Vitamin D and Disease Severity in Multiple Sclerosis-Baseline Data From the Randomized Controlled Trial (EVIDIMS)

Objective: To investigate the associations between hypovitaminosis D and disease activity in a cohort of relapsing remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) patients. Methods: In 51 RRMS and 2 CIS patients on stable interferon-β-1b (IFN-β-1b) treatment recruited to the EVIDIMS study (Efficacy of Vitamin D Supplementation in Multiple Sclerosis (NCT01440062) baseline serum vitamin D levels were evaluated. Patients were dichotomized based on the definition of vitamin D deficiency which is reflected by a < 30 vs. ≥ 30 ng/ml level of 25-hydroxyvitamin D (25(OH)D). Possible associations between vitamin D deficiency and both clinical and MRI features of the disease were analyzed. Results: Median (25, 75% quartiles, Q) 25(OH)D level was 18 ng/ml (12, 24). Forty eight out of 53 (91%) patients had 25(OH)D levels < 30 ng/ml (p < 0.001). Patients with 25(OH)D ≥ 30 ng/ml had lower median (25, 75% Q) T2-weighted lesion counts [25 (24, 33)] compared to patients with 25(OH)D < 30 ng/ml [60 (36, 84), p = 0.03; adjusted for age, gender and disease duration: p < 0.001]. Expanded disability status scale (EDSS) score was negatively associated with serum 25(OH)D levels in a multiple linear regression, including age, sex, and disease duration (adjusted: p < 0.001). Interpretation: Most patients recruited in the EVIDIMS study were vitamin D deficient. Higher 25(OH)D levels were associated with reduced T2 weighted lesion count and lower EDSS scores

Imaging markers of disability in aquaporin-4 immunoglobulin G seropositive neuromyelitis optica: a graph theory study

Neuromyelitis optica spectrum disorders lack imaging biomarkers associated with disease course and supporting prognosis. This complex and heterogeneous set of disorders affects many regions of the central nervous system, including the spinal cord and visual pathway. Here, we use graph theory-based multimodal network analysis to investigate hypothesis-free mixed networks and associations between clinical disease with neuroimaging markers in 40 aquaporin-4-immunoglobulin G antibody seropositive patients (age = 48.16 ± 14.3 years, female:male = 36:4) and 31 healthy controls (age = 45.92 ± 13.3 years, female:male = 24:7). Magnetic resonance imaging measures included total brain and deep grey matter volumes, cortical thickness and spinal cord atrophy. Optical coherence tomography measures of the retina and clinical measures comprised of clinical attack types and expanded disability status scale were also utilized. For multimodal network analysis, all measures were introduced as nodes and tested for directed connectivity from clinical attack types and disease duration to systematic imaging and clinical disability measures. Analysis of variance, with group interactions, gave weights and significance for each nodal association (hyperedges). Connectivity matrices from 80% and 95% F-distribution networks were analyzed and revealed the number of combined attack types and disease duration as the most connected nodes, directly affecting changes in several regions of the central nervous system. Subsequent multivariable regression models, including interaction effects with clinical parameters, identified associations between decreased nucleus accumbens (β = −0.85, P = 0.021) and caudate nucleus (β = −0.61, P = 0.011) volumes with higher combined attack type count and longer disease duration, respectively. We also confirmed previously reported associations between spinal cord atrophy with increased number of clinical myelitis attacks. Age was the most important factor associated with normalized brain volume, pallidum volume, cortical thickness and the expanded disability status scale score. The identified imaging biomarker candidates warrant further investigation in larger-scale studies. Graph theory-based multimodal networks allow for connectivity and interaction analysis, where this method may be applied in other complex heterogeneous disease investigations with different outcome measures

Building a Framework for Logical Cryptanalysis of Hash Functions

Solving systems of boolean equations (SAT) is one of the NP-Complete problems in Computer Science traditionally thought intractable. However, the past 20 years of SAT solving competitions has resulted in the development of many fast solvers which have seen adoption by industry for model checking and partially for automated theorem proving. Starting in the early 2000s, the application of SAT solvers to cryptography was developed by F. Massacci, resulting in the field of logical cryptanalysis. While many of the existing publications in the field are geared towards creating benchmarks for further developing solvers, we seek to apply SAT to cryptographic hash functions to study their effectiveness at collision resistance. Over the past year, we have developed a framework for studying hash functions and new techniques for analyzing the security margin of cryptographic hash functions. We have applied this framework to the winner of NIST's hash competition, the Keccak/SHA-3 hash function, as standardized by FIPS-202 in 2015. In this talk, we will discuss the creation of new techniques for analyzing SHA-3, their impact on SHA-3's security margin, and their mathematical implications

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Ninja data analysis with a detection pipeline based on the Hilbert-Huang Transform

The Ninja data analysis challenge allowed the study of the sensitivity of data analysis pipelines to binary black hole numerical relativity waveforms in simulated Gaussian noise at the design level of the LIGO observatory and the VIRGO observatory. We analyzed NINJA data with a pipeline based on the Hilbert Huang Transform, utilizing a detection stage and a characterization stage: detection is performed by triggering on excess instantaneous power, characterization is performed by displaying the kernel density enhanced (KD) time-frequency trace of the signal. Using the simulated data based on the two LIGO detectors, we were able to detect 77 signals out of 126 above SNR 5 in coincidence, with 43 missed events characterized by signal to noise ratio SNR less than 10. Characterization of the detected signals revealed the merger part of the waveform in high time and frequency resolution, free from time-frequency uncertainty. We estimated the timelag of the signals between the detectors based on the optimal overlap of the individual KD time-frequency maps, yielding estimates accurate within a fraction of a millisecond for half of the events. A coherent addition of the data sets according to the estimated timelag eventually was used in a characterization of the event.Comment: Accepted for publication in CQG, special issue NRDA proceedings 200

Pain in AQP4-IgG-positive and MOG-IgG-positive neuromyelitis optica spectrum disorders

Background: Pain is a frequent symptom in aquaporin-4-immunoglobulin-G-positive neuromyelitis optica spectrum disorders (AQP4-IgG-pos. NMOSD). Data on pain in myelin-oligodendrocyteglycoprotein- immunoglobulin-G autoimmunity with a clinical NMOSD phenotype (MOG-IgG-pos. NMOSD) are scarce. Objective: The objective of this paper is to investigate pain in MOG-IgG-pos. NMOSD, AQP4-IgG-pos. NMOSD and NMOSD without AQP4/MOG-IgG detection (AQP4/MOG-IgG-neg. NMOSD). Methods: Forty-nine MOG-IgG-pos. (n=14), AQP4-IgG-pos. (n=29) and AQP4/MOG-IgG-neg. (n=6) NMOSD patients were included in this cross-sectional baseline analysis from an ongoing observational study. We identified spinal cord lesions on magnetic resonance imaging, assessed pain by the painDETECT and McGill Pain questionnaires, quality of life by Short Form Health Survey, and depression by Beck Depression Inventory. Results: Twelve MOG-IgG-pos. NMOSD patients (86%), 24 AQP4-IgG-pos. NMOSD patients (83%), and all AQP4/MOG-IgG-neg. NMOSD patients (100%) suffered from pain. MOG-IgG-pos. NMOSD patients had mostly neuropathic pain and headache; AQP4-IgG-pos. and AQP4/MOG-IgG-neg. NMOSD patients had mostly neuropathic pain. A history of myelitis was less frequent in MOG-IgGpos. NMOSD than in AQP4-IgG-pos. NMOSD patients. Pain influenced quality of life in all patients. Thirty-six percent of patients with pain received pain medication; none of them were free of pain. Conclusions: Pain is a frequent symptom of patients with MOG-IgG-pos. NMOSD and is as important as in AQP4-IgG-pos. and AQP4/MOG-IgG-neg. NMOSD. Despite its impact on quality of life, pain is insufficiently alleviated by medication

PT-symmetric photonic quantum systems with gain and loss do not exist

We discuss the impact of gain and loss on the evolution of photonic quantum states and find that PT-symmetric quantum optics in gain/loss systems is not possible. Within the framework of macroscopic quantum electrodynamics we show that gain and loss are associated with non-compact and compact operator transformations, respectively. This implies a fundamentally different way in which quantum correlations between a quantum system and a reservoir are built up and destroyed