Recurrence of disease activity during pregnancy after cessation of fingolimod in multiple sclerosis

Background: Fingolimod is an effective treatment for active relapsing-remitting multiple sclerosis (MS). Discontinuation of therapy may be followed by recurrence of disease activity. Thus, female MS patients may be at risk of relapse during pregnancy after stopping fingolimod. Objectives and methods: To report the disease course during pregnancy of five women who interrupted therapy with fingolimod for pregnancy. Results: All patients experienced relapses during pregnancy and/or postpartum after stopping fingolimod. Conclusion: The risk of recurrence of disease activity during pregnancy after stopping fingolimod may be substantial. This should be considered and discussed with MS patients who are planning to become pregnant

On a class of non-Hermitian matrices with positive definite Schur complements

Given a positive definite nXn matrix A and a Hermitian mXm matrix D, we characterize under which conditions there exists a strictly contractive matrix K such that the non-Hermitian block-matrix with the enties A and -AK in the first row and K^*A and D in the second has a positive definite Schur complement with respect to its submatrix A. Additionally, we show that K can be chosen such that diagonalizability of the block-matrix is guaranteed and we compute its spectrum. Moreover, we show a connection to the recently developed frame theory for Krein spaces

Subgraph Mining

Graphs are often used as models in very different application areas ranging from networks to molecules and proteins. Having graphs in a graph database it is an interesting problem to find small graph parts, so called subgraphs, that appear in a certain number of graphs within the database. Possible subgraphs of a set of graphs form a lattice that must be searched to find the subgraphs that appear most frequently. Two steps are necessary for this search: first new possible subgraphs must be generated, secondly it must be checked how often a newly generated subgraph appears in the database. Additionally intelligent pruning methods, inexact graph matching and background knowledge can be incorporated in the mining algorithms

Graph based molecular data mining - an overview

Abstract – In the last years quite a lot of algorithms concerning frequent graph pattern mining have been published. In this paper an overview on the different methods for graph data mining is given, starting with the greedy searches proposed in the middle of the ninties. The ILP-based approaches are taken into account as well as ideas influenced by basket analyses proposed lately. A remaining question is how the different approaches can be tailored to meet the needs for mining molecules. In this area special problems occur as molecules are not just “normal arbitrary graphs”. There are structures that are typical and frequent as rings and chains, some node types resp. atoms occur more often than others. It is an unsolved question how chemically isomorphic mining can be handled

Parallel Mining for Frequent Fragments on a Shared-Memory Multiprocessor - Results and Java-Obstacles

Although in the last years about a dozen sophisticated algorithms for mining frequent subgraphs have been proposed, it still takes too long to search big databases with 100,000 graphs and more. Even the currently fastest algorithms like gSpan, FFSM, Gaston, or MoFa need hours to complete their tasks

Mining Molecular Datasets on Symmetric Multiprocessor Systems

Although in the last years about a dozen sophisticated algorithms for mining frequent subgraphs have been proposed, it still takes too long to search big databases with 100,000 graphs and more. Even the currently fastest algorithms like gSpan, FFSM, Gaston, or MoFa need hours to complete their tasks.This paper presents thread-based parallel versions of MoFa [5] and gSpan [26] that achieve speedups up to 11 on a shared-memory SMP system using 12 processors. We discuss the design space of the parallelization, the results, and the obstacles, that are caused by the irregular search space and by the current state of Java technology

Edgar: the embedding-based graph miner

Abstract. In this paper we present the novel graph mining algorithm Edgar which is based on the well-known gSpan algorithm. The need for another subgraph miner results from procedural abstraction (an important technique to reduce code size in embedded systems). Assembler code is represented as a data flow graph and subgraph mining on this graph returns frequent code fragments that can be extracted into procedures. When mining for procedural abstraction, it is not the number of data flow graphs in which a fragment occurs that is important but the number of all the non-overlapping occurrences in all graphs. Several changes in the mining process have therefore become necessary. As traditional pruning strategies are inappropriate, Edgar uses a new embedding-based frequency; on average, saves 160 % more instructions compared to classical approaches.

Predictors for multiple sclerosis relapses after switching from natalizumab to fingolimod

Background: Risks of natalizumab (NAT) therapy have to be weighed against disease recurrence after stopping NAT. Objectives: The objective of this paper is to identify risk factors for recurrence of relapses after switching from NAT to fingolimod (FTY) in relapsing–remitting multiple sclerosis (RRMS). Methods: Patients (n = 33) were treated with NAT for ≥1 year, and then switched to FTY within 24 weeks (mean follow-up on FTY 81.1 (SD 26.5) weeks). Annual relapse rates (ARR) and Expanded Disability Status Scale scores (EDSS) were assessed. Descriptive statistics, univariate logistic regression analysis, and receiver operating characteristic curves were conducted. Results: Overall, 20 patients (61%) had relapses after discontinuation of NAT and 16 (48%) during FTY therapy. The maximum incidence of relapses occurred between weeks 13–24 post-NAT. The last EDSS during the switching period predicted relapses during subsequent FTY therapy. EDSS >3 separated most powerfully between the groups (sensitivity 64%, specificity 88%) and significantly predicted relapses (relative risk 3.27, 95% CI: 1.5–7.3). Seventy-five percent of patients with EDSS ≤ 3 remained free of relapses, compared to 18% of patients with EDSS >3. Conclusions: There was an increase of the ARR in the first year after switching from NAT to FTY. Last EDSS during the switching period was a predictor of relapses during FTY