In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

FrosPy: A Modular Python Toolbox for Normal Mode Seismology

We present free oscillations Python (FrosPy), a modular Python toolbox for normal mode seismology, incorporating several Python core classes that can easily be used and be included in larger Python programs. FrosPy is freely available and open source online. It provides tools to facilitate pre- and postprocessing of seismic normal mode spectra, including editing large time series and plotting spectra in the frequency domain. It also contains a comprehensive database of center frequencies and quality factor (Q) values based on 1D reference model preliminary reference Earth model for all normal modes up to 10 mHz and a collection of published measurements of center frequencies, Q values, and splitting function (or structure) coefficients. FrosPy provides the tools to visualize and convert different formats of splitting function coefficients and plot these as maps. By giving the means of using and comparing normal mode spectra and splitting function measurements, FrosPy also aims to encourage seismologists and geophysicists to learn about normal mode seismology and the study of the Earth’s free oscillation spectra and to incorporate them into their own research or use them for educational purposes.

IMPLICIT NONLINEAR NORMAL MODE INITIALIZATION FOR A BAROTROPIC PRIMITIVE EQUATION LIMITED AREA MODEL

A simple version of implicit nonlinear normal mode initialization is applied to a limited area one-level primitive equation model over a tropical domain. The model formulation is based on shallow water equations in spherical co-ordinate and potential enstrophy conserving finite difference scheme is employed. The model is used for predicting the movement of a typical monsoon depression formed over the Bay of Bengal. The above scheme is found to be very effective as it requires only three iterations for attaining balance between the mass and wind tields. However this model is not able to predict the movement of the depression very ac-curately due to the limitations of such a one-level model.

Quantitative Radiomics Approach to Assess Acute Radiation Dermatitis in Breast Cancer Patients

We applied a radiomics approach to skin surface images to objectively assess radiodermatitis in patients undergoing radiotherapy for breast cancer. A prospective cohort study of 20 patients was conducted. Skin surface images in normal, polarised, and UV modes were acquired using a skin analysis device before starting radiotherapy (‘before RT’), 7 days after the first treatment ('RT_D7'), on ‘RT_D14’, and 10 days after radiotherapy ended (‘after RT_D10’). Eighteen types of radiomic feature ratios were calculated. We measured skin doses in ipsilateral breasts using OSLDs on the first day of radiotherapy. Clinical evaluation of acute radiodermatitis was performed using the RTOG scoring criteria on ‘RT_D14’ and ‘after RT_D10’. Several statistical analysis methods were used to test the performance of radiomic features as indicators of radiodermatitis evaluation. As the skin was damaged by radiation, the energy for normal mode and sum variance for polarised and UV modes decreased significantly for ipsilateral breasts. The radiomic feature ratios at ‘RT_D7’ had strong correlations to skin doses and those at ‘RT_D14’ and ‘after RT_D10’ with statistical significance. The energy for normal mode and sum variance for polarised and UV modes demonstrated the potential to evaluate and predict acute radiation, which assists in its appropriate management.

MODERNIZATION OF THE LUBRICATION SYSTEM OF THE TURBOCHARGER BEARING OF THE DIESEL ENGINE

The problem of increasing the unit power of the engine without making changes to its design is solve by using a turbo supercharger. However, due to the intensity of the turbochargers operating mode, which are characterized by engine speed variability due to changing load indicators during operation (the number of rotor revolutions varies from 30000 min-1 to 120000 min-1, engine exhaust gases have a temperature of about 7500C), there is a need to improve the efficiency of the turbocharger bearing lubrication system. The purpose of the research is to ensure the operability and increase the reliability of turbochargers of diesel engines. To achieve this goal, a constructive solution for the lubrication system of the bearing assembly was propose, i.e., a membrane-type hydraulic accumulator was structurally provided in the lubrication system of the bearing assembly. Experimental studies were conduct to identify the operability and effectiveness of this constructive solution. The experiment was carried out on the KAMAZ-740 engine, the turbocharger shaft drive was carried out in normal mode, that is, from exhaust gases. L-02-40 fuel was use, SAE 10W–40 API was use as a lubricant. The turbocharger shaft speed varied from minimum to maximum by changing the engine speed and then stopping it. During the experiments, the following parameters of the turbocharger operation were measure: the duration of inertial rotation of the turbocharger rotor; the duration of pressure reduction in the turbocharger lubrication system. The dependences of the influence of the duration of the pressure drop in the turbocharger lubrication system and the duration of rotation of the turbocharger shaft by inertia with parallel inclusion of the accumulator in its lubrication system and in the normal mode of lubrication of the bearing are reveal. It is established that the installation of a device for feeding the turbocharger bearing during a sharp reduction in engine speed will increase the run-out of the turbocharger rotor from 30 to 65 s while maintaining the normal operating mode of the turbocharger lubrication system

Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.