Fingerprinting environmental conditions and related stress using stable isotopic composition of rice (Oryza sativa L.) grain organic matter

Sea level rise (SLR) is a primary factor responsible for inundation of low-lying coastal regions across the world, which in turn governs the agricultural productivity. In this study, rice (Oryza sativa L.) cultivated seasonally in the Kuttanad Wetland, a SLR prone region on the southwest coast of India, were analysed for oxygen, hydrogen and carbon isotopic ratios (delta O-18, delta H-2 and delta C-13) to distinguish the seasonal environmental conditions prevalent during rice cultivation. The region receives high rainfall during the wet season which promotes large supply of fresh water to the local water bodies via the rivers. In contrast, during the dry season reduced river discharge favours sea water incursion which adversely affects the rice cultivation. The water for rice cultivation is derived from regional water bodies that are characterised by seasonal salinity variation which co-varies with the delta O-18 and delta H-2 values. Rice cultivated during the wet and the dry season bears the isotopic imprints of this water. We explored the utility of a mechanistic model to quantify the contribution of two prominent factors, namely relative humidity and source water composition in governing the seasonal variation in oxygen isotopic composition of rice grain OM. delta C-13 values of rice grain OM were used to deduce the stress level by estimating the intrinsic water use efficiency (WUEi) of the crop during the two seasons. 1.3 times higher WUE, was exhibited by the same genotype during the dry season. The approach can be extended to other low lying coastal agro-ecosystems to infer the growth conditions of cultivated crops and can further be utilised for retrieving paleo-environmental information from well preserved archaeological plant remains. (c) 2015 Elsevier Ltd. All rights reserved.

Folding of Protein L with Implications for Collapse in the Denatured State Ensemble

A fundamental question in protein folding is whether the coil to globule collapse transition occurs during the initial stages of folding (burst phase) or simultaneously with the protein folding transition. Single molecule fluorescence resonance energy transfer (FRET) and small-angle X-ray scattering (SAXS) experiments disagree on whether Protein L collapse transition occurs during the burst phase of folding. We study Protein L folding using a coarse-grained model and molecular dynamics simulations. The collapse transition in Protein L is found to be concomitant with the folding transition. In the burst phase of folding, we find that FRET experiments overestimate radius of gyration, R-g, of the protein due to the application of Gaussian polymer chain end-to-end distribution to extract R-g from the FRET efficiency. FRET experiments estimate approximate to 6 angstrom decrease in R-g when the actual decrease is approximate to 3 angstrom on guanidinium chloride denaturant dilution from 7.5 to 1 M, thereby suggesting pronounced compaction in the protein dimensions in the burst phase. The approximate to 3 angstrom decrease is close to the statistical uncertainties of the R-g data measured from SAXS experiments, which suggest no compaction, leading to a disagreement with the FRET experiments. The transition-state ensemble (TSE) structures in Protein L folding are globular and extensive in agreement with the Psi-analysis experiments. The results support the hypothesis that the TSE of single domain proteins depends on protein topology and is not stabilized by local interactions alone.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.