Utilizing RNA-seq Data to Infer Bacterial Transcription Termination Sites and Validate Predictions.

The transcription termination process is an important part of the gene expression process in the cell. It has been studied extensively, but many aspects of the mechanism are not well understood. The widespread availability of experimental RNA-seq data from high-throughput experiments provides a unique opportunity to infer the end of the transcription units genome wide. This data is available for both Rho-dependent and Rho-independent termination pathways that drive transcription termination in bacteria. Our book chapter gives an overview of the current knowledge of Rho-independent transcription termination mechanisms and the prediction approaches currently deployed to infer the termination sites. Thereafter, we describe our method that uses cluster hairpins to detect Rho-independent transcription termination sites. These clusters are a group of hairpins that lies at <15 bp from each other and are together capable of enforcing the termination process. The idea of a group of hairpins being extensively used for transcription termination is new, and results show that at least 52% of the total cases are of this type, while in the remaining cases, a single strong hairpin is capable of driving transcription termination. The reads derived from the RNA-seq data for corresponding bacteria have been used to validate the predicted sites. The predictions that match these RNA-seq derived sites have higher confidence, and we find almost 98% of the predicted sites, including alternate termination sites, to match the RNA-seq data. We discuss the features of predicted hairpins in detail for a better understanding of the Rho-independent transcription termination mechanism in bacteria. We also explain how users can use the tools developed by us to do transcription terminator predictions and design their experiments through genome-level visualization of the transcription termination sites from the precomputed INTERPIN database.

Prediction of Novel Trigonal Chloride Superionic Conductors as Promising Solid Electrolytes for All-Solid-State Lithium Batteries.

Recently emerging lithium ternary chlorides have attracted increasing attention for solid-state electrolytes (SSEs) due to their favorable combination between ionic conductivity and electrochemical stability. However, a noticeable discrepancy in Li-ion conductivity persists between chloride SSEs and organic liquid electrolytes, underscoring the need for designing novel chloride SSEs with enhanced Li-ion conductivity. Herein, an intriguing trigonal structure (i.e., Li3SmCl6 with space group P3112) is identified using the global structure searching method in conjunction with first-principles calculations, and its potential for SSEs is systematically evaluated. Importantly, the structure of Li3SmCl6 exhibits a high ionic conductivity of 15.46 mS cm-1 at room temperature due to the 3D lithium percolation framework distinct from previous proposals, associated with the unique in-plane cation ordering and stacking sequences. Furthermore, it is unveiled that Li3SmCl6 possesses a wide electrochemical window of 0.73-4.30 V vs Li+/Li and excellent chemical interface stability with high-voltage cathodes. Several other Li3MCl6 (M = Er, and In) materials with isomorphic structures to Li3SmCl6 are also found to be potential chloride SSEs, suggesting the broader applicability of this structure. This work reveals a new class of ternary chloride SSEs and sheds light on strategy for structure searching in the design of high-performance SSEs.

Design and analysis of a lower limb assistive exoskeleton robot.

BACKGROUND: In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. OBJECTIVE: The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. METHODS: The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. RESULTS: The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. CONCLUSION: A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects.

A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second-ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

Integrated prediction and control of mobility changes of young talents in the field of science and technology based on convolutional neural network.

As the scientific and technological levels continue to rise, the dynamics of young talent within these fields are increasingly significant. Currently, there is a lack of comprehensive models for predicting the movement of young professionals in science and technology. To address this gap, this study introduces an integrated approach to forecasting and managing the flow of these talents, leveraging the power of convolutional neural networks (CNNs). The performance test of the proposed method shows that the prediction accuracy of this method is 76.98%, which is superior to the two comparison methods. In addition, the results showed that the average error of the model was 0.0285 lower than that of the model based on the recurrent prediction error (RPE) algorithm learning algorithm, and the average time was 41.6 s lower than that of the model based on the backpropagation (BP) learning algorithm. In predicting the flow of young talent, the study uses flow characteristics including personal characteristics, occupational characteristics, organizational characteristics and network characteristics. Through the above results, the study found that convolutional neural network can effectively use these features to predict the flow of young talents, and its model is superior to other commonly used models in processing speed and accuracy. The above results indicate that the model can provide organizations and government agencies with useful information about the flow trend of young talents, and help them to formulate better talent management strategies.

A New Residual Subsidence Prediction Method of Short Working Face Goaf for Safety Construction of Urban Viaduct.

