Remarks by David F. Cavers to Duke Students Converning the Origin of and Vision for Law and Contemporary Problems

Objectives To present a method for generating reference maps of typical brain characteristics of groups of subjects using a novel combination of rapid quantitative Magnetic Resonance Imaging (qMRI) and brain normalization. The reference maps can be used to detect significant tissue differences in patients, both locally and globally. Materials and Methods A rapid qMRI method was used to obtain the longitudinal relaxation rate (R1), the transverse relaxation rate (R2) and the proton density (PD). These three tissue properties were measured in the brains of 32 healthy subjects and in one patient diagnosed with Multiple Sclerosis (MS). The maps were normalized to a standard brain template using a linear affine registration. The differences of the mean value ofR1, R2 and PD of 31 healthy subjects in comparison to the oldest healthy subject and in comparison to an MS patient were calculated. Larger anatomical structures were characterized using a standard atlas. The vector sum of the normalized differences was used to show significant tissue differences. Results The coefficient of variation of the reference maps was high at the edges of the brain and the ventricles, moderate in the cortical grey matter and low in white matter and the deep grey matter structures. The elderly subject mainly showed significantly lower R1 and R2 and higher PD values along all sulci. The MS patient showed significantly lower R1 and R2 and higher PD values at the edges of the ventricular system as well as throughout the periventricular white matter, at the internal and external capsules and at each of the MS lesions. Conclusion Brain normalization of rapid qMRI is a promising new method to generate reference maps of typical brain characteristics and to automatically detect deviating tissue properties in the brain

The Role of Attorney Fee Shifting in Public Interest Litigation

BACKGROUND: Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 and PD, and partial volume voxels are intermediate in this space. The qMRI parameters, however, depend on the main magnetic field strength. Therefore, longitudinal studies can be seriously limited by system upgrades. The aim of this work was to apply one recently described brain tissue segmentation method, based on qMRI, at both 1.5 T and 3.0 T field strengths, and to investigate similarities and differences. METHODS: In vivo qMRI measurements were performed on 10 healthy subjects using both 1.5 T and 3.0 T MR scanners. The brain tissue segmentation method was applied for both 1.5 T and 3.0 T and volumes of WM, GM, CSF and brain parenchymal fraction (BPF) were calculated on both field strengths. Repeatability was calculated for each scanner and a General Linear Model was used to examine the effect of field strength. Voxel-wise t-tests were also performed to evaluate regional differences. RESULTS: Statistically significant differences were found between 1.5 T and 3.0 T for WM, GM, CSF and BPF (p<0.001). Analyses of main effects showed that WM was underestimated, while GM and CSF were overestimated on 1.5 T compared to 3.0 T. The mean differences between 1.5 T and 3.0 T were -66 mL WM, 40 mL GM, 29 mL CSF and -1.99% BPF. Voxel-wise t-tests revealed regional differences of WM and GM in deep brain structures, cerebellum and brain stem. CONCLUSIONS: Most of the brain was identically classified at the two field strengths, although some regional differences were observed

Combining Cluster Validation Indices for Detecting Label Noise

In this paper, we show that cluster validation indices can be used for filtering mislabeled instances or class outliers prior to training in supervised learning problems. We propose a technique, entitled Cluster Validation Index (CVI)-based Outlier Filtering, in which mislabeled instances are identified and eliminated from the training set, and a classification hypothesis is then built from the set of remaining instances. The proposed approach assigns each instance several cluster validation scores representing its potential of being an outlier with respect to the clustering properties the used validation measures assess. We examine CVI-based Outlier Filtering and compare it against the Local Outlier Factor (LOF) detection method on ten data sets from the UCI data repository using five well-known learning algorithms and three different cluster validation indices. In addition, we study and compare three different approaches for combining the selected cluster validation measures. Our results show that for most learning algorithms and data sets, the proposed CVI-based outlier filtering algorithm outperforms the baseline method (LOF). The greatest increase in classification accuracy has been achieved by using union or ranked-based median strategies to assemble the used cluster validation indices and global filtering of mislabeled instances

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C

Health-related quality of life across all stages of autosomal dominant polycystic kidney disease

Background. A limited number of studies have assessed health related quality of life (HRQoL) in autosomal dominant polycystic kidney disease (ADPKD). Results to date have been conflicting and studies have generally focused on patients with later stages of the disease. This study aimed to assess HRQoL in ADPKD across all stages of the disease, from patients with early chronic kidney disease (CKD) to patients with end-stage renal disease. Methods. A study involving cross-sectional patient-reported outcomes and retrospective clinical data was undertaken April December 2014 in Denmark, Finland, Norway and Sweden. Patients were enrolled into four mutually exclusive stages of the disease: CKD stages 1-3; CKD stages 4-5; transplant recipients; and dialysis patients. Results. Overall HRQoL was generally highest in patients with CKD stages 1-3, followed by transplant recipients, patients with CKD stages 4-5 and patients on dialysis. Progressive disease predominately had an impact on physical health, whereas mental health showed less variation between stages of the disease. A substantial loss in quality of life was observed as patients progressed to CKD stages 4-5. Conclusions. Later stages of ADPKD are associated with reduced physical health. The value of early treatment interventions that can delay progression of the disease should be considered.Peer reviewe

Real-world costs of autosomal dominant polycystic kidney disease in the Nordics

Background: There is limited real-world data on the economic burden of patients with autosomal dominant polycystic kidney disease (ADPKD). The objective of this study was to estimate the annual direct and indirect costs of patients with ADPKD by severity of the disease: chronic kidney disease (CKD) stages 1-3; CKD stages 4-5; transplant recipients; and maintenance dialysis patients. Methods: A retrospective study of ADPKD patients was undertaken April-December 2014 in Denmark, Finland, Norway and Sweden. Data on medical resource utilisation were extracted from medical charts and patients were asked to complete a self-administered questionnaire. Results: A total of 266 patients were contacted, 243 (91%) of whom provided consent to participate in the study. Results showed that the economic burden of ADPKD was substantial at all levels of the disease. Lost wages due to reduced productivity were large in absolute terms across all disease strata. Mean total annual costs were highest in dialysis patients, driven by maintenance dialysis care, while the use of immunosuppressants was the main cost component for transplant care. Costs were twice as high in patients with CKD stages 4-5 compared to CKD stages 1-3. Conclusions: Costs associated with ADPKD are significant and the progression of the disease is associated with an increased frequency and intensity of medical resource utilisation. Interventions that can slow the progression of the disease have the potential to lead to substantial reductions in costs for the treatment of ADPKD.Peer reviewe

Altered expression of autoimmune regulator in infant down syndrome thymus, a possible contributor to an autoimmune phenotype.

To access publisher's full text version of this article click on the hyperlink at the bottom of the pageDown syndrome (DS), caused by trisomy of chromosome 21, is associated with immunological dysfunctions such as increased frequency of infections and autoimmune diseases. Patients with DS share clinical features, such as autoimmune manifestations and specific autoantibodies, with patients affected by autoimmune polyendocrine syndrome type 1. Autoimmune polyendocrine syndrome type 1 is caused by mutations in the autoimmune regulator (AIRE) gene, located on chromosome 21, which regulates the expression of tissue-restricted Ags (TRAs) in thymic epithelial cells. We investigated the expression of AIRE and TRAs in DS and control thymic tissue using quantitative PCR. AIRE mRNA levels were elevated in thymic tissue from DS patients, and trends toward increased expression of the AIRE-controlled genes INSULIN and CHRNA1 were found. Immunohistochemical stainings showed altered cell composition and architecture of the thymic medulla in DS individuals with increased frequencies of AIRE-positive medullary epithelial cells and CD11c-positive dendritic cells as well as enlarged Hassall's corpuscles. In addition, we evaluated the proteomic profile of thymic exosomes in DS individuals and controls. DS exosomes carried a broader protein pool and also a larger pool of unique TRAs compared with control exosomes. In conclusion, the increased AIRE gene dose in DS could contribute to an autoimmune phenotype through multiple AIRE-mediated effects on homeostasis and function of thymic epithelial cells that affect thymic selection processes.Swedish Research Council 80409601 Marianne and Marcus Wallenberg Foundation Region Vastra Gotaland ALFGBG-771712 Arbetsmarknadens Forsakringsaktiebolag 100258 IngaBritt and Arne Lundbergs Research Foundation AnnMari and Per Ahlqvists Foundation Gothenburg Medical Society Wilhelm and Martina Lundgrens Research Foundatio

Heightened immune response to autocitrullinated porphyromonas gingivalis peptidylarginine deiminase: a potential mechanism for breaching immunologic tolerance in rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is characterised by autoimmunity to citrullinated proteins, and there is increasing epidemiologic evidence linking Porphyromonas gingivalis to RA. P gingivalis is apparently unique among periodontal pathogens in possessing a citrullinating enzyme, peptidylarginine deiminase (PPAD) with the potential to generate antigens driving the autoimmune response. Objectives: To examine the immune response to PPAD in patients with RA, individuals with periodontitis (PD) and controls (without arthritis), confirm PPAD autocitrullination and identify the modified arginine residues. Methods: PPAD and an inactivated mutant (C351A) were cloned and expressed and autocitrullination of both examined by immunoblotting and mass spectrometry. ELISAs using PPAD, C351A and another P gingivalis protein arginine gingipain (RgpB) were developed and antibody reactivities examined in patients with RA (n=80), individuals with PD (n=44) and controls (n=82). Results: Recombinant PPAD was a potent citrullinating enzyme. Antibodies to PPAD, but not to Rgp, were elevated in the RA sera (median 122 U/ml) compared with controls (median 70 U/ml; p&#60;0.05) and PD (median 60 U/ml; p&#60;0.01). Specificity of the anti-peptidyl citrullinated PPAD response was confirmed by the reaction of RA sera with multiple epitopes tested with synthetic citrullinated peptides spanning the PPAD molecule. The elevated antibody response to PPAD was abolished in RA sera if the C351A mutant was used on ELISA. Conclusions: The peptidyl citrulline-specific immune response to PPAD supports the hypothesis that, as a bacterial protein, it might break tolerance in RA, and could be a target for therapy