High-Throughput Phenotyping using Computer Vision and Machine Learning

High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.Comment: Presented for the Smoky Mountains Computational Sciences and Engineering Conference: Best Paper Awar

Endodontic management of dental pain in an inhibitor positive, severe Hemophilia A patient: A brief review and report of a case

Dental health care providers often have to deal with patients requiring special care during treatment planning, and certain precautions while carrying out the procedures, and patients with bleeding disorders are one of them. Hemophilia, an X-linked blood dyscrasia, is the most common bleeding disorder. While hemophilia-A is a deficiency of factor VIII, hemophilia-B (Christmas disease) is a deficiency of factor IX. The present paper presents a case discussing endodontic management of mandibular molars with irreversible pulpits in an inhibitor positive severe hemophilia-A patient. As such patients may require administration of inferior alveolar nerve blocks, so adequate factor levels should be ensured before initiation of local anesthetics. Furthermore, the authors have tried to highlight the barriers to oral health care suffered by such patients and the larger role played by the physicians and oral health care providers in the prevention, early detection, and timely intervention in these cases

Performance Optimization for Distributed Computing via Gen AI

Methods, systems, and computer program products for performance optimization of distributed computing resources using generative artificial intelligence (AI) are provided. An example method may include receiving data associated with performance metrics of a distributed computing system and data associated with a cost function for activities engaged in with the distributed computing system. The method may include providing, as an input to a generative AI algorithm, the data associated with performance metrics of the distributed computing system, the data associated with a cost function for activities engaged in with the distributed computing system, and data associated with a knowledge graph regarding requirements for use of one or more resources of the distributed computing system to generate an output of the generative AI algorithm. The output of the generative AI algorithm comprises data for optimization of one or more resources of the distributed computing system and executing an optimization procedure on the distributed computing system

High resolution mapping of QTLs for fruit color and firmness in Amrapali/Sensation mango hybrids

IntroductionMango (Mangifera indica L.), acclaimed as the ‘king of fruits’ in the tropical world, has historical, religious, and economic values. It is grown commercially in more than 100 countries, and fresh mango world trade accounts for ~3,200 million US dollars for the year 2020. Mango is widely cultivated in sub-tropical and tropical regions of the world, with India, China, and Thailand being the top three producers. Mango fruit is adored for its taste, color, flavor, and aroma. Fruit color and firmness are important fruit quality traits for consumer acceptance, but their genetics is poorly understood.MethodsFor mapping of fruit color and firmness, mango varieties Amrapali and Sensation, having contrasting fruit quality traits, were crossed for the development of a mapping population. Ninety-two bi-parental progenies obtained from this cross were used for the construction of a high-density linkage map and identification of QTLs. Genotyping was carried out using an 80K SNP chip array.Results and discussionInitially, we constructed two high-density linkage maps based on the segregation of female and male parents. A female map with 3,213 SNPs and male map with 1,781 SNPs were distributed on 20 linkages groups covering map lengths of 2,844.39 and 2,684.22cM, respectively. Finally, the integrated map was constructed comprised of 4,361 SNP markers distributed on 20 linkage groups, which consisted of the chromosome haploid number in Mangifera indica (n =20). The integrated genetic map covered the entire genome of Mangifera indica cv. Dashehari, with a total genetic distance of 2,982.75 cM and an average distance between markers of 0.68 cM. The length of LGs varied from 85.78 to 218.28 cM, with a mean size of 149.14 cM. Phenotyping for fruit color and firmness traits was done for two consecutive seasons. We identified important consistent QTLs for 12 out of 20 traits, with integrated genetic linkages having significant LOD scores in at least one season. Important consistent QTLs for fruit peel color are located at Chr 3 and 18, and firmness on Chr 11 and 20. The QTLs mapped in this study would be useful in the marker-assisted breeding of mango for improved efficiency

Research Online Visibility of LIS Faculties at Central Universities in North India

The study examines the Google Scholar profile of LIS faculties employed in central universities of North India to determine their research online visibility. Data was obtained by doing manual searches on Google scholar on 4 July 2022 with the appropriate name of the faculties and their affiliation. The study found that 74 % of the faculty have a Google Scholar profile. Findings show that Prof. Margam Madhusudhan (DU) is leading among the faculties with a citation count of 1715, the highest number of publications, 162, and the highest i10 index of 31. Further, Prof. Bhaskar Mukherjee (BHU) and Prof. Margam Madhusudhan (DU) have the highest-ranked h-index, with 18 each leading the list. The authors advocate that a GS profile can be used to assess the research productivity of the faculty and that the authors’ work is more accessible if they create a Google Scholar profile for personal and institutional ranking purposes. The study also recommends displaying thrust areas for faculty members to boost the visibility of their areas of interest, which can be used for collaboration by other faculties or researchers with similar interests in India and overseas.</jats:p

Protein engineering for biofuel production: Recent development

The unstable and unsure handiness of crude oil sources moreover the rising price of fuels have shifted international efforts to utilize renewable resources for the assembly of greener energy and a replacement which might additionally meet the high energy demand of the globe. Biofuels represent a sustainable, renewable, and also the solely predictable energy supply to fossil fuels. During the green production of Biofuels, several in vivo processes place confidence in the conversion of biomass to sugars by engineered enzymes, and the subsequent conversion of sugars to chemicals via designed proteins in microbial production hosts. Enzymes are indispensable within the effort to provide fuels in an ecologically friendly manner. They have the potential to catalyze reactions with high specificity and potency while not using dangerous chemicals. Nature provides an in depth assortment of enzymes, however usually these should be altered to perform desired functions in needed conditions. Presently available enzymes like cellulose are subject to tight induction and regulation systems and additionally suffer inhibition from numerous end products. Therefore, more impregnable and economical catalyst preparations ought to be developed for the enzymatic method to be more economical. Approaches like protein engineering, reconstitution of protein mixtures and bio prospecting for superior enzymes are gaining importance. Advances in enzyme engineering allow the planning and/or directed evolution of enzymes specifically tailored for such industrial applications. Recent years have seen the production of improved enzymes to help with the conversion of biomass into fuels. The assembly of the many of those fuels is feasible due to advances in protein engineering. This review discusses the distinctive challenges that protein engineering faces in the method of changing lignocellulose to biofuels and the way they're addressed by recent advances in this field

Verloren negatively regulates the expression of IMD pathway dependent antimicrobial peptides in Drosophila

AbstractDrosophila immune deficiency (IMD) pathway is similar to the human tumor necrosis factor receptor (TNFR) signaling pathway and is preferentially activated by Gram-negative bacterial infection. Recent studies highlighted the importance of IMD pathway regulation as it is tightly controlled by numbers of negative regulators at multiple levels. Here, we report a new negative regulator of the IMD pathway, Verloren (Velo). Silencing of Velo led to constitutive expression of the IMD pathway dependent antimicrobial peptides (AMPs), and Escherichia coli stimulation further enhanced the AMP expression. Epistatic analysis indicated that Velo knock-down mediated AMP upregulation is dependent on the canonical members of the IMD pathway. The immune fluorescent study using overexpression constructs revealed that Velo resides both in the nucleus and cytoplasm, but the majority (~ 75%) is localized in the nucleus. We also observed from in vivo analysis that Velo knock-down flies exhibit significant upregulation of the AMP expression and reduced bacterial load. Survival experiments showed that Velo knock-down flies have a short lifespan and are susceptible to the infection of pathogenic Gram-negative bacteria, P. aeruginosa. Taken together, these data suggest that Velo is an additional new negative regulator of the IMD pathway, possibly acting in both the nucleus and cytoplasm.</jats:p