Dynamics of Shifting Cultivation Landscapes in Northern Lao PDR Between 2000 and 2009 Based on an Analysis of MODIS Time Series and Landsat Images

The rotational nature of shifting cultivation poses several challenges to its detection by remote sensing. Consequently, there is a lack of spatial data on the dynamics of shifting cultivation landscapes on a regional, i.e. sub-national, or national level. We present an approach based on a time series of Landsat and MODIS data and landscape metrics to delineate the dynamics of shifting cultivation landscapes. Our results reveal that shifting cultivation is a land use system still widely and dynamically utilized in northern Laos. While there is an overall reduction in the areas dominated by shifting cultivation, some regions also show an expansion. A review of relevant reports and articles indicates that policies tend to lead to a reduction while market forces can result in both expansion and reduction. For a better understanding of the different factors affecting shifting cultivation landscapes in Laos, further research should focus on spatially explicit analyses.

Effects of Landscape Segregation on Livelihood Vulnerability: Moving From Extensive Shifting Cultivation to Rotational Agriculture and Natural Forests in Northern Laos

This study investigates four decades of socio-economic and environmental change in a shifting cultivation landscape in the northern uplands of Laos. Historical changes in land cover and land use were analyzed using a chronological series of remote sensing data. Impacts of landscape change on local livelihoods were investigated in seven villages through interviews with various stakeholders. The study reveals that the complex mosaics of agriculture and forest patches observed in the study area have long constituted key assets for the resilience of local livelihood systems in the face of environmental and socio-economic risks. However, over the past 20 years, a process of segregating agricultural and forest spaces has increased the vulnerability of local land users. This process is a direct outcome of policies aimed at increasing national forest cover, eradicating shifting cultivation and fostering the emergence of more intensive and commercial agricultural practices. We argue that agriculture-forest segregation should be buffered in such a way that a diversity of livelihood opportunities and economic development pathways can be maintained.

Agonist specific L-type Ca(2+)-current stimulation in ventricular myocytes by a novel steroid-like compound

In cardiac muscle the amplitude of Ca(2+) transients can be increased by enhancing Ca(2+) influx. Among the processes leading to increased Ca(2+) influx, agonists of the L-type Ca(2+)-channel can play an important role. Known pharmacological Ca(2+)-channel agonists act on different binding sites on the channel protein, which may lead not only to enhanced peak currents, but also to distinct changes in other biophysical characteristics of the current. In this study, membrane currents were recorded with the patch-clamp technique in the whole-cell configuration in guinea pig isolated ventricular myocytes in combination with confocal fluorescence Ca(2+) imaging techniques and a variety of pharmacological tools. Testing a new positive inotropic steroid-like compound, we found that it increased the L-type Ca(2+)-current by 2.5-fold by shifting the voltage-dependence of activation by 20.2 mV towards negative potentials. The dose-response relationship revealed two vastly different affinities (EC(50(high-affinity))=4.5+/-1.7 nM, EC(50(low-affinity))=8.0+/-1.1 microM) exhibiting differential pharmacological interactions with three classes of Ca(2+)-current antagonists, suggesting more than one binding site on the channel protein. Therefore, we identified and characterized a novel positive inotropic compound (F90927) as a member of a new class of Ca(2+)-channel agonists exhibiting unique features, which set it apart from other presently known L-type Ca(2+)-channel agonists.

A Data-Analytic Strategy for Protein-Biomarker Discovery: Profiling of High-Dimensional Proteomic Data for Cancer Detection

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of "signature" protein profiles specific to each pathologic state (e.g., normal vs. cancer) or differential profiles between experimental conditions (e.g., treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data analytic strategy for discovering protein biomarkers based on such high-dimensional mass-spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data analytic strategy takes properties of the SELDI mass-spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After these pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Extensions to Gene Set Enrichment

Motivation: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture moderate but coordinated changes in the expression of sets of functionally related genes. We propose number of extensions to GSEA, which uses different statistics to describe the association between genes and phenotype of interest. We make use of dimension reduction procedures, such as principle component analysis to identify gene sets containing coordinated genes. We also address the problem of overlapping among gene sets in this paper. Results: We applied our methods to the data come from a clinical trial in acute lymphoblastic leukemia (ALL) [1]. We identified interesting gene sets using different statistics. We find that gender may have effects on the gene expression in addition to the phenotype effects. Investigating overlap among interesting gene sets indicate that overlapping could alter the interpretation of the significant results.

GENE SET ENRICHMENT ANALYSIS MADE SIMPLE

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, chose an appropriate cut-off, and create a list of candidate genes. This approach has been criticized for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, many of these methods seem overly complicated. Furthermore, the most popular method, Gene Set Enrichment Analysis (GSEA), is based on a statistical test known for its lack of sensitivity. In this paper we compare the performance of a simple alternative to GSEA.We find that this simple solution clearly outperforms GSEA.We demonstrate this with eight different microarray datasets.

[Evaluation of the "Schema-focused Emotive Behavioural Therapy" (SET) for Patients with Personality Disorders: Results of a Randomised Controlled Trial.]

The "Schema-focussed Emotive Behavioral Therapy" (SET) was developed by our research group as a new group therapy approach for patients with personality disorders from all clusters (A to C; DSM-IV). It was evaluated in a randomised controlled study (n = 93). Data were collected before and after treatment as well as one year after study entry. A completer analysis was conducted with matched subgroups (n = 60). After therapy, SET patients improved in the outcome domains interactional behavior, strain, and symptomatic complaints (IIP-D, GAF, VEV-VW, BSI-P). Furthermore, they showed a significant lower dropout rate. At the follow-up assessment, Cluster C patients of the experimental group deteriorated with regard to symptomatic complaints (BSI-P). In contrast, cluster B patients improved more over time compared to control subjects. SET seems to be an adequate and effective group therapy with effects that seem to be stable over time, especially for patients with Cluster B diagnosis.