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.     

Farmers' preferences for adopting on-farm concentration of raw milk: Results from a discrete choice experiment in Germany

Milk concentrates are used in the manufacturing of dairy products such as yogurt and cheese or are processed into milk powder. Processes for the nonthermal separation of water and valuable milk ingredients are becoming increasingly widespread at farm level. The technical barriers to using farm-manufactured milk concentrate in dairies are minimal, hence the suspicion that the practice of on-farm raw milk concentration is still fairly uncommon for economic reasons. This study, therefore, set out to investigate farmers' potential willingness to adopt a raw milk concentration plant. The empirical analysis was based on discrete choice experiments with 75 German dairy farmers to identify preferences and the possible adoption of on-farm raw milk concentration. The results showed that, in particular, farmers who deemed the current milk price to be insufficient viewed on-farm concentration using membrane technology as an option for diversifying their milk sales. We found no indication that adoption would be impeded by a lack of trustworthy information on milk processing technologies or capital.     

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.     

Food-grade carrageenans and their implications in health and disease

Food additives, often used to guarantee the texture, shelf-life, taste, and appearance of processed foods, have gained widespread attention due to their increased link to the growing incidence of chronic diseases. As one of the most common additives, carrageenans have been used in human diets for hundreds of years. While classified as generally recognized as safe (GRAS) for human consumption, numerous studies since the 1980s have suggested that carrageenans, particularly those with random coil conformations, may have adverse effects on gastrointestinal health, including aggravating intestinal inflammation. While these studies have provided some evidence of adverse effects, the topic is still controversial. Some have suggested that the negative consequence of the consumption of carrageenans may be structure dependent. Furthermore, pre-existing conditions may predispose individuals to varied outcomes of carrageenan intake. In this review, structure–function relationships of various carrageenans in the context of food safety are discussed. We reviewed the molecular mechanisms by which carrageenans exert their biological effects. We summarized the findings associated with carrageenan intake in animal models and clinical trials. Moreover, we examined the interactions between carrageenans and the gut microbiome in the pathogenesis of gastrointestinal disorders. This review argues for personalized guidance on carrageenan intake based on individuals’ health status. Future research efforts that aim to close the knowledge gap on the effect of low-dose and chronic carrageenan intake as well as interactions among food additives should be conducive to the improved safety profile of carrageenans in processed food products.     

电商平台产品共同购买网络的影响因素研究

共同购买网络的推荐系统应用越加广泛，仅基于网络内生结构变量研究其共同购买链接的经济意义已有局限，故加入网络口碑这一外生变量，进行更全面的分析。采用了社会网络方法中指数随机图模型进行建模，主要围绕产品销售量、产品入度、差评率和评论数4个方面因素，探究其对共同购买网络中共同购买链接形成的影响。结果显示，销售量、产品入度和评论数对共同购买链接形成的影响呈正比关系，而差评率则会显著地降低产品共同购买的几率。该指数随机图构建出的共同购买网络为在线电商平台管理网络口碑和推荐系统优化设计提供有益参考。     

基于Shearlet变换的自适应地震资料随机噪声压制

传统的阈值选取方法是对所有变换域系数使用统一阈值，但对Shearlet变换而言，各尺度、各方向的有效信号和噪声均存在差异，因此全局硬阈值存在一定局限性；局部阈值可根据一定范围内的系数分布情况确定。针对地震数据去噪过程中传统阈值选取方法的局限性，通过改进自适应阈值函数压制随机噪声，在局部阈值的基础上改进贝叶斯阈值，形成一种适用于Shearlet变换的自适应阈值函数。具体做法为：将信噪比与阈值函数有机关联，并将信噪比作为阈值设定的因素，即不同的信噪比的权值系数不同，可以自适应求取不同尺度阈值，从而最大限度地改善去噪效果，避免有效信号损失，实现自适应去噪。模型试算与实际资料去噪效果表明，在保证有效信号不受损失的情况下，所提方法可恢复被噪声掩盖的弱信号，有效改善去噪效果。     

优化特征提取的互动式人脸活体检测研究

针对人脸识别系统中出现的通过照片或视频“欺诈”解锁问题，提出一种活体检测方法，通过随机指令判断被检测对象是否为真人。利用HOG特征和随机森林算法对摄像头采集的图像进行人脸检测，预测脸部68个特征点位置，把68个特征点位置和脸部位置的相对位置归一化后作为姿态的特征，提取的姿态特征与SVM分类器相结合，训练出分类效果较好的头部姿态估计分类器。通过多次实验，对特征提取方法进行优化，进一步提高检测准确率。最后，使用随机互动式命令序列对被检测人发出指令，实现活体检测。该方法对头部姿态估计具有较高的鲁棒性，并且可以有效地防范照片和视频认证攻击。     

Reliable techniques for integrated circuit identification and true random number generation using 1.5-transistor flash memory

Flash memory bits, like other integrated circuit (IC) devices, are prone to random variability in their actual versus nominal characteristics. We present the use of 1.5-transistor flash memory cells in physically unclonable functions leveraging their erase speed variability. This type of memory is interesting for the internet of things due to its wide availability as intellectual property at foundries. Using experimentally measured results, we show simple methods that provide high reliability with no or limited need for helper data and error correction. High quality fingerprints for IC identification are demonstrated. Moreover, techniques to remove systematic variations from the array response are shown, allowing the resulting binary strings to pass all National Institute of Standards and Technology tests for randomness. Consequently, with low complexity helper functions, true random numbers can be readily produced.