Developing a Framework for Automated Scenario- Based e-Learning Design

Scenario-based e-learning can be used to enable students to develop expertise, in situations that are rare or infrequent, too hazardous for the inexperienced, too expensive to permit practice and failure, or simply not available. Developing automated courses requires significant technical ability, separate from the subject expertise of the educator. This paper introduces a framework developed to simplify this process, along with a scenario authoring and cloud-based training environment, Pandora

An analysis of the features of successful written submissions to government inquiries

Objective: Government inquiries present a policy window for advocates to influence policy. Evidence on how to write influential submissions, however, is sparse. We aimed to identify features of successful written submissions to the Parliament of Australia's Inquiry into Food Pricing and Food Security in Remote Indigenous Communities (Inquiry). Method: A scoping review was conducted to identify influential features of written submissions to government inquiries. A content analysis of a sub-sample of government Inquiry submissions and their recommendations was then coded for influential features. The frequency of submission recommendations incorporated into the final Inquiry report was recorded, as was their link to influential features. Results: Thirty features were identified. Results from 21 submissions indicate that when writing a submission to a government inquiry, advocates should: (1) ensure their submission is clear and concise; (2) convey the authority of both the writer and supporting evidence; and (3) where possible, align submission recommendations with the government agenda. Conclusions: We encourage future research to test the framework of influential features on other inquiry topics and in other countries to increase the reliability of results. Implications for Public Health: This study consolidates and presents a list of features that advocates can consider incorporating when writing a submission to a government inquiry.</p

TCR Translocations at the Normal-malignant T Cell Interface

Hematopoiesis is the process leading to production and maturation of peripheral blood cells. All blood cells are derived from hematopoietic stem cells (HSCs) which reside in hematopoietic organs. In mammals, the site of hematopoiesis changes during development, which is sequentially taking place in different organs starting with primitive erythrocytes in the yolk sac, the aorta-gonad mesonephros (AGM) region, the fetal lever, and finally the bone marrow (BM) during adulthood. Blood cells are short-lived, and with a daily demand for more than a billion new hematopoietic cells, a continuous replenishment of progenitor cells committed to specific hematopoietic lineages is required. HSCs are at the top of the hematopoietic hierarchy, and are the only source of progenitors. HSCs comprise 0.005-0.01% of the bone marrow, and their unique properties, i.e. the ability of self-renewal and multi-lineage differentiation potential in combination with a specific stem cell microenvironment/ niche, enable these cells to sustain the hematopoietic system. These cells differentiate into progenitor cells, either into common lymphoid progenitors (CLP) or common myeloid progenitors (CMP), which in due course differentiate into mature blood cells, providing cells to the myeloid or lymphoid system respectively 6. CLPs carry the potential to give rise to B cells, T cells (via the thymus) and NK cells, whereas CMPs have the potential to differentiate into erythrocytes, megakaryocytes, macrophages, and granulocytes. Dendritic cells can arise from both progenitor types. The process of hematopoietic lineage determination is tightly regulated by the BM microenvironment’s extrinsic factors, such as growth factors and cytokines mediated by cell-cell interactions, which sustain survival and proliferation of committed cells. Equally important in determining cell fate are the lineage- and cell-type-specific gene expression signatures (intrinsic factors). These signatures are based on the up and down regulation of transcription factors apparently regulated by the epigenetic-micro RNAs regulatory circuit. The strict regulation of both extrinsic and intrinsic signals is of utmost importance, as deregulation of the expression of these factors could result in hematopoietic malignancies such as leukemia or lymphoma. Such deregulation of gene expression is usually caused by irreversible molecular-cytogenetic changes introduced into the genomic DNA sequence. These changes can be caused by mutations, translocations and deletions concerning genes involved in cell cycle, differentiation, proliferation, and self-renewal processes. During the last decade it has become evident that, next to genetic aberrations, epigenetic alterations can also contribute to tumorigenesis, for example through gene silencing due to aberrant methylation.

Tools for educational innovation

How does an instructor keep abreast of educational technology changes and developments? What tools, apps and add-ins are available and how can they be used innovatively? This session will cover a variety of resources and tools that answer these two questions plus address how to pick the best tools for your instructional environment

A MUSE map of the central Orion Nebula (M 42)

We present a new integral-field spectroscopic dataset of the central part of the Orion Nebula (M 42), observed with the MUSE instrument at the ESO VLT. We reduced the data with the public MUSE pipeline. The output products are two FITS cubes with a spatial size of ~5.9'x4.9' (corresponding to ~0.76 pc x 0.63 pc) and a contiguous wavelength coverage of 4595...9366 Angstrom, spatially sampled at 0.2". We provide two versions with a sampling of 1.25 Angstrom and 0.85 Angstrom in dispersion direction. Together with variance cubes these files have a size of 75 and 110 GiB on disk. They represent one of the largest integral field mosaics to date in terms of information content. We make them available for use in the community. To validate this dataset, we compare world coordinates, reconstructed magnitudes, velocities, and absolute and relative emission line fluxes to the literature and find excellent agreement. We derive a two-dimensional map of extinction and present de-reddened flux maps of several individual emission lines and of diagnostic line ratios. We estimate physical properties of the Orion Nebula, using the emission line ratios [N II] and [S III] (for the electron temperature $T_e$) and [S II] and [Cl III] (for the electron density $N_e$), and show two-dimensional images of the velocity measured from several bright emission lines.Comment: Resubmitted to A&A after incorporating referee comments; access to full dataset via http://muse-vlt.eu/science/data-release

An empirical, Bayesian approach to modelling crop yield: Maize in USA

We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18 °C–19 °C, corresponding to a daily maximum temperature of 24 °C–25 °C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2 °C, relative to 1981–2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate

The Nearby Supernova Factory

The Nearby Supernova Factory (SNfactory) is an ambitious project to find and study in detail approximately 300 nearby Type Ia supernovae (SNe~Ia) at redshifts 0.03<z<0.08. This program will provide an exceptional data set of well-studied SNe in the nearby smooth Hubble flow that can be used as calibration for the current and future programs designed to use SNe to measure the cosmological parameters. The first key ingredient for this program is a reliable supply of Hubble-flow SNe systematically discovered in unprecedented numbers using the same techniques as those used in distant SNe searches. In 2002, 35 SNe were found using our test-bed pipeline for automated SN search and discovery. The pipeline uses images from the asteroid search conducted by the Near Earth Asteroid Tracking group at JPL. Improvements in our subtraction techniques and analysis have allowed us to increase our effective SN discovery rate to ~12 SNe/month in 2003.Comment: 7 pages, 3 figures to be published in New Astronomy Review

Generative Quantum Learning of Joint Probability Distribution Functions

Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we design quantum machine learning algorithms to model copulas. We show that any copula can be naturally mapped to a multipartite maximally entangled state. A variational ansatz we christen as a `qopula' creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or GHZ states for multiple variables. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables for historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits and show that our results outperform those obtained through equivalent classical generative learning. Further, we present theoretical arguments for exponential advantage in our model's expressivity over classical models based on communication and computational complexity arguments.Comment: 19 pages, 11 figures. v2: published versio

Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: A cohort study using OpenSAFELY-TPP

Background: Healthcare across all sectors, in the UK and globally, was negatively affected by the COVID-19 pandemic. We analysed healthcare services delivered to people with pancreatic cancer from January 2015 to March 2023 to investigate the effect of the COVID-19 pandemic. Methods: With the approval of NHS England, and drawing from a nationally representative OpenSAFELY-TPP dataset of 24 million patients (over 40% of the English population), we undertook a cohort study of people diagnosed with pancreatic cancer. We queried electronic healthcare records for information on the provision of healthcare services across the pancreatic cancer pathway. To estimate the effect of the COVID-19 pandemic, we predicted the rates of healthcare services if the pandemic had not happened. We used generalised linear models and the pre-pandemic data from January 2015 to February 2020 to predict rates in March 2020 to March 2023. The 95% confidence intervals of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results: The rate of pancreatic cancer and diabetes diagnoses in the cohort was not affected by the pandemic. There were 26,840 people diagnosed with pancreatic cancer from January 2015 to March 2023. The mean age at diagnosis was 72 (±11 SD), 48% of people were female, 95% were of White ethnicity, and 40% were diagnosed with diabetes. We found a reduction in surgical resections by 25-28% during the pandemic. In addition, 20%, 10%, and 4% fewer people received body mass index, glycated haemoglobin, and liver function tests, respectively, before they were diagnosed with pancreatic cancer. There was no impact of the pandemic on the number of people making contact with primary care, but the number of contacts increased on average by 1-2 per person amongst those who made contact. Reporting of jaundice decreased by 28%, but recovered within 12 months into the pandemic. Emergency department visits, hospital admissions, and deaths were not affected. Conclusions: The pandemic affected healthcare in England across the pancreatic cancer pathway. Positive lessons could be learnt from the services that were resilient and those that recovered quickly. The reductions in healthcare experienced by people with cancer have the potential to lead to worse outcomes. Current efforts should focus on addressing the unmet needs of people with cancer. Funding: This work was jointly funded by the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157). This work was funded by Medical Research Council (MRC) grant reference MR/W021390/1 as part of the postdoctoral fellowship awarded to AL and undertaken at the Bennett Institute, University of Oxford. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), or the Department of Health and Social Care. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication