Chesapeake Coastal Community Flood Vulnerability--Prediction and Verification

Fast moving hurricanes and stationary nor’easters have resulted in significant flood damage in Chesapeake tidewater communities. The Chesapeake Bay region is one of A m erica’s most vulnerable regions with respect to sea-level rise, which will only increase storm surge impacts over upcoming decades. While the general trends are well documented, there is limited information relevant to specific communities’ relative flood risk and response. The dearth o f data is especially troublesome given the lengthy period o f time generally needed for communities to plan and implement adaptive action. This study contributes to the regional understanding of flood and sea-level rise vulnerability by applying physical, social, and combined vulnerability indices to tidally influenced localities along the Chesapeake Bay. Unlike other combinations of physical and socioeconomic data, the physical vulnerability index for this study is calculated at a scale that can directly link into social vulnerability index information at local and regional levels. The research also considers the distribution of coastal natural capital (in the form o f marshes and forests) alongside these indices at comparable scales. By calculating the indices for conditions o f the early 2000s, this study also tested their predictive value against Hurricane Isabel, a landmark 2003 storm that flooded areas across the region. Systematic verification “hindcasts” o f past events are relatively rare for vulnerability index evaluation. By attempting to establish connections between real flooding data, socioeconomic activity, and vulnerability indices, this study questions whether theoretical vulnerability indices work as true proxies for real world conditions. The results question the true utility o f these indices by showing limited relationships between vulnerability and changes in community socio-economic activity. The research also emphasizes the need for more data collection and consideration in order to better comprehensively understand coastal flood impacts and their management implications

Developing a Framework to Identify Local Business and Government Vulnerability to Sea-Level Rise: A Case Study of Coastal Virginia

In this paper we develop methods for identifying local business and government vulnerabilities to sea-level rise and the natural hazards associated with it. Unlike the fairly large literature on measuring social vulnerability to natural hazards, there are very few papers that discuss methods for measuring local business or local government vulnerability even though businesses and governments are also differentially affected natural hazards. Our goal is to create measures that are easily replicable using readily available data and that are easy to explain to local planners, policy makers, and citizens. We implement our measures of local business and government vulnerability for our study area, Coastal Virginia. We then combine those measures with a physical vulnerability measure to identify the areas in Coastal Virginia where planners and policy makers need to more closely examine the potential impacts of sea-level rise on their local businesses and government. While our methods are tailored to Coastal Virginia, they could be easily applied in other areas threatened by sea-level rise

Informing social decision making: Physical vulnerability to sea level rise

Social indices on census tract and other geopolitical levels are increasingly being considered to inform decision making. In a flooding and sea level rise context, the likelihood of an area flooding is an important component of adaptation and the decision-making framework, however it is frequently modeled on a continuous scale. In our work, we have developed an index of physical vulnerability to flooding on a census tract scale, specifically designed to complement social vulnerability indices

Summary Tables: Lancaster County, Virginia Shoreline Inventory Report

The Shoreline Inventory Summary Tables quantify observed conditions based on river systems, such as the combined length of linear features (e.g. shoreline miles surveyed, miles of bulkhead and revetment), the total number of point features (e.g. docks, boathouses, boat ramps) & total acres of polygon features (tidal marshes)

Lancaster County, Virginia Shoreline Inventory Report Methods and Guidelines

The data inventory developed for the Shoreline Inventory is based on a three tiered shoreline assessment approach. This assessment characterizes conditions that can be observed from a small boat navigating along the shoreline or by using observations made remotely at the desktop using high resolution imagery. The three tiered shoreline assessment approach divides the shorezone into three regions: the immediate riparian zone, evaluated for land use the bank, evaluated for height, cover and natural protection the shoreline, describing the presence of shoreline structures for shore protection and recreational purposes. The 2015 Inventory for Lancaster County was generated using on-screen, digitizing techniques in ArcGIS&tm; - ArcMap v10.2.2 while viewing conditions observed in 2015 Bing high resolution oblique imagery and 2013 imagery from the Virginia Base Mapping Program (VBMP). These data sources allowed the inventory to be generated without additional field work. Three GIS shapefiles are developed. The first describes land use and bank conditions (LancasterCo _lubc_2015). The second reports shoreline structures that are described as arcs or lines (LancasterCo _sstru_2015). The final shapefile includes all structures that are represented as points (LancasterCo_astru_2015). The shapefiles use a shoreline basemap updated in-house from the 2013 VBMP high resolution digital terrain model. The shoreline is re-coded to reflect features and attributes observed. The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain to data

Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.Comment: 18 pages, 9 figure

Suppression of quantum oscillations and the dependence on site energies in electronic excitation transfer in the Fenna-Matthews-Olson trimer

Energy transfer in the photosynthetic complex of the Green Sulfur Bacteria known as the Fenna-Matthews-Olson (FMO) complex is studied theoretically taking all three subunits (monomers) of the FMO trimer and the recently found eighth bacteriochlorophyll (BChl) molecule into account. We find that in all considered cases there is very little transfer between the monomers. Since it is believed that the eighth BChl is located near the main light harvesting antenna we look at the differences in transfer between the situation when BChl 8 is initially excited and the usually considered case when BChl 1 or 6 is initially excited. We find strong differences in the transfer dynamics, both qualitatively and quantitatively. When the excited state dynamics is initialized at site eight of the FMO complex, we see a slow exponential-like decay of the excitation. This is in contrast to the oscillations and a relatively fast transfer that occurs when only seven sites or initialization at sites 1 and 6 is considered. Additionally we show that differences in the values of the electronic transition energies found in the literature lead to a large difference in the transfer dynamics

Back to the future of soil metagenomics

JN was funded by a fellowship from the French MENESR.Peer reviewedPeer Reviewe

Four distinct trajectories of tau deposition identified in Alzheimer’s disease

Alzheimer’s Disease Neuroimaging Initiative.Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These ‘subtypes’ were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of ‘typical AD’ and a revisiting of tau pathological staging.J.W.V. acknowledges support from the government of Canada through a tri-council Vanier Canada Graduate Doctoral fellowship from the McGill Centre for Integrative Neuroscience and the Healthy Brains, Healthy Lives initiative, and from the National Institutes of Health (NIH) (no. T32MH019112). A.L.Y. is supported by a Medical Research Council Skills Development Fellowship (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (no. MR/S03546X/1). N.P.O. and D.C.A. acknowledge support from the UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and D.C.A. acknowledges support from the Engineering and Physical Sciences Research Council grant no. EP/M020533/1. M.J.G. is supported by the Miguel Servet program (no. CP19/00031) and a research grant (no. PI20/00613) of the Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional. R.L.J. acknowledges support from the NIH (no. K99AG065501). This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant no. 666992. The BioFINDER studies are supported by the Swedish Research Council (no. 2016-00906), the Knut and Alice Wallenberg Foundation (no. 2017-0383), the Marianne and Marcus Wallenberg Foundation (no. 2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer’s Foundation (no. AF-939932), the Swedish Brain Foundation (no. FO2019-0326), the Swedish Parkinson Foundation (no. 1280/20), the Skåne University Hospital Foundation (no. 2020-O000028), Regionalt Forskningsstöd (no. 2020-0314) and the Swedish Federal Government under the ALF agreement (no. 2018-Projekt0279). The Tau PET study in Gangnam Severance Hospital was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (nos. NRF2018R1D1A1B07049386 and NRF2020R1F1A1076154) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI18C1159). We also thank B. L. Miller, H. J. Rosen, M. Gorno Tempini and W. Jagust for supporting the UCSF tau-PET studies, which were funded through the following sources: National Institute on Aging (NIA) no. R01 AG045611 (G.D.R.), no. P50 AG23501 (B.L.M., H.J.R., G.D.R.), no. P01 AG019724 (B.L.M., H.J.R., G.D.R.). The precursor of 18F-flortaucipir was provided by AVID Radiopharmaceuticals. The precursor of 18F-flutemetamol was sponsored by GE Healthcare. The precursor of 18F-RO948 was provided by Roche. Data collection and sharing for this project were funded by ADNI (NIH grant no. U01 AG024904) and Department of Defense ADNI (award no. W81XWH-12-2-0012). ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bioclinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EUROIMMUN; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research Development; Johnson & Johnson Pharmaceutical Research Development; Lumosity; Lundbeck; Merck; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California