Shobhit Institute of Engineering & Technology, also known as Shobhit University, is a private university located in Meerut, Uttar Pradesh, India. It is a Deemed University under section 3 of the University Grants Commission Act, 1956 and accredited by National Accreditation and Assessment Council (NAAC).
The advancement of novel technologies, coupled with bioinformatics, has led to the discovery of additional genes, such as long noncoding RNAs (lncRNAs), that are associated with drug resistance. LncRNAs are composed of over 200 nucleotides and do not possess any protein coding function. These lncRNAs exhibit lower conservation across species, are typically expressed at low levels, and often display high specificity towards specific tissues and developmental stages. The LncRNA MALAT1 plays crucial regulatory roles in various aspects of genome function, encompassing gene transcription, splicing, and epigenetics. Additionally, it is involved in biological processes related to the cell cycle, cell differentiation, development, and pluripotency. Recently, MALAT1 has emerged as a novel mechanism contributing to drug resistance or sensitivity, attracting significant attention in the field of cancer research. This review aims to explore the mechanisms through which MALAT1 confers resistance to chemotherapy and radiotherapy in cancer cells.
As an organization, enhancing customer satisfaction and retention rates needs to be one of their primary objectives. There has been a notable spike in attention recently to logistics process improvements that help salvage value from recycled goods. The present research develops a production inventory approach that is divided into three separate components, i.e., renovating proceedings, producing procedures, and reinstating the stock process. Restoring, reconstructing, and reprocessing items that have been rejected or reached the end of their practical lifespan is known as refurbishment. Reverse logistics' primary goal is to regulate the transit of merchandise, supplies, and commodities from the end user to the supply chain's starting point in a way that maximizes value and reduces ecological effects. It may reduce trash, which is one of its main benefits when integrated into a sustainable economic structure. The several modes of transportation used in logistics to transfer packages and products throughout the world are the source of carbon emissions. The distinctive feature of green inventory administration is the addition of ecological considerations to conventional commercial concerns. The mathematical approach to the framework is to minimize the total expenditure of stockpiles, including carbon emission costs. Models have undergone sensitivity evaluations and computational examples.
Tomato leaf curl New Delhi virus (ToLCNDV), a bipartite begomovirus transmitted by the whitefly (Bemisia tabaci), has emerged as a significant limiting factor in cucumber (Cucumis sativus L.) cultivation across tropical and subtropical regions. Despite its economic significance, the genetic and molecular determinants of resistance to ToLCNDV in cucumber remain largely unexplored. The present study employed a QTL-seq-based approach to unravel the genetic architecture of ToLCNDV resistance and identify candidate genomic regions associated with this complex trait. Whole-genome resequencing of resistant (DC 91) and susceptible (DC 773) parental lines, along with resistant and susceptible F₂ bulks, was performed using the Illumina HiSeq platform. High-quality reads were aligned to the C. sativus reference genome to identify genome-wide single-nucleotide polymorphisms (SNPs). Δ(SNP-index) analysis revealed a major quantitative trait locus (QTL) on chromosome 7 (Cy-2) conferring ToLCNDV resistance, spanning approximately 1.8 Mb of the genomic region. Three closely linked markers, SSR00931, one InDel (AMS12), and one CAPS marker (AMS13) were identified and developed for future marker-assisted selection (MAS). Expression profiling of genes within the QTL region via qRT-PCR revealed several putative candidate genes involved in defense signaling cascades, including those encoding leucine-rich repeat (LRR) receptor-like kinases, pathogenesis-related proteins, and enzymes associated with secondary metabolite biosynthesis. These genes are likely to contribute to viral restriction by modulating host defense pathways, suppressing virus replication, and reinforcing cellular barriers. The discovery of this major-effect QTL and closely linked markers provides a valuable genomic resource for precision breeding of ToLCNDV-resistant cucumber cultivars. The present study sheds light on the molecular basis of begomovirus-host interactions in cucurbits.
Silver nanoparticles were synthesized using the extract of Aloe vera leaves and the presence of light enhanced the synthesis of phytogenic silver nanoparticles. The colour change from opaque white to brown is the initial indication for the synthesis of silver nanoparticles, that was further confirmed by UV-Vis spectroscopy exhibiting λmax at 440 nm. The highest synthesis of AgNPs was recorded at 50 °C. Atomic force microscopy confirmed the average size of silver nanoparticles as ˂100 nm and zeta potential as 77.4 nm with a surface charge of -21.4 mV. X-ray diffraction analysis showed crystalline nature of phytogenic silver nanoparticles. Green nanoparticles showed antimicrobial activity against bacteria (Escherichia coli DH5α and Bacillus subtilis subsp. subtilis JJBS250) and a thermophilic mould, Myceliophthora thermophila BJAMDU7 with a minimum inhibitory concentration of approximately 100 µg/ml. Phytogenic silver nanoparticles did not show hemolysis and cytotoxicity activity. Further, green nanoparticles inhibited the growth of Plasmodium falciparum showing antimalarial potential. Malachite green and gentian violet were decolorized 82
Surveillance systems generate large volumes of video data, making fast and reliable anomaly detection essential for public-safety and smart-city applications. Existing video anomaly detection (VAD) methods often struggle with imbalanced anomaly occurrence, domain shifts, noise interference, and deployment inefficiency. This paper proposes a hybrid CNN–SRU-LSTM–MIL framework that integrates spatio–temporal CNN features, an annealed top-k MIL ranking loss, efficient SRU-based temporal modeling, and LSTM reconstruction to enhance accuracy, robustness, and runtime performance. To support real-world deployment, we incorporate pruning, quantization, knowledge distillation, and corruption-aware preprocessing. Experiments on UCF-Crime, CUHK Avenue, ShanghaiTech, UMN, and a real-world traffic dataset show consistent improvements, including ≈ 2% AUC gains over state-of-the-art baselines and real-time throughput ( ≈ 24 FPS on RTX GPUs; 10–14 FPS on Jetson Xavier NX after pruning and 8-bit quantization). These results demonstrate that the proposed framework is accurate, efficient, and suitable for practical intelligent surveillance applications.
