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On the practice of ignoring center-patient interactions in evaluating hospital performance

We evaluate the performance of medical centers based on a continuous or binary patient outcome (e.g., 30-day mortality). Common practice adjusts for differences in patient mix through outcome regression models, which include patient-specific baseline covariates (e.g., age and disease stage) besides center effects. Because a large number of centers may need to be evaluated, the typical model postulates that the effect of a center on outcome is constant over patient characteristics. This may be violated, for example, when some centers are specialized in children or geriatric patients.

SFINX: Straightforward Filtering Index for Affinity Purification Mass Spectrometry Data Analysis

Affinity purification-mass spectrometry is one of the most common techniques for the analysis of protein-protein interactions, but inferring bona fide interactions from the resulting data sets remains notoriously difficult. We introduce SFINX, a Straightforward Filtering INdeX that identifies true-positive protein interactions in a fast, user-friendly, and highly accurate way. SFINX outperforms alternative techniques on two benchmark data sets and is available via the Web interface at

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks

Background: Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time.

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications.

Polyploidy and genome evolution in plants

Plant genomes vary in size and complexity, fueled in part by processes of whole-genome duplication (WGD; polyploidy) and subsequent genome evolution. Despite repeated episodes of WGD throughout the evolutionary history of angiosperms in particular, the genomes are not uniformly large, and even plants with very small genomes carry the signatures of ancient duplication events. The processes governing the evolution of plant genomes following these ancient events are largely unknown.

Comparative in silico analysis of SSRs in coding regions of high confidence predicted genes in Norway spruce (Picea abies) and Loblolly pine (Pinus taeda)

Background: Microsatellites or simple sequence repeats (SSRs) are DNA sequences consisting of 1-6 bp tandem repeat motifs present in the genome. SSRs are considered to be one of the most powerful tools in genetic studies. We carried out a comparative study of perfect SSR loci belonging to class I (>= 20) and class II (>= 12 and

Network service chaining with optimized network function embedding supporting service decompositions

The rise of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) introduce opportunities for service providers to reduce CAPEX/OPEX and to offer and quickly deploy novel network services. In particular, SDN and NFV enable the flexible composition of network functions, a generic service concept known as network service chaining (NSC).