Estimating Peak Demand for Beach Parking Spaces

The United States Army Corps of Engineers planning guidance stipulates that in order for local beach communities to qualify for Federal cost share funds for Hurricane and Storm Damage Reduction beach renourishment projects, the community must provide public beach access and parking to satisfy peak demand. This study presents a method for estimating peak demand for beach parking spaces in the presence of parking constraints. A Tobit regression model is developed to estimate the number of parking spaces that would be necessary to meet unconstrained demand on a given percentage of peak demand days. For example, the model can be used to estimate the number of parking spaces that would be adequate to meet peak demand on 90% of peak parking days. The Tobit model provides a promising framework for estimating peak parking demand under constrained parking conditions, a situation that characterizes most beach communities.

Allelic imbalances of chromosomes 8p and 18q and their roles in distant relapse of early stage, node-negative breast cancer

INTRODUCTION: Identification of breast cancer patients at risk for postoperative distant relapse is an important clinical issue. Existing pathological markers can predict disease recurrence only to a certain extent, and there is a need for more accurate predictors. METHODS: Using 'counting alleles', a novel experimental method, we determined allelic status of chromosomes 8p and 18q in a case-control study with 65 early stage, node negative, invasive ductal carcinomas (IDCs). The association between allelic imbalance (AI) of both chromosomal markers and distant relapses was examined. RESULTS: Eighty percent of tumors contained 8pAI and sixty-eight percent of tumors contained 18qAI. However, none of the tumor samples retained both chromosome 8p and 18q alleles. More importantly, tumors with 8pAI but not 18qAI were more likely to have distant relapse compared to tumors with 18qAI but not 8pAI. CONCLUSION: Our finding suggests that differential allelic loss of chromosomes 8p and 18q may represent subtypes of early stage IDC with different tumor progression behaviors

Non-Conjugated Small Molecule FRET for Differentiating Monomers from Higher Molecular Weight Amyloid Beta Species

Background: Systematic differentiation of amyloid (Aβ) species could be important for diagnosis of Alzheimer's disease (AD). In spite of significant progress, controversies remain regarding which species are the primary contributors to the AD pathology, and which species could be used as the best biomarkers for its diagnosis. These controversies are partially caused by the lack of reliable methods to differentiate the complicated subtypes of Aβ species. Particularly, differentiation of Aβ monomers from toxic higher molecular weight species (HrMW) would be beneficial for drug screening, diagnosis, and molecular mechanism studies. However, fast and cheap methods for these specific aims are still lacking. Principal Findings: We demonstrated the feasibility of a non-conjugated FRET (Förster resonance energy transfer) technique that utilized amyloid beta (Aβ) species as intrinsic platforms for the FRET pair assembly. Mixing two structurally similar curcumin derivatives that served as the small molecule FRET pair with Aβ40 aggregates resulted in a FRET signal, while no signal was detected when using Aβ40 monomer solution. Lastly, this FRET technique enabled us to quantify the concentrations of Aβ monomers and high molecular weight species in solution. Significance: We believe that this FRET technique could potentially be used as a tool for screening for inhibitors of Aβ aggregation. We also suggest that this concept could be generalized to other misfolded proteins/peptides implicated in various pathologies including amyloid in diabetes, prion in bovine spongiform encephalopathy, tau protein in AD, and α-synuclein in Parkinson disease.National Institute on Aging (K25AG036760

Effects of HLA single chain trimer design on peptide presentation and stability

MHC class I “single-chain trimer” molecules, coupling MHC heavy chain, β2-microglobulin, and a specific peptide into a single polypeptide chain, are widely used in research. To more fully understand caveats associated with this design that may affect its use for basic and translational studies, we evaluated a set of engineered single-chain trimers with combinations of stabilizing mutations across eight different classical and non-classical human class I alleles with 44 different peptides, including a novel human/murine chimeric design. While, overall, single-chain trimers accurately recapitulate native molecules, care was needed in selecting designs for studying peptides longer or shorter than 9-mers, as single-chain trimer design could affect peptide conformation. In the process, we observed that predictions of peptide binding were often discordant with experiment and that yields and stabilities varied widely with construct design. We also developed novel reagents to improve the crystallizability of these proteins and confirmed novel modes of peptide presentation

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease

Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps. Results: Across three independent datasets, group separation showed high accuracy for AD dementia vs. controls (AUC$\geq$0.92) and moderate accuracy for MCI vs. controls (AUC$\approx$0.75). Relevance maps indicated that hippocampal atrophy was considered as the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r$\approx$-0.86, p<0.001). Conclusion: The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code available on GitHu

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Human and mouse essentiality screens as a resource for disease gene discovery

The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery. Discovery of causal variants for monogenic disorders has been facilitated by whole exome and genome sequencing, but does not provide a diagnosis for all patients. Here, the authors propose a Full Spectrum of Intolerance to Loss-of-Function (FUSIL) categorization that integrates gene essentiality information to aid disease gene discovery