Functional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and... Show moreFunctional assessment of in vitro neuronal networks-of relevance for disease modelling and drug testing-can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ''MEA-ToolBox'', a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient's hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles. Show less
Albers, E.A.C.; Fraterman, I.; Walraven, I.; Wilthagen, E.; Schagen, S.B.; Ploeg, I.M. van der; ... ; Ligt, K.M. de 2022
Purpose The use of Patient-Reported Outcome Measures (PROMs) for individual patient management within clinical practice is becoming increasingly important. New evidence about graphic visualization... Show morePurpose The use of Patient-Reported Outcome Measures (PROMs) for individual patient management within clinical practice is becoming increasingly important. New evidence about graphic visualization formats for PROMs scores has become available. This systematic literature review evaluated evidence for graphic visualization formats of PROMs data in clinical practice for patients and clinicians, for both individual and group level PROMs data. Methods Studies published between 2000 and 2020 were extracted from CINAHL, PubMed, PsychInfo, and Medline. Studies included patients >= 18 years old in daily clinical practice. Papers not available in English, without full-text access, or that did not specifically describe visualization of PROMs data were excluded. Outcomes were: visualization preferences; interpretation accuracy; guidance for clinical interpretation. Results Twenty-five out of 789 papers were included for final analysis. Most frequently studied formats were: bar charts, line graphs, and pie charts. Patients preferred bar charts and line graphs as these were easy and quick for retrieving information about their PROMs scores over time. Clinicians' interpretation accuracy and preferences were similar among graphic visualization formats. Scores were most often compared with patients' own previous scores; to further guide clinical interpretation, scores were compared to norm population scores. Different 'add-ons' improved interpretability for patients and clinicians, e.g. using colors, descriptions of measurement scale directionality, descriptive labels, and brief definitions. Conclusion There was no predominant graphical visualization format approach in terms of preferences or interpretation accuracy for both patients and clinicians. Detailed clarification of graph content is essential.Plain English summary Patient-Reported Outcome Measures (PROMs) capture patients' self-reported health through the use of questionnaires. PROMs measure health related quality of life, daily functioning, and symptom experience, which are becoming increasingly important to incorporate in clinical practice for individual patient management. To present PROMs within clinical practice, raw or summarized PROMs scores can be visualized in graphical formats. To be useful during clinical encounters, both patients and clinicians ought to interpret such formats correctly. New evidence about graphic visualization formats for PROMs scores has become available. Therefore, we systematically reviewed the literature to evaluate evidence for graphic visualization formats of PROMs data in clinical practice. In 25 included papers, most studies used graphical formats like bar charts, line graphs, and pie charts for presenting PROMs scores. There was no predominant graphical visualization format approach in terms of preferences or interpretation accuracy for both patients and clinicians. Patients preferred bar charts and line graphs as these were easy and quick for retrieving information about their PROMs scores over time. Clinicians' interpretation accuracy and preferences were similar among graphic visualization formats. The graphical interpretation of PROMs data for patients and clinicians can be improved by using colors, descriptions of measurement scale directionality, descriptive labels, and brief definitions. Show less
Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided... Show moreEmbedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this article, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods. Code is available at https://github.com/tao-aimi/DeepRecursiveEmbedding. Show less
In the past years access to EU law has been significantly enhanced via services such as EUR-Lex. This development not only allows for easy retrieval of individual legal acts, but for collecting... Show moreIn the past years access to EU law has been significantly enhanced via services such as EUR-Lex. This development not only allows for easy retrieval of individual legal acts, but for collecting information about the evolution of EU law in the aggregate as well. This contribution argues that by charting and analysing the evolution of the body of EU law over time, we can understand better the nature and development of the EU as a political system. The text examines the legislative productivity of the EU over the past 15 years as an illustration. Further, it showcases recent examples of the use of novel data-analytic techniques to analyse the body of EU law for the purposes of understanding the EU legal system, the institutions, and the polity that produced the legal acts. The contribution concludes by arguing that it is important to transmit basic facts and insights about the evolution of EU law and law-making to the general public as well, in order to counter the threat of Euroscepticism and perceptions of democratic deficit in the EU. Show less
With tomography it is possible to reconstruct the interior of an object without destroying. It is an important technique for many applications in, e.g., science, industry, and medicine. The runtime... Show moreWith tomography it is possible to reconstruct the interior of an object without destroying. It is an important technique for many applications in, e.g., science, industry, and medicine. The runtime of conventional reconstruction algorithms is typically much longer than the time it takes to perform the tomographic experiment, and this prohibits the real-time reconstruction and visualization of the imaged object. The research in this dissertation introduces various techniques such as new parallelization schemes, data partitioning methods, and a quasi-3D reconstruction framework, that significantly reduce the time it takes to run conventional tomographic reconstruction algorithms without affecting image quality. The resulting methods and software implementations put reconstruction times in the same ballpark as the time it takes to do a tomographic scan, so that we can speak of real-time tomographic reconstruction. Show less
