Interleukin-1β Modulation of the Mechanobiology of Primary Human Pulmonary Fibroblasts: Potential Implications in Lung Repair

Pro-inflammatory cytokines like interleukin-1β (IL-1β) are upregulated during early responses to tissue damage and are expected to transiently compromise the mechanical microenvironment. Fibroblasts are key regulators of tissue mechanics in the lungs and other organs. However, the effects of IL-1β on fibroblast mechanics and functions remain unclear. Here we treated human pulmonary fibroblasts from control donors with IL-1β and used Atomic Force Microscopy to unveil that IL-1β significantly reduces the stiffness of fibroblasts concomitantly with a downregulation of filamentous actin (F-actin) and alpha-smooth muscle (α-SMA). Likewise, COL1A1 mRNA was reduced, whereas that of collagenases MMP1 and MMP2 were upregulated, favoring a reduction of type-I collagen. These mechanobiology changes were functionally associated with reduced proliferation and enhanced migration upon IL-1β stimulation, which could facilitate lung repair by drawing fibroblasts to sites of tissue damage. Our observations reveal that IL-1β may reduce local tissue rigidity by acting both intracellularly and extracellularly through the downregulation of fibroblast contractility and type I collagen deposition, respectively. These IL-1β-dependent mechanical effects may enhance lung repair further by locally increasing pulmonary tissue compliance to preserve normal lung distension and function. Moreover, our results support that IL-1β provides innate anti-fibrotic protection that may be relevant during the early stages of lung repair.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

The control of sea lice in Atlantic salmon by selective breeding

Sea lice threaten the welfare of farmed Atlantic salmon and the sustainability of fish farming across the world. Chemical treatments are the major method of control but drug resistance means that alternatives are urgently needed. Selective breeding can be a cheap and effective alternative. Here, we combine experimental trials and diagnostics to provide a practical protocol for quantifying resistance to sea lice. We then combined quantitative genetics with epidemiological modelling to make the first prediction of the response to selection, quantified in terms of reduced need for chemical treatments. We infected over 1400 young fish with Lepeophtheirus salmonis, the most important species in the Northern Hemisphere. Mechanisms of resistance were expressed early in infection. Consequently, the number of lice per fish and the ranking of families were very similar at 7 and 17 days post infection, providing a stable window for assessing susceptibility to infection. The heritability of lice numbers within this time window was moderately high at 0.3, confirming that selective breeding is viable. We combined an epidemiological model of sea lice infection and control on a salmon farm with genetic variation in susceptibility among individuals. We simulated 10 generations of selective breeding and examined the frequency of treatments needed to control infection. Our model predicted that substantially fewer chemical treatments are needed to control lice outbreaks in selected populations and chemical treatment could be unnecessary after 10 generations of selection. Selective breeding for sea lice resistance should reduce the impact of sea lice on fish health and thus substantially improve the sustainability of Atlantic salmon production.     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.     

Alloying behavior and novel properties of CoCrFeNiMn high-entropy alloy fabricated by mechanical alloying and spark plasma sintering

An equiatomic CoCrFeNiMn high-entropy alloy was synthesized by mechanical alloying (MA) and spark plasma sintering (SPS). During MA, a solid solution with refined microstructure of 10 nm which consists of a FCC phase and a BCC phase was formed. After SPS consolidation, only one FCC phase can be detected in the HEA bulks. The as-sintered bulks exhibit high compressive strength of 1987 MPa. An interesting magnetic transition associated with the structure coarsening and phase transformation was observed during SPS process.     

苯乳酸抑制链格孢霉作用靶位的分析

以从自然腐败的樱桃上分离的链格孢霉（Alternaria sp.）LD3.0086为指示菌，研究苯乳酸对链格孢霉的主要抑制作用靶位。应用分光光度法测定苯乳酸对链格孢霉的最小抑菌浓度，通过卡尔科弗卢尔荧光增白剂染液（calcofluor white，CFW）染色观察苯乳酸对菌丝顶端生长的破坏作用，利用扫描电子显微镜和透射电子显微镜观察链格孢霉的超微结构变化，通过测定苯乳酸作用前后链格孢霉上清液中N-乙酰葡萄糖胺质量浓度变化研究苯乳酸对菌丝细胞壁的破坏作用，应用荧光双染色法观察苯乳酸对链格孢霉菌丝细胞膜的损伤作用。结果表明，12.5 mmol/L的苯乳酸能有效抑制链格孢霉的生长；与对照组（无菌水处理）相比，苯乳酸处理后链格孢霉顶端生长细胞无明显形变，经12.5 mmol/L苯乳酸处理的链格孢霉上清液中N-乙酰葡萄糖胺质量浓度基本不变；苯乳酸处理24 h，链格孢霉菌丝细胞壁表面无明显损伤，细胞内结构发生明显变化；苯乳酸短时间（4 h）处理链格孢霉，菌丝细胞膜仍较为完整，加入苯乳酸较长时间（8 h）后细胞膜发生破裂。综合分析可知，苯乳酸对链格孢霉的主要作用靶位应不是菌丝体的细胞壁和细胞膜，而是在菌丝体内部，通过破坏菌丝内部细胞器结构或引起细胞内的生化反应，从而抑制链格孢霉的生长和繁殖，发挥抑菌活性。     

基于张量分解融合RGB-D图像的物体识别

为了充分利用RGB-D图像的深度图像信息，提出了基于张量分解的物体识别方法。首先将RGB-D图像构造成一个四阶张量，然后将该四阶张量分解为一个核心张量和四个因子矩阵，再利用相应的因子矩阵将原张量进行投影，获得融合后的RGB-D数据，最后输入到卷积神经网络中进行识别。RGB-D数据集中三组相似物体的识别结果表明，利用张量分解融合RGB-D图像的物体识别准确率高于未采用张量分解的物体识别准确率，并且单一错分实例的准确率最高可提升99%。     

碳酸盐岩岩溶储层多井评价方法及地质应用

多井评价是建立在单井精细解释对比分析基础上的预测储层及含油气性平面分布规律的技术方法,而常规测井资料无法区分碳酸盐岩岩溶储层,因而利用多井评价结果确定岩溶发育程度在平面上的分布规律就显得十分重要。为此,在岩心标定成像测井的基础上,对四川盆地高石梯—磨溪地区15口井的成像测井岩溶发育特征进行分析,建立了中二叠统茅口组岩溶发育各分带的标准成像图版,利用交会图及直方图分析各分带的常规测井响应特征,在此基础上形成了电成像测井刻度常规测井识别岩溶发育带的新方法。研究结果表明:①高石梯—磨溪地区茅口组岩溶带自上而下可划分为风化壳残积带、垂直渗流岩溶带、水平潜流岩溶带以及受岩溶作用较弱的基岩;②风化壳残积带在成像测井图像显示为"暗—亮—暗"条带状模式,垂直渗流岩溶带为垂直线状与暗色斑状组合模式,水平潜流岩溶带为水平线状—层状与斑状组合模式,基岩整体显示为亮色块状模式偶见线状或斑状特征;③有效储层主要发育在垂直渗流带和水平潜流带的顶部;④该区茅口组岩溶发育主要受裂缝发育控制,而裂缝发育又与断层关系密切。结论认为,该新方法对碳酸盐岩岩溶储层的多井评价具有普遍适用性,为四川盆地中二叠统风险探井的部署提供了技术支撑。     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.