Prediction of anxious depression using multimodal neuroimaging and machine learning.

Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.

Cytotoxic T-lymphocytes in acute myeloid leukemia: Monitoring prognosis and guiding treatment choice.

BACKGROUND: Cytotoxic T-lymphocyte (CTL)-mediated therapy has become the central theme of cancer immunotherapy. The present study emphasized the role of CTLs in acute myeloid leukemia (AML) and aimed to understand the role of CTLs cytogenetic markers in monitoring AML prognostic outcomes and clinical treatment responses. METHODS: Seurat was employed to analyze single-cell RNA sequencing data in GSE116256. CellChat was used to detect cell-cell interactions to determine the central role of CTLs. The marker genes of CTLs were extracted and randomForestSRC was employed to construct a random forest model. The prognosis, immune checkpoint expression, immune cell infiltration, immunotherapy response and drug sensitivity of AML patients were evaluated according to the model. RESULTS: Seven types of cellular components of AML were identified in GSE116256, and CTLs radiated the most interactions with other cell types. Random forest analysis screened out six marker genes for construction of the model. The risk score calculated according to the model was positively correlated with immune score, immune cell infiltration, expression of multiple immune checkpoints and immune effect pathway. The response rate of immunotherapy was significantly higher and more sensitive to 14 drugs in high-risk samples than in low-risk samples, whereas low-risk patients showed a higher sensitivity to six drugs. CONCLUSIONS: The present study emphasized the central role of CTLs in cell communication and established a random forest regression model based on its cytogenetic markers, which helps to stratify the prognosis of AML, promotes the understanding of the phenotype of AML and may also guide the treatment choice of AML patients, which contributed to stratification of AML prognosis, promoted understanding of the phenotype of AML and may guide treatment selection in patients with AML.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model.

OBJECTIVE: The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients. METHODS: Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights. RESULTS: High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1. CONCLUSION: The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution.

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Prevalence of childhood hypertension and associated factors in Zhejiang Province: a cross-sectional analysis based on random forest model and logistic regression.

With childhood hypertension emerging as a global public health concern, understanding its associated factors is crucial. This study investigated the prevalence and associated factors of hypertension among Chinese children. This cross-sectional investigation was conducted in Pinghu, Zhejiang province, involving 2,373 children aged 8-14 years from 12 schools. Anthropometric measurements were taken by trained staff. Blood pressure (BP) was measured in three separate occasions, with an interval of at least two weeks. Childhood hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ age-, sex-, and height-specific 95th percentile, across all three visits. A self-administered questionnaire was utilized to collect demographic, socioeconomic, health behavioral, and parental information at the first visit of BP measurement. Random forest (RF) and multivariable logistic regression model were used collectively to identify associated factors. Additionally, population attributable fractions (PAFs) were calculated. The prevalence of childhood hypertension was 5.0% (95% confidence interval [CI]: 4.1-5.9%). Children with body mass index (BMI) ≥ 85th percentile were grouped into abnormal weight, and those with waist circumference (WC) > 90th percentile were sorted into central obesity. Normal weight with central obesity (NWCO, adjusted odds ratio [aOR] = 5.04, 95% CI: 1.96-12.98), abnormal weight with no central obesity (AWNCO, aOR = 4.60, 95% CI: 2.57-8.21), and abnormal weight with central obesity (AWCO, aOR = 9.94, 95% CI: 6.06-16.32) were associated with an increased risk of childhood hypertension. Childhood hypertension was attributable to AWCO mostly (PAF: 0.64, 95% CI: 0.50-0.75), followed by AWNCO (PAF: 0.34, 95% CI: 0.19-0.51), and NWCO (PAF: 0.13, 95% CI: 0.03-0.30). Our results indicated that obesity phenotype is associated with childhood hypertension, and the role of weight management could serve as potential target for intervention.

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

Systematic coupling and multistage interactive response of the urban land use efficiency and ecological environment quality.

