Diurnal and seasonal variation of mixing ratio and delta C-13 of air CO2 observed at an urban station Bangalore, India

We present here observations on diurnal and seasonal variation of mixing ratio and delta C-13 of air CO2, from an urban station-Bangalore (BLR), India, monitored between October 2008 and December 2011. On a diurnal scale, higher mixing ratio with depleted delta C-13 of air CO2 was found for the samples collected during early morning compared to the samples collected during late afternoon. On a seasonal scale, mixing ratio was found to be higher for dry summer months (April-May) and lower for southwest monsoon months (June-July). The maximum enrichment in delta C-13 of air CO2 (-8.04 +/- 0.02aEuro degrees) was seen in October, then delta C-13 started depleting and maximum depletion (-9.31 +/- 0.07aEuro degrees) was observed during dry summer months. Immediately after that an increasing trend in delta C-13 was monitored coincidental with the advancement of southwest monsoon months and maximum enrichment was seen again in October. Although a similar pattern in seasonal variation was observed for the three consecutive years, the dry summer months of 2011 captured distinctly lower amplitude in both the mixing ratio and delta C-13 of air CO2 compared to the dry summer months of 2009 and 2010. This was explained with reduced biomass burning and increased productivity associated with prominent La Nina condition. While compared with the observations from the nearest coastal and open ocean stations-Cabo de Rama (CRI) and Seychelles (SEY), BLR being located within an urban region captured higher amplitude of seasonal variation. The average delta C-13 value of the end member source CO2 was identified based on both diurnal and seasonal scale variation. The delta C-13 value of source CO2 (-24.9 +/- 3aEuro degrees) determined based on diurnal variation was found to differ drastically from the source value (-14.6 +/- 0.7aEuro degrees) identified based on seasonal scale variation. The source CO2 identified based on diurnal variation incorporated both early morning and late afternoon sample; whereas, the source CO2 identified based on seasonal variation included only afternoon samples. Thus, it is evident from the study that sampling timing is one of the important factors while characterizing the composition of end member source CO2 for a particular station. The difference in delta C-13 value of source CO2 obtained based on both diurnal and seasonal variation might be due to possible contribution from cement industry along with fossil fuel / biomass burning as predominant sources for the station along with differential meteorological conditions prevailed.

Structural studies on Mycobacterium tuberculosis RecA: Molecular plasticity and interspecies variability

Structures of crystals of Mycobacterium tuberculosis RecA, grown and analysed under different conditions, provide insights into hitherto underappreciated details of molecular structure and plasticity. In particular, they yield information on the invariant and variable features of the geometry of the P-loop, whose binding to ATP is central for all the biochemical activities of RecA. The strengths of interaction of the ligands with the P-loop reveal significant differences. This in turn affects the magnitude of the motion of the `switch' residue, Gln195 in M. tuberculosis RecA, which triggers the transmission of ATP-mediated allosteric information to the DNA binding region. M. tuberculosis RecA is substantially rigid compared with its counterparts from M smegmatis and E. coli, which exhibit concerted internal molecular mobility. The interspecies variability in the plasticity of the two mycobacterial proteins is particularly surprising as they have similar sequence and 3D structure. Details of the interactions of ligands with the protein, characterized in the structures reported here, could be useful for design of inhibitors against M. tuberculosis RecA.

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

On the variability in fracture toughness of `ductile' bulk metallic glasses

The mode I fracture toughness, K-Ic, of ductile bulk metallic glasses (BMGs) exhibits a high degree of specimen-to-specimen variability. By conducting fracture experiments in modes I and II, we demonstrate that the observed high variability in mode I, vis-a-vis mode II, is a result of highly variable propensity for the conversion of shear bands into cracks in mode I whereas in mode II, crack growth direction is fixed. Thus, the measured variability in K-Ic is intrinsic to the nature of BMGs. (C) 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Decreasing intensity of monsoon low-frequency intraseasonal variability over India

Understanding the changing nature of the intraseasonal oscillatory (ISO) modes of Indian summer monsoon manifested by active and break phase, and their association with extreme rainfall events are necessary for probabilistic estimation of flood-related risks in a warming climate. Here, using ground-based observed rainfall, we define an index to measure the strength of monsoon ISOs and show that the relative strength of the northward-propagating low-frequency ISO (20-60 days) modes have had a significant decreasing trend during the past six decades, possibly attributed to the weakening of large-scale circulation in the region during monsoon season. This reduction is compensated by a gain in synoptic-scale (3-9 days) variability. The decrease in low-frequency ISO variability is associated with a significant decreasing trend in the percentage of extreme events during the active phase of the monsoon. However, this decrease is balanced by significant increasing trends in the percentage of extreme events in the break and transition phases. We also find a significant rise in the occurrence of extremes during early and late monsoon months, mainly over eastern coastal regions. Our study highlights the redistribution of rainfall intensity among periodic (low-frequency) and non-periodic (extreme) modes in a changing climate scenario.

Hemagglutinin sequence conservation guided stem immunogen design from influenza A H3 subtype

Seasonal epidemics caused by influenza A (H1 and H3 subtypes) and B viruses are a major global health threat. The traditional, trivalent influenza vaccines have limited efficacy because of rapid antigenic evolution of the circulating viruses. This antigenic variability mediates viral escape from the host immune responses, necessitating annual vaccine updates. Influenza vaccines elicit a protective antibody response, primarily targeting the viral surface glycoprotein hemagglutinin (HA). However, the predominant humoral response is against the hypervariable head domain of HA, thereby restricting the breadth of protection. In contrast, the conserved, subdominant stem domain of HA is a potential ``universal'' vaccine candidate. We designed an HA stem-fragment immunogen from the 1968 pandemic H3N2 strain (A/Hong Kong/1/68) guided by a comprehensive H3 HA sequence conservation analysis. The biophysical properties of the designed immunogen were further improved by C-terminal fusion of a trimerization motif, ``isoleucine-zipper'', or ``foldon''. These immunogens elicited cross-reactive, antiviral antibodies and conferred partial protection against a lethal, homologous HK68 virus challenge in vivo. Furthermore, bacterial expression of these immunogens is economical and facilitates rapid scale-up.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.

Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.

Microstructural evolution and strength variability in microwires

Tensile experiments on cold-drawn Ni microwires with diameters from similar to 115 to 50 gm revealed high strengths, with significant strength variability for finer wires with diameters less than similar to 50 gm. The wires showed pronounced necking at fracture. The coarser wires with diameters > 50 mu m exhibited conventional ductile cup-cone fracture, with dimples in the central zone and peripheral shear lips, whereas finer wires failed by shear with knife or chisel-edge fractures. Shear bands were observed in all samples. Further, through- section microscopy of selected fractured samples revealed that the shear bands did not go across the enitre specimen for the coarser wires. The shear bands led to grain fragmention, with a reduction in grain aspect ratio as well as rotations away from the initial < 111 > orientations. The strength data were analysed based on a Weibull approach. The data could be rationalized in terms of failure from volume defects in coarser wires, with a high Weibull modulus, and from surface defects in finer wires, with a low Weibull modulus and greater variability. (C) 2015 Elsevier B.V. All rights reserved.