Region Outliers Research Articles

The Traditional Chinese Medicine Houttuynia cordata Thunb decoction alters intestinal barrier function via an EGFR dependent MAPK (ERK1/2) signalling pathway

BackgroundA traditionally prepared aqueous extract (= decoction) of Houttuynia cordata Thunb (Yu xing cao) (HC) is widely used in Traditional Chinese Medicine (TCM) to treat inflammatory disease. Previous chemical and biological studies on HC have mainly focused on organic extracts rather than the aqueous decoction, which is the traditional formulation. PurposeThe study aimed to investigate whether the chemical composition of HC aqueous decoction (HCD) varies with geographical sourcing, to investigate the mechanism of action of HCD, and to determine if chemical variation impacts on HCDs anti-inflammatory activity. MethodSixteen samples of HC were purchased from Sichuan, Hubei and Anhui provinces in the People's Republic of China (PRC) and were prepared by the traditional decoction method to yield their corresponding HCDs. A Quality Control (QC) sample was prepared by combining individual HCD extracts. HCDs were analysed by Nuclear Magnetic Resonance (NMR) and High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS). The anti-inflammatory activities associated with intestinal barrier function of HCD were studied by tumor necrosis factor-α (TNF-α) activated Caco-2 monolayers in vitro and in vivo using Dextran Sulfate Sodium (DSS)-induced murine colitis. Proteins involved in inflammation, mRNA levels, disease severity scores, and histology involved in intestinal inflammation were analysed. ResultsHCD samples exhibited different chemical fingerprints and three regional outliers were identified by Principal Component Analysis (PCA). Fifteen phytochemical metabolites were identified and quantified. HCD showed in vitro anti-inflammatory activity, enhancing zonula occludens-1 (ZO-1), occludin, interleukin (IL)-10 and decreasing IL-1β, IL-6 and epidermal growth factor receptor (EGFR) via an EGFR-dependent mitogen-activated protein kinase (MAPK) extracellular signal-regulated kinase 1/2 (ERK 1/2) signaling pathway. This beneficial effect on intestinal inflammation was also seen in the in vivo colitis model at a molecular level in colonic tissues. ConclusionThis study shows that the test HCDs were chemically different, resulting in different levels of activity on intestinal barrier function and inflammation. Moreover, a “Daodi” product showed the greatest biological activity in this study, thus validating the importance of the “Daodi” quality material in TCM and supporting the traditional used of HCD for the treatment of inflammation.

Recent Trends in Globalization of Industry R&D Clinical Trials: Are Emerging Markets Losing their Allure?

Background: This paper expands on our initial research published in this journal in 2014 which analyzed global trends in biopharmaceutical R&D clinical trials (BPCT) covering the period 2007 – 2012. In this paper, we have built on this work and used an enhanced and improved methodology (analysis of the Phase II and Phase III BPCT data from ClinicalTrials.gov clinical trial registry with the number of active, rather than just the newly added, clinical trial sites utilized as a surrogate of BPCT activity) to analyze geographic participation of countries and regions in BPCTs during the period 2009-2014. </P><P> Discussion: Our data demonstrate that the developed clinical BPCT markets North America and Western Europe have been successful to reduce, and to a certain degree reverse, loss of market share to the emerging markets, and keep the market share of developed markets well above the 70% mark. While almost all emerging markets lost market share at a regional level, there were two regional outliers. The first being Central and Eastern Europe (CEE), which as a region managed to protect its market share position amidst the developed markets offensive. In terms of patient accessibility to clinical trials (population-adjusted number of BPCT sites), core Central Europe outperformed Western Europe, and some countries (Bulgaria, Hungary, Czech Republic) also exceeded levels in North America. The other outlier on the opposite end of the spectrum is MENA1 (Middle East and North Africa – excluding Israel), which contributes almost 2% of the global spend on pharmaceuticals, but attracted less than 0.2% of the BPCTs market share. Population-adjusted accessibility to clinical trials of novel compounds in MENA is 200x below US levels, and more than 20x below global average. </P><P> Conclusion: Patient accessibility to clinical trials in the developed markets is roughly 20x higher than in a majority of the developing markets, however patient accessibility to BPCTs in several countries in CEE, as well as Israel, exceeds the US levels. Our data also demonstrate substantial imbalance between participation in development of new products (expressed as BPCT market share) and consumption of marketed pharmaceuticals (expressed as market share of pharmaceutical sales) across several emerging markets, most notably MENA. Moreover, this strong consumption bias is further increasing over time across a majority of emerging markets, driven by low (and declining across the majority of the emerging markets) BPCT market share. This contrasts the rapid growth of pharmaceutical sales across a majority of the emerging markets, significantly exceeding the rate of growth in the developed countries.

Finding big shots: small-area mapping and spatial modelling of obesity among Swiss male conscripts.

BackgroundIn Switzerland, as in other developed countries, the prevalence of overweight and obesity has increased substantially since the early 1990s. Most of the analyses so far have been based on sporadic surveys or self-reported data and did not offer potential for small-area analyses. The goal of this study was to investigate spatial variation and determinants of obesity among young Swiss men using recent conscription data.MethodsA complete, anonymized dataset of conscription records for the 2010–2012 period were provided by Swiss Armed Forces. We used a series of Bayesian hierarchical logistic regression models to investigate the spatial pattern of obesity across 3,187 postcodes, varying them by type of random effects (spatially unstructured and structured), level of adjustment by individual (age and professional status) and area-based [urbanicity and index of socio-economic position (SEP)] characteristics.ResultsThe analysed dataset consisted of 100,919 conscripts, out of which 5,892 (5.8 %) were obese. Crude obesity prevalence increased with age among conscripts of lower individual and area-based SEP and varied greatly over postcodes. Best model’s estimates of adjusted odds ratios of obesity on postcode level ranged from 0.61 to 1.93 and showed a strong spatial pattern of obesity risk across the country. Odds ratios above 1 concentrated in central and north Switzerland. Smaller pockets of elevated obesity risk also emerged around cities of Geneva, Fribourg and Lausanne. Lower estimates were observed in North-East and East as well as south of the Alps. Importantly, small regional outliers were observed and patterning did not follow administrative boundaries. Similarly as with crude obesity prevalence, the best fitting model confirmed increasing risk of obesity with age and among conscripts of lower professional status. The risk decreased with higher area-based SEP and, to a lesser degree – in rural areas.ConclusionIn Switzerland, there is a substantial spatial variation in obesity risk among young Swiss men. Small-area estimates of obesity risk derived from conscripts records contribute to its understanding and could be used to design further studies and interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s40608-016-0092-6) contains supplementary material, which is available to authorized users.

Transformation of the Macroregional Structure of the International Trade under Globalization

Against increasing number of works dealing with multiple aspects of globalization many researchers and observers tend to believe that the international trade is getting more globalized. The article sets criteria – the share of regional trade and the degree of matching of a region’s (or country’s) geographical structure of exports to that of the world’s imports – which allow us to understand whether the international trade can be referred to as a really global one. The results of the research show that many countries tend to trade with their regional counterparts rather than with regional outliers. Besides, the article considers the peculiarities of the geographical structure of international trade in various product groups and reveals that raw materials and fuels tend to be traded inter-regionally, whereas finished goods as well as intermediate goods are mainly exchanged within regions. As long as intra-industry trade is thought to be one of the features of the modern stage of globalization, exports-imports operations in automotive, offi ce and consumer electronics, clothing and furniture industries are considered as examples of such trade. The main finding was that the intra-industry trade tended to have stronger presence in technologically advanced industries, whereas inter-industry trade was more typical of labor-intensive ones. Finally, the article presents a classification of countries based on the peculiarities of the geographical structure of their exports. It shows that today the external trade of very few countries can be thought of as a really global one.

The Spatial Outlier Mining Algorithm based on the KNN Graph

In order to solve the defect in the spatial outlier mining algorithm that the spatial objects may be affected by their surrounding abnormal neighbors, a Based K-Nearest Neighbor (BKNN) algorithm was proposed based on the working principle of KNN Graph, which could effectively identify the spatial outliers by using cutting edge strategies. The core idea of BKNN is to calculate the dissimilarity of the non-space attribute values the between adjacent objects, and to find the find the largest local outlier or outlier regions by cropping off the edges with the largest dissimilarity. The experiments for the spatial outlier mining algorithm BKNN based on the KNN Graph were carried out in the real datasets FMR and WNV. The example of the algorithm and the time complexity were analyzed and the results were compared to those of the existing classical algorithms, which verified that this algorithm could improve the accuracy of spatial outlier mining and simultaneously mine spatial region outliers.

A GRAPH-BASED APPROACH TO DETECT ABNORMAL SPATIAL POINTS AND REGIONS

Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorological images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, assigns the differences of nonspatial attribute as edge weights, and continuously cuts high-weight edges to identify isolated points or regions that are much dissimilar to their neighboring objects. The proposed algorithms have three major advantages compared with other existing spatial outlier detection methods: accurate in detecting both point and region outliers, capable of avoiding false outliers, and capable of computing the local outlierness of an object within subgraphs. We present time complexity of the algorithms, and show experiments conducted on US housing and Census data to demonstrate the effectiveness of the proposed approaches.

Lobular Involution, Mammographic Density, and Breast Cancer Risk: Visualizing the Future?

Lobular Involution, Mammographic Density, and Breast Cancer Risk: Visualizing the Future?

Minimum Spanning Tree-based Structural Similarity Clustering for Image Mining with Local Region Outliers

mining is more than just an extension of data mining to image domain. Image mining is a technique commonly used to extract knowledge directly from image. Image segmentation is the first step in image mining. We treat image segmentation as graph partitioning problem. In this paper we propose a novel algorithm, Minimum Spanning Tree based Structural Similarity Clustering for Image Mining with Local Region Outliers (MSTSSCIMLRO) to segment the given image and to detect anomalous pattern (outliers). In MSTSSCIMLRO algorithm we use weighted Euclidean distance for edges, which is key element in building the graph from image. MST-based image segmentation is fast and efficient method of generating a set of segments from an image. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of segments. The algorithm works in two phases. The first phase of the algorithm creates optimal number of clusters/segments, where as the second phase of the algorithm further segments the optimal number of clusters/segments and detect local region outliers

High performance computing for spatial outliers detection using parallel wavelet transform

Wavelet analysis is a practical tool to study signal analysis and image processing. Traditional Fourier transform can also transfer the signal into frequency domain, but wavelet analysis is more attractive for its features of multi-resolution and localization of frequency. Recently, there has been significant development in the use of wavelet methods in the data mining process. However, the objective of the study described in this paper is twofold: designing a wavelet transform algorithm on the multiprocessor architecture and using this algorithm in mining spatial outliers of meteorological data. Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Outlier detection reveals important and valuable information from large spatial data sets. As region outliers are commonly multi-scale objects, wavelet analysis is an effective tool to study them. In this paper, we present a wavelet based approach and its applicability in outlier detection. We design a suite of algorithms to effectively discover region outliers and also a parallel algorithm is designed to bring efficiency and speedup for the wavelet analysis. The applicability and effectiveness of the developed algorithms are evaluated on real-world meteorological dataset.

Detecting and tracking regional outliers in meteorological data

Detecting spatial outliers can help identify significant anomalies in spatial data sequences. In the field of meteorological data processing, spatial outliers are frequently associated with natural disasters such as tornadoes and hurricanes. Previous studies on spatial outliers mainly focused on identifying single location points over a static data frame. In this paper, we propose and implement a systematic methodology to detect and track regional outliers in a sequence of meteorological data frames. First, a wavelet transformation such as the Mexican Hat or Morlet is used to filter noise and enhance the data variation. Second, an image segmentation method, λ-connected segmentation, is employed to identify the outlier regions. Finally, a regression technique is applied to track the center movement of the outlying regions for consecutive frames. In addition, we conducted experimental evaluations using real-world meteorological data and events such as Hurricane Isabel to demonstrate the effectiveness of our proposed approach.