Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.     

Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

基于机器学习的直接电离质谱爆炸物检测方法

直接电离质谱系统在现场快速检测中的应用日益广泛,主要用于爆炸物、毒品、食品添加剂等的检测.然而,直接电离质谱系统中质谱信号波动大且同一浓度样品峰强呈现对数正态分布,严重影响了检出限附近低浓度样品的检测准确性.该研究将乙酰水杨酸(115个样品)作为爆炸物模拟物,利用介质阻挡放电离子源与质谱系统,研究了基于机器学习的直接电...     

The symmetry and loading-independency of multiple inclusions enclosing uniform stresses in an infinite elastic plane

The identification of multiple interacting inclusions with uniform internal stresses in an infinite elastic matrix subjected to a uniform remote loading is of fundamental importance in the mechanics and design of particulate composite materials. In anti-plane shear and plane deformations, certain sufficient conditions have been established in the literature which guarantee uniform internal stresses inside multiple interacting inclusions displaying various symmetries when the matrix is subjected ...     

离子导体驱动的介电弹性体软机器研究进展

基于介电弹性体的软机器在柔性机器人、能量收集、柔性电子等领域有着广泛的应用.传统的介电弹性器件由不透明的流体碳膏电极或其他硬材料制作的电子导体来驱动.可拉伸透明离子导体作为电极驱动介电弹性体时,两种软材料互相结合,为软机器提供了一种独特的实现方式.论文综述了离子导体驱动的介电弹性体软机器的近期研究进展,展望了未来的研究方向和存在的问题.     

基于FPGA的微型直流电机控制器IP核设计

为了实现阵列天线波束高效控制和系统小型化, 实现多轴微型直流伺服系统达到高速、并行控制, 根据Nios Ⅱ处理器的Avalon总线规范, 利用Verilog硬件描述语言, 设计了一种基于现场可编程逻辑门阵列(FPGA)的微型直流电机控制器IP核。该控制器采用了有限状态机的方法, 分多模块实现硬件控制功能。针对微型直流电机特性, 采用了比例-积分-微分（PID）算法、分段控制及启停监测等控制策略。最后利用软件Quartus Ⅱ及DE0开发板进行了仿真和实验验证, 实验表明, 该控制器具有响应速度快、定位精度高、可靠性高、体积小等特点, 可以可靠地对各路微型直流电机进行独立控制, 且控制性能良好, 能够实现天线单元快速、准确的相位控制。     

改进加权支持向量机回归方法器件易损性评估

加权支持向量机回归算法,几乎都是以样本输入空间中的一个重要特征量的函数来确定权值,造成了在高维特征空间中作回归可能存在较大误差。针对这一问题,提出利用高维特征空间中的欧基里德距离来确定权值的方法,构造了一种改进的加权支持向量机回归算法,并将其应用到电子器件高功率微波易损性评估中。仿真结果表明：该方法具有比模糊神经网络法、标准支持向量机回归算法和一般的加权支持向量机回归算法更高的预测精度。由于增加了权值的计算过程,相对于标准支持向量机回归和模糊神经网络方法,该方法的效率较低,但与一般的加权支持向量机回归算法相当。     

混响室频率搅拌方式及其场环境特性分析

从本征模的角度阐明了频率搅拌技术的混响原理,并根据激励信号的频域特征,系统研究了频率搅拌方式下获取实时均匀场与统计均匀场的三种有效实现途径。选取线性扫频这一搅拌方式为主要研究对象,检验了混响室频率搅拌下的最低可用频率、场均匀性与统计特性三项主要技术指标。试验结果表明：线性扫频搅拌方式下,混响室的最低可用频域与机械搅拌方式基本一致;场均匀性满足IEC 61000-4-21标准规定的容差要求;场值波动规律符合理论分布模型。     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

一致空间的理想导集映射

本文介绍一致空间的理想导集映射,并对这些映射的像展开研究.同时,介绍I-Hurewicz有界性的概念,研究I-Hurewicz有界性的基础拓扑运算性质.最后,得到一致空间中I-Hurewicz有界性的一个等价刻画.