The Evolution of Luminous Compact Blue Galaxies: Disks or Spheroids?

Luminous compact blue galaxies (LCBGs) are a diverse class of galaxies characterized by high luminosities, blue colors, and high surface brightnesses. Residing at the high luminosity, high mass end of the blue sequence, LCBGs sit at the critical juncture of galaxies that are evolving from the blue to the red sequence. Yet we do not understand what drives the evolution of LCBGs, nor how they will evolve. Based on single-dish HI observations, we know that they have a diverse range of properties. LCBGs are HI-rich with M(HI)=10^{9-10.5} M(sun), have moderate M(dyn)=10^{10-12} M(sun), and 80% have gas depletion timescales less than 3 Gyr. These properties are consistent with LCBGs evolving into low-mass spirals or high mass dwarf ellipticals or dwarf irregulars. However, LCBGs do not follow the Tully-Fisher relation, nor can most evolve onto it, implying that many LCBGs are not smoothly rotating, virialized systems. GMRT and VLA HI maps confirm this conclusion revealing signatures of recent interactions and dynamically hot components in some local LCBGs, consistent with the formation of a thick disk or spheroid. Such signatures and the high incidence of close companions around LCBGs suggest that star formation in local LCBGs is likely triggered by interactions. The dynamical masses and apparent spheroid formation in LCBGs combined with previous results from optical spectroscopy are consistent with virial heating being the primary mechanism for quenching star formation in these galaxies.Comment: 4 pages, 1 figure, to appear in "Hunting for the Dark: The Hidden Side of Galaxy Formation", Malta, 19-23 Oct. 2009, eds. V.P. Debattista & C.C. Popescu, AIP Conf. Se

Smart City Puebla: measuring smartness

Objective of the study: this empirical study revisits the meaning and scope of the ‘smart city’ concept, measuring ‘smartness’ in an emerging market setting. Methodology / approach: a data reduction exercise is conducted through a principal component analysis of 22 smart city variables and a two-step cluster analysis for the 217 municipalities of the State of Puebla (Mexico), so as to identify the defining challenges to ‘smartness’ in a developing economy city. Originality / Relevance: the prevailing models that measure urban ‘smartness’, notably Giffinger’s and Cities in Motion, arguably miss to capture the socioeconomic challenges of cities in a developing market context. Main results: two distinctive factors emerge from the data reduction exercise, namely ‘marginalization’, referring to social and economic inequalities, and ‘access to services’, particularly public health and education, to define the challenges emerging market cities would need to address in their path to ‘smartness’. Theoretical / methodological contributions: we introduce a revised approach to measure city ‘smartness’, claiming that access to public services (education and health) helps reduce social inequality and marginalization, which are core indicators to redefine smart cities in emerging markets. Social / management contributions: even if the analysis is carried out on data from a single region, our findings could be a meaningful input to a more generalizable model to measure city ‘smartness’ in emerging markets, with implications to multiple stakeholders, particularly policy-makers, suggesting basic inequalities and access to education and health services should be addressed, before attempting to improve traditional smart city indicators

Integrated modeling to achieve global goals: lessons from the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative

Humanity is challenged with making progress toward global biodiversity, freshwater, and climate goals, while providing food and nutritional security for everyone. Our current food and land-use systems are incompatible with this ambition making them unsustainable. Papers in this special feature introduce a participatory, integrated modeling approach applied to provide insights on how to transform food and land-use systems to sustainable trajectories in 12 countries: Argentina, Australia, Canada, China, Germany, Finland, India, Mexico, Rwanda, Sweden, the UK, and USA. Papers are based on work completed by members of the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative, a network of in-country research teams engaging policymakers and other local stakeholders to co-develop future food and land-use scenarios and modeling their national and global sustainability impacts. Here, we discuss the key leverage points, methodological advances, and multi-sector engagement strategies presented and applied in this collection of work to set countries and our planet on course for achieving food security, biodiversity, freshwater, and climate targets by 2050

The impact of scaling up cervical cancer screening and treatment services among women living with HIV in Kenya: a modelling study

Introduction: We aimed to quantify health outcomes and programmatic implications of scaling up cervical cancer (CC) screening and treatment options for women living with HIV in care aged 18–65 in Kenya. Methods: Mathematical model comparing from 2020 to 2040: (1) visual inspection with acetic acid (VIA) and cryotherapy (Cryo); (2) VIA and Cryo or loop excision electrical procedure (LEEP), as indicated; (3) human papillomavirus (HPV)-DNA testing and Cryo or LEEP; and (4) enhanced screening technologies (either same-day HPV-DNA testing or digitally enhanced VIA) and Cryo or LEEP. Outcomes measured were annual number of CC cases, deaths, screening and treatment interventions, and engaged in care (numbers screened, treated and cured) and five yearly age-standardised incidence. Results: All options will reduce CC cases and deaths compared with no scale-up. Options 1–3 will perform similarly, averting approximately 28 000 (33%) CC cases and 7700 (27%) deaths. That is, VIA screening would yield minimal losses to follow-up (LTFU). Conversely, LTFU associated with HPV-DNA testing will yield a lower care engagement, despite better diagnostic performance. In contrast, option 4 would maximise health outcomes, averting 43 200 (50%) CC cases and 11 800 (40%) deaths, given greater care engagement. Yearly rescreening with either option will impose a substantial burden on the health system, which could be reduced by spacing out frequency to three yearly without undermining health gains. Conclusions: Beyond the specific choice of technologies to scale up, efficiently using available options will drive programmatic success. Addressing practical constraints around diagnostics’ performance and LTFU will be key to effectively avert CC cases and deaths

Sustainability implications of Rwanda’s Vision 2050 long-term development strategy

Improving livelihoods in Rwanda requires overcoming food insecurity and malnutrition. Vision 2050 is Rwanda’s long-term development strategy, yet little is known about its potential trade-offs for the country’s biodiversity, forest cover, and greenhouse gas (GHG) emissions. Scenario analysis can provide insights into how to achieve such goals more sustainably. Here, we use the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Calculator, a simple integrated assessment tool, to explore potential sustainability implications by 2050 through two scenarios: (1) Current Trends and (2) Vision 2050. The Vision 2050 pathway incorporates components of the government’s long-term development strategy and associated national agricultural policy targets. It includes greater increases in crop productivity and decreases in post-harvest losses, and shifts to more sustainable diets, compared to the Current Trends pathway. Results show that the Vision 2050 pathway would, relative to Current Trends, lead to a greater decrease in agricultural land area and an increase in non-forested natural land-cover area, with consequent decreases in GHG emissions from agriculture, increases in carbon sequestration, and increases in the share of land that can support biodiversity conservation. Shifts to a healthier diet in the Vision 2050 pathway would only be compatible with national agricultural priorities if these diets favor consumption of foods that underpin sustainable livelihoods in Rwanda, such as beans, cassava, potatoes, sweet potatoes, banana, and corn. We discuss the potential for integrated land-use planning and adoption of agroecological farming practices to help Rwanda achieve food security, livelihood, biodiversity, and climate mitigation goals in tandem

FABLE Calculator 2020 update

The FABLE Calculator (“the Calculator”) is an Excel accounting tool used to study the potential evolution of food and land-use systems over the period 2000-2050. It focuses on agriculture as the main driver of land-use change and tests the impact of different policies and changes in the drivers of these systems through the combination of a large number of scenarios. It includes 76 raw and processed agricultural products from the crop and livestock sectors (Appendix 1) and relies extensively on the FAOSTAT (2020) database for input data. For every 5-year time step over the period 2000-2050, the Calculator computes the level of agricultural activity, land use change, food consumption, trade, greenhouse gas (GHG) emissions, water use, and biodiversity conservation according to selected scenarios. Users can replace data from global databases with national or subnational data

Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies

Report 29: The impact of the COVID-19 epidemic on all-cause attendances to emergency departments in two large London hospitals: an observational study

The health care system in England has been highly affected by the surge in demand due to patients afflicted by COVID-19. Yet the impact of the pandemic on the care seeking behaviour of patients and thus on Emergency department (ED) services is unknown, especially for non-COVID-19 related emergencies. In this report, we aimed to assess how the reorganisation of hospital care and admission policies to respond to the COVID-19 epidemic affected ED attendances and emergency hospital admissions. We performed time-series analyses of present year vs historic (2015-2019) trends of ED attendances between March 12 and May 31 at two large central London hospitals part of Imperial College Healthcare NHS Trust (ICHNT) and compared these to regional and national trends. Historic attendances data to ICHNT and publicly available NHS situation reports were used to calibrate time series auto-regressive integrated moving average (ARIMA) forecasting models. We thus predicted the (conterfactual) expected number of ED attendances between March 12 (when the first public health measure leading to lock-down started in England) to May 31, 2020 (when the analysis was censored) at ICHNT, at all acute London Trusts and nationally. The forecasted trends were compared to observed data for the same periods of time. Lastly, we analysed the trends at ICHNT disaggregating by mode of arrival, distance from postcode of patient residence to hospital and primary diagnosis amongst those that were subsequently admitted to hospital and compared these data to an average for the same period of time in the years 2015 to 2019. During the study period (January 1 to May 31, 2020) there was an overall decrease in ED attendances of 35% at ICHNT, of 50% across all London NHS Trusts and 53% nationally. For ICHNT, the decrease in attendances was mainly amongst those aged younger than 65 and those arriving by their own means (e.g. personal or public transport). Increasing distance (km) from postcode of residence to hospital was a significant predictor of reduced attendances, which could not be explained by weighted (for population numbers) mean index of multiple deprivation. Non-COVID emergency admissions to hospital after March 12 fell by 48% at ICHNT compared to previous years. This was seen across all disease areas, including acute coronary syndromes, stroke and cancer-related emergencies. The overall non-COVID-19 hospitalisation mortality risk did not differ (RR 1.13, 95%CI 0.94-1.37, p=0.19), also in comparison to previous years. Our findings suggest emergency healthcare seeking to hospitals drastically changed amongst the population within the catchment area of ICHNT. This trend was echoed regionally and nationally, suggesting those suffering a medical emergency may not have attended other (i.e. closer-to-home) hospitals. Furthermore, our time-series analyses showed that, even after COVID-19 cases and deaths decreased (i.e. from early April), non-COVID-19 ED attendances did not increase. The impact of emergency triaging systems (e.g. 111 calls) and alternative (e.g. private hospital, chemist) health services on these trends remains unknown. However, another recent report found increased non-COVID excess deaths in the community, which may be partially explained by people experiencing an emergency and not attending health services at all. Whether those that attended ED services have done so with longer delays from the moment of emergency onset also remains unknown. National analyses into the factors causing reduced attendances to ED services and strategies to revert these negative trends are urgently needed