Consistent quantum mechanics admits no mereotopology

It is standardly assumed in discussions of quantum theory that physical systems can be regarded as having well-defined Hilbert spaces. It is shown here that a Hilbert space can be consistently partitioned only if its components are assumed not to interact. The assumption that physical systems have well-defined Hilbert spaces is, therefore, physically unwarranted.Comment: 10 pages; to appear in Axiomathe

Validity of new child-specific thoracic gas volume prediction equations for air-displacement plethysmography

BACKGROUND: To determine the validity of the recently developed child-specific thoracic gas volume (TGV) prediction equations for use in air-displacement plethysmography (ADP) in diverse pediatric populations. METHODS: Three distinct populations were studied: European American and African American children living in Birmingham, Alabama and European children living in Lisbon, Portugal. Each child completed a standard ADP testing protocol, including a measured TGV according to the manufactures software criteria. Measured TGV was compared to the predicted TGV from current adult-based ADP proprietary equations and to the recently developed child-specific TGV equations of Fields et al. Similarly, percent body fat, derived using the TGV prediction equations, was compared to percent body fat derived using measured TGV. RESULTS: Predicted TGV from adult-based equations was significantly different from measured TGV in girls from each of the three ethnic groups (P < 0.05), however child-specific TGV estimates did not significantly differ from measured TGV in any of the ethnic or gender groups. Percent body fat estimates using adult-derived and child-specific TGV estimates did not differ significantly from percent body fat measures using measured TGV in any of the groups. CONCLUSION: The child-specific TGV equations developed by Fields et al. provided a modest improvement over the adult-based TGV equations in an ethnically diverse group of children

Marker genes for circulating tumour cells predict survival in metastasized breast cancer patients

We investigated the prognostic significance of circulating breast cancer cells in peripheral blood detected by quantitative RT-PCR of marker genes in patients with advanced breast cancer. Blood samples from 94 breast cancer patients with metastatic disease (M1) were examined for circulating tumour cells by studying the mRNA expression of CK19, p1B, PS2 and EGP2 by real-time PCR. Using a score function, developed for predicting circulating tumour cells by quadratic discriminant analysis (QDA), the four expression levels were combined into a single discriminant value. Tumour cells were present in 24 out of 94 (31%) of the patients. In 77% (72 out of 94) of the patients distant metastatic disease was localised in the bone. In 36% (26 out of 72) of the patients with bone metastases at the time of blood sampling, a positive QDA for the four genes was found, in contrast to only 14% (three out of 22) without bone involvement. Overall survival rates by Kaplan-Meier revealed no prognostic effect for the presence of bone metastases (P=0.93). However, patients with a positive QDA value did have a progression-free survival at 1 year of 3% and overall survival at 2 years of 17%, against 22 and 36% for patients with a negative QDA value (P=0.015 and 0.0053, respectively). Breast cancer patients with metastatic disease have a significantly worse progression-free and overall survival when circulating tumour cells can be detected in their peripheral bloo

DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions

<p>Abstract</p> <p>Background</p> <p>Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, <it>Drosophila melanogaster</it>.</p> <p>Description</p> <p>DroID, the <it>Drosophila </it>Interactions Database, is a comprehensive interactions database designed specifically for <it>Drosophila</it>. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link.</p> <p>Conclusion</p> <p>DroID is the most comprehensive interactions database available for <it>Drosophila</it>. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at <url>http://www.droidb.org</url>.</p

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

<p>Abstract</p> <p>Background</p> <p>Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.</p> <p>Results</p> <p>In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from <it>S. cervisiae </it>hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of <it>S. cerevisiae</it>, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.</p> <p>Conclusion</p> <p>Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.</p

A Pair of Dopamine Neurons Target the D1-Like Dopamine Receptor DopR in the Central Complex to Promote Ethanol-Stimulated Locomotion in Drosophila

Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol

Network-Free Inference of Knockout Effects in Yeast

Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network

Poverty and Well-being in Post-Apartheid South Africa: An Overview of Data, Outcomes and Policy

WP 2006-03 January 2006This is an overview of poverty and well-being in the first decade of post-apartheid South Africa. It is an introduction to a volume that brings together some of the most prominent academic research done on this topic for the 10-year review process in South Africa. This overview highlights three key aspects of the picture that the detailed research paints. First, data quality and comparability has been a constant issue in arriving at a consensus among analysts on the outcomes for households and individuals in postapartheid South Africa. Second, while the outcomes on unemployment, poverty and inequality are indeed bad, the outcomes on social indicators and access to public services are much more encouraging. Third, the prospects for rapid and sustained economic growth, without which poverty and well-being cannot be addressed in the long run, are themselves negatively affected by increasing inequality, poverty and unemployment

High quality RNA isolation from Aedes aegypti midguts using laser microdissection microscopy

Background: Laser microdissection microscopy (LMM) has potential as a research tool because it allows precise excision of target tissues or cells from a complex biological specimen, and facilitates tissue-specific sample preparation. However, this method has not been used in mosquito vectors to date. To this end, we have developed an LMM method to isolate midgut RNA using Aedes aegypti