A comparison of three materials used for tactile symbols to communicate colour to children and young people with visual impairments

A series of 14 tactile symbols were developed to represent different colours and shades for children and young people who are blind or have visual impairment. A study compared three different methods for representing the symbols: (1) embroidered thread, (2) heated ‘swell’ paper, and (3) representation in plastic using Additive Manufacturing (AM; three-dimensional printing). The results show that for all three materials, the recognition of particular symbols varied between 2.40 and 3.95 s. The average times for the three materials across all colours were 2.26 s for AM material, 3.20 s for swell paper, and 4.03 s for embroidered symbols. These findings can be explained by the fact that the AM material (polylactide) is firmer and more easily perceived tactually than the other two materials. While AM plastic offers a potentially useful means to communicate colours for appropriate objects, traditional media are still important in certain contexts

Before the Pandemic Ends: Making Sure This Never Happens Again

Introduction On 30 January 2020, the World Health Organization (WHO) declared a Global Health Emergency of international concern attendant to the emergence and spread of SARS-CoV-2, nearly two months after the first reported emergence of human cases in Wuhan, China. In the subsequent two months, global, national and local health personnel and infrastructures have been overwhelmed, leading to suffering and death for infected people, and the threat of socio-economic instability and potential collapse for humanity as a whole. This shows that our current and traditional mode of coping, anchored in responses after the fact, is not capable of dealing with the crisis of emerging infectious disease. Given all of our technological expertise, why is there an emerging disease crisis, and why are we losing the battle to contain and diminish emerging diseases? Part of the reason is that the prevailing paradigm explaining the biology of pathogen-host associations (coevolution, evolutionary arms races) has assumed that pathogens must evolve new capacities - special mutations – in order to colonize new hosts and produce emergent disease (e.g. Parrish and Kawaoka, 2005). In this erroneous but broadly prevalent view, the evolution of new capacities creates new opportunities for pathogens. Further, given that mutations are both rare and undirected, the highly specialized nature of pathogen-host relationships should produce an evolutionary firewall limiting dissemination; by those definitions, emergences should be rare (for a historical review see Brooks et al., 2019). Pathogens, however, have become far better at finding us than our traditional understanding predicts. We face considerable risk space for pathogens and disease that directly threaten us, our crops and livestock – through expanding interfaces bringing pathogens and hosts into increasing proximity, exacerbated by environmental disruption and urban density, fueled by globalized trade and travel. We need a new paradigm that explains what we are seeing. Additional section headers: The Stockholm Paradigm The DAMA Protocol A Sense of Urgency and Long-Term Commitment Reference

The ATP-Binding Cassette Proteins of the Deep-Branching Protozoan Parasite Trichomonas vaginalis

The ATP binding cassette (ABC) proteins are a family of membrane transporters and regulatory proteins responsible for diverse and critical cellular process in all organisms. To date, there has been no attempt to investigate this class of proteins in the infectious parasite Trichomonas vaginalis. We have utilized a combination of bioinformatics, gene sequence analysis, gene expression and confocal microscopy to investigate the ABC proteins of T. vaginalis. We demonstrate that, uniquely among eukaryotes, T. vaginalis possesses no intact full-length ABC transporters and has undergone a dramatic expansion of some ABC protein sub-families. Furthermore, we provide preliminary evidence that T. vaginalis is able to read through in-frame stop codons to express ABC transporter components from gene pairs in a head-to-tail orientation. Finally, with confocal microscopy we demonstrate the expression and endoplasmic reticulum localization of a number of T. vaginalis ABC transporters

Lentiviral Hematopoietic Stem Cell Gene Therapy Benefits Metachromatic Leukodystrophy

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Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Differential methylation relative to breast cancer subtype and matched normal tissue reveals distinct patterns

