Ycasd - a tool for capturing and scaling data from graphical representations

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses

Ycasd - a tool for capturing and scaling data from graphical representations

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses

Which parameters to use for sleep quality monitoring in team sport athletes? A systematic review and metaanalysis

Background: Sleep quality is an essential component of athlete's recovery. However, a better understanding of the parameters to adequately quantify sleep quality in team sport athletes is clearly warranted. Objective: To identify which parameters to use for sleep quality monitoring in team sport athletes. Methods: Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus, SPORTDiscus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. For the meta-analysis, effect sizes with 95% CI were calculated and heterogeneity was assessed using a random-effects model. The coefficient of variation (CV) with 95% CI was also calculated to assess the level of instability of each parameter. Results: In general, 30 measuring instruments were used for monitoring sleep quality. A meta-analysis was undertaken on 15 of these parameters. Four objective parameters inferred by actigraphy had significant results (sleep efficiency with small CV and sleep latency, wake episodes and total wake episode duration with large CV). Six subjective parameters obtained from questionnaires and scales also had meaningful results (Pittsburgh Sleep Quality Index (sleep efficiency), Likert scale (Hooper), Likert scale (no reference), Liverpool Jet-Lag Questionnaire, Liverpool Jet-Lag Questionnaire (sleep rating) and RESTQ (sleep quality)). Conclusions: These data suggest that sleep efficiency using actigraphy, Pittsburgh Sleep Quality Index, Likert scale, Liverpool Jet-Lag Questionnaire and RESTQ are indicated to monitor sleep quality in team sport athletes

Strength training to prevent falls in older adults: A systematic review with meta-analysis of randomized controlled trials

We performed a systematic review with meta-analysis of randomized controlled trials (RCTs) to assess the effects of strength training (ST), as compared to alternative multimodal or unimodal exercise programs, on the number of falls in older adults (=60 years). Ten databases were consulted (CINAHL, Cochrane Library, EBSCO, EMBASE, PEDro, PubMed, Scielo, Scopus, SPORTDiscus and Web of Science), without limitations on language or publication date. Eligibility criteria were as follows: RCTs with humans =60 years of age of any gender with one group performing supervised ST and a group performing another type of exercise training, reporting data pertaining falls. Certainty of evidence was assessed with Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Meta-analysis used a random effects model to calculate the risk ratio (RR) for number of falls. Five RCTs with six trials were included (n = 543, 76% women). There was no difference between ST and alternative exercise interventions for falls (RR = 1.00, 95% CI 0.77–1.30, p = 0.99). The certainty of evidence was very low. No dose–response relationship could be established. In sum, ST showed comparable RR based on number of falls in older adults when compared to other multimodal or unimodal exercise modalities, but evidence is scarce and heteroge-neous, and additional research is required for more robust conclusions. Registration: PROSPERO CRD42020222908

Transforming the Reading Experience of Scientific Documents with Polymorphism

Despite the opportunities created by digital reading, documents remain mostly static and mimic paper. Any improvement in the shape or form of documents has to come from authors who contend with current digital formats, workflows, and software and who impose a presentation to readers. Instead, I propose the concept of polymorphic documents which are documents that can change in form to offer better representations of the information they contain. I believe that multiple representations of the same information can help readers, and that any document can be made polymorphic, with no intervention from the original author. This thesis presents four projects investigating what information can be obtained from existing documents, how this information can be better represented, and how these representations can be generated using only the source document. To do so, I draw upon theories showing the benefit of presenting information using multiple representations; the design of interactive systems to support morphing representations; and user studies to evaluate system usability and the benefits of the new representations on reader comprehension

Biomathematische Modellierung von Chemo- und Immuntherapie bei aggressiven Non-Hodgkin-Lymphomen

Dosis- und Zeitintensivierungen von Chemotherapie verbesserten das ereignisfreie Überleben bei Patienten mit aggressiven Non-Hodgkin-Lymphomen. Klinische Studien zeigten jedoch, dass zu starke Therapien in schlechteren Überlebensraten resultieren können. Rituximab ist ein monoklonaler Antikörper, der zu einem Durchbruch der Immuntherapie bei CD20-positiven B-Zell-Lymphomen geführt hat. Unterschiede bei den Überlebensraten zwischen einzelnen Therapievarianten werden durch Rituximab allerdings abgeschwächt. In dieser Promotionsarbeit wurde ein Modell entwickelt, welches diese Phänomene aus klinischen Studien durch die Annahme eines Anti-Tumor-Effekts des Immunsystems erklärt. Ein Differentialgleichungsmodell beschreibt die Dynamiken und Interaktionen zwischen Tumor- und Immunzellen unter Immunchemotherapie. Spezielle Parameter des Modells wurden durch Überlebenskurven aus klinischen Studien geschätzt. Dazu wurde ein Algorithmus entwickelt, der die Heterogenität der Überlebens- und Rezidivraten innerhalb eines Patientenkollektivs auf die Variabilität einiger weniger Parameter zurückführt. Das Modell wurde so an verschiedene Patientenkollektive angepasst. Schlechtere Ergebnisse bei zu intensiven Therapien werden im Modell durch eine zu starke Schädigung des Immunsystems erklärt, welches nicht mehr in der Lage ist, den residualen Tumor nach Therapieende zu bekämpfen. Ein weiterer Bestandteil des Modells ist die Vorhersage neuer Chemo- sowie Immuntherapievarianten, um vielversprechende Therapieszenarien zu ermitteln, die in die Konzeption neuer klinischen Studien einfließen können. Prognosen in Abhängigkeit von bestimmten Risikogruppen der Patienten können gestellt werden, indem Modellparameter mit messbaren Risikofaktoren assoziiert werden. Die wesentlichen Ergebnisse dieser Arbeit werden in zwei Publikationen vorgestellt

Ycasd - a tool for capturing and scaling data from graphical representations

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses

Ycasd - a tool for capturing and scaling data from graphical representations

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses

Ycasd - a tool for capturing and scaling data from graphical representations

Background: Mathematical modelling of biological processes often requires a large variety of different data sets for parameter estimation and validation. It is common practice that clinical data are not available in raw formats but are provided as graphical representations. Hence, in order to include these data into environments used for model simulations and statistical analyses, it is necessary to extract them from their presentations in the literature. For this purpose, we developed the freely available open source tool ycasd. After establishing a coordinate system by simple axes definitions, it supports convenient retrieval of data points from arbitrary figures. Results: After describing the general functionality and providing an overview of the programme interface, we demonstrate on an example how to use ycasd. A major advantage of ycasd is that it does not require a certain input file format to open and process figures. All options of ycasd are accessible through a single window which eases handling and speeds up data extraction. For subsequent processing of extracted data points, results can be formatted as a Matlab or an R matrix. We extensively compare the functionality and other features of ycasd with other publically available tools. Finally, we provide a short summary of our experiences with ycasd in the context of modelling. Conclusions: We conclude that our tool is suitable for convenient and accurate data retrievals from graphical representations such as papers. Comparison of tools reveals that ycasd is a good compromise between easy and quick capturing of scientific data from publications and complexity. Our tool is routinely applied in the context of biological modelling, where numerous time series data are required to develop models. The software can also be useful for other kinds of analyses for which published data are required but are not available in raw formats such as systematic reviews and meta-analyses