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This study proposed to collect and analyze the daily activities of dromedary camels kept under farm conditions by training a model depending on frames as opposed to long-term windows. Any change in daily activities gives information on the camel’s health status, and an early warning can be generated to issue a signal upon identifying any diseased camels. Five daily activities: eating, standing, sitting, drinking, and sleeping, were recorded, and analyzed in two phases, each of 7 days of continuous video recordings in November and December 2022. Phase 1 was applied on two camels to test the time elapsed for each daily activity and Phase 2 was applied on 4 camels to determine the difference in daily activities between different camel individuals. The average standing and sleeping activities reduced from 9.8 h to 6.0 h, and from 4.3 h to 2.8 h in phases 1 and 2, respectively. While the average sitting and eating activities increased from 6.2 h to 9.9 h and from 3 h to 4.7 h in phases 1 and 2, respectively. The average drinking activity for all tested camels was 43 min in both phases. All camels were active in the eating, drinking, and standing activities during the early morning hours and after 16:00 O’clock till evening. While during noon and early afternoon, they were sitting under shadows. During the evening and nighttime, they mainly sat, occasionally moving their heads, with some standing and walking activities. The average eating, standing, and sleeping activities did not differ between the 4 tested camels throughout the experiment. While the drinking and sitting activities showed an average variation of 25% and 12%, respectively, in between the tested camels. In conclusion, the camel’s daily activities can be monitored by using the deep learning model. This model efficiently monitors and improves the health of camels kept on farms in remote areas beyond human control.

Legumes are essential and play a significant role in maintaining food standards and augmenting physiochemical soil properties through the biological nitrogen fixation process. Biotic and abiotic factors are the main factors limiting legume production. Classical breeding methodologies have been explored extensively about the problem of truncated yield in legumes but have not succeeded at the desired rate. Conventional breeding improved legume genotypes but with more resources and time. Recently, the invention of next-generation sequencing (NGS) and high-throughput methods for genotyping have opened new avenues for research and developments in legume studies. During the last decade, genome sequencing for many legume crops documented. Sequencing and re-sequencing of important legume species have made structural variation and functional genomics conceivable. NGS and other molecular techniques such as the development of markers; genotyping; high density genetic linkage maps; quantitative trait loci (QTLs) identification, expressed sequence tags (ESTs), single nucleotide polymorphisms (SNPs); and transcription factors incorporated into existing breeding technologies have made possible the accurate and accelerated delivery of information for researchers. The application of genome sequencing, RNA sequencing (transcriptome sequencing), and DNA sequencing (re-sequencing) provide considerable insights for legume development and improvement programs.Moreover, RNA-Seq helps to characterize genes, including differentially expressed genes, and can be applied for functional genomics studies, especially when there is limited information available for the studied genomes. Genome-based crop development studies and the availability of genomics data as well as decision-making gears look be specific for breeding programs. This review mainly presents an overview of the path from classical breeding to new emerging genomics tools, which will trigger and accelerate genomics-assisted breeding for recognition of novel genes for yield and quality characters for sustainable legume crop production. Keywords: Legumes, Classical breeding, RNA sequencing, Transcriptome, Improvement

The Klotho is known as lifespan enhancing protein involved in antagonizing the effect of Wnt proteins. Wnt proteins are stem cell regulators, and uninterrupted exposure of Wnt proteins to the cell can cause stem and progenitor cell senescence, which may lead to aging. Keeping in mind the importance of Klotho in Wnt signaling, in silico approaches have been applied to study the important interactions between Klotho and Wnt3 and Wnt3a (wingless-type mouse mammary tumor virus (MMTV) integration site family members 3 and 3a). The main aim of the study is to identify important residues of the Klotho that help in designing peptides which can act as Wnt antagonists. For this aim, a protein engineering study is performed for Klotho, Wnt3 and Wnt3a. During the theoretical analysis of homology models, unexpected role of number of disulfide bonds and secondary structure elements has been witnessed in case of Wnt3 and Wnt3a proteins. Different in silico experiments were carried out to observe the effect of correct number of disulfide bonds on 3D protein models. For this aim, total of 10 molecular dynamics (MD) simulations were carried out for each system. Based on the protein-protein docking simulations of selected protein models of Klotho with Wnt3 and Wnt3a, different peptides derived from Klotho have been designed. Wnt3 and Wnt3a proteins have three important domains: Index finger, N-terminal domain and a patch of similar to 10 residues on the solvent exposed surface of palm domain. Protein-peptide docking of designed peptides of Klotho against three important domains of palmitoylated Wnt3 and Wnt3a yields encouraging results and leads better understanding of the Wnt protein inhibition by proposed Klotho peptides. Further in vitro studies can be carried out to verify effects of novel designed peptides as Wnt antagonists.

Dengue virus is a major issue of tropical and sub-tropical regions. The proliferation of virus results in immense number of deaths each year because of unavailability of on-shelf drugs. This issue necessitates the design of novel anti-Dengue drugs. The protease enzyme pathway is the critical target for drug design due to its significance in the replication, survival and other cellular activities of Dengue virus. Keeping in mind the worsening situation regarding Dengue virus, approximately eighteen million drug-like compounds from the ZINC small molecule database have been screened against Nonstructural Protein 3 (NS3) previously by our group. In this study, in order to investigate the effect of extended time of molecular dynamics (MD) simulations on structural and dynamical profiles of used complexes, simulation run time is increased from 50-ns to 100-ns for the each system. In addition, a well-known Dengue virus inhibitor (MB21) from literature is used as reference structure (positive control) to compare the proposed molecules. Post-processing MD analyses including Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations were conducted to predict binding free energies of inhibitors from derived trajectory frames of MD simulations. Identified compounds are further directed to Quantum-Polarized Ligand Docking (QPLD), molecular fingerprint-based virtual screening of another small molecule database (Otava Drug Like small molecule database), and Structure-based Pharmacophore Modeling (E-Pharmacophore). Finally, cell proliferation and cytotoxicity tests as well as pre and post-treatment on HUH7 cells infected with DENV2 NGC strain are applied for four identified hit molecules (ZINC36681949, ZINC44921800, ZINC95518765 and ZINC39500661) to check whether these drugs inhibit DENV2 from entry and/or exit pathways. Based on cell-based Dengue quantification assays, there is no effect seen on pre-treatment of cells with these compounds indicating that the early infection processes of virus is not affected. In contrast, the post-treatment of cells with these compounds after Dengue virus infection has resulted in a significant 1 log PFU/ml reduction of the virus infectious titre. (C) 2017 Elsevier Inc. All rights reserved.

Dengue virus is a major issue of tropical and sub-tropical regions. Dengue virus has been the cause behind the major alarming epidemics in the history with mass causalities from the decades. Unavailability of on-shelf drugs for the prevention of further proliferation of virus inside the human body results in immense number of deaths each year. This issue necessitates the design of novel anti-dengue drug. The protease enzyme pathway is the critical target for drug design due to its significance in the replication, survival and other cellular activities of dengue virus. Therefore, approximately eighteen million compounds from the ZINC database have been virtually screened against nonstructural protein 3 (NS3). The incremental construction algorithm of Glide docking program has been used with its features high throughput virtual screening (HTVS), standard precision (SP), extra precision (XP) and in combination of Prime module, induced fit docking (IFD) approach has also been applied. Five top-ranked compounds were then selected from the IFD results with better predicted binding energies with the catalytic triad residues (His51, Asp75, and Ser135) that may act as potential inhibitors for the underlying target protease enzyme. The top-ranked compounds ZINC95518765, ZINC44921800, ZINC71917414, ZINC39500661, ZINC36681949 have shown the predicted binding energies of -7.55, -7.36, -8.04, -8.41, -9.18kcal/mol, respectively, forming binding interactions with three catalytically important amino acids. Top-docking poses of compounds are then used in molecular dynamics (MD) simulations. In computational studies, our proposed compounds confirm promising results against all the four serotypes of dengue virus, strengthening the opportunity of these compounds to work as potential on-shelf drugs against dengue virus. Further experimentation on the proposed compounds can result in development of strong inhibitors.

Pakistan possesses a rich and vast source of natural products (NPs). Some of these secondary metabolites have been identified as potent therapeutic agents. However, the medicinal usage of most of these compounds has not yet been fully explored. The discoveries for new scaffolds of NPs as inhibitors of certain enzymes or receptors using advanced computational drug discovery approaches are also limited due to the unavailability of accurate 3D structures of NPs. An organized database incorporating all relevant information, therefore, can facilitate to explore the medicinal importance of the metabolites from Pakistani Biodiversity. The Chemical Database of Pakistan (ChemDP; release 01) is a fully-referenced, evolving, web-based, virtual database which has been designed and developed to introduce natural products (NPs) and their derivatives from the biodiversity of Pakistan to Global scientific communities. The prime aim is to provide quality structures of compounds with relevant information for computer-aided drug discovery studies. For this purpose, over 1000 NPs have been identified from more than 400 published articles, for which 2D and 3D molecular structures have been generated with a special focus on their stereochemistry, where applicable. The PM7 semiempirical quantum chemistry method has been used to energy optimize the 3D structure of NPs. The 2D and 3D structures can be downloaded as .sdf, .mol, .sybyl, .mol2, and .pdb files - readable formats by many chemoinformatics/bioinformatics software packages. Each entry in ChemDP contains over 100 data fields representing various molecular, biological, physico-chemical and pharmacological properties, which have been properly documented in the database for end users. These pieces of information have been either manually extracted from the literatures or computationally calculated using various computational tools. Cross referencing to a major data repository i.e. ChemSpider has been made available for overlapping compounds. An android application of ChemDP is available at its website. The ChemDP is freely accessible at www.chemdp.com.