High Predictive Ability Research Articles

T4Seeker: a hybrid model for type IV secretion effectors identification.

The type IV secretion system is widely present in various bacteria, such as Salmonella, Escherichia coli, and Helicobacter pylori. These bacteria use the type IV secretion system to secrete type IV secretion effectors, infect host cells, and disrupt or modulate the communication pathways. In this study, type III and type VI secretion effectors were used as negative samples to train a robust model. The area under the curve of T4Seeker on the validation and independent test sets were 0.947 and 0.970, respectively, demonstrating the strong predictive capacity and robustness of T4Seeker. After comparing with the classic and state-of-the-art T4SE identification models, we found that T4Seeker, which is based on traditional features and large language model features, had a higher predictive ability. The T4Seeker proposed in this study demonstrates superior performance in the field of T4SEs prediction. By integrating features at multiple levels, it achieves higher predictive accuracy and strong generalization capability, providing an effective tool for future T4SE research.

Association mapping and genomic prediction for processing and end-use quality traits in wheat (Triticum aestivum L.).

End-use and processing traits in wheat (Triticum aestivum L.) are crucial for varietal development but are often evaluated only in the advanced stages of the breeding program due to the amount of grain needed and the labor-intensive phenotyping assays. Advances in genomic resources have provided new tools to address the selection for these complex traits earlier in the breeding process. We used association mapping to identify key variants underlying various end-use quality traits and evaluate the usefulness of genomic prediction for these traits in hard red spring wheat from the Northern United States. A panel of 383 advanced breeding lines and cultivars representing the diversity of the University of Minnesota wheat breeding program was genotyped using the Illumina 90K single nucleotide polymorphism array and evaluated in multilocation trials using standard assessments of end-use quality. Sixty-three associations for grain or flour characteristics, mixograph, farinograph, and baking traits were identified. The majority of these associations were mapped in the vicinity of glutenin/gliadin or other known loci. In addition, a putative novel multi-trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross-validation and forward prediction, respectively. The deployment of 46 markers from GWAS to predict dough-rheology traits yielded low to moderate PA for various traits. The results of this study suggest that genomic prediction for end-use traits in early generations can be effective for mixograph and farinograph assays but not baking assays.

Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.

Aim: Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms.Materials & methods: A total of 178 patients with CIN (+) and 1511 with CIN (-) were included.Results: CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients.Conclusion: IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.

Prospectively predicting 6-month risk for non-suicidal self-injury among adolescents after psychiatric hospitalization based on a predictive model

BackgroundIt is challenging to predict the occurrence of non-suicidal self-injury (NSSI) among adolescents over short periods. Moreover, the predictive value of indices for NSSI remains elusive. Thus, this study aimed to identify predictors of NSSI within 6 months among adolescents after psychiatric hospitalization by establishing a risk assessment model.MethodsA total of 632 high-risk participants were included in this study. The distribution characteristics of adolescent NSSI were initially assessed through a cross-sectional survey, following which risk factors were identified using logistic regression analysis. The risk score method was then used to construct a 6-month risk assessment model for NSSI. Lastly, the predictive effect of the model was evaluated by indicators such as the area under the receiver operating characteristic (ROC) curve and the positive predictive value.ResultsAfter 6 months, 412 cases of NSSI were identified. According to the logistic regression model, the frequency of relapses, medication status, and NSSI history were identified as influencing factors. Higher scores on the Impulsive Behavior Scale and Pittsburgh Sleep Quality Index were associated with a higher risk of NSSI. Conversely, higher scores on the Pain and Belief Perception Scale were correlated with a lower risk of NSSI. Moreover, the area under the ROC curve for the predictive model was 0.9989, with a 95% confidence interval of (0.9979, 0.9999), highlighting its high predictive ability and accuracy. The predictive model was validated using 78 patients, yielding an area under the ROC curve of 0.9703 and a 95% confidence interval of (0.9167, 0.9999), demonstrating outstanding predictability.ConclusionThese results collectively showed that the predictive model could accurately predict adolescent NSSI. Thus, the model’s primary variables may be applied to predict the risk of NSSI in the clinical setting.

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the disease. Whilst many DR mutations in the Mtb genome are known, analysis of large datasets generated using whole genome sequencing (WGS) platforms can reveal new variants through the assessment of genotype-phenotype associations. Here, we apply tree-based ensemble methods to a dataset comprised of 35,777 Mtb WGS and phenotypic drug-susceptibility test data across first- and second-line drugs. We compare model performance across models trained using mutations in drug-specific regions and genome-wide variants, and find high predictive ability for both first-line (area under ROC curve (AUC); range 88.3–96.5) and second-line (AUC range 84.1–95.4) drugs. To aggregate information from low-frequency variants, we pool mutations by functional impact and observe large improvements in predictive accuracy (e.g., sensitivity: pyrazinamide + 25%; ethionamide + 10%). We further characterise loss-of-function mutations observed in resistant phenotypes, uncovering putative markers of resistance (e.g., ndh 293dupG, Rv3861 78delC). Finally, we profile the distribution of known DR-associated single nucleotide polymorphisms across discretised minimum inhibitory concentration (MIC) data generated from phenotypic testing (n = 12,066), and identify mutations associated with highly resistant phenotypes (e.g., inhA − 779G > T and 62T > C). Overall, our work demonstrates that applying machine learning to large-scale WGS data is useful for providing insights into predicting Mtb binary drug resistance and MIC phenotypes, thereby potentially assisting diagnosis and treatment decision-making for infection control.

Risk stratification of residual abscess after surgical treatment for gastroduodenal perforation

AbstractAimsResidual abscess is a major complication after emergency surgery for gastroduodenal (GD) perforation. However, there is little evidence regarding potential risk factors contributing to its development. Establishing a risk stratification strategy would be valuable for the entire management process.MethodsThis single‐center, retrospective study analyzed 115 consecutive patients who underwent surgery for GD perforation between 2010 and 2023 at a secondary emergency care hospital. Patients were divided into two groups based on the presence or absence of residual abscesses. Potential risk factors for abscess formation were evaluated from various aspects.ResultsThe incidence of residual abscesses was 19.1% (22 of 115). Multivariable analysis revealed that current use of nonsteroidal antiinflammatory drugs (odds ratio [OR] 3.76, p = 0.037), cancer chemotherapy (OR 13.56, p = 0.005), and preoperative renal dysfunction (OR 4.72, p = 0.018) were independent predictors. A potential scoring model could be created using these three parameters, and the number of risk factors correlated with the likelihood of developing a residual abscess (0 vs. 1 vs. ≥2; 6.2% vs. 29.4% vs. 50.0%, p < 0.001). From a bacteriological point of view, the presence of Enterococcus in the ascites culture was closely related to its occurrence with 100% probability. Moreover, regarding early detection of this complication, C‐reactive protein on postoperative d 5 had the highest predictive ability with an area under the curve of 0.818.ConclusionThe risk of residual abscess formation after surgical treatment of GD perforation can be assessed utilizing both preoperative and postoperative information.

Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.

Predictive ability and prognostic correlation of global longitudinal strain in patients with negative dipyridamole stress echocardiography by wall motion criteria

Abstract Background Dipyridamole stress echocardiography (DSE) is an established technique to detect coronary artery disease (CAD) and for risk stratification. Speckle Tracking Echocardiography (STE), as an objective and quantitative evaluation of ventricular function, may add incremental value to conventional stress echo analysis. Purpose To assess the prognostic value of rest (Rest-GLS) and peak (Peak-GLS) stress global longitudinal strain (GLS) in patients with suspected CAD and negative DSE according to wall motion index (WMI) criteria. Methods We retrospectively enrolled patients who underwent DSE at our centre for suspected CAD, with normal WMI. Strain Reserve was calculated as Rest GLS – Peak-GLS (Delta-GLS). We also collected data regarding other conventional parameters such as coronary flow reserve (CFR) of left anterior descending (LAD), left ventricle contractile reserve (LVCR), delta left ventricular ejection fraction (Delta-LVEF), heart rate response (HRR). Our primary endpoint was a composite of all-cause death and coronary revascularization. Receiver operating characteristic (ROC) curves were used to compare the predictive ability of the parameters. Adjusted logistic regression analyses were used to assess associations, also modelled as a restricted cubic spline. Results A total of 355 patients were included in the analysis (median age 68.2, IQR 58.2-75.2, 38.0% female). At follow-up, 58 patients met the primary endpoint. Figure 1 shows the ROC curves of different DSE parameters. Delta-GLS and Peak-GLS showed high predictive ability of composite endpoint events (area under the curve [AUC] 0.87, 95% Confidence Interval [CI] 0.82-0.92, AUC, 0.85, 95% CI 0.79-0.91, respectively), which was significantly higher compared with other DSE parameters, included Basal-GLS (P for difference for all &amp;lt; 0.001). After adjustments for age, previous CAD, sex, and diabetes, reducing values of Rest-GLS, Peak-GLS and Delta-GLS were significantly associated with higher odds of experiencing composite endpoint events (Basal-GLS, odds ratio [OR] 1.13, 95% CI 1.04-1.24; Peak-GLS, OR 1.45, 95% CI 1.32-1.63; Delta-GLS, OR 1.73, 95% CI 1.50-2.04). Plotting the results of the logistic regression analysis on a restricted cubic spline, reducing values of Peak-GLS and Delta-GLS showed a linear increase for the risk of adverse events, while Rest-GLS showed a plateau for lower values. Conclusion The use of GLS parameters in DSE can help to better stratify the risk for the patients since they are associated with adverse cardiovascular events.Figure 1Figure 2

Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles

ContextThe invasion of annual grasses in western U.S. rangelands promotes high litter accumulation throughout the landscape that perpetuates a grass-fire cycle threatening biodiversity.ObjectivesTo provide novel evidence on the potential of fine spatial and structural resolution remote sensing data derived from Unmanned Aerial Vehicles (UAVs) to separately estimate the biomass of vegetation and litter fractions in sagebrush ecosystems.MethodsWe calculated several plot-level metrics with ecological relevance and representative of the biomass fraction distribution by strata from UAV Light Detection and Ranging (LiDAR) and Structure-from-Motion (SfM) datasets and regressed those predictors against vegetation, litter, and total biomass fractions harvested in the field. We also tested a hybrid approach in which we used digital terrain models (DTMs) computed from UAV LiDAR data to height-normalize SfM-derived point clouds (UAV SfM-LiDAR).ResultsThe metrics derived from UAV LiDAR data had the highest predictive ability in terms of total (R2 = 0.74) and litter (R2 = 0.59) biomass, while those from the UAV SfM-LiDAR provided the highest predictive performance for vegetation biomass (R2 = 0.77 versus R2 = 0.72 for UAV LiDAR). In turn, SfM and SfM-LiDAR point clouds indicated a pronounced decrease in the estimation performance of litter and total biomass.ConclusionsOur results demonstrate that high-density UAV LiDAR datasets are essential for consistently estimating all biomass fractions through more accurate characterization of (i) the vertical structure of the plant community beneath top-of-canopy surface and (ii) the terrain microtopography through thick and dense litter layers than achieved with SfM-derived products.

Severity Stratification of Coronary Artery Disease Using Novel Inner Ellipse-Based Foveal Avascular Zone Biomarkers.

Given the similarities between the retinal and coronary microvasculature, the retina holds promising potential to serve as a non-invasive screening tool for coronary artery disease (CAD). We aimed to develop novel inner ellipse-based metrics and discern whether foveal avascular zone (FAZ) alterations can serve as indicators for CAD presence and severity. Patients admitted to the Department of Cardiology who underwent coronary angiography were included. This resulted in an inclusion of 212 patients, of which 73 had no CAD. During the same visit, 6 × 6-mm (nominal size) fovea-centered optical coherence tomography angiography images of both eyes were acquired. The Gensini score (GS) was utilized to quantify CAD severity. Six known FAZ shape metrics were assessed and three novel biomarkers based on the inner ellipse were defined: absolute inner ellipse difference, Hausdorff distance, and Chamfer distance. Eight out of nine metrics showed significant associations with the GS in the left eye. However, significant differences across three CAD severity groups were only demonstrated by the novel metrics. Utilizing the Chamfer distance, age, and sex, patients with and without CAD could be distinguished with an average area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.84-0.95). Moreover, three CAD severity groups could be discerned with a macro average AUC of 0.77 (95% CI, 0.72-0.84). A comprehensive assessment of FAZ shape descriptors was performed, and a strong association with CAD was found. The inner ellipse-based biomarkers especially demonstrated high predictive abilities for CAD presence and severity.

Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction

BackgroundResearch comparing artificial intelligence and machine learning (AI/ML) methods with classical statistical methods applied to large population health databases is limited. ObjectivesThis retrospective cohort study aimed to compare the predictive performance of AI/ML algorithms against conventional multivariate logistic regression models using linked health administrative data. MethodsUsing Ontario's population health databases, we created a cohort of residents of the city of Ottawa, Ontario, who underwent a PCR test for COVID-19 between March 10, 2020, and May 13, 2021. Using demographic, socio-economic and health data (including COVID-19 PCR test results and available, symptom data), we developed predictive models for the purpose of COVID-19 case identification using the following approaches: classical multivariate logistic regression (LR); deep neural network (DNN); random forest (RF); and gradient boosting trees (GBT). Model performance comparisons were made using the area under the curve (AUC) swarm plot for 10-fold cross-validation. ResultsThe cohort consisted of n = 351,248 Ottawa residents tested for COVID-19 during the study period. Among whom, a total of n = 883,879 unique COVID-19 tests were performed (2.6 % positive test results). Inclusion of COVID-19 symptoms data in the analysis improved model performance and variable predictive value across all tested models (p < 0.0001), with the 10-fold cross-validation AUC increasing to near or over 0.7 in all models when symptoms data were included. In various pairwise comparisons, the GBT method had the highest predictive ability (AUC = 0.796 ± 0.017), significantly outperforming multivariate logistic regression and the other AI/ML approaches. ConclusionsConventional multivariate regression-based models are better than some and worse than other machine learning algorithms to provide good predictive accuracy in a moderate dataset with a reasonable number of features. However, whenever possible, the AI/ML GBT approach should be considered.

