Toxoplasma gondii GRA9 Regulates the Activation of NLRP3 Inflammasome to Exert Anti-Septic Effects in Mice

Dense granule proteins (GRAs) are essential components in Toxoplasma gondii, which are suggested to be promising serodiagnostic markers in toxoplasmosis. In this study, we investigated the function of GRA9 in host response and the associated regulatory mechanism, which were unknown. We found that GRA9 interacts with NLR family pyrin domain containing 3 (NLRP3) involved in inflammation by forming the NLRP3 inflammasome. The C-terminal of GRA9 (GRA9C) is essential for GRA9–NLRP3 interaction by disrupting the NLRP3 inflammasome through blocking the binding of apoptotic speck-containing (ASC)-NLRP3. Notably, Q200 of GRA9C is essential for the interaction of NLRP3 and blocking the conjugation of ASC. Recombinant GRA9C (rGRA9C) showed an anti-inflammatory effect and the elimination of bacteria by converting M1 to M2 macrophages. In vivo, rGRA9C increased the anti-inflammatory and bactericidal effects and subsequent anti-septic activity in CLP- and E. coli- or P. aeruginosa-induced sepsis model mice by increasing M2 polarization. Taken together, our findings defined a role of T. gondii GRA9 associated with NLRP3 in host macrophages, suggesting its potential as a new candidate therapeutic agent for sepsis.     

海绵负载纳米Al2O3复合吸附剂去除水中硒研究

研究制备了海绵负载纳米Al2O3微球的复合吸附剂(NAS),并用于对Se(Ⅳ)和Se(Ⅵ)的吸附。结果表明,合成的纳米Al2O3微球(NAO)的平均尺寸为200~400 nm,在海绵上负载NAO会使其分散性更好。当NAO负载量分别为80 mg/g和60 mg/g时,NAS对Se(Ⅳ)和Se(Ⅵ)的吸附性能为佳,分别需要60、120 min达到平衡,适应pH为2~5;两者均符合准2级动力学模型;NAS对Se(Ⅳ)、Se(Ⅵ)的最大吸附容量分别为137.2、143.9 mg/g,能很好地与Freundlich模型拟合,说明NAS表面不均匀,且属于多层吸附。经过2次的循环,对Se(Ⅳ)和Se(Ⅵ)的去除率有所降低,但均仍保持在一定的水平,说明NAS可再生循环利用。NAS作为一种新型吸附剂去除水中Se具有较好的应用前景。     

基于3D打印技术制造柔性传感器研究进展

与传统的涂覆、沉积等加工手段相比，使用3D打印技术可制造复杂立体功能结构的传感器，将3D打印与柔性传感技术结合可以促进未来生物医疗、人工智能等领域的发展。本文介绍了国内外基于3D打印技术制造柔性传感器的最新进展，其中包括聚酰亚胺等多种基底材料、纳米金属等多种打印传感材料；按照熔融沉积、黏弹性墨水沉积、粉末烧结熔化、还原光聚合和材料喷射的制造原理分别阐述了多种传感器的材料选择、成型特点，并对制造方法进行总结分析。虽然3D打印制造柔性传感器件存在着缺乏行业标准及多种类打印材料等问题，但经过不断创新与发展，3D打印将成为柔性传感领域极佳的制造手段。     

非连续采空区矿柱力学分析的数值模拟

为探讨非连续采空区矿柱力学原理，通过FLAC3D数值模拟技术，对下向水平分层进路式胶结充填采矿法开采形成的非连续采空区中矿柱的受力状态进行了分析。研究表明，非连续采空区中矿柱所受的力主要为覆岩的自重应力，水平应力对其影响不大；随着埋深的增加矿柱承受的垂直方向的力近似呈直线增加；随着非连续采空区跨度的增加（进路数量增加），非连续采空区受垂直方向的应力逐渐由围岩两侧向矿柱中间过渡。     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Congestion management by determining optimal location of series FACTS devices using hybrid bacterial foraging and Nelder–Mead algorithm

In this paper, the Thyristor-Controlled Series-Compensated (TCSC) devices are located for congestion management in the power system by considering the non-smooth fuel cost function and penalty cost of emission. For this purpose, it is considered that the objective function of the proposed optimal power flow (OPF) problem is minimizing fuel and emission penalty cost of generators. A hybrid method that is the combination of the bacterial foraging (BF) algorithm with Nelder–Mead (NM) method (BF-NM) is employed to solve the OPF problems. The optimal location of the TCSC devices are then determined for congestion management. The size of the TCSC is obtained by using of the BF-NM algorithm to minimize the cost of generation, cost of emission, and cost of TCSC. The simulation results on IEEE 30-bus, modified IEEE 30-bus and IEEE 118-bus test system confirm the efficiency of the proposed method for finding the optimal location of the TCSC with non-smooth non-convex cost function and emission for congestion management in the power system. In addition, the results clearly show that a better solution can be achieved by using the proposed OPF problem in comparison with other intelligence methods.     

Lipase‐catalyzed synthesis of aliphatic poly(β‐thioether ester) with various methylene group contents: thermal properties,crystallization and degradation

A series of novel aliphatic poly(β‐thioether ester)s with various methylene group contents were prepared by direct lipase‐catalyzed polycondensation of the monomer with an acid‐labile β‐thiopropionate group. The polycondensation reaction using immobilized lipase B from Candida antarctica was carried out in diphenyl ether at 90 °C. Poly(β‐thioether ester)s with high molecular weights of 20 500–57 000 Da and narrow polydispersities in the range 1.40–1.48 were obtained. Thermogravimetric analysis, differential scanning calorimetry and wide‐angle X‐ray diffraction were used to investigate the thermal properties and crystal structures of these polyesters. All the poly(β‐thioether ester)s were semicrystalline polymers and thermally stable up to at least 200 °C. In vitro degradation studies showed that they can rapidly degrade under acidic conditions by the hydrolysis of the β‐thiopropionate groups, suggesting their potential as acid‐degradable polymeric materials. © 2019 Society of Chemical Industry     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.