lignocellulosic source material for bioenergy production. The lignocellulosic feedstock is primarily composed of cellulose, hemicellulose, and lignin cell wall polymers. The hemicellulose and lignin polymers induce crosslinks in the cell wall, by firmly associating with cellulose microfibrils, and thereby, denying considerable access of cellulose to cellulase enzymes. This issue has been addressed by various researchers through downregulating several genes associated in monolignol biosynthesis in Arabidopsis, Poplar, Rice and Switchgrass to increase ethanol recovery. Similarly, xylan biosynthetic genes are also targeted to genetically culminate its accumulation in the secondary cell walls. Regulation of cellulose synthases (CesA) proves to be an effective tool in addressing the negative impact of these two factors. Modification in the expression of cellulose synthase aids in reducing cellulose crystallinity as well as polymerisation degree which in turn increases ethanol recovery. The engineered bioenergy crops and various fungal strains with state of art biomass processing techniques presents the most recent integrative biotechnology model for cost effective green fuels generation along with production of key value-added products with minuscule disturbances in the environment. Plant breeding strategies utilizing the existing variability for biomass traits will be key in developing dual purpose varieties. For this purpose, reorientation of conventional breeding techniques for incorporating useful biomass traits will be effective.

Biochemical and SSR based molecular characterization of elite rice varieties for straw lignocellulose.

BACKGROUND: Lignocellulosic biomass from rice straw possesses enormous potential in generating bioenergy thereby reducing the dependence of human on non-renewable fuel sources. Developing rice varieties of such calibre necessitates biochemical characterization as well as assessing the presence of genetic diversity among the rice genotypes with respect to cellulose content. METHODS AND RESULTS: Forty-three elite rice genotypes were selected for biochemical characterization and SSR marker-based genetic fingerprinting. For genotyping, 13 cellulose synthase specific polymorphic markers were used. The diversity analysis was performed using TASSEL 5.0 and GenAlE × 6.51b2, software program. Of the 43 rice varieties, CR-Dhan-601, CR-Dhan-1014, Mahanadi, Jagabandhu, Gouri, Samanta and Chandrama were found to possess desirable lignocellulosic composition with respect to harnessing green fuels. The marker OsCESA-1.3 expressed the highest PIC (0.640), while the marker OsCESA-6.3 of lowest PIC (0.128). A moderate average estimate (0.367) of PIC was observed under current set of genotypes and marker system. The dendrogram analysis grouped the rice genotypes into two principal clusters i.e., cluster I and II. Cluster-II is monogenetic, while cluster-I is having 42 genotypes. CONCLUSIONS: The moderate level of both PIC and H average estimates indicate the narrow genetic bases of the germplasms. The varieties falling under different clusters possessing desirable lignocellulosic composition can be used in a hybridization programme to develop bioenergy efficient varieties. The promising varietal combinations that can be used as parents for developing bioenergy efficient genotypes are Kanchan / Gobinda, Mahanadi / Ramachandi, Mahanadi / Rambha, Mahanadi / Manika, Rambha / Manika, Rambha / Indravati and CR-Dhan-601 / Manika as they offer an advantage of higher cellulose accumulation. This study helped in identification of suitable dual purpose rice varieties for biofuel production without compromising food security.

Anxiety sensitivity as a predictor, and non-specific therapeutic factors as predictors and mediators of CBT outcome for obsessive-compulsive disorder in a naturalistic mental health setting.

OBJECTIVE: We examined the unique predictive strength of anxiety sensitivity (AS) and the role of expectancy, credibility, and therapeutic alliance (TA) as predictors and mediators of cognitive-behavioral treatment (CBT) outcomes in obsessive-compulsive disorder (OCD). METHOD: The current study is a prospective cohort study. Participants (N = 116) were treatment-seeking individuals with a primary diagnosis of OCD. Independent raters assessed patients on Yale-Brown Obsessive-Compulsive Scale (YBOCS) and Anxiety Sensitivity Index-3 at baseline, post-intervention, and three-month follow-up. Participants responded to the Credibility and Expectancy questionnaire and Working Alliance Inventory-Short revised at baseline, first-session, and mid-session. RESULTS: The individual addition of AS, end-of-first-session credibility and expectancy, mid-session credibility and expectancy, and therapeutic alliance predicted significant CBT outcomes. There was a moderate positive correlation between baseline OCD severity and the global score of AS, but a weak one with AS dimensions. Both expectancy and credibility significantly improved from baseline to end-of-first-session treatment. End-of-first and third-session outcome expectancies, not credibility, have significant, indirect effects on OCD CBT outcomes. CONCLUSIONS: AS, within-session credibility and expectancies and TA independently predict CBT outcomes. Within-sessions outcome expectancies mediate CBT outcomes in OCD, not credibility. Expectancy and credibility both include state-like elements that can be influenced to enhance the outcomes of CBT. Proposals for reducing treatment barriers in CBT for OCD are offered.

