Thermal Gravitino Production and Collider Tests of Leptogenesis

Considering gravitino dark matter scenarios, we obtain the full gauge-invariant result for the relic density of thermally produced gravitinos to leading order in the Standard Model gauge couplings. For the temperatures required by thermal leptogenesis, we find gaugino mass bounds which will be probed at future colliders. We show that a conceivable determination of the gravitino mass will allow for a unique test of the viability of thermal leptogenesis in the laboratory.Comment: 5 pages, 3 figures, revised version matches published versio

Confusions about ‘Inner’ and ‘Outer’ Voices: Conceptual Problems in the Study of Auditory Verbal Hallucinations

Both in research on Auditory Verbal Hallucinations (AVHs) and in their clinical assessment, it is common to distinguish between voices that are experienced as ‘inner’ (or ‘internal’, ‘inside the head’, ‘inside the mind’, ...) and voices that are experienced as ‘outer’ (‘external’, ‘outside the head’, ‘outside the mind’, ...). This inner/outer-contrast is treated not only as an important phenomenological variable of AVHs, it is also often seen as having diagnostic value. In this article, we argue that the distinction between ‘inner’ and ‘outer’ voices is ambiguous between different readings, and that lack of disambiguation in this regard has led to flaws in assessment tools, diagnostic debates and empirical studies. Such flaws, we argue furthermore, are often linked to misreadings of inner/outer-terminology in relevant 19th and early twentieth century work on AVHs, in particular, in connection with Kandinsky’s and Jaspers’s distinction between hallucinations and pseudo-hallucinations.publishedVersio

Metabolic control during the neonatal period in phenylketonuria:associations with childhood IQ

Background In phenylketonuria, treatment and subsequent lowering of phenylalanine levels usually occur within the first month of life. This study investigated whether different indicators of metabolic control during the neonatal period were associated with IQ during late childhood/early adolescence. Methods Overall phenylalanine concentration during the first month of life (total "area under the curve"), proportion of phenylalanine concentrations above upper target level (360 mu mol/L) and proportion below lower target level (120 mu mol/L) during this period, diagnostic phenylalanine levels, number of days until phenylalanine levels were 360 mu mol/L during the first month of life negatively correlated with IQ in late childhood/early adolescence. Separately, phenylalanine concentrations during different periods within the first month of life (0-10 days, 11-20 days, 21-30 days) were negatively correlated with later IQ as well, but correlation strengths did not differ significantly. No further significant associations were found. Conclusions In phenylketonuria, achievement of target-range phenylalanine levels during the neonatal period is important for cognition later in life, also when compared to other indicators of metabolic control. Impact In phenylketonuria, it remains unclear during which age periods or developmental stages metabolic control is most important for later cognitive outcomes. Phenylalanine levels during the neonatal period were clearly and negatively related to later IQ, whereas no significant associations were observed for other indices of metabolic control. This emphasizes the relative importance of this period for cognitive development in phenylketonuria. No further distinctions were observed in strength of associations with later IQ between different indicators of metabolic control during the neonatal period. Thus, achievement of good metabolic control within 1 month after birth appears "safe" with respect to later cognitive outcomes

Evaluating automatic LFG f-structure annotation for the Penn-II treebank

Lexical-Functional Grammar (LFG: Kaplan and Bresnan, 1982; Bresnan, 2001; Dalrymple, 2001) f-structures represent abstract syntactic information approximating to basic predicate-argument-modifier (dependency) structure or simple logical form (van Genabith and Crouch, 1996; Cahill et al., 2003a) . A number of methods have been developed (van Genabith et al., 1999a,b, 2001; Frank, 2000; Sadler et al., 2000; Frank et al., 2003) for automatically annotating treebank resources with LFG f-structure information. Until recently, however, most of this work on automatic f-structure annotation has been applied only to limited data sets, so while it may have shown lsquoproof of conceptrsquo, it has not yet demonstrated that the techniques developed scale up to much larger data sets. More recent work (Cahill et al., 2002a,b) has presented efforts in evolving and scaling techniques established in these previous papers to the full Penn-II Treebank (Marcus et al., 1994). In this paper, we present a number of quantitative and qualitative evaluation experiments which provide insights into the effectiveness of the techniques developed to automatically derive a set of f-structures for the more than 1,000,000 words and 49,000 sentences of Penn-II. Currently we obtain 94.85% Precision, 95.4% Recall and 95.09% F-Score for preds-only f-structures against a manually encoded gold standard

FuGEFlow: data model and markup language for flow cytometry

<p>Abstract</p> <p>Background</p> <p>Flow cytometry technology is widely used in both health care and research. The rapid expansion of flow cytometry applications has outpaced the development of data storage and analysis tools. Collaborative efforts being taken to eliminate this gap include building common vocabularies and ontologies, designing generic data models, and defining data exchange formats. The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard was recently adopted by the International Society for Advancement of Cytometry. This standard guides researchers on the information that should be included in peer reviewed publications, but it is insufficient for data exchange and integration between computational systems. The Functional Genomics Experiment (FuGE) formalizes common aspects of comprehensive and high throughput experiments across different biological technologies. We have extended FuGE object model to accommodate flow cytometry data and metadata.</p> <p>Methods</p> <p>We used the MagicDraw modelling tool to design a UML model (Flow-OM) according to the FuGE extension guidelines and the AndroMDA toolkit to transform the model to a markup language (Flow-ML). We mapped each MIFlowCyt term to either an existing FuGE class or to a new FuGEFlow class. The development environment was validated by comparing the official FuGE XSD to the schema we generated from the FuGE object model using our configuration. After the Flow-OM model was completed, the final version of the Flow-ML was generated and validated against an example MIFlowCyt compliant experiment description.</p> <p>Results</p> <p>The extension of FuGE for flow cytometry has resulted in a generic FuGE-compliant data model (FuGEFlow), which accommodates and links together all information required by MIFlowCyt. The FuGEFlow model can be used to build software and databases using FuGE software toolkits to facilitate automated exchange and manipulation of potentially large flow cytometry experimental data sets. Additional project documentation, including reusable design patterns and a guide for setting up a development environment, was contributed back to the FuGE project.</p> <p>Conclusion</p> <p>We have shown that an extension of FuGE can be used to transform minimum information requirements in natural language to markup language in XML. Extending FuGE required significant effort, but in our experiences the benefits outweighed the costs. The FuGEFlow is expected to play a central role in describing flow cytometry experiments and ultimately facilitating data exchange including public flow cytometry repositories currently under development.</p

Breaking tolerance to the natural human liver autoantigen cytochrome P450 2D6 by virus infection

Autoimmune liver diseases, such as autoimmune hepatitis (AIH) and primary biliary cirrhosis, often have severe consequences for the patient. Because of a lack of appropriate animal models, not much is known about their potential viral etiology. Infection by liver-tropic viruses is one possibility for the breakdown of self-tolerance. Therefore, we infected mice with adenovirus Ad5 expressing human cytochrome P450 2D6 (Ad-2D6). Ad-2D6–infected mice developed persistent autoimmune liver disease, apparent by cellular infiltration, hepatic fibrosis, “fused” liver lobules, and necrosis. Similar to type 2 AIH patients, Ad-2D6–infected mice generated type 1 liver kidney microsomal–like antibodies recognizing the immunodominant epitope WDPAQPPRD of cytochrome P450 2D6 (CYP2D6). Interestingly, Ad-2D6–infected wild-type FVB/N mice displayed exacerbated liver damage when compared with transgenic mice expressing the identical human CYP2D6 protein in the liver, indicating the presence of a stronger immunological tolerance in CYP2D6 mice. We demonstrate for the first time that infection with a virus expressing a natural human autoantigen breaks tolerance, resulting in a chronic form of severe, autoimmune liver damage. Our novel model system should be instrumental for studying mechanisms involved in the initiation, propagation, and precipitation of virus-induced autoimmune liver diseases

Results from the CERN pilot CLOUD experiment

During a 4-week run in October–November 2006, a pilot experiment was performed at the CERN Proton Synchrotron in preparation for the Cosmics Leaving OUtdoor Droplets (CLOUD) experiment, whose aim is to study the possible influence of cosmic rays on clouds. The purpose of the pilot experiment was firstly to carry out exploratory measurements of the effect of ionising particle radiation on aerosol formation from trace H2SO4 vapour and secondly to provide technical input for the CLOUD design. A total of 44 nucleation bursts were produced and recorded, with formation rates of particles above the 3 nm detection threshold of between 0.1 and 100 cm -3 s -1, and growth rates between 2 and 37 nm h -1. The corresponding H2O concentrations were typically around 106 cm -3 or less. The experimentally-measured formation rates and htwosofour concentrations are comparable to those found in the atmosphere, supporting the idea that sulphuric acid is involved in the nucleation of atmospheric aerosols. However, sulphuric acid alone is not able to explain the observed rapid growth rates, which suggests the presence of additional trace vapours in the aerosol chamber, whose identity is unknown. By analysing the charged fraction, a few of the aerosol bursts appear to have a contribution from ion-induced nucleation and ion-ion recombination to form neutral clusters. Some indications were also found for the accelerator beam timing and intensity to influence the aerosol particle formation rate at the highest experimental SO2 concentrations of 6 ppb, although none was found at lower concentrations. Overall, the exploratory measurements provide suggestive evidence for ion-induced nucleation or ion-ion recombination as sources of aerosol particles. However in order to quantify the conditions under which ion processes become significant, improvements are needed in controlling the experimental variables and in the reproducibility of the experiments. Finally, concerning technical aspects, the most important lessons for the CLOUD design include the stringent requirement of internal cleanliness of the aerosol chamber, as well as maintenance of extremely stable temperatures (variations below 0.1 °C

The class reconstruction number of maximal planar graphs

The reconstruction number rn(G) of a graph G was introduced by Harary and Plantholt as the smallest number of vertex-deleted subgraphs G i = G − v i in the deck of G which do not all appear in the deck of any other graph. For any graph theoretic property P , Harary defined the P -reconstruction number of a graph G ∈ P as the smallest number of the G i in the deck of G , which do not all appear in the deck of any other graph in P We now study the maximal planar graph reconstruction number ℳrn(G) , proving that its value is either 1 or 2 and characterizing those with value 1.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41581/1/373_2005_Article_BF01788528.pd

Lactation and neonatal nutrition: defining and refining the critical questions.

This paper resulted from a conference entitled "Lactation and Milk: Defining and refining the critical questions" held at the University of Colorado School of Medicine from January 18-20, 2012. The mission of the conference was to identify unresolved questions and set future goals for research into human milk composition, mammary development and lactation. We first outline the unanswered questions regarding the composition of human milk (Section I) and the mechanisms by which milk components affect neonatal development, growth and health and recommend models for future research. Emerging questions about how milk components affect cognitive development and behavioral phenotype of the offspring are presented in Section II. In Section III we outline the important unanswered questions about regulation of mammary gland development, the heritability of defects, the effects of maternal nutrition, disease, metabolic status, and therapeutic drugs upon the subsequent lactation. Questions surrounding breastfeeding practice are also highlighted. In Section IV we describe the specific nutritional challenges faced by three different populations, namely preterm infants, infants born to obese mothers who may or may not have gestational diabetes, and infants born to undernourished mothers. The recognition that multidisciplinary training is critical to advancing the field led us to formulate specific training recommendations in Section V. Our recommendations for research emphasis are summarized in Section VI. In sum, we present a roadmap for multidisciplinary research into all aspects of human lactation, milk and its role in infant nutrition for the next decade and beyond

Interaction testing and polygenic risk scoring to estimate the association of common genetic variants with treatment resistance in schizophrenia