The planned viaduct in Jining, Shandong is a priority project in the city. However, the 63 working faces of a mine in Jining is only 3 m away from the planned viaduct, posing a serious threat to the safety of the viaduct's construction. Consequently, it is essential to evaluate the stability of the planned viaduct's goaf area under the influence of the 63 working faces. However, the 63 working faces are short faces, and there is a lack of corresponding prediction of surface residual subsidence. To address this issue, this paper employs theoretical analysis and numerical simulation to uncover the foundation deformation mechanism and characteristics of fractured rock and soil mass in the short goaf. Subsequently, a residual subsidence prediction method for the short goaf was proposed for the viaduct mined-out area. This new approach was implemented for the planned viaduct in Jining, and its effectiveness was validated through InSAR and leveling monitoring results. The research findings offer technical support for viaduct construction in areas affected by underground mining.

Predicting the occurrence of antagonism within ternary guanidine mixture pollutants based on the concentration ratio of components.

The widespread prevalence and coexistence of diverse guanidine compounds pose substantial risks of potential toxicity interactions, synergism or antagonism, to environmental organisms. This complexity presents a formidable challenge in assessing the risks associated with various pollutants. Hence, a method that is both accurate and universally applicable for predicting toxicity interactions within mixtures is crucial, given the unimaginable diversity of potential combinations. A toxicity interaction prediction method (TIPM) developed in our past research was employed to predict the toxicity interaction, within guanidine compound mixtures. Here, antagonism were found in the mixtures of three guanidine compounds including chlorhexidine (CHL), metformin (MET), and chlorhexidine digluconate (CDE) by selecting Escherichia coli (E. coli) as the test organism. The antagonism in the mixture was probably due to the competitive binding of all three guanidine compounds to the anionic phosphates of E. coli cell membranes, which eventually lead to cell membrane rupture. Then, a good correlation between toxicity interactions (antagonisms) and components' concentration ratios (pis) within binary mixtures (CHL-MET, CHL-CDE, MET-CDE) was established. Based on the correlation, the TIPM was constructed and accurately predicted the antagonism in the CHL-MET-CDE ternary mixture, which once again proved the accuracy and applicability of the TIPM method. Therefore, TIPM can be suggested to identify or screen rapidly the toxicity interaction within ternary mixtures exerting potentially adverse effects on the environment.

A transformer-based genomic prediction method fused with knowledge-guided module.

Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.

Lightweight RepVGG-Based Cross-Modality Data Prediction Method for Solid Rocket Motors.

Solid rocket motors (SRMs) have been popularly used in the current aerospace industry. Performance indicators, such as pressure and thrust, are of great importance for rocket monitoring and design. However, the measurement of such signals requires high economic and time costs. In many practical situations, the thrust measurement error is large and requires manual correction. In order to address this challenging problem, a lightweight RepVGG-based cross-modality data prediction method is proposed for SRMs. An end-to-end data prediction framework is established by transforming data across different modalities. A novel RepVGG deep neural network architecture is built, which is able to automatically learn features from raw data and predict new time-series data of different modalities. The effectiveness of the proposed method is extensively validated with the field SRM data. The accurate prediction of the thrust data can be achieved by exploring the pressure data. After calculation, the percentage error between the predicted data and the actual data is less than 5%. The proposed method offers a promising tool for cross-modality data prediction in real aerospace industries for SRMs.

Strength Degradation of Foamed Lightweight Soil Due to Chemical Erosion and Wet-Dry Cycle and Its Empirical Model.

Foamed lightweight soils (FLS) have been extensively used as backfill material in the construction of transportation infrastructures. However, in the regions consisting of salt-rich soft soil, the earth structure made by FLS experiences both fluctuation of groundwater and chemical environment erosion, which would accelerate the deterioration of its long-term performance. This study conducted laboratory tests to explore the deterioration of FLS in strength after being eroded by sulfate attack and/or wet-dry cycling, where the influencing factors of FLS density, concentration of sulfate solution, and cation type (i.e., Na+ and Mg2+) were considered. An unconfined compressive test (UCT) was conducted, and the corrosion-resistant coefficient (CRC) was adopted to evaluate the erosion degree after the specimens experienced sulfate attack and/or dry-wet cycling for a certain period. The research results show that the erosion of the FLS specimen under the coupling effect of sulfate attack and dry-wet cycling was more remarkable than that only under chemical soaking, and Na2SO4 solution had a severe erosion effect as compared with MgSO4 solution when other conditions were kept constant. An empirical model is proposed based on the test results, and its reliability has been verified with other test results from the literature. The proposed model provides an alternative for engineers to estimate the strength deterioration of FLS on real structures in a preliminary design.

