Primordial Black Holes: sirens of the early Universe

Primordial Black Holes (PBHs) are, typically light, black holes which can form in the early Universe. There are a number of formation mechanisms, including the collapse of large density perturbations, cosmic string loops and bubble collisions. The number of PBHs formed is tightly constrained by the consequences of their evaporation and their lensing and dynamical effects. Therefore PBHs are a powerful probe of the physics of the early Universe, in particular models of inflation. They are also a potential cold dark matter candidate.Comment: 21 pages. To be published in "Quantum Aspects of Black Holes", ed. X. Calmet (Springer, 2014

Restaurant outbreak of Legionnaires' disease associated with a decorative fountain: an environmental and case-control study

BACKGROUND: From June to November 2005, 18 cases of community-acquired Legionnaires' disease (LD) were reported in Rapid City South Dakota. We conducted epidemiologic and environmental investigations to identify the source of the outbreak. METHODS: We conducted a case-control study that included the first 13 cases and 52 controls randomly selected from emergency department records and matched on underlying illness. We collected information about activities of case-patients and controls during the 14 days before symptom onset. Environmental samples (n = 291) were cultured for Legionella. Clinical and environmental isolates were compared using monoclonal antibody subtyping and sequence based typing (SBT). RESULTS: Case-patients were significantly more likely than controls to have passed through several city areas that contained or were adjacent to areas with cooling towers positive for Legionella. Six of 11 case-patients (matched odds ratio (mOR) 32.7, 95% CI 4.7-infinity) reported eating in Restaurant A versus 0 controls. Legionella pneumophila serogroup 1 was isolated from four clinical specimens: 3 were Benidorm type strains and 1 was a Denver type strain. Legionella were identified from several environmental sites including 24 (56%) of 43 cooling towers tested, but only one site, a small decorative fountain in Restaurant A, contained Benidorm, the outbreak strain. Clinical and environmental Benidorm isolates had identical SBT patterns. CONCLUSION: This is the first time that small fountain without obvious aerosol-generating capability has been implicated as the source of a LD outbreak. Removal of the fountain halted transmission

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

Observation of Coherent Elastic Neutrino-Nucleus Scattering

The coherent elastic scattering of neutrinos off nuclei has eluded detection for four decades, even though its predicted cross-section is the largest by far of all low-energy neutrino couplings. This mode of interaction provides new opportunities to study neutrino properties, and leads to a miniaturization of detector size, with potential technological applications. We observe this process at a 6.7-sigma confidence level, using a low-background, 14.6-kg CsI[Na] scintillator exposed to the neutrino emissions from the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. Characteristic signatures in energy and time, predicted by the Standard Model for this process, are observed in high signal-to-background conditions. Improved constraints on non-standard neutrino interactions with quarks are derived from this initial dataset

The natural history and management of hamstring injuries

Hamstring injuries in sport can be debilitating. The anatomical complexity of this muscle makes uniform assessment of injury epidemiology difficult and insures that post-injury management strategies must be individually focused. This article reviews the anatomy of the hamstring, its role in athletic movement, common mechanisms of injury, and management guidelines with the goal of return into sporting activity in mind

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

Clinical features and predictors of mortality in admitted patients with community- and hospital-acquired legionellosis: A Danish historical cohort study

<p>Abstract</p> <p>Background</p> <p>Legionella is a common cause of bacterial pneumonia. Community-acquired [CAL] and hospital-acquired legionellosis [HAL] may have different presentations and outcome. We aimed to compare clinical characteristics and examine predictors of mortality for CAL and HAL.</p> <p>Methods</p> <p>We identified hospitalized cases of legionellosis in 4 Danish counties from January 1995 to December 2005 using the Danish national surveillance system and databases at departments of clinical microbiology. Clinical and laboratory data were retrieved from medical records; vital status was obtained from the Danish Civil Registration System. We calculated 30- and 90-day case fatality rates and identified independent predictors of mortality using logistic regression analyses.</p> <p>Results</p> <p>We included 272 cases of CAL and 60 cases of HAL. Signs and symptoms of HAL were less pronounced than for CAL and time from in-hospital symptoms to legionellosis diagnosis was shorter for CAL than for HAL (5.5 days vs. 12 days p < 0.001). Thirty-day case fatality was 12.9% for CAL and 33.3% for HAL; similarly 90-day case fatalities in the two groups were 15.8% and 55.0%, respectively. In a logistic regression analysis (excluding symptoms and laboratory tests) age >65 years (OR = 2.6, 95% CI: 1.1-5.9) and Charlson comorbidty index ≥2 (OR = 2.7, 95% CI: 1.1-6.5) were associated with an increased risk of death in CAL. We identified no statistically significant predictors of 30-day mortality in HAL.</p> <p>Conclusions</p> <p>Signs and symptoms were less pronounced in HAL compared to CAL. Conversely, 30-day case fatality was almost 3 times higher. Clinical awareness is important for the timely diagnosis and treatment especially of HAL. There is a need for further studies of prognostic factors in order to improve the therapeutic approach to legionellosis and potentially reduce mortality.</p

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

<p>Abstract</p> <p>Background</p> <p>Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.</p> <p>Results</p> <p>To evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3~6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge.</p> <p>Conclusion</p> <p>Our experiments demonstrate the effectiveness of integrating unlabeled and likely labeled data to augment a PPI text classification system. Our mixed model is suitable for ranking purposes whereas our hierarchical model is better for filtering. In addition, our results indicate that supervised weighting schemes outperform unsupervised ones. Our newly-proposed weighting scheme, TFBRF, which considers documents that do not contain the target word, avoids some of the biases found in traditional weighting schemes. Our experiment results show TFBRF to be the most effective among several other top weighting schemes.</p

Social Modulation during Songbird Courtship Potentiates Midbrain Dopaminergic Neurons

Synaptic transmission onto dopaminergic neurons of the mammalian ventral tegmental area (VTA) can be potentiated by acute or chronic exposure to addictive drugs. Because rewarding behavior, such as social affiliation, can activate the same neural circuitry as addictive drugs, we tested whether the intense social interaction of songbird courtship may also potentiate VTA synaptic function. We recorded glutamatergic synaptic currents from VTA of male zebra finches who had experienced distinct social and behavioral conditions during the previous hour. The level of synaptic transmission to VTA neurons, as assayed by the ratio of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) to N-methyl-D-aspartic acid (NMDA) glutamate receptor mediated synaptic currents, was increased after males sang to females, and also after they saw females without singing, but not after they sang while alone. Potentiation after female exposure alone did not appear to result from stress, as it was not blocked by inhibition of glucocorticoid receptors. This potentiation was restricted to synapses of dopaminergic projection neurons, and appeared to be expressed postsynaptically. This study supports a model in which VTA dopaminergic neurons are more strongly activated during singing used for courtship than during non-courtship singing, and thus can provide social context-dependent modulation to forebrain areas. More generally, these results demonstrate that an intense social encounter can trigger the same pathways of neuronal plasticity as addictive drugs