Deep and self-taught learning for protein accessible surface area prediction

ASA captures the degree of burial or surface accessibility of a protein residue. It is a very important indicator of the behavior of amino acids within a protein as well. It can be used to find protein interactions, interfaces, folding states, etc. Calculation of the ASA requires the presence of the structure of the protein. However, structure determination for proteins is expensive and requires significant technical effort. As a consequence, the prediction of ASA is a very important and fundamental problem in Bioinformatics and Proteomics. In this work, we have investigated self-taught machine learning methods along with deep neural network to predict the residue level accessible surface area (ASA) of a protein. We have found that deep learning neural networks can predict the ASA of the residues in a protein accurately. Furthermore, the proposed deep learning based method does not require the use of computationally demanding features such as the position specific scoring matrix (PSSM) which have been used in previous works. A simple Blosum62 matrix based position dependent representation of amino acids in a sequence window gives comparable performance. This is particularly attractive for proteome wide prediction of ASA. We have used various self-taught learning schemes for obtaining an optimal feature representation from unlabeled data. These include a sparse and regularized autoencoder neural network and a dictionary based learning scheme. We have used unlabeled data from the protein universe in an attempt to improve the feature representation. We have also evaluated the performance of a stochastic gradient based predictor of accessible surface area for different feature representations

The influence of seasonal variations on yield components of sunflower

The wider adaptability of the crop and wide range of climatic conditions of Pakistan make it possible to have two crops of sunflower in a year. Field experiments, one in spring and one in autumn were conducted at the University of Arid Agriculture, Rawalpindi, Pakistan to evaluate the influence of seasonal variation on yield and yield components of sunflower. Four sunflower hybrids were planted in randomized complete block design with three replications. Two central rows were harvested for the measurement of yield and yield components. It was observed that head size of spring crop was larger than autumn crop which was considered to be the result of overall better plant structure, length of crop life cycle, slow and gradual rise in cumulative degree days. Contrary to head size, thousand seed weight of autumn crop was found to be more than that of spring crop. Lesser seed weight of spring crop may be the result of competition for assimilates which left many seeds malnourished as larger head might have encouraged the initial setting of seeds. However, final yield of spring crop was greater than that of autumn crop. It led to the conclusion that having spring crop is the best option, however, autumn crop could be supplementary one to increase the production of oil seeds

Influence of environmental variations on physiological attributes of sunflower

High degree of adaptability, wide range of climatic conditions, high photosynthetic capacity, maximum stomatal conductance and efficient hydraulic mechanism allow sunflower crop to be productive in broad range of environments. Combined effects of environmental factors not only modify plant phenology but also cause many physiological changes. Field experiments, one each in spring and autumn were conducted at Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Pakistan for 2 years (2007 and 2008) to document the effect of environmental variations on the physiological functions of sunflower hybrids. Four sunflower hybrids, Alisson-RM, Parasio-24, MG-2 and S-278 were planted in randomized complete block design with 4 replications. The data on physiological attributes like photosynthetic rate, stomatal conductance and transpiration rate at 10 days interval after complete emergence to 60 days after emergence (DAE) was recorded. Overall higher values of photosynthetic rate, stomatal conductance and transpiration rate were recorded during spring as compared to autumn for both the years. Photosynthates accumulation and utilization was depressed in cold imposing a restriction on biomass production than at warm temperature. Physiological performance of all the hybrids during spring at the start was slower as compared to autumn. Progressive increase in photosynthetic rate, stomatal conductance and transpiration was recorded with the gradual increase in temperature up to a certain level during spring but further increase in temperature caused decline in these attributes. However during autumn, values of all these 3 physiological attributes were higher at the start those declined with gradual decrease in temperature later in the season

ISLAND: in-silico proteins binding affinity prediction using sequence information

Background: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem

DO DIFFERENT SECTORS AFFECT EQUITY RISK PREMIUMS IN EMERGING MARKETS? EVIDENCE FROM ASIA

This paper explores intricacies of the higher equity risk premia of emerging Asian economies within the context of industrial composition. The conventional ex-post empirical analysis is executed to scrutinize the impact of industries on the country's stock performance, diverging from the contemporary literature on finance, which was restricted to "total market indexes." By utilizing the DataStream’s Total Return Indices (TRIs) data of emerging market industries, this study highlights the contribution of various industries towards higher equity risk premiums across prominent Asian emerging countries. The study unearths several salient empirical findings. Primarily, the study confirms the "high-volatile highperformance nature" in conjunction with the time-varying dynamics of excess returns for emerging markets at the industry level. Secondly, the study's findings identify the industries accountable for the most significant contribution to higher stock premia of emerging markets at both the country and dynamic context levels. Thirdly, we observe that certain industries demonstrate greater exposure to global factors than others. It is, therefore, argued that these observations provide a crucial indication for international portfolio diversification. The investigation of diversification opportunities due to the impact of global factors on country indexes, and the existence of some industries that offer little but advantageous insurance components provide valuable insights for the higher equity premia of emerging markets. The overall study findings suggest that foreign portfolio investors must not only diversify across countries but also across industries to generate augmented returns in emerging stock markets

Cumulative Effect of Temperature and Solar Radiation on Wheat Yield

The impact of temperature and solar radiations were studied as determinant factor for spring wheat grain yield. The data obtained at anthesis and maturity for grain number (GN), grain weight (GW) and grain yield (Y) were examined with mean temperature at anthesis (T1) and maturity (T2), solar radiation at anthesis (SR1) and maturity (SR2) and photothermal quotient (PTQ) at anthesis (PTQ1) and maturity (PTQ2). The data obtained was subjected to Statistica 8 software and scatter plot regression model was developed at 95% confidence interval with crop data and climate variables (T1, T2, SR1, SR2, PTQ1 and PTQ2). Results clearly indicated that yield remained directly proportional to solar radiation and temperature plus solar radiation (PTQ) while inversely to temperature under optimum other environmental resources. Direct relationship between PTQ and yield parameters confirmed that it determined crop yield and its management for variable environmental conditions need to be opted by adopting suitable sowing time as an adaptation strategy under changing climate

1:1 relationship among simulated (x-axis) and observed (y-axis) physiological, grain quality traits and grain yield in response to treatments.

<p>1:1 relationship among simulated (x-axis) and observed (y-axis) physiological, grain quality traits and grain yield in response to treatments.</p

Mean comparison of different quality traits and grain yield at different levels of treatments.

<p>Notes: Means with similar letter(s) have no significant difference while different letters a,b,c,d,e showed that means are significantly different from each other at 0.05 probability levels, ns = non-significant.</p><p>Mean comparison of different quality traits and grain yield at different levels of treatments.</p