The Ensemble of Conformations of Antifreeze Glycoproteins (AFGP8): A Study Using Nuclear Magnetic Resonance Spectroscopy.

The primary sequence of antifreeze glycoproteins (AFGPs) is highly degenerate, consisting of multiple repeats of the same tripeptide, Ala-Ala-Thr*, in which Thr* is a glycosylated threonine with the disaccharide beta-d-galactosyl-(1,3)-alpha-N-acetyl-d-galactosamine. AFGPs seem to function as intrinsically disordered proteins, presenting challenges in determining their native structure. In this work, a different approach was used to elucidate the three-dimensional structure of AFGP8 from the Arctic cod Boreogadus saida and the Antarctic notothenioid Trematomus borchgrevinki. Dimethyl sulfoxide (DMSO), a non-native solvent, was used to make AFGP8 less dynamic in solution. Interestingly, DMSO induced a non-native structure, which could be determined via nuclear magnetic resonance (NMR) spectroscopy. The overall three-dimensional structures of the two AFGP8s from two different natural sources were different from a random coil ensemble, but their "compactness" was very similar, as deduced from NMR measurements. In addition to their similar compactness, the conserved motifs, Ala-Thr*-Pro-Ala and Ala-Thr*-Ala-Ala, present in both AFGP8s, seemed to have very similar three-dimensional structures, leading to a refined definition of local structural motifs. These local structural motifs allowed AFGPs to be considered functioning as effectors, making a transition from disordered to ordered upon binding to the ice surface. In addition, AFGPs could act as dynamic linkers, whereby a short segment folds into a structural motif, while the rest of the AFGPs could still be disordered, thus simultaneously interacting with bulk water molecules and the ice surface, preventing ice crystal growth

NMR Spectroscopy Analysis Reveals Differential Metabolic Responses in Arabidopsis Roots and Leaves Treated with a Cytokinesis Inhibitor

In plant cytokinesis, de novo formation of a cell plate evolving into the new cell wall partitions the cytoplasm of the dividing cell. In our earlier chemical genomics studies, we identified and characterized the small molecule endosidin-7, that specifically inhibits callose deposition at the cell plate, arresting late-stage cytokinesis in arabidopsis. Endosidin-7 has emerged as a very valuable tool for dissecting this essential plant process. To gain insights regarding its mode of action and the effects of cytokinesis inhibition on the overall plant response, we investigated the effect of endosidin-7 through a nuclear magnetic resonance spectroscopy (NMR) metabolomics approach. In this case study, metabolomics profiles of arabidopsis leaf and root tissues were analyzed at different growth stages and endosidin-7 exposure levels. The results show leaf and root-specific metabolic profile changes and the effects of endosidin-7 treatment on these metabolomes. Statistical analyses indicated that the effect of endosidin-7 treatment was more significant than the developmental impact. The endosidin-7 induced metabolic profiles suggest compensations for cytokinesis inhibition in central metabolism pathways. This study further shows that long-term treatment of endosidin-7 profoundly changes, likely via alteration of hormonal regulation, the primary metabolism of arabidopsis seedlings. Hormonal pathway-changes are likely reflecting the plant’s responses, compensating for the arrested cell division, which in turn are leading to global metabolite modulation. The presented NMR spectral data are made available through the Metabolomics Workbench, providing a reference resource for the scientific community

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Molecular Thermodynamics Using Nuclear Magnetic Resonance (NMR) Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy is perhaps the most widely used technology from the undergraduate teaching labs in organic chemistry to advanced research for the determination of three-dimensional structure as well as dynamics of biomolecular systems... The NMR spectrum of a molecule under a given experimental condition is unique, providing both quantitative and structural information. In particular, the quantitative nature of NMR spectroscopy offers the ability to follow a reaction pathway of the given molecule in a dynamic process under well-defined experimental conditions. To highlight the use of NMR when determining the molecular thermodynamic parameters, a review of three distinct applications developed from our laboratory is presented. These applications include the thermodynamic parameters of (a) molecular oxidation from time-dependent kinetics, (b) intramolecular rotation, and (c) intermolecular exchange. An experimental overview and the method of data analysis are provided so that these applications can be adopted in a range of molecular systems

HcBench: Methodology, Development, and Characterization of a Customer Usage Representative Big Data/Hadoop Benchmark

Big Data analytics using Map-Reduce over Hadoop has become a leading edge paradigm for distributed programming over large server clusters. The Hadoop platform is used extensively for interactive and batch analytics in ecommerce, telecom, media, retail, social networking, and being actively evaluated for use in other areas. However, to date no industry standard or customer representative benchmarks exist to measure and evaluate the true performance of a Hadoop cluster. Current Hadoop micro-benchmarks such as HiBench-2, GridMix-3, Terasort, etc. are narrow functional slices of applications that customers run to evaluate their Hadoop clusters. However, these benchmarks fail to capture the real usages and performance in a datacenter environment. Given that typical datacenter deployments of Hadoop process a wide variety of analytic interactive and query jobs in addition to batch transform jobs under strict Service Level Agreement (SLA) requirements, performance benchmarks used to evaluate clusters must capture the effects of concurrently running such diverse job types in production environments. In this paper, we present the methodology and the development of a customer datacenter usage representative Hadoop benchmark HcBench which includes a mix of large number of customer representative interactive, query, machine learning, and transform jobs, a variety of data sizes, and includes compute, storage 110, and network intensive jobs, with inter-job arrival times as in a typical datacenter environment. We present the details of this benchmark and discuss application level, server and cluster level performance characterization collected on an Intel Sandy Bridge Xeon Processor Hadoop cluster