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Abstract India is currently the world's largest producer of papaya (Carica papaya L.), producing fruits for both the domestic market and export. Morphological sex identification of papaya plants is not possible at seedling stage, it is commonly cultivated by planting 2–3 seedlings in a pit, followed by uprooting of the undesirable male plants after flowering which not only increases the cost of inputs (seeds & other basic inputs) and labour but also restricts efficient usage of land. This causes great losses to papaya producers and the identification of the sex of seedlings during the nursery stage would be an important advance. In our study random amplified polymorphic DNA (RAPD) and sequence characterized amplified region (SCAR) analysis were used to differentiate between the sexual forms of papaya. Analysis of sex types at seedling stage with two marker system also confirmed the same accuracy level and chances for the false negative result also minimized to zero. This method could reduce both the time and costs associated with breeding programs. [ABSTRACT FROM AUTHOR]

Liquefactive necrosis of the lung tissue caused by microbial infection, lung abscess is characterised by formation of cavities containing necrotic debris. In the vast majority of cases of lung abscess, polymicrobial bacteria can be found with predominance of anaerobes. Mycobacterium has been described as a very rare causative agent of community acquired lung abscess. We are presenting a case of middle aged diabetic female, who had bilateral lung abscesses, aetiology of which was established to be tubercular. Astonishing it may sound; based upon extensive web and library search, it's the first case report on tubercular lung abscess in a diabetic from India, and perhaps from the world itself. [ABSTRACT FROM AUTHOR]

Present study is among the first attempts to prioritize the prominent Indian states for better utilization of available solar resources. In this context, potentiality indices are performed on the basis of six prime factors that influence the effective utilization of solar energy and ranking is done accordingly. Firstly, in order to determine the weightage and hierarchy of the evaluation parameters modified digital logic (MDL) approach is employed. Availability of solar radiation is found to be the most influential parameter. Thereafter, fuzzy-analytical hierarchy process (AHP) is used for determination of the potentiality index and the corresponding ranking of different states. The ranks thus obtained are compared to current installed capacity ranks. It is found that a few states despite of high potential for the exploitation of solar energy are not getting proper attention by their respective governments. In order to promote the solar energy exploitation in such states it becomes vital to identify the vital parameters that may lead to the improvement in the current ranking. In this direction, sensitivity analysis is performed to determine the percentage change in distinct parameters which is required for the upgradation in the ranking. Such studies are capable to make a paradigm shift in technology utilization and formulation of energy policies as the proposed approach provides a concrete layout for re-evaluation of current policies as well as formulation of newer ones. [ABSTRACT FROM AUTHOR]

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral., (© 2021. The Author(s).)

In the current study, a total of 214 blood samples from dogs in and around Ludhiana, Punjab (India), suspected for canine babesiosis were examined with conventional and molecular assays. Examination of Giemsa-stained peripheral thin blood smears revealed an overall prevalence of 7.47% (16/214) for canine babesiosis encompassing 0.93% (2/214) of large Babesia and 6.54% (14/214) of Babesia gibsoni. However, molecular diagnosis revealed 15.42% (33/214) samples positive for B. gibsoni infection as evident by the presence of 671 bp amplicon. The results of multivariate analysis showed that the prevalence of B. gibsoni was associated with various risk factors, namely, age (P<0.001; OR: 0.398; CI 95%: 0.080-1.799), sex (P=0.022; OR: 0.849; CI 95%: 0.403-1.791), breed of host (P=0.371; OR: 3.345; CI 95%: 1.045-10.710), and season (P=0.230; OR: 2.143; CI 95%: 0.788-5.830). The prevalence of B. gibsoni was higher in summer as compared to winter season and in younger dogs, while breed and sex of the host were not significantly associated with the occurrence of the disease.