Identification of novel serological autoantibodies in Chinese prostate cancer patients using high-throughput protein arrays.

Autoantibody (AAb) has a prominent role in prostate cancer (PCa), with few studies profiling the AAb landscape in Chinese patients. Therefore, the AAb landscape in Chinese patients was characterized using protein arrays. First, in the discovery phase, Huprot arrays outlined autoimmune profiles against ~ 21,888 proteins from 57 samples. In the verification phase, the PCa-focused arrays detected 25 AAbs selected from the discovery phase within 178 samples. Then, PCa was detected using a backpropagation artificial neural network (BPANN) model. In the validation phase, an enzyme-linked immunosorbent assay (ELISA) was used to validate four AAb biomarkers from 196 samples. Huprot arrays profiled distinct PCa, benign prostate diseases (BPD), and health AAb landscapes. PCa-focused array depicted that IFIT5 and CPOX AAbs could distinguish PCa from health with an area under curve (AUC) of 0.71 and 0.70, respectively. PAH and FCER2 AAbs had AUCs of 0.86 and 0.88 in discriminating PCa from BPD. Particularly, PAH AAb detected patients in the prostate-specific antigen (PSA) gray zone with an AUC of 0.86. Meanwhile, the BPANN model of 4-AAb (IFIT5, PAH, FCER2, CPOX) panel attained AUC of 0.83 among the two cohorts for detecting patients with gray-zone PSA. In the validation cohort, the IFIT5 AAb was upregulated in PCa compared to health (p < 0.001). Compared with BPD, PAH and FCER2 AAbs were significantly elevated in PCa (p = 0.012 and 0.039). We have demonstrated the first extensive profiling of autoantibodies in Chinese PCa patients, identifying novel diagnostic AAb biomarkers, especially for identification of gray-zone-PSA patients.

Role of BP-ANN in simulating greenhouse gas emissions from global aquatic ecosystems via carbon component-environmental factor coupling.

Inland waters (IW), estuarine areas (EA), and offshore areas (OA) function as aquatic systems in which the transport of carbon components results in the release of greenhouse gases (GHGs). Interconnected subsystems exhibit a greater greenhouse effect than individual systems. Despite this, there is a lack of research on how carbon loading and its components impact GHG emissions in various aquatic systems. In this study, we analyzed 430 aquatic sites to explore trade-off mechanisms among dissolved organic carbon (DOC), particulate organic carbon, dissolved inorganic carbon (DIC), and GHGs. The results revealed that IW emerged as the most significant GHG source, possessing a comprehensive global warming potential (GWP) of 0.78 ± 0.08 (10-2 Pg CO2-ep ha-1 year-1) for combined carbon dioxide, methane, and nitrous oxide. This surpassed the cumulative potentials of EA and OA (0.35 ± 0.05 (10-2 Pg CO2-ep ha-1 year-1)). Additionally, structural equation modeling indicated that GHG emissions resulted from a combination of carbon component loading and environmental factors. DOC exhibited a positive correlation with GWPs when influenced by biodegradable DOC. Total alkalinity and pH influenced DIC, leading to elevated pCO2 in aquatic systems, thereby enhancing GWPs. Predictive modeling using backpropagation artificial neural networks (BP-ANN) for GWPs, incorporating carbon components and environmental factors, demonstrated a good fit (R2 = 0.6078, RMSEaverage = 0.069, p > 0.05) between observed and predicted values. Enhancing the estimation of aquatic region feedback to GHG changes was achieved by incorporating corresponding water quality parameters. In summary, this study underscores the pivotal role of carbon components and environmental factors in aquatic regions for GHG emissions. The application of BP-ANN to estimate greenhouse effects from aquatic regions is highlighted, providing theoretical and experimental support for future advancements in monitoring and developing policies concerning the influence of water quality on GHG emissions.

Prediction of biological nutrients removal in full-scale wastewater treatment plants using H2O automated machine learning and back propagation artificial neural network model: Optimization and comparison.

The effective control of total nitrogen (ETN) and total phosphorus (ETP) in effluent is challenging for wastewater treatment plants (WWTPs). In this work, automated machine learning (AutoML) (mean square error = 0.4200 âˆ¼ 3.8245, R2 = 0.5699 âˆ¼ 0.6219) and back propagation artificial neural network (BPANN) model (mean square error = 0.0012 âˆ¼ 6.9067, R2 = 0.4326 âˆ¼ 0.8908) were used to predict and analyze biological nutrients removal in full-scale WWTPs. Interestingly, BPANN model presented high prediction performance and general applicability for WWTPs with different biological treatment units. However, the AutoML candidate models were more interpretable, and the results showed that electricity carbon emission dominated the prediction. Meanwhile, increasing data volume and types of WWTP hardly affected the interpretable results, demonstrating its wide applicability. This study demonstrated the validity and the specific advantages of predicting ETN and ETP using H2O AutoML and BPANN model, which provided guidance on the prediction and improvement of biological nutrients removal in WWTPs.

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks.

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.

Integrating metabolomic data with machine learning approach for discovery of Q-markers from Jinqi Jiangtang preparation against type 2 diabetes.

BACKGROUND: Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little research has been done to identify and classify its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. METHODS: This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishment of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled by using the untargeted UPLC-LTQ-Orbitrap metabolomic approach. The chemical features obtained which were associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV). RESULTS: Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of partial least square (PLS) model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. With this machine learning model, 10 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted. CONCLUSIONS: This proposed artificial intelligence approach is desirable for quick and easy identification of Q-markers with bioactivity from JQJT preparation.

Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network.

In this study, a combined alkaline (ALK) and ultrasonication (ULS) sludge lysis-cryptic pretreatment and anoxic/oxic (AO) system (AO + ALK/ULS) was developed to enhance biological nitrogen removal (BNR) in domestic wastewater with a low carbon/nitrogen (C/N) ratio. A real-time control strategy for the AO + ALK/ULS system was designed to optimize the sludge lysate return ratio (RSLR) under variable sludge concentrations and variations in the influent C/N (⩽ 5). A multi-layered backpropagation artificial neural network (BPANN) model with network topology of 1 input layer, 3 hidden layers, and 1 output layer, using the Levenberg-Marquardt algorithm, was developed and validated. Experimental and predicted data showed significant concurrence, verified with a high regression coefficient (R2 = 0.9513) and accuracy of the BPANN. The BPANN model effectively captured the complex nonlinear relationships between the related input variables and effluent output in the combined lysis-cryptic + BNR system. The model could be used to support the real-time dynamic response and process optimization control to treat low C/N domestic wastewater.

Application of a Backpropagation Artificial Neural Network in Predicting Plasma Concentration and Pharmacokinetic Parameters of Oral Single-Dose Rosuvastatin in Healthy Subjects.

A backpropagation artificial neural network (BPANN) model was established for the prediction of the plasma concentration and pharmacokinetic parameters of rosuvastatin (RVST) in healthy subjects. The data (demographic characteristics and results of clinical laboratory tests) were collected from 4 bioequivalence studies using reference 10-mg RVST calcium tablets. After the data were cleaned using extreme gradient boosting, 13 important factors were extracted to construct the BPANN model. The model was fully validated, and mean impact values (MIVs) were calculated. The model was used to predict the plasma concentration and pharmacokinetic parameters of oral single-dose RVST in healthy subjects under fasting and fed conditions. The predicted and measured values were compared in order to evaluate the accuracy of prediction. The constructed model performed well in validation. The top 3 factors ranked by MIV related to RVST concentration are fasting/fed, time, and creatinine clearance. The time-concentration profiles of the measured and predicted data agreed well. There were no significant differences (P > .05) in the area under the concentration-time curve from 0 to the last measurable concentration (AUC0-t ) and extrapolated to infinity (AUC0-∞ ), half-time of elimination, peak concentration, and time to peak concentration of the measured data and data predicted by BPANN. The BPANN model has an accurate prediction ability and can be used to predict RVST concentration and pharmacokinetic parameters in healthy subjects.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater.

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH4+, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R2), the BPANN model demonstrated significant performance with R2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.