Quantum Well Intermixing in 2 μm InGaAs Multiple Quantum Well structures

Quantum well intermixing in 2μm emitting structures is presented for the first time. A photoluminescence and electroluminescence differential shift of 160nm is achieved between SiNx and SiO2 capped regions demonstrating potential for monolithic integration

Human and mouse essentiality screens as a resource for disease gene discovery

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery. Discovery of causal variants for monogenic disorders has been facilitated by whole exome and genome sequencing, but does not provide a diagnosis for all patients. Here, the authors propose a Full Spectrum of Intolerance to Loss-of-Function (FUSIL) categorization that integrates gene essentiality information to aid disease gene discovery

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Second nature? : the socio-spatial production of disability

Social inequalities associated with disability are a disturbing feature of contemporary Western societies. The pervasiveness of this structural oppression means that millions of lives are overshadowed by disablement. This study sets out to situate this fact theoretically, historically, and geographically. Broadly speaking, disability is the socially imposed state of exclusion which physically impaired individuals may be forced to endure. Such a view contrasts with popular, or common sense, understandings which see the experience of disablement as `second nature' to impaired people. An important claim of the thesis is that disability is a socio-spatial oppression which social theory must no longer ignore. Further, historical materialism provides the explanatory foundations for a social theory of disability. It is asserted from the outset that the form of historical materialism needed to achieve this task is one which takes the human body and space to be central theoretical considerations. Accordingly, the study uses a spatially-focused historical materialism to analyse the question of disability, and does this through carefully designed empirical case studies of the everyday experience of disablement in different times and places. The study asks the question: How have changes in the socio-spatial organisation of society affected the lived experience of physical impairment? A response is made in the form of a comparative analysis of the lived experience of impairment in feudal England and colonial (nineteenth-century) Melbourne. Five important data sets exist which relate to the experience of impairment in both societies, and these are consulted in the course of the study. The most substantial empirical resource is the set of case records (1850-1900) of the Melbourne Ladies' Benevolent Society, an important philanthropic organisation which operated in colonial Melbourne. The research demonstrates that socio-spatial changes affect the lived experience of impairment by transforming the material structures of everyday life. It is argued that past transformations in the mode of production have had profound social consequences for physically impaired people. In particular, the analysis shows that the socio-spatial organisation of industrial capitalism was an oppressive source of disablement for physically impaired people. The study concludes that a transformation in the present mode of production (capitalism) is a necessary first step towards ending the oppression of disability

An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland’s pandemic response ✩

The COVID-19 pandemic presented an immediate need for the Irish Government to establish modelling capacity in order to inform public health decision making. A broad-based interdisciplinary team was created at short notice, drawing together related expertise from the academic and health sectors. This paper documents one of a number of modelling solutions developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advised the Irish Government on COVID-19 responses during the pandemic. The model inputs included surveillance data, epidemiological data, demographic data, vaccination schedules, vaccine efficacy estimates, estimates of social contacts, and new variant data. Outputs from the model supported policy discussions, including: decisions on the timing of public health restrictions, simulating the effects of school reopening on overall disease transmission, exploring the impact of vaccination across different age cohorts, and generating scenarios on the plausible impact on cases caused by the Omicron variant. An innovative aspect of the solution was the use of a modular design, with three benefits: (1) it enabled a simplification of the disease transmission structure; (2) it provided a practical workflow to coordinate activities; and (3) it speeded up the process of scenario generation and the requirement to provide timely and informative scenario analysis to support Ireland’s pandemic response. Given the paper’s applied and practical focus, it presents a record of modelling and scenario outputs as they were developed, presented and deployed during the actual outbreak - therefore all simulation results and scenarios are documented “as they happened”, and without the benefit of hindsight.</p

Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effective contact rates

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’