dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing living network meta-analysis and living pairwise meta-analysis We compared the emergence of strong evidence against the null hypothesis (defined as crossing monitoring boundaries) between living pairwise and network meta-analyses. Among comparisons with strong evidence we examined whether boundaries were crossed as a result of indirect evidence. We analysed data in a 2×2 table using McNemar's exact test and estimated differences in the probability of providing strong evidence. We plotted Kaplan-Meier curves and calculated the hazard ratio from a frailty time-to-event regression model to describe the time needed to cross the boundary while accounting for the paired nature of the data. 31 For comparisons with strong evidence against the null hypothesis we recorded how many studies that directly compared the treatments were published after the boundaries had been crossed.
Database of network meta-analyses Out of 456 published network meta-analyses included in the original database,  12   Of the 49 comparisons, 29 (59%) were informed by both direct and indirect evidence in the network meta-analysis. The P values from testing for agreement between indirect and direct evidence ranged between 0. 11 and 0. 99 (see appendix table  1 ). Thirteen comparisons (27%) were not examined directly in any RCT; seven (14%) were based on direct evidence only.
We acknowledge that strong evidence against the null hypothesis does not necessarily translate into strong recommendations. 48 uideline panels typically consider the quality of evidence in the results from meta-analysis before making recommendations, often using the GRADE system. 49 Evaluation of the confidence in the results from network meta-analysis is a matter of ongoing research,  [50] [51] [52]  and none of the included networks in our database attempted such an evaluation. We did not consider other components such as the risk of publication bias, the limitations of the RCTs included in the networks, or the comprehensiveness of the literature search and accuracy of the data extraction. Selective publication of studies of the comparison of interest or of studies of comparisons that contributed indirect evidence or a high risk of bias in the conduct and analysis of studies, could have affected our conclusions.
Study level data and R codes are available in a GitHub repository. Instructions to access them can be found in appendix N. Transparency: The lead author (GS) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4. 0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. See: http://creativecommons.
In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions. Results: We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli. , and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA.
Introduction Constraint-based analysis on genome-scale metabolic models (GEMs) is a popular method to study metabolism and cellular physiology. Flux Balance Analysis (FBA), in particular, has been used to predict network-level behaviors, such as specific growth rate, gene essentiality, etc. The MATLAB-based COBRA toolbox  (Schellenberger et al. , 2011)  and its Python counterpart COBRApy  (Ebrahim et al. , 2013)  are today the most popular tools to perform such studies, and offer an intuitive interface to model GEMs using a linear programming formulation. However, FBA-derived approaches often lead to flux distributions that are contradicting with physiology and bioenergetics due to the lack of thermodynamic constraints in their formulation  (Ataman and Hatzimanikatis, 2015; Soh and Hatzimanikatis, 2010).
Implementation The Python package pyTFA is built to integrate with COBRApy  (Ebrahim et al. , 2013) , and takes advantage of Optlang  (Jensen et al. , 2017)  for solver agnosticism and model operations. The MATLAB implementation matTFA is built on top of The COBRA Toolbox  (Schellenberger et al. , 2011) : In the current implementation, the code uses SEED IDs  (Overbeek et al. , 2005)  to match metabolites with a table of thermodynamic information taken from  (Jankowski et al. , 2008). It is also possible to input additional D f G o manually.
Conclusion We propose the software package to add thermodynamic information to constraint-based metabolic models. The resulting formulation is amenable to different types of analysis with high value for the Metabolic Engineering and Systems Biology communities. We demonstrated it with a case study of a reduced system for E. coli focusing on glycolysis, as well as the original GEM. Our package is available for MATLAB, and Python 3, on GitHub: respectively, https://github. com/EPFL-LCSB/matTFA and https://github. com/ EPFL-LCSB/pytfa.
Description of the clinical photographic posture assessment tool (CPPAT) CPPAT is a software-based program with a graphical interface for the analysis of four to six photographs of a patient's posture (front, back, left and right) acquired in standing using a standard procedure (see Fortin et al. ,  [18, 19]  for more details). We have shown excellent test-retest and inter-rater reliability for marker placement among a senior and novice physical therapists (reliability coefficients between 0. 90 and 1. 00 and standard error of measurement ranging from 0. 5°to 3. 0°and 3 to 6 mm)  [18]. The software uses interactive click-on markers with the computer mouse. The user selects each specific marker from the graphical interface and places it directly on the corresponding anatomical landmark or anatomical reference points (e.
g. eyes, upper end, lower end and center of the waist) on the person's photographs (see Fig. 2 ). The program allows zooming in on a marker for more accuracy. Different sets of markers are available according to each view (anterior, posterior or lateral). Following the selection of the markers associated with the calculation of an angle, its value can be displayed. All measurements can be exported in Excel-or Word formats. We thus choose to study the acceptance of the CPPAT because this tool has good demonstrated reliability and validity, allows posture assessment of the whole body, was designed to be user-friendly and follows a standardized procedure.
Following the training period, participants were asked to collect the number of patients assessed with the tool and time spent for photo acquisition and for photo-processing with the software program on a data sheet for three months. The second part consisted of a post-trial focus group discussion in small groups of up to six participants. Before beginning the discussion, participants submitted their data sheet with the number of patients analysed with the tool, together with the time for photo acquisition and photo-processing. Subsequently, van Schaik et al. 's  [22]  questionnaire was used to measure technology acceptance in terms of perceived ease of use, perceived usefulness and intention to use (see Additional file 2). The focus group discussion was conducted by the researchers in UK (J B-S) and in France (CF) and a physical therapist research assistant (J-F A-F) in Quebec (Canada).
Statistical analysis Descriptive statistics (mean and SD), confidence intervals and t-tests were used to characterize the number of patients assessed with the method and time for photo acquisition and photo-processing with the CPPAT. The data were examined for normality; skew and kurtosis were not extreme (|z[skew]| < 1. 8; z[kurtosis] < 1. 1) and the distributions were not significantly different from the normal distribution(Komolgorov-Smirnov test: p >. 05). t-tests were used to determine if scores obtained for each sub-scale differed from the neutral score (represented by a value of 4 on a seven-point Likert scale). We assessed reliability of each acceptance measure scale by calculating Cronbach's alpha. Correlation analysis examined the association between the three acceptance variables.
Barriers/disadvantages The principal barriers stated were time to do a complete analysis of posture  (19) , the quality of human-computer interaction  (18) , non-automation of posture index calculation and photo transfer  (18)  and lack of versatility  (14). Within the time category, participants included the time to take the photo, to transfer the photo into the software program, as well as processing the photo. For human-computer interaction, participants indicated that it was hard to print or copy the processed photo, the software program was only functioning on Windows systems (not on tablet, iPhone or MAC computer), it was complex to export data and the technology was complex in general for older therapists. Regarding non-automation, the most frequent answers were manual processing of the photo, a few software bugs, manual importation of photo, and manual conversion from pixels to cm for linear posture indices.
This may also explain why for half of our participants the graphical interface of the software program was seen as user-friendly while not for the others. Other barriers for the CPPAT acceptance were the lack of automation of posture index calculation and photo transfer, and the lack of versatility of the tool in terms of positions of posture acquisition and computer operating system. Further development of the tool focusing on automation of photo transfer and posture index analysis would contribute to a substantially decreased time for photo-processing and may thus promote an increased adoption of the tool. Some participants mentioned that they could not take photos in other positions apart from standing or could not add new posture indices. In the present study, they were asked to take photos in a standing position but we showed in a previous study that it is also possible to take and analyse photos in a sitting position  [17].
Few participants mentioned the need to have the software program functioning on Windows systems as well as on tablet, iPhone or MAC computer. Other mobile APPs have been developed to measure posture and showed promising results, but posture indices measurement errors of these APPs and their validity still need to be documented  [16, 20]. Some participants also indicated that less than 30 min should be taken for a complete assessment of posture. Being able to integrate automation of photo transfer and of posture index calculation into the CPPAT should allow clinicians to have a more efficient tool and may promote adherence to this tool. To