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Cognitive radio networks can achieve high spectrum efficiency through dynamic spectrum allocation to the secondary users (SU) by way of using primary user (PU) spectrum. An efficient handoff scheme for cognitive radio networks using hybrid priority queuing model has been proposed using discretion rule. This method is divided into two parts: (1) hybrid priority queuing model with discretion rule is proposed by prioritizing the secondary users in the channel and then calculate the waiting time during the spectrum handoff and (2) to obtain better efficiency by reducing the waiting time, the learning rate of the secondary users from the other users should be increased. This can be done with multi teacher apprentice learning (MAL) along with sparse regression by considering multiple users into consideration at a time instead of a single user for better efficiency of the spectrum. The normalized load, arrival rate and the service time of the proposed model is compared to the existing models with respect to the average data delivery time of the users.

MotivationContinuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as ‘big data records’ to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of disease outcomes provide an excellent ground for identifying important mutations by developing machine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2.ResultsWe describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely ‘Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal’ were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a ‘temporal modeling approach’ to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on ‘past data’ in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern.Contactsharmila.mande@tcs.comSupplementary informationSupplementary data are enclosed.

Cutaneous larvae migrans or creeping eruption is a serpiginous cutaneous eruption caused by the accidental penetration and migration of animal hookworm (nematode) larvae through the epidermis and subcutaneous tissue. The infection occurs most frequently in warmer climates and is associated with pruritus and burning sensations. We report a case of 48 years old farmer with vesicular lesion on left ankle progressing into a serpiginous track on the skin surface without any characteristic symptoms. Treatment with oral and topical drugs led to successful resolution of lesions within 10 days.

The organic food industry in India is in the early stages of growth. Higher disposable income and greater health awareness have resulted in an increased domestic demand for organic food. There is huge premium in selling organic products, not only to export markets but also to affluent, health conscious domestic consumers. India is endowed with an abundance of labour and has diverse agroclimatic region that is well suited to year round agriculture. It still has strong traditional agricultural practices. Can India make use of this comparative advantage to introduce sustainable agriculture practices and at the same time improve incomes of small and marginal farmers?On the supply side, small and marginal farmers realize that there is an opportunity to get higher net incomes even if yields are low in organic agriculture. This is because the price of pesticides and chemicals has increased significantly over the last few decades resulting in a significant increase in the cost of production. Organic farming cost could be 50% to 60% less when compared to inorganic farming practices.In addition to domestic demand side, globalized markets provide significant opportunities for Indian agriculture to capture a larger share of the global demand for organic food. This paper analyzes the growth of the organic food industry in relation to domestic and export demand. We also look at the supply side to determine if organic farming and sustainable agricultural practices could help improve farmers’ income. Finally, this paper analyses existing policy framework towards organic agriculture and how small and marginal farmers could possibly benefit in this niche market.