Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

A new method of predicting the saturation pressure of oil reservoir and its application

Saturation pressure is a vital parameter of oil reservoir which can reflect the oilfield characteristics and determine the oilfield development process, and it is determined by experiments in the laboratory in general. However, there was only one well with saturation pressure test in this target reservoir, and it is necessary to determine whether this parameter is right or not.In this work, we present a new method for quickly determining saturation pressure using machine learning algorithms, including random forest regressor (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN or NN). Using these approaches, saturation pressure was obtained by using the initial solution gas-oil ratio (GOR), temperature, API gravity and other reservoir-fluid data available in the oilfields. Compared with the empirical formula for saturation pressure calculation, the calculated result shows that the accuracy given from machine learning is higher than that from other formulas at home and abroad, and has a good match with the lab test. On the basis of the calculated saturation pressure, it can determine whether the reservoir enters into the stage of dissolved gas drive or not, which also provides the basis for maintaining the reservoir pressure by water injection in advance, rational development decision-making and work over measures.This approach above can provide technical guidance for predicting the saturation pressure in the development of different kinds of reservoirs, including the sandstone reservoirs and carbonate reservoirs.     

守住青山绿水,遏制污染毒瘤

峰林平原是我国南方山水的典型代表,其特殊的岩溶地貌类型具有珍稀性和不可替代性,是具有较高的旅游价值的地质景观。但是由于特殊的水文地质结构和强烈的人类活动,峰林平原区的地下水污染问题十分突出,严重影响了地方的旅游产业和经济发展,对当地居民的身体健康产生巨大威胁。本文简述了峰林平原水文地质结构特点、污染现状、成因及危害,提出了一系列从源头上解决峰林平原区地下水污染的对策建议。     

基于LSTM-RF模型的城市道路交通流预测

交通流预测作为信号协调和出行时间预测等任务的基础,成为了交通领域的研究点。对于交通流预测问题,研究人员提出了多种方法,但这些方法大多只使用交通流数据的时域信息进行交通流预测,忽略了空间相关性对于预测目标路段流的影响,导致预测精度不理想。基于组合模型的思想提出了一种称为LSTM-RF的交通流预测模型。在交通流预测过程中,首先使用LSTM模型提取预测目标路段的时序特征,再将其预测值与采集的相邻上下游路段信息同时作为随机森林模型的输入特征,进行交通流时空相关性分析,获得最终的预测结果。并通过贵阳市车牌识别系统采集的城区132条路段的交通流数据进行实验验证。结果表明:该方法在预测精度上优于单一模型,并且预测误差相比单一模型有明显减少。     

Forest parameters retrieval with dual-baseline polarimetric SAR interferometry based on clustering analysis

Forest parameters inversion is an important application of Polarimetric Synthetic Aperture Radar Interferometry(PolInSAR). To resolve the volume decorrelation coefficient ambiguity of single-baseline PolInSAR, a new dual-baseline PolInSAR approach is proposed in this paper. The correlation of dual-baseline data is fully investigated to resolve the ambiguity problem effectively, and the performance of the proposed approach is proved to be of great robustness in a very small baseline ratio system. Finally, experimental results with ESA dual-baseline PolInSAR simulated data validate the effectiveness and robustness of the proposed approach.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

基于随机森林与多元线性回归的咪唑啉衍生物缓蚀剂的构效关系研究

以15种不同十一烷基咪唑啉衍生物缓蚀剂为研究对象,采用随机森林与多元线性回归相结合考察了分子结构对其缓蚀效率(IE)的影响。首先,从能量、电荷、分子表面与信息量、立体构象与拓扑特征6个方面对15种咪唑啉类缓蚀剂进行了全面表征,共得到55个分子结构参数;然后运用随机森林(RF)与多元线性回归(MLR)分别对特征参数进行了优化,筛选出top10的8个重叠参数。从8个参数中随机挑选3个,使用留一法(LOO)进行多元线性回归模型构建,最终得到了最优的特征组合为分子总能量(Te)、信息含量(Ic)与分子折射率(Mr)。基于此得到的最优定量结构-缓蚀效率关系模型,其相关系数R~2为0.843,关系式表示为IE=-5.517-0.010 1×Te+15.601 7×Ic+0.222×Mr。考察样本后去掉一个奇异样本,其相对误差达到18.9%,剩余14个样本留一法建模,模型效果大大提高,其R~2为0.911。结果表明,Te、Ic、Mr与缓蚀效率具有较高的正相关性,分子结构越稳定、对称性好及折射率高,则其IE值就越高,为设计新型高效的缓蚀剂提供了理论指导。     

森林抚育对森林生态系统的影响

伴随着我国社会经济的稳固发展,人们的生活质量逐渐有了明显改善,而在生态问题趋于严重使人们的环境保护意识显著提高的过程中,森林抚育对于森林生态系统的作用和价值愈发凸显。对此,文章针对森林抚育,从提高森林利用率、维持森林动植物生态平衡、增加可利用水资源等多个方面就其对森林生态系统的影响进行了分析,旨在给予相关森林保护工作者可行的帮助,并以此促进我国森林开发工作的可持续发展。     

Bacteriocin-producing Enterococcus faecalis KT2W2G isolated from mangrove forests in southern Thailand: Purification,characterization and safety evaluation

The aim of this work was to purify and characterize the bacteriocin produced by Enterococcus faecalis KT2W2G isolated from the mangrove forest in southern Thailand, in order to evaluate its potential as a new food protective agent. The active peptide from the cell-free supernatant of Ent. faecalis KT2W2G was purified in 4 steps: (i) precipitation with 70% saturated ammonium sulfate, (ii) elution on a reversed phase cartridge (Sep-Pak C₈) using different concentrations of acetonitrile, (iii) cation-exchange chromatography and (iv) final purification by reversed phase-HPLC on a C₈ column. Each purification step increased the specific activity and reduced the amount of contaminating non-bacteriocin proteins. The specific activity of purified bacteriocin was 13,470.53 AU/mg of protein, which corresponded to a 48.10-fold increase. Tricine–SDS-PAGE of the purified bacteriocin gave molecular weight ranging between 3.5 and 6.5 kDa. The activity of the partially purified bacteriocin was unaffected by pH (2.0–12.0) and thermostable, but was sensitive to proteolytic enzymes. This bacteriocin maintained full stability after storage at −20, 4 and 37 °C for 2 months. It was stable when incubated for 1 month at 4 °C in 0–30% NaCl. Inhibitory spectrum of this bacteriocin showed a wide range of activities against other LAB, food-spoilage and food-borne pathogens. Ent. faecalis KT2W2G was sensitive to kanamycin, tetracycline and vancomycin but resistant to ampicillin, gentamicin and penicillin. PCR amplification demonstrated that Ent. faecalis KT2W2G does not harbor virulence genes cylA, cylB and esp but has virulence genes ace, asa1 and efaAfs. The bacteriocin and its producing strain may find application as bio-preservatives for reduction of food-spoilage and food-borne pathogens in food products.