Impact of Driving Cycles on Greenhouse Gas (GHG) Emissions, Global Warming Potential (GWP) and Fuel Economy for SI Car Real World Driving

The transport sector is one of the major contributors to greenhouse gas emissions. This study investigated three greenhouse gases emitted from road transport: CO2, N2O and CH4 emissions as a function of engine warm up and driving cycles. Five different urban driving cycles were developed and used including free flow driving and congested driving. An in-vehicle FTIR (Fourier Transform Inferred) emission measurement system was installed on a EURO2 emission compliant SI (Spark Ignition) car for emissions measurement at a rate of 0.5 HZ under real world urban driving conditions. This emission measurement system was calibrated on a standard CVS (Constant Volume Sampling) measurement system and showed excellent agreement on CO2 measurement with CVS results. The N2O and CH4 measurement was calibrated using calibration gas in lab. A MAX710 real time in-vehicle fuel consumption measurement system was installed in the test vehicle and real time fuel consumption was then obtained. The temperatures across the TWC (Three Way Catalyst) and engine out exhaust gas lambda were measured. The GHG (greenhouse gas) mass emissions and consequent GWP (Global Warming Potential) for different urban diving conditions were analyzed and presented. The results provided a better understanding of traffic related greenhouse gas emission profile in urban area and will contribute to the control of climate change

Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists

Background Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop ‘indicator markers’ found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. Methods We examined EHRs of 5,843 patients in the General Practice Research Database, aged ≥30y, with a first coded diagnosis of RA between 2005 and 2008. Lists of indicator markers for RA were developed initially by panels of clinicians drawing up code-lists and then modified based on scrutiny of available data. The prevalence of indicator markers, and their temporal relationship to RA codes, was examined in patients from 3y before to 14d after recorded RA diagnosis. Findings Indicator markers were common throughout EHRs of RA patients, with 83.5% having 2 or more markers. 34% of patients received a disease-specific prescription before RA was coded; 42% had a referral to rheumatology, and 63% had a test for rheumatoid factor. 65% had at least one joint symptom or sign recorded and in 44% this was at least 6-months before recorded RA diagnosis. Conclusion Indicator markers of RA may be valuable for case definition in cases which do not yet have a diagnostic code. The clinical diagnosis of RA is likely to occur some months before it is coded, shown by markers frequently occurring ≥6 months before recorded diagnosis. It is difficult to differentiate delay in diagnosis from delay in recording. Information concealed in free text may be required for the accurate identification of patients and to assess the quality of care in general practice

Cost Effectiveness of Internet Interventions: Review and Recommendations

10.1007/s12160-009-9131-6Annals of Behavioral Medicine38140-45ABME

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

A BMP7 variant inhibits tumor angiogenesis in vitro and in vivo through direct modulation of endothelial cell biology

Bone morphogenetic proteins (BMPs), members of the TGF-\u3b2 superfamily, have numerous biological activities including control of growth, differentiation, and vascular development. Using an in vitro co-culture endothelial cord formation assay, we investigated the role of a BMP7 variant (BMP7v) in VEGF, bFGF, and tumor-driven angiogenesis. BMP7v treatment led to disruption of neo-endothelial cord formation and regression of existing VEGF and bFGF cords in vitro. Using a series of tumor cell models capable of driving angiogenesis in vitro, BMP7v treatment completely blocked cord formation. Pre-treatment of endothelial cells with BMP7v significantly reduced their cord forming ability, indicating a direct effect on endothelial cell function. BMP7v activated the canonical SMAD signaling pathway in endothelial cells but targeted gene knockdown using shRNA directed against SMAD4 suggests this pathway is not required to mediate the anti-angiogenic effect. In contrast to SMAD activation, BMP7v selectively decreased ERK and AKT activation, significantly decreased endothelial cell migration and down-regulated expression of critical RTKs involved in VEGF and FGF angiogenic signaling, VEGFR2 and FGFR1 respectively. Importantly, in an in vivo angiogenic plug assay that serves as a measurement of angiogenesis, BMP7v significantly decreased hemoglobin content indicating inhibition of neoangiogenesis. In addition, BMP7v significantly decreased angiogenesis in glioblastoma stem-like cell (GSLC) Matrigel plugs and significantly impaired in vivo growth of a GSLC xenograft with a concomitant reduction in microvessel density. These data support BMP7v as a potent anti-angiogenic molecule that is effective in the context of tumor angiogenesis

Evaluation of lifestyle interventions to treat elevated cardiometabolic risk in primary care (E-LITE): a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Efficacy research has shown that intensive individual lifestyle intervention lowers the risk for developing type 2 diabetes mellitus and the metabolic syndrome. Translational research is needed to test real-world models of lifestyle interventions in primary care settings.</p> <p>Design</p> <p>E-LITE is a three-arm randomized controlled clinical trial aimed at testing the feasibility and potential effectiveness of two lifestyle interventions: information technology-assisted self-management, either alone or in combination with care management by a dietitian and exercise counselor, in comparison to usual care. Overweight or obese adults with pre-diabetes and/or metabolic syndrome (n = 240) recruited from a community-based primary care clinic are randomly assigned to one of three treatment conditions. Treatment will last 15 months and involves a three-month intensive treatment phase followed by a 12-month maintenance phase. Follow-up assessment occurs at three, six, and 15 months. The primary outcome is change in body mass index. The target sample size will provide 80% power for detecting a net difference of half a standard deviation in body mass index at 15 months between either of the self-management or care management interventions and usual care at a two-sided α level of 0.05, assuming up to a 20% rate of loss to 15-month follow-up.</p> <p>Secondary outcomes include glycemic control, additional cardiovascular risk factors, and health-related quality of life. Potential mediators (e.g., treatment adherence, caloric intake, physical activity level) and moderators (e.g., age, gender, race/ethnicity, baseline mental status) of the intervention's effect on weight change also will be examined.</p> <p>Discussion</p> <p>This study will provide objective evidence on the extent of reductions in body mass index and related cardiometabolic risk factors from two lifestyle intervention programs of varying intensity that could be implemented as part of routine health care.</p> <p>Trial registration</p> <p>NCT00842426</p

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

Contains fulltext : 75361.pdf (publisher's version ) (Open Access)14 p

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

Group-based memory rehabilitation for people with multiple sclerosis: subgroup analysis of the ReMiND trial

Background/Aim: Memory problems are frequently reported in people with multiple sclerosis (MS). These can be debilitating and affect individuals and their families. This sub-group analysis focused on the effectiveness of memory rehabilitation in patients with MS. Methods: Data were extracted from a single blind randomised controlled trial, the ReMiND trial, which also included participants with traumatic brain injury and stroke. Participants were randomly allocated to compensation or restitution treatment programmes, or a self-help control. The programmes were manual-based and comprised two individual and ten group sessions. Outcome measures included assessments of memory, mood and activities of daily living. A total of 39 patients with MS participated in this study (ten males (26%), 29 females (74%); mean±SD age: 48.3±10.8 years). Results: Comparison of groups showed no significant effect of treatment on memory, but there were significant differences between compensation and restitution on self-report symptoms of emotional distress at both 5- (p=0.04) and 7-month (p=0.05) follow-up sessions. The compensation group showed less distress than the restitution group. Conclusions: Individuals with MS who received compensation memory rehabilitation reported significantly less emotional distress than those who received restitution. Further research is needed to explore why self-reported memory problems did not differ between groups