The National Institute of Science and Technology (NIST) was established in 1996 by a group of academicians and technocrats educated in the top institutes of India and abroad. It is promoted by SM Charitable Educational Trust under the vision and guidance of its founder chairman, Dr. Sukant K. Mahapatra, Ph.D, Stevens Institute of Technology, NJ, USA . The institute is approved by the All India Council for Technical Education (AICTE) and is affiliated to the BPUT. The institute campus of 70 acres is located in the Pallur Hills about 12 km from Berhampur and three hours drive from Bhubaneswar.It is the first NRI educational venture in the state of Odisha and first engineering college under Berhampur University. The primary objective of the founders was to make NIST as a center of academic excellence and research in the field of science and technology in their home state of Odisha.The institute has grown under the leadership of Dr. Sukant K. Mohapatra – Founder & Chairman. In its 24 years, it has produced four BOYSCAST faculty scholars who have been funded by Government of India to pursue postgraduate research in the United States and Germany. NIST has produced three Fulbright scholars. Three of its faculty have won the Samant Chandrasekhar Young Scientist Award given by the Government of odisha. Two AICTE funded emeritus professors worked at NIST. The faculty of the institute have been awarded three URSI Young Scientist Awards. More than twenty-five post doctorate scholarships have been awarded to institute faculty. The faculty members of the institute are alumni of IIN, IISc, ISI, Jadavpur University and Calcutta University, etc. More than 35 faculty of the institute are from the CET alone which is a testimony to its ability to attract and retain top-notch faculty. The faculties of the institute have published more than 250 journal papers in refereed journals and are authors of about 24 books. The institute also has its own publishing house, the NIST Press. The institute has received a number of grants, projects, special funding, travel funding, SDPs, FIST grant, scholarships, etc., from the DST, CSIR, AICTE, IT companies, etc.NIST is a considered as a benchmark research institution in eastern India. The institute has recently signed MoU's for research collaboration in the field of nanosciences, communications, semiconductor technology with the National Taiwan University and University of Electro Communications, Japan. NIST has signed MoU with industries like Sandhur and SunMoksha for collaborative work in the area of nano technology and renewal energy. NIST, in association with IIT, Khargapur, is also part of the MHRD Virtual Laboratory project.
The prediction of friction and wear is crucial in engineering, and current measurement methods primarily rely on experimentation. However, experimental measurements are limited by high costs, lengthy cycle times, and dependence on specific working conditions. To address these challenges, we propose a mesoscopic friction and wear model of elastoplastic metallic materials to reduce the dependence on friction and wear testing. This model integrates a mesoscopic coupled plasticity-damage model with a three-dimensional non-Gaussian rough surface characterization method. Based on this model, taking the connecting rod-bearing bushing contact pair of a diesel engine as an example, the effects of sliding displacement, normal force, and temperature on the friction and wear are simulated, and the prediction ability of the model is verified through friction and wear testing. Finally, we investigate the necessity of incorporating size effects into the mesoscopic friction and wear model. The study indicates that since the material has obvious size effects at the asperities, it must be considered in the mesoscopic friction and wear model.
Objective: To construct a machine learning diagnostic model for hereditary hearing loss based on GJB2 and SLC26A4 genes and perform interpretability analysis using SHapley Additive explanations (SHAP). Methods: The data of genetic variants and hearing status were collected from 1 539 individuals at the Deafness Molecular Diagnosis Center of the Chinese PLA General Hospital from June 2015 to August 2024. Participants were categorized by expert diagnosis as hereditary hearing loss patients or non-hereditary hearing loss individuals, and were randomly assigned to a training set (n=1 077) and a test set (n=462) in a 7∶3 ratio using a computer-generated random sequence. Using non-zero coefficient variants of GJB2 and SLC26A4 genes screened by least absolute shrinkage and selection operator (LASSO) regression, six machine learning models including logistic regression, decision tree, random forest, gradient boosting (GB), eXtreme Gradient Boosting and k-nearest neighbors were constructed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with the DeLong test, and accuracy, precision, sensitivity, F1 score and specificity were calculated. The best model was compared against intermediate-level physicians to assess its clinical value, and SHAP was applied for interpretation. Results: A total of 748 hereditary hearing loss patients (391 males and 357 females) aged 29 (13, 36) years and 791 non-hereditary hearing loss individuals (405 males and 386 females) aged 27 (12, 36) years were included. LASSO regression yielded 121 non-zero coefficient variants: 34 in the GJB2 gene and 87 in the SLC26A4 gene. The GB model produced an AUC of 0.975 (95%CI: 0.967-0.983), outperforming each of the other five models (all P<0.05). Moreover, the GB model also showed higher precision [98.5% (95%CI: 97.8%-99.2%) vs 92.6% (95%CI: 90.4%-94.4%)] and specificity [98.7% (95%CI: 98.1%-99.3%) vs 93.2% (95%CI: 91.2%-94.8%)] than intermediate-level physicians (both P<0.001). SHAP identified the top 10 impactful variants in the GB model: nine pathogenic variants (GJB2: p.Leu79CysfsTer3, p.His100ArgfsTer14, p.Val37Ile, p.Gly59AlafsTer18; SLC26A4: c.919-2A>G, p.His723Arg, p.Asn392Tyr, p.Thr410Met, p.Val659Leu) and one benign variant (GJB2: p.Val27Ile). The model tends to diagnose individuals carrying pathogenic homozygous or compound heterozygous variants as hereditary hearing loss patients. Conclusion: The machine learning models incorporating the GJB2 and SLC26A4 genes are of referential value for the auxiliary diagnosis of hereditary hearing loss, with the GB model demonstrating the best diagnostic performance.
Emerging evidence suggests that the extracellular matrix (ECM) possesses a “memory” that can influence cell physiology and recellularization outcomes. Understanding this memory is essential to allow the use of bioengineered organs derived from diseased ECM, offering a solution to the critical organ shortage. To address this, we investigated whether the memory of ECM derived from metabolic dysfunction-associated steatohepatitis (MASH) livers impacts disease establishment following transplantation. Partial orthotopic transplantation of decellularized MASH-derived ECM was performed in control and MASH recipients. Histological analysis confirmed complete recellularization; however, molecular and metabolomic analyses revealed that MASH ECM stimulated de novo lipogenesis and fibrogenesis, inducing impaired lipid oxidation and mitochondrial dysfunction, which contributed to disease progression by promoting altered lipid turnover and inflammatory signalling. In vitro analysis revealed that MASH-ECM disrupted calcium signalling and promoted the maintenance of a pathological phenotype. Although derived from diseased livers, human ECM can promote cell survival and permissiveness. In conclusion, diseased ECM memory impacts cell physiology, suggesting that the scaffold can drive disease progression independently of the cellular environment. Thus, further studies are needed to develop strategies capable of reversing the pathological memory associated with ECM to allow its use in liver transplantation.
Several community empowerment programs related to environmental topics have been reported in Indonesia. Various programs have been carried out. Several studies report the success and failure of these programs. This article aims to summarize the strategies reported to support the success of the future empowerment program. This literature review can contribute to the field of environmental science, community empowerment, and other relevant studies. The success strategy topic was chosen based on the results of a bibliometric analysis using Harzing's Publish or Perish application and VOSviewer. A total of 20 relevant articles were then selected through Google Scholar and analyzed descriptively. The results show that 10 strategies are needed to support the success of community empowerment programs addressing environmental issues
Muntingia calabura L. has been reported to possess diverse pharmacological effects, with flavonoid derivatives recognized as the main bioactive constituents. This study aimed to identify flavonoids from M. calabura fruit extract and to explore their pharmacological potential as anti-aging agents targeting key proteins through a network pharmacology approach and in silico analyses. The research was conducted in several stages, including maceration extraction and identification of flavonoid compounds from the ethanol extract of M. calabura fruit. The identified compounds were subsequently evaluated for physicochemical properties and skin permeability using web-based tools. Compounds meeting these criteria were further analyzed through network pharmacology, molecular docking, and molecular dynamics simulations. A total of 41 compounds were identified, including 36 flavonoid derivatives. Among them, 6-hydroxy-7-methoxy-2-(2-phenylethyl)-chromone (compound 16) demonstrated strong multi-target interactions with aging-related proteins, including collagenase, gelatinase, elastase, and tyrosinase. Physicochemical and skin permeability evaluations indicated that 38 compounds were suitable for further analysis, while network pharmacology linked 19 compounds to aging-related proteins, particularly matrix metalloproteinases (MMPs) and tyrosinase (TYR). Docking studies revealed that compound 16 exhibited binding affinities of –8.65 kcal/mol (collagenase), –8.91 kcal/mol (gelatinase), –7.26 kcal/mol (elastase), and –1.31 kcal/mol (tyrosinase). Molecular dynamics simulations confirmed the stability of these interactions. Collectively, these findings suggest that the ethanol extract of M. calabura fruit may exert anti-aging effects, supported by the presence of stable, multi-target flavonoid derivatives, making it a promising candidate for further research and development.
