Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century 1-4 , as well as on whether the changes observed are due to global warming 5,6 or regional urbanization 7 . Although a uniform and consistent decrease in moderate rainfall has been reported 1,3 , a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory [8][9][10][11][12][13][14][15] to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability. We show that when generalized extreme value theory 8,16-18 is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches 2-4 . Furthermore, our space-time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.There is considerable debate in the recent literature about the nature of space-time trends in extreme rainfall over India 1-3 and their attribution to aspects of global change, specifically, global climate change 5,6 versus regional urbanization patterns 7 . Previous researchers have drawn a variety of conclusions 1-4 about trends in rainfall extremes during the Indian monsoon from a regular 1 • ×1 • (or similar) gridded daily rainfall dataset over India for 1951-2003. The differences can probably be attributed to the corresponding definitions of extremes, levels of spatial aggregation and areas of coverage. The use of fixed thresholds over a 12 • × 10 • box labelled as Central India suggested an increasing trend in rainfall extremes concurrent with decreasing moderate rainfall, resulting in no discernible net trends 1 . However, the use of variable percentile-based thresholds over each individual 1 • × 1 • grid 2 , analysis based on homogeneous regions 3 and analysis with a percentile-based definition of the frequency and intensity of rainfall extremes 4 showed no discernible spatially uniform trends in rainfall extremes over India. Field significance tests do not statistically support the hypothesis of increasing trends in heavy rain events 2,3 , and the region labelled as Central India in ref. 1 may not be meteorologically homogeneous 19 . However, the use of additional data (1901-2004; ref. 5) confirmed the findings of ref. 1 when identical definitions of extremes, aggregations and coverage were used. The often conflicting insights about Indian rainfall extremes in the recent literature point to the importance of effective characterization of extremes, especially for understanding and communicating their relevance to impacts and policy.Here we show that rainfall extremes over I...

Hydroxymethylbilane synthase (HMBS), the third enzyme in the heme biosynthetic pathway, catalyzes the head-to-tail condensation of four molecules of porphobilinogen (PBG) to form the linear tetrapyrrole 1-hydroxymethylbilane (HMB). Mutations in human () cause acute intermittent porphyria (AIP), an autosomal-dominant disorder characterized by life-threatening neurovisceral attacks. Although the 3D structure of hHMBS has been reported, the mechanism of the stepwise polymerization of four PBG molecules to form HMB remains unknown. Moreover, the specific roles of each of the critical active-site residues in the stepwise enzymatic mechanism and the dynamic behavior of hHMBS during catalysis have not been investigated. Here, we report atomistic studies of HMB stepwise synthesis by using molecular dynamics (MD) simulations, mutagenesis, and in vitro expression analyses. These studies revealed that the hHMBS active-site loop movement and cofactor turn created space for the elongating pyrrole chain. Twenty-seven residues around the active site and water molecules interacted to stabilize the large, negatively charged, elongating polypyrrole. Mutagenesis of these active-site residues altered the binding site, hindered cofactor binding, decreased catalysis, impaired ligand exit, and/or destabilized the enzyme. Based on intermediate stages of chain elongation, R26 and R167 were the strongest candidates for proton transfer to deaminate the incoming PBG molecules. Unbiased random acceleration MD simulations identified R167 as a gatekeeper and facilitator of HMB egress through the space between the enzyme's domains and the active-site loop. These studies identified the specific active-site residues involved in each step of pyrrole elongation, thereby providing the molecular bases of the active-site mutations causing AIP.

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

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Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs’ mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.