We describe an ongoing project to digitize information about plant specimens and make it available to botanists in the field. This first requires digital images and models, and then effective retrieval and mobile computing mechanisms for accessing this information. We have almost completed a digital archive of the collection of type specimens at the Smithsonian Institution Department of Botany. Using these and additional images, we have also constructed prototype electronic field guides for the flora of Plummers Island. Our guides use a novel computer vision algorithm to compute leaf similarity. This algorithm is integrated into image browsers that assist a user in navigating a large collection of images to identify the species of a new specimen. For example, our systems allow a user to photograph a leaf and use this image to retrieve a set of leaves with similar shapes. We measured the effectiveness of one of these systems with recognition experiments on a large dataset of images, and with user studies of the complete retrieval system. In addition, we describe future directions for acquiring models of more complex, 3D specimens, and for using new methods in wearable computing to interact with data in the 3D environment in which it is acquired.
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better templates.PharmRF is a pharmacophore-based scoring function for selecting the best crystal structures with the potential to attain high enrichment rates in pharmacophore-based virtual screening prospectively. The PharmRF scoring function is trained and tested on the PDBbind v2018 protein-ligand complex dataset and employs a random forest regressor to correlate protein pocket descriptors and ligand pharmacophoric elements with binding affinity. PharmRF score represents the calculated binding affinity which identifies highaffinity ligands by thorough pruning of all the PDB entries available for a particular protein of interest with a high PharmRF score. Ligands with high PharmRF scores can provide a better basis for structure-based pharmacophore enumerations with a better enrichment rate. Evaluated on 10 protein-ligand systems of the DUD-E dataset, PharmRF achieved superior performance (average success rate: 77.61%, median success rate: 87.16%) than Vina docking score (75.47%, 79.39%). PharmRF was further evaluated using the CASF-2016 benchmark set yielding a moderate correlation of 0.591 with experimental binding affinity, similar in performance to 25 scoring functions tested on this dataset. Independent assessment of PharmRF on 8 protein-ligand systems of LIT-PCBA dataset exhibited average and median success rates of 57.55% and 74.72% with 4 targets attaining success rate > 90%.
Background and Objective Andrographis paniculata belonging to Acanthaceae family is well-known potential hepatoprotective herb. The role of diet on human health and diseases including Hepatocellular Carcinoma has been demonstrated by innumerable studies which have also testified that plant-derived micro-RNAs, transferred to animals via oral consumption wherein it alters host gene expression and this provides the evidence for inter-species gene regulation. Herein, we aim to explore the role of predicted miRNAs of A. paniculata on human health.Methods and ResultsIn the present study, A. paniculata miRNAs have been predicted and their impact on human transcriptome is uncovered by employing computational approach. By implementing miRNA prediction on A. paniculata genomic data and plant miRNA sequences present in miRBase repository, we have identified 20 miRNAs which are confined to 16 miR families. 197 human target genes were predicted for the identified miRNAs, which were further subjected to Gene Ontology analysis and network analysis revealing their significant role in various biological processes and signaling pathways. The identified hub proteins HGF, NRP2 and CCND1 show their significant involvement in cancerous pathways including STAT3, MAPK, TGF-β, mTOR and VEGFR pathways. Deregulation of these pathways has been reported as oncogenic response involved in Hepatocellular Carcinoma, cell proliferation and metastasis.ConclusionThe findings of our study suggest potentially beneficiary role of A. paniculata miRNAs in regulating cancer via cross-species genetic regulation. Conclusively, our study sustains the promising theory of inter-species gene regulation.
Drug-induced liver injury (DILI) is one of the most severe adverse effects (AEs) causing life-threatening conditions, such as acute liver failure. t has also been recognized as the single most common cause of safety- related post-market withdrawals or warnings Due to the nature and idiosyncrasy of clinical forms of DILI, attempts to develop new predictive approaches to evaluate the risk of a medication being a hepatotoxicant have been difficult. The FDA Adverse Event Reporting System (AERS) provides post-market data illustrating AE morbidity. A quantitative structure –activity relationship (QSAR) model for DILI prediction with satisfactory output is urgently needed. In this study, we documented a high-quality QSAR model for predicting the hepatotoxicity risk of DILI by integrating the use of eight effective classifiers and molecular descriptors given by the VlifeMds program. For the present QSAR study, data set of 99 compounds (withdrawn and approved drugs) collected from different databases were taken. Multiple linear regression and partial least square analysis methods had developed two dimensional QSAR models, and then validated for internal and external predictions. The 2D QSAR model developed was statistically important, and was highly predictive. The validation methods presented essential statistical parameters that proved the model's predictive ability. The developed 2D QSAR model revealed the significance of SsssCE-index, SsOHcount, SsssNcount and SdssPcount descriptors. These findings will prove to be an important guide for furtherdesigning and developing new hepatotoxicity activity.
Hepatocellular carcinoma (HCC) is one of the most common and fatal malignancy in humans and proves to be the third most common cause of cancer-related death. Thus, HCC contributes to major international health problem because its incidence is exponentially increasing in many countries. One of the main reasons for the lethality of HCC is the lack of diagnostic markers for early detection of the disease. At late stages, HCC shows a high clinical heterogeneity with poor prognosis i.e. high tumor recurrence is observed in 60-70% of cases within 5 years after surgery. One of the major reasons is that most patients with HCC were diagnosed at advanced stages. It is crucial to find out new therapeutic targets and novel diagnostic biomarkers for the early diagnosis and timely treatment of HCC and to develop preventive strategies and therapeutic interventions based on an improved understanding of molecular hepato carcinogenesis. Therefore, it is still urgent to further explore the exact molecular mechanisms of the development, progression, invasion, and metastasis of HCC. It has been shown that both genetic and epigenetic alterations are crucial for the initiation of HCC, thus making epigenetics a promising and attractive field for identifying the subset of patients at a high risk of recurrence and with dismal survival outcomes.However, the underlying molecular mechanisms remain unknown. Thus, it is urgent and important to dig the hub molecules and to uncover the key molecular mechanisms. Due to the advances made in research based on next generation sequencers, it is now possible to detect and analyse epigenetic abnormalities associated with cancer. In this review article we are trying to explore previously reported to play key role in HCC development and progression such as, DNA methylation, various histone modifications, chromatin remodelling, and non-coding RNA associated gene silencing are considered to be transcriptional regulatory mechanisms associated with gene expression changes.
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