Development of hazard prediction test and interventions for two-wheeled electric vehicle riders in China

Objective This paper aimed to develop a hazard prediction test and enhance two-wheeled electric vehicle (TWEV) riders’ hazard perception and prediction capabilities via interventions by executing two distinct studies. Study 1 aimed to develop and validate a hazard prediction test. Study 2 evaluated the efficacy of two interventions, self-commentary and what happens next (WHN), integrating expert commentary. Method For Study 1, a video-based hazard prediction test was developed through video recording and clipping, with participants categorized into high and low prediction ability groups for experimentation. Data analysis employed the receiver operating characteristic (ROC) curve. Study 2 categorized participants into four groups: self-commentary with licenses (SCL), self-commentary without licenses (SCNL), WHN with licenses (WHNL), and WHN without licenses (WHNNL), for conducting a one-week intervention experiment. Data collected from participants’ pre-intervention, post-intervention, and aftereffect tests were subjected to repeated measures analysis of variance (ANOVA). Results Analysis of the ROC curve indicated the test can distinguish the riders with different hazard prediction levels. ANOVA results demonstrated that the measurement time had a significant positive effect on scores (p < 0.001). Both interventions significantly improved hazard prediction ability (p < 0.05), and the effect persisted one week after administration. The effect of the WHN intervention was significantly greater than the self-commentary method across all time points. Conclusion The hazard prediction test developed in this study could assess riders’ hazard prediction ability, with the identified interventions demonstrating effectiveness in enhancing this ability. These findings suggested potential application in future qualification tests for TWEV riders, contributing to enhanced traffic safety awareness among TWEV riders in China, thus advancing overall traffic safety.

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches

To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). The cohort included all multiparous women who delivered a singleton birth at 20–42 weeks’ gestation in an Ontario hospital between April 1, 2012 and March 31, 2014. The primary outcome was PTB < 37 weeks, with spontaneous PTB analyzed as a secondary outcome. Stepwise logistic regression and the Boruta machine learning were used to select the important variables during the first and second trimester. For building prediction models, the whole data set were divided for the two independent parts: two-third for training the classifiers (Logistic regression, random forests, decision trees, and artificial neural networks) and one-third for model validation. Then, the training data set were balanced by random over sampling technique. The best hyper parameters were obtained by the tenfold cross validation. The performance of all models was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristics (AUC). The cohort included 145,846 births, of which 8125 (5.57%) were preterm. In first-trimester models, the strongest predictors of PTB were previous PTB, preexisting diabetes, and abnormal pregnancy‐associated plasma protein-A. In the testing data set, the highest predictive ability was seen for artificial neural networks, with an area under the receiver operating characteristic curve (AUC) of 68.8% (95% CI 67.6–70.1%). In second-trimester models, addition of infant sex, attendance at first-trimester appointment, medication exposure, and abnormal alpha-fetoprotein concentrations increased the AUC to 72.1% (95% CI 71.1–73.1%) with logistic regression. With the inclusion of the variable complications during pregnancy, the AUC increased to 80.5% (95% CI 79.6–81.5%) using logistic regression. For both overall and spontaneous PTB, during both the first and second trimesters, models yielded negative predictive values of 97%. Overall, machine learning and logistic regression produced similar performance for prediction of PTB. For overall and spontaneous PTB, both first- and second-trimester models provided negative predictive values of ~ 97%, higher than that of fetal fibronectin.

