Identifying functionally important cis-peptide containing segments in proteins and their utility in molecular function annotation

Cis-peptide embedded segments are rare in proteins but often highlight their important role in molecular function when they do occur. The high evolutionary conservation of these segments illustrates this observation almost universally, although no attempt has been made to systematically use this information for the purpose of function annotation. In the present study, we demonstrate how geometric clustering and level-specific Gene Ontology molecular-function terms (also known as annotations) can be used in a statistically significant manner to identify cis-embedded segments in a protein linked to its molecular function. The present study identifies novel cis-peptide fragments, which are subsequently used for fragment-based function annotation. Annotation recall benchmarks interpreted using the receiver-operator characteristic plot returned an area-under-curve >0.9, corroborating the utility of the annotation method. In addition, we identified cis-peptide fragments occurring in conjunction with functionally important trans-peptide fragments, providing additional insights into molecular function. We further illustrate the applicability of our method in function annotation where homology-based annotation transfer is not possible. The findings of the present study add to the repertoire of function annotation approaches and also facilitate engineering, design and allied studies around the cis-peptide neighborhood of proteins.

Classification of Protein Kinases on the Basis of Both Kinase and Non-Kinase Regions

Background: Protein phosphorylation is a generic way to regulate signal transduction pathways in all kingdoms of life. In many organisms, it is achieved by the large family of Ser/Thr/Tyr protein kinases which are traditionally classified into groups and subfamilies on the basis of the amino acid sequence of their catalytic domains. Many protein kinases are multidomain in nature but the diversity of the accessory domains and their organization are usually not taken into account while classifying kinases into groups or subfamilies. Methodology: Here, we present an approach which considers amino acid sequences of complete gene products, in order to suggest refinements in sets of pre-classified sequences. The strategy is based on alignment-free similarity scores and iterative Area Under the Curve (AUC) computation. Similarity scores are computed by detecting common patterns between two sequences and scoring them using a substitution matrix, with a consistent normalization scheme. This allows us to handle full-length sequences, and implicitly takes into account domain diversity and domain shuffling. We quantitatively validate our approach on a subset of 212 human protein kinases. We then employ it on the complete repertoire of human protein kinases and suggest few qualitative refinements in the subfamily assignment stored in the KinG database, which is based on catalytic domains only. Based on our new measure, we delineate 37 cases of potential hybrid kinases: sequences for which classical classification based entirely on catalytic domains is inconsistent with the full-length similarity scores computed here, which implicitly consider multi-domain nature and regions outside the catalytic kinase domain. We also provide some examples of hybrid kinases of the protozoan parasite Entamoeba histolytica. Conclusions: The implicit consideration of multi-domain architectures is a valuable inclusion to complement other classification schemes. The proposed algorithm may also be employed to classify other families of enzymes with multidomain architecture.

Chemical toughening of epoxies. II. Mechanical, thermal, and microscopic studies of epoxies toughened with hydroxyl-terminated poly(butadiene-co-acrylonitrile)

Diglycidyl ether–bisphenol-A-based epoxies toughened with various levels (0–12%) of chemically reacted liquid rubber, hydroxyl-terminated poly(butadiene-co-acrylonitrile) (HTBN) were studied for some of the mechanical and thermal properties. Although the ultimate tensile strength showed a continuous decrease with increasing rubber content, the toughness as measured by the area under the stress-vs.-strain curve and flexural strength reach a maximum around an optimum rubber concentration of 3% before decreasing. Tensile modulus was found to increase for concentrations below 6%. The glass transition temperature Tg as measured by DTA showed no variation for the toughened formulations. The TGA showed no variations in the pattern of decomposition. The weight losses for the toughened epoxies at elevated temperatures compare well with that of the neat epoxy. Scanning electron microscopy revealed the presence of a dual phase morphology with the spherical rubber particles precipitating out in the cured resin with diameter varying between 0.33 and 6.3 μm. In contrast, a physically blended rubber–epoxy showed much less effect towards toughening with the precipitated rubber particles of much bigger diameter (0.6–21.3 μm).

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

Magnetocaloric effect and nature of magnetic transition in nanoscale Pr0.5Ca0.5MnO3

Systematic measurements pertinent to the magnetocaloric effect and nature of magnetic transition around the transition temperature are performed in the 10 nm Pr0.5Ca0.5MnO3 nanoparticles (PCMO10). Maxwell's relation is employed to estimate the change in magnetic entropy. At Curie temperature (T-C) similar to 83.5 K, the change in magnetic entropy (-Delta S-M) discloses a typical variation with a value 0.57 J/kg K, and is found to be magnetic field dependent. From the area under the curve (Delta S vs T), the refrigeration capacity is calculated at T-C similar to 83.5K and it is found to be 7.01 J/kg. Arrott plots infer that due to the competition between the ferromagnetic and anti-ferromagnetic interactions, the magnetic phase transition in PCMO10 is broadly spread over both in temperature as well as magnetic field coordinates. Upon tuning the particle size, size distribution, morphology, and relative fraction of magnetic phases, it may be possible to enhance the magnetocalorific effect further in PCMO10. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4759372]

Forest area estimation and reporting: implications for conservation, management and REDD

Periodic estimation, monitoring and reporting on area under forest and plantation types and afforestation rates are critical to forest and biodiversity conservation, sustainable forest management and for meeting international commitments. This article is aimed at assessing the adequacy of the current monitoring and reporting approach adopted in India in the context of new challenges of conservation and reporting to international conventions and agencies. The analysis shows that the current mode of monitoring and reporting of forest area is inadequate to meet the national and international requirements. India could be potentially over-reporting the area under forests by including many non-forest tree categories such as commercial plantations of coconut, cashew, coffee and rubber, and fruit orchards. India may also be under-reporting deforestation by reporting only gross forest area at the state and national levels. There is a need for monitoring and reporting of forest cover, deforestation and afforestation rates according to categories such as (i) natural/primary forest, (ii) secondary/degraded forests, (iii) forest plantations, (iv) commercial plantations, (v) fruit orchards and (vi) scattered trees.

Influence of curvature on the device physics of thin film transistors on flexible substrates

Thin film transistors (TFTs) on elastomers promise flexible electronics with stretching and bending. Recently, there have been several experimental studies reporting the behavior of TFTs under bending and buckling. In the presence of stress, the insulator capacitance is influenced due to two reasons. The first is the variation in insulator thickness depending on the Poisson ratio and strain. The second is the geometric influence of the curvature of the insulator-semiconductor interface during bending or buckling. This paper models the role of curvature on TFT performance and brings to light an elegant result wherein the TFT characteristics is dependent on the area under the capacitance-distance curve. The paper compares models with simulations and explains several experimental findings reported in literature. (C) 2014 AIP Publishing LLC.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Estimation of `drainable' storage - A geomorphological approach

Storage of water within a river basin is often estimated by analyzing recession flow curves as it cannot be `instantly' estimated with the aid of available technologies. In this study we explicitly deal with the issue of estimation of `drainable' storage, which is equal to the area under the `complete' recession flow curve (i.e. a discharge vs. time curve where discharge continuously decreases till it approaches zero). But a major challenge in this regard is that recession curves are rarely `complete' due to short inter-storm time intervals. Therefore, it is essential to analyze and model recession flows meaningfully. We adopt the wellknown Brutsaert and Nieber analytical method that expresses time derivative of discharge (dQ/dt) as a power law function of Q : -dQ/dt = kQ(alpha). However, the problem with dQ/dt-Q analysis is that it is not suitable for late recession flows. Traditional studies often compute alpha considering early recession flows and assume that its value is constant for the whole recession event. But this approach gives unrealistic results when alpha >= 2, a common case. We address this issue here by using the recently proposed geomorphological recession flow model (GRFM) that exploits the dynamics of active drainage networks. According to the model, alpha is close to 2 for early recession flows and 0 for late recession flows. We then derive a simple expression for drainable storage in terms the power law coefficient k, obtained by considering early recession flows only, and basin area. Using 121 complete recession curves from 27 USGS basins we show that predicted drainable storage matches well with observed drainable storage, indicating that the model can also reliably estimate drainable storage for `incomplete' recession events to address many challenges related to water resources. (C) 2014 Elsevier Ltd. All rights reserved.

Application of principal component analysis to gas sensing characteristics of Cr0.8Fe0.2NbO4 thick film array

The transient changes in resistances of Cr0.8Fe0.2NbO4 thick film sensors towards specified concentrations of H-2, NH3, acetonitrile, acetone, alcohol, cyclohexane and petroleum gas at different operating temperatures were recorded. The analyte-specific characteristics such as slopes of the response and retrace curves, area under the curve and sensitivity deduced from the transient curve of the respective analyte gas have been used to construct a data matrix. Principal component analysis (PCA) was applied to this data and the score plot was obtained. Distinguishing one reducing gas from the other is demonstrated based on this approach, which otherwise is not possible by measuring relative changes in conductivity. This methodology is extended for three Cr0.8Fe0.2NbO4 thick film sensor array operated at different temperatures. (C) 2015 Elsevier B.V. All rights reserved.

Nucleosome core protein: Asymmetric dissociation of the octamer

The octameric nucleosomal core-histone complex, (H2A)2-(H2B)2-(H3)2-(H4)2, isolated from rat liver, undergoes dissociation during gel exclusion chromatography as a result of dilution occurring in the columns. The elution pattern at pH 7.0 and 4°C showed a sharp leading peak containing all four histones but predominantly H3 and H4, and a trailing peak containing equal amounts of histones H2A and H2B. As column length was increased the area under the leading peak decreased and that under the trailing peak increased. In addition the relative positions of the two peaks varied with column length. From an analysis of the data on increase in elution volume of the leading peak in relation to column length an apparent molecular weight of 86 000 was calculated for the undissociated molecule. Its apparent molecular weight, histone composition and pattern of further dissociation in relation to column length suggest that this species is the hexamer, (H2A-H2B)-(H3)2-(H4)2. At pH 7.0 and 4°C the dissociation of the core complex appears to be as follows: (H2A)2-(H2B)2-(H3)2-(H4)2 → (H2A-H2B) + (H2A-H2B)-(H3)2-(H4)2 → 2(H2A-H2B) + (H3)2-(H4)2 This dissociation was accelerated by an increase in temperature or decrease in pH and was accompanied by marked conformational changes as judged by circular dichroism measurements.

A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye Using Proper Orthogonal Decomposition

Glaucoma is the second leading cause of blindness worldwide. Often, the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this paper, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L-1-norm and L-2-norm, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUCs) was used to compare the diagnostic performance of the POD-induced parameters with the parameters of the topographic change analysis (TCA) method. The IMED and L-2-norm parameters in the POD framework provided the highest AUC of 0.94 at 10 degrees. field of imaging and 0.91 at 15 degrees. field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88, respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management.

A Strategy for Conserving the Biodiversity of the Uttara Kannada District in South India

Taking the various values ascribed to biodiversity as its point of departure rather many years ago, the present study aims at deriving a conservation strategy for Uttara Kannada. This hilly district, with the highest proportion of its area under forests in South India, is divided into five ecological zones: coastal, northern evergreen, southern evergreen, moist deciduous, and dry deciduous. The heavily-populated coastal zone includes mangrove forests and estuarine wetlands. The evergreen forests are particularly rich in the diversity of plant species which they support - including wild relatives of a number of cultivated plants. They also serve a vital function in watershed conservation. The moist deciduous forests are rich in bird species; both moist and dry deciduous forests include a number of freshwater ponds and lakes that support a high diversity of aquatic birds.Reviewing the overall distribution of biodiversity, we identify specific localities - including estuaries, evergreen forests, and moist deciduous forests - which should be set aside as Nature reserves. These larger reserves must be complemented by a network of traditionally-protected sacred groves and sacred trees that are distributed throughout the district and that protect today, for instance, the finest surviving stand of dipterocarp trees.We also spell out the necessary policy-changes in overall development strategy that should stem the ongoing decimation of biodiversity. These include (1) revitalizing community-based systems of sustainable management of village forests and protection of sacred groves and trees; (2) reorienting the usage-pattern of reserve forests from production of a limited variety of timber and softwood species for industrial consumers, to production of a larger diversity of non-wood forest produce of commercial value to support the rural economy; (3) utilizing marginal lands under private ownership for generating industrial wood supplies; and (4) provision of incentives for in situ maintenance of land-races of cultivated plants - especially evergreen, fruit-yielding trees - by the local people.It is proposed that this broad framework be now taken to the local communities, and that an action-plan be developed on the basis of inputs provided - and initiatives taken - by them.

Potential and economics of forestry options for carbon sequestration in India

There is a need to understand the carbon (C) sequestration potential of the forestry option and its financial implications for each country.In India the C emissions from deforestation are estimated to be nearly offset by C sequestration in forests under succession and tree plantations. India has nearly succeeded in stabilizing the area under forests and has adequate forest conservation strategies. Biomass demands for softwood, hardwood and firewood are estimated to double or treble by the year 2020. A set of forestry options were developed to meet the projected biomass needs, and keeping in mind the features of land categories available, three scenarios were developed: potential; demand-driven; and programme-driven scenarios. Adoption of the demand-driven scenario, targeted at meeting the projected biomass needs, is estimated to sequester 78 Mt of C annually after accounting for all emissions resulting from clearfelling and end use of biomass. The demand-driven scenario is estimated to offset 50% of national C emission at 1990 level. The cost per t of C sequestered for forestry options is lower than the energy options considered. The annual investment required for implementing the demand-driven scenario is estimated to be US$ 2.1 billion for six years and is shown to be feasible. Among forestry options, the ranking based on investment cost per t of C sequestered from least cost to highest cost is; natural regeneration-agro-forestry-enhanced natural regeneration (< US$ 2.5/t C)-timber-community-softwood forestry (US$ 3.3 to 7.3 per t of C).

Comparative Assessment of Techniques for Bioresource Monitoring Using GIS and Remote Sensing

Growing concern over the status of global and regional bioenergy resources has necessitated the analysis and monitoring of land cover and land use parameters on spatial and temporal scales. The knowledge of land cover and land use is very important in understanding natural resources utilization, conversion and management. Land cover, land use intensity and land use diversity are land quality indicators for sustainable land management. Optimal management of resources aids in maintaining the ecosystem balance and thereby ensures the sustainable development of a region. Thus sustainable development of a region requires a synoptic ecosystem approach in the management of natural resources that relates to the dynamics of natural variability and the effects of human intervention on key indicators of biodiversity and productivity. Spatial and temporal tools such as remote sensing (RS), geographic information system (GIS) and global positioning system (GPS) provide spatial and attribute data at regular intervals with functionalities of a decision support system aid in visualisation, querying, analysis, etc., which would aid in sustainable management of natural resources. Remote sensing data and GIS technologies play an important role in spatially evaluating bioresource availability and demand. This paper explores various land cover and land use techniques that could be used for bioresources monitoring considering the spatial data of Kolar district, Karnataka state, India. Slope and distance based vegetation indices are computed for qualitative and quantitative assessment of land cover using remote spectral measurements. Differentscale mapping of land use pattern in Kolar district is done using supervised classification approaches. Slope based vegetation indices show area under vegetation range from 47.65 % to 49.05% while distance based vegetation indices shoes its range from 40.40% to 47.41%. Land use analyses using maximum likelihood classifier indicate that 46.69% is agricultural land, 42.33% is wasteland (barren land), 4.62% is built up, 3.07% of plantation, 2.77% natural forest and 0.53% water bodies. The comparative analysis of various classifiers, indicate that the Gaussian maximum likelihood classifier has least errors. The computation of talukwise bioresource status shows that Chikballapur Taluk has better availability of resources compared to other taluks in the district.