A dynamic model of coalbed methane emission from boreholes in front of excavation working face: numerical model and its application.

China's current energy consumption is primarily fueled by coal, increasing coal mining with growing energy demand. Coal and gas outburst accidents are common problems in coal mining, and prediction methods are fundamental for preventing such accidents. The gas emission characteristics of boreholes are a combination of comprehensive coal properties and coal seam gas occurrence status; thus, the accurate prediction of gas emissions from boreholes is crucial for preventing such hazards. This paper presents a method for measuring the gas flow rate in continuous boreholes, which is used to predict outburst danger in front of the working face. The model was compared with field measurement data and found suitable for research. The effects of different initial gas pressures, different borehole radius, and different burial depths on gas emissions from boreholes were studied. The results showed that (1) initial gas pressure is the main influencing factor of gas gushing. The amount of gas emission during drilling and the attenuation of gas pressure are more sensitive to pressure. An increase in gas pressure considerably increases the amount of gas gushing out of drilling holes. (2) The increase in the drilling radius increases the generation of coal cuttings, the area of the drilling hole wall, and the degree of damage to the drilling hole wall. Consequently, the amount of gas gushing out of the drilling hole increases. (3) In situ stress occurs mainly because of the increase in gas pressure with an increase in burial depth and the increase in gas desorption caused by the increase in damage to the borehole wall. This study provides a new outburst prediction method, which involves identifying outburst hazards through the gas gushing out of the borehole. The results are expected to aid the control of underground coal and gas outbursts and ensure the safe production of coal mines.

Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.

PURPOSE: To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively. METHODS: 135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set. RESULTS: In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996). CONCLUSION: We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.

Empirical Compression Model of Ultra-High-Performance Concrete Considering the Effect of Cement Hydration on Particle Packing Characteristics.

The mix design of UHPC has always been based on a large number of experiments; in order to reduce the number of repeated experiments, in this study, silica fume (SF), fly ash (FA), and limestone powder (LP) were used as the raw materials to conduct 15 groups of experiments to determine the particle size distribution (PSD) properties of UHPC. A model of multi-component hydration based on the SF, FA, and LP pozzolanic reactions was devised to quantify the rate and total heat release during the hydration process. Additionally, a microscopic pore development model, which was based on the accumulation of hydration products, was established to measure the effect of these products on the particle-packing properties. Utilizing this model, a UHPC strength prediction technique was formulated to precisely forecast the compressive strength based on a restricted experimental data set. The applicability of this prediction method was verified using 15 sets of existing experimental data along with the data collected from 4 research articles. The results show that the prediction method can predict the strength values of different mix proportions with an accuracy rate of over 80%.

Corrigendum: An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

[This corrects the article DOI: 10.3389/fncom.2023.1145209.].

Tabletability and compactibility of α-lactose monohydrate powders of different particle size. II: predicted relationships.

This study aimed to evaluate a material sparing method to predict tabletability and compactibility relationships. Seven α-lactose monohydrate powders with varying particle size were used as test materials. The compressibility of the powders was determined experimentally, whereas tabletability and compactibility profiles were derived both experimentally and predicted. In the prediction method, two experimental compression parameters (Kawakita b-1 and Heckel plastic stiffness) and a single tensile strength reference value were used, all necessary data obtained from a single compression experiment. For both predicted and experimental relationships, compaction and tableting parameters (performance indicators) were calculated. The correction for viscoelastic recovery was successful in generating compressibility profiles that corresponded to the series of experimental out-of-die tablet porosities. For both the tabletability and compactibility, the experimental and predicted profiles showed a high degree of similarity. Good correlations were obtained between the predicted and experimental compaction and tableting parameters. It is concluded that the hybrid prediction method is a material sparing method, which can give good approximations of tabletability and compactibility relationships. The prediction method has the potential to be included as a part of a protocol for the characterisation of the tableting performance of particulate solids.

Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature.

