Improved Detection of Remote Homologues Using Cascade PSI-BLAST: Influence of Neighbouring Protein Families on Sequence Coverage

Background: Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. Methodology/Principal Findings: We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of similar to 100% and Mathew's correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. Conclusions/Significance: Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the `bridging' role of related families.

Limit laws for coverage in backbone-sensor networks

We study coverage in sensor networks having two types of nodes, namely, sensor nodes and backbone nodes. Each sensor is capable of transmitting information over relatively small distances. The backbone nodes collect information from the sensors. This information is processed and communicated over an ad hoc network formed by the backbone nodes, which are capable of transmitting over much larger distances. We consider two models of deployment for the sensor and backbone nodes. One is a PoissonPoisson cluster model and the other a dependently thinned Poisson point process. We deduce limit laws for functionals of vacancy in both models using properties of association for random measures.

Impacts of climate change on regional water resources

Water is the most important medium through which climate change influences human life. Rising temperatures together with regional changes in precipitation patterns are some of the impacts of climate change that have implications on water availability, frequency and intensity of floods and droughts, soil moisture, water quality, water supply and water demands for irrigation and hydropower generation. In this article we provide an introduction to the emerging field of hydrologic impacts of climate change with a focus on water availability, water quality and irrigation demands. Climate change estimates on regional or local spatial scales are burdened with a considerable amount of uncertainty, stemming from various sources such as climate models, downscaling and hydrological models used in the impact assessments and uncertainty in the downscaling relationships. The present article summarizes the recent advances on uncertainty modeling and regional impacts of climate change for the Mahanadi and Tunga-Bhadra Rivers in India.

Regional impacts of climate change on irrigation water demands

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U-2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U-2. Consequently, the reference evapotranspiration, modeled by the Penman-Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U-2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright (c) 2012 John Wiley & Sons, Ltd.

Regional variations in the nonlinearity and anisotropy of bovine aortic elastin

Arterial walls have a regular and lamellar organization of elastin present as concentric fenestrated networks in the media. In contrast, elastin networks are longitudinally oriented in layers adjacent to the media. In a previous model exploring the biomechanics of arterial elastin, we had proposed a microstructurally motivated strain energy function modeled using orthotropic material symmetry. Using mechanical experiments, we showed that the neo-Hookean term had a dominant contribution to the overall form of the strain energy function. In contrast, invariants corresponding to the two fiber families had smaller contributions. To extend these investigations, we use biaxial force-controlled experiments to quantify regional variations in the anisotropy and nonlinearity of elastin isolated from bovine aortic tissues proximal and distal to the heart. Results from this study show that tissue nonlinearity significantly increases distal to the heart as compared to proximally located regions (). Distally located samples also have a trend for increased anisotropy (), with the circumferential direction stiffer than the longitudinal, as compared to an isotropic and relatively linear response for proximally located elastin samples. These results are consistent with the underlying tissue histology from proximally located samples that had higher optical density (), fiber thickness (), and trend for lower tortuosity () in elastin fibers as compared to the thinner and highly undulating elastin fibers isolated from distally located samples. Our studies suggest that it is important to consider elastin fiber orientations in investigations that use microstructure-based models to describe the contributions of elastin and collagen to arterial mechanics.

Enhancing Surface Coverage and Growth in Layer-by-Layer Assembly of Protein Nanoparticles

Thin films of bovine serum albumin (BSA) nanoparticles are fabricated via layer-by-layer assembly. The surface of BSA nanoparticles have two oppositely acting functional groups on the surface: amine (NH2) and carboxylate (COO-). The protonation and deprotonation of these functional groups at different pH vary the charge density on the particle surface, and entirely different growth can be observed by varying the nature of the complementary polymer and the pH of the particles. The complementary polymers used in this study are poly(dimethyldiallylammonium chloride) (PDDAC) and poly(acrylic acid) (PAA). The assembly of BSA nanoparticles based on electrostatic interaction with PDDAC suffers from the poor loading of the nanoparticles. The assembly with PAA aided by a hydrogen bonding interaction shows tremendous improvement in the growth of the assembly over PDDAC. Moreover, the pH of the BSA nanoparticles was observed to affect the loading of nanoparticles in the LbL assembly with PAA significantly.

Formulation of a mathematical approach to regional frequency analysis

Estimation of design quantiles of hydrometeorological variables at critical locations in river basins is necessary for hydrological applications. To arrive at reliable estimates for locations (sites) where no or limited records are available, various regional frequency analysis (RFA) procedures have been developed over the past five decades. The most widely used procedure is based on index-flood approach and L-moments. It assumes that values of scale and shape parameters of frequency distribution are identical across all the sites in a homogeneous region. In real-world scenario, this assumption may not be valid even if a region is statistically homogeneous. To address this issue, a novel mathematical approach is proposed. It involves (i) identification of an appropriate frequency distribution to fit the random variable being analyzed for homogeneous region, (ii) use of a proposed transformation mechanism to map observations of the variable from original space to a dimensionless space where the form of distribution does not change, and variation in values of its parameters is minimal across sites, (iii) construction of a growth curve in the dimensionless space, and (iv) mapping the curve to the original space for the target site by applying inverse transformation to arrive at required quantile(s) for the site. Effectiveness of the proposed approach (PA) in predicting quantiles for ungauged sites is demonstrated through Monte Carlo simulation experiments considering five frequency distributions that are widely used in RFA, and by case study on watersheds in conterminous United States. Results indicate that the PA outperforms methods based on index-flood approach.

Development of a regional model for the North Indian Ocean

We have developed a one-way nested Indian Ocean regional model. The model combines the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory's (GFDL) Modular Ocean Model (MOM4p1) at global climate model resolution (nominally one degree), and a regional Indian Ocean MOM4p1 configuration with 25 km horizontal resolution and 1 m vertical resolution near the surface. Inter-annual global simulations with Coordinated Ocean-Ice Reference Experiments (CORE-II) surface forcing over years 1992-2005 provide surface boundary conditions. We show that relative to the global simulation, (i) biases in upper ocean temperature, salinity and mixed layer depth are reduced, (ii) sea surface height and upper ocean circulation are closer to observations, and (iii) improvements in model simulation can be attributed to refined resolution, more realistic topography and inclusion of seasonal river runoff. Notably, the surface salinity bias is reduced to less than 0.1 psu over the Bay of Bengal using relatively weak restoring to observations, and the model simulates the strong, shallow halocline often observed in the North Bay of Bengal. There is marked improvement in subsurface salinity and temperature, as well as mixed layer depth in the Bay of Bengal. Major seasonal signatures in observed sea surface height anomaly in the tropical Indian Ocean, including the coastal waveguide around the Indian peninsula, are simulated with great fidelity. The use of realistic topography and seasonal river runoff brings the three dimensional structure of the East India Coastal Current and West India Coastal Current much closer to observations. As a result, the incursion of low salinity Bay of Bengal water into the southeastern Arabian Sea is more realistic. (C) 2013 Elsevier Ltd. All rights reserved.

SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth's terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specific, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the significant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identified and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensification, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Modifications in Trypsin Digestion Protocol for Increasing the Efficiency and Coverage

Standard trypsin digestion protocol of proteins followed by MALDI-MS analysis has been realized as an important tool for the identification and characterization of proteins. In this article, we proposed the elimination of the step of `staining/de-staining of gel pieces' in in-gel digestion protocol in order to improve the efficiency of trypsin digestion. Coomassie dye is known to interfere with digestion of proteins by trypsin and the procedure of staining-de-staining could result in loss of photoaffinity probe, post translational modifications and catalytic activities of enzymes. Further, we studied parameters like hydrophobicity and isoelectric point, and attempted to quantitatively relate it to the efficiency of trypsin digestion. We suggest that properties of proteins should be considered and trypsin digestion protocol should be appropriately modified as per sequence and other information.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Analytical approach to quantile estimation in regional frequency analysis based on fuzzy framework

Regional frequency analysis is widely used for estimating quantiles of hydrological extreme events at sparsely gauged/ungauged target sites in river basins. It involves identification of a region (group of watersheds) resembling watershed of the target site, and use of information pooled from the region to estimate quantile for the target site. In the analysis, watershed of the target site is assumed to completely resemble watersheds in the identified region in terms of mechanism underlying generation of extreme event. In reality, it is rare to find watersheds that completely resemble each other. Fuzzy clustering approach can account for partial resemblance of watersheds and yield region(s) for the target site. Formation of regions and quantile estimation requires discerning information from fuzzy-membership matrix obtained based on the approach. Practitioners often defuzzify the matrix to form disjoint clusters (regions) and use them as the basis for quantile estimation. The defuzzification approach (DFA) results in loss of information discerned on partial resemblance of watersheds. The lost information cannot be utilized in quantile estimation, owing to which the estimates could have significant error. To avert the loss of information, a threshold strategy (TS) was considered in some prior studies. In this study, it is analytically shown that the strategy results in under-prediction of quantiles. To address this, a mathematical approach is proposed in this study and its effectiveness in estimating flood quantiles relative to DFA and TS is demonstrated through Monte-Carlo simulation experiments and case study on Mid-Atlantic water resources region, USA. (C) 2015 Elsevier B.V. All rights reserved.

Maximum magnitude estimation considering the regional rupture character

The main objective of the paper is to develop a new method to estimate the maximum magnitude (M (max)) considering the regional rupture character. The proposed method has been explained in detail and examined for both intraplate and active regions. Seismotectonic data has been collected for both the regions, and seismic study area (SSA) map was generated for radii of 150, 300, and 500 km. The regional rupture character was established by considering percentage fault rupture (PFR), which is the ratio of subsurface rupture length (RLD) to total fault length (TFL). PFR is used to arrive RLD and is further used for the estimation of maximum magnitude for each seismic source. Maximum magnitude for both the regions was estimated and compared with the existing methods for determining M (max) values. The proposed method gives similar M (max) value irrespective of SSA radius and seismicity. Further seismicity parameters such as magnitude of completeness (M (c) ), ``a'' and ``aEuro parts per thousand b `` parameters and maximum observed magnitude (M (max) (obs) ) were determined for each SSA and used to estimate M (max) by considering all the existing methods. It is observed from the study that existing deterministic and probabilistic M (max) estimation methods are sensitive to SSA radius, M (c) , a and b parameters and M (max) (obs) values. However, M (max) determined from the proposed method is a function of rupture character instead of the seismicity parameters. It was also observed that intraplate region has less PFR when compared to active seismic region.

Algorithm for Autonomous Reorganization of Mobile Wireless Camera Sensor Networks to Improve Coverage

In this paper, sensing coverage by wireless camera-embedded sensor networks (WCSNs), a class of directional sensors is studied. The proposed work facilitates the autonomous tuning of orientation parameters and displacement of camera-sensor nodes in the bounded field of interest (FoI), where the network coverage in terms of every point in the FoI is important. The proposed work is first of its kind to study the problem of maximizing coverage of randomly deployed mobile WCSNs which exploits their mobility. We propose an algorithm uncovered region exploration algorithm (UREA-CS) that can be executed in centralized and distributed modes. Further, the work is extended for two special scenarios: 1) to suit autonomous combing operations after initial random WCSN deployments and 2) to improve the network coverage with occlusions in the FoI. The extensive simulation results show that the performance of UREA-CS is consistent, robust, and versatile to achieve maximum coverage, both in centralized and distributed modes. The centralized and distributed modes are further analyzed with respect to the computational and communicational overheads.