Rapid urbanization and industrialization have greatly contributed to boosting regional economic growth and mitigating the problem of poverty, but blind expansion of cities and towns has not only caused the inefficient use of urban land resources but also caused the deterioration in the urban ecological environment. Within the current context of emphasizing high-quality development, achieving synergy between the efficient use of urban land and ecological environmental protection is an urgent task for promoting new urbanization construction. In this study, cities in the upper reaches of the Yangtze River (URYR) were adopted as the research object, a theoretical analysis framework for the urban land use efficiency (ULUE) and ecological environment quality (EEQ) was established, the ULUE was measured by using the Slacks-Based Measure (SBM) model, the coupling coordination and interactive corresponding response relationship between the ULUE and EEQ were analyzed, and the influencing factors of the coupling coordination between these two systems were explored by using the random forest model. The following conclusions can be obtained: in 2020, compared with those in 2006, both the ULUE and EEQ were improved, and the two systems exhibited interactions and significant spatiotemporal heterogeneity. The coupling coordination degree (CCD) between the ULUE and EEQ could facilitate maintaining the original state, and the transfer of the CCD exhibited a significant spatial correlation with the state of neighbouring cities. The effect of the ULUE on the EEQ indicated nonlinear characteristics, while the effect of the EEQ on the ULUE was manifested as inhibition initially and then promotion. The random forest regression results showed that the population density, landscape agglomeration and connectivity, market conditions, government intervention, and industrial institutions are the key influencing factors of the CCD. Finally, this study provides policy implications for innovative urban land use modelling, environmental regulation, and industrial transformation and upgrading.

Unraveling the impact of digital transformation on green innovation through microdata and machine learning.

How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018. It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ii) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (iii) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (iv) random-forest analysis shows that DT exhibits an inverted U-shaped non-linear effect on green innovation, including the "IT productivity paradox." This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation. Furthermore, it imparts insights into the mechanisms and contextual limitations governing this association.

Modeling the feasibility of Se-rich corn cultivation in Se-deficient agricultural fields using random forest algorithm.

Selenium constitutes an essential trace element for the human body. Moderate Se intake plays a pivotal role in preserving overall health. The absorption of Se by plants is primarily influenced by the available Se levels in soils, rather than by the soil total Se content, offering potential for exploring Se-rich crops in Se-deficient regions. In this study, we explore the factors influencing the Se bioaccumulation coefficient in corn based on a land quality geochemical survey at a 1:50,000 scale and establish predictive models for corn seed Se content using random forest and multiple linear regression approaches. The results indicate that the surface soil in the study area is deficient in Se (0.18-1.21 mg/kg), but 54% of the corn grain samples met the standards for Se-rich products (0.02-0.30 mg/kg). The factors influencing the Se biological enrichment coefficient in corn seeds are soil pH and CaO and MgO content, with impact levels of 0.54, 0.42, and 0.35, respectively. Compared to multiple linear regression models, the RF model provides more accurate and reliable predictions of corn Se content. The random forest model indicates that approximately 41% of the farmland within the study area is conducive to the cultivation of naturally Se-rich corn, which is a 26% increase in the planting area compared to recommendations based solely on soil Se content. In this research, we introduce an innovative methodological framework for organically cultivating naturally Se-rich corn within regions affected by Se deficiency.

Random effect generalized linear model-based predictive modelling of traffic noise.

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

Quantification of Central and Eastern China's atmospheric CH4 enhancement changes and its contributions based on machine learning approach.

Methane is the second largest anthropogenic greenhouse gas, and changes in atmospheric methane concentrations can reflect the dynamic balance between its emissions and sinks. Therefore, the monitoring of CH4 concentration changes and the assessment of underlying driving factors can provide scientific basis for the government's policy making and evaluation. China is the world's largest emitter of anthropogenic methane. However, due to the lack of ground-based observation sites, little work has been done on the spatial-temporal variations for the past decades and influencing factors in China, especially for areas with high anthropogenic emissions as Central and Eastern China. Here to quantify atmospheric CH4 enhancements trends and its driving factors in Central and Eastern China, we combined the most up-to-date TROPOMI satellite-based column CH4 (xCH4) concentration from 2018 to 2022, anthropogenic and natural emissions, and a random forest-based machine learning approach, to simulate atmospheric xCH4 enhancements from 2001 to 2018. The results showed that (1) the random forest model was able to accurately establish the relationship between emission sources and xCH4 enhancement with a correlation coefficient (R²) of 0.89 and a root mean-square error (RMSE) of 11.98 ppb; (2)The xCH4 enhancement only increased from 48.21±2.02 ppb to 49.79±1.87 ppb from the year of 2001 to 2018, with a relative change of 3.27%±0.13%; (3) The simulation results showed that the energy activities and waste treatment were the main contributors to the increase in xCH4 enhancement, contributing 68.00% and 31.21%, respectively, and the decrease of animal ruminants contributed -6.70% of its enhancement trend.

Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China.

OBJECTIVES: Scrub typhus is an increasingly serious public health problem, which is becoming the most common vector-borne disease in Guangzhou. This study aimed to analyse the correlation between scrub typhus incidence and potential factors and rank the importance of influential factors. METHODS: We collected monthly scrub typhus cases, meteorological variables, rodent density (RD), Normalised Difference Vegetation Index (NDVI), and land use type in Guangzhou from 2006 to 2019. Correlation analysis and a random forest model were used to identify the risk factors for scrub typhus and predict the importance rank of influencing factors related to scrub typhus incidence. RESULTS: The epidemiological results of the scrub typhus cases in Guangzhou between 2006 and 2019 showed that the incidence rate was on the rise. The results of correlation analysis revealed that a positive relationship between scrub typhus incidence and meteorological factors of mean temperature (Tmean ), accumulative rainfall (RF), relative humidity (RH), sunshine hours (SH), and NDVI, RD, population density, and green land coverage area (all p < 0.001). Additionally, we tested the relationship between the incidence of scrub typhus and the lagging meteorological factors through cross-correlation function, and found that incidence was positively correlated with 1-month lag Tmean , 2-month lag RF, 2-month lag RH, and 6-month lag SH (all p < 0.001). Based on the random forest model, we found that the Tmean was the most important predictor among the influential factors, followed by NDVI. CONCLUSIONS: Meteorological factors, NDVI, RD, and land use type jointly affect the incidence of scrub typhus in Guangzhou. Our results provide a better understanding of the influential factors correlated with scrub typus, which can improve our capacity for biological monitoring and help public health authorities to formulate disease control strategies.

Interpreting Highly Variable Indoor PM2.5 in Rural North China Using Machine Learning.

Household air pollution associated with solid fuel use is a long-standing public concern. The global population mainly using solid fuels for cooking remains large. Besides cooking, large amounts of coal and biomass fuels are burned for space heating during cold seasons in many regions. In this study, a wintertime multiple-region field campaign was carried out in north China to evaluate indoor PM2.5 variations. With hourly resolved data from ∼1600 households, key influencing factors of indoor PM2.5 were identified from a machine learning approach, and a random forest regression (RFR) model was further developed to quantitatively assess the impacts of household energy transition on indoor PM2.5. The indoor PM2.5 concentration averaged at 120 µg/m3 but ranged from 16 to ∼400 µg/m3. Indoor PM2.5 was ∼60% lower in families using clean heating approaches compared to those burning traditional coal or biomass fuels. The RFR model had a good performance (R2 = 0.85), and the interpretation was consistent with the field observation. A transition to clean coals or biomass pellets can reduce indoor PM2.5 by 20%, and further switching to clean modern energies would reduce it an additional 30%, suggesting many significant benefits in promoting clean transitions in household heating activities.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis.

BACKGROUND AND AIM: Severe acute pancreatitis (SAP) in patients progresses rapidly and can cause multiple organ failures associated with high mortality. We aimed to train a machine learning (ML) model and establish a nomogram that could identify SAP, early in the course of acute pancreatitis (AP). METHODS: In this retrospective study, 631 patients with AP were enrolled in the training cohort. For predicting SAP early, five supervised ML models were employed, such as random forest (RF), K-nearest neighbors (KNN), and naive Bayes (NB), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by the calibration curve and AUC. They were externally validated by an independent cohort of 109 patients with AP. RESULTS: In the training cohort, the AUC of RF, KNN, and NB models were 0.969, 0.954, and 0.951, respectively, while the AUC of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Ranson and Glasgow scores were only 0.796, 0.847, and 0.837, respectively. In the validation cohort, the RF model also showed the highest AUC, which was 0.961. The AUC for the nomogram was 0.888 and 0.955 in the training and validation cohort, respectively. CONCLUSIONS: Our findings suggested that the RF model exhibited the best predictive performance, and the nomogram provided a visual scoring model for clinical practice. Our models may serve as practical tools for facilitating personalized treatment options and improving clinical outcomes through pre-treatment stratification of patients with AP.

Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives.

Nitrate (NO3-) pollution of groundwater is a global concern in agricultural areas. To gain a comprehensive understanding of the sources and destiny of nitrate in soil and groundwater within intensive agricultural areas, this study employed a combination of chemical indicators, dual isotopes of nitrate (δ15N-NO3- and δ18O-NO3-), random forest model, and Bayesian stable isotope mixing model (MixSIAR). These approaches were utilized to examine the spatial distribution of NO3- in soil profiles and groundwater, identify key variables influencing groundwater nitrate concentration, and quantify the sources contribution at various depths of the vadose zone and groundwater with different nitrate concentrations. The results showed that the nitrate accumulation in the cropland and kiwifruit orchard at depths of 0-400 cm increased, leading to subsequent leaching of nitrate into deeper vadose zones and ultimately groundwater. The mean concentration of nitrate in groundwater was 91.89 mg/L, and 52.94% of the samples exceeded the recommended grade III value (88.57 mg/L) according to national standards. The results of the random forest model suggested that the main variables affecting the nitrate concentration in groundwater were well depth (16.6%), dissolved oxygen (11.6%), and soil nitrate (10.4%). The MixSIAR results revealed that nitrate sources vary at different soil depths, which was caused by the biogeochemical process of nitrate. In addition, the highest contribution of nitrate in groundwater, both with high and low concentrations, was found to be soil nitrogen (SN), accounting for 56.0% and 63.0%, respectively, followed by chemical fertilizer (CF) and manure and sewage (M&S). Through the identification of NO3- pollution sources, this study can take targeted measures to ensure the safety of groundwater in intensive agricultural areas.

Development and validation of a random forest model to predict functional outcome in patients with intracerebral hemorrhage.

OBJECTIVE: To develop and validate a machine learning (ML)-based model to predict functional outcome in Chinese patients with intracerebral hemorrhage (ICH). METHODS: This retrospective cohort study enrolled patients with ICH between November 2017 and November 2020. The follow-up period ended in February 2021. The study population was divided into training and testing sets with a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator (LASSO) regression for feature selection. The selected variables were incorporated into the random forest algorithm to construct the prediction model. The predictive performance of the model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and calibration curve. RESULTS: A total of 412 ICH patients were included, with 288 in the training set, and 124 in the testing set. Twelve attributes were selected: neurological deterioration, Glasgow Coma Scale (GCS) score at 24 h, baseline GCS score, time from onset to the emergency room, blood glucose, diastolic blood pressure (DBP) change in 24 h, hematoma volume change in 24 h, systemic immune-inflammatory index (SII), systolic blood pressure (SBP) change in 24 h, serum creatinine, serum sodium, and age. In the testing set, the accuracy, AUC, sensitivity, specificity, PPV, and NPV of the model were 0.895, 0.964, 0.872, 0.906, 0.810, and 0.939, respectively. The calibration curves showed a good calibration capability of the model. CONCLUSION: This developed random forest model performed well in predicting 3-month poor functional outcome for Chinese ICH patients.

A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.

BACKGROUND: Postoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. METHODS: Patients who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. RESULTS: A total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, respectively. CONCLUSIONS: The random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical application value.

Using Downscaled GRACE Mascon Data to Assess Total Water Storage in Mississippi Alluvial Plain Aquifer.

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R2 for each model was 0.88. Large R2 and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.

Young Novice Drivers' Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior.

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

A Random Forest Model for Peptide Classification Based on Virtual Docking Data.

The affinity of peptides is a crucial factor in studying peptide-protein interactions. Despite the development of various techniques to evaluate peptide-receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide-receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.