Transport planning for health : explaining and evaluating barriers and opportunities to intersectoral collaboration

The establishment in the 1980s of a European Health for All strategy and set of targets has implications for transport planning policy across Europe. Potentially, Health for All provides a means by which transport planning can better promote public health. Key to this is the ability of local Health for All projects to develop effective intersectoral collaboration with transport planning professionals. The progress of Health for All in influencing priorities in transport planning is explored through a comparative study of Health for All collaborating cities in Denmark, the Netherlands and England between 1986 and 1996. Three cities are used as case studies: Copenhagen, Groningen, and Sheffield. The study examines the extent to which transport policies have been influenced by Health for All strategies and targets. Archival and interview data are used to explore the nature and degree of intersectoral collaboration between Health for All projects and transport planning departments. The research contributes to knowledge about how Health for All can influence transport planning in promoting health, an issue largely neglected in the literature. There are similarities and differences between Health for All projects studied in the way that they have sought to develop intersectoral collaboration on transport issues. The research highlights common barriers to effective collaboration, but also how Health for All projects could develop collaborative initiatives. It indicates that health needs to be translated into values such as quality of life, equity, and environmental protection, found to be policy drivers within transport planning. Quality of life and equity are identified in Health for All targets but were not drawn on sufficiently by health promoters. The study findings also underscore the importance of national policy frameworks on both health and transport which can provide common ground between the two sectors. The most successful city Health for All project was characterised by concerns for environmental protection and quality of life, with supportive national government frameworks for health promotion and transport planning

Mock galaxy redshift catalogues from simulations: implications for Pan-STARRS1

We describe a method for constructing mock galaxy catalogues which are well suited for use in conjunction with large photometric surveys. We use the semi-analytic galaxy formation model of Bower et al. implemented in the Millennium simulation. We apply our method to the specific case of the surveys soon to commence with PS1, the first of 4 telescopes planned for the Pan-STARRS system. PS1 has 5 photometric bands (grizy), and will carry out an all-sky 3pi survey and a medium deep survey (MDS) over 84 sq.deg. We calculate the expected magnitude limits for extended sources in the two surveys. We find that, after 3 years, the 3pi survey will have detected over 10^8 galaxies in all 5 bands, 10 million of which will lie at redshift z>0.9, while the MDS will have detected over 10^7 galaxies with 0.5 million lying at z>2. These numbers at least double if detection in the shallowest band, y is not required. We then evaluate the accuracy of photometric redshifts estimated using an off-the-shelf photo-z code. With the grizy bands alone it is possible to achieve an accuracy in the 3pi survey of Delta z/(1+z)~0.06 for 0.25<z<0.8, which could be reduced by about 15% using near infrared photometry from the UKIDDS survey, but would increase by about 25% for the deeper sample without the y band photometry. For the MDS an accuracy of Delta z/(1+z)~0.05 is achievable for 0.02<z<1.5 using grizy. A dramatic improvement in accuracy is possible by selecting only red galaxies. In this case, Delta z/(1+z)~0.02-0.04 is achievable for ~100 million galaxies at 0.4<z<1.1 in the 3pi survey and for 30 million galaxies in the MDS at 0.4<z<2. We investigate the effect of using photo-z in the estimate of the baryonic acoustic oscillation scale. We find that PS1 will achieve a similar accuracy in this estimate as a spectroscopic survey of 20 million galaxies.Comment: 23 pages, 18 figures, accepted by MNRA

Communications Biophysics

Contains research objectives and summary of research.National Institutes of Health (Grant 5 PO1 GM14940-07)National Institutes of Health (Grant 1 RO1 NS11000-01)Clarence J. LeBel FundNational Institutes of Health (Grant 1 RO1 NS10737-01)National Aeronautics and Space Administration (Grant NGL 22-009-304)Boston City Hospital Purchase Order 1176-21335B-D Electrodyne Division, Becton Dickinson and Company (Grant)Chicago Musical Instrument Company (Grant

pygwb: Python-based library for gravitational-wave background searches

The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the Universe and the population of GW sources within it. We present a new, user-friendly Python--based package for gravitational-wave data analysis to search for an isotropic GWB in ground--based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one's own needs. We describe the individual modules which make up {\tt pygwb}, following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline which combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.Comment: 32 pages, 14 figure

Communications Biophysics

Contains research objectives and summary of research on thirteen research projects split into four section.National Institutes of Health (Grant 1 RO1 NS10737-01)National Institutes of Health (Grant 1 ROI NS10916-01)National Institutes of Health (Grant 5 RO1 NS11000-02)National Institutes of Health (Grant 1 RO1 NS11153-01)Harvard M.I.T. Rehabilitation Engineering CenterU. S. Department of Health, Education, and Welfare, Grant 23-P-55854National Institutes of Health (Grant 1 RO1 NS11680-01)Norlin Music, Inc.Clarence J. LeBel FundNational Institutes of Health (Grant 1 RO1 NS11080-01A1)National Institutes of Health (Grant 5 TO1 GM01555-08)M.I.T. Health Sciences FundBoston City Hospital Purchase Order 1176-05-21335-C

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment