Definition of valid proteomic biomarkers: a bayesian solution

Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary proteome data was collected from healthy adult males and females, and analysed to find biomarkers that differentiated between genders. We believe that models that incorporate sparsity in terms of variables are desirable for biomarker selection, as proteomics data typically contains a huge number of variables (peptides) and few samples making the selection process potentially unstable. This suggests the application of a two-level hierarchical Bayesian probit regression model for variable selection which assumes a prior that favours sparseness. The classification performance of this method is shown to improve that of the Probabilistic K-Nearest Neighbour model

Forecasting GDP Growth using Disaggregated GDP Revisions

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include revisions to GDP and its components. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, models that include revisions to components of GDP improve forecast accuracy. The first revision to consumption is particularly relevant in that every model that includes the revision outperforms the baseline model. Measured by root mean squared forecasting error (RMSFE), improvements are quite sizable, with many models increasing forecasting performance by 5% or more, and with top-performing models forecasting 0.24 percentage points closer to the advance estimate of growth. We use Bayesian model averaging to underscore that our results are driven by the informational content of revisions. The posterior probability of models with the first revision to consumption is significantly higher than our baseline model, despite strong priors that the latter should be the preferred forecasting model

Forecasting GDP: Do Revisions Matter?

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth

Forecasting GDP: Do Revisions Matter?

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth

Assessment and treatment of distorted schemas in sexual offenders

The aim of this review is to examine the literature related to the assessment and treatment of sex offenders’ distorted schemas. Where appropriate, the review draws upon current insights from the field of social cognition to aid in the critical evaluation of the findings. First, the review considers the various different methodologies for assessing distorted schemas, discussing their strengths and limitations. Second, the review examines the work related to the treatment of sex offenders’ schemas. Suggestions for future research, and the implications for clinical practice, are highlighted in the article

eHealth Literacy and Web 2.0 Health Information Seeking Behaviors Among Baby Boomers and Older Adults

Background: Baby boomers and older adults, a subset of the population at high risk for chronic disease, social isolation, and poor health outcomes, are increasingly utilizing the Internet and social media (Web 2.0) to locate and evaluate health information. However, among these older populations, little is known about what factors influence their eHealth literacy and use of Web 2.0 for health information. Objective: The intent of the study was to explore the extent to which sociodemographic, social determinants, and electronic device use influences eHealth literacy and use of Web 2.0 for health information among baby boomers and older adults. Methods: A random sample of baby boomers and older adults (n=283, mean 67.46 years, SD 9.98) participated in a cross-sectional, telephone survey that included the eHealth literacy scale (eHEALS) and items from the Health Information National Trends Survey (HINTS) assessing electronic device use and use of Web 2.0 for health information. An independent samples t test compared eHealth literacy among users and non-users of Web 2.0 for health information. Multiple linear and logistic regression analyses were conducted to determine associations between sociodemographic, social determinants, and electronic device use on self-reported eHealth literacy and use of Web 2.0 for seeking and sharing health information. Results: Almost 90% of older Web 2.0 users (90/101, 89.1%) reported using popular Web 2.0 websites, such as Facebook and Twitter, to find and share health information. Respondents reporting use of Web 2.0 reported greater eHealth literacy (mean 30.38, SD 5.45, n=101) than those who did not use Web 2.0 (mean 28.31, SD 5.79, n=182), t217.60=−2.98, P=.003. Younger age (b=−0.10), more education (b=0.48), and use of more electronic devices (b=1.26) were significantly associated with greater eHealth literacy (R2 =.17, R2adj =.14, F9,229=5.277, P<.001). Women were nearly three times more likely than men to use Web 2.0 for health information (OR 2.63, Wald= 8.09, df=1, P=.004). Finally, more education predicted greater use of Web 2.0 for health information, with college graduates (OR 2.57, Wald= 3.86, df =1, P=.049) and post graduates (OR 7.105, Wald= 4.278, df=1, P=.04) nearly 2 to 7 times more likely than non-high school graduates to use Web 2.0 for health information. Conclusions: Being younger and possessing more education was associated with greater eHealth literacy among baby boomers and older adults. Females and those highly educated, particularly at the post graduate level, reported greater use of Web 2.0 for health information. More in-depth surveys and interviews among more diverse groups of baby boomers and older adult populations will likely yield a better understanding regarding how current Web-based health information seeking and sharing behaviors influence health-related decision making

Patients’ willingness to attend the NHS cardiovascular health checks in primary care: A qualitative interview study

Background: The NHS Cardiovascular Health Check (NHSHC) programme was introduced in England in 2009 to reduce cardiovascular disease mortality and morbidity for all patients aged 40 to 74 years old. Programme cost-effectiveness was based on an assumed uptake of 75% but current estimates of uptake in primary care are less than 50%. The purpose of this study was to identify factors influencing patients’ willingness to attend an NHSHC. For those who attended, their views, experiences and their future willingness to engage in the programme were explored. Method: Telephone or face-to-face interviews were conducted with patients who had recently been invited for an NHSHC by a letter from four general practices in Torbay, England. Patients were purposefully sampled (by gender, age, attendance status). Interviews were audio recorded, transcribed verbatim and analysed thematically. Results: 17 attendees and 10 non-attendees were interviewed. Patients who attended an NHSHC viewed it as worthwhile. Proactive attitudes towards their health, a desire to prevent disease before they developed symptoms, and a willingness to accept screening and health check invitations motivated many individuals to attend. Non-attendees cited not seeing the NHSHC as a priority, or how it differed from regular monitoring already received for other conditions as barriers to attendance. Some non-attendees actively avoided GP practices when feeling well, while others did not want to waste health professionals’ time. Misunderstandings of what the NHSHC involved and negative views of what the likely outcome might be were common. Conclusion: While a minority of non-attendees simply had made an informed choice not to have an NHSHC, improving the clarity and brevity of invitational materials, better advertising, and simple administrative interventions such as sending reminder letters, have considerable potential to improve NHSHC uptake

Poly(β-Amino Ester)-Nanoparticle Mediated Transfection of Retinal Pigment Epithelial Cells In Vitro and In Vivo

A variety of genetic diseases in the retina, including retinitis pigmentosa and leber congenital amaurosis, might be excellent targets for gene delivery as treatment. A major challenge in non-viral gene delivery remains finding a safe and effective delivery system. Poly(beta-amino ester)s (PBAEs) have shown great potential as gene delivery reagents because they are easily synthesized and they transfect a wide variety of cell types with high efficacy in vitro. We synthesized a combinatorial library of PBAEs and evaluated them for transfection efficacy and toxicity in retinal pigment epithelial (ARPE-19) cells to identify lead polymer structures and transfection formulations. Our optimal polymer (B5-S5-E7 at 60 w/w polymer∶DNA ratio) transfected ARPE-19 cells with 44±5% transfection efficacy, significantly higher than with optimized formulations of leading commercially available reagents Lipofectamine 2000 (26±7%) and X-tremeGENE HP DNA (22±6%); (p<0.001 for both). Ten formulations exceeded 30% transfection efficacy. This high non-viral efficacy was achieved with comparable cytotoxicity (23±6%) to controls; optimized formulations of Lipofectamine 2000 and X-tremeGENE HP DNA showed 15±3% and 32±9% toxicity respectively (p>0.05 for both). Our optimal polymer was also significantly better than a gold standard polymeric transfection reagent, branched 25 kDa polyethyleneimine (PEI), which achieved only 8±1% transfection efficacy with 25±6% cytotoxicity. Subretinal injections using lyophilized GFP-PBAE nanoparticles resulted in 1.1±1×103-fold and 1.5±0.7×103-fold increased GFP expression in the retinal pigment epithelium (RPE)/choroid and neural retina respectively, compared to injection of DNA alone (p = 0.003 for RPE/choroid, p<0.001 for neural retina). The successful transfection of the RPE in vivo suggests that these nanoparticles could be used to study a number of genetic diseases in the laboratory with the potential to treat debilitating eye diseases

Design and Construction of Multigenic Constructs for Plant Biotechnology Using the GoldenBraid Cloning Strategy

GoldenBraid (GB) is an iterative and standardized DNA assembling system specially designed for Multigene Engineering in Plant Synthetic Biology. GB is based on restriction–ligation reactions using type IIS restriction enzymes. GB comprises a collection of standard DNA pieces named “GB parts” and a set of destination plasmids (pDGBs) that incorporate the multipartite assembly of standardized DNA parts. GB reactions are extremely efficient: two transcriptional units (TUs) can be assembled from several basic GBparts in one T-DNA less than 24 h. Moreover, larger assemblies comprising 4–5 TUs are routinely built in less than 2 working weeks. Here we provide a detailed view of the GB methodology. As a practical example, a Bimolecular Fluorescence Complementation construct comprising four TUs in a 12 kb DNA fragment is presented.Sarrion-Perdigones, A.; Palací, J.; Granell Richart, A.; Orzáez Calatayud, DV. (2014). Design and Construction of Multigenic Constructs for Plant Biotechnology Using the GoldenBraid Cloning Strategy. Methods in Molecular Biology. 1116:133-151. doi:10.1007/978-1-62703-764-8_10S1331511116Haseloff J, Ajioka J (2009) Synthetic biology, history, challenges and prospects. J R Soc Interface 6(Suppl 4):S389–S391Check E (2005) Synthetic biology, designs on life. Nature 438:417–418Kosuri S, Eroshenko N, LeProust EM et al (2010) Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips. Nat Biotechnol 28:1295–1299Ellis T, Adie T, Baldwin GS (2011) DNA assembly for synthetic biology, from parts to pathways and beyond. Integr Biol 3:109–118Gibson DG, Young L, Chuang R-Y et al (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat Methods 6: 343–345Gibson DG, Glass JI, Lartigue C et al (2010) Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329:52–56Sarrion-Perdigones A, Falconi EE, Zandalinas SI et al (2011) GoldenBraid, an iterative cloning system for standardized assembly of reusable genetic modules. PLoS One 6:e21622Sarrion-Perdigones A, Vilar-Vazquez M et al (2013) GoldenBraid2.0, A comprehensive DNA assembly framework for plant synthetic biology. Plant Physiol 162:1618–1631Engler C, Gruetzner R, Kandzia R (2009) Golden gate shuffling, a one-pot DNA shuffling method based on type IIs restriction enzymes. PLoS One 4:e5553Engler C, Kandzia R, Marillonnet S (2008) A one pot, one step, precision cloning method with high throughput capability. PLoS One 3:e3647Bracha-Drori K, Shichrur K, Katz A et al (2004) Detection of protein-protein interactions in plants using bimolecular fluorescence complementation. Plant J 40:419–427Smaczniak C, Immink RG, Muino JM et al (2012) Characterization of MADS-domain transcription factor complexes in Arabidopsis flower development. Proc Natl Acad Sci U S A 109:1560–1565de Folter S, Immink RG, Kieffer M et al (2005) Comprehensive interaction map of the Arabidopsis MADS Box transcription factors. Plant Cell 17:1424–1433Lorenz WW, McCann RO, Longiaru M et al (1991) Isolation and expression of a cDNA encoding Renilla reniformis luciferase. Proc Natl Acad Sci U S A 88:4438–4442Voinnet O, Pinto YM, Baulcombe DC (1999) Suppression of gene silencing: a general strategy used by diverse DNA and RNA viruses of plants. Proc Natl Acad Sci U S A 96: 14147–14152Hellens RP, Edwards EA, Leyland NR et al (2000) pGreen: a versatile and flexible binary Ti vector for Agrobacterium-mediated plant transformation. Plant Mol Biol 42:819–832Butelli E, Titta L, Giorgio M et al (2008) Enrichment of tomato fruit with health-promoting anthocyanins by expression of select transcription factors. Nat Biotechnol 26: 1301–1308Kapila J, DeRycke R, VanMontagu M et al (1997) An Agrobacterium-mediated transient gene expression system for intact leaves. Plant Sci 122:101–10