CD4 T Cell-Derived IFN-γ Plays a Minimal Role in Control of Pulmonary Mycobacterium tuberculosis Infection and Must Be Actively Repressed by PD-1 to Prevent Lethal Disease

IFN-γ–producing CD4 T cells are required for protection against Mycobacterium tuberculosis (Mtb) infection, but the extent to which IFN-γ contributes to overall CD4 T cell-mediated protection remains unclear. Furthermore, it is not known if increasing IFN-γ production by CD4 T cells is desirable in Mtb infection. Here we show that IFN-γ accounts for only ~30% of CD4 T cell-dependent cumulative bacterial control in the lungs over the first six weeks of infection, but \u3e80% of control in the spleen. Moreover, increasing the IFN-γ–producing capacity of CD4 T cells by ~2 fold exacerbates lung infection and leads to the early death of the host, despite enhancing control in the spleen. In addition, we show that the inhibitory receptor PD-1 facilitates host resistance to Mtb by preventing the detrimental over-production of IFN-γ by CD4 T cells. Specifically, PD-1 suppressed the parenchymal accumulation of and pathogenic IFN-γ production by the CXCR3+KLRG1-CX3CR1- subset of lung-homing CD4 T cells that otherwise mediates control of Mtb infection. Therefore, the primary role for T cell-derived IFN-γ in Mtb infection is at extra-pulmonary sites, and the host-protective subset of CD4 T cells requires negative regulation of IFN-γ production by PD-1 to prevent lethal immune-mediated pathology

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl

Using unoccupied aerial vehicles (UAVs) to map seagrass cover from Sentinel-2 imagery

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R2 = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R2 = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km2 of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m2 resolution to underpin evidence-based management and conservation of seagrass.publishedVersio

Bringing an Effective Behavioral Weight Loss Intervention for People With Serious Mental Illness to Scale

People with serious mental illnesses (SMIs) die 10–20 years earlier than the general population, mainly due to cardiovascular disease. Obesity is a key driver of cardiovascular risk in this group. Because behavioral weight loss interventions tailored to the needs of people with SMI have been shown to lead to clinically significant weight loss, achieving widespread implementation of these interventions is a public health priority. In this Perspective, we consider strategies for scaling the ACHIEVE behavioral weight loss intervention for people with SMI, shown to be effective in a randomized clinical trial (RCT), to mental health programs in the U.S. and internationally. Given the barriers to high-fidelity implementation of the complex, multi-component ACHIEVE intervention in often under-resourced mental health programs, we posit that substantial additional work is needed to realize the full public health potential of this intervention for people with SMI. We discuss considerations for successful “scale-up,” or efforts to expand ACHIEVE to similar settings and populations as those included in the RCT, and “scale-out,” or efforts to expand the intervention to different mental health program settings/sub-populations with SMI. For both, we focus on considerations related (1) intervention adaptation and (2) implementation strategy development, highlighting four key domains of implementation strategies that we believe need to be developed and tested: staff capacity building, leadership engagement, organizational change, and policy strategies. We conclude with discussion of the types of future research needed to support ACHIEVE scale-up/out, including hybrid trial designs testing the effectiveness of intervention adaptations and/or implementations strategies

Tropospheric Ozone Assessment Report: assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty

Using unoccupied aerial vehicles (UAVs) to map seagrass cover from Sentinel-2 imagery

Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R2 = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R2 = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km2 of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m2 resolution to underpin evidence-based management and conservation of seagrass

Quantifying the intra-habitat variation of seagrass beds with unoccupied aerial vehicles (UAVs)

Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world’s seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8–11.9% (SE of 8.2–14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15–20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Need for Cardiovascular Risk Reduction in Persons With Serious Mental Illness: Design of a Comprehensive Intervention

Persons with serious mental illness (SMI) comprise a high-risk group for cardiovascular disease (CVD)-related mortality with rates at least twice those of the overall US. Potentially modifiable CVD risk behaviors (tobacco smoking, obesity, physical inactivity, unhealthy diet) and risk factors (hypertension, diabetes, dyslipidemia) are all markedly elevated in persons with SMI. Evaluations of programs implementing integrated medical care into specialty mental health settings have not shown meaningful effects on CVD risk factor reduction. Rigorously tested, innovative interventions are needed to address the large burden of CVD risk in populations with SMI. In this article, we describe the design of a comprehensive 18-month intervention to decrease CVD risk that we are studying in a randomized clinical trial in a community mental health organization with psychiatric rehabilitation programs. The individual-level intervention incorporated health behavior coaching and care coordination/care management to address all seven CVD risk behaviors and risk factors, and is delivered by a health coach and nurse. If successful, the intervention could be adopted within current integrated care models and significantly improve the physical health of persons with SMI