Measurement of single top quark production in the tau+jets channnel using boosted decision trees at D0

The top quark is the heaviest known matter particle and plays an important role in the Standard Model of particle physics. At hadron colliders, it is possible to produce single top quarks via the weak interaction. This allows a direct measurement of the CKM matrix element V{sub tb} and serves as a window to new physics. The first direct measurement of single top quark production with a tau lepton in the final state (the tau+jets channel) is presented in this thesis. The measurement uses 4.8 fb{sup -1} of Tevatron Run II data in p{bar p} collisions at {radical}s = 1.96 TeV acquired by the D0 experiment. After selecting a data sample and building a background model, the data and background model are in good agreement. A multivariate technique, boosted decision trees, is employed in discriminating the small single top quark signal from a large background. The expected sensitivity of the tau+jets channel in the Standard Model is 1.8 standard deviations. Using a Bayesian statistical approach, an upper limit on the cross section of single top quark production in the tau+jets channel is measured as 7.3 pb at 95% confidence level, and the cross section is measured as 3.4{sub -1.8}{sup +2.0} pb. The result of the single top quark production in the tau+jets channel is also combined with those in the electron+jets and muon+jets channels. The expected sensitivity of the electron, muon and tau combined analysis is 4.7 standard deviations, to be compared to 4.5 standard deviations in electron and muon alone. The measured cross section in the three combined final states is {sigma}(p{bar p} {yields} tb + X,tqb + X) = 3.84{sub -0.83}{sup +0.89} pb. A lower limit on |V{sub tb}| is also measured in the three combined final states to be larger than 0.85 at 95% confidence level. These results are consistent with Standard Model expectations

Charmed meson decay constants in three-flavor lattice QCD

We present the first lattice QCD calculation with realistic sea quark content of the D^+ meson decay constant f_{D^+}. We use the MILC Collaboration's publicly available ensembles of lattice gauge fields, which have a quark sea with two flavors (up and down) much lighter than a third (strange). We obtain f_{D^+} = 201 +/- 3 +/- 17 MeV, where the errors are statistical and a combination of systematic errors. We also obtain f_{D_s} = 249 +/- 3 +/- 16 MeV for the D_s meson.Comment: note added on recent CLEO measurement; PRL versio

Genome sequencing defines phylogeny and spread of methicillin-resistant Staphylococcus aureus in a high transmission setting.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial infection. Whole-genome sequencing of MRSA has been used to define phylogeny and transmission in well-resourced healthcare settings, yet the greatest burden of nosocomial infection occurs in resource-restricted settings where barriers to transmission are lower. Here, we study the flux and genetic diversity of MRSA on ward and individual patient levels in a hospital where transmission was common. We repeatedly screened all patients on two intensive care units for MRSA carriage over a 3-mo period. All MRSA belonged to multilocus sequence type 239 (ST 239). We defined the population structure and charted the spread of MRSA by sequencing 79 isolates from 46 patients and five members of staff, including the first MRSA-positive screen isolates and up to two repeat isolates where available. Phylogenetic analysis identified a flux of distinct ST 239 clades over time in each intensive care unit. In total, five main clades were identified, which varied in the carriage of plasmids encoding antiseptic and antimicrobial resistance determinants. Sequence data confirmed intra- and interwards transmission events and identified individual patients who were colonized by more than one clade. One patient on each unit was the source of numerous transmission events, and deep sampling of one of these cases demonstrated colonization with a "cloud" of related MRSA variants. The application of whole-genome sequencing and analysis provides novel insights into the transmission of MRSA in under-resourced healthcare settings and has relevance to wider global health.The authors acknowledge financial support from the UKCRC Translational Infection Research (TIR) Initiative and the Medical Research Council (Grant number G1000803), with contributions to the grant from the Biotechnology and Biological Sciences Research Council, the National Institute for Health Research on behalf of the Department of Health, and the Chief Scientist Office of the Scottish Government Health Directorate (to Professor Peacock); from Wellcome Trust grant number 098051 awarded to the Wellcome Trust Sanger Institute; and the NIHR Cambridge Biomedical Research Centre (to Professor Peacock). S.Y.C.T. is an Australian National Health and Medical Research Council Career Development Fellow (1065736)This is the final version of the article. It first appeared at http://www.genome.org/cgi/doi/10.1101/gr.174730.114

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

Ocean Acidification-Induced Food Quality Deterioration Constrains Trophic Transfer

Our present understanding of ocean acidification (OA) impacts on marine organisms caused by rapidly rising atmospheric carbon dioxide (CO2) concentration is almost entirely limited to single species responses. OA consequences for food web interactions are, however, still unknown. Indirect OA effects can be expected for consumers by changing the nutritional quality of their prey. We used a laboratory experiment to test potential OA effects on algal fatty acid (FA) composition and resulting copepod growth. We show that elevated CO2 significantly changed the FA concentration and composition of the diatom Thalassiosira pseudonana, which constrained growth and reproduction of the copepod Acartia tonsa. A significant decline in both total FAs (28.1 to 17.4 fg cell−1) and the ratio of long-chain polyunsaturated to saturated fatty acids (PUFA:SFA) of food algae cultured under elevated (750 µatm) compared to present day (380 µatm) pCO2 was directly translated to copepods. The proportion of total essential FAs declined almost tenfold in copepods and the contribution of saturated fatty acids (SFAs) tripled at high CO2. This rapid and reversible CO2-dependent shift in FA concentration and composition caused a decrease in both copepod somatic growth and egg production from 34 to 5 eggs female−1 day−1. Because the diatom-copepod link supports some of the most productive ecosystems in the world, our study demonstrates that OA can have far-reaching consequences for ocean food webs by changing the nutritional quality of essential macromolecules in primary producers that cascade up the food web

