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Introduction The inflammatory bowel diseases (IBDs), consisting of ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation of the gastrointestinal tract in genetically susceptible individuals exposed to [...], Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory bowel diseases (IBDs) of unknown etiology that are associated with an aberrant mucosal immune response. Neoangiogenesis and vascular injury are observed in IBD along with increased lymphangiogenesis. While the pathogenic role of angiogenesis in IBD is well characterized, it is not clear how or if increased lymphangiogenesis promotes disease. Here, we determined that enhancing lymphangiogenesis and lymphatic function reduces experimental IBD. Specifically, we demonstrated that adenoviral induction of prolymphangiogenic factor VEGF-C provides marked protection against the development of acute and chronic colitis in 2 different animal models. VEGF-C-dependent protection was observed in combination with increased inflammatory cell mobilization and bacterial antigen clearance from the inflamed colon to the draining lymph nodes. Moreover, we found that the VEGF-C/VEGFR3 pathway regulates macrophage (MΦ) plasticity and activation both in cultured MΦs and in vivo, imparting a hybrid M1-M2 phenotype. The protective function of VEGF-C was meditated by the so-called resolving MΦs during chronic experimental colitis in a STAT6-dependent manner. Together, these findings shed light on the contribution of lymphatics to the pathogenesis of gut inflammation and suggest that correction of defective lymphatic function with VEGF-C has potential as a therapeutic strategy for IBD.

Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well. [ABSTRACT FROM AUTHOR]

This paper presents the well-known symmetrical components transformation (SCT) by means of a different approach from the traditional matrix analysis: a topological lecture provides, in fact, the results obtained by the existing analysis in a more suitable form for network applications. This approach leaves more space to the intuitive analysis, simplifying the application of such a method for teaching purposes, and completing the traditional mathematical view. Moreover, the use of graph concept allows the unified analysis of unbalanced faults leading the matter to the general theory of balanced three-phase systems. Typical for the physical balanced networks, the real convenience of SCT is also confirmed from the topological point of view, even for the fault analysis. Index Terms--Network topology, poly-phase system, symmetrical components transformation (SCT).

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell. [ABSTRACT FROM AUTHOR]

The methodology of continuous improvement in manufacturing is a well-known practice originated by the 'Japanese approach' to industrial production. However, some needs still exist for an easy implementation of the improvement procedures it involves. In fact, in practical implementations in the manufacturing system, difficulties are encountered for clearly identifying the existing weak point to attack. To respond to this concern, the authors developed a special parameter called operating system effectiveness (OSE) that was very effective in measuring the performances of a manufacturing system and in detecting the causes of low productivity, thus offering a powerful tool for implementing process improvement measures. The article describes this approach by first setting the problem and reviewing the state of the art of similar methods already available in literature. Afterwards, the limits of existing methods are discussed and the new OSE parameter is presented and analysed in detail explaining how its components take into account both efficiency and effectiveness, thus effectively guiding the implementation of an improvement process. Then, a structured procedure centred on the OSE, called OSE-structured improvement approach (OSE-SIA), is presented as a practical tool to monitor, diagnose and improve the performances of a manufacturing system, by precisely and selectively detecting the causes of reduced productivity. Finally, a demonstration of how the procedure works is presented through an application case in the electronic manufacturing sector. [ABSTRACT FROM AUTHOR]

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Reflectarray antennas are low-profile high-gain systems widely applied in the aerospace industry. The increase in their application is leading to the problem of getting more advanced performance while keeping the system as simple as possible. In these cases, their design cannot be conducted via analytical methods, thus evolutionary optimization algorithms are often implemented. Indeed, the design is characterized by the presence of many local minima, by high number of design variables, and by the high computational burden required to evaluate the antenna performance. The purpose of this paper is to develop, implement, and test a complete Optimization Environment that can be applied to achieve high scanning capabilities with a reflectarray. The design of the optimization environment has been selected to be flexible enough to be applied also with other different algorithms. [ABSTRACT FROM AUTHOR]

The correct design of a Wireless Sensor Network (WSN) is a very important task because it can highly influence its installation and operational costs. An important aspect that should be addressed with WSN is the routing definition in multi-hop networks. This problem is faced with different methods in the literature, and here it is managed with a recently developed swarm intelligence algorithm called Social Network Optimization (SNO). In this paper, the routing definition in WSN is approached with two different problem codifications and solved with SNO and Particle Swarm Optimization. The first codification allows the optimization algorithm more degrees of freedom, resulting in a slower and in many cases sub-optimal solution. The second codification reduces the degrees of freedom, speeding significantly the optimization process and blocking in some cases the convergence toward the real best network configuration. [ABSTRACT FROM AUTHOR]

The urokinase-type plasminogen activator receptor (uPAR) has a well-established role in cancer progression, but it has been little studied at earlier stages of cancer initiation. Here, we show that uPAR deficiency in the mouse dramatically reduces susceptibility to the classical two-stage protocol of inflammatory skin carcinogenesis. uPAR genetic deficiency decreased papilloma formation and accelerated keratinocyte differentiation, effects mediated by Notch1 hyperactivation. Notably, Notch1 inhibition in uPAR-deficient mice rescued their susceptibility to skin carcinogenesis. Clinically, we found that human differentiated keratoacanthomas expressed low levels of uPAR and high levels of activated Notch1, with opposite effects in proliferating tumors, confirming the relevance of the observations in mice. Furthermore, we found that TACE-dependent activation of Notch1 in basal kerantinocytes was modulated by uPAR. Mechanistically, uPAR sequestered TACE within lipid rafts to prevent Notch1 activation, thereby promoting cell proliferation and tumor formation. Given that uPAR signaling is nonessential for normal epidermal homeostasis, our results argue that uPAR may present a promising disease-specific target for preventing skin cancer development. [ABSTRACT FROM AUTHOR]