AISECT University

Next generation sequencing (NGS) remains underutilized in clinical microbiology applications despite providing broad pathogen spectrum detection superior to other molecular methods. This is primarily because of lower sensitivity of metagenomic NGS (mNGS) compared to PCR, lengthy turn-around times, cost, and complexity of data analysis. Capture sequencing is a technique that can mitigate some of the limitations of mNGS. Using probes that are engineered to selectively bind and pull down desired nucleic acids, capture sequencing enriches for targets of interest and can result in up to a 10,000-fold increase in sensitivity compared to mNGS. In this study, we describe the application of capture sequencing on Oxford Nanopore Technology's portable sequencer, the MinION MK1C. We examined the performance of VirCapSeq-VERT and TBDCapSeq, two distinct capture sequencing assays that target vertebrate viruses and tick-borne pathogens, respectively. Both assays were originally established on the Illumina platform. To enable sequencing on the MinION instrument, we developed a modified hybrid workflow using our established library preparation and capture protocol for Illumina, followed by the addition of the ONT sequencing adaptor. In tests using contrived and clinical samples, we compared sensitivity thresholds and sequencing output, including pathogen genome coverage and relevant read counts. The addition of capture enrichment to MinION NGS provided significant improvement in pathogen detection when compared to mNGS. Assessment of assay performance on pathogen-positive samples revealed equivalent sensitivity on the MinION MK1C and Illumina NextSeq.We found that the elevated read counts and sequencing depth generated by Illumina NGS were offset by the greater read length obtained on the MinION MK1C and resulted in comparable pathogen genome coverage between the two platforms. This study demonstrates the utility for employment of VirCapSeq and TBDCapSeq on different sequencing platforms and suggest the potential of the MinION platform for broad-spectrum clinical diagnostics.

The mandate for high data rates in mobile communication is increasing and will continue to do so in the future. Although the latest network technologies can meet this demand, they result in more-dense networks. Networks like ultra-dense networks and massive multiple-input multiple-output provide very high data rates, but they cannot meet the future demand. The main issue with existing networks is inter-cell interference and variations in quality of service esp. at the cell edges, leading to research on new network architectures that offer intelligent coordination and collaboration capabilities are being researched, like user-centric cell-free (UC-CF) massive-multipleinput-multiple-output (mMIMO). This network combines the best of ultradense networks and mMIMO and eliminates cell edge problems. It is served by access points that cooperate and coordinate with each other. This paper reviews the challenges and opportunities in physical layer parameterfronthaul signaling for UC-CF mMIMO. We discuss the basics of the network, the importance of fronthaul signaling, and propose various approaches in the literature to address challenges and identify research gaps and provide future directions. Our aims to provide a comprehensive overview of the current state of fronthaul signaling and highlight the key issues that need to be addressed to realize its full potential.

A field experiment was conducted at the Agricultural Research Farm of Ravindra Nath Tagore University of Agriculture, Bhopal, Madhya Pradesh (India), to study the effect of nutrient management through foliar application of nutrients in lentil (Lens culinaris M.) under rainfed conditions. The experiment was laid out in a Randomized Block Design (RBD) with six treatments and four replications to minimize soil heterogeneity. The experimental field was divided into four blocks, each comprising six unit plots, resulting in a total of 24 plots. Various growth parameters such as plant height (37.70 cm), number of branches per plant (16.25), dry weight (8.65 g), number of root nodules per plant (17.00) and weight of root nodules were recorded maximum under the application of 2% urea. Yield attributes including number of pods per plant, number of seeds per pod, seed index, seed yield, straw yield, net returns, and benefit–cost ratio were also assessed. The results revealed that foliar application of 2% urea significantly enhanced growth and yield parameters and outperformed all other treatments at all growth stages. The highest grain yield (1736 kg ha-1) of lentil cultivar RVL 13-7 was recorded with the application of 2% urea under Vertisol conditions of Madhya Pradesh. Although foliar application of 2% DAP and Jeevamrut improved crop performance, their effects were comparatively lower, while water spray and control treatments recorded the lowest values. Despite reports on the positive effects of foliar nutrition in pulse crops, information on the relative performance of different inorganic nutrient sprays and organic liquid formulations in lentil under rainfed Vertisol conditions is limited. Moreover, comprehensive studies linking crop growth, yield and economic returns under such moisture-stressed environments are scarce.

The Group based Compressive Sensing (CS) methods such as Grupp-sparse representation (GSR) have been widely used for storing the image data. However, conventional sparse representation models assume independence among transform coefficients and fail to capture the inherent correlations in natural images. Consequently, these methods often yield over-smoothed reconstructions, texture loss, and degraded performance under noise or model mismatch. To overcome these limitations, in this paper a restricted-Threshold based GSR (RT-GSR) based CS recovery framework is proposed, that optimally selects the restricted iteration counts for standard GSR method to avoided over smothering of reconstructed image. Unlike traditional methods RT-GSR imposes joint sparsity on groups of similar patches, effectively modeling both spatial and structural dependencies. Proposed RT-GSR framework incorporates an iteration restriction mechanism based on an early PSNR stabilization threshold, enabling the algorithm to automatically terminate once the reconstruction quality becomes stable. it is concluded that as the sub rate increases the restriction count also decreases and PSNR increases by using RT-GSR.

In this study, the phytochemical profile and total phenolic and flavonoid contents of triphala and its constituent parts, Terminalia bellirica, Terminalia chebula, and Emblica officinalis, were assessed. After preparing methanolic extracts from the dried fruits, the primary secondary metabolites were qualitatively screened. All samples tested positive for flavonoids, alkaloids, phenolics, and other bioactive compounds. Quantitative estimation was performed using colorimetric methods. The Total Phenolic Content (TPC) was highest in E. officinalis (117.50 mg GAE/g), while T. bellirica showed the highest Total Flavonoid Content (TFC) (104.39 mg QE/g). The Triphala extract demonstrated moderate levels of both phenolics (90.41 mg GAE/g) and flavonoids (51.04 mg QE/g), reflecting the cumulative contribution of its constituents. These findings support the antioxidant potential and therapeutic value of Triphala and its ingredients, justifying their traditional use in Ayurvedic medicine.