The Buffer Gas Beam: An Intense, Cold, and Slow Source for Atoms and Molecules

Beams of atoms and molecules are stalwart tools for spectroscopy and studies of collisional processes. The supersonic expansion technique can create cold beams of many species of atoms and molecules. However, the resulting beam is typically moving at a speed of 300-600 m/s in the lab frame, and for a large class of species has insufficient flux (i.e. brightness) for important applications. In contrast, buffer gas beams can be a superior method in many cases, producing cold and relatively slow molecules in the lab frame with high brightness and great versatility. There are basic differences between supersonic and buffer gas cooled beams regarding particular technological advantages and constraints. At present, it is clear that not all of the possible variations on the buffer gas method have been studied. In this review, we will present a survey of the current state of the art in buffer gas beams, and explore some of the possible future directions that these new methods might take

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p

Essential versus accessory aspects of cell death: recommendations of the NCCD 2015

Cells exposed to extreme physicochemical or mechanical stimuli die in an uncontrollable manner, as a result of their immediate structural breakdown. Such an unavoidable variant of cellular demise is generally referred to as ‘accidental cell death’ (ACD). In most settings, however, cell death is initiated by a genetically encoded apparatus, correlating with the fact that its course can be altered by pharmacologic or genetic interventions. ‘Regulated cell death’ (RCD) can occur as part of physiologic programs or can be activated once adaptive responses to perturbations of the extracellular or intracellular microenvironment fail. The biochemical phenomena that accompany RCD may be harnessed to classify it into a few subtypes, which often (but not always) exhibit stereotyped morphologic features. Nonetheless, efficiently inhibiting the processes that are commonly thought to cause RCD, such as the activation of executioner caspases in the course of apoptosis, does not exert true cytoprotective effects in the mammalian system, but simply alters the kinetics of cellular demise as it shifts its morphologic and biochemical correlates. Conversely, bona fide cytoprotection can be achieved by inhibiting the transduction of lethal signals in the early phases of the process, when adaptive responses are still operational. Thus, the mechanisms that truly execute RCD may be less understood, less inhibitable and perhaps more homogeneous than previously thought. Here, the Nomenclature Committee on Cell Death formulates a set of recommendations to help scientists and researchers to discriminate between essential and accessory aspects of cell death

Spintronics: Fundamentals and applications

Spintronics, or spin electronics, involves the study of active control and manipulation of spin degrees of freedom in solid-state systems. This article reviews the current status of this subject, including both recent advances and well-established results. The primary focus is on the basic physical principles underlying the generation of carrier spin polarization, spin dynamics, and spin-polarized transport in semiconductors and metals. Spin transport differs from charge transport in that spin is a nonconserved quantity in solids due to spin-orbit and hyperfine coupling. The authors discuss in detail spin decoherence mechanisms in metals and semiconductors. Various theories of spin injection and spin-polarized transport are applied to hybrid structures relevant to spin-based devices and fundamental studies of materials properties. Experimental work is reviewed with the emphasis on projected applications, in which external electric and magnetic fields and illumination by light will be used to control spin and charge dynamics to create new functionalities not feasible or ineffective with conventional electronics.Comment: invited review, 36 figures, 900+ references; minor stylistic changes from the published versio

Primary Hyperparathyroidism Influences the Expression of Inflammatory and Metabolic Genes in Adipose Tissue

Background: Primary hyperparathyroidism (PHPT) is characterised by increased production of parathyroid hormone (PTH) resulting in elevated serum calcium levels. The influence on bone metabolism with altered bone resorption is the most studied clinical condition in PHPT. In addition to this, patients with PHPT are at increased risk of non-skeletal diseases, such as impaired insulin sensitivity, arterial hypertension and increased risk of death by cardiovascular diseases (CVD), possibly mediated by a chronic low-grade inflammation. The aim of this study was to investigate whether adipose tissue reflects the low-grade inflammation observed in PHPT patients. Methodology/Principal Findings: Subcutaneous fat tissue from the neck was sampled from 16 non-obese patients with PHPT and from 16 patients operated for benign thyroid diseases, serving as weight-matched controls. RNA was extracted and global gene expression was analysed with Illumina BeadArray Technology. We found 608 differentially expressed genes (q-value,0.05), of which 347 were up-regulated and 261 were down-regulated. Gene ontology analysis showed that PHPT patients expressed increased levels of genes involved in immunity and defense (e.g. matrix metallopeptidase 9, S100 calcium binding protein A8 and A9, CD14, folate receptor 2), and reduced levels of genes involved in metabolic processes. Analysis of transcription factor binding sites present in the differentially expressed genes corroborated the up-regulation of inflammatory processes. Conclusions/Significance: Our findings demonstrate that PHPT strongly influences gene regulation in fat tissue, which may result in altered adipose tissue function and release of pathogenic factors that increase the risk of CVD