Priorities for Mediterranean marine turtle conservation and management in the face of climate change.

As climate-related impacts threaten marine biodiversity globally, it is important to adjust conservation efforts to mitigate the effects of climate change. Translating scientific knowledge into practical management, however, is often complicated due to resource, economic and policy constraints, generating a knowledge-action gap. To develop potential solutions for marine turtle conservation, we explored the perceptions of key actors across 18 countries in the Mediterranean. These actors evaluated their perceived relative importance of 19 adaptation and mitigation measures that could safeguard marine turtles from climate change. Of importance, despite differences in expertise, experience and focal country, the perceptions of researchers and management practitioners largely converged with respect to prioritizing adaptation and mitigation measures. Climate change was considered to have the greatest impacts on offspring sex ratios and suitable nesting sites. The most viable adaptation/mitigation measures were considered to be reducing other pressures that act in parallel to climate change. Ecological effectiveness represented a key determinant for implementing proposed measures, followed by practical applicability, financial cost, and societal cost. This convergence in opinions across actors likely reflects long-standing initiatives in the Mediterranean region towards supporting knowledge exchange in marine turtle conservation. Our results provide important guidance on how to prioritize measures that incorporate climate change in decision-making processes related to the current and future management and protection of marine turtles at the ocean-basin scale, and could be used to guide decisions in other regions globally. Importantly, this study demonstrates a successful example of how interactive processes can be used to fill the knowledge-action gap between research and management.

Spatial heterogeneity and temporal stability characterize future climatic refugia in Mediterranean Europe.

Climate plays a major role in shaping biodiversity patterns over time and space, with ongoing changes leading to the reorganization of ecosystems, which challenges conservation initiatives. Identifying areas that could serve as possible climate change refugia for future biodiversity is, thus, critical for both conservation and management. Here, we identify potential future climatic refugia within the Euro-Mediterranean biome, which is a global biodiversity hotspot, while accounting for multiple emission climate change projections over the next 50 years. We developed two metrics of climatic variability: temporal stability and spatial heterogeneity. We then used a systematic conservation planning approach to identify climate-based priority areas. While we used a climate-based, species-neutral methodology, we deliberately implemented low climatic velocity thresholds, so that the identified climatic refugia would even be compatible with the needs of species with low dispersal capacity, such as plants. Our projections showed that future climatic refugia would be more frequently observed in mid-altitudes, for gradients with steep elevations, and mainly in the eastern part of the Euro-Mediterranean biome, with possible conflicts with existing land uses and future conservation implications. Climatic, land use, and topography results indicated that only a limited number of refugia would be hosted by high elevation habitats (>1500 m), raising possible concerns about the biodiversity of Mediterranean mountain regions. Our analyses show that the current network of protected areas captures future climatic refugia disproportionally, despite their importance for safeguarding present and future biodiversity in the Mediterranean. Key climatic refugia could limit the impacts of future climate change on biodiversity in mid-altitude and mountainous regions, and should be included in management guidelines for a climate-ready conservation design in the Mediterranean biome.

Split bootstrap hierarchical modeling of antibiotics abuse in China.

In the 1990s, China experienced a high degree of antibiotics abuse, which resulted in increased drug resistance. As a result, the World Health Organization introduced a program for children under the age of 5 years who had an acute respiratory tract infection. We analyze the data pertaining to the treatment provided by doctors in several hospitals in China in order to understand the relationships in the data. The data are nested in a three-level hierarchical structure with small cluster sizes ranging from 2 to 10. While large sample theory provides a mechanism to construct confidence intervals and test hypotheses about regression coefficients, the estimation algorithms often fail to converge when they are applied to small cluster sizes. This paper presents a combination of the cluster bootstrap and primary unit splitting methods, called split bootstrap, which is a novel combination that can be used as an alternative when analyzing data pertaining to the abuse of antibiotics in China with small cluster sizes. The split bootstrap method provides accurate estimations with a minimal reduction in precision.

Alternative methods for virtual heart transplant-Size matching for pediatric heart transplantation with and without donor medical images available.

BACKGROUND: Listed pediatric heart transplant patients have the highest solid-organ waitlist mortality rate. The donor-recipient body weight (DRBW) ratio is the clinical standard for allograft size matching but may unnecessarily limit a patient's donor pool. To overcome DRBW ratio limitations, two methods of performing virtual heart transplant fit assessments were developed that account for patient-specific nuances. Method 1 uses an allograft total cardiac volume (TCV) prediction model informed by patient data wherein a matched allograft 3-D reconstruction is selected from a virtual library for assessment. Method 2 uses donor images for a direct virtual transplant assessment. METHODS: Assessments were performed in medical image reconstruction software. The allograft model was developed using allometric/isometric scaling assumptions and cross-validation. RESULTS: The final predictive model included gender, height, and weight. The 25th-, 50th-, and 75th-percentiles for TCV percentage errors were -13% (over-prediction), -1%, and 8% (under-prediction), respectively. Two examples illustrating the potential of virtual assessments are presented. CONCLUSION: Transplant centers can apply these methods to perform their virtual assessments using existing technology. These techniques have potential to improve organ allocation. With additional experience and refinement, virtual transplants may become standard of care for determining suitability of donor organ size for an identified recipient.

The Effect of Blood Rheology and Inlet Boundary Conditions on Realistic Abdominal Aortic Aneurysms under Pulsatile Flow Conditions.

BACKGROUND: The effects of non-Newtonian rheology and boundary conditions on various pathophysiologies have been studied quite extensively in the literature. The majority of results present qualitative and/or quantitative conclusions that are not thoroughly assessed from a statistical perspective. METHODS: The finite volume method was employed for the numerical simulation of seven patient-specific abdominal aortic aneurysms. For each case, five rheological models and three inlet velocity boundary conditions were considered. Outlier- and heteroscedasticity-robust ANOVA tests assessed the simultaneous effect of rheological specifications and boundary conditions on fourteen variables that capture important characteristics of vascular flows. RESULTS: The selection of inlet velocity profiles appears as a more critical factor relative to rheological specifications, especially regarding differences in the oscillatory characteristics of computed flows. Response variables that relate to the average tangential force on the wall over the entire cycle do not differ significantly across alternative factor levels, as long as one focuses on non-Newtonian specifications. CONCLUSIONS: The two factors, namely blood rheological models and inlet velocity boundary condition, exert additive effects on variables that characterize vascular flows, with negligible interaction effects. Regarding thrombus-prone conditions, the Plug inlet profile offers an advantageous hemodynamic configuration with respect to the other two profiles.

Selection of Bifurcated Grafts' Dimensions during Aorto-Iliac Vascular Reconstruction Based on Their Hemodynamic Performance.

During the vascular surgical reconstruction of aorto-iliac occlusive/aneurysmal disease, bifurcated grafts are used where vascular surgeons intra-operatively select the size and the relative lengths of the parent and daughter portions of the graft. Currently, clinical practice regarding the selection of the most favorable geometric configuration of the graft is an understudied research subject: decisions are solely based on the clinical experience of the operating surgeon. This manuscript aims to evaluate the hemodynamic performance of various diameters, D, of bifurcated aortic grafts and relate those with proximal/distal part length ratios (the angle φ between the limbs is used as a surrogate marker of the main body-to-limb length ratio) in order to provide insights regarding the effects of different geometries on the hemodynamic environment. To this end, a computationally intensive set of simulations is conducted, and the resulting data are analyzed with modern statistical regression tools. A negative curvilinear relationship of TAWSS with both φ and D is recorded. It is shown that the angle between limbs is a more important predictor for the variability of TAWSS, while the graft's diameter is an important determinant for the variability of OSI. Large percentages of the total graft area with TAWSS < 0.4 Pa, which correspond to thrombogenic stimulating environments, are only observed for large values of φ and D > 20 mm. This variable ranges from 10% (for the smallest values of φ and D) to 55% (for the largest φ and D values). Our findings suggest that grafts with the smallest possible angle between the limbs (i.e., smallest parent-to-daughter length ratio) present the most favorable hemodynamic performance, yielding the smallest percentage of total graft area under thrombogenic simulating environments. Similarly, grafts with the smallest acceptable diameter should be preferred for the same reason. Especially, grafts with diameters greater than 20 mm should be avoided, given the abrupt increase in estimated thrombogenic areas.

Bayesian estimation of subset threshold autoregressions: short-term forecasting of traffic occupancy.

Traffic management authorities in metropolitan areas use real-time systems that analyze high-frequency measurements from fixed sensors, to perform short-term forecasting and incident detection for various locations of a road network. Published research over the last 20 years focused primarily on modeling and forecasting of traffic volumes and speeds. Traffic occupancy approximates vehicular density through the percentage of time a sensor detects a vehicle within a pre-specified time interval. It exhibits weekly periodic patterns and heteroskedasticity and has been used as a metric for characterizing traffic regimes (e.g. free flow, congestion). This article presents a Bayesian three-step model building procedure for parsimonious estimation of Threshold-Autoregressive (TAR) models, designed for location- day- and horizon-specific forecasting of traffic occupancy. In the first step, multiple regime TAR models reformulated as high-dimensional linear regressions are estimated using Bayesian horseshoe priors. Next, significant regimes are identified through a forward selection algorithm based on Kullback-Leibler (KL) distances between the posterior predictive distribution of the full reference model and TAR models with fewer regimes. Given the regimes, the forward selection algorithm can be implemented again to select significant autoregressive terms. In addition to forecasting, the proposed specification and model-building scheme, may assist in determining location-specific congestion thresholds and associations between traffic dynamics observed in different regions of a network. Empirical results applied to data from a traffic forecasting competition, illustrate the efficacy of the proposed procedures in obtaining interpretable models and in producing satisfactory point and density forecasts at multiple horizons.

Zoonotic cutaneous leishmaniasis in central Tunisia: spatio temporal dynamics.

BACKGROUND: Zoonotic cutaneous leishmaniasis (ZCL) is endemic in many rural areas of the Southern and Eastern Mediterranean region where different transmission patterns of the disease have been described. This study was carried out in a region located in Central Tunisia and aimed to investigate the spatio-temporal dynamics of the disease from 1999 to 2004. METHODS: Incident ZCL cases were defined by clinical diagnosis, confirmed by a positive skin test and/or parasitological examination. Annual ZCL rates were calculated for 94 regional sectors that comprise the study region of Sidi-Bouzid. Spatial and temporal homogeneity were initially investigated by chi-squared tests. Next, spatial scan statistics were used to identify spatial, temporal and spatio-temporal clusters that display abnormally high incidence rates. A hierarchical Bayesian Poisson regression model with spatial effects was fitted to signify explanatory socio-geographic factors related to spatial rate variability. Temporal ZCL dynamics for the 94 sectors were described via a linear mixed model. RESULTS: A total of 15 897 ZCL cases were reported in the 6-year study period, with an annual incidence rate of 669.7/100 000. An outbreak of the disease was detected in 2004 (1114/100 000). Spatial clustering is evident for the whole time period. The most likely cluster according to the spatial scan statistic, contains seven sectors with abnormally high incidence rates and approximately 5% of the total population. ZCL rates per sector are mostly related to the urban/rural index; sectoral population density and the number of inhabitants per household do not appear to contribute much to the explanation of rate variability. The dynamics of the disease within the study period are satisfactorily described by quadratic curves that differ for urban and rural areas. CONCLUSIONS: ZCL rates vary across space and time; rural/urban areas and environmental factors may explain part of this variation. In the study region, the Sidi Saâd dam-constructed in the early eighties and identified by previous studies as a major reason for the first outbreak of the disease-seems to be still related to increased ZCL rates. The most likely spatial cluster of high incidence rates contains regions located close to the dam. Our findings of increased incidences in urban areas support the hypothesis of increased incidences in peri-urban environments due to changes in sandfly/rodent living habits over recent years.

A robust approach for exploring hemodynamics and thrombus growth associations in abdominal aortic aneurysms.

Longitudinal studies of vascular diseases often need to establish correspondence between follow-up images, as the diseased regions may change shape over time. In addition, spatial data structures should be taken into account in the statistical analyses to avoid inferential errors. This study investigates the association between hemodynamics and thrombus growth in abdominal aortic aneurysms (AAAs) while emphasizing on the abovementioned methodological issues. Six AAA surfaces and their follow-ups were three-dimensionally reconstructed from computed-tomography images. AAA surfaces were mapped onto a rectangular grid which allowed identification of corresponding regions between follow-ups. Local thrombus thickness was measured at initial and follow-up surfaces and computational fluid dynamic simulations provided time-average wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time. Six Bayesian regression models, which account for spatially correlated measurements, were employed to explore associations between hemodynamics and thrombus growth. Results suggest that spatial regression models based on TAWSS and OSI offer superior predictive performance for thrombus growth relative to alternative specifications. Ignoring the spatial data structure may lead to improper assessment with regard to predictor significance.

Accounting for exhaust gas transport dynamics in instantaneous emission models via smooth transition regression.

Collecting and analyzing high frequency emission measurements has become very usual during the past decade as significantly more information with respect to formation conditions can be collected than from regulated bag measurements. A challenging issue for researchers is the accurate time-alignment between tailpipe measurements and engine operating variables. An alignment procedure should take into account both the reaction time of the analyzers and the dynamics of gas transport in the exhaust and measurement systems. This paper discusses a statistical modeling framework that compensates for variable exhaust transport delay while relating tailpipe measurements with engine operating covariates. Specifically it is shown that some variants of the smooth transition regression model allow for transport delays that vary smoothly as functions of the exhaust flow rate. These functions are characterized by a pair of coefficients that can be estimated via a least-squares procedure. The proposed models can be adapted to encompass inherent nonlinearities that were implicit in previous instantaneous emissions modeling efforts. This article describes the methodology and presents an illustrative application which uses data collected from a diesel bus under real-world driving conditions.