Social Dimensions of Urban Flood Experience, Exposure, and Concern

With growing urban populations and climate change, urban flooding is an important global issue, even in dryland regions. Flood risk assessments are usually used to identify vulnerable locations and populations, flooding experience patterns, or levels of concern about flooding, but rarely are all of these approaches combined. Furthermore, the social dynamics of flood concerns, exposure, and experience are underexplored. We combined geographic and survey data on household‐level measures of flood experience, concern, and exposure in Utah\u27s urbanizing Wasatch Front. We asked: (1) Are socially vulnerable groups more likely to be exposed to flood risk? (2) How common are flooding experiences among urban residents, and how are these experiences related to sociodemographic characteristics and exposure? and (3) How concerned are urban residents about flooding, and does concern vary by exposure, flood experience, and sociodemographic characteristics? Although floodplain residents were more likely to be White and have higher incomes, respondents who were of a racial/ethnic minority, were older, had less education, and were living in floodplains were more likely to report flood experiences and concern about flooding. Flood risk management approaches need to address social as well as physical sources of vulnerability to floods and recognize social sources of variation in flood experiences and concern

Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation

Towards Whole Placenta Segmentation At Late Gestation Using Multi-View Ultrasound Images

We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.Wellcome Trust IEH Award; EPSRC Centre for Medical Engineering; National Institute for Health Research (NIHR); King’s College London; NHS Foundation Trus

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

The effectiveness of e-Learning on biosecurity practice to slow the spread of invasive alien species

Online e-Learning is increasingly being used to provide environmental training. Prevention measures including biosecurity are essential to reducing the introduction and spread of invasive alien species (IAS) and are central to international and national IAS policy. This paper is the first to evaluate the effectiveness of e-Learning as a tool to increase awareness, risk perception and biosecurity behaviour in relation to IAS among individuals conducting work activities or research (fieldwork) in the field. We surveyed participants (a mixture of students and professionals) before, and 6 months after undertaking an e-Learning course on IAS and biosecurity practices. Awareness of IAS and self-reported biosecurity behaviour increased after e-Learning among students and professionals. Students had a lower awareness of IAS than professionals before training (20% of students vs 60% of professionals), but after training students showed a greater increase in awareness which led to similar levels of awareness post-training (81%). Prior to training, risk perception was also lower amongst students than professionals (33% of students and 59% of professionals were aware of the risk that their activities posed to the accidental spread of IAS). There was no change in risk perception amongst professionals after training, however training led to a doubling of risk perception in students. E-Learning also led to an increase in reported biosecurity behaviour and cleaning practices and there were higher levels of biosecurity cleaning amongst professionals. The higher awareness and better biosecurity amongst professionals is likely to reflect their familiarity with the issues of IAS and day-to-day activities in the field. Our results suggest that e-Learning is an effective tool to raise awareness and encourage behaviour change among field workers and researchers in an attempt to reduce the risk of accidental introduction and spread of IAS