Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

Associations between obesity (BMI and waist circumference) and socio-demographic factors, physical activity, dietary habits, life events, resilience, mood, perceived stress and hopelessness in healthy older Europeans

Abstract: Background: It is important to understand the psycho-social context of obesity to inform prevention and treatment of obesity at both the individual and public health level. Methods: Representative samples of middle-aged adults aged >= 43 years were recruited in Great Britain (GB) (n = 1182) and Portugal (n = 540) and interviewed to explore associations between body mass index (BMI), waist circumference (WC), demographic factors, physical activity, dietary habits (FFQ), life events (LES), Resilience (RS11), Mood (MS), Hopelessness (BDI) and Perceived Stress (PSS4). BMI (kg/m(2)) and WC (cm) were dependent variables in separate multiple linear regression models for which predictors were entered in 4 blocks: 1. demographic factors; 2. stressful life events; 3. diet/activity; and, 4. psychological measures. Results: In the GB sample, BMI (kg/m(2)) was predicted by less education, illness in a close friend or relative, frequent alcohol consumption and sedentary behaviour. Among the Portuguese, higher BMI (kg/m(2)) was predicted by lower resilience. Being male and less education were independent predictors of having a larger WC (cm) in both countries. Within GB, not working, illness in a close friend or relative, sedentary lifestyle and lower resilience were also independent predictors of a larger WC (cm). Conclusions: These data suggest that intervention to treat and/or prevent obesity should target males, particularly those who have left education early and seek to promote resilience. In GB, targeting those with high alcohol consumption and encouraging physical activity, particularly among those who have experienced illness in a close friend or relative may also be effective in reducing obesity

A large fraction of HLA class I ligands are proteasome-generated spliced peptides

The proteasome generates the epitopes presented on human leukocyte antigen (HLA) class I molecules that elicit CD8(+) T cell responses. Reports of proteasome-generated spliced epitopes exist, but they have been regarded as rare events. Here, however, we show that the proteasome-generated spliced peptide pool accounts for one-third of the entire HLA class I immunopeptidome in terms of diversity and one-fourth in terms of abundance. This pool also represents a unique set of antigens, possessing particular and distinguishing features. We validated this observation using a range of complementary experimental and bioinformatics approaches, as well as multiple cell types. The widespread appearance and abundance of proteasome-catalyzed peptide splicing events has implications for immunobiology and autoimmunity theories and may provide a previously untapped source of epitopes for use in vaccines and cancer immunotherapy

A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation

Mangrove forests provide many ecosystem services but are among the world's most threatened ecosystems. Mangroves vary substantially according to their geomorphic and sedimentary setting; while several conceptual frameworks describe these settings, their spatial distribution has not been quantified. Here, we present a new global mangrove biophysical typology and show that, based on their 2016 extent, 40.5% (54,972 km2) of mangrove systems were deltaic, 27.5% (37,411 km2) were estuarine and 21.0% (28,493 km2) were open coast, with lagoonal mangroves the least abundant (11.0%, 14,993 km2). Mangroves were also classified based on their sedimentary setting, with carbonate mangroves being less abundant than terrigenous, representing just 9.6% of global coverage. Our typology provides a basis for future research to incorporate geomorphic and sedimentary setting in analyses. We present two examples of such applications. Firstly, based on change in extent between 1996 and 2016, we show while all types exhibited considerable declines in area, losses of lagoonal mangroves (- 6.9%) were nearly twice that of other types. Secondly, we quantify differences in aboveground biomass between mangroves of different types, with it being significantly lower in lagoonal mangroves. Overall, our biophysical typology provides a baseline for assessing restoration potential and for quantifying mangrove ecosystem service provision

Harnessing big data to support the conservation and rehabilitation of mangrove forests globally

Mangrove forests are found on sheltered coastlines in tropical, subtropical, and some warm temperate regions. These forests support unique biodiversity and provide a range of benefits to coastal communities, but as a result of large-scale conversion for aquaculture, agriculture, and urbanization, mangroves are considered increasingly threatened ecosystems. Scientific advances have led to accurate and comprehensive global datasets on mangrove extent, structure, and condition, and these can support evaluation of ecosystem services and stimulate greater conservation and rehabilitation efforts. To increase the utility and uptake of these products, in this Perspective we provide an overview of these recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation. We describe a new platform for visualizing and disseminating these datasets to the global science community, non-governmental organizations, government officials, and rehabilitation practitioners and highlight future directions and collaborations to increase the uptake and impact of large-scale mangrove research. This Perspective reviews the role of global-scale research in stimulating policy action and on-the-ground conservation for mangrove ecosystems. We outline the current state of knowledge in terms of global analyses and examine the challenge of translating this research in action

Nilotinib and Imatinib Are Comparably Effective in Reducing Growth of Human Eosinophil Leukemia Cells in a Newly Established Xenograft Model

We developed a xenograft model of human Chronic Eosinophilic Leukemia (CEL) to study disease progression and remission-induction under therapy with tyrosine kinase inhibitors using imatinib and nilotinib as examples. The FIP1L1/PDGFRA+ human CEL cell lineEOL-1 was injected intravenously into scid mice, and MR imaging and FACS analysis of mouse blood samples were performed to monitor disease development and the effects of imatinib and nilotinib. Organ infiltration was analyzed in detail by immunohistochemistry after sacrifice. All animals developed CEL and within one week of therapy, complete remissions were seen with both imatinib and nilotinib, resulting in reduced total tumor volumes by MR-imaging and almost complete disappearance of EOL-1 cells in the peripheral blood and in tissues. The new model system is feasible for the evaluation of new tyrosine kinase inhibitors and our data suggest that nilotinib may be a valuable additional targeted drug active in patients with FIP1L1/PDGFRA+ CEL

A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions

A globally relevant change taxonomy and evidence-based change framework for land monitoring

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State�Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation ‘impact (pressure)’, with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into ‘impact (pressure)’ categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from groundbased, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes—including land degradation, desertification and ecosystem restoration—the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions