Plant proteomics in India and Nepal: current status and challenges ahead

Plant proteomics has made tremendous contributions in understanding the complex processes of plant biology. Here, its current status in India and Nepal is discussed. Gel-based proteomics is predominantly utilized on crops and non-crops to analyze majorly abiotic (49 %) and biotic (18 %) stress, development (11 %) and post-translational modifications (7 %). Rice is the most explored system (36 %) with major focus on abiotic mainly dehydration (36 %) stress. In spite of expensive proteomics setup and scarcity of trained workforce, output in form of publications is encouraging. To boost plant proteomics in India and Nepal, researchers have discussed ground level issues among themselves and with the International Plant Proteomics Organization (INPPO) to act in priority on concerns like food security. Active collaboration may help in translating this knowledge to fruitful applications. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12298-013-0198-y) contains supplementary material, which is available to authorized users

The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

Abstract We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (&gt;90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.</jats:p

Emergence and Evolution

The aminoacyl-tRNA synthetases (aaRSs) are essential components of the protein synthesis machinery responsible for defining the genetic code by pairing the correct amino acids to their cognate tRNAs. The aaRSs are an ancient enzyme family believed to have origins that may predate the last common ancestor and as such they provide insights into the evolution and development of the extant genetic code. Although the aaRSs have long been viewed as a highly conserved group of enzymes, findings within the last couple of decades have started to demonstrate how diverse and versatile these enzymes really are. Beyond their central role in translation, aaRSs and their numerous homologs have evolved a wide array of alternative functions both inside and outside translation. Current understanding of the emergence of the aaRSs, and their subsequent evolution into a functionally diverse enzyme family, are discussed in this chapter