Editorial: The engaged university

Gateways has been a place where university researchers and community members join together to better understand the broad range of issues confronting communities across the globe, including academic communities. It is well positioned to promote a healthy debate among community members, researchers and policy-makers around scores of problems. We will continue to be a resource that is free to the thousands of our readers

Plant Community Composition and Structure Monitoring for Agate Fossil Beds National Monument

Agate Fossil Beds National Monument (AGFO) plays a vital role in protecting and managing some of the last remnants of native mixed-grass prairie in the region. The Northern Great Plains Inventory & Monitoring Network (NGPN) and Fire Ecology Program (FireEP) surveyed 12 long-term monitoring plots in Agate Fossil Beds National Monument in 2012 as part of an effort to better understand the condition of plant communities in the park. We measured plant diversity and cover, looked for the presence of exotic species that may be newly invading the park, and evaluated the amount of human and natural disturbance at all plots. This effort was the second year in a multiple-year venture to document the current status and long-term trends in plant communities in AGFO. At the end of five years, there will be an in-depth report describing the status of the plant community. In addition to upland plant monitoring, we also sampled vegetation at 12 sites along the riparian corridor at AGFO as part of a pilot study to develop a long-term monitoring approach for this area. The riparian corridor is narrow and not adequately represented in our standard sampling, but is of great ecological and management importance to the park. In 2013, we will also revisit legacy plots that were established as part of the Prairie Cluster prototype monitoring. In this report, we provide a simple summary of our results from sampling in 2012

Gateways: Expanding knowledge through broader participation

This new journal, Gateways: International Journal of Community Research and Engagement, responds to a growing global movement of university-collaborative research initiatives. It also strives to fill a gap created by the sparse number of journals which publish outcomes of community-engaged research and work concerning community engagement. We seek articles based on research that is the result of actively engaged research-practitioner collaborative projects, has the potential of informing community-based activities or develops understanding of community engagement. Combining different knowledge bases that have traditionally been separated into academic and non-academic worlds can dramatically increase information flowing to scholars, community leaders and activists seeking to improve the quality of life in local communities around the world. We also wish to encourage work that contributes to the scholarship of engagement

Higher Education Financial Wellness Alliance (HEFWA) Survey of Financial Wellness Programs in Higher Education 2020

In January 2020, the Higher Education Financial Wellness Alliance conducted a nation-wide survey to capture the landscape of financial wellness-related programming in colleges and universities nation-wide. For the purposes of the survey the term financial wellness refers to a range of programs and resources that support college students’ overall financial health and aid students in making well-informed financial decisions

Life cycle energy and carbon assessment of double skin façades for office refurbishments

In countries like the UK, the upkeep of existing buildings is where the greatest opportunities for achieving carbon reduction targets lie. Façades are the physical barriers between outdoors and indoors, and their upgrade can arguably be amongst the most effective interventions to improve the existing stock. Double Skin Façades (DSFs) represent a possible solution for low-carbon refurbishment due to their capability to reduce energy consumption, and the related carbon emissions, of the building they are applied to. Although much research exists on maximising the operational energy savings of DSFs, little is known about their life cycle performance. This article addresses such a knowledge gap through a comparative life cycle assessment between DSF refurbishments and an up-to-standard, single-skin alternative. This study adopts a parametric approach where 128 DSF configurations have been analysed through primary data. Energy and carbon (both operational and embodied) are the units assessed in this research. Results show that DSFs are more energy-efficient than single-skin in 98% of the cases, and more carbon-efficient in 85% of the cases. Not only does this study represent the first broad parametric approach to evaluating life cycle energy and carbon of DSFs within its given context, but it also informs environmentally-aware design and application of DSFs

MILEPOST GCC: machine learning based research compiler

International audienceTuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically

Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection

The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from the local laboratory information systems of a sentinel network of 23 laboratories across England. An algorithm based on frequency of HCV RNA testing within a defined time period was designed to identify treated patients. Validation of the algorithm was undertaken for one center by comparison with treatment data recorded in a clinical database managed by the Trent HCV Study Group. In total, 267,887 HCV RNA test results from 100,640 individuals were extracted. Of these, 78.9% (79,360) tested positive for viral RNA, indicating an active infection, 20.8% (16,538) of whom had a repeat pattern of HCV RNA testing suggestive of treatment monitoring. Annual numbers of individuals treated increased rapidly from 468 in 2002 to 3,295 in 2009, but decreased to 3,110 in 2010. Approximately two thirds (63.3%; 10,468) of those treated had results consistent with a sustained virological response, including 55.3% and 67.1% of those with a genotype 1 and non-1 virus, respectively. Validation against the Trent clinical database demonstrated that the algorithm was 95% sensitive and 93% specific in detecting treatment and 100% sensitive and 93% specific for detecting treatment outcome. Conclusions: Laboratory testing activity, collected through a sentinel surveillance program, has enabled the first country-wide analysis of treatment and response among HCV-infected individuals. Our approach provides a sensitive, robust, and sustainable method for monitoring service provision across Englan

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

International audienceTuning compiler optimizations for rapidly evolving hardwaremakes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describeMilepostGCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor.We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilatio

CMB-S4: Forecasting Constraints on Primordial Gravitational Waves

CMB-S4---the next-generation ground-based cosmic microwave background (CMB) experiment---is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semi-analytic projection tool, targeted explicitly towards optimizing constraints on the tensor-to-scalar ratio, $r$, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2--3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments given a desired scientific goal. To form a closed-loop process, we couple this semi-analytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for $r > 0.003$ at greater than $5\sigma$, or, in the absence of a detection, of reaching an upper limit of $r < 0.001$ at $95\%$ CL.Comment: 24 pages, 8 figures, 9 tables, submitted to ApJ. arXiv admin note: text overlap with arXiv:1907.0447

Basic science232. Certolizumab pegol prevents pro-inflammatory alterations in endothelial cell function

Background: Cardiovascular disease is a major comorbidity of rheumatoid arthritis (RA) and a leading cause of death. Chronic systemic inflammation involving tumour necrosis factor alpha (TNF) could contribute to endothelial activation and atherogenesis. A number of anti-TNF therapies are in current use for the treatment of RA, including certolizumab pegol (CZP), (Cimzia ®; UCB, Belgium). Anti-TNF therapy has been associated with reduced clinical cardiovascular disease risk and ameliorated vascular function in RA patients. However, the specific effects of TNF inhibitors on endothelial cell function are largely unknown. Our aim was to investigate the mechanisms underpinning CZP effects on TNF-activated human endothelial cells. Methods: Human aortic endothelial cells (HAoECs) were cultured in vitro and exposed to a) TNF alone, b) TNF plus CZP, or c) neither agent. Microarray analysis was used to examine the transcriptional profile of cells treated for 6 hrs and quantitative polymerase chain reaction (qPCR) analysed gene expression at 1, 3, 6 and 24 hrs. NF-κB localization and IκB degradation were investigated using immunocytochemistry, high content analysis and western blotting. Flow cytometry was conducted to detect microparticle release from HAoECs. Results: Transcriptional profiling revealed that while TNF alone had strong effects on endothelial gene expression, TNF and CZP in combination produced a global gene expression pattern similar to untreated control. The two most highly up-regulated genes in response to TNF treatment were adhesion molecules E-selectin and VCAM-1 (q 0.2 compared to control; p > 0.05 compared to TNF alone). The NF-κB pathway was confirmed as a downstream target of TNF-induced HAoEC activation, via nuclear translocation of NF-κB and degradation of IκB, effects which were abolished by treatment with CZP. In addition, flow cytometry detected an increased production of endothelial microparticles in TNF-activated HAoECs, which was prevented by treatment with CZP. Conclusions: We have found at a cellular level that a clinically available TNF inhibitor, CZP reduces the expression of adhesion molecule expression, and prevents TNF-induced activation of the NF-κB pathway. Furthermore, CZP prevents the production of microparticles by activated endothelial cells. This could be central to the prevention of inflammatory environments underlying these conditions and measurement of microparticles has potential as a novel prognostic marker for future cardiovascular events in this patient group. Disclosure statement: Y.A. received a research grant from UCB. I.B. received a research grant from UCB. S.H. received a research grant from UCB. All other authors have declared no conflicts of interes