A comparative study on pyrolysis kinetics and thermodynamic parameters of little millet and sunflower stems biomass using thermogravimetric analysis.

Several biochemical and thermochemical routes including pyrolysis, liquefaction, combustion and gasification are used to convert biomass to several bioproducts and green fuels. The current investigation included two important biomass namely, little millet stem (LMS) and sunflower stem (SS), whose potentiality as useful feedstocks is largely unexplored. The presence of considerable level of cellulose accumulation (approx. 30 %), volatiles (approx. 67 %) and high heating value (approx. 14 MJ/kg) in both the biomass, inferred their potentiality to be used as feedstocks in the pyrolysis process. The estimate of activation energy for LMS was reported as 191.14 kJ/mol (FWO), 191.46 kJ/mol (KAS) whereas for SS, the activation energy was estimated as 166.52 kJ/mol (FWO) and 162.68 kJ/mol (KAS). The difference between change in enthalpy and activation energy was small (5 to 6 kJ/mol) for both the biomasses, indicating the feasibility of combustion process. From Z(α) analyses, the experimental curve was seen passing through different theoretical curves, indicating complex nature of pyrolysis process for both the biomass.

Brief cognitive behavior group therapy for social anxiety among medical students: A randomized placebo-controlled trial.

OBJECTIVE: To compare brief cognitive behavior group therapy (bCBGT) for social anxiety disorder (SAD) to a credible placebo, psychoeducational-supportive therapy (PST), in a sample of medical students. METHOD: This was a single-center, rater-blind, randomized, attention placebo-controlled, parallel-group study. Participants were 50 consenting undergraduate medical students of a state government medical college in Cuttack, India having a primary diagnosis of SAD, who recieved 6 weekly 2-h group sessions. Assessments were carried out at baseline, post intervention and at two-month follow. Independent raters assessed the participants on the Liebowitz Social Anxiety Scale and Clinical Global Impression- Improvement scale (CGI-I). Social Phobia Inventory (SPIN), a self-rated measure, was administered in the same periods. RESULTS: bCBGT group improved significantly across periods from pre-treatment to post-treatment and from pre-treatment to two-month follow-up. bCBGT was statistically superior to PST at the post-treatment and follow-up assessments and showed large effect sizes at both post-treatment and follow-up. CONCLUSIONS: A 6-session bCBGT is an efficacious treatment for SAD among medical students. A longer follow-up and replication in other groups, and clinical settings are necessary for generalization to a broader SAD population.

Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers.

The objective of this study was to build a machine learning model that can predict healing of diabetes-related foot ulcers, using both clinical attributes extracted from electronic health records (EHR) and image features extracted from photographs. The clinical information and photographs were collected at an academic podiatry wound clinic over a three-year period. Both hand-crafted color and texture features and deep learning-based features from the global average pooling layer of ResNet-50 were extracted from the wound photographs. Random Forest (RF) and Support Vector Machine (SVM) models were then trained for prediction. For prediction of eventual wound healing, the models built with hand-crafted imaging features alone outperformed models built with clinical or deep-learning features alone. Models trained with all features performed comparatively against models trained with hand-crafted imaging features. Utilization of smartphone and tablet photographs taken outside of research settings hold promise for predicting prognosis of diabetes-related foot ulcers.

Diabetic Wound Segmentation using Convolutional Neural Networks.

Image segmentation is a common goal in many medical applications, as its use can improve diagnostic capability and outcome prediction. In order to assess the wound healing rate in diabetic foot ulcers, some parameters from the wound area are measured. However, heterogeneity of diabetic skin lesions and the noise present in images captured by digital cameras make wound extraction a difficult task. In this work, a Deep Learning based method for accurate segmentation of wound regions is proposed. In the proposed method, input images are first processed to remove artifacts and then fed into a Convolutional Neural Network (CNN), producing a probability map. Finally, the probability maps are processed to extract the wound region. We also address the problem of removing some false positives. Experiments show that our method can achieve high performance in terms of segmentation accuracy and Dice index.