Second-Generation Objects in the Universe: Radiative Cooling and Collapse of Halos with Virial Temperatures Above 10^4 Kelvin

The first generation of protogalaxies likely formed out of primordial gas via H2-cooling in cosmological minihalos with virial temperatures of a few 1000K. However, their abundance is likely to have been severely limited by feedback processes which suppressed H2 formation. The formation of the protogalaxies responsible for reionization and metal-enrichment of the intergalactic medium, then had to await the collapse of larger halos. Here we investigate the radiative cooling and collapse of gas in halos with virial temperatures Tvir > 10^4K. In these halos, efficient atomic line radiation allows rapid cooling of the gas to 8000 K; subsequently the gas can contract nearly isothermally at this temperature. Without an additional coolant, the gas would likely settle into a locally gravitationally stable disk; only disks with unusually low spin would be unstable. However, we find that the initial atomic line cooling leaves a large, out-of-equilibrium residual free electron fraction. This allows the molecular fraction to build up to a universal value of about x(H2) = 10^-3, almost independently of initial density and temperature. We show that this is a non--equilibrium freezeout value that can be understood in terms of timescale arguments. Furthermore, unlike in less massive halos, H2 formation is largely impervious to feedback from external UV fields, due to the high initial densities achieved by atomic cooling. The H2 molecules cool the gas further to about 100K, and allow the gas to fragment on scales of a few 100 Msun. We investigate the importance of various feedback effects such as H2-photodissociation from internal UV fields and radiation pressure due to Ly-alpha photon trapping, which are likely to regulate the efficiency of star formation.Comment: Revised version accepted by ApJ; some reorganization for clarit

MC-Simulation of the Transverse Double Spin Asymmetry for RHIC

Using {\sc Sphinx tt}, a new MC simulation program for transverse polarized nucleon--nucleon scattering based on {\sc Pythia~5.6}, we calculate the transverse double spin asymmetry $A^{TT}$ in the Drell-Yan process. If one assumes (quite arbitrarily) that the transversity parton distribution $\delta q(x,Q^2)$ equals the helicity distribution $\Delta q(x,Q^2)$ at some low $Q_0^2$ scale, the resulting asymmetry is of order 1\%. In this case is $A^{TT}$ would hardly be be measurable with PHENIX at RHIC.Comment: 17 pages, 5 figure

Living biointerfaces based on non-pathogenic bacteria to direct cell differentiation

Genetically modified Lactococcus lactis, non-pathogenic bacteria expressing the FNIII7-10 fibronectin fragment as a protein membrane have been used to create a living biointerface between synthetic materials and mammalian cells. This FNIII7-10 fragment comprises the RGD and PHSRN sequences of fibronectin to bind α5β1 integrins and triggers signalling for cell adhesion, spreading and differentiation. We used L. lactis strain to colonize material surfaces and produce stable biofilms presenting the FNIII7-10 fragment readily available to cells. Biofilm density is easily tunable and remains stable for several days. Murine C2C12 myoblasts seeded over mature biofilms undergo bipolar alignment and form differentiated myotubes, a process triggered by the FNIII7-10 fragment. This biointerface based on living bacteria can be further modified to express any desired biochemical signal, establishing a new paradigm in biomaterial surface functionalisation for biomedical applications

Eradication of chronic myeloid leukemia stem cells: a novel mathematical model predicts no therapeutic benefit of adding G-CSF to imatinib

Imatinib mesylate induces complete cytogenetic responses in patients with chronic myeloid leukemia (CML), yet many patients have detectable BCR-ABL transcripts in peripheral blood even after prolonged therapy. Bone marrow studies have shown that this residual disease resides within the stem cell compartment. Quiescence of leukemic stem cells has been suggested as a mechanism conferring insensitivity to imatinib, and exposure to the Granulocyte-Colony Stimulating Factor (G-CSF), together with imatinib, has led to a significant reduction in leukemic stem cells in vitro. In this paper, we design a novel mathematical model of stem cell quiescence to investigate the treatment response to imatinib and G-CSF. We find that the addition of G-CSF to an imatinib treatment protocol leads to observable effects only if the majority of leukemic stem cells are quiescent; otherwise it does not modulate the leukemic cell burden. The latter scenario is in agreement with clinical findings in a pilot study administering imatinib continuously or intermittently, with or without G-CSF (GIMI trial). Furthermore, our model predicts that the addition of G-CSF leads to a higher risk of resistance since it increases the production of cycling leukemic stem cells. Although the pilot study did not include enough patients to draw any conclusion with statistical significance, there were more cases of progression in the experimental arms as compared to continuous imatinib. Our results suggest that the additional use of G-CSF may be detrimental to patients in the clinic

