Accurate Prediction of Kinematic Viscosity of Biodiesels and their Blends with Diesel Fuels

Viscosity of mixtures of biodiesels (admixtures) and mixtures of biodiesel/diesel (blends) is a important parameter for determining their combustion behavior. There is no universal and general model for prediction of viscosity of these systems at different conditions. Hence, developing simple, accurate, and general models for prediction of viscosity of these systems is of great importance. In this work, three computer-based models named multilayer perceptron neural network (MLP-NN), radial basis function optimized by particle swarm optimization (PSO-RBF), and adaptive neuro fuzzy inference system optimized by hybrid approach (Hybrid-ANFIS) were developed for prediction of viscosity of blends and admixtures. A number of 966 experimental data covering wide ranges of influencing parameters were utilized to develop the models. The accuracy of predictions of the developed models was examined by using different statistical quality measure approaches as well as comparing their results with the predictions of literature models. Results showed that the developed models present accurate predictions and are superior to the literature models. The predictions of PSO-RBF model were also better than Hybrid-ANFIS and MLP-NN models.     

Is it better to combine predictions?

We have compared the accuracy of the individual protein secondarystructure prediction methods: PHD, DSC, NNSSP and Predator againstthe accuracy obtained by combing the predictions of the methods.A range of ways of combing predictions were tested: voting,biased voting, linear discrimination, neural networks and decisiontrees. The combined methods that involve `learning' (the non-votingmethods) were trained using a set of 496 non-homologous domains;this dataset was biased as some of the secondary structure predictionmethods had used them for training. We used two independenttest sets to compare predictions: the first consisted of 17non-homologous domains from CASP3 (Third Community Wide Experimenton the Critical Assessment of Techniques for Protein StructurePrediction); the second set consisted of 405 domains that wereselected in the same way as the training set, and were non-homologousto each other and the training set. On both test datasets themost accurate individual method was NNSSP, then PHD, DSC andthe least accurate was Predator; however, it was not possibleto conclusively show a significant difference between the individualmethods. Comparing the accuracy of the single methods with thatobtained by combing predictions it was found that it was betterto use a combination of predictions. On both test datasets itwas possible to obtain a ~3% improvement in accuracy by combingpredictions. In most cases the combined methods were statisticallysignificantly better (at P = 0.05 on the CASP3 test set, andP = 0.01 on the EBI test set). On the CASP3 test dataset therewas no significant difference in accuracy between any of thecombined method of prediction: on the EBI test dataset, lineardiscrimination and neural networks significantly outperformedvoting techniques. We conclude that it is better to combinepredictions.     

Density of fluoride glasses through artificial intelligence techniques

Artificial Intelligence techniques have been employed for the first time to train a dataset of 1258 distinct fluoride glasses collected from published literature to predict the density of novel oxy-fluoro glasses based on their chemical composition and ionic radii. The glass dataset was split based on the linear and non-linear variation to predict the glass density using various AI models like gradient descent, random forest regression and artificial neural networks. High concentration of boron in glass specimens resulted in scattering of datapoints in packing factor relation and density prediction. The random forest regression model fit the combined glass dataset with the highest R² of 0.980. In case of boron-rich glasses, their non-linear behavior restricted the R² for ANNs to 0.792 as optimum with the tanh activation function.     

MemDis: Predicting Disordered Regions in Transmembrane Proteins

Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.     

Identifying Protein-Protein Interaction Sites Using Covering Algorithm

Identification of protein-protein interface residues is crucial for structural biology. This paper proposes a covering algorithm for predicting protein-protein interface residues with features including protein sequence profile and residue accessible area. This method adequately utilizes the characters of a covering algorithm which have simple, lower complexity and high accuracy for high dimension data. The covering algorithm can achieve a comparable performance (69.62%, Complete dataset; 60.86%, Trim dataset with overall accuracy) to a support vector machine and maximum entropy on our dataset, a correlation coefficient (CC) of 0.2893, 58.83% specificity, 56.12% sensitivity on the Complete dataset and 0.2144 (CC), 53.34% (specificity), 65.59% (sensitivity) on the Trim dataset in identifying interface residues by 5-fold cross-validation on 61 protein chains. This result indicates that the covering algorithm is a powerful and robust protein-protein interaction site prediction method that can guide biologists to make specific experiments on proteins. Examination of the predictions in the context of the 3-dimensional structures of proteins demonstrates the effectiveness of this method.     

Artificial intelligence density model for oxide glasses

A comprehensive study to perform glass density prediction employing artificial intelligence using a dataset of 6630 oxide glass samples. The prediction is done based on Ionic packing ratio as the independent variable and experimental densities from the dataset as the dependent variable. Random forest regression and artificial neural networks were observed as the best models training the density datasets. The random forest regression had the least average R² score for large datasets. Artificial neural networks employing sigmoid and ReLU activation functions dominate in predicting the glass density as compared to tanh and identity activation functions. Based on this study we can theoretically predict the density of any oxide glass to an extent of maximum accuracy for a known glass composition.     

以离子液体密度为例的分子性质预测模型建模方法探讨

分子性质预测模型是针对特定应用需求筛选设计化学品的有力工具,然而诸多相关建模过程中的测试集划分、交叉验证、算法选择等关键环节普遍存在严谨性不足的问题,模型真实预测性能难以保证。以基团贡献法预测离子液体密度为例,探讨了分子性质预测模型建模过程中数据集划分和交叉验证的重要性,提出了自动基团划分方法并研究了数据集中基团涉及分子个数对预测精度的影响。通过对比五种回归算法（多重线性回归、岭回归、随机森林、支持向量机、神经网络）,基于岭回归的基团贡献模型预测性能最佳,在由1078种离子液体、共计23034个数据点组成的数据集上得到的平均相对误差为1.88%。     

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high-level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation.     

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

基于迁移学习工况划分的裂解炉收率PSO-LS-SVM建模

乙烯裂解炉收率的实时预测对于生产的先进控制及节能降耗具有重要意义。实际生产过程中,不同工况的收率具有较大差别,采用单一工况、单一模型无法满足生产需要。考虑到裂解炉不同运行过程中的相似性,同时为了减小建模过程中典型样本的采集成本,有效利用历史数据,辅以迁移学习算法实现工况的高精度划分。不同工况采用泛化能力强、训练速度高的最小二乘支持向量机建模,并利用粒子群算法对LS-SVM的参数寻优,进一步提高模型精度,从而实现了多工况、多模型的高精度收率预测。基于某乙烯厂现场数据的实验结果表明,多工况、多模型的预测效果更准确合理,PSO优化LS-SVM建立的裂解炉收率模型预测精度更高,趋势跟踪性能良好。     

Mechanisms of the Cytotoxic Effect of Selenium Nanoparticles in Different Human Cancer Cell Lines

In recent decades, studies on the functional features of Se nanoparticles (SeNP) have gained great popularity due to their high biocompatibility, stability, and pronounced selectivity. A large number of works prove the anticarcinogenic effect of SeNP. In this work, the molecular mechanisms regulating the cytotoxic effects of SeNP, obtained by laser ablation, were studied by the example of four human cancer cell lines: A-172 (glioblastoma), Caco-2, (colorectal adenocarcinoma), DU-145 (prostate carcinoma), MCF-7 (breast adenocarcinoma). It was found that SeNP had different concentration-dependent effects on cancer cells of the four studied human lines. SeNP at concentrations of less than 1 μg/mL had no cytotoxic effect on the studied cancer cells, with the exception of the A-172 cell line, for which 0.5 μg/mL SeNP was the minimum concentration affecting its metabolic activity. It was shown that SeNP concentration-dependently caused cancer cell apoptosis, but not necrosis. In addition, it was found that SeNP enhanced the expression of pro-apoptotic genes in almost all cancer cell lines, with the exception of Caco-2 and activated various pathways of adaptive and pro-apoptotic signaling pathways of UPR. Different effects of SeNP on the expression of ER-resident selenoproteins and selenium-containing glutathione peroxidases and thioredoxin reductases, depending on the cell line, were established. In addition, SeNP triggered Ca²⁺ signals in all investigated cancer cell lines. Different sensitivity of cancer cell lines to SeNP can determine the induction of the process of apoptosis in them through regulation of the Ca²⁺ signaling system, mechanisms of ER stress, and activation of various expression patterns of genes encoding pro-apoptotic proteins.     

Metabolic and Lipidomic Assessment of Kidney Cells Exposed to Nephrotoxic Vancomycin Dosages

Vancomycin is a glycopeptide antibiotic used against multi-drug resistant gram-positive bacteria such as Staphylococcus aureus (MRSA). Although invaluable against resistant bacteria, vancomycin harbors adverse drug reactions including cytopenia, ototoxicity, as well as nephrotoxicity. Since nephrotoxicity is a rarely occurring side effect, its mechanism is incompletely understood. Only recently, the actual clinically relevant concentration the in kidneys of patients receiving vancomycin was investigated and were found to exceed plasma concentrations by far. We applied these clinically relevant vancomycin concentrations to murine and canine renal epithelial cell lines and assessed metabolic and lipidomic alterations by untargeted and targeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analyses. Despite marked differences in the lipidome, both cell lines increased anabolic glucose reactions, resulting in higher sorbitol and lactate levels. To the best of our knowledge, this is the first endometabolic profiling of kidney cells exposed to clinically relevant vancomycin concentrations. The presented study will provide a valuable dataset to nephrotoxicity researchers and might add to unveiling the nephrotoxic mechanism of vancomycin.     

头孢菌素C发酵过程状态变量及效益函数预报方法

生物发酵过程中关键变量（如产物浓度和基质浓度等）是过程监控和控制的重要依据,难以在线测量,通常采用离线取样分析获得,取样时间间隔长,数据滞后大,难以满足实时控制的需要．近年来提出的各种软测量方法在一定程度上解决了该问题,但这些方法在预报精度上尚有欠缺,且预报区间较短．文献[5]提出了一种基于神经网络的滚动学习-预报技术,并将该技术用到生产波动相对较小的青霉素发酵过程中,实现了产量的高精度、宽区间的预报,但在生产波动较大的发酵过程中是否适用仍需进一步的探讨．     

Ultra-high temperature ceramics melting temperature prediction via machine learning

Melting temperature has great influence on the high temperature properties and working temperature limits of ultra-high temperature ceramics (UHTCs) In order to bypass the challenge in the measurement of ultra-high melting points, this paper proposed a novel method to predict UHTCs melting temperature via machine learning. A dataset including more than ten thousand melting temperature data has been established, which covers 8 elements and most of the known non-oxide UHTCs. We built up an element to ceramic system framework by back propagation artificial neural network (BPANN) with the accuracy approaching to 90% and the correlation coefficients approaching to 0.95. Our work provides a probability to get the high accuracy melting temperature of UHTCs, and a more convenient way to develop novel materials with higher working temperature. The given case of melting temperature prediction of Hf-C-N ceramics proves the generality of the artificial neural network (ANN). An inter-validation of melting temperature prediction using our network with materials thermodynamics and density functional theory (DFT) has been demonstrated, indicating that our network is of powerful prediction ability.     

Application of FTIR spectroscopy in determining sesamol in sesame seed oil

A new analytical method was developed for determining sesamol in sesame seed oil by FTIR spectroscopy. Sesamol was also spiked at 0 to 1000 mg/kg in freshly refined, bleached, and deodorized palm olein (RBDPOo) and groundnut (peanut) oil. FTIR spectra were recorded using a transmission (NaCl) cell accessory at room temperature, and the partial least squares regression statistical method was used to derive calibration models for each oil. The standard errors of calibration were 6.07, 5.88, and 4.24 mg/100 g for sesame, RBDPOo, and groundnut oils, with coefficients of determination (R ²) of 0.9947, 0.9940, and 0.9662, respectively. The calibration models were validated by the “leave-one-out” cross-validation method, and the R ² of validation, the standard errors of prediction, and SD of the differences for repeatability and accuracy were computed. Our results support the premise that FTIR spectroscopy is an efficient and accurate method for determining minor components such as sesamol in edible oils.     

Prediction of disulfide connectivity in proteins with support vector machine

Disulfide bonds stabilize protein structures and play an important role in protein folding. Predicting disulfide connectivity precisely is an important task for determining the structural/functional relationships of proteins. The accuracy obtained by conventional disulfide connectivity predictions using sequence information only is limited. In this study, we aimed to develop a new method to improve the prediction accuracy of disulfide connectivity using support vector machine (SVM) with prior knowledge of disulfide bonding states and evolutionary information. The separations among the oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Our previous prediction of disulfide connectivity for non-redundant proteins in SwissProt release no. 39 (SP39) sharing less than 30% sequence identity has yielded the accuracy of 49% using CSP method alone. In this study, for proteins from the same dataset, an even better fourfold cross-validation accuracy of 62% was achieved using SVM with CSP as a feature.     

基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法

针对过程数据不足,且具有强非线性和多尺度特性的新间歇过程,结合迁移学习方法与多尺度核学习方法的优势,提出了一种基于多尺度核JYMKPLS（Joint-Y multi-scale kernel partial least squares）迁移模型的间歇过程产品质量在线预测方法。该方法首先通过迁移学习利用相似源域的旧过程数据提高新间歇过程建模效率和质量预测的精度。然后,针对间歇过程数据的非线性和多尺度特性问题,引入了多尺度核函数以更好地拟合数据变化的趋势,从而提高模型的预测精度。此外,提出模型在线更新和数据剔除,通过在线持续改善迁移模型对新间歇过程的匹配程度,以消除相似过程间的差异性给迁移学习带来的不利影响,从而不断地提升预测精度。最后,通过仿真验证了所提方法的有效性,结果表明,与传统的数据驱动建模方法相比,本文所提方法能够有效提高建模效率和预测精度。