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.     

Improving water retention of chicken breast meats by CaCl2 combined with pulsed electric fields

Frozen poultry meat is the most widely consumed animal-based food. However, water loss often leads to quality loss of poultry meat. Therefore, the present study sought to investigate the combined effect of calcium chloride (CaCl₂) and pulsed electric fields (PEF) treatment on chicken breast meats and the mechanisms underlying protein degradation. The results showed that the synergistic effect was superior to the single treatment. Compared with the untreated group, the combination of CaCl₂ and PEF increased water holding capacity of chicken breast meats by 16.61% and decreased cooking loss by 28.93%. Low-field nuclear magnetic resonance (LF-NMR) results indicated that the synergistic treatment promoted water molecules' binding capacity in myofibrils of poultry meat, which exhibited higher immobilised water. Additionally, the combination of CaCl₂ and PEF led to increased degradation of proteins of high-molecular weight and surface hydrophobicity of myofibrillar protein. Furthermore, the extension of the protein molecule and microenvironmental changes promoted interaction between protein and water. In conclusion, the synergistic treatment of CaCl₂ and PEF enhanced water retention and improved physicochemical properties of the myofibrillar protein in chicken breast meats.     

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.     

Construction and analysis of photovoltaic directly coupled conditions in PEM electrolyzer

The Markov model and the PEM electrolyzer system model for directly coupled photovoltaic are combined to construct an efficient and reliable working condition that fits the fluctuation characteristics of solar energy. The working condition is designed through genetic algorithm so that the average coupling efficiency of the system can reach 98.8%. Then, the durability and recovery test are conducted on the basis of the constructed conditions. It is found that the attenuation rate at the current density of 1A/cm² under the photovoltaic fluctuating condition reached 7.8mV/h, which is twice that under the constant current condition. The charge transfer impedance (R_ct) is the main factor leading to the degradation. It is proved by the recovery experiment that the increase of R_ct is related to the pollution of metal ions. After pickling to remove some metal ions, R_ct can be significantly reduced by 46.8% and 65.2%, respectively. After the durability test, the voltammetric charges under the photovoltaic fluctuating condition and the constant current condition are reduced by 48.3% and 19.1% It indicates that the photovoltaic fluctuation condition will accelerate the attenuation of the effective reaction area of MEA, which is irreversible even after pickling. It can be observed from the SEM images that the catalyst layer of MEA has more obvious peeling under the photovoltaic fluctuation condition, which is not conducive to material transmission and destroys the transmission channel of ions and electrons. This result can provide a reliable reference for the coupling design of PEM electrolyzer and renewable energy in the future.     

Pulsed Electromagnetic Field Stimulation in Osteogenesis and Chondrogenesis: Signaling Pathways and Therapeutic Implications

Mesenchymal stem cells (MSCs) are the main cell players in tissue repair and thanks to their self-renewal and multi-lineage differentiation capabilities, they gained significant attention as cell source for tissue engineering (TE) approaches aimed at restoring bone and cartilage defects. Despite significant progress, their therapeutic application remains debated: the TE construct often fails to completely restore the biomechanical properties of the native tissue, leading to poor clinical outcomes in the long term. Pulsed electromagnetic fields (PEMFs) are currently used as a safe and non-invasive treatment to enhance bone healing and to provide joint protection. PEMFs enhance both osteogenic and chondrogenic differentiation of MSCs. Here, we provide extensive review of the signaling pathways modulated by PEMFs during MSCs osteogenic and chondrogenic differentiation. Particular attention has been given to the PEMF-mediated activation of the adenosine signaling and their regulation of the inflammatory response as key player in TE approaches. Overall, the application of PEMFs in tissue repair is foreseen: (1) in vitro: to improve the functional and mechanical properties of the engineered construct; (2) in vivo: (i) to favor graft integration, (ii) to control the local inflammatory response, and (iii) to foster tissue repair from both implanted and resident MSCs cells.     

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。     

马尔可夫跳变线性系统最优控制的研究现状与进展

马尔可夫跳变线性系统（MJLS）是一种具有多个模态的随机系统，系统在各个模态之间的跳变转移由一组马尔可夫链来决定。MJLS模型因其在表示过程中可以产生突变而更能精确的描述实际工程应用中的系统。近年来，MJLS的最优控制问题成为了研究的热点，动态规划、极大值原理以及线性矩阵不等式等成为了解决此类问题的主流方法。本文对MJLS最优控制领域的研究现状进行了综述。分别对一般情况下、带有噪声的情况下、带有时滞的情况下以及某些特定情况下的MLJS最优控制问题的国内外研究现状进行论述。最后进行了总结并提出MJLS最优控制领域未来值得关注的研究方向。     

具有自动装卸搬运机器人的制造单元建模与分析

针对具有自动装卸搬运机器人的制造单元，着重考虑其双重资源约束的特点，提出了该系统的有限缓冲开排队网的建模方法。根据工件的加工、装卸与搬运等工艺流程，定义该系统的排队网模型的各种状态，基于连续时间马尔可夫链，采用精确解法对该模型进行分析求解，获得系统一系列的稳态性能指标。为了验证排队网建模方法的有效性，建立其对应的仿真模型，分别采用排队网模型的数值计算与仿真模型的仿真统计2种方法求出系统的性能指标进行对比，并基于排队网模型对该制造单元的系统性能进行分析，为该类制造单元的资源配置优化和设施布局优化等问题提供重要的理论支撑。