Milespetersen2950

Helminthostachys zeylanica is a traditional folk herb used to improve inflammation and fever in Taiwan. Previous studies showed that H. zeylanica extract could ameliorate lipopolysaccharide-induced acute lung injury in mice. CC-930 The aim of this study was to investigate whether H. zeylanica water (HZW) and ethyl acetate (HZE) extracts suppressed eosinophil infiltration and airway hyperresponsiveness (AHR) in asthmatic mice, and decreased the inflammatory response and oxidative stress in tracheal epithelial cells. Human tracheal epithelial cells (BEAS-2B cells) were pretreated with various doses of HZW or HZE (1 μg/ml-10 μg/ml), and cell inflammatory responses were induced with IL-4/TNF-α. In addition, female BALB/c mice sensitized with ovalbumin (OVA), to induce asthma, were orally administered with HZW or HZE. The result demonstrated that HZW significantly inhibited the levels of proinflammatory cytokines, chemokines, and reactive oxygen species in activated BEAS-2B cells. HZW also decreased ICAM-1 expression and blocked monocytic cells from adhering to inflammatory BEAS-2B cells in vitro. Surprisingly, HZW was more effective than HZE in suppressing the inflammatory response in BEAS-2B cells. Our results demonstrated that HZW significantly decreased AHR and eosinophil infiltration, and reduced goblet cell hyperplasia in the lungs of asthmatic mice. HZW also inhibited oxidative stress and reduced the levels of Th2 cytokines in bronchoalveolar lavage fluid. Our findings suggest that HZW attenuated the pathological changes and inflammatory response of asthma by suppressing Th2 cytokine production in OVA-sensitized asthmatic mice.Effective designation of Protected Areas (PAs) requires the careful consideration of their social impacts as these are perceived by people. These refer to a variety of issues such as the distribution of power, social equity, social relations and more importantly the impact of PAs on human wellbeing. A number of studies have emerged in the past decade aiming to capture social impacts of PAs across the world through non-monetary assessments taking into consideration people's perceptions. Although Europe is the region with the largest in proportion number of Protected Areas across the world it is also a region with very limited scientific evidence on this topic. As the European Union is preparing to implement its new Biodiversity Strategyto ipkmplement this paper aims to provide the first comprehensive review of the literature regarding social impacts of European PAs and highlight new directions for current policy frameworks in the region. The paper focuses on the perceived non-economic social costs and benefits of PAs and identifies 7 key categories of social impacts. We propose that policy planning for biodiversity conservation in Europe should incorporate subjective assessments of social costs and benefits with the aim to achieve an increase of benefits for people and their equal distribution across social groups.Recently, the ATOMKI experiment has reported new evidence for the excess of e + e - events with a mass ∼ 17 MeV in the nuclear transitions of 4 He, that they previously observed in measurements with 8 Be. These observations could be explained by the existence of a new vector X 17 boson. So far, the search for the decay X 17 → e + e - with the NA64 experiment at the CERN SPS gave negative results. Here, we present a new technique that could be implemented in NA64 aiming to improve the sensitivity and to cover the remaining X 17 parameter space. If a signal-like event is detected, an unambiguous observation is achieved by reconstructing the invariant mass of the X 17 decay with the proposed method. To reach this goal an optimization of the X 17 production target, as well as an efficient and accurate reconstruction of two close decay tracks, is required. A dedicated analysis of the available experimental data making use of the trackers information is presented. This method provides independent confirmation of the NA64 published results [1], validating the tracking procedure. The detailed Monte Carlo study of the proposed setup and the background estimate show that the goal of the proposed search is feasible.Few studies have specifically focused on meaning in life in African Americans and many important questions remain, including whether effects of meaning in life are direct or moderated by levels of stress. In a national sample of 909 African Americans, we tested meaning in life as a prospective predictor of changes in depressive symptoms and positive affect over a 2.5-year period. Controlling for demographics and hassles, meaning in life predicted decreased depressive symptoms and increased positive affect across the span of 2.5 years. Moderation effects were not found for hassles, age, or income. Gender moderated the effect of meaning on positive affect such that effects were stronger for women than for men. These results suggest that, for African Americans, meaning in life appears to robustly protect against future depressive symptoms and promote positive affect over time unaffected by amount of stress experienced or most demographic factors.Genetic programming has found recent success as a tool for learning sets of features for regression and classification. Multidimensional genetic programming is a useful variant of genetic programming for this task because it represents candidate solutions as sets of programs. These sets of programs expose additional information that can be exploited for building block identification. In this work, we discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. We investigate methods for biasing the components of programs that are promoted in order to guide search towards useful and complementary feature spaces. We study two main approaches 1) the introduction of new objectives and 2) the use of specialized semantic variation operators. We find that a semantic crossover operator based on stagewise regression leads to significant improvements on a set of regression problems. The inclusion of semantic crossover produces state-of-the-art results in a large benchmark study of open-source regression problems in comparison to several state-of-the-art machine learning approaches and other genetic programming frameworks.