A data calibration method for micro air quality detectors based on a LASSO regression and NARX neural network combined model

Elucidating hepatocellular carcinoma progression: a novel prognostic miRNA-mRNA network and signature analysis

There is increasing evidence that miRNAs play an important role in the prognosis of HCC. There is currently a lack of acknowledged models that accurately predict patient prognosis. The aim of this study is to create a miRNA-based model to precisely forecast a patient's prognosis and a miRNA-mRNA network to investigate the function of a targeted mRNA. TCGA miRNA dataset and survival data of HCC patients were downloaded for differential analysis. The outcomes of variance analysis were subjected to univariate and multivariate Cox regression analyses and LASSO analysis. We constructed and visualized prognosis-related models and subsequently used violin plots to probe the function of miRNAs in tumor cells. We predicted the target mRNAs added those to the String database, built PPI protein interaction networks, and screened those mRNA using Cytoscape. The hub mRNA was subjected to GO and KEGG analysis to determine its biological role. Six of them were associated with prognosis: hsa-miR-139-3p, hsa-miR-139-5p, hsa-miR-101-3p, hsa-miR-30d-5p, hsa-miR-5003-3p, and hsa-miR-6844. The prognostic model was highly predictive and consistently performs, with the C index exceeding 0.7 after 1, 3, and 5 years. The model estimated significant differences in the Kaplan-Meier plotter and the model could predict patient prognosis independently of clinical indicators. A relatively stable miRNA prognostic model for HCC patients was constructed, and the model was highly accurate in predicting patients with good stability over 5 years. The miRNA-mRNA network was constructed to explore the function of mRNA.

Least Absolute Shrinkage and Selection Operator-Based Prediction of the Binding Constant of p-Sulfonatocalix[6]/[8]arenes with Alkaloids

p-Sulfonatocalix[n]arenes (SCnA) have demonstrated great potential for drug encapsulation through host-guest complexation to improve solubility, stability, and bioavailability. In this study, the solubilization effect of SCnA (n = 4, 6, 8) on 95 active compounds derived from traditional Chinese medicine (TCM) was investigated. Based on the significant solubilization effect on alkaloids, SC6A/SC8A and 76 alkaloids were selected as the host and guest, respectively, to determine the binding constant by competitive fluorescence titration. LASSO regression was adopted to investigate the mechanism of the complex of SCnA with alkaloids. The binding constant of alkaloids-SC6A and alkaloids-SC8A was related to the alkaloid alkalinity. Also, the electronegativity, polarization, first ionization potential, hydrogen bond potential, the molecular size, and shape of alkaloids are critical properties to determine alkaloids-SC6A binding constant as well as electronegativity, polarization, hydrophobicity, and the molecular size and shape of alkaloids play an important role for the alkaloids-SC8A binding constant.

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model

A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4-95.4%.

Perceived parenting styles and incidence of major depressive disorder: results from a 6985 freshmen cohort study

BACKGROUND

Even though a fair amount of studies focus on depression among college students, the effect of perceived parenting styles on the incidence of major depressive disorder (MDD) among representative freshmen in Chinese context is scarcely studied. The aim of this study is to investigate the effect of parenting styles on MDD in Chinese freshmen.

METHODS

A total of 9,928 Chinese freshmen were recruited in 2018. 6985 valid questionnaires were collected at one-year follow-up. Composite International Diagnostic Interview 3.0 (CIDI-3.0) was used for the diagnosis of MDD. Egna Minnen Beträffande Uppfostran (EMBU) questionnaire and Beck Depression Inventory-II (BDI-II) were used to assess parenting styles and baseline depressive symptoms, respectively. The associations between parenting styles and MDD incidence was analyzed with logistic regression.

RESULTS

The incidence of MDD in freshmen was 2.23% (95%CI: 1.91-2.60%). Maternal overprotection (OR = 1.03, 95%CI: 1.01-1.05) and disharmony relationship between parents (OR = 2.35, 95% CI: 1.42-3.89) increased the risk of new-onset MDD in freshmen, respectively. Mild depressive symptoms (OR = 2.06, 95%CI: 1.06-4.02), moderate (OR = 4.64, 95%CI: 2.55-8.44) and severe depressive symptoms (OR = 7.46, 95%CI: 2.71-20.52) at baseline increased the risk of new-onset MDD.

CONCLUSIONS

Maternal overprotection, disharmony relationship between parents and baseline depressive symptoms are risk factors for new-onset MDD in Chinese freshmen.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions

Reasonable cultivation is an important part of the protection work of endangered species. The timely and nondestructive monitoring of chlorophyll can provide a basis for the accurate management and intelligent development of cultivation. The image analysis method has been applied in the nutrient estimation of many economic crops, but information on endangered tree species is seldom reported. Moreover, shade control, as the common seedling management measure, has a significant impact on chlorophyll, but shade levels are rarely discussed in chlorophyll estimation and are used as variables to improve model accuracy. In this study, 2-year-old seedlings of tropical and endangered Hopea hainanensis were taken as the research object, and the SPAD value was used to represent the relative chlorophyll content. Based on the performance comparison of RGB and multispectral (MS) images using different algorithms, a low-cost SPAD estimation method combined with a machine learning algorithm that is adaptable to different shade conditions was proposed. The SPAD values changed significantly at different shade levels (p < 0.01), and 50% shade in the orthographic direction was conducive to chlorophyll accumulation in seedling leaves. The coefficient of determination (R ²), root mean square error (RMSE), and average absolute percent error (MAPE) were used as indicators, and the models with dummy variables or random effects of shade greatly improved the goodness of fit, allowing better adaption to monitoring under different shade conditions. Most of the RGB and MS vegetation indices (VIs) were significantly correlated with the SPAD values, but some VIs exhibited multicollinearity (variance inflation factor (VIF) > 10). Among RGB VIs, RGRI had the strongest correlation, but multiple VIs filtered by the Lasso algorithm had a stronger ability to interpret the SPAD data, and there was no multicollinearity (VIF < 10). A comparison of the use of multiple VIs to estimate SPAD indicated that Random forest (RF) had the highest fitting ability, followed by Support vector regression (SVR), linear mixed effect model (LMM), and ordinary least squares regression (OLR). In addition, the performance of MS VIs was superior to that of RGB VIs. The R ² of the optimal model reached 0.9389 for the modeling samples and 0.8013 for the test samples. These findings reinforce the effectiveness of using VIs to estimate the SPAD value of H. hainanensis under different shade conditions based on machine learning and provide a reference for the selection of image data sources.

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

In a world where humanity's interests come first, the environment is flooded with pollutants produced by humans' urgent need for expansion. Air pollution and climate change are side effects of humans' inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM_2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM_2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN-LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms.

Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for air quality prediction worldwide and features Busan, South Korea as its model city. The paper proposes the application of an attention-based convolutional BiLSTM autoencoder model. The proposed deep learning model has been trained on a distributed framework, referred to data parallelism, to forecast the intensity of particle pollution (PM2.5 and PM10). The algorithm automatically learns the intrinsic correlation among the particle pollution in different locations. Each location’s meteorological and traffic data is extensively exploited to improve the model’s performance. The model has been trained using air quality particle data and car traffic information. The traffic information is obtained by a device which counts cars passing a specific area through the YOLO algorithm, and then sends the data to a stacked deep autoencoder to be encoded alongside the meteorological data before the final prediction. In addition, multiple one-dimensional CNN layers are used to obtain the local spatial features jointly with a stacked attention-based BiLSTM layer to figure out how air quality particles are correlated in space and time. The evaluation of the new attention-based convolutional BiLSTM autoencoder model was derived from data collected and retrieved from comprehensive experiments conducted in South Korea. The results not only show that the framework outperforms the previous models both on short- and long-term predictions but also indicate that traffic information can improve the accuracy of air quality forecasting. For instance, during PM2.5 prediction, the proposed attention-based model obtained the lowest MAE (5.02 and 22.59, respectively, for short-term and long-term prediction), RMSE (7.48 and 28.02) and SMAPE (17.98 and 39.81) among all the models, which indicates strong accuracy between observed and predicted values. It was also found that the newly proposed model had the lowest average training time compared to the baseline algorithms. Furthermore, the proposed framework was successfully deployed in a cloud server in order to provide future air quality information in real time and when needed.