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Polygenic hazard score is associated with prostate cancer in multi-ethnic populations
Get PDFGenetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10−180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset
Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems
Get PDFSimultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing potential for handheld clinical periodontal imaging
Organising knowledge generation and dissemination in the Dutch high-water protection programme–a sender-receiver approach
Get PDFIn 2012 the Dutch High-Water Protection Programme (HWPP) was initiated. This programme prioritised dike strengthening projects for the near future with a yearly budget of around 350 million Euros. A safety assessment 2011–2013 indicated the need to strengthen 748 km of dikes. To achieve this, it was recognised that generation and dissemination of state-of-the-art-knowledge was necessary. For this purpose, four Spatial and Technical Research Projects (STRPs) were initiated. The challenge for these STRPs is to generate and disseminate the developed knowledge that is relevant for other dike strengthening projects within the HWPP. This paper examines whether the STRPs have successfully undertaken activities to generate and disseminate new knowledge to relevant stakeholders. We examine how the generation and dissemination of knowledge from the STRPs to the HWPP-projects and water management organisations in the Netherlands took place and might be further facilitated
Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation
Get PDFRationale, aims and objectives: The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data. Methods: E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated. Results: We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called ‘indicators’ of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems. Conclusions: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice
The Many Faces of Teacher Differentiation: Using Q Methodology to Explore Teachers Preferences for Differentiated Instruction
Get PDFDifferentiated instruction occurs when teachers use students’ level of readiness, interests, and learning preferences to adjust the content, process, or products, which increases engagement and academic performance. However, teachers cannot offer every form of differentiation to every student all the time. There exist limits of resources as well as an essential balance that teachers need to make in terms of benefits for student learning on the one hand and classroom efficiency on the other. Additionally, the choices teachers make in terms of differentiation are also rooted in and stem from their personal beliefs systems. The goal of this research was to investigate teachers’ preferences for differentiating their instruction by using Q methodology. 32 teachers, coming from a single Dutch secondary school, completed a paper version of a Q sort containing 33 statements. Three groups of teachers were identified who emphasized 1) content mastery over students’ interests, 2) offering options over content growth, and 3) students’ interests or experiences over deliberate teaching. Therefore investigating teacher’s personal differentiation preferences will offer insight into what teachers choose to focus on in their differentiation, as well as, what teachers to not to emphasize. Each cluster is explored in terms of possible effects on student learning. Additionally, the implications for teacher development are also discussed per cluster
What a drag it is getting old? Mental health and loneliness beyond age 50
Get PDFThis paper studies mental health and loneliness in the Netherlands for individuals beyond age 50. The analysis is based on panel data over the period 2008 to 2018 and focuses on the effects of life events and ageing. It appears that mental health gets worse and loneliness increases if individuals lose their partner (through divorce or death) or become unemployed. On average, the mental health of males and high educated females improves at retirement. With respect to ageing, the main conclusions are that mental health improves while loneliness goes down at least up to the high 70s. From the perspective of mental health and loneliness, it does not seem to be a drag getting old
Promoting water consumption among Dutch children: an evaluation of the social network intervention Share H2 O
Get PDFBackground: There is a need to develop and improve interventions promoting healthy drinking behaviors among children. A promising method could be to stimulate peer influence within children’s social networks. In the Share H2O social network intervention (SNI), peer influence was utilized by selecting a subset of influential children and training them as ‘influence agents’ to promote water consumption—as an alternative to SSBs. Previous research has mainly focused on the process of selecting influence agents. However, the process of motivating influence agents to promote the behavior has hardly received any research attention. Therefore, in the SNI Share H2O SNI, this motivation process was emphasized and grounded in the self-determination theory (SDT). This study evaluated the implementation of the Share H2O SNI, focusing on whether and how applying SDT-based techniques can motivate the influence agents and, indirectly, their peers. Methods: This study included data collected in the Netherlands from both the influence agents (n = 37) and the peers (n = 112) in the classroom networks of the influence agents. Self-reported measurements assessed the influence agents’ enjoyment of the training, duration and perceived autonomy support during the training, and changes in their intrinsic motivation and water consumption before and after the start of the intervention. Changes in the peers’ intrinsic motivation, perceived social support, and social norms were measured before and after the start of the intervention. Results: The influence agents enjoyed the training, the duration was adequate, and perceived it as autonomy supportive. There was an increase in the influence agents’ intrinsic motivation to drink water and their actual water consumption. Providing personal meaningful rationales seemed to have motivated the influence agents. The intrinsic motivation and perceived descriptive norm of the peers remained stable. The peers reported an increase in their perceived social support and injunctive norm concerning water drinking after the intervention. Influence agents appeared to mainly use face-to-face strategies, such as modeling, talking to peers, and providing social support to promote the behavior. Conclusions: The current findings provided preliminary evidence of the promising effects of using SDT-based techniques in an SNI to motivate the influence agents and, indirectly, their peers. Trial registration: NTR, NL6905, Registered 9 January 2018, https://www.trialregister.nl/trial/690
Making Green Power Purchase Agreements More Predictable and Reliable for Companies
Get PDFTo comply with sustainability goals, many companies buy green energy to serve their energy demand. This is
typically done by engaging in bilateral power purchase agreements (PPA) with renewable energy producers
(REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future
energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially attractive for sellers,
providing price certainty, unlike trading in electricity markets. However, PPAs can bring quantity uncertainty for
buyers due to the uncertainty of future green energy delivery. This uncertainty in the long-term endangers
sustainability targets, and in the short-term complicates reliable and cost-efficient demand matching. Thus,
multiple strategies have been used in PPAs to encourage sellers to provide accurate and good-faith predictions of
their short-term and longer-term future production. Yet, it has been shown that REPs can have incentives to
misreport predicted values. This has discouraged some companies from engaging in PPAs. In this paper, we first
investigate how PPA structure and pricing can incentivize REPs to provide more reliable predictions. This shifts
the risk of production uncertainty to REPs, increasing the chance that REPs adopt batteries. We further study how
having batteries for REPs affects their own revenue as well as the reliability of their energy predictions for
buyers. We use analytical and simulation approaches to propose a decision tree for a win-win PPA structure,
which improves reliability for buyers while maintaining profitability for REPs
A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts
Get PDFBackground: Prognostic models developed in general cohorts with a mixture of heart failure (HF) phenotypes, though more widely applicable, are also likely to yield larger prediction errors in settings where the HF phenotypes have substantially different baseline mortality rates or different predictor-outcome associations. This study sought to use individual participant data meta-analysis to develop an HF phenotype stratified model for predicting 1-year mortality in patients admitted with acute HF. Methods: Four prospective European cohorts were used to develop an HF phenotype stratified model. Cox model with two rounds of backward elimination was used to derive the prognostic index. Weibull model was used to obtain the baseline hazard functions. The internal-external cross-validation (IECV) approach was used to evaluate the generalizability of the developed model in terms of discrimination and calibration. Results: 3577 acute HF patients were included, of which 2368 were classified as having HF with reduced ejection fraction (EF) (HFrEF; EF < 40%), 588 as having HF with midrange EF (HFmrEF; EF 40–49%), and 621 as having HF with preserved EF (HFpEF; EF ≥ 50%). A total of 11 readily available variables built up the prognostic index. For four of these predictor variables, namely systolic blood pressure, serum creatinine, myocardial infarction, and diabetes, the effect differed across the three HF phenotypes. With a weighted IECV-adjusted AUC of 0.79 (0.74–0.83) for HFrEF, 0.74 (0.70–0.79) for HFmrEF, and 0.74 (0.71–0.77) for HFpEF, the model showed excellent discrimination. Moreover, there was a good agreement between the average observed and predicted 1-year mortality risks, especially after recalibration of the baseline mortality risks. Conclusions: Our HF phenotype stratified model showed excellent generalizability across four European cohorts and may provide a useful tool in HF phenotype-specific clinical decision-making