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Agriculture is the majority source of income for many people not just in the Indian subcontinent but around the world and hence forms the backbone of the economy. Present-day difficulties like unpredictability in weather conditions, water scarcity, and volatility due to demand- supply fluctuations create the need for the farmer to be equipped with modern day techniques. More specifically, topics like less yield of crops due to unpredictable climate, faulty irrigation resources, and soil fertility level depletions needto be communicated. Hence there is a requirement to modify the abundant agriculture data into modern day technologies and make them conveniently accessible to farmers. A technique that can be implemented in crop yield predictionis Machine learning. Numerous machine learning techniqueslike regression, clustering, classification and prediction can be employed in crop yield forecasting. Algorithms like Naïve Bayes, support vector machines, decision trees, linear and logistic regression, and artificial neural networks can be employed in the prediction. The wide array of available algorithms poses a selection dilemma with reference to the selected crop. The purpose of this study is to investigate how different machine learning algorithms may be used to forecast agricultural production and present an approach in the context of big data computing for crop yield prediction and fertilizer recommendation using machine learning techniques

The detection of selection signatures assists in understanding domestication, evolution, and the identification of genomic regions related to adaptation and production traits in buffaloes. The emergence of high-throughput technologies like Next Generation Sequencing and SNP genotyping had expanded our ability to detect these signatures of selection. In this study, we sought to identify signatures of selection in five buffalo populations (Brazilian Murrah, Bulgarian Murrah, Indian Murrah, Nili-Ravi, and Kundi) using Axiom Buffalo 90 K Genotyping Array data. Using seven different methodologies (Tajima's D, CLR, ROH, iHS, F

The impact of Covid-19 lockdowns on Delhi's albedo is discussed in this research along with its possible causes. The pollution control board's 24-hour data is utilized to examine the situation in Delhi during the previous five years across five areas. Delhi's five most diverse areas, Okhla, Dwarka, Mundka, Mandir Marg, and Anand Vihar, are evaluated. This paper is divided into three periods, i.e., pre-lockdown, during-lockdown, and post-lockdown periods, for a clear understanding of the results. PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, Ozone, and Atmospheric Temperature are the ten parameters used for analysis. The analysis revealed that pollutants in Delhi are positively correlated to the atmospheric temperature and negatively correlated to albedo. Moreover, it was found that atmospheric temperature levels were minimum for Delhi when lockdowns were imposed.

The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions.

The BoLA class II DQA and DQB genes in crossbred cattle were studied using PCR-RFLP, cloning, and sequencing techniques. Seventy-two crossbred cattle (Vrindavani) were used in the current study. HaeIII and XbaI restriction enzymes digested DQA exon 2-3, revealing seven (HaeIII-A-G) and three (XbaI A-C) motifs, respectively. The BoLA-DQB gene was analyzed using PCR-RFLP with PstI and TaqI restriction enzymes, yielding five restriction motifs for each restriction enzyme (PstI-A-E and TaqI-A-E). In crossbred cattle, addition, deletion, and substitutions were observed in distinct sequences, resulting in variations in overall gene length. Changes in nucleotides at positions 64-80, 110-200, and 207-264 were largely responsible for polymorphism in DQA exon 2. The phylogenetic analysis predicted a high degree of nucleotide and amino acid changes in DQA exon 2-3 and DQB exon 2. DQA genes had a nucleotide dissimilarity of 0.3-25.4 percent, while DQB genes had a nucleotide dissimilarity of 1.5-14.3 percent. We cloned and sequenced 20 genotypes based on PCR-RFLP of the DQA and DQB genes. The current study observed variation in the DQA and DQB genes and will serve as a foundation for future research on the BoLA DQA and DQB genes.

Objective To acquaint with the presentation and management of the cystic artery aneurysm by enriching the reviewed literature with our own experience. Background Cystic artery pseudoaneurysm is an uncommon entity with varied clinical presentation. Inflammation and trauma are associated with most of the cases. Limited experience with the condition challenges the management of individual cases. Materials and methods We retrieved all the reported cases of cystic artery pseudoaneurysm, published up to December 2019, from the PubMed database and excluded those arising as postoperative complications. A total of 59 cases were analyzed, and we also included our experience of managing a case of cystic artery pseudoaneurysm. Results Abdominal pain (77.9%) was the most common presentation followed by upper GI bleed (64.4%), while 19 patients (32.2%) had presented with classic Quincke's Triad. Most of the cases were diagnosed following the rupture of the pseudoaneurysm (n = 49, 83.05%). Fifteen patients presented with shock. Hyperbilirubinemia (59.3%) and anemia (55.9%) were the commonest laboratory findings. Although CT angiogram remains the investigation of choice, a conventional angiogram is the gold standard and sufficed as the definitive management in 20 cases. Cholecystectomy formed the definitive management in the rest of the cases. We successfully managed a middle-aged female patient of cystic artery aneurysm with xanthogranulomatous cholecystitis by open cholecystectomy. Conclusion Cystic artery pseudoaneurysms are amenable to successful management with careful evaluation and timely cholecystectomy or angioembolization or a combination of both.

The present study was conducted on the MHC class I (BoLA-A/BuLA-A) gene in Sahiwal, Jersey, Hariana, and Tharparkar breeds of cattle and Murrah, Mehsana, and Bhadawari breeds of buffalo to study the polymorphism. Exons 7-8 of the MHC class I gene was first characterized for polymorphism study in buffalo and the results reveal that this gene has a higher level of nucleotide changes than the cattle. Genes were investigated for polymorphisms in 285 animals of cattle and buffalo breeds. Molecular characterization of the MHC class I (BoLa-A/Bula-A) gene reveals a higher degree of polymorphism at the nucleotide level in cattle and buffalo. Results revealed this region has a higher level of polymorphisms in buffalo as campared to the cattle. Alul restriction patterns were monomorphic except for three different patterns but it was able to illustrate the differences in buffalo and cattle. SSCP analysis of exons 7-8 showed remarkable differences in cattle and buffalo. Sequence analysis revealed more closeness of Murrah breed with crossbred and indigenous cattle than Holstein Friesian. Exon 8 had more deletion and stop codon as compared to exon 7. The investigation confirmed that MHC class I BoLa-A/Bula-A exons 7-8 is highly polymorphic in buffalo as compared to cattle.

Logic encryption has emerged to be a potential solution to the problem of Intellectual Property (IP)-Piracy and counterfeiting. However, in the recent past, several attacks have been mounted on existing logic encryption strategies to extract the secret key. SAT attack, the most predominant one among them, exploits the unprotected Design-for-Testability (DfT) infrastructure as a backdoor to launch attacks on sequential circuits. Protecting the DfT infrastructure is of paramount importance to ensure the security of an Integrated Chip (IC). In this paper, we propose a new logic encryption scheme which dynamically obfuscates the scan operation for an unauthorized attempt of scan access. A detailed security analysis on the proposed secure DfT infrastructure demonstrates its ability to thwart SAT attack without compromising the testability of the design. A methodical key-gate placement strategy enables the proposed scheme to eliminate the leakage of key information through weak key-gate locations, offering protection against path sensitization and logic cone based attacks. Unlike other state-of-the-art SAT preventive schemes, our proposed method does not suffer from poor output corruption, which is a fundamental requirement of a logic encryption scheme.

Natural fibers derived from diverse animal species have gained increased attention in recent years due to their favorable environmental effects, long-term sustainability benefits, and remarkable physical and mechanical properties that make them valuable raw materials used for textile and non-textile production. Domestication and selective breeding for the economically significant fiber traits play an imperative role in shaping the genomes and, thus, positively impact the overall productivity of the various fiber-producing species. These selection pressures leave unique footprints on the genome due to alteration in the allelic frequencies at specific loci, characterizing selective sweeps. Recent advances in genomics have enabled the discovery of selection signatures across the genome using a variety of methods. The increased demand for 'green products' manufactured from natural fibers necessitates a detailed investigation of the genomes of the various fiber-producing plant and animal species to identify the candidate genes associated with important fiber attributes such as fiber diameter/fineness, color, length, and strength, among others. The objective of this review is to present a comprehensive overview of the concept of selection signature and selective sweeps, discuss the main methods used for its detection, and address the selection signature studies conducted so far in the diverse fiber-producing animal species.

Background We observe the emergence of several promoter-variant viral strains in India during recent years. The variant viral promoters contain additional copies of transcription factor binding sites present in the viral modulatory region or enhancer, including RBEIII, LEF-1, Ap-1 and/or NF-κB. These sites are crucial for governing viral gene expression and latency. Here, we infer that one variant viral promoter R2N3-LTR containing two copies of RBF-2 binding sites (an RBEIII site duplication) and three copies of NF-κB motifs may demonstrate low levels of gene expression noise as compared to the canonical RN3-LTR or a different variant R2N4-LTR (a duplication of an RBEIII site and an NF-κB motif). To demonstrate this, we constructed a panel of sub-genomic viral vectors of promoter-variant LTRs co-expressing two reporter proteins (mScarlet and Gaussia luciferase) under the dual-control of Tat and Rev. We established stable pools of CEM.NKR-CCR5 cells (CEM-CCR5RL reporter cells) and evaluated reporter gene expression under different conditions of cell activation. Results The R2N3-LTR established stringent latency that was highly resistant to reversal by potent cell activators such as TNF-α or PMA, or even to a cocktail of activators, compared to the canonical RN3- or the variant R2N4-LTR. The R2N3-LTR exhibited low-level basal gene expression in the absence of cell activation that enhanced marginally but significantly when activated. In the presence of Tat and Rev, trans-complemented in the form of an infectious virus, the R2N3-LTR demonstrated gene expression at levels comparable to the wild-type viral promoter. The R2N3-LTR is responsive to Tat and Rev factors derived from viral strains representing diverse genetic subtypes. Conclusion With extremely low-level transcriptional noise, the R2N3-LTR can serve as an excellent model to examine the establishment, maintenance, and reversal of HIV-1 latency. The R2N3-LTR would also be an ideal viral promoter to develop high-throughput screening assays to identify potent latency-reversing agents since the LTR is not affected by the usual background noise of the cell.

Manganese neurotoxicity has been reported to cause a neurodegenerative disease known as parkinsonism. Previous reports have shown that the expression of the KH-type splicing regulatory protein (KHSRP), a nucleic acid-binding protein, and NLRP3 is increased upon Mn exposure. However, the relation between these two during Mn toxicity has not been fully deduced. The mouse neuroblastoma (N2a) and SD rats are treated with LPS and MnCl2 to evaluate the expression of KHSRP and NLRP3. Further, the effect of the NLRP3 inhibitor MCC950 is checked on the expression of NLRP3, KHSRP and pro-inflammatory markers (TNFα, IL-18 and IL-1β) as well as the caspase-1 enzyme. Our results demonstrated an increment in NLRP3 and KHSRP expression post-MnCl2 exposure in N2a cells and rat brain, while on the other hand with LPS exposure only NLRP3 expression levels were elevated and KHSRP was found to be unaffected. An increased expression of KHSRP, NLRP3, pro-inflammatory markers and the caspase-1 enzyme was observed to be inhibited with MCC950 treatment in MnCl2-exposed cells and rats. Manganese exposure induces NLRP3 and KHSRP expression to induce neuroinflammation, suggesting a correlation between both which functions in toxicity-related pathways. Furthermore, MCC950 treatment reversed the role of KHSRP from anti-inflammatory to pro-inflammatory.

This paper presents a signal processing and machine learning (ML) based methodology to leverage Electromagnetic (EM) emissions from an embedded device to remotely detect a malicious application running on the device and classify the application into a malware family. We develop Fast Fourier Transform (FFT) based feature extraction followed by Support Vector Machine (SVM) and Random Forest (RF) based ML models to detect a malware. We further propose methods to learn characteristic behavior of different malwares from EM traces to reveal similarities to known malware families and improve efficiency of malware analysis. We propose to use Discrete Wavelet Transform (DWT) based feature extraction from spectrograms of EM side-channel traces and perform ML on the extracted features to learn fine-grained patterns of malware families. The experimental demonstration on Open-Q 820 development platform demonstrate $0.99~F_{1}~score$ in detecting malware and $0.88~F_{1}~score$ in uniquely classifying malwares among 8 malware family evaluated using Support Vector Machines (SVM) and Random Forest (RF) Machine Learning(ML) models. We also demonstrate capability of proposed framework in identifying new unknown applications with 0.99 recall and unknown malware family with 0.87 recall.

Objectives: - The aim of the present study is to evaluate the clinical profile, investigations, treatment protocol and the response to the therapy in patient of COPD presenting with Respiratory failure. Materials and Methods: - All those patient with COPD with respiratory failure admitted in IPD in department of Respiratory Medicine R. D. Gardi Medical College Ujjain included in the study. Patients were evaluated by a detailed history, clinical examination, chest radiograph, arterial blood gas analysis and relevant blood investigation and were put on treatment. All the information was written in a proforma and the results were analysed. Results: - Majority of the patients were males (83.3%) with mean age of 65 years. In our study, the most common risk factor for COPD with respiratory failure was found to be smoking (80%). Nearly 70% cases were having type 2 respiratory failure. The main causes of exacerbations were bronchospasm(88.3%) and secondary infection (68.4%). 65% of the patients were discharged on room air with medical treatment, 23.3% patients were discharge on home oxygen with medical treatment, 3.33% patients discharged on NIV along with medical treatment and 5 patients died during hospitalization. Conclusion: - It is very important to take holistic approach toward COPD patient with respiratory failure. Early diagnosis, prompt treatment, pulmonary rehabilitation and vaccination are mainstay of management modalities and to prevent further exacerbation in patients.

The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India.