3,004 research outputs found
Are would-be authoritarians right? Democratic support and citizens’ left-right self-placement in former left- and right- authoritarian countries
Conventional wisdom dictates that the more citizens lean toward either end of the ideological spectrum, the lower their support for democracy. The main model pitted against this ‘rigidity-of-the-extremes model’ is the ‘rigidity-of-the-right model’. This model assumes that rightist citizens are less supportive. This study proposes and empirically demonstrates the validity of an alternative model, which we call ‘the authoritarian legacy model’. This model predicts that whether leftist or rightist citizens are less supportive of democracy depends on countries’ experience with left- or right- authoritarianism. To evaluate its validity, we present a systematic comparative investigation of the relation between citizens’ ideological and democratic beliefs, using European and World Values Survey data from 38 European countries (N = 105,495; 1994-2008). In line with this model, our analyses demonstrate that democratic support is lowest among leftist citizens in former left-authoritarian countries and among rightist citizens in former right-authoritarian countries. We find that this relation persists even among generations that grew up after authoritarian rule. These findings suggest that traditional ideological rigidity models are unsuitable for the study of citizens’ democratic beliefs
Long-Ranged Orientational Order in Dipolar Fluids
Recently Groh and Dietrich claimed the thermodynamic state of a dipolar fluid
depends on the shape of the fluid's container. For example, a homogeneous fluid
in a short fat container would phase separate when transferred to a tall skinny
container of identical volume and temperature. Their calculation thus lacks a
thermodynamic limit. We show that removal of demagnetizing fields restores the
true, shape independent, thermodynamic limit. As a consequence, spontaneously
magnetized liquids display inhomogeneous magnetization textures.Comment: 3 pages, LaTex, no figures. Submitted as comment to PRL, May 199
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure
Paraneoplastic cerebellar degeneration associated with antineuronal antibodies: analysis of 50 patients
Paraneoplastic cerebellar degeneration (PCD) is a heterogeneous group of
disorders characterized by subacute cerebellar ataxia, specific tumour
types and (often) associated antineuronal antibodies. Nine specific
antineuronal antibodies are associated with PCD. We examined the relative
frequency of the antineuronal antibodies associated with PCD and compared
the neurological symptoms and signs, associated tumours, disability and
survival between groups of PCD with different antibodies. Also, we
attempted to identify patient-, tumour- and treatment-related
characteristics associated with functional outcome and survival. In a
12-year period, we examined >5000 samples for the presence of antineuronal
antibodies. A total of 137 patients were identified with a paraneoplastic
neurological syndrome and high titre (> or =400) antineuronal antibodies.
Fifty (36%) of these patients had antibody-associated PCD, including 19
anti-Yo, 16 anti-Hu, seven anti-Tr, six anti-Ri and two anti-mGluR1.
Because of the low number, the anti-mGluR1 patients were excluded from the
statistical analysis. While 100% of patients with anti-Yo, anti-Tr and
anti-mGluR1 antibodies suffered PCD, 86% of anti-Ri and only 18% of
anti-Hu patients had PCD. All patients presented with subacute cerebellar
ataxia progressive over weeks to months and stabilized within 6 months.
The majority of patients in all antibody groups had both truncal and
appendicular ataxia. The frequency of nystagmus and dysarthria was lower
in anti-Ri patients (33 and 0%). Later in the course of the disease,
involvement of non-cerebellar structures occurred most frequently in
anti-Hu patients (94%). In 42 patients (84%), a tumour was detected. The
most commonly associated tumours were gynaecological and breast cancer
(anti-Yo and anti-Ri), lung cancer (anti-Hu) and Hodgkin's lymphoma
(anti-Tr and anti-mGluR1). In one anti-Hu patient, a suspect lung lesion
on CT scan disappeared while the PCD evolved. Seven patients improved by
at least 1 point on the Rankin scale, while 16 remained stable and 27
deteriorated. All seven patients that improved received antitumour
treatment for their underlying cancer, resulting in complete remission.
The functional outcome was best in the anti-Ri patients, with three out of
six improving neurologically and five were able to walk at the time of
last follow-up or death. Only four out of 19 anti-Yo and four out of 16
anti-Hu patients remained ambulatory. Also, survival from time of
diagnosis was significantly worse in the anti-Yo (median 13 months) and
anti-Hu (median 7 months) patients compared with anti-Tr (median >113
months) and anti-Ri (median >69 months). Patients receiving antitumour
treatment (with or without immunosuppressive therapy) lived significantly
longer [hazard ratio (HR) 0.3; 95% confidence interval (CI) 0.1-0.6; P =
0.004]. Patients > or =60 years old lived somewhat shorter from time of
diagnosis, although statistically not significant (HR 2.9; CI 1.0-8.5; P =
0.06)
Separating the effects of 24-hour urinary chloride and sodium excretion on blood pressure and risk of hypertension:Results from PREVEND
OBJECTIVE:Research into dietary factors associated with hypertension has focused on the sodium component of salt. However, chloride has distinct physiological effects that may surpass the effect of sodium on blood pressure. This study aims to separate the specific effects of chloride and sodium intake on blood pressure. METHODS:We studied 5673 participants from the Prevention of Renal and Vascular End-Stage Disease(PREVEND) study. Urinary chloride(uCl) and sodium(uNa) were measured in two 24-hour collections. We used generalized-linear-regression to evaluate the relation of uCl and uNa with baseline blood pressure and Cox-proportional-hazards-analysis to assess the association with hypertension. Multicollinearity was assessed with Ridge regression. RESULTS:Baseline 24-hour uCl was 135±39mmol and uNa was 144±54mmol. The correlation between uCl and uNa was high (Pearson's r = 0.96). UCl and uNa had similar non-significant positive and linear associations with blood pressure. In 3515 normotensive patients, 1021 patients developed hypertension during a median follow-up of 7.4 years. UCl and uNa had a comparable but non-significant J-shaped effect on the risk of hypertension. Adding both uCl and uNa to the same model produced instability, demonstrated by Ridge coefficients that converged or changed sign. The single index of uNa minus uCl showed a non-significant higher risk of hypertension of 2% per 10mmol/24-hour difference (HR1.02, 95%CI 0.98-1.06). CONCLUSION:UCl and uNa had similar positive but non-significant associations with blood pressure and risk of hypertension and their effects could not be disentangled. Hence, the alleged adverse effects of high salt intake could be due to sodium, chloride or both. This encourages further study into the effect of chloride in order to complement dietary recommendations currently focused on sodium alone
Omineca Herald, October, 04, 1979
The relation between progression of cerebral small vessel disease (SVD) and gait decline is uncertain, and diffusion tensor imaging (DTI) studies on gait decline are lacking. We therefore investigated the longitudinal associations between (micro) structural brain changes and gait decline in SVD using DTI. 275 participants were included from the Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort (RUN DMC), a prospective cohort of participants with cerebral small vessel disease aged 50–85years. Gait (using GAITRite) and magnetic resonance imaging measures were assessed during baseline (2006–2007) and follow-up (2011−2012). Linear regression analysis was used to investigate the association between changes in conventional magnetic resonance and diffusion tensor imaging measures and gait decline. Tract-based spatial statistics analysis was used to investigate region-specific associations between changes in white matter integrity and gait decline. 56.2% were male, mean age was 62.9years (SD8.2), mean follow-up duration was 5.4years (SD0.2) and mean gait speed decline was 0.2m/s (SD0.2). Stride length decline was associated with white matter atrophy (β=0.16, p=0.007), and increase in mean white matter radial diffusivity and mean diffusivity, and decrease in mean fractional anisotropy (respectively, β=−0.14, p=0.009; β=−0.12, p=0.018; β=0.10, p=0.049), independent of age, sex, height, follow-up duration and baseline stride length. Tract-based spatial statistics analysis showed significant associations between stride length decline and fractional anisotropy decrease and mean diffusivity increase (primarily explained by radial diffusivity increase) in multiple white matter tracts, with the strongest associations found in the corpus callosum and corona radiata, independent of traditional small vessel disease markers. White matter atrophy and loss of white matter integrity are associated with gait decline in older adults with small vessel disease after 5years of follow-up. These findings suggest that progression of SVD might play an important role in gait decline. Keywords: Cerebral small vessel disease (SVD), MRI, Diffusion tensor imaging (DTI), Tract-based spatial statistics (TBSS), Gai
Measuring health-related quality of life in colorectal cancer patients:systematic review of measurement properties of the EORTC QLQ-CR29
Measuring health-related quality of life in colorectal cancer patients:systematic review of measurement properties of the EORTC QLQ-CR29
Tensor Regression with Applications in Neuroimaging Data Analysis
Classical regression methods treat covariates as a vector and estimate a
corresponding vector of regression coefficients. Modern applications in medical
imaging generate covariates of more complex form such as multidimensional
arrays (tensors). Traditional statistical and computational methods are proving
insufficient for analysis of these high-throughput data due to their ultrahigh
dimensionality as well as complex structure. In this article, we propose a new
family of tensor regression models that efficiently exploit the special
structure of tensor covariates. Under this framework, ultrahigh dimensionality
is reduced to a manageable level, resulting in efficient estimation and
prediction. A fast and highly scalable estimation algorithm is proposed for
maximum likelihood estimation and its associated asymptotic properties are
studied. Effectiveness of the new methods is demonstrated on both synthetic and
real MRI imaging data.Comment: 27 pages, 4 figure
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