Importance About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n=10 501) and individuals with non-TRS (n=20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r² = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r² = 1.09%; P = .04). Conclusions and Relevance In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.Funding/Support: This work was supported by Medical Research Council Centre grant MR/ L010305/1, Medical Research Council Program grant MR/P005748/1, and Medical Research Council Project grants MR/L011794/1 and MC_PC_17212 to Cardiff University and by the National Centre for Mental Health, funded by the Welsh Government through Health and Care Research Wales. This work acknowledges the support of the Supercomputing Wales project, which is partially funded by the European Regional Development Fund via the Welsh Government. Dr Pardiñas was supported by an Academy of Medical Sciences Springboard Award (SBF005\1083). Dr Andreassen was supported by the Research Council of Norway (grants 283798, 262656, 248980, 273291, 248828, 248778, and 223273); KG Jebsen Stiftelsen, South-East Norway Health Authority, and the European Union’s Horizon 2020 Research and Innovation Programme (grant 847776). Dr Ajnakina was supported by an National Institute for Health Research postdoctoral fellowship (PDF-2018-11-ST2-020). Dr Joyce was supported by the University College London Hospitals/UCL University College London Biomedical Research Centre. Dr Kowalec received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement (793530) from the government of Canada Banting postdoctoral fellowship programme and the University of Manitoba. Dr Sullivan was supported by the Swedish Research Council (Vetenskapsrådet, D0886501), the European Union’s Horizon 2020 programme (COSYN, 610307) and the US National Institute of Mental Health (U01 MH109528 and R01 MH077139). The Psychiatric Genomics Consortium was partly supported by the National Institute Of Mental Health (grants R01MH124873). The Sweden Schizophrenia Study was supported by the National Institute Of Mental Health (grant R01MH077139). The STRATA consortium was supported by a Stratified Medicine Programme grant to Dr MacCabe from the Medical Research Council (grant MR/L011794/1), which funded the research and supported Drs Pardiñas, Smart, Kassoumeri, Murray, Walters, and MacCabe. Dr Smart was supported by a Collaboration for Leadership in Applied Health Research and Care South London at King’s College Hospital National Health Service Foundation Trust. The AESOP (US) cohort was funded by the UK Medical Research Council (grant G0500817). The Belfast (UK) cohort was funded by the Research and Development Office of Northern Ireland. The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Genetics and Psychosis project (London, UK) cohort was funded by the UK National Institute of Health Research Specialist Biomedical Research Centre for Mental Health, South London and the Maudsley National Health Service Mental Health Foundation Trust (SLAM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework program (HEALTH-F2-2009-241909, project EU-GEI). The Lausanne (Switzerland) cohort was funded by the Swiss National Science Foundation (grants 320030_135736/1, 320030-120686, 324730-144064, 320030-173211, and 171804); the National Center of Competence in Research Synaptic Bases of Mental Diseases from the Swiss National Science Foundation (grant 51AU40_125759); and Fondation Alamaya. The Oslo (Norway) cohort was funded by the Research Council of Norway (grant 223273/F50, under the Centers of Excellence funding scheme, 300309, 283798) and the South-Eastern Norway Regional Health Authority (grants 2006233, 2006258, 2011085, 2014102, 2015088, and 2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework program (HEALTH-F2-2010–241909, project EU-GEI). The Prague (Czech Republic) cohort was funded by the Ministry of Health of the Czech Republic (grant NU20-04-00393). The Santander (Spain) cohort was funded by the following grants to Dr Crespo-Facorro: Instituto de Salud Carlos III (grants FIS00/3095, PI020499, PI050427, and PI060507), Plan Nacional de Drogas Research (grant 2005-Orden sco/3246/2004), SENY Fundatio Research (grant 2005-0308007), Fundacion Marques de Valdecilla (grant A/02/07, API07/011) and Ministry of Economy and Competitiveness and the European Fund for Regional Development (grants SAF2016-76046-R and SAF2013-46292-R). The West London (UK) cohort was funded by The Wellcome Trust (grants 042025, 052247, and 064607)