How Cells Communicate with Each Other in the Tumor Microenvironment: Suggestions to Design Novel Therapeutic Strategies in Cancer Disease

Connexin- and pannexin (Panx)-formed hemichannels (HCs) and gap junctions (GJs) operate an interaction with the extracellular matrix and GJ intercellular communication (GJIC), and on account of this they are involved in cancer onset and progression towards invasiveness and metastatization. When we deal with cancer, it is not correct to omit the immune system, as well as neglecting its role in resisting or succumbing to formation and progression of incipient neoplasia until the formation of micrometastasis, nevertheless what really occurs in the tumor microenvironment (TME), which are the main players and which are the tumor or body allies, is still unclear. The goal of this article is to discuss how the pivotal players act, which can enhance or contrast cancer progression during two important process: “Activating Invasion and Metastasis” and the “Avoiding Immune Destruction”, with a particular emphasis on the interplay among GJIC, Panx-HCs, and the purinergic system in the TME without disregarding the inflammasome and cytokines thereof derived. In particular, the complex and contrasting roles of Panx1/P2X7R signalosome in tumor facilitation and/or inhibition is discussed in regard to the early/late phases of the carcinogenesis. Finally, considering this complex interplay in the TME between cancer cells, stromal cells, immune cells, and focusing on their means of communication, we should be capable of revealing harmful messages that help the cancer growth and transform them in body allies, thus designing novel therapeutic strategies to fight cancer in a personalized manner.     

中高碳铝镇静钢形成MnS塑性夹杂的工艺开发与实践

 为提高中高碳钢产品的抗疲劳性能,利用中高碳钢的成分特点,研究开发了中高碳铝镇静钢中MnS以Al₂O₃为形核质点的非均质形核工艺,将钢中Al₂O₃脆性夹杂用塑性MnS包裹,解决了疲劳应力钢因脆性非金属夹杂引起的疲劳断裂问题。通过对微细、弥散Al₂O₃夹杂生成条件、MnS非均质形核析出热力学条件的研究,开展了钢中关键元素的成分设计、精炼及连铸集成工艺的设计与开发。工业实践表明,低活度氧条件下进行铝终脱氧可以形成3～5 μm微细弥散的Al₂O₃夹杂,并作为非均质形核的核心在二次枝晶晶间的凝固末端析出弥散、细小的粒状MnS;通过梯度脱氧、真空碳脱氧以及保护浇铸等操作可以有效稳定控制钢中全氧含量,提高钢水洁净度,成品T[O]质量分数平均为0.000 618%,较原工艺的0.000 739%降低了16%;成品的夹杂物中MnS及MnS包裹Al₂O₃夹杂所占比例大于96%,与世界领先产品的夹杂物控制水平相当,考虑到产品使用过程中Al₂O₃夹杂外部的MnS包裹层必须足够厚,塑性夹杂才能起作用,建立了MnS “有效包裹率” 的概念,当硬相夹杂物被MnS包裹且硬相夹杂物的最大半径不大于MnS包裹部位半径的1/2时,认为MnS对硬相夹杂物实现了“有效包裹”;MnS塑性夹杂工艺可明显提高材料的疲劳性能,成品的平均断裂韧性为83.47 MPa·m1/2,较原工艺的67.31 MPa·m1/2提高了24%。     

一种新颖的SPMSM驱动系统自适应死区补偿方法

逆变器死区效应将造成电机电流畸变,为提高表贴式永磁同步电机电流控制精度,提出一种自适应死区电压补偿策略。该方法利用参考电流建立d、q轴死区电压核函数,能够在表贴式永磁同步电机电阻、电感参数未知的情况下,对d、q轴死区电压的幅值进行辨识,将辨识的电压幅值与其核函数相乘得到所需补偿的死区电压。从而解决了输死区电压造成的相电流畸变难题。仿真结果表明,所提出的电流控制策略能够有效减少相电流谐波含量、提高电流控制精度。     

The concept of smartness in cyber–physical systems and connection to urban environment

The next-generation systems are expected to be largely cyber–physical systems (CPSs) that autonomously control physical processes, through sensors and actuators typically in real-time feedback and cooperative control loops distributed among physical and cyber environments. The rapid technological advancements enhance the smartness of these CPSs, pushing their boundaries of performance and efficiency by embedding new information and communication technologies. However, to what extent CPSs should be smarter so that they do not compromise safety and security of safety critical systems? is an open research question. Towards this goal, the purpose of this study is to establish a grounded theory to analyse what makes these systems smart? and eventually, how to find a balance between smartness and safety risks? In this precinct, this article aims to develop a conceptual framework, define the dimensions and derive the characteristics that make CPSs smart. The proposed approach combines an automated informetric and systematic analysis of literature pertinent to the topic of smartness across anthropology, science, engineering and technology. The analysis of a case study building and the discussions presented herein support the connection between the existing understanding of CPSs and smartness offered by the building design approach in urban environment.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

Cloud computing adoption in Ghana; accounting for institutional factors

In spite of increasing adoption of Cloud Computing (CC) by organizations in developed countries, the rate of adoption of the technology in sub-Saharan Africa has stagnated, although it has the potential to accelerate the speed of digital transformation. This paper contributes to the extant literature in this light by examining how the institutional environment influences the adoption of cloud computing. We investigated the role of mimetic, coercive and normative institutional pressures in predicting adoption outcomes of cloud computing among organizations in the region. After testing three main hypotheses and ten corollaries, and analyzing data collected from seventy-nine organizations with the partial least square structural equation modelling (PLS-SEM), the study found that the institutional pressures (mimetic, coercive and normative) explain 27% of the variance in cloud computing adoption. The mimetic – CC adoption path coefficient of 0.35 made the biggest contribution to the model while the normative – CC adoption path was the least contributor with a path coefficient of 0.18. The findings explain the determinants of adoption of CC in environments of low adoption and institutional challenges.     

FogMed: A Fog-Based Framework for Disease Prognosis Based Medical Sensor Data Streams

Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This framework aims to improve the disease prediction accuracy by achieving two objectives: QoS guarantee of fog services and anomaly prediction of Medical data streams. We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed. The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.