Sequence-Order Frequency Matrix - Sampling and Machine learning with Smith-Waterman (SOFM-SMSW) for Protein Remote Homology Detection

Background: Protein Remote Homology Detection (PRHD) is used to find the homologous proteins which are similar in function and structure but sharing low sequence identity. In general, the Sequence-Order Frequency Matrix (SOFM) was used for protein remote homology detection. In the SOFM Top-n-gram (SOFM-Top) algorithm, the probability of substrings was calculated based on the highest probability value of substrings. Moreover, SOFM-Smith Waterman (SOFM-SW) algorithm combines the SOFM with local alignment for protein remote homology detection. However, the computation complexity of SOFM based PRHD is high since it processes all protein sequences in SOFM.Objective: Sequence-Order Frequency Matrix - Sampling and Machine learning with Smith-Waterman (SOFM-SMSW) algorithm is proposed for predicting the protein remote homology. The SOFM-SMSW algorithm used the PVS method to select the optimum target sequences based on the uniform distribution measure.Method: This research work considers the most important sequences for PRHD by introducing Proportional Volume Sampling (PVS). After sampling the protein sequences, a feature vector is constructed and labeling is performed based on the concatenation between two protein sequences. Then, a substitution score which represents the structural alignment is learned using k-Nearest Neighbor (k-NN). Based on the learned substitution score and alignment score, the protein homology is detected using Smith-Waterman algorithm and Support Vector Machine (SVM). By selecting the most important sequences, the accuracy of PRHD is improved and the computational complexity for PRHD is reduced by using structural alignment along with the local alignment.Results: The performance of the proposed SOFM-SMSW algorithm is tested with SCOP database and it has been compared with various existing algorithms such as SVM Top-N-gram, SVM pairwise, GPkernal, Long Short-Term Memory (LSTM), SOFM Top-N-gram and SOFM-SW. Conclusion: The experimental results illustrate that the proposed SOFM-SMSW algorithm has better accuracy, precision, recall, ROC and ROC 50 for PRHD than the other existing algorithms.

Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes

Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.

Blurring correction in the electro-optical scanning systems on board a spacecraft

The authors analyze the existing ways of blurring elimination. It determines the technique and problem solving procedure of blurring correction in electro-optical scanning systems on board a spacecraft. The proposed technique is implemented at the initial stages of the survey and is based on the optimization of CCD matrix sampling rate according to the maximum of parameters dispersion of brightness image fields. In contrast to the known approaches, brightness differences of adjacent lines of survey route are values to be measured. The results of testing the technique on conditional frames of lines with different degrees of blurring obtained by distorting the signals of a real space image are presented. The authors’ new approach based on the established functional relationship between the level of blurring, survey parameters and errors of their determination was applied for the blurring simulation. The obtained results prove the possibility of reducing the initial blurring of images being formed on board a spacecraft to the magnitude of tenths of a pixel.

TIMSS 2015: Illustrating Advancements in Large-Scale International Assessments

International large-scale assessments of student achievement such as International Association for the Evaluation of Educational Achievement’s Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study and Organization for Economic Cooperation and Development’s Program for International Student Assessment that have come to prominence over the past 25 years owe a great deal in methodological terms to pioneering work by National Assessment of Educational Progress (NAEP). Using TIMSS as an example, this article describes how a number of core techniques, such as matrix sampling, student population sampling, item response theory scaling with population modeling, and resampling methods for variance estimation, have been adapted and implemented in an international context and are fundamental to the international assessment effort. In addition to the methodological contributions of NAEP, this article illustrates how the large-scale international assessments go beyond measuring student achievement by representing important aspects of community, home, school, and classroom contexts in ways that can be used to address issues of importance to researchers and policymakers.

The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy

Several works show that the linear Angle of Arrival (AoA) methods such as Projection Matrix (PM) have low computational complexity compared to the subspace methods. Although the PM method is classified as a subspace method, it does not need decomposition of the measured matrix. This work investigates the effect of the sampled columns within the covariance matrix on the projection matrix construction. To the authors’ knowledge, this investigation has not been addressed in the literature. Unlike the subspace methods such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Minimum Norm, Propagator, etc., which have to use a specific number of columns, we demonstrate this aspect is not applicable in the PM method. To this end, the projection matrix is formed based on a various number of sampled columns to estimate the arrival angles. A theoretical analysis is accomplished to illustrate the relationship between the number of the sampled columns and the degrees of freedom (DOFs). The analysis shows that with the same aperture size, the DOFs can be increased by increasing only the number of sampled columns in the projection matrix calculation step. An intensive Monte Carlo simulation for different scenarios is presented to validate the theoretical claims. The estimation accuracy of the PM method, based on the proposed selected sampling methodology outperforms all the other techniques with less complexity compared to the Capon and MUSIC methods. The estimation accuracy is evaluated in terms of Root Mean Square Error (RMSE) and the Probability of Successful Detection (PSD). The results are presented and discussed.