Experimental study and life prediction for aero-engine turbine blade considering creep-fatigue interaction effect

As an important failure form of the turbine blade, creep-fatigue interaction damage affects the safe operation and maintenance strategy of aero-engine, and has been the focus of scientific research and the academic community. Firstly, based on the Kachanov-Rabotnov-Lemaitre continuum damage mechanics theory and the nonlinear symmetry of creep damage and fatigue damage, a creep-fatigue life prediction model is constructed considering the interaction effect in this paper. Then, based on the thermal-fluid–solid multi-physical field coupling numerical simulation of the turbine blade, the equivalent method of creep-fatigue load spectrum was explored according to the equal damage criterion and linear damage rule, and the creep-fatigue interaction test of smooth samples of the blade material was conducted to analyze the creep-fatigue fracture morphology. Finally, the stress term in the creep-fatigue life prediction model of blade material is modified by the correction factor α, and the modified creep-fatigue life prediction model of the turbine blade is constructed considering the interaction effect. The results show that the modified creep-fatigue life prediction model considering the interaction effect has a high life prediction ability with an error of 3.9%. The above research has important scientific research value for the life extension design of turbine blades and the improvement of aero-engine maintenance strategy.

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Early vs. delayed QTc prolongation in acute poisoning: A prognostic accuracy study-A case series.

Given the limited capacity of intensive care units in many countries, it is crucial to identify reliable prognostic markers to optimize poisoning patients management and improve outcomes. This study aimed to assess the predictive value of three variables, namely the initial QTc interval (iQTc) measured within two hours of admission, the delayed QTc interval (dQTc) measured between 6 and 12 hours of entry, and the QTc interval trend over time (ΔQTc), for mortality in patients with undifferentiated poisoning. A retrospective case series was conducted on 70 patients with undifferentiated poisoning admitted to the intensive care unit (ICU) of Afzalipour Hospital between March 21, 2021, and March 20, 2023. The results of the multivariate analysis revealed that dQTc, base deficit, and creatinine were independently associated with mortality (P value < 0.001). The dQTc had the highest predictive ability, with an area under the curve (AUC) of 0.84, followed by ΔQTc with an AUC of 0.76, and iQTc with an AUC of 0.67. Additionally, the results of the Generalized Estimating Equation model with repeated measurements revealed a higher odds ratio for dQTc (OR, 6.33; 95% CI, 2.54-15.79) compared to iQTc (OR, 4.92; 95% CI, 1.71-14.17). The study concluded that monitoring the dQTc interval could provide valuable prognostic information in acute poisoning cases.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods

AbstractThe broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency.

Plasma metabolites and inflammatory proteins profiling predict outcome of Fufang Duzhong Jiangu granules treating Kashin-Beck disease.

To investigate predictive biomarkers that could be used to identify patients' response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-Beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG). Plasma was collected from 12 KBD patients before treatment and 1month after FDJG treatment. LC-MS and olink proteomics were employed for obtaining plasma metabolomics profiling and inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict the drug efficacy. The receiver operating characteristic curve was used to evaluate the prediction accuracy of biomarkers. The changes in metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, d-glutamine and d-glutamate metabolism, nitrogen metabolism and NF-kappa B signaling pathway. Three metabolites were identified as potential predictors: N-undecanoylglycine, β-aminopropionitrile and PC [18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)]. For inflammatory protein, interleukin-8 was identified as a predictive biomarker to detect responders. Combined use of these four biomarkers had high predictive ability (area under the curve = 0.972).

Multi-factor fuzzy sets decision system forecasting consumer insolvency risk

The objective of this study is to develop a multi-factor decision system predicting insolvency risk for natural persons with the use of fuzzy sets. Considering that the financial situation of households is affected by various endogenous and exogenous factors, the main assumption of this study is that the system for predicting financial difficulties should not be limited to the use of only a few financial variables concerning consumers, but also include variables describing the environment. The author proposes a system consisting of three different forecasting models that connect the macroeconomic and microeconomic environments. It monitors the economic situation of households by also identifying those environmental variables, which may directly, or indirectly, endanger the consumer, such as unemployment rate (job market situation), inflation and interest rates, exchange rates, or economic situation in the country (GDP growth rate, the dynamics of retail sales, etc.). Moreover, the created cause-and-effect tool is in the form of a flexible application that can be easily adapted to changing economic conditions. Another unique feature of the study is the proposed use of newly developed ratios in household finance, similar to that in financial ratio analysis, which is commonly used in corporate finance. The proposed ratios demonstrated high predictive abilities. The paper also identifies the predictive capabilities of selected macroeconomic variables from the perspective of their impact on the risk of consumer insolvency. The research relies on four samples consisting of a total of 2400 consumers from Taiwan and Poland. The author created three forecasting models separately for the South-East Asian and Central European regions, and two multi-factor systems, each consisting of 1260 decision rules. The findings clearly showed that fuzzy logic is a significantly more effective method compared to traditional models based on classical logic.