Measuring the Hubble constant with Type Ia supernovae as near-infrared standard candles

The most precise local measurements of $H_0$ rely on observations of Type Ia supernovae (SNe Ia) coupled with Cepheid distances to SN Ia host galaxies. Recent results have shown tension comparing $H_0$ to the value inferred from CMB observations assuming $\Lambda$CDM, making it important to check for potential systematic uncertainties in either approach. To date, precise local $H_0$ measurements have used SN Ia distances based on optical photometry, with corrections for light curve shape and colour. Here, we analyse SNe Ia as standard candles in the near-infrared (NIR), where intrinsic variations in the supernovae and extinction by dust are both reduced relative to the optical. From a combined fit to 9 nearby calibrator SNe with host Cepheid distances from Riess et al. (2016) and 27 SNe in the Hubble flow, we estimate the absolute peak $J$ magnitude $M_J = -18.524\;\pm\;0.041$ mag and $H_0 = 72.8\;\pm\;1.6$ (statistical) $\pm$ 2.7 (systematic) km s$^{-1}$ Mpc$^{-1}$. The 2.2 $\%$ statistical uncertainty demonstrates that the NIR provides a compelling avenue to measuring SN Ia distances, and for our sample the intrinsic (unmodeled) peak $J$ magnitude scatter is just $\sim$0.10 mag, even without light curve shape or colour corrections. Our results do not vary significantly with different sample selection criteria, though photometric calibration in the NIR may be a dominant systematic uncertainty. Our findings suggest that tension in the competing $H_0$ distance ladders is likely not a result of supernova systematics that could be expected to vary between optical and NIR wavelengths, like dust extinction. We anticipate further improvements in $H_0$ with a larger calibrator sample of SNe Ia with Cepheid distances, more Hubble flow SNe Ia with NIR light curves, and better use of the full NIR photometric data set beyond simply the peak $J$-band magnitude.Comment: 13 pages, replaced to match published version in A&A, code available at https://github.com/sdhawan21/irh

Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs

To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics. To address the generic issues of contour tracking, the yConvex Hypergraph (yCHG) model was proposed by Kanna et al [1]. In this work, we propose a parallel approach to implement the yCHG model by exploiting massively parallel cores of NVIDIA's Compute Unified Device Architecture (CUDA). We perform our experiments on the MODIS satellite image database by NASA, and based on our analysis we observe that the performance of the serial implementation is better on smaller images, but once the threshold is achieved in terms of image resolution, the parallel implementation outperforms its sequential counterpart by 2 to 10 times (2x-10x). We also conclude that an increase in the number of hyperedges in the ROI of a given size does not impact the performance of the overall algorithm.Comment: 1 page, 1 figure published in Proceedings of the 27th ACM International Conference on Supercomputing, ICS 2013, Eugene, Oregon, US

OGLE-TR-56

In early 2003 our team announced the discovery of the second extrasolar transiting planet, around the faint star OGLE-TR-56 (V = 16.6), based on the detection of small changes in the radial velocity of the primary. The star was originally identified as a candidate by the OGLE team from the shallow and periodic dips in its brightness. We present here new precise radial velocity measurements that confirm the variation measured earlier, supporting the conclusion that the companion is indeed a planet. Additional photometric observations are also available, which combined with the spectroscopy yield improved parameters (mass and radius) for the planet

MOLECULAR DOCKING STUDY OF NEUROPROTECTIVE PLANT-DERIVED BIOMOLECULES IN PARKINSON'S DISEASE

Objective: The objective of this study was to explore the therapeutic role of biomolecules in targeting the altered expression of Parkin in PD pathogenesis.Methods: We employed various in silico tools such as drug-likeness parameters, namely, Lipinski filter analysis, Muscle tool for phylogenetic analysis, Castp Server for active site prediction, molecular docking studies using AutoDock 4.2.1 and LIGPLOT1.4.5 were carried out.Results: Our results show that neuroprotective activity of Quercetin to be most effective and can provide their possible clinical relevance in PD. Further, initial screenings of the molecules were done based on Lipinski's rule of five. CastP server used to predict the ligand binding site suggests that this protein can be utilized as a potential drug target. Finally, we have found Quercetin to be most effective amongst four biomolecules in modulating Parkin based on minimum inhibition constant, Ki and highest negative free energy of binding with the maximum interacting surface area in a course of docking studies.Conclusion: This research could provide a potential therapeutic window for the treatment of PD

AN IN SILICO STUDY OF NARINGENIN-MEDIATED NEUROPROTECTION IN PARKINSON'S DISEASE

  Objective: Naringenin is a dietary biomolecule with broad spectrum of activities which protects neurons from various neurotoxic insults and improves cognition and motor function in neurodegenerative diseases. DJ-1 has both, ubiquitin E3 ligase as well as chaperonic activity, and loss of ubiquitin E3 ligase activity of DJ-1 has been found to be associated with familial Parkinson's disease (PD). Naringenin induced E3 ligase activity of DJ-1 which can have possible clinical relevance in PD.Methods: Various in silico parameters such as phylogenetic analysis, homology modeling, active site prediction, and molecular docking studies using AutoDock 4.2.1 and LIGPLOT1.4.5 were carried out.Results: Three-dimensional structure of DJ-1 was generated and Ramachandran plot was obtained for quality assessment. RAMPAGE displayed 99.5% of residues in the most favored regions. 0% residues in additionally allowed and 0.5% disallowed regions of DJ-1 protein. Further, initial screenings of the molecules were done based on Lipinski's rule of five. CastP server used to predict the ligand binding site suggests that this protein can be utilized as a potential drug target. Finally, we have found naringenin to be most effective among four biomolecules in modulating DJ-1 based on minimum inhibition constant, Ki, and highest negative free energy of binding with maximum interacting surface area in the course of docking studies.Conclusion: Our study suggests that based on different in silico parameters and molecular docking studies, naringenin can provide a new avenue for PD therapeutics

A Conceptual Architecture for a Quantum-HPC Middleware

Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) coupled to classical computing nodes (HPC). With the increasing scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum-HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high-performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly.Comment: 12 pages, 3 figure