Social Sector Business Ventures: The Critical Factors That Maximize Success

This paper seeks to help social sector leaders understand the factors that they should consider when launching revenue-generating business ventures. Given that much of the research on social sector business ventures is based on the personal experiences of individual practitioners, there is a wide array of advice for organizational leaders who are thinking about launching business ventures. Consequently, we approach the subject of social sector business ventures in a systematic and analytic way in order to determine what organizational leaders really need to know about launching successful ventures. We introduce a framework called "business in a box" that separates the process of thinking about launching business ventures from the organizational characteristics and dynamics that influence these ventures. We assert that organizational leaders who wish to maximize the success of their business ventures must explore (1) what is "inside" the box (The Business and its Context) to understand the business fundamentals of launching a venture and (2) what is "outside" the box (Assets and Internal Destructive Forces) to understand the forces and dynamics within the organizational context that impact these ventures.This publication is Hauser Center Working Paper No. 43. The Hauser Center Working Paper Series was launched during the summer of 2000. The Series enables the Hauser Center to share with a broad audience important works-in-progress written by Hauser Center scholars and researchers

Changes in PM2.5 Peat Combustion Source Profiles with Atmospheric Aging in an Oxidation Flow Reactor

Smoke from laboratory chamber burning of peat fuels from Russia, Siberia, the USA (Alaska and Florida), and Malaysia representing boreal, temperate, subtropical, and tropical regions was sampled before and after passing through a potential-aerosol-mass oxidation flow reactor (PAM-OFR) to simulate intermediately aged (∼2 d) and well-aged (∼7 d) source profiles. Species abundances in PM2.5 between aged and fresh profiles varied by several orders of magnitude with two distinguishable clusters, centered around 0.1 % for reactive and ionic species and centered around 10 % for carbon. Organic carbon (OC) accounted for 58 %–85 % of PM2.5 mass in fresh profiles with low elemental carbon (EC) abundances (0.67 %–4.4 %). OC abundances decreased by 20 %–33 % for well-aged profiles, with reductions of 3 %–14 % for the volatile OC fractions (e.g., OC1 and OC2, thermally evolved at 140 and 280 ∘C). Ratios of organic matter (OM) to OC abundances increased by 12 %–19 % from intermediately aged to well-aged smoke. Ratios of ammonia (NH3) to PM2.5 decreased after intermediate aging. Well-aged NH+4 and NO−3 abundances increased to 7 %–8 % of PM2.5 mass, associated with decreases in NH3, low-temperature OC, and levoglucosan abundances for Siberia, Alaska, and Everglades (Florida) peats. Elevated levoglucosan was found for Russian peats, accounting for 35 %–39 % and 20 %–25 % of PM2.5 mass for fresh and aged profiles, respectively. The water-soluble organic carbon (WSOC) fractions of PM2.5 were over 2-fold higher in fresh Russian peat (37.0±2.7 %) than in Malaysian (14.6±0.9 %) peat. While Russian peat OC emissions were largely water-soluble, Malaysian peat emissions were mostly water-insoluble, with WSOC ∕ OC ratios of 0.59–0.71 and 0.18–0.40, respectively. This study shows significant differences between fresh and aged peat combustion profiles among the four biomes that can be used to establish speciated emission inventories for atmospheric modeling and receptor model source apportionment. A sufficient aging time (∼7 d) is needed to allow gas-to-particle partitioning of semi-volatilized species, gas-phase oxidation, and particle volatilization to achieve representative source profiles for regional-scale source apportionment

Sars-Cov-2 Serostatus and Covid-19 Illness Characteristics By Variant Time Period in Non-Hospitalized Children and adolescents

OBJECTIVE: to describe COVID-19 illness characteristics, risk factors, and SARS-CoV-2 serostatus by variant time period in a large community-based pediatric sample. DESIGN: Data were collected prospectively over four timepoints between October 2020 and November 2022 from a population-based cohort ages 5 to 19 years old. SETTING: State of Texas, USA. PARTICIPANTS: Participants ages 5 to 19 years were recruited from large pediatric healthcare systems, Federally Qualified Healthcare Centers, urban and rural clinical practices, health insurance providers, and a social media campaign. EXPOSURE: SARS-CoV-2 infection. MAIN OUTCOME(S) AND MEASURE(S): SARS-CoV-2 antibody status was assessed by the Roche Elecsys RESULTS: Over half (57.2%) of the sample (N = 3911) was antibody positive. Symptomatic infection increased over time from 47.09% during the pre-Delta variant time period, to 76.95% during Delta, to 84.73% during Omicron, and to 94.79% during the Omicron BA.2. Those who were not vaccinated were more likely (OR 1.71, 95% CI 1.47, 2.00) to be infected versus those fully vaccinated. CONCLUSIONS: Results show an increase in symptomatic COVID-19 infection among non-hospitalized children with each progressive variant over the past two years. Findings here support the public health guidance that eligible children should remain up to date with COVID-19 vaccinations

Antibody Duration after infection From Sars-Cov-2 in the Texas Coronavirus antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not)

The effect of multiple internal representations on context rich instruction

This paper presents n-coding, a theoretical model of multiple internal mental representations. The n-coding construct is developed from a review of cognitive and imaging studies suggesting the independence of information processing along different modalities: verbal, visual, kinesthetic, social, etc. A study testing the effectiveness of the n-coding construct in an algebra-based mechanics course is presented. Four sections differing in the level of n-coding opportunities were compared. Besides a traditional instruction section used as a control group, each of the remaining three treatment sections were given context rich problems following the 'cooperative group problem solving' approach which differed by the level of n-coding opportunities designed into their laboratory environment. To measure the effectiveness of the construct, problem solving skills were assessed as was conceptual learning using the Force Concept Inventory. However, a number of new measures taking into account students' confidence in concepts were developed to complete the picture of student learning. Results suggest that using the developed n-coding construct to design context rich environments can generate learning gains in problem solving, conceptual knowledge and concept-confidence.Comment: Submitted to the American Journal of Physic

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment