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Malnutrition affects growth and development in humans and causes socio-economic losses. Normal maize is deficient in essential amino acids, lysine and tryptophan; and vitamin-A. Crop biofortification is a sustainable and economical approach to alleviate micronutrient malnutrition. We combined favorable alleles of crtRB1 and lcyE genes into opaque2 ( o2 )-based four inbreds viz . QLM11, QLM12, QLM13, and QLM14 using marker-assisted backcross breeding. These are parents of quality protein maize versions of two elite hybrids viz . Buland and PMH1, grown in India. Gene-based SSRs for o2 and InDel markers for crtRB1 and lcyE were successfully employed for foreground selection in BC 1 F 1 , BC 2 F 1 , and BC 2 F 2 generations. The recurrent parent genome recovery ranged from 88.9 to 96.0% among introgressed progenies. Kernels of pyramided lines possessed a high concentration of proA (7.14–9.63 ppm), compared to 1.05 to 1.41 ppm in the recurrent parents, while lysine and tryptophan ranged from 0.28–0.44% and 0.07–0.09%, respectively. The reconstituted hybrids (RBuland and RPMH1) showed significant enhancement of endosperm proA (6.97–9.82 ppm), tryptophan (0.07–0.09%), and lysine (0.29–0.43%), while grain yield was at par with their original versions. The dissemination of reconstituted hybrids holds significant promise to alleviate vitamin-A deficiency and protein-energy malnutrition in developing countries.

Face detection is important part of face recognition system. In face recognition, face detection is taken not so seriously. Face detection is taken for granted; primarily focus is on face recognition. Also, many challenges associated with face detection, increases the value of TN (True Negative). A lot of work has been done in field of face recognition. But in field of face detection, especially with problems of face occlusion and non-uniform illumination, not so much work has been done. It directly affects the efficiency of applications linked with face detection, example face recognition, surveillance, etc. So, these reasons motivate us to do research in field of face detection, especially with problems of face occlusion and non-uniform illumination. The main objective of this article is to detect face in still image. Experimental work has been conducted on images having problem of face occlusion and non-uniform illumination. Experimental images have been taken from public dataset AR face dataset and Color FERET dataset. One manual dataset has also been created for experimental purpose. The images in this manual dataset have been taken from the internet. This involves making the machine intelligent enough to acquire the human perception and knowledge to detect, localize and recognize the face in an arbitrary image with the same ease as humans do it. This article proposes an efficient technique for face detection from still images under occlusion and non-uniform illumination. The authors have presented a face detection technique using a combination of YCbCr, HSV and L × a × b color model. The proposed technique improved results in terms of Accuracy, Detection Rate, False Detection Rate and Precision. This technique can be useful in the surveillance and security related applications.

Heat shock proteins (HSPs) have a significant role in protein folding and are considered as prominent candidates for development of heat-tolerant crops. Understanding of wheat HSPs has great importance since wheat is severely affected by heat stress, particularly during the grain filling stage. In the present study, efforts were made to identify HSPs in wheat and to understand their role during plant development and under different stress conditions. HSPs in wheat genome were first identified by using Position-Specific Scoring Matrix (PSSMs) of known HSP domains and then also confirmed by sequence homology with already known HSPs. Collectively, 753 TaHSPs including 169 TaSHSP , 273 TaHSP40 , 95 TaHSP60 , 114 TaHSP70 , 18 TaHSP90 and 84 TaHSP100 were identified in the wheat genome. Compared with other grass species, number of HSPs in wheat was relatively high probably due to the higher ploidy level. Large number of tandem duplication was identified in TaHSP s, especially TaSHSP s. The TaHSP genes showed random distribution on chromosomes, however, there were more TaHSP s in B and D sub-genomes as compared to the A sub-genome. Extensive computational analysis was performed using the available genomic resources to understand gene structure, gene expression and phylogentic relationship of TaHSPs . Interestingly, apart from high expression under heat stress, high expression of TaSHSP was also observed during seed development. The study provided a list of candidate HSP genes for improving thermo tolerance during developmental stages and also for understanding the seed development process in bread wheat.

Key messageA total of seven glutathione reductase (GR) genes were identified in Triticum aestivum, which were used for comparative structural characterization, phylogenetic analysis and expression profiling with the GR genes of other cereal plants. The modulated gene expression and enzyme activity revealed the role of GRs in abiotic stress response in T. aestivum.Glutathione reductase (GR) is an enzymatic antioxidant that converts oxidized glutathione (GSSG) into reduced glutathione (GSH) through the ascorbate–glutathione cycle. In this study, a total of seven GR genes forming two homeologous groups were identified in the allohexaploid genome of bread wheat (Triticum aestivum). Besides, we identified three GR genes in each Aegilops tauschii, Brachypodium distachyon, Triticum urartu and Sorghum bicolor, which were used for comparative characterization. Phylogenetic analysis revealed the clustering of GR proteins into two groups; class I and class II, which were predicted to be localized in cytoplasm and chloroplast, respectively. The exon–intron and conserved motif patterns were almost conserved in each group, in which a maximum of 10 and 17 exons were present in chloroplastic and cytoplasmic GRs, respectively. The protein structure analysis confirmed the occurrence of conserved pyridine nucleotide disulfide oxidoreductase (Pyr_redox) and pyridine nucleotide disulfide oxidoreductase dimerization (Pyr_redox_dim) domains in each GR. The active site of GR proteins consisted of two conserved cysteine residues separated by four amino acid residues. Promoter analysis revealed the occurrence of growth and stress-related cis-active elements. Tissue-specific expression profiling suggested the involvement of GRs in both vegetative and reproductive tissue development in various plants. The differential expression of TaGR genes and enhanced GR enzyme activity suggested their roles under drought, heat, salt and arsenic stress. Interaction of GRs with other proteins and chemical compounds of the ascorbate–glutathione cycle revealed their coordinated functioning. The current study will provide a foundation for the validation of the precise role of each GR gene in future studies.

In the digital age, hospital websites are essential for providing healthcare information and services. This research introduces an automated tool, WUAHP, created in Python utilizing BeautifulSoup for HTML parsing. This facilitates the extraction of structural and content-based components essential for usability assessment. It assesses websites based on five principal criteria: Navigational efficiency, operational efficiency, accessibility, responsiveness & compatibility, and security—each subdivided into many sub-criteria. Each measure is evaluated on a scale from 0 (least desirable) to 1 (most ideal) utilizing normalized modules. The entropy weighting method is utilized to impartially allocate weights according to data variability. Usability scores are subsequently confirmed via user feedback and aligned with Nielsen’s heuristic usability standards. The tool was utilized on fifty healthcare websites. The results indicated significant variability, with HW9 attaining the greatest usability score of 97% and HW39 the lowest at 12%. The ultimate usability scores varied from 12 to 97%, underscoring disparities in design efficacy. WUAHP provides web developers and healthcare providers with an effective method to assess and enhance website usability. The technology establishes a basis for future applications in training machine learning models for automated, large-scale website assessment.

Quorum sensing (QS) and signal molecules used for interspecies communication are well defined in mesophiles, but there is still a plethora of microorganisms in which existence and mechanisms of QS need to be explored, thermophiles being among them. In silico analysis has revealed the presence of autoinducer-2 (AI-2) class of QS signaling molecules in thermophiles, synthesized by LuxS (AI-2 synthase), though the functions of this system are not known. In this study, LuxS of Meiothermus ruber was used for understanding the mechanism and functions of AI-2 based QS among thermophilic bacteria. The luxS gene of M. ruber was expressed in luxS− deletion mutant of Escherichia coli. Complementation of luxS resulted in significant AI-2 activity, enhanced biofilm formation, and antibiotic susceptibility. Transcriptome analysis showed significant differential expression of 204 genes between the luxS-complemented and luxS− deletion mutant of E. coli. Majority of the genes regulated by luxS belonged to efflux pumps. This elucidation may contribute towards finding novel alternatives against incessant antibiotic resistance in bacteria.Key Points• Expression of luxS in luxS−E. coli resulted in increase in biofilm index.• Reduction in the MIC of antibiotics was observed after complementation of luxS.• Downregulation of efflux pump genes was observed after complementation of luxS.• Transcriptome analysis showed that 204 genes were differentially regulated significantly.

In today’s digital era, hospital websites serve as crucial informational resources, providing patients with easy access to medical services. Ensuring the usability of these websites is essential, as it directly impacts users’ ability to navigate and retrieve vital medical information. Despite the recognized importance of website usability in the healthcare sector, there is a notable lack of empirical studies leveraging machine learning to assess this usability. This study aims to fill this gap by evaluating the user-friendliness of hospital websites using machine learning models, including Decision Trees, Random Forests, Ridge Regression, and Support Vector Regression. The dataset used in this analysis was generated by a custom-built automated tool developed by the author, which assessed the usability of 100 hospital websites. Among the models, Random Forest Regression and Ridge Regression demonstrated exceptional performance, achieving an accuracy rate of 98% and 87% respectively. Key metrics such as R-square, Mean Square Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) confirmed the model’s predictive accuracy. Cross-validation further highlighted the robustness of Ridge Regression exhibiting low overfitting. The importance rankings consistently underscored the critical role of overall usability in predicting website performance. The study recommends expanding the dataset to include a broader range of healthcare websites, integrating user interaction data, and exploring advanced analytical techniques like deep learning to enhance future usability assessments. These advancements could lead to optimized website design and improved digital healthcare experiences for a diverse range of users.

The incidents of violence against doctors, leading to grievous injury and even death, seem to be on an increasing trend in recent years. There is a paucity of studies on workplace violence against doctors and its effect, in India. The present study was conducted to assess workplace violence faced by doctors, its effect on the psycho-social wellbeing of the treating doctor and, subsequently, on patient management. The present nationwide cross-sectional study was conducted from November 2019 -April 2020. The sample size was calculated assuming the prevalence of workplace violence as 50%, with 20% non-response. Doctors, working in private and/or public set-up, with [greater than or equal to]1 year clinical experience, were included. A pre-tested study tool- Google form-was sent to study participants via social media platforms. The Microsoft Excel spreadsheet was downloaded from google drive and data was analysed using STATA-12 statistical software. A total of 617 responses were received from doctors all over India; out of which 477 (77.3%) doctors had ever faced workplace violence. \"Actual or perceived non-improvement or deterioration of patient's condition\" (40.0%), followed by \"perception of wrong treatment given\" (37.3%) were the main causes of workplace violence; and the family members/relatives were the major perpetrators (82.2%). More than half of the participants reported \"loss of self-esteem\", \"feeling of shame\" and \"stress/depression/anxiety/ideas of persecution\" after the incident. Management by surgical interventions (p-value<0.001) and handling of emergency/complicated cases (p-value<0.001) decreased significantly with an increase in severity of workplace violence; while the suggestion of investigations and referrals increased (p-value<0.001). Workplace violence has a significant effect on the psycho-social well-being of doctors, as well as on patient management; which may escalate discontent and distrust among the general public, thereby increasing incidents of workplace violence-in a self-propagating vicious cycle.

This paper presents a novel bio-inspired algorithm called Sandpiper Optimization Algorithm (SOA) and applies it to solve challenging real-life problems. The main inspiration behind this algorithm is the migration and attacking behaviour of sandpipers. These two steps are modeled and implemented computationally to emphasize intensification and diversification in the search space. The comparison of proposed SOA algorithm is performed with nine competing optimization algorithms over 44 benchmark functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. Further, SOA algorithm is hybridized with decision tree machine-learning algorithm to solve real-life applications. The experimental results demonstrated that the proposed algorithm is able to solve challenging constrained optimization problems and outperforms the other state-of-the-art optimization algorithms.

Autonomous underwater vehicles (AUVs) are essential for marine exploration, monitoring, and surveillance, especially in hazardous or inaccessible environments for human divers. Underwater imaging systems frequently face considerable difficulties in detecting and tracking objects due to image degradation resulting from light scattering, colour distortion, and haze. Conventional enhancement methods—like histogram equalisation and gamma correction—struggle with non-uniform illumination and frequently do not maintain critical structural details and perceptual quality. To address these limitations, this work proposes a novel framework for underwater image dehazing and enhancement that incorporates three essential components: a generative adversarial network (GAN), a bottleneck attention module (BAM), and an enhanced Retinex-based contrast enhancement technique. The GAN acquires the intricate correspondence between deteriorated and high-quality underwater images, facilitating the restoration of fine textures and the attenuation of noise. The BAM selectively amplifies spatial and channel-specific features, thereby augmenting the network’s capacity to preserve natural hues and intricate details. The modified Retinex algorithm adaptively distinguishes between illumination and reflectance components, facilitating context-sensitive contrast enhancement across various lighting conditions. This integrated architecture facilitates collaborative learning among generative modelling, attention-driven feature refinement, and physics-based enhancement. The proposed method undergoes thorough evaluation on the underwater Image enhancement benchmark (UIEB) dataset, which consists of 890 authentic underwater images. This study presents exceptional quantitative performance across various evaluation metrics: a UIQM score of 3.71 (indicating image quality), a PSNR of 28.4 dB (reflecting signal fidelity), an SSIM of 0.88 (representing structural similarity), and a perceptual LPIPS score of 0.082. The low LPIPS score underscores the perceptual realism of the enhanced images, correlating effectively with human visual preferences. These results distinctly surpass current classical and learning-based enhancement methods, demonstrating the efficacy and resilience of this approach for practical underwater vision applications.