On The Origin Of The Gamma Rays From The Galactic Center

The region surrounding the center of the Milky Way is both astrophysically rich and complex, and is predicted to contain very high densities of dark matter. Utilizing three years of data from the Fermi Gamma Ray Space Telescope (and the recently available Pass 7 ultraclean event class), we study the morphology and spectrum of the gamma ray emission from this region and find evidence of a spatially extended component which peaks at energies between 300 MeV and 10 GeV. We compare our results to those reported by other groups and find good agreement. The extended emission could potentially originate from either the annihilations of dark matter particles in the inner galaxy, or from the collisions of high energy protons that are accelerated by the Milky Way's supermassive black hole with gas. If interpreted as dark matter annihilation products, the emission spectrum favors dark matter particles with a mass in the range of 7-12 GeV (if annihilating dominantly to leptons) or 25-45 GeV (if annihilating dominantly to hadronic final states). The intensity of the emission corresponds to a dark matter annihilation cross section consistent with that required to generate the observed cosmological abundance in the early universe (sigma v ~ 3 x 10^-26 cm^3/s). We also present conservative limits on the dark matter annihilation cross section which are at least as stringent as those derived from other observations.Comment: 13 pages, 11 figure

Who approves/pays for additional monitoring?

Major considerations in the provision of healthcare are availability, affordability, accessibility, and appropriateness, especially in the setting of heart failure where disease burden is growing, developments have been rapid and newer biomarkers, diagnostic and imaging techniques, monitoring systems, devices, procedures, and drugs have all been developed in a relatively short period of time. Many monitoring and diagnostic systems have been developed but the disproportionate cost of conducting trials of their effectiveness has limited their uptake. There are added complexities, in that the utilization of doctors for the supervision of the monitoring results may be optimal in one setting and not in another because of differences in the characteristics of organization of healthcare provision, making even interpretation of the trials we have had, still difficult to interpret. New technologies are continuously changing the approach to healthcare and will reshape the structure of the healthcare systems in the future. Mobile technologies can empower patients and carers by giving them more control over their health and social care needs and reducing their dependence on healthcare professionals for monitoring their health, but a significant problem is the integration of the multitude of monitored parameters with clinical data and the recognition of intervention thresholds. Digital technology can help, but we need to prove its cost/efficacy and how it will be paid for. Governments in many European countries and worldwide are trying to establish frameworks that promote the convergence of standards and regulations for telemedicine solutions and yet simultaneously health authorities are closely scrutinizing healthcare spending, with the objective of reducing and optimizing expenditure in the provision of health services. There are multiple factors to be considered for the reimbursement models associated with the implementation of physiological monitoring yet it remains a challenge in cash-strapped health systems

Dark Matter Annihilation in The Galactic Center As Seen by the Fermi Gamma Ray Space Telescope

We analyze the first two years of data from the Fermi Gamma Ray Space Telescope from the direction of the inner 10 degrees around the Galactic Center with the intention of constraining, or finding evidence of, annihilating dark matter. We find that the morphology and spectrum of the emission between 1.25 degrees and 10 degrees from the Galactic Center is well described by a the processes of decaying pions produced in cosmic ray collisions with gas, and the inverse Compton scattering of cosmic ray electrons in both the disk and bulge of the Inner Galaxy, along with gamma rays from known points sources in the region. The observed spectrum and morphology of the emission within approximately 1.25 degrees (~175 parsecs) of the Galactic Center, in contrast, departs from the expectations for by these processes. Instead, we find an additional component of gamma ray emission that is highly concentrated around the Galactic Center. The observed morphology of this component is consistent with that predicted from annihilating dark matter with a cusped (and possibly adiabatically contracted) halo distribution (density proportional to r^{-gamma}, with gamma=1.18 to 1.33). The observed spectrum of this component, which peaks at energies between 1-4 GeV (in E^2 units), can be well fit by a 7-10 GeV dark matter particle annihilating primarily to tau leptons with a cross section in the range of 4.6 x 10^-27 to 5.3 x 10^-26 cm^3/s, depending on how the dark matter distribution is normalized. We also discuss other sources for this emission, including the possibility that much of it originates from the Milky Way's supermassive black hole.Comment: 23 pages, 16 figure

Image-Based Assessment of Drought Response in Grapevines

Many plants can modify their leaf profile rapidly in response to environmental stress. Image-based data are increasingly used to retrieve reliable information on plant water status in a non-contact manner that has the potential to be scaled to high-throughput and repeated through time. This paper examined the variation of leaf angle as measured by both 3D images and goniometer in progressively drought stressed grapevine. Grapevines, grown in pots, were subjected to a 21-day period of drought stress receiving 100% (CTRL), 60% (IRR60%) and 30% (IRR30%) of maximum soil available water capacity. Leaf angle was (i) measured manually (goniometer) and (ii) computed by a 3D reconstruction method (multi-view stereo and structure from motion). Stomatal conductance, leaf water potential, fluorescence (Fv/Fm), leaf area and 2D RGB data were simultaneously collected during drought imposition. Throughout the experiment, values of leaf water potential ranged from −0.4 (CTRL) to −1.1 MPa (IRR30%) and it linearly influenced the leaf angle when measured manually (R2 = 0.86) and with 3D image (R2 = 0.73). Drought was negatively related to stomatal conductance and leaf area growth particularly in IRR30% while photosynthetic parameters (i.e., Fv/Fm) were not impaired by water restriction. A model for leaf area estimation based on the number of pixels of 2D RGB images developed at a different phenotyping robotized platform in a closely related experiment was successfully employed (R2 = 0.78). At the end of the experiment, top view 2D RGB images showed a ∼50% reduction of greener fraction (GGF) in CTRL and IRR60% vines compared to initial values, while GGF in IRR30% increased by approximately 20%

Polygenic Parkinson's Disease Genetic Risk Score as Risk Modifier of Parkinsonism in Gaucher Disease

Background: Biallelic pathogenic variants in GBA1 are the cause of Gaucher disease (GD) type 1 (GD1), a lysosomal storage disorder resulting from deficient glucocerebrosidase. Heterozygous GBA1 variants are also a common genetic risk factor for Parkinson's disease (PD). GD manifests with considerable clinical heterogeneity and is also associated with an increased risk for PD. Objective: The objective of this study was to investigate the contribution of PD risk variants to risk for PD in patients with GD1. Methods: We studied 225 patients with GD1, including 199 without PD and 26 with PD. All cases were genotyped, and the genetic data were imputed using common pipelines. Results: On average, patients with GD1 with PD have a significantly higher PD genetic risk score than those without PD (P = 0.021). Conclusions: Our results indicate that variants included in the PD genetic risk score were more frequent in patients with GD1 who developed PD, suggesting that common risk variants may affect underlying biological pathways. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson's Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale