Mixing patterns and the spread of close-contact infectious diseases

Surprisingly little is known regarding the human mixing patterns relevant to the spread of close-contact infections, such as measles, influenza and meningococcal disease. This study aims to estimate the number of partnerships that individuals make, their stability and the degree to which mixing is assortative with respect to age. We defined four levels of putative at-risk events from casual (physical contact without conversation) to intimate (contact of a sexual nature), and asked university student volunteers to record details on those they contacted at these levels on three separate days. We found that intimate contacts are stable over short time periods whereas there was no evidence of repeat casual contacts with the same individuals. The contacts were increasingly assortative as intimacy increased. Such information will aid the development and parameterisation of models of close contact diseases, and may have direct use in outbreak investigations

Using data-driven rules to predict mortality in severe community acquired pneumonia

Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available. © 2014 Wu et al

Generalized probabilities taking values in non-Archimedean fields and topological groups

We develop an analogue of probability theory for probabilities taking values in topological groups. We generalize Kolmogorov's method of axiomatization of probability theory: main distinguishing features of frequency probabilities are taken as axioms in the measure-theoretic approach. We also present a review of non-Kolmogorovian probabilistic models including models with negative, complex, and $p$-adic valued probabilities. The latter model is discussed in details. The introduction of $p$-adic (as well as more general non-Archimedean) probabilities is one of the main motivations for consideration of generalized probabilities taking values in topological groups which are distinct from the field of real numbers. We discuss applications of non-Kolmogorovian models in physics and cognitive sciences. An important part of this paper is devoted to statistical interpretation of probabilities taking values in topological groups (and in particular in non-Archimedean fields)

Upper limits on the strength of periodic gravitational waves from PSR J1939+2134

The first science run of the LIGO and GEO gravitational wave detectors presented the opportunity to test methods of searching for gravitational waves from known pulsars. Here we present new direct upper limits on the strength of waves from the pulsar PSR J1939+2134 using two independent analysis methods, one in the frequency domain using frequentist statistics and one in the time domain using Bayesian inference. Both methods show that the strain amplitude at Earth from this pulsar is less than a few times $10^{-22}$.Comment: 7 pages, 1 figure, to appear in the Proceedings of the 5th Edoardo Amaldi Conference on Gravitational Waves, Tirrenia, Pisa, Italy, 6-11 July 200

Improving the sensitivity to gravitational-wave sources by modifying the input-output optics of advanced interferometers

We study frequency dependent (FD) input-output schemes for signal-recycling interferometers, the baseline design of Advanced LIGO and the current configuration of GEO 600. Complementary to a recent proposal by Harms et al. to use FD input squeezing and ordinary homodyne detection, we explore a scheme which uses ordinary squeezed vacuum, but FD readout. Both schemes, which are sub-optimal among all possible input-output schemes, provide a global noise suppression by the power squeeze factor, while being realizable by using detuned Fabry-Perot cavities as input/output filters. At high frequencies, the two schemes are shown to be equivalent, while at low frequencies our scheme gives better performance than that of Harms et al., and is nearly fully optimal. We then study the sensitivity improvement achievable by these schemes in Advanced LIGO era (with 30-m filter cavities and current estimates of filter-mirror losses and thermal noise), for neutron star binary inspirals, and for narrowband GW sources such as low-mass X-ray binaries and known radio pulsars. Optical losses are shown to be a major obstacle for the actual implementation of these techniques in Advanced LIGO. On time scales of third-generation interferometers, like EURO/LIGO-III (~2012), with kilometer-scale filter cavities, a signal-recycling interferometer with the FD readout scheme explored in this paper can have performances comparable to existing proposals. [abridged]Comment: Figs. 9 and 12 corrected; Appendix added for narrowband data analysi

(Re) defining salesperson motivation: current status, main challenges, and research directions

The construct of motivation is one of the central themes in selling and sales management research. Yet, to-date no review article exists that surveys the construct (both from an extrinsic and intrinsic motivation context), critically evaluates its current status, examines various key challenges apparent from the extant research, and suggests new research opportunities based on a thorough review of past work. The authors explore how motivation is defined, major theories underpinning motivation, how motivation has historically been measured, and key methodologies used over time. In addition, attention is given to principal drivers and outcomes of salesperson motivation. A summarizing appendix of key articles in salesperson motivation is provided

Network Analysis of Differential Expression for the Identification of Disease-Causing Genes

Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes

Molecular Biomarker Analyses Using Circulating Tumor Cells

Evaluation of cancer biomarkers from blood could significantly enable biomarker assessment by providing a relatively non-invasive source of representative tumor material. Circulating Tumor Cells (CTCs) isolated from blood of metastatic cancer patients hold significant promise in this regard.Using spiked tumor-cells we evaluated CTC capture on different CTC technology platforms, including CellSearch and two biochip platforms, and used the isolated CTCs to develop and optimize assays for molecular characterization of CTCs. We report similar performance for the various platforms tested in capturing CTCs, and find that capture efficiency is dependent on the level of EpCAM expression. We demonstrate that captured CTCs are amenable to biomarker analyses such as HER2 status, qRT-PCR for breast cancer subtype markers, KRAS mutation detection, and EGFR staining by immunofluorescence (IF). We quantify cell surface expression of EGFR in metastatic lung cancer patient samples. In addition, we determined HER2 status by IF and FISH in CTCs from metastatic breast cancer patients. In the majority of patients (89%) we found concordance with HER2 status from patient tumor tissue, though in a subset of patients (11%), HER2 status in CTCs differed from that observed in the primary tumor. Surprisingly, we found CTC counts to be higher in ER+ patients in comparison to HER2+ and triple negative patients, which could be explained by low EpCAM expression and a more mesenchymal phenotype of tumors belonging to the basal-like molecular subtype of breast cancer.Our data suggests that molecular characterization from captured CTCs is possible and can potentially provide real-time information on biomarker status. In this regard, CTCs hold significant promise as a source of tumor material to facilitate clinical biomarker evaluation. However, limitations exist from a purely EpCAM based capture system and addition of antibodies to mesenchymal markers could further improve CTC capture efficiency to enable routine biomarker analysis from CTCs

Severe pneumococcal pneumonia: impact of new quinolones on prognosis

<p>Abstract</p> <p>Background</p> <p>Most guidelines have been proposing, for more than 15 years, a β-lactam combined with either a quinolone or a macrolide as empirical, first-line therapy of severe community acquired pneumonia (CAP) requiring ICU admission. Our goal was to evaluate the outcome of patients with severe CAP, focusing on the impact of new rather than old fluoroquinolones combined with β-lactam in the empirical antimicrobial treatments.</p> <p>Methods</p> <p>Retrospective study of consecutive patients admitted in a 16-bed general intensive care unit (ICU), between January 1996 and January 2009, for severe (Pneumonia Severity Index > or = 4) community-acquired pneumonia due to non penicillin-resistant <it>Streptococcus pneumoniae </it>and treated with a β-lactam combined with a fluoroquinolone.</p> <p>Results</p> <p>We included 70 patients of whom 38 received a β-lactam combined with ofloxacin or ciprofloxacin and 32 combined with levofloxacin. Twenty six patients (37.1%) died in the ICU. Three independent factors associated with decreased survival in ICU were identified: septic shock on ICU admission (AOR = 10.6; 95% CI 2.87-39.3; p = 0.0004), age > 70 yrs. (AOR = 4.88; 95% CI 1.41-16.9; p = 0.01) and initial treatment with a β-lactam combined with ofloxacin or ciprofloxacin (AOR = 4.1; 95% CI 1.13-15.13; p = 0.03).</p> <p>Conclusion</p> <p>Our results suggest that, when combined to a β-lactam, levofloxacin is associated with lower mortality than ofloxacin or ciprofloxacin in severe pneumococcal community-acquired pneumonia.</p