BACKGROUND: Femoral neck fracture (FNF) is a very common traumatic disorder and a major cause of blood supply disruption to the femoral head, which may lead to a severe long-term complication, osteonecrosis of femoral head (ONFH). Early prediction and evaluation of ONFH after FNF could facilitate early treatment and may prevent or reverse the development of ONFH. In this review paper, we will review all the prediction methods reported in the previous literature. METHODS: Studies on the prediction of ONFH after FNF were included in PubMed and MEDLINE databases with articles published before October 2022. Further screening criteria were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This study highlights all the advantages and disadvantages of the prediction methods. RESULTS: There were a total of 36 studies included, involving 11 methods to predict ONFH after FNF. Among radiographic imaging, superselective angiography could directly visualize the blood supply of the femoral head, but it is an invasive examination. As noninvasive detection methods, dynamic enhanced magnetic resonance imaging (MRI) and SPECT/CT are easy to operate, have a high sensitivity, and increase specificity. Though still at the early stage of development in clinical studies, micro-CT is a method of highly accurate quantification that can visualize femoral head intraosseous arteries. The prediction model relates to artificial intelligence and is easy to operate, but there is no consensus on the risk factors of ONFH. For the intraoperative methods, most of them are single studies and lack clinical evidence. CONCLUSION: After reviewing all the prediction methods, we recommend using dynamic enhanced MRI or single photon emission computed tomography/computed tomography in combination with the intraoperative observation of bleeding from the holes of proximal cannulated screws to predict ONFH after FNF. Moreover, micro-CT is a promising imaging technique in clinical practice.

An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.

Human motion prediction is one of the fundamental studies of computer vision. Much work based on deep learning has shown impressive performance for it in recent years. However, long-term prediction and human skeletal deformation are still challenging tasks for human motion prediction. For accurate prediction, this paper proposes a GCN-based two-stage prediction method. We train a prediction model in the first stage. Using multiple cascaded spatial attention graph convolution layers (SAGCL) to extract features, the prediction model generates an initial motion sequence of future actions based on the observed pose. Since the initial pose generated in the first stage often deviates from natural human body motion, such as a motion sequence in which the length of a bone is changed. So the task of the second stage is to fine-tune the predicted pose and make it closer to natural motion. We present a fine-tuning model including multiple cascaded causally temporal-graph convolution layers (CT-GCL). We apply the spatial coordinate error of joints and bone length error as loss functions to train the fine-tuning model. We validate our model on Human3.6m and CMU-MoCap datasets. Extensive experiments show that the two-stage prediction method outperforms state-of-the-art methods. The limitations of proposed methods are discussed as well, hoping to make a breakthrough in future exploration.

Multimodal deep learning methods enhance genomic prediction of wheat breeding.

While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype-environment interaction (GE); however, unlike conventional GP models, DL has not been investigated for when genomics is linked with phenomics. In this study we used 2 wheat data sets (DS1 and DS2) to compare a novel DL method with conventional GP models. Models fitted for DS1 were GBLUP, gradient boosting machine (GBM), support vector regression (SVR) and the DL method. Results indicated that for 1 year, DL provided better GP accuracy than results obtained by the other models. However, GP accuracy obtained for other years indicated that the GBLUP model was slightly superior to the DL. DS2 is comprised only of genomic data from wheat lines tested for 3 years, 2 environments (drought and irrigated) and 2-4 traits. DS2 results showed that when predicting the irrigated environment with the drought environment, DL had higher accuracy than the GBLUP model in all analyzed traits and years. When predicting drought environment with information on the irrigated environment, the DL model and GBLUP model had similar accuracy. The DL method used in this study is novel and presents a strong degree of generalization as several modules can potentially be incorporated and concatenated to produce an output for a multi-input data structure.

Evaluation and prediction method of railway passenger long-term vibration comfort under complex operating conditions.

Vibration contributes large increases in railway passenger discomfort during long-term sitting. Discomfort caused by vibration may differ in different operation conditions. This paper conducted field measurements to investigate the interrelationships between the three. Participants completed a 240-min train journey with their whole-body vibration, subjective comfort ratings and train operating parameters being recorded. A large correlation was observed between the estimated vibration dose value and subjective comfort. The relationship that vibration magnitude significantly increases with increasing the train speed and tunnel density was also found and quantified. A vibration exposure limit of 2.08 m/s1.75 corresponding to the boundary between subjective ratings of comfortable and discomfortable was obtained. Based on the exposure limit and the quantified relationship, a vibration comfort prediction method that can calculate the passenger's maximum tolerance time under a given operation condition was proposed and may help in determining the optimal operating speed and tunnels distribution to alleviate vibration discomfort. Practitioner summary: Similar to the guide to effect of vibration on health in current standard, a vibration exposure limit regarding comfort was provided for reference when assessing long-term vibration comfort. Meanwhile, a prediction method was proposed for determining the best train operating speed and tunnels distribution, thereby alleviating railway passengers' vibration discomfort.