| 1. |
- Chaudhary, Amit, et al.
(författare)
-
Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions
- 2022
-
Ingår i: International Journal of Biological Macromolecules. - : Elsevier BV. - 0141-8130 .- 1879-0003. ; 220, s. 743-753
-
Forskningsöversikt (refereegranskat)abstract
- Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non-cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition reduces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N- and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered regions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins.
|
|
| 2. |
- Gupta, Alok C., et al.
(författare)
-
Long-term Multiband Near-infrared Variability of the Blazar OJ 287 during 2007-2021
- 2022
-
Ingår i: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 260:2, s. 39-
-
Tidskriftsartikel (refereegranskat)abstract
- We present the most extensive and well-sampled long-term multiband near-infrared (NIR) temporal and spectral variability study of OJ 287, considered to be the best candidate binary supermassive black hole blazar. These observations were made between 2007 December and 2021 November. The source underwent similar to 2-2.5 mag variations in the J, H, and Ks NIR bands. Over these long-term timescales there were no systematic trends in either flux or spectral evolution with time or with the source's flux states. However, on shorter timescales, there are significant variations in flux and spectra indicative of strong changes during different activity states. The NIR spectral energy distributions show diverse facets at each flux state, from the lowest to the highest. The spectra are, in general, consistent with a power-law spectral profile (within 10%) and many of them indicate minor changes (observationally insignificant) in the shift of the peak. The NIR spectra generally steepen during bright phases. We briefly discuss these behaviors in the context of blazar emission scenarios/mechanisms, OJ 287's well-known traditional behavior, and implications for models of the source central engine invoked for its long-term optical semiperiodic variations.
|
|
| 3. |
- Kumar Singh, Abhinav, et al.
(författare)
-
An Integrated Statistical-Machine Learning Approach for Runoff Prediction
- 2022
-
Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:3
-
Tidskriftsartikel (refereegranskat)abstract
- Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.
|
|
