Unbiased and Consistent Nested Sampling via Sequential Monte Carlo

We introduce a new class of sequential Monte Carlo methods called Nested Sampling via Sequential Monte Carlo (NS-SMC), which reframes the Nested Sampling method of Skilling (2006) in terms of sequential Monte Carlo techniques. This new framework allows convergence results to be obtained in the setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An additional benefit is that marginal likelihood estimates are unbiased. In contrast to NS, the analysis of NS-SMC does not require the (unrealistic) assumption that the simulated samples be independent. As the original NS algorithm is a special case of NS-SMC, this provides insights as to why NS seems to produce accurate estimates despite a typical violation of its assumptions. For applications of NS-SMC, we give advice on tuning MCMC kernels in an automated manner via a preliminary pilot run, and present a new method for appropriately choosing the number of MCMC repeats at each iteration. Finally, a numerical study is conducted where the performance of NS-SMC and temperature-annealed SMC is compared on several challenging and realistic problems. MATLAB code for our experiments is made available at https://github.com/LeahPrice/SMC-NS .Comment: 45 pages, some minor typographical errors fixed since last versio

Poster Session - STREAMS: Improving Student Success in STEM at Bridgewater State University

STREAMS, an NSF-STEP grant held by Bridgewater State University, implements best-practice approaches to increasing the number of STEM graduates. STREAMS interventions include a summer bridge program, a mentoring program, curricular changes promoting inquiry-based teaching, Structured Learning Assistance in gateway courses, a Residential Learning Community, and better transfer student advising and articulation. This presentation will focus on the assessment strategies that encourage curricular change and evidence of increased student success in science and math at Bridgewater

Gender: An Important Factor in the Implementation of Services for Juvenile Offenders

The Child Welfare League of America (2003) reported that between 1980 and 2000 the arrest rate for boys declined by 11% but increased for girls by 35%. A well tested case management approach being applied more commonly in juvenile justice is the Risk-Needs-Responsivity (RNR) approach, which suggests that interventions and services should be commensurate with ones level of risk and specific dynamic risk factors (criminogenic needs). The RNR model tends to be seen as gender-neutral , based on assumption that it works equally well with both sexes. Few studies have examined whether gender differences exist in the effectiveness of RNR-type case planning. Vitopoulos et al., (2012) examined possible RNR differences between justice-involved boys and girls using the Youth Level of Service/Case Management Inventory (YLS/CMI). Across all of the criminogenic need areas (e.g. antisocial attitudes, peer affiliations), only the personality domain was significantly different by gender, such that more girls than boys seemed to have a problem inthis area. They did not find any gender differences in the matching of services to needs identified; however, a higher match between clinician-recommended needs and assigned treatment services (service-to-needs match) predicted a decrease in boys\u27 re-offending but not in girls\u27 reoffending. Given the paucity of research, we are left to question the applicability of some RNR principles or the quality of their implementation for girl offenders. Using the Structured Assessment of Violence Risk for Youth (SAVRY)) in three probation officies to measure both risk level and dynamic risk factors (criminogenic needs), we examined whether within a large sample of youth there were gender differences in the (a) criminogenic needs identified, (b) ability of probation officers (POs) to match services to needs in their case planning and (c) the association of the serve-need match to recidivism

Vitamin D deficiency: A new risk factor for type 2 diabetes?

Recent compelling evidence suggests a role of vitamin D deficiency in the pathogenesis of insulin resistance and insulin secretion derangements, with a consequent possible interference with type 2 diabetes mellitus. The mechanism of this link is incompletely understood. In fact, vitamin D deficiency is usually detected in obesity in which insulin resistance is also a common finding. The coexistence of insulin resistance and vitamin D deficiency has generated several hypotheses. Some cross-sectional and prospective studies have suggested that vitamin D deficiency may play a role in worsening insulin resistance; others have identified obesity as a risk factor predisposing individuals to exhibit both vitamin D deficiency and insulin resistance. The available data from intervention studies are largely confounded, and inadequate considerations of seasonal effects on 25(OH)D concentrations are also a common design flaw in many studies. On the contrary, there is strong evidence that obesity might cause both vitamin D deficiency and insulin resistance, leaving open the possibility that vitamin D and diabetes are not related at all. Although it might seem premature to draw firm conclusions on the role of vitamin D supplementation in reducing insulin resistance and preventing type 2 diabetes, this manuscript will review the circumstances leading to vitamin D deficiency and how such a deficiency can eventually independently affect insulin sensitivity. © 2012 S. Karger AG, Basel

Towards a New System for the Assessment of the Quality in Care Pathways: An Overview of Systematic Reviews

Clinical or care pathways are developed by a multidisciplinary team of healthcare practitioners, based on clinical evidence, and standardized processes. The evaluation of their framework/content quality is unclear. The aim of this study was to describe which tools and domains are able to critically evaluate the quality of clinical/care pathways. An overview of systematic reviews was conducted, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, using Medline, Embase, Science Citation Index, PsychInfo, CINAHL, and Cochrane Library, from 2015 to 2020, and with snowballing methods. The quality of the reviews was assessed with Assessment the Methodology of Systematic Review (AMSTAR-2) and categorized with The Leuven Clinical Pathway Compass for the definition of the five domains: processes, service, clinical, team, and financial. We found nine reviews. Three achieved a high level of quality with AMSTAR-2. The areas classified according to The Leuven Clinical Pathway Compass were: 9.7% team multidisciplinary involvement, 13.2% clinical (morbidity/mortality), 44.3% process (continuity-clinical integration, transitional), 5.6% financial (length of stay), and 27.0% service (patient-/family-centered care). Overall, none of the 300 instruments retrieved could be considered a gold standard mainly because they did not cover all the critical pathway domains outlined by Leuven and Health Technology Assessment. This overview shows important insights for the definition of a multiprinciple framework of core domains for assessing the quality of pathways. The core domains should consider general critical aspects common to all pathways, but it is necessary to define specific domains for specific diseases, fast pathways, and adapting the tool to the cultural and organizational characteristics of the health system of each country

Life cycle inventory data for the Italian agri-food sector: background, sources and methodological aspects

AbstractPurposeFor the development of any life cycle assessment study, the practitioner frequently integrates primary data collected on-field, with background data taken from various life cycle inventory databases which are part of most commercial LCA software packages. However, such data is often not generally applicable to all product systems since, especially concerning the agri-food sector, available datasets may not be fully representative of the site specificity of the food product under examination. In this context, the present work investigates the background, sources and methodological aspects that characterise the most known commercial databases containing agri-food data, with a focus on four agri-food supply chains (olive oil, wine, wheat products and citrus fruit), which represent an important asset for the Italian food sector.MethodsSpecifically, the paper entails a review of currently available LCI databases and their datasets with a twofold scope: firstly, to understand how agri-food data is modelled in these databases for a coherent and consistent representation of regional scenarios and to verify whether they are also suitable for the Italian context and, secondly, to identify and analyse useful and relevant methodological approaches implemented in the existing LCI databases when regional data are modelled.ResultsBased on the aforementioned review, it is possible to highlight some problems which may arise when developing an LCI pertaining to the four Italian agri-food supply chains, namely:1. The need for specific inventory datasets to tackle the specificities of agri-food product systems.2. The lack of datasets, within the existing DBs, related to the Italian context and to the abovementioned supply chains. In fact, at present, in the currently available LCI DBs, there are very few (or in some cases none) datasets related to Italian wine, olive oil, wheat-based products and citrus fruit. The few available datasets often contain some data related to the Italian context but also approximate data with that of product systems representing other countries.Furthermore, the present study allowed to identify and discuss the main aspects to be used as starting elements for modelling regional data to be included in a future Italian LCI database of the abovementioned four supply chains.ConclusionsThe results of the present study represent a starting point for the collection of data and its organisation, in order to develop an Italian LCI agri-food database with datasets which are representative of the regional specificities of four agri-food supply chains which play an important role in the Italian economy

Life cycle inventory data for the Italian agri-food sector: background, sources and methodological aspects

Purpose: For the development of any life cycle assessment study, the practitioner frequently integrates primary data collected on-field, with background data taken from various life cycle inventory databases which are part of most commercial LCA software packages. However, such data is often not generally applicable to all product systems since, especially concerning the agri-food sector, available datasets may not be fully representative of the site specificity of the food product under examination. In this context, the present work investigates the background, sources and methodological aspects that characterise the most known commercial databases containing agri-food data, with a focus on four agri-food supply chains (olive oil, wine, wheat products and citrus fruit), which represent an important asset for the Italian food sector. Methods: Specifically, the paper entails a review of currently available LCI databases and their datasets with a twofold scope: firstly, to understand how agri-food data is modelled in these databases for a coherent and consistent representation of regional scenarios and to verify whether they are also suitable for the Italian context and, secondly, to identify and analyse useful and relevant methodological approaches implemented in the existing LCI databases when regional data are modelled. Results: Based on the aforementioned review, it is possible to highlight some problems which may arise when developing an LCI pertaining to the four Italian agri-food supply chains, namely: 1. The need for specific inventory datasets to tackle the specificities of agri-food product systems. 2. The lack of datasets, within the existing DBs, related to the Italian context and to the abovementioned supply chains. In fact, at present, in the currently available LCI DBs, there are very few (or in some cases none) datasets related to Italian wine, olive oil, wheat-based products and citrus fruit. The few available datasets often contain some data related to the Italian context but also approximate data with that of product systems representing other countries. Furthermore, the present study allowed to identify and discuss the main aspects to be used as starting elements for modelling regional data to be included in a future Italian LCI database of the abovementioned four supply chains. Conclusions: The results of the present study represent a starting point for the collection of data and its organisation, in order to develop an Italian LCI agri-food database with datasets which are representative of the regional specificities of four agri-food supply chains which play an important role in the Italian economy