How neighbourhood governance entities are located within broader networks of governance : a cross-national comparison

This research considers how neighbourhood-based governance entities are located in broader networks of governance, by undertaking an international comparative investigation in two cities (Baltimore, Maryland in the US and Bristol in England). It uses an empirically-grounded approach to ascertain the function of such governance forms according to the way they are structured and operate. It assesses the governance context, focused at the urban level, within which these entities are located, considering the key actors and their relative power and the focus on deprived neighbourhoods versus broader strategies. This leads to consideration of how these strategies and actors shape the functions of neighbourhood governance and the implications in terms of the relative power vested at the neighbourhood level. The research demonstrates the localist and privatist nature of Baltimore's urban governance context, and the centrist and managerial nature of Bristol's. Within both networks a policy subsystem is evident with regard to neighbourhood approaches, but it is the broader governance network which determines the state and market imperatives pursued. In Baltimore and latterly in Bristol, tackling deprivation is a subservient agenda to the predominant imperative of growth. This highlights the importance of the two cities' shared neo-liberal context despite their different governmental systems. In Baltimore, neighbourhoods do not gain resource from the city-level governance network if they lack the assets this network seeks. The function of neighbourhood governance which results is self-help, as long as neighbourhoods have the capacity to do so. In Bristol, neighbourhood governance is steered by central government via its funding regimes and policy approaches. Changes in these have heralded a shift from the targeting of deprived areas via area-based initiatives' to seeking to link deprived neighbourhoods to the benefits of broader growth. Neighbourhood governance entities are being steered to adopt self-help strategies, irrespective of their capacity to do so.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

Introduction: Current guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy. Results: Using data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment. Discussion: Higher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

IntroductionCurrent guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.ResultsUsing data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.DiscussionHigher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

Candidate composite biomarker to inform drug treatments for diabetic kidney disease

IntroductionCurrent guidelines recommend renin angiotensin system inhibitors (RASi) as key components of treatment of diabetic kidney disease (DKD). Additional options include sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide 1 receptor agonists (GLP1a), and mineralocorticoid receptor antagonists (MCRa). The identification of the optimum drug combination for an individual is difficult because of the inter-, and longitudinal intra-individual heterogeneity of response to therapy.ResultsUsing data from a large observational study (PROVALID), we identified a set of parameters that can be combined into a meaningful composite biomarker that appears to be able to identify which of the various treatment options is clinically beneficial for an individual. It uses machine-earning techniques to estimate under what conditions a treatment of RASi plus an additional treatment is different from the treatment with RASi alone. The measure of difference is the annual percent change (ΔeGFR) in the estimated glomerular filtration rate (ΔeGFR). The 1eGFR is estimated for both the RASi-alone treatment and the add-on treatment.DiscussionHigher estimated increase of eGFR for add-on patients compared with RASi-alone patients indicates that prognosis may be improved with the add-on treatment. The personalized biomarker value thus identifies which patients may benefit from the additional treatment

Naive Bayes ant colony optimization for designing high dimensional experiments

In a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naive Bayes classifier (NBC) that is, the naive Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani

The relationship between brand constructs and motivational patterns in crowdfunding decisions

Crowdfunding (CF) platforms are emerging as new source of resources to support either business or not-for-profit entrepreneurial projects. This phenomenon has received increasing attention by academic scholars. One of the most important existing streams of literature is the one of backers’ motivations. To the best of our knowledge, no study has so far considered the possible role of brand constructs in backers’ funding decisions. This is due to the typical CF setting, where project proponents usually don’t have a strong brand to rely on and backers have no significant reason to feel emotionally connected to a given CF platform. However, the scenario is changing: companies and other organizations seem to be increasingly intrigued by the idea of using CF as a marketing tool. We aim to deepen our understanding of this very recent phenomenon by analyzing a special empirical setting, which is the one of CF platforms created by Universities to fund (above all) their scientific research projects. These projects have mostly to do with the progress and well-being of society, so we should expect more of other-oriented reasons for funding. Nevertheless, since all the stakeholders of a given University (starting from students) could have strong reasons to conceive themselves as “in-groups” we expect this can affect the CF intention (as a brand supportive behavior) as well as the reasons behind it