MOA: Massive Online Analysis, a framework for stream classification and clustering.

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. It contains collection of offline and online for both classification and clustering as well as tools for evaluation. In particular, for classification it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. For clustering, it implements StreamKM++, CluStream, ClusTree, Den-Stream, D-Stream and CobWeb. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily apply and compare several algorithms to real world data set and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license

Personalized Brain-Computer Interface Models for Motor Rehabilitation

We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.Comment: 6 pages, 6 figures, conference submissio

Application of multivariate analysis in the processing of medical data

Medical data frequently represent multidimensional datasets as investigated factors and clinical and laboratory parameters coverage is huge. This research area is very important in terms of practical applications. We were given monthly lipid metabolism and hormonal status data of children (including children suffering from obesity) of Siberian region during a year. In this article some research results appear

Neural Signatures of Motor Skill in the Resting Brain

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task -- as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.Comment: 2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019

Microstructural investigation of hybrid CAD/CAM restorative dental materials by micro-CT and SEM

Objectives: An increasing number of CAD/CAM (computer-aided design/computer-aided manufacturing) hybrid materials have been introduced to the dental market in recent years. In addition, CAD/CAM hybrid materials for additive manufacturing (AM) are becoming more attractive in digital dentistry. Studies on material microstructures using micro-computed tomography ($\mu$-CT) combined with scanning electron microscopy (SEM) have only been available to a limited extent so far. Methods: One CAD/CAM three-dimensional- (3D-) printable hybrid material (VarseoSmile Crown plus) and two CAD/CAM millable hybrid materials (Vita Enamic; Voco Grandio), as well as one direct composite material (Ceram.x duo), were included in the present study. Cylindrical samples with a diameter of 2 mm were produced from each material and investigated by means of synchrotron radiation $\mu$-CT at a voxel size of 0.65 $\mu$m. Different samples from the same materials, obtained by cutting and polishing, were investigated by SEM. Results: The 3D-printed hybrid material showed some agglomerations and a more irregular distribution of fillers, as well as a visible layered macrostructure and a few spherical pores due to the printing process. The CAD/CAM millable hybrid materials revealed a more homogenous distribution of ceramic particles. The direct composite material showed multiple air bubbles and microstructural irregularities based on manual processing. Significance: The $\mu$-CT and SEM analysis of the materials revealed different microstructures even though they belong to the same class of materials. It could be shown that $\mu$-CT and SEM imaging are valuable tools to understand microstructure and related mechanical properties of materials.Comment: 22 pages, 3 tables, 11 figures including supplementary materia

Use of smartphone application versus written titration charts for basal insulin titration in adults with type 2 diabetes and suboptimal glycaemic control (My Dose Coach) : multicentre, open-label, parallel, randomised controlled trial

Background The majority of people with type 2 diabetes who require insulin therapy use only basal insulin in combination with other anti-diabetic agents. We tested whether using a smartphone application to titrate insulin could improve glycaemic control in people with type 2 diabetes who use basal insulin. Methods This was a 12-week, multicentre, open-label, parallel, randomised controlled trial conducted in 36 diabetes practices in Germany. Eligible participants had type 2 diabetes, a BMI ≥25.0 kg/m2, were on basal insulin therapy or were initiating basal insulin therapy, and had suboptimal glycaemic control (HbA1c >7.5%; 58.5 mmol/mol). Block randomisation with 1:1 allocation was performed centrally. Participants in the intervention group titrated their basal insulin dose using a smartphone application (My Dose Coach) for 12 weeks. Control group participants titrated their basal insulin dose according to a written titration chart. The primary outcome was the baseline-adjusted change in HbA1c at 12 weeks. The intention-to-treat analysis included all randomised participants. Results Between 13 July 2021 and 21 March 2022, 251 study participants were randomly assigned (control group: n = 123; intervention group: n = 128), and 236 completed the follow-up phase (control group: n = 119; intervention group: n = 117). Regarding the HbA1c a model-based adjusted between-group difference of −0.31% (95% CI: 0.01%–0.69%; p = 0.0388) in favour of the intervention group was observed. There were 30 adverse events reported: 16 in the control group, 14 in the intervention group. Of these, 15 adverse events were serious. No event was considered to be related to the investigational device. Interpretation Study results suggest that utilizing this digital health smartphone application for basal insulin titration may have resulted in a comparatively greater reduction in HbA1c levels among individuals with type 2 diabetes, as compared to basal insulin titration guided by a written titration schedule. No negative effect on safety outcomes was observed. Funding Sanofi-Aventis Deutschland GmbH

CT fluoroscopy‐guided pancreas transplant biopsies: a retrospective evaluation of predictors of complications and success rates

To identify predictors of biopsy success and complications in CT-guided pancreas transplant (PTX) core biopsy. We retrospectively identified all CT fluoroscopy-guided PTX biopsies performed at our institution (2000-2017) and included 187 biopsies in 99 patients. Potential predictors related to patient characteristics (age, gender, body mass index (BMI), PTX age, PTX volume) and procedure characteristics (biopsy depth, needle size, access path, number of samples, interventionalist's experience) were correlated with biopsy success (sufficient tissue for histologic diagnosis) and the occurrence of complications. Biopsy success (72.2%) was more likely to be obtained in men [+25.3% (10.9, 39.7)] and when the intervention was performed by an experienced interventionalist [+27.2% (8.1, 46.2)]. Complications (5.9%) occurred more frequently in patients with higher PTX age [OR: 1.014 (1.002, 1.026)] and when many (3-4) tissue samples were obtained [+8.7% (-2.3, 19.7)]. Multivariable regression analysis confirmed male gender [OR: 3.741 (1.736, 8.059)] and high experience [OR: 2.923 (1.255, 6.808)] (biopsy success) as well as older PTX age [OR: 1.019 (1.002, 1.035)] and obtaining many samples [OR: 4.880 (1.240, 19.203)] (complications) as independent predictors. Our results suggest that CT-guided PTX biopsy should be performed by an experienced interventionalist to achieve higher success rates, and not more than two tissue samples should be obtained to reduce complications. Caution is in order in patients with older transplants because of higher complication rates