Due to the heterogeneous nature of breast cancer and the widespread use of single-gene studies, there is limited knowledge of multi-gene, locus-specific DNA methylation patterns in relation to molecular subtype and clinical features. We, therefore, quantified DNA methylation of 70 candidate gene loci in 140 breast tumors and matched normal tissues and determined associations with gene expression and tumor subtype. Using Sequenom’s EpiTYPER platform, approximately 1,200CpGs were interrogated and revealed six DNA methylation patterns in breast tumors relative to matched normal tissue. Differential methylation of several gene loci was observed within all molecular subtypes, while other patterns were subtype-dependent. Methylation of numerous gene loci was inversely correlated with gene expression, and in some cases, this correlation was only observed within specific breast tumor subtypes. Our findings were validated on a larger set of tumors and matched adjacent normal tissue from The Cancer Genome Atlas dataset, which utilized methylation data derived from both Illumina Infinium 27 and 450k arrays. These findings highlight the need to control for subtype when interpreting DNA methylation results, and the importance of interrogating multiple CpGs across varied gene regions.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-013-2738-0) contains supplementary material, which is available to authorized users

Oral abstracts 3: RA Treatment and outcomesO13. Validation of jadas in all subtypes of juvenile idiopathic arthritis in a clinical setting

Background: Juvenile Arthritis Disease Activity Score (JADAS) is a 4 variable composite disease activity (DA) score for JIA (including active 10, 27 or 71 joint count (AJC), physician global (PGA), parent/child global (PGE) and ESR). The validity of JADAS for all ILAR subtypes in the routine clinical setting is unknown. We investigated the construct validity of JADAS in the clinical setting in all subtypes of JIA through application to a prospective inception cohort of UK children presenting with new onset inflammatory arthritis. Methods: JADAS 10, 27 and 71 were determined for all children in the Childhood Arthritis Prospective Study (CAPS) with complete data available at baseline. Correlation of JADAS 10, 27 and 71 with single DA markers was determined for all subtypes. All correlations were calculated using Spearman's rank statistic. Results: 262/1238 visits had sufficient data for calculation of JADAS (1028 (83%) AJC, 744 (60%) PGA, 843 (68%) PGE and 459 (37%) ESR). Median age at disease onset was 6.0 years (IQR 2.6-10.4) and 64% were female. Correlation between JADAS 10, 27 and 71 approached 1 for all subtypes. Median JADAS 71 was 5.3 (IQR 2.2-10.1) with a significant difference between median JADAS scores between subtypes (p < 0.01). Correlation of JADAS 71 with each single marker of DA was moderate to high in the total cohort (see Table 1). Overall, correlation with AJC, PGA and PGE was moderate to high and correlation with ESR, limited JC, parental pain and CHAQ was low to moderate in the individual subtypes. Correlation coefficients in the extended oligoarticular, rheumatoid factor negative and enthesitis related subtypes were interpreted with caution in view of low numbers. Conclusions: This study adds to the body of evidence supporting the construct validity of JADAS. JADAS correlates with other measures of DA in all ILAR subtypes in the routine clinical setting. Given the high frequency of missing ESR data, it would be useful to assess the validity of JADAS without inclusion of the ESR. Disclosure statement: All authors have declared no conflicts of interest. Table 1Spearman's correlation between JADAS 71 and single markers DA by ILAR subtype ILAR Subtype Systemic onset JIA Persistent oligo JIA Extended oligo JIA Rheumatoid factor neg JIA Rheumatoid factor pos JIA Enthesitis related JIA Psoriatic JIA Undifferentiated JIA Unknown subtype Total cohort Number of children 23 111 12 57 7 9 19 7 17 262 AJC 0.54 0.67 0.53 0.75 0.53 0.34 0.59 0.81 0.37 0.59 PGA 0.63 0.69 0.25 0.73 0.14 0.05 0.50 0.83 0.56 0.64 PGE 0.51 0.68 0.83 0.61 0.41 0.69 0.71 0.9 0.48 0.61 ESR 0.28 0.31 0.35 0.4 0.6 0.85 0.43 0.7 0.5 0.53 Limited 71 JC 0.29 0.51 0.23 0.37 0.14 -0.12 0.4 0.81 0.45 0.41 Parental pain 0.23 0.62 0.03 0.57 0.41 0.69 0.7 0.79 0.42 0.53 Childhood health assessment questionnaire 0.25 0.57 -0.07 0.36 -0.47 0.84 0.37 0.8 0.66 0.4

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages