Insights into the Ligand Binding to Bromodomain-Containing Protein 9 (BRD9): A Guide to the Selection of Potential Binders by Computational Methods

The estimation of the binding of a set of molecules against BRD9 protein was carried out through an in silico molecular dynamics-driven exhaustive analysis to guide the identification of potential novel ligands. Starting from eight crystal structures of this protein co-complexed with known binders and one apo form, we conducted an exhaustive molecular docking/molecular dynamics (MD) investigation. To balance accuracy and an affordable calculation time, the systems were simulated for 100 ns in explicit solvent. Moreover, one complex was simulated for 1 µs to assess the influence of simulation time on the results. A set of MD-derived parameters was computed and compared with molecular docking-derived and experimental data. MM-GBSA and the per-residue interaction energy emerged as the main indicators for the good interaction between the specific binder and the protein counterpart. To assess the performance of the proposed analysis workflow, we tested six molecules featuring different binding affinities for BRD9, obtaining promising outcomes. Further insights were reported to highlight the influence of the starting structure on the molecular dynamics simulations evolution. The data confirmed that a ranking of BRD9 binders using key parameters arising from molecular dynamics is advisable to discard poor ligands before moving on with the synthesis and the biological tests.

Stability Analysis and Structure Optimization of Unequal-Pitch End Mills

The damping performance of unequal tooth milling cutters is controlled by the pitch parameters. How to improve the vibration damping and dynamic balance of milling cutters needs to be further studied. This paper analyzes the pitch angle through the stability of the lobe diagram and the spectral characteristics, and unequal-pitch end mills with asymmetric structure were determined to have better cutting stability. Due to the principle error of the asymmetrical tool, dynamic balance accuracy is poor. The dynamic balance of the tool is analyzed, and the centroid model of the tool is established. In order to improve the dynamic balance accuracy of tools, the parameters of the groove shape are analyzed and optimized, and balance accuracy is improved. Through modal and milling-force analysis, the relative vibration displacement and cutting force of the optimized tool were reduced by 17% and 10%, respectively, which determined that such tools have better dynamic performance. Here, unequal tooth end mills could reduce vibration and had higher accuracy in dynamic balance by adjusting the parameters of the pitch angles and chip pockets, so that the tool could have higher cutting stability.

MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction

Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.

Studying problems related to the motional physiology of tennists

The article is devoted to the problems connected with the physiology of tennis players' movements. The main aim of the article was to determine the key factors which directly influence on movements and actions of tennis players during training process and participation in professional tournaments. Methods of the research: the analysis of scientific and methodical sources and observations. Object of the research: the educational and training process of tennis players aimed at improvement of movements. Results. The human strength is the ability to overcome the external resistance or to resist it due to the power of muscles. Each of the factors studied plays an important role in the training of athletes. The physiology of movement of tennis players consists of many components such as strength, agility, speed, ability to maintain balance, accuracy of movement in space, flexibility and endurance. Each of the mentioned elements plays not a small role and requires a special training. The urgency of the subject of the analysis of physiology of tennis players' movements consists in the increase of popularity of this kind of sport on the territory of Ukraine. As of 2021, more than 50 representatives of the country are included in the WTA and ATP ratings. It directly indicates a high level of preparation of domestic sportsmen. Conclusions. The physiology of a tennis player's movement consists of many factors (strength, agility, speed, ability to keep balance, accuracy of movement in space, flexibility and endurance), each of which plays an important role to reach the goals. Most of them are related to each other and create certain combinations. However, having one property does not guarantee the other, creates obstacles. However, they can be solved through constant training. Studying in detail the physiology of an athlete's movement is necessary in order to understand what characteristics may arise. More detailed researches, connected with physiology of movements, will help to correct correctly the preparatory process in future not only for the future professionals, but also for the present representatives of the Ukrainian national tennis team.

A Machine Learning Model for the Prognosis of Pulseless Electrical Activity during Out-of-Hospital Cardiac Arrest

Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20–30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.

Design and Optimization of Variable Pitch End Mills Based on Dynamic Balance Accuracy

Variable pitch end mills are widely used in the high-speed milling process due to having better vibration reduction properties. However, because of the unequal pitch angles and the asymmetrical structure of end mills, there are principle error, poor dynamic balance accuracy, and serious tool vibration problems in the milling process. In order to improve the dynamic balance accuracy of variable pitch end mills, the structure of the end mill is designed and optimized based on the minimum eccentricity criterion in this paper. Firstly, by analyzing the dynamic balance of variable pitch end mills, the relationship between it and eccentricity is defined, and the end section model and the centroid equation are developed through the structure design of end mills. Then, the optimization method of variable pitch end mills is analyzed, the eccentricity decreases from e0= 150um to e'0 = 3um based on this method. the structure of the end mill is optimized to meet the design criteria, which improves the balance accuracy level of end mills. Finally, through the modal analysis for the optimized structure of the variable pitch end mill, the maximum relative displacement in the mode is reduced by about 17%, it is verified that the optimized end mills have better dynamic performances, which is of great significance for alleviating the cutting vibration and improving the cutting stability.

Parsimonious neural networks learn interpretable physical laws

Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models. Less explored is its use to discover interpretable physical laws from data. We propose parsimonious neural networks (PNNs) that combine neural networks with evolutionary optimization to find models that balance accuracy with parsimony. The power and versatility of the approach is demonstrated by developing models for classical mechanics and to predict the melting temperature of materials from fundamental properties. In the first example, the resulting PNNs are easily interpretable as Newton’s second law, expressed as a non-trivial time integrator that exhibits time-reversibility and conserves energy, where the parsimony is critical to extract underlying symmetries from the data. In the second case, the PNNs not only find the celebrated Lindemann melting law, but also new relationships that outperform it in the pareto sense of parsimony vs. accuracy.

Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images

Retinal blood vessel morphological abnormalities are generally associated with cardiovascular, cerebrovascular, and systemic diseases, automatic artery/vein (A/V) classification is particularly important for medical image analysis and clinical decision making. However, the current method still has some limitations in A/V classification, especially the blood vessel edge and end error problems caused by the single scale and the blurred boundary of the A/V. To alleviate these problems, in this work, we propose a vessel-constraint network (VC-Net) that utilizes the information of vessel distribution and edge to enhance A/V classification, which is a high-precision A/V classification model based on data fusion. Particularly, the VC-Net introduces a vessel-constraint (VC) module that combines local and global vessel information to generate a weight map to constrain the A/V features, which suppresses the background-prone features and enhances the edge and end features of blood vessels. In addition, the VC-Net employs a multiscale feature (MSF) module to extract blood vessel information with different scales to improve the feature extraction capability and robustness of the model. And the VC-Net can get vessel segmentation results simultaneously. The proposed method is tested on publicly available fundus image datasets with different scales, namely, DRIVE, LES, and HRF, and validated on two newly created multicenter datasets: Tongren and Kailuan. We achieve a balance accuracy of 0.9554 and F1 scores of 0.7616 and 0.7971 for the arteries and veins, respectively, on the DRIVE dataset. The experimental results prove that the proposed model achieves competitive performance in A/V classification and vessel segmentation tasks compared with state-of-the-art methods. Finally, we test the Kailuan dataset with other trained fusion datasets, the results also show good robustness. To promote research in this area, the Tongren dataset and source code will be made publicly available. The dataset and code will be made available at https://github.com/huawang123/VC-Net.

Wind resistance calculation of large rotary mesh antenna and its influence on dynamic balance precision

Due to the wind resistance which acts on the main reflector of large rotary mesh antenna, the correct balancing result of satellite antenna is difficult to be gotten in the ground dynamic balancing test. In order to solve this problem, the dynamic balance method of large rotary mesh antenna which is under the influence of wind resistance in both low pressure environment and standard atmospheric pressure environment on the ground is studied. Based on the theoretical analysis and the experimental data of two-dimensional flow around circular cylinder, a new method of the large rotary mesh antenna wind resistance calculation is proposed, according to the CFD analysis of the three dimensional flow field. Through the dynamic equivalent method, the distributed wind resistance acted on the main reflector of the mesh antenna in the rotating state is equivalent to the principal vector and principal moment of the action point in each quadrant, and then transformed into the eccentric mass on the distribution plane. It provides a feasible and innovative way to estimate the influence of wind resistance on the dynamic balance accuracy of large mesh antenna, so as to compensate the wind resistance effect. Combined with the ground dynamic balancing requirements of a certain type of satellite mesh antenna, the whole finite element model of the mesh antenna is established, the simulation of ground dynamic balancing test is carried out, and the influence of wind resistance on the ground dynamic balancing results of the antenna is analyzed in this study, which provides important data for compensating the influence of wind resistance and ensuring the on-orbit balancing accuracy of the antenna.

Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise

Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.