Least Dependent Component Analysis Based on Mutual Information

We propose to use precise estimators of mutual information (MI) to find least dependent components in a linearly mixed signal. On the one hand this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand it has the advantage, compared to other implementations of `independent' component analysis (ICA) some of which are based on crude approximations for MI, that the numerical values of the MI can be used for: (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output, by comparing the pairwise MIs with those of re-mixed components; (iii) clustering the output according to the residual interdependencies. For the MI estimator we use a recently proposed k-nearest neighbor based algorithm. For time sequences we combine this with delay embedding, in order to take into account non-trivial time correlations. After several tests with artificial data, we apply the resulting MILCA (Mutual Information based Least dependent Component Analysis) algorithm to a real-world dataset, the ECG of a pregnant woman. The software implementation of the MILCA algorithm is freely available at http://www.fz-juelich.de/nic/cs/softwareComment: 18 pages, 20 figures, Phys. Rev. E (in press

Hierarchical clustering using mutual information

We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use this both in the Shannon (probabilistic) version of information theory and in the Kolmogorov (algorithmic) version. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and to the output of independent components analysis (ICA) as illustrated with the ECG of a pregnant woman

On Fine Stochastic Simulations of Liposome-Encapsulated PUREsystem™

The PURESystem™(for short: PS) is a defined set of about 80 different macromolecular species which can perform protein synthesis starting from a coding DNA. To understand the processes that take place inside a liposome with entrapped PS, several simulation approaches, of either a deterministic or stochastic nature, have been proposed in the literature. To correctly describe some peculiar phenomena that are observed only in very small liposomes (such as power-law distribution of solutes and supercrowding effect), a stochastic approach seems necessary, due to the very small average number of molecules contained in these liposomes. Here we recall the results reported in other works published by us and by other Authors, discussing the importance of a stochastic simulation approach and of a fine description of the system: both these aspects, in fact, were not properly acknowledged in such previous papers

Conventional and semi-automatic histopathological analysis of tumor cell content for multigene sequencing of lung adenocarcinoma.

Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification. TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath). Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%. Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions

A Hilbert space embedding for distributions

Abstract. We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation. Kernel methods are widely used in supervised learning [1, 2, 3, 4], however they are much less established in the areas of testing, estimation, and analysis of probability distributions, where information theoretic approaches [5, 6] have long been dominant. Recent examples include [7] in the context of construction of graphical models, [8] in the context of feature extraction, and [9] in the context of independent component analysis. These methods have by and large a common issue: to compute quantities such as the mutual information, entropy, or Kullback-Leibler divergence, we require sophisticated space partitioning and/o

Late Thrombectomy in Clinical Practice

Background and purpose!#!To provide real-world data on outcome and procedural factors of late thrombectomy patients.!##!Methods!#!We retrospectively analyzed patients from the multicenter German Stroke Registry. The primary endpoint was clinical outcome on the modified Rankin scale (mRS) at 3 months. Trial-eligible patients and the subgroups were compared to the ineligible group. Secondary analyses included multivariate logistic regression to identify predictors of good outcome (mRS ≤ 2).!##!Results!#!Of 1917 patients who underwent thrombectomy, 208 (11%) were treated within a time window ≥ 6-24 h and met the baseline trial criteria. Of these, 27 patients (13%) were eligible for DAWN and 39 (19%) for DEFUSE3 and 156 patients were not eligible for DAWN or DEFUSE3 (75%), mainly because there was no perfusion imaging (62%; n = 129). Good outcome was not significantly higher in trial-ineligible (27%) than in trial-eligible (20%) patients (p = 0.343). Patients with large trial-ineligible CT perfusion imaging (CTP) lesions had significantly more hemorrhagic complications (33%) as well as unfavorable outcomes.!##!Conclusion!#!In clinical practice, the high number of patients with a good clinical outcome after endovascular therapy ≥ 6-24 h as in DAWN/DEFUSE3 could not be achieved. Similar outcomes are seen in patients selected for EVT ≥ 6 h based on factors other than CTP. Patients triaged without CTP showed trends for shorter arrival to reperfusion times and higher rates of independence

Outcome after Thrombectomy and Intravenous Thrombolysis in Patients with Acute Ischemic Stroke : A Prospective Observational Study

Background and Purpose - In patients with ischemic stroke, randomized trials showed a better functional outcome after endovascular therapy with new-generation thrombectomy devices compared with medical treatment, including intravenous thrombolysis. However, effects on mortality and the generalizability of results to routine clinical practice are uncertain. Methods - In a prospective observational register-based study patients with ischemic stroke treated either with thrombectomy, intravenous thrombolysis, or their combination were included. Primary outcome was the modified Rankin scale score (0 [no symptoms] to 6 [death]) at 3 months. Ordinal logistic regression was used to estimate the common odds ratio as treatment effects (shift analysis). Propensity score matching was applied to compare patients treated either with intravenous thrombolysis alone or with intravenous thrombolysis plus thrombectomy. Results - Among 2650 recruited patients, 1543 received intravenous thrombolysis, 504 underwent thrombectomy, and 603 received intravenous thrombolysis in combination with thrombectomy. Later time-to-treatment was associated with worse outcomes among patients treated with thrombectomy plus thrombolysis. In 241 pairs of propensity score-matched patients with a proximal intracranial occlusion, thrombectomy plus thrombolysis was associated with improved functional outcome (common odds ratio, 1.84; 95% confidence interval, 1.32-2.57), and reduced mortality (15% versus 33%;