Mapping Samudra Tapu glacier:A holistic approach utilizing radar and optical remote sensing data for glacier radar facies mapping and velocity estimation

Himalayan glaciers have shown more sensitivity and visible changes to the climate change and global warming in the last 150 years. The highly rugged topography and inaccessible remote areas makes satellite images as the most appropriate source of information retrieval. We performed remote sensing based glacier change study for Samudra Tapu glacier, located in the Chandra basin of North-West Himalaya. In the present study, the capabilities of both optical and microwave remote sensing data was analysed in glacier change study in terms of its coverage, shift in equilibrium line altitude (ELA) and surface velocity over a period from 2000 to 2021. Multi Sensor (RISAT-1, Sentinel-1) time series of C-band SAR data along with a object oriented classification technique were used to identify different glacier facies such as percolation facies, icefalls, bare ice facies, refreeze snow and supraglacial debris. These classified maps were also used to detect the snow line and firn line along with ELA, aided with elevation information from digital elevation model (DEM). It was identified that more than 50 % of the total glacier area still lies into accumulation region. Further, we estimated the glacier surface velocity using Differential Interferometric Synthetic Aperture Radar (DInSAR) technique using European Remote Sensing Satellite (ERS-1/2) tandem data of 1996. High value of coherence was observed from the SAR return signal for one-day temporal difference. A mean velocity of 17–24 cm/day was found for the months of March and May 1996, highest flow rates were seen in the high accumulation area located in the Eastern and Southern Aspect of glacier. Spatial analysis of velocity patterns with respect to slope and aspect show that high rates of flow was found in southern slopes and movement rates generally increase with increase in slope. Feature tracking approach was used to estimate the glacier flow for long term and seasonal basis using optical and SAR datasets (IRS-1C, 1D PAN, Landsat-7, 8 PAN, and TANDEM-x) during 1999–2020 period. The results suggest that glacier flow varies with season, i.e., high velocity during spring-summer season, as compared to late summer or winter and, the rate of ice flow changes over the years. The mean glacier velocity reduced to 49.5 m/year during 2013–2020 time, as compared to 67.67 m/year during 1999–2003 time. These results of reducing glacier velocity and changing snow line altitude indicates enhanced glacier's melt rate and overall negative mass balance for Smudra tapu glacier.</p

First Results from MFOSC-P : Low Resolution Optical Spectroscopy of a Sample of M dwarfs within 100 parsecs

Mt. Abu Faint Object Spectrograph and Camera (MFOSC-P) is an in-house developed instrument for Physical Research Laboratory (PRL) 1.2m telescope at Mt. Abu India, commissioned in February 2019. Here we present the first science results derived from the low resolution spectroscopy program of a sample of M Dwarfs carried out during the commissioning run of MFOSC-P between February-June 2019. M dwarfs carry great significance for exoplanets searches in habitable zone and are among the promising candidates for the observatory's several ongoing observational campaigns. Determination of their accurate atmospheric properties and fundamental parameters is essential to constrain both their atmospheric and evolutionary models. In this study, we provide a low resolution (R$\sim$500) spectroscopic catalogue of 80 bright M dwarfs (J$<$10) and classify them using their optical spectra. We have also performed the spectral synthesis and $\chi^2$ minimisation techniques to determine their fundamental parameters viz. effective temperature and surface gravity by comparing the observed spectra with the most recent BT-Settl synthetic spectra. Spectral type of M dwarfs in our sample ranges from M0 to M5. The derived effective temperature and surface gravity are ranging from 4000 K to 3000 K and 4.5 to 5.5 dex, respectively. In most of the cases, the derived spectral types are in good agreement with previously assigned photometric classification.Comment: Accepted for Publication in MNRA

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

How Much Aromatic Naphthalene and Graphene Are?

Naphthalene, (Aromatic stabilization Energy; ASE, 50-60 kcal/mol) polyacenes and graphene are considered aromatic. Existing models for polyacenes predict a linearly increasing ASE and give little insights into their high reactivity and decreasing stability. Graphene’s aromaticity has been studied earlier qualitatively suggesting alternate Clar’s sextet and two-electrons per ring, but ASE estimates have not been reported yet. In this paper, various Heat of Hydrogenation (HoH) and isodesmic schemes have been proposed and compared for the estimation of naphthalene ASE. Results show that HoH schemes are simple to design, are equivalent to isodesmic schemes, and unconjugated unsaturated reference systems predict ASE values in agreement with literature reports. Partially aromatic reference systems underestimate ASE. HoH schemes require calculations for a smaller number of structures, and offer scope for experimental validation, and involve enthalpy differences. Polyacene (X-axis extensions of benzene) ASE estimates (using HoH scheme) correlate well with experimental instability data and offer new physical insights explaining the absence of arbitrarily larger polyacenes. ASEs extrapolated from quadratic and logarithmic regression models have been used to estimate the largest polyacene with limiting ASE values. ASE values for Pyrene (Y-axis extension of benzene) and higher analogues (here called pyrene-vertacenes) are estimated using HoH schemes. Further truncated graphene models and graphene are approximated as combinations of polyacene and pyrene-vertacene units. First ever ASE and molecular sizes (22-255 nM) estimates predict nanometer size ranges for flat graphene in agreement with recent experiments and offer new physical insights. These ASE and size estimates for graphene may prove useful in the design of novel energy (hydrogen) storage, electronic and material science applications.</p

SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates