Development and testing of a database of NIH research funding of AAPM members: A report from the AAPM Working Group for the Development of a Research Database (WGDRD).

PURPOSE: To produce and maintain a database of National Institutes of Health (NIH) funding of the American Association of Physicists in Medicine (AAPM) members, to perform a top-level analysis of these data, and to make these data (hereafter referred to as the AAPM research database) available for the use of the AAPM and its members. METHODS: NIH-funded research dating back to 1985 is available for public download through the NIH exporter website, and AAPM membership information dating back to 2002 was supplied by the AAPM. To link these two sources of data, a data mining algorithm was developed in Matlab. The false-positive rate was manually estimated based on a random sample of 100 records, and the false-negative rate was assessed by comparing against 99 member-supplied PI_ID numbers. The AAPM research database was queried to produce an analysis of trends and demographics in research funding dating from 2002 to 2015. RESULTS: A total of 566 PI_ID numbers were matched to AAPM members. False-positive and -negative rates were respectively 4% (95% CI: 1-10%, N = 100) and 10% (95% CI: 5-18%, N = 99). Based on analysis of the AAPM research database, in 2015 the NIH awarded $USD 110M to members of the AAPM. The four NIH institutes which historically awarded the most funding to AAPM members were the National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Heart Lung and Blood Institute, and National Institute of Neurological Disorders and Stroke. In 2015, over 85$116M, which is lower than the historic mean of $120M (in 2015 USD). CONCLUSIONS: A database of NIH-funded research awarded to AAPM members has been developed and tested using a data mining approach, and a top-level analysis of funding trends has been performed. Current funding of AAPM members is lower than the historic mean. The database will be maintained by members of the Working group for the development of a research database (WGDRD) on an annual basis, and is available to the AAPM, its committees, working groups, and members for download through the AAPM electronic content website. A wide range of questions regarding financial and demographic funding trends can be addressed by these data. This report has been approved for publication by the AAPM Science Council

Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation

Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions

The conservation value of human-modified landscapes for the world's primates

Land-use change pushes biodiversity into human-modified landscapes, where native ecosystems are surrounded by anthropic land covers (ALCs). Yet, the ability of species to use these emerging covers remains poorly understood. We quantified the use of ALCs by primates worldwide, and analyzed species' attributes that predict such use. Most species use secondary forests and tree plantations, while only few use human settlements. ALCs are used for foraging by at least 86 species with an important conservation outcome: those that tolerate heavily modified ALCs are 26% more likely to have stable or increasing populations than the global average for all primates. There is no phylogenetic signal in ALCs use. Compared to all primates on Earth, species using ALCs are less often threatened with extinction, but more often diurnal, medium or large-bodied, not strictly arboreal, and habitat generalists. These findings provide valuable quantitative information for improving management practices for primate conservation worldwide

Mobile and wearable computing in patagonian wilderness

Recent advances in mobile and wearable technology in the last few years have made the optimization of data collection processes possible in diverse fields. Users currently have access to small portable devices that are not only sensitive to their activity, but also to their interaction with their environment. These growing technological advances are in constant development , and have given way to the study and redesign of processes that can be tailored to fit any particular needs. Even users that are far from urbanization, without access to electricity can make use of these possibilities. These technologies can substantially improve their productivity, by allowing them to concentrate solely on their own tasks instead of on the interactions with the computational method used to support their activities. This study presents results and indicators relating to the application these tools within the field of Flora information retrieval, in areas far from urban centers.Instituto de Investigación en Informátic

Extinction Debt in Source-Sink Metacommunities

In an increasingly modified world, understanding and predicting the consequences of landscape alteration on biodiversity is a challenge for ecologists. To this end, metacommunity theory has developed to better understand the complexity of local and regional interactions that occur across larger landscapes. While metacommunity ecology has now provided several alternative models of species coexistence at different spatial scales, predictions regarding the consequences of landscape alteration have been done exclusively for the competition-colonization trade off model (CC). In this paper we investigate the effects of landscape perturbation on source-sink metacommunities. We show that habitat destruction perturbs the equilibria among species competitive effects within the metacommunity, driving both direct extinctions and an indirect extinction debt. As in CC models, we found a time lag for extinction following habitat destruction that varied in length depending upon the relative importance of direct and indirect effects. However, in contrast to CC models, we found that the less competitive species are more affected by habitat destruction. The best competitors can sometimes even be positively affected by habitat destruction, which corresponds well with the results of field studies. Our results are complementary to those results found in CC models of metacommunity dynamics. From a conservation perspective, our results illustrate that landscape alteration jeopardizes species coexistence in patchy landscapes through complex indirect effects and delayed extinctions patterns

When is the Best Time to Sample Aquatic Macroinvertebrates in Ponds for Biodiversity Assessment?

Ponds are sites of high biodiversity and conservation value, yet there is little or no statutory monitoring of them across most of Europe. There are clear and standardized protocols for sampling aquatic macroinvertebrate communities in ponds but the most suitable time(s) to undertake the survey(s) remains poorly specified. This paper examined the aquatic macroinvertebrate communities from 95 ponds within different landuse types over three seasons (spring, summer and autumn) to determine the most appropriate time to undertake sampling to characterise biodiversity. The combined samples from all three seasons provided the most comprehensive record of the aquatic macroinvertebrate taxa recorded within ponds (alpha and gamma diversity). Samples collected during the autumn survey yielded significantly greater macroinvertebrate richness (76% of the total diversity) than either spring or summer surveys. Macroinvertebrate diversity was greatest during autumn in meadow and agricultural ponds but taxon richness among forest and urban ponds did not differ significantly temporally. The autumn survey provided the highest measures of richness for Coleoptera, Hemiptera and Odonata. However, richness of the aquatic insect order Trichoptera was highest in spring and lowest in autumn. The results illustrate that multiple surveys, covering more than one season, provide the most comprehensive representation of macroinvertebrate biodiversity. When sampling can only be undertaken on one occasion, the most appropriate time to undertake surveys to characterise the macroinvertebrate community biodiversity is during the autumn; although this may need to be modified if other floral and faunal groups need to be incorporated in to the sampling programme

The heterogeneity of wooded-agricultural landscape mosaics influences woodland bird community assemblages

Context Landscape heterogeneity (the composition and configuration of different landcover types) plays a key role in shaping woodland bird assemblages in wooded-agricultural mosaics. Understanding how species respond to landscape factors could contribute to preventing further decline of woodland bird populations. Objective To investigate how woodland birds with different species traits respond to landscape heterogeneity, and to identify whether specific landcover types are important for maintaining diverse populations in wooded-agricultural environments. Methods Birds were sampled from woodlands in 58 2 x 2 km tetrads across southern Britain. Landscape heterogeneity was quantified for each tetrad. Bird assemblage response was determined using redundancy analysis combined with variation partitioning and response trait analyses. Results For woodland bird assemblages, the independent explanatory importance of landscape composition and landscape configuration variables were closely interrelated. When considered simultaneously during variation partitioning, the community response was better represented by compositional variables. Different species responded to different landscape features and this could be explained by traits relating to woodland association, foraging strata and nest location. Ubiquitous, generalist species, many of which were hole-nesters or ground foragers, correlated positively with urban landcover while specialists of broadleaved woodland avoided landscapes containing urban areas. Species typical of coniferous woodland correlated with large conifer plantations. Conclusions At the 2 x 2 km scale, there was evidence that the availability of resources provided by proximate landcover types was highly important for shaping woodland bird assemblages. Further research to disentangle the effects of composition and configuration at different spatial scales is advocated

The impacts of landscape structure on the winter movements and habitat selection of female red deer

An area of research that has recently gained more attention is to understand how species respond to environmental change such as the landscape structure and fragmentation. Movement is crucial to select habitats but the landscape structure influences the movement patterns of animals. Characterising the movement characteristics, utilisation distribution (UD) and habitat selection of a single species in different landscapes can provide important insights into species response to changes in the landscape. We investigate these three fields in female red deer (Cervus elaphus) in southern Sweden, in order to understand how landscape structure influences their movement and feeding patterns. Movements are compared between two regions, one dominated by a fragmented agriculture-forest mosaic and the other by managed homogenous forest. Red deer in the agriculture-dominated landscape had larger UDs compared to those in the forest-dominated area, moved larger distances between feeding and resting and left cover later in the day but used a similar duration for their movements, suggesting faster travelling speeds between resting and feeding locations. The habitat selection patterns of red deer indicate a trade-off between forage and cover, selecting for habitats that provide shelter during the day and forage by night. However, the level of trade-off, mediated through movement and space use patterns, is influenced by the landscape structure. Our approach provides further understanding of the link between individual animal space use and changing landscapes and can be applied to many species able to carry tracking devices