βLact-Pred: A Predictor Developed for Identification of Beta-Lactamases Using Statistical Moments and PseAAC via 5-Step Rule

Beta-lactamase (β-lactamase) produced by different bacteria confers resistance against β-lactam-containing drugs. The gene encoding β-lactamase is plasmid-borne and can easily be transferred from one bacterium to another during conjugation. By such transformations, the recipient also acquires resistance against the drugs of the β-lactam family. β-Lactam antibiotics play a vital significance in clinical treatment of disastrous diseases like soft tissue infections, gonorrhoea, skin infections, urinary tract infections, and bronchitis. Herein, we report a prediction classifier named as βLact-Pred for the identification of β-lactamase proteins. The computational model uses the primary amino acid sequence structure as its input. Various metrics are derived from the primary structure to form a feature vector. Experimentally determined data of positive and negative beta-lactamases are collected and transformed into feature vectors. An operating algorithm based on the artificial neural network is used by integrating the position relative features and sequence statistical moments in PseAAC for training the neural networks. The results for the proposed computational model were validated by employing numerous types of approach, i.e., self-consistency testing, jackknife testing, cross-validation, and independent testing. The overall accuracy of the predictor for self-consistency, jackknife testing, cross-validation, and independent testing presents 99.76%, 96.07%, 94.20%, and 91.65%, respectively, for the proposed model. Stupendous experimental results demonstrated that the proposed predictor “βLact-Pred” has surpassed results from the existing methods.

DETERMINATION OF GALAXY DISTRIBUTION WITH STATISTICAL MOMENTS

In this report we discuss topological studies of large scale structure of the Universe (LSS) from XMM-Newton, Sloan Digital Sky Survey and simulated data of galaxy distribution. Early works in this mentioned field were based on genus statistics, which is averaged curvature of isosurface of smoothed density field. Later, significant number of other methods was developed. This comprise Euler characteristics, Minkowski functionals, Voronoi clustering, alpha shapes, Delanuay tesselation, Morse theory, Hessian matrix and Soneira-Peebles models. In practice, modern topology methods are reducedto calculation of the three Betti numbers which shall be interpreted as a number of galaxy clusters, filaments and voids. Such an approach was applied by different authors both for simulated and observed LSS data. Topology methods are generally verified using LSS simulations. Observational data normally includes SDSS, CFHTLS and other surveys. These data have many systematical and statistical errors and gaps. Furthermore, there is also a problem of underlying dark matter distribution. The situation is not better in relation to calculations of the power spectrum and its power law index which does not provide a clear picture as well. In this work we propose some tools to solve above problems. First, we performed topology description of simple LSS models such as cubic, graphite-like and random Gaussian distribution of matter. Our next idea is to set a task for LSS topology assessment using X-ray observations of the galaxies. Although, here could be a major complication due to current lack of detected high energy emitting galaxies. Nevertheless, we are expecting to get sufficient results in the future encouraging comprehensive X-ray data. Here we present analysis of statistical moments for four galaxy samples and compare them with the behavior of Betti numbers. Finally, we consider the options of applying artificial neural networks to observed galaxies and fill the data deficiency. This shall enable to define topology at least for superimposed superclusters and other LSS elements.

Time since hemorrhages of traumatic and non-traumatic genesis into the substance of the human brain formation using distribution of linear dichroism fluctuations reconstruction

For a forensic expert-practitioner, it is especially important to objectively diagnose and time since the formation of hemorrhage (TSFH) in the substance of the human brain (SHB) of traumatic and non-traumatic origin, as there are cases when the external examination of the corpse at the scene are absent, and at internal research find hemorrhages in a brain. In forensic practice, to verify the cause of death, physical-optical methods are successfully used, which are based on laser irradiation of biological tissues with subsequent mathematical and statistical processing of the obtained data. Previous studies on the possibility of differentiating the cause of death by traditional polarization methods have yielded positive results, which suggests the possibility of their suitability for verification of the genesis of hemorrhage into the brain. For a forensic expert-practitioner, the main thing is objectivity, accuracy and speed of obtaining the result, which could fully satisfy the methods of laser polarimetry in the case of determining the TSFH of traumatic and non-traumatic origin in SHB. Therefore, it is necessary to continue the development and research of these methods for this purpose. Aim of the work. To substantiate the possibility of using the method of differential Mueller-matrix mapping of phase anisotropy to determine the temporal dynamics of maps of linear birefringence of histological sections of human brain in determining the age of hemorrhage in human brain substance and to develop forensic criteria for determining the age. death due to cerebral infarction of ischemic and hemorrhagic origin. Materials and methods. To achieve this goal, we studied native histological preparations SHB from 130 corpses with a known time of death. The cause of death was TBI (group II (n=35)), cerebral infarction of ischemic origin (group III (n=32)), hemorrhagic stroke (group IV (n=33)), acute coronary insufficiency (group I – comparison group (n=30)). The values of the distribution of the coordinates of the polarization parameters at the points of the microscopic images at the location of the standard Stokes polarimeter were measured. Experimental measurements of Stokes-parametric images of biological layers were performed according to the method presented in the sources. Subsequently, the obtained data were subjected to statistical processing and evaluation of the obtained results. Statistical moments (SM) of the 1st-4th orders (mean (SM1), variance (SM2), asymmetry (SM3) and excess (SM4)) of each map were determined. Results and discussion. Comparative analysis of polarization Mueller-matrix mapping images of SHB sections from all groups revealed the destruction of the polycrystalline structure formed by optically active protein complexes of the brain substance, which indicates a decrease in absolute values and range of their scatter with increasing hemorrhage time. This is indicated by the coordinate inhomogeneity of the Mueller-matrix invariant maps of histological sections of SHB of all groups. For histograms that characterize the distributions of the Mueller-matrix invariant samples from all (comparison groups 1 and experimental 2-4) groups, are characterized by individual and significant variations in the values of statistical moments. Due to this, with increasing hemorrhage time, the value of the mean (SM1) and variance (SM2) decreases. Asymmetry (SM3) and excess (SM4), on the contrary, increase. The analysis of the results of statistical processing of the topographic structure of LD tomograms of fibrillar networks of histological sections of SHB dead from all groups shows a greater temporal dynamics of necrotic destruction of nervous tissue. Accordingly, there is a faster time decrease in the absolute values and the range of scatter of the LD value with increasing TSFH. That is, the diagnostic sensitivity of the statistical moments of the 3rd and 4th orders for azimuthal-invariant Mueller-matrix differentiation of nerve tissue samples of the brain of the deceased of control group 1 and all experimental groups 2-4 (p<0,05) was revealed. Conclusions. A series of studies of the effectiveness of a new in forensic practice method of differential Mueller-matrix mapping of partially depolarizing histological sections of SHB and tomographic reproduction of optical anisotropy parameters of their polycrystalline structure revealed a high level of accuracy of differentiation and formation of genesis, even under conditions of small geometric thickness of experimental samples. The range of linear change of values of statistical moments of the 1st - 4th orders which characterize distributions of size of LD of fibrillar networks of histologic sections of SHB of the dead from all groups, makes 24 h. In the range of 6-24 hours, the accuracy of determining the TSFH using statistical processing of the topographic structure of LD tomograms of fibrillar networks of histological sections of TSFH is (30±5) minutes.

3D Mueller Matrix Reconstruction of the Optical Anisotropy Parameters of Myocardial Histopathology Tissue Samples

Diseases affecting myocardial tissues are currently a leading cause of death in developed nations. Fast and reliable techniques for analysing and understanding how tissues are affected by disease and respond to treatment are fundamental to combating the effects of heart disease. A 3D Mueller matrix method that reconstructs the linear and circular birefringence and dichroism parameters has been developed to image the biological structures in myocardial tissues. The required optical data is gathered using a Stokes polarimeter and then processed mathematically to recover the individual optical anisotropy parameters, expanding on existing 2D Mueller matrix implementations by combining with a digital holography approach. Changes in the different optical anisotropy parameters are rationalised with reference to the general tissue structure, such that the structures can be identified from the anisotropy distributions. The first to fourth order statistical moments characterising the distribution of the parameters of the optical anisotropy of the polycrystalline structure of the partially depolarising layer of tissues in different phase sections of their volumes are investigated and analysed. The third and fourth order statistical moments are found to be the most sensitive to changes in the phase and amplitude anisotropy. The possibility of forensic medical differentiation of death in cases of acute coronary insufficiency (ACI) and coronary heart disease (CHD) is considered as a diagnostic application. The optimal phase plane (θ∗=0.7rad) has been found, in which excellent differentiation accuracy is achieved ACI and CHD -Ac(ΔZ4(θ∗,ΦL,ΔL))=93.05%÷95.8%. A comparative analysis of the accuracy of the Mueller-matrix reconstruction of the parameters of the optical anisotropy of the myocardium in different phase planes (θ=0.9rad and θ=1.2rad), as well as the 2D Mueller-matrix reconstruction method was carried out. This work demonstrates that a 3D Mueller matrix method can be used to effectively analyse the optical anisotropy parameters of myocardial tissues with potential for definitive diagnostics in forensic medicine.

Statistical error estimation methods for engineering-relevant quantities from scale-resolving simulations

Scale-resolving simulations, such as large eddy simulations, have become affordable tools to investigate the flow in turbomachinery components. The resulting time-resolved flow field is typically analyzed using first- and second-order statistical moments. However, two sources of uncertainty are present when recording statistical moments from scale-resolving simulations: the influence of initial transients and statistical errors due to the finite number of samples. In this paper, both are systematically analyzed for several quantities of engineering interest using time series from a long-time large eddy simulation of the low-pressure turbine cascade T106C. A set of statistical tools to either remove or quantify these sources of uncertainty is assessed. First, the Marginal Standard Error Rule is used to detect the end of the initial transient. The method is validated for integral and local quantities and guidelines on how to handle spatially varying initial transients are formulated. With the initial transient reliably removed, the statistical error is estimated based on standard error relations considering correlations in the time series. The resulting confidence intervals are carefully verified for quantities of engineering interest utilizing cumulative and simple moving averages. Furthermore, the influence of periodic content from large scale vortex shedding on the error estimation is studied. Based on the confidence intervals, the required averaging interval to reduce the statistical uncertainty to a specific level is indicated for each considered quantity.

Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture

In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.

iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC

Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington’s, Alzheimer’s, Parkinson’s, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou’s Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.

Skewness and Kurtosis of the Surface Wave in the Coastal Zone of the Black Sea

Purpose. The aim of the study is to analyze variability of the statistical moments characterizing deviations of the sea surface elevation distributions from the Gaussian one. Methods and Results. Field studies of the sea waves’ characteristics were carried out from the stationary oceanographic platform located in the Black Sea near the Southern coast of Crimea. The data obtained both in summer and winter, were used. The statistical moments were calculated separately for wind waves and swell. The measurements were performed in a wide range of meteorological conditions and wave parameters (wind speed varied from 0 to 26 m/s, wave age – from 0 to 5.2 and steepness – from 0.005 to 0.095). For wind waves, the coefficients of skewness correlation with the waves’ steepness and age were equal to 0.46 and 0.38. The kurtosis correlation coefficients with these parameters were small (0.09 and 0.07), but with the confidence level 99.8% – significant. For swell, the correlation coefficients were 1.5 – 2.0 times lower. Conclusions. The statistical moments of the sea surface elevations of the third and higher orders are the indicators of the wave field nonlinearity, which should be taken into account when solving a wide range of the applied and fundamental problems. The deviations of the surface elevation distributions from the Gaussian one are not described unambiguously by the waves’ steepness and age. At the fixed values of these parameters, a large scatter in the values of the surface elevations’ asymmetry and kurtosis is observed. This imposes significant limitations on the possibility of applying the nonlinear wave models based on the wave profile expansion by small parameter (steepness) degrees, in engineering calculations.

Skewness and Kurtosis of the Surface Wave in the Coastal Zone of the Black Sea

Purpose. The aim of the study is to analyze variability of the statistical moments characterizing deviations of the sea surface elevation distributions from the Gaussian. Methods and Results. Field studies of the sea waves’ characteristics were carried out from the stationary oceanographic platform located in the Black Sea near the Southern coast of Crimea. The data obtained both in summer and winter, were used. The statistical moments were calculated separately for wind waves and swell. The measurements were performed in a wide range of meteorological conditions and wave parameters (wind speed varied from 0 to 26 m/s, wave age – from 0 to 5.2 and steepness – from 0.005 to 0.095). For wind waves, the coefficients of skewness correlation with the waves’ steepness and age were equal to 0.46 and 0.38. The kurtosis correlation coefficients with these parameters were small (0.09 and 0.07), but with the confidence level 99.8% – significant. For swell, the correlation coefficients were 1.5 – 2.0 times lower. Conclusions. The statistical moments of the sea surface elevations of the third and higher orders are the indicators of the wave field nonlinearity, which should be taken into account when solving a wide range of the applied and fundamental problems. The deviations of the surface elevation distributions from the Gaussian one are not described unambiguously by the steepness and wave age. At the fixed values of these parameters, a large scatter in the skewness and kurtosis of the surface elevations is observed. This imposes significant limitations on the possibility of applying the nonlinear wave models based on the wave profile expansion by small parameter (steepness) degrees, in engineering calculations.