922017 research outputs found
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Cholesteryl ester transfer protein (CETP) inhibition is associated with reduced risk of Sjögren’s disease
No full textAn Incremental Cognitive Architecture of Consciousness with Global Workspace Theory
No full textThis paper revisits the GlobalWorkspace Theory as a neuroscientifically plausible theory for developing conscious cognitive architecture. Based on the discussion of the authors’ previous implementations, the Global Workspace Theory’s compatibility with the working mechanisms underneath human brains is enhanced by demonstrating cognitive features of attention and consciousness. To progress in the incremental research pathway for the architecture implementation, two principles of work are stressed. The primitive cognitive mechanisms are emphasised more than the functional complexity and the revision-update cycle for implementation is appreciated for its engineering feasibility. Along the incremental pathway, the idea for possible following attempts is shared. Based on the discussion of completed and following implementations, the authors’ works are consistent with the Biologically Inspired Cognitive Architecture roadmap
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata
No full textMost statistical agencies release randomly selected samples of Census microdata, usually with sample fractions under 10% and with other forms of statistical disclosure control (SDC) applied. An alternative to SDC is data synthesis, which has been attracting growing interest, yet there is no clear consensus on how to measure the associated utility and disclosure risk of the data. The ability to produce synthetic Census microdata, where the utility and associated risks are clearly understood, could mean that more timely and wider-ranging access to microdata would be possible. This paper follows on from previous work by the authors which mapped synthetic Census data on a risk-utility (R-U) map. The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data. Three commonly used data synthesis packages are compared with some interesting results. Further work is needed in several directions but the methodology looks very promising.</p
Unintended consequences of patient online access to health records: a qualitative study in UK primary care
No full textLoneliness, Social Isolation, and Healthcare Utilization in the General Population
No full textObjectives Due to increasing pressure on healthcare resources, knowledge of factors that affect healthcare utilization (HCU) is important. However, the evidence of a longitudinal association between loneliness and social isolation respectively, and HCU is limited. The present prospective cohort study investigated the association of loneliness and social isolation with HCU in the general population over time. Methods Data from the 2013 Danish “How are you?" survey (n = 27.501) were combined with individual-level register data with almost complete follow-up over a 6-year follow-up period (2013-2018). Negative binomial regression analyses were performed while adjusting for baseline demographics and pre-existing chronic disease. Results Loneliness measured was significantly associated with more general practice contacts (incident-rate ratio (IRR) = 1.03, 95% confidence interval (CI) [1.02, 1.04]), more emergency treatments (IRR = 1.06, 95% CI [1.03, 1.10]), more emergency admissions (IRR = 1.06, 95% CI [1.03, 1.06]), and hospital admission days (IRR=1.05, 95% CI [1.00, 1.11]) across the 6-year follow-up period. No significant associations were found between social isolation and HCU with one minor exception, in which social isolation was associated with fewer planned outpatient treatments (IRR = .97, 95% CI [.94, .99]). Wald test demonstrated that the association of loneliness with emergency admissions and hospital admissions days was not significantly different from the effects of social isolation on those outcomes. Conclusions Our findings suggest that loneliness slightly increased the number of general practice contacts and emergency room treatments. Overall, the effects of loneliness and social isolation on HCU were small
Concurrent multi-scale modeling of granular materials: Role of coarse-graining in FEM-DEM coupling
No full textThe finite element method (FEM) is commonly used for modeling continuum media, while particle simulation methods like the so-called discrete element method (DEM) are used for discrete systems. Coupling the discrete (DEM) and continuum (FEM) methods is conventionally achieved through a direct mapping between discrete particles and finite elements. Coarse-graining (CG) is a micro-macro transition method (discrete to continuum) that maps discrete particle data onto smooth, differentiable fields that satisfy the continuum equations. By choosing an appropriate length scale (the coarse-graining width c), the coarse-grained fields are then homogenized and projected onto a FEM spatial discretization.This concept is utilized here to reformulate FEM-DEM coupling methods, both surface and volume, where in the limiting case of c → 0, the classical coupling is recovered. For surface coupling, the discrete particle-surface contact forces are first mapped onto a continuous surface traction field (using CG) which is then coupled to the continuum FEM model. For volume coupling (also known as the Arlequin framework), the homogenization operators are enriched with CG functions, offering a non-local coupling approach between discrete particles, their continuum fields, and the finite element formulation.The CG enrichment represents a new strategy that consists of (1) a particle-to-continuum mapping and (2) a continuum-to-continuum coupling based on “CG-enriched homogenization” (CGH). It is shown for surface coupling that the CG-enriched formulation not only leads to more accurate results, conserving symmetry, but also reduces energies generated by the coupling. For volume coupling, there is consistently less numerical dissipation with than without CG-enrichment, especially when the load contains high-frequency content. Finally, the optimal CG widths are identified for very simple test cases, with which the surface/volume coupling performs best.CGH can be potentially extended beyond the present examples, by considering other continuum fields (e.g., higher-order) and equations (e.g., multi-physics), and used to formulate other concurrent multi-scale modeling methods
A Benchmark for Multi-Class Object Counting and Size Estimation Using Deep Convolutional Neural Networks
No full textAutomatic object counting and object size estimation in digital images can be very useful in many real-world applications such as surveillance, smart farming, intelligent traffic systems, etc. However, most existing research mainly focus on scenarios where only one type of object is considered due to the lack of proper datasets. Furthermore, they use the traditional detection algorithms for size estimation and can only do segmenting tasks but cannot identify different types of objects and return corresponding individual size information. To fill these gaps, we create a synthetic dataset and propose a benchmark for multi-class object counting and size estimation (MOCSE) within a unified framework. We create the dataset MOCSE13 by using Unity to generate synthetic images for 13 different objects (fruits and vegetables). Besides, we propose a deep architecture approach for multi-class object counting and object size estimation. Our proposed models with different backbones are evaluated on the synthetic dataset. The experimental results provide a benchmark for multi-class object counting and size estimation and the synthetic dataset can be served as a proper testbed for future studies
Prognostic and predictive factors for locoregional and systemic therapies in hepatocellular carcinoma
No full textHepatocellular carcinoma (HCC) is a growing health concern, with an estimated global incidence of over 1 million by 2025. In its intermediate and advanced stages, HCC remains a challenging condition to treat, despite a recently expanded array of systemic therapies, which continues to grow. Extensive efforts have accordingly been made to identify predictive factors to guide treatment decisions. However, currently only one predictive biomarker is in widespread clinical use, namely elevated alpha-fetoprotein for second-line systemic therapy with ramucirumab. This article reviews known prognostic and predictive biomarkers for patients with HCC who are treated with locoregional and systemic therapies, including recent controversies around the potential impact of HCC aetiology on the efficacy of systemic therapies
Enhanced biosynthesis of Polyhydroxyalkanoates by continuous feeding of volatile fatty acids in Haloferax mediterranei
No full textIn this work, the biosynthesis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) in Haloferax mediterranei was enhanced by continuous feeding of volatile fatty acids. Using this strategy, polymer production was doubled to around 5 g L–1 PHBV when compared to pulse-fed fed-batch fermentations. Polymer productivity and yield increased up to 12.8 mg L–1 h–1 and 0.63 g g–1 respectively when the carbon concentration in the fermentation media was kept constant at 0.25 molar. This biopolymer production was achieved in less than half the time when compared to pulse feeding, effectively quadrupling the overall PHA productivity. Control over co-polymer composition was achieved and maintained at around 40 mol% 3-hydroxyvalerate (3HV). A correlation between substrate consumption and cell growth was observed, providing a crucial tool for feeding rate selection in future fermentations. The higher productivity and yield obtained with the novel feeding strategy will be key to future industrial scale PHA production
Pathway-specific Polygenic Risk Scores (PRS) identify OSA-related pathways differentially moderating genetic susceptibility to CAD
No full textBackground—Obstructive sleep apnea (OSA) and its features, such as chronic intermittent hypoxia (IH), may differentially affect specific molecular pathways and processes in the pathogenesis of coronary artery disease (CAD) and influence the subsequent risk and severity of CAD events. In particular, competing adverse (e.g. inflammatory) and protective (e.g. increased coronary collateral blood flow) mechanisms may operate, but remain poorly understood. We hypothesize that common genetic variation in selected molecular pathways influences the likelihood of CAD events differently in individuals with and without OSA, in a pathway-dependent manner. Methods—We selected a cross-sectional sample of 471,877 participants from the UK Biobank, among whom we ascertained 4,974 to have OSA, 25,988 to have CAD, and 711 to have both. We calculated pathway-specific polygenic risk scores (PS-PRS) for CAD, based on 6.6 million common variants evaluated in the CARDIoGRAMplusC4D genome-wide association study (GWAS), annotated tospecific genes and pathways using functional genomics databases. Based on evidence of involvement with IH and CAD, we tested PS-PRS for the HIF-1, VEGF, NFkB and TNF signaling pathways. Results—In a multivariable-adjusted logistic generalized additive model, elevated PS-PRSs for the KEGG VEGF pathway (39 genes) associated with protection for CAD in OSA (interaction odds ratio 0.86, p = 6E-04). By contrast, the genome-wide CAD PRS did not show evidence of statistical interaction with OSA.Conclusions—We find evidence that pathway-specific genetic risk of CAD differs between individuals with and without OSA in a qualitatively pathway-dependent manner, consistent with the previously studied phenomena whereby features of OSA may have both positive and negative effects on CAD. These results provide evidence that gene-by-environment interaction influences CAD riskin certain pathways among people with OSA, an effect that is not well-captured by the genome-wise wide PRS. These results can be followed up to study how OSA interacts with genetic risk at the molecular level, and potentially to personalize OSA treatment and reduce CAD risk according to individual pathway-specific genetic risk profiles