Monitoring Global Change Research Articles

Characterizing microglia activation: a spatial statistics approach to maximize information extraction

SummaryMicroglia play an important role in the pathology of CNS disorders, however, there remains significant uncertainty about the neuroprotective/degenerative role of these cells due to a lack of techniques to adequately assess their complex behaviour in response to injury. This talk briefly describes a novel technique for microglia analysis, combining improved immunohistological image analysis with spatial statistical techniques (Ripley‐K function and Dixon's χ2‐test). Using this approach, a comprehensive set of morphological parameters describing microglia activation status was developed to characterise microgliosis in a murine optic nerve injury model. In addition to monitoring global changes in microglia density, nearest neighbour distance, and regularity index, cluster analyses based on changes in soma size and roundness were used to yield novel insights into the behaviour of different microglia phenotypes. These methods should be considered a generic tool to quantitatively assess microglia activation status, to profile these phenotypic changes into microglia subpopulations, and to map their spatial distributions in virtually every CNS region and disease state.

Human pluripotent stem cell-derived cardiomyocytes: Genome-wide expression profiling of long-term in vitro maturation in comparison to human heart tissue.

Cardiomyocyte-like cells (CMs) derived from human pluripotent stem cells (hPSCs) present a valuable model for human disease modeling, studying early human development and, potentially, developing cell therapeutic approaches. However, the specification of early hPSC-derived CMs into defined cardiac subtypes such as atrial and ventricular cells is not well understood and, thus, poorly controlled. Moreover, the maturation status of hPSC-CMs is not well defined, yet it is known that these cells undergo at least some degree of maturation upon longer term in vitro culture. To gain insight into this process, and to assess their developmental status, we have recently generated a data set of hPSC-CMs monitoring global changes in gene expression upon long term maintenance in vitro, in comparison to human atrial and ventricular heart samples (GEO accession number GEO: GSE64189). These data present a rich resource for evaluating the maturation status of hPSC-CMs, for identifying suitable markers for subtype-specific gene expression, as well as for the generation of functional hypotheses. Here, we provide additional details and quality checks of this data set, and exemplify how it can be used to identify maturation-associated as well as cardiac subtype-specific markers.

Snowfall trends and variability in Qinghai, China

Snow is an important component of a climate system, and very sensitive to climate change, and it can be an indicator of checking and monitoring global change. This paper is concerned with identifying changes in the time series of snowfall in Qinghai, located in western China. We used the Mann-Kendall test to evaluate the annual and monthly trends in snowfall in Qinghai during the periods 1957/58–2006/07 based on data obtained from 25 stations. Significant positive and negative trends at the 90, 95, and 99 percent confidence levels were detected for numerous stations. In order to understand the regional pattern of snowfall in Qinghai, the detected trends are spatially interpolated using the inverse distance weighted interpolation method in a GIS environment. The results indicate that in January snowfall in the most area of Qinghai shows a significant upward trend, especially in the Delingha and Nomhon area in northern Qaidam Basin, as well as southeastern Qinghai, however, there is no significant downward trend area. The trend analysis indicates that annual snowfall has a significant upward trend in Delingha and Nomhon area in northern Qaidam Basin, and the downward trends occurred in the Qilian mountain area and Gonghe Basin. This is especially the case for highlighting the spatial structure of snowfall trends. The research results help in further understanding the regional pattern of snowfall in Qinghai and its response to global climatic warming.

A GIS-based Spatial Pattern Analysis Model for eco-region mapping and characterization

Growing concerns about global climate change, biodiversity maintenance, natural resources conservation, and long-term ecosystem sustainability have been responsible for the transformation of traditional single resource management approaches into integrated ecosystem management models. Eco-regions are large ecosystems of regional extent that contain smaller ecosystems of similar response potential and resource production capabilities. They can be used as a geographical framework for organizing and reporting resource information, setting bioecological recovery criteria, extrapolating site-level management, and monitoring global change. The objective of this research is to develop a quantitative, multivariate regionalization model that is capable of delineating eco-regions at multiple levels from remotely sensed information and other environmental and natural resources spatial data. The Spatial Pattern Analysis Model developed in this study uses a region-growing algorithm to generate spatially contiguous regions from primitive polygonal land units. The algorithm merges the most similar pair of neighbouring units at each iteration, based on satisfying certain similarity criteria until all units are grouped into one. This model was utilized to develop an eco-region map of Nebraska with three hierarchical levels. In the mapping process, the STATSGO data set was used to build the primitive map units. Environmental parameters included in the model were multi-temporal AVHRR data, soil rooting depth, organic matter content, available water capacity, and long-term annual averages of water balance and growing degree day totals. Development of the model provides a new and useful approach to eco-region mapping for resource managers and researchers. The method is automated and efficient, reduces the judgement biases and uncertainty of manual analyses, and can be replicated for other regions or for the regionalization of other themes.