Identifying spatial and temporal suicide clusters in a Californian county

Barriers to suicide cluster detection and monitoring include requiring advanced software and statistical knowledge. We tested face validity of a simple method using readily accessible household software, Excel 3D Maps, to identify suicide clusters in this county, years 2014–2019. For spatial and temporal clusters, respectively, we defined meaningful thresholds of suicide density as 1.39/km2 and 33.9/yearly quarter, defined as the 95th percentile of normal logarithmic and normal scale distributions of suicide density per area in each ZIP Code Tabulated Area and 24 yearly quarters from all years. We generated heat maps showing suicide densities per 2.5 km viewing diameter. We generated a one-dimensional temporal map of 3-month meaningful cluster(s). We identified 21 total population spatial clusters and one temporal cluster. For greater accessibility, we propose an alternative method to traditional scan statistics using Excel 3D Maps potentially broadly advantageous in detecting, monitoring, and intervening at suicide clusters

Mapping breast cancer blood flow index, composition, and metabolism in a human subject using combined diffuse optical spectroscopic imaging and diffuse correlation spectroscopy

Diffuse optical spectroscopic imaging (DOSI) and diffuse correlation spectroscopy (DCS) are modelbased near-infrared (NIR) methods that measure tissue optical properties (broadband absorption, mu(a), and reduced scattering, mu(s)) and blood flow (blood flow index, BFI), respectively. DOSI-derived mu(a) values are used to determine composition by calculating the tissue concentration of oxy- and deoxyhemoglobin(HbO2,HbR), water, and lipid. We developed and evaluated a combined, coregistered DOSI/ DCS handheld probe for mapping and imaging these parameters. We show that uncertainties of 0.3 mm(-1) (37%) in mu(s) and 0.003 mm(-1) (33%) in mu(a) lead to similar to 53% and 9% errors in BFI, respectively. DOSI/ DCS imaging of a solid tissue-simulating flow phantom and a breast cancer patient reveals well-defined spatial distributions of BFI and composition that clearly delineates both the flow channel and the tumor. BFI reconstructed with DOSI-corrected mu(a) and mu(s) values had a tumor/ normal contrast of 2.7, 50% higher than the contrast using commonly assumed fixed optical properties. In conclusion, spatially coregistered imaging of DOSI and DCS enhances intrinsic tumor contrast and information content. This is particularly important for imaging diseased tissues where there are significant spatial variations in mu(a) and mu(s) as well as potential uncoupling between flow and metabolism. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication,Funding Agencies|National Institute of Biomedical Imaging and Bioengineering [P41EB015890]; National Cancer Institute [R01CA142989, U54CA136400]; Chao Family Comprehensive Cancer Center [P30CA62203]; Arnold and Mabel Beckman Foundation; Fulbright Visiting Scholar grant; Swedish Governmental Agency for Innovation Systems (VINNOVA) [2015-0153]; NIH [P41-EB015893, 1R01NS060653]</p