Reverse mathematics of matroids

Matroids generalize the familiar notion of linear dependence from linear algebra. Following a brief discussion of founding work in computability and matroids, we use the techniques of reverse mathematics to determine the logical strength of some basis theorems for matroids and enumerated matroids. Next, using Weihrauch reducibility, we relate the basis results to combinatorial choice principles and statements about vector spaces. Finally, we formalize some of the Weihrauch reductions to extract related reverse mathematics results. In particular, we show that the existence of bases for vector spaces of bounded dimension is equivalent to the induction scheme for \Sigma^0_2 formulas

Identification of the nus B gene product of Escherichia coli

Escherichia coli nus B mutants fail to support the activity of a phage λ gene product, pN, which regulates phage gene expression by influencing transcription termination. We report the identification of the nus B protein on SDS-polyacrylamide gels as a 14,500 dalton protein.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47548/1/438_2004_Article_BF00293941.pd

Antipsychotic dose escalation as a trigger for Neuroleptic Malignant Syndrome (NMS): literature review and case series report

Background: “Neuroleptic malignant syndrome” (NMS) is a potentially fatal idiosyncratic reaction to any medication which affects the central dopaminergic system. Between 0.5% and 1% of patients exposed to antipsychotics develop the condition. Mortality rates may be as high as 55% and many risk factors have been reported. Although rapid escalation of antipsychotic dose is thought to be an important risk factor, to date it has not been the focus of a published case series or scientifically defined. <p/>Aims: To identify cases of NMS and review risk factors for its development with a particular focus on rapid dose escalation in the 30 days prior to onset. <p/>Methodology: A review of the literature on rapid dose escalation was undertaken and a pragmatic definition of “rapid dose escalation” was made. NMS cases were defined using DSM-IV criteria and systematically identified within a secondary care mental health service. A ratio of titration rate was calculated for each NMS patient and “rapid escalators” and “non rapid escalators” were compared. <p/>Results: 13 cases of NMS were identified. A progressive mean dose increase 15 days prior to the confirmed episode of NMS was observed (241.7mg/day during days 1-15 to 346.9mg/day during days 16-30) and the mean ratio of dose escalation for NMS patients was 1.4. Rapid dose escalation was seen in 5/13 cases and non rapid escalators had markedly higher daily cumulative antipsychotic dose compared to rapid escalators. <p/>Conclusions: Rapid dose escalation occurred in less than half of this case series (n=5, 38.5%), although there is currently no consensus on the precise definition of rapid dose escalation. Cumulative antipsychotic dose – alongside other known risk factors - may also be important in the development of NMS

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Knowledge brokering between researchers and policymakers in Fiji to develop policies to reduce obesity: a process evaluation

The importance of using research evidence in decision making at the policy level has been increasingly recognized. However, knowledge brokering to engage researchers and policymakers in government and non-government organizations is challenging. This paper describes and evaluates the knowledge exchange processes employed by the Translational Research on Obesity Prevention in Communities (TROPIC) project that was conducted from July 2009 to April 2012 in Fiji. TROPIC aimed to enhance: the evidence-informed decision making skills of policy developers; and awareness and utilization of local and other obesity-related evidence to develop policies that could potentially improve the nation&rsquo;s food and physical activity environments. The specific research question was: Can a knowledge brokering approach advance evidence-informed policy development to improve eating and physical activity environments in Fiji. <br /

A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self- assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered beta-sheets as their size increases

Sabotage in Contests: A Survey

A contest is a situation in which individuals expend irretrievable resources to win valuable prize(s). ‘Sabotage’ is a deliberate and costly act of damaging a rival’s' likelihood of winning the contest. Sabotage can be observed in, e.g., sports, war, promotion tournaments, political or marketing campaigns. In this article, we provide a model and various perspectives on such sabotage activities and review the economics literature analyzing the act of sabotage in contests. We discuss the theories and evidence highlighting the means of sabotage, why sabotage occurs, and the effects of sabotage on individual players and on overall welfare, along with possible mechanisms to reduce sabotage. We note that most sabotage activities are aimed at the ablest player, the possibility of sabotage reduces productive effort exerted by the players, and sabotage may lessen the effectiveness of public policies, such as affirmative action, or information revelation in contests. We discuss various policies that a designer may employ to counteract sabotage activities. We conclude by pointing out some areas of future research