A Social Cloud for Public eResearch

Abstract—Scientific researchers faced with extremely large computations or the requirement of storing vast quantities of data have come to rely on distributed computational models like cloud computing. However, distributed computation is typically complex and expensive. The Social Cloud for Public eResearch aims to provide researchers with a platform to exploit social networks to reach out to users who would otherwise be unlikely to donate computational time for scientific and other research oriented projects. In this paper we explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through established social networks. We specifically look at integrating Facebook and BOINC, and discuss the architecture of the functional system and the novel social engineering algorithms that power it. I

Collaborative eResearch in a Social Cloud

Abstract—Social networks provide a useful basis for enabling collaboration among groups of individuals. This is applicable not only to social communities but also to the scientific community. Already scientists are leveraging social networking concepts in projects to form groups, share information and communicate with their peers. For scientific projects which require large computing resources, one useful aspect of collaboration is the sharing of computing resources among project members. A social network provides an ideal platform to share these resources. This paper introduces a framework for Social Cloud computing with a view towards collaboration and resource sharing within a scientific community. The architecture of a Social Cloud, where individ-uals or institutions contribute the capacity of their computing resources by means of Virtual Machines leased through the social network, is outlined. Members of the Social Cloud can contribute, request, and use Virtual Machines from other members, as well as form Virtual Organizations among groups of members

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Efficacy of Sofosbuvir, Velpatasvir, and GS-9857 in Patients With Hepatitis C Virus Genotype 2, 3, 4, or 6 Infections in an Open-Label, Phase 2 Trial

Background & Aims Studies are needed to determine the optimal regimen for patients with chronic hepatitis C virus (HCV) genotype 2, 3, 4, or 6 infections whose prior course of antiviral therapy has failed, and the feasibility of shortening treatment duration. We performed a phase 2 study to determine the efficacy and safety of the combination of the nucleotide polymerase inhibitor sofosbuvir, the NS5A inhibitor velpatasvir, and the NS3/4A protease inhibitor GS-9857 in these patients. Methods We performed a multicenter, open-label trial at 32 sites in the United States and 2 sites in New Zealand from March 3, 2015 to April 27, 2015. Our study included 128 treatment-naïve and treatment-experienced patients (1 with HCV genotype 1b; 33 with HCV genotype 2; 74 with HCV genotype 3; 17 with genotype HCV 4; and 3 with HCV genotype 6), with or without compensated cirrhosis. All patients received sofosbuvir-velpatasvir (400 mg/100 mg fixed-dose combination tablet) and GS-9857 (100 mg) once daily for 6–12 weeks. The primary end point was sustained virologic response 12 weeks after treatment (SVR12). Results After 6 weeks of treatment, SVR12s were achieved by 88% of treatment-naïve patients without cirrhosis (29 of 33; 95% confidence interval, 72%–97%). After 8 weeks of treatment, SVR12s were achieved by 93% of treatment-naïve patients with cirrhosis (28 of 30; 95% CI, 78%–99%). After 12 weeks of treatment, SVR12s were achieved by all treatment-experienced patients without cirrhosis (36 of 36; 95% CI, 90%–100%) and 97% of treatment-experienced patients with cirrhosis (28 of 29; 95% CI, 82%–100%). The most common adverse events were headache, diarrhea, fatigue, and nausea. Three patients (1%) discontinued treatment due to adverse events. Conclusions In a phase 2 open-label trial, we found sofosbuvir-velpatasvir plus GS-9857 (8 weeks in treatment-naïve patients or 12 weeks in treatment-experienced patients) to be safe and effective for patients with HCV genotype 2, 3, 4, or 6 infections, with or without compensated cirrhosis

Diverse University Students Across the United States Reveal Promising Pathways to Hunter Recruitment and Retention

Declining participation in hunting, especially among young adult hunters, affects the ability of state and federal agencies to achieve goals for wildlife management and decreases revenue for conservation. For wildlife agencies hoping to engage diverse audiences in hunter recruitment, retention, and reactivation (R3) efforts, university settings provide unique advantages: they contain millions of young adults who are developmentally primed to explore new activities, and they cultivate a social atmosphere where new identities can flourish. From 2018 to 2020, we surveyed 17,203 undergraduate students at public universities across 22 states in the United States to explore R3 potential on college campuses and assess key demographic, social, and cognitive correlates of past and intended future hunting behavior. After weighting to account for demographic differences between our sample and the larger student population, 29% of students across all states had hunted in the past. Students with previous hunting experience were likely to be white, male, from rural areas or hunting families, and pursuing degrees related to natural resources. When we grouped students into 1 of 4 categories with respect to hunting (i.e., non-hunters [50%], potential hunters [22%], active hunters [26%], and lapsed hunters [3%]), comparisons revealed differences based on demographic attributes, beliefs, attitudes, and behaviors. Compared to active hunters, potential hunters were more likely to be females or racial and ethnic minorities, and less likely to experience social support for hunting. Potential hunters valued game meat and altruistic reasons for hunting, but they faced unique constraints due to lack of hunting knowledge and skills. Findings provide insights for marketing and programming designed to achieve R3 objectives with a focus on university students. © 2021 The Wildlife Society

Dysregulation of PRMT5 in chronic lymphocytic leukemia promotes progression with high risk of Richter's transformation

: Richter's Transformation (RT) is a poorly understood and fatal progression of chronic lymphocytic leukemia (CLL) manifesting histologically as diffuse large B-cell lymphoma. Protein arginine methyltransferase 5 (PRMT5) is implicated in lymphomagenesis, but its role in CLL or RT progression is unknown. We demonstrate herein that tumors uniformly overexpress PRMT5 in patients with progression to RT. Furthermore, mice with B-specific overexpression of hPRMT5 develop a B-lymphoid expansion with increased risk of death, and Eµ-PRMT5/TCL1 double transgenic mice develop a highly aggressive disease with transformation that histologically resembles RT; where large-scale transcriptional profiling identifies oncogenic pathways mediating PRMT5-driven disease progression. Lastly, we report the development of a SAM-competitive PRMT5 inhibitor, PRT382, with exclusive selectivity and optimal in vitro and in vivo activity compared to available PRMT5 inhibitors. Taken together, the discovery that PRMT5 drives oncogenic pathways promoting RT provides a compelling rationale for clinical investigation of PRMT5 inhibitors such as PRT382 in aggressive CLL/RT cases

CfA3: 185 Type Ia Supernova Light Curves from the CfA

We present multi-band photometry of 185 type-Ia supernovae (SN Ia), with over 11500 observations. These were acquired between 2001 and 2008 at the F. L. Whipple Observatory of the Harvard-Smithsonian Center for Astrophysics (CfA). This sample contains the largest number of homogeneously-observed and reduced nearby SN Ia (z < 0.08) published to date. It more than doubles the nearby sample, bringing SN Ia cosmology to the point where systematic uncertainties dominate. Our natural system photometry has a precision of 0.02 mag or better in BVRIr'i' and roughly 0.04 mag in U for points brighter than 17.5 mag. We also estimate a systematic uncertainty of 0.03 mag in our SN Ia standard system BVRIr'i' photometry and 0.07 mag for U. Comparisons of our standard system photometry with published SN Ia light curves and comparison stars, where available for the same SN, reveal agreement at the level of a few hundredths mag in most cases. We find that 1991bg-like SN Ia are sufficiently distinct from other SN Ia in their color and light-curve-shape/luminosity relation that they should be treated separately in light-curve/distance fitter training samples. The CfA3 sample will contribute to the development of better light-curve/distance fitters, particularly in the few dozen cases where near-infrared photometry has been obtained and, together, can help disentangle host-galaxy reddening from intrinsic supernova color, reducing the systematic uncertainty in SN Ia distances due to dust.Comment: Accepted to the Astrophysical Journal. Minor changes from last version. Light curves, comparison star photometry, and passband tables are available at http://www.cfa.harvard.edu/supernova/CfA3

2017 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1004/thumbnail.jp

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18