Unemployment of Non-Western Immigrants in the Great Recession

Abstract: This paper examines whether unemployment of non-western immigrant workers in the Netherlands was disproportionally affected by the Great Recession. We analyze unemployment data covering the period November 2007 to February 2013 finding that the Great Recession affected unemployment rates of non-western immigrant workers in absolute terms more than unemployment rates of native workers. However, in relative terms there is not much of a difference. We also find that the sensitivity of individual job finding rates to the aggregate state of the labor market does not differ between natives and non-western immigrants. In combination our findings suggest that the Great Recession did not have a different impact on the unemployment of non-westerns immigrants and native Dutch.

The Molecular Clockwork of the Fire Ant Solenopsis invicta

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Ponatinib for Treating Acute Lymphoblastic Leukaemia: An Evidence Review Group Perspective of a NICE Single Technology Appraisal

As part of its single technology appraisal (STA) process, the UK National Institute for Health and Care Excellence (NICE) invited the manufacturer (Incyte Corporation) of ponatinib (Inclusig®) to submit evidence of its clinical and cost effectiveness for previously treated Philadelphia-chromosome-positive acute lymphoblastic leukaemia (Ph+ ALL) and chronic myeloid leukaemia. This paper focusses on Ph+ ALL. The School of Health and Related Research Technology Appraisal Group at the University of Sheffield was commissioned to act as the independent evidence review group (ERG). This article presents the critical review of the company's submission by the ERG and the outcome of the NICE guidance. The clinical-effectiveness evidence in the company's submission was derived from a phase II, single-arm, open-label, non-comparative study. Given the lack of comparative evidence, a naïve indirect comparison was performed against re-induction chemotherapy comparing major cytogenetic response and complete remission. Best supportive care (BSC) was assumed to produce no disease response. Despite the limited evidence and potential for biases, this study demonstrated that ponatinib was likely to be an effective treatment for patients with Ph+ ALL. The company submitted a state transition model that analysed the incremental cost effectiveness of ponatinib versus re-induction therapy and BSC for the treatment of Ph+ ALL in patients whose disease is resistant to dasatinib, who are intolerant to dasatinib and for whom subsequent treatment with imatinib is not clinically appropriate or who have the threonine-315-isoleucine mutation. This population was further subdivided into those who were suitable for allogeneic stem cell transplant (allo-SCT) and those who were not. The company's revised economic evaluation, following the clarification process, estimated incremental cost-effectiveness ratios (ICERs) in those suitable for allo-SCT of £31,123 per quality-adjusted life-year (QALY) gained for ponatinib compared with re-induction chemotherapy and £26,624 per QALY gained compared with BSC. For those for whom allo-SCT was unsuitable, the company-estimated ICER compared with BSC was £33,954 per QALY gained. Following a critique of the model, the ERG undertook exploratory analyses that, when combined, produced a range in ICERs (due to uncertainty of the most appropriate overall survival function) of dominant (being less expensive and providing more QALYs) to £11,727 per QALY gained compared with re-induction chemotherapy and between £7892 and £31,696 per QALY gained compared with BSC for those in whom allo-SCT was suitable. For those in whom allo-SCT was not suitable, the ERG estimated that ponatinib was dominant. During the consultation period, the company agreed a revised patient access scheme (PAS) that reduced the ICER ranges to £7156 to £29,995 per QALY gained versus BSC and to less than £5000 per QALY gained versus re-induction chemotherapy. In people for whom allo-SCT was unsuitable, ponatinib dominated BSC. The NICE appraisal committee concluded that ponatinib is a cost-effective use of UK NHS resources in the considered population, subject to the company providing the agreed discount in the PAS

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

A method for identifying genetic heterogeneity within phenotypically defined disease subgroups.

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.We acknowledge the help of the Diabetes and Inflammation Laboratory Data Service for access and quality control procedures on the data sets used in this study. The JDRF/Wellcome Trust Diabetes and Inflammation Laboratory is in receipt of a Wellcome Trust Strategic Award (107212; J.A.T.) and receives funding from the NIHR Cambridge Biomedical Research Centre. J.L. is funded by the NIHR Cambridge Biomedical Research Centre and is on the Wellcome Trust PhD program in Mathematical Genomics and Medicine at the University of Cambridge. C.W. is funded by the MRC (grant MC_UP_1302/5). We thank M. Simmonds, S. Gough, J. Franklyn, and O. Brand for sharing their AITD genetic association data set and all patients with AITD and control subjects for participating in this study. The AITD UK national collection was funded by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript