Associations between urbanicity and malaria at local scales in Uganda

Background: Sub-Saharan Africa is expected to show the greatest rates of urbanization over the next 50 years. Urbanization has shown a substantial impact in reducing malaria transmission due to multiple factors, including unfavourable habitats for Anopheles mosquitoes, generally healthier human populations, better access to healthcare, and higher housing standards. Statistical relationships have been explored at global and local scales, but generally only examining the effects of urbanization on single malaria metrics. In this study, associations between multiple measures of urbanization and a variety of malaria metrics were estimated at local scales. Methods: Cohorts of children and adults from 100 households across each of three contrasting sub-counties of Uganda (Walukuba, Nagongera and Kihihi) were followed for 24 months. Measures of urbanicity included density of surrounding households, vegetation index, satellite-derived night-time lights, land cover, and a composite urbanicity score. Malaria metrics included the household density of mosquitoes (number of female Anopheles mosquitoes captured), parasite prevalence and malaria incidence. Associations between measures of urbanicity and malaria metrics were made using negative binomial and logistic regression models. Results: One site (Walukuba) had significantly higher urbanicity measures compared to the two rural sites. In Walukuba, all individual measures of higher urbanicity were significantly associated with a lower household density of mosquitoes. The higher composite urbanicity score in Walukuba was also associated with a lower household density of mosquitoes (incidence rate ratio = 0.28, 95 % CI 0.17–0.48, p < 0.001) and a lower parasite prevalence (odds ratio, OR = 0.44, CI 0.20–0.97, p = 0.04). In one rural site (Kihihi), only a higher density of surrounding households was associated with a lower parasite prevalence (OR = 0.15, CI 0.07–0.34, p < 0.001). And, in only one rural site (Nagongera) was living where NDVI ≤0.45 associated with higher incidence of malaria (IRR = 1.35, CI 1.35–1.70, p = 0.01). Conclusions: Urbanicity has been shown previously to lead to a reduction in malaria transmission at large spatial scales. At finer scales, individual household measures of higher urbanicity were associated with lower mosquito densities and parasite prevalence only in the site that was generally characterized as being urban. The approaches outlined here can help better characterize urbanicity at the household level and improve targeting of control interventions

Reconstructing the 3-D Trajectories of CMEs in the Inner Heliosphere

A method for the full three-dimensional (3-D) reconstruction of the trajectories of coronal mass ejections (CMEs) using Solar TErrestrial RElations Observatory (STEREO) data is presented. Four CMEs that were simultaneously observed by the inner and outer coronagraphs (COR1 and 2) of the Ahead and Behind STEREO satellites were analysed. These observations were used to derive CME trajectories in 3-D out to ~15Rsun. The reconstructions using COR1/2 data support a radial propagation model. Assuming pseudo-radial propagation at large distances from the Sun (15-240Rsun), the CME positions were extrapolated into the Heliospheric Imager (HI) field-of-view. We estimated the CME velocities in the different fields-of-view. It was found that CMEs slower than the solar wind were accelerated, while CMEs faster than the solar wind were decelerated, with both tending to the solar wind velocity.Comment: 17 pages, 10 figures, 1 appendi

Differential expression of CD49a and CD49b determines localization and function of tumor-infiltrating CD8(+) T cells

CD8(+) T-cell infiltration and effector activity in tumors are correlated with better overall survival of patients, suggesting that the ability of T cells to enter and remain in contact with tumor cells supports tumor control. CD8(+) T cells express the collagen-binding integrins CD49a and CD49b, but little is known about their function or how their expression is regulated in the tumor microenvironment (TME). Here, we found that tumor-infiltrating CD8(+) T cells initially expressed CD49b, gained CD49a, and then lost CD49b over the course of tumor outgrowth. This differentiation sequence was driven by antigen-independent elements in the TME, although T-cell receptor (TCR) stimulation further increased CD49a expression. Expression of exhaustion markers and CD49a associated temporally but not mechanistically. Intratumoral CD49a-expressing CD8(+) T cells failed to upregulate TCR-dependent Nur77 expression, whereas CD69 was constitutively expressed, consistent with both a lack of productive antigen engagement and a tissue-resident memory-like phenotype. Imaging T cells in live tumor slices revealed that CD49a increased their motility, especially of those in close proximity to tumor cells, suggesting that it may interfere with T-cell recognition of tumor cells by distracting them from productive engagement, although we were not able to augment productive engagement by short-term CD49a blockade. CD49b also promoted relocalization of T cells at a greater distance from tumor cells. Thus, our results demonstrate that expression of these integrins affects T-cell trafficking and localization in tumors via distinct mechanisms, and suggests a new way in which the TME, and likely collagen, could promote tumor-infiltrating CD8(+) T-cell dysfunction.Experimental cancer immunology and therap

Influential Periods in Longitudinal Clinical Cardiovascular Health Scores

The prevalence of ideal cardiovascular health (CVH) among adults in the United States is low and decreases with age. Our objective was to identify specific age windows when the loss of CVH accelerates, to ascertain preventive opportunities for intervention. Data were pooled from 5 longitudinal cohorts (Project Heartbeat!, Cardiovascular Risk in Young Finns Study, The Bogalusa Heart Study, Coronary Artery Risk Development in Young Adults, Special Turku Coronary Risk Factor Intervention Project) from the United States and Finland from 1973 to 2012. Individuals with clinical CVH factors (i.e., body mass index, blood pressure, cholesterol, blood glucose) measured from ages 8 to 55 years were included. These factors were categorized and summed into a clinical CVH score ranging from 0 (worst) to 8 (best). Adjusted, segmented, linear mixed models were used to estimate the change in CVH over time. Among the 18,343 participants, 9,461 (52%) were female and 12,346 (67%) were White. The baseline mean (standard deviation) clinical CVH score was 6.9 (1.2) at an average age of 17.6 (8.1) years. Two inflection points were estimated: at 16.9 years (95% confidence interval: 16.4, 17.4) and at 37.2 years (95% confidence interval: 32.4, 41.9). Late adolescence and early middle age appear to be influential periods during which the loss of CVH accelerates.publishedVersionPeer reviewe

Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding

Durden J, Schoening T, Althaus F, et al. Perspectives in Visual Imaging for Marine Biology and Ecology: From Acquisition to Understanding. In: Hughes RN, Hughes DJ, Smith IP, Dale AC, eds. Oceanography and Marine Biology: An Annual Review. 54. Boca Raton: CRC Press; 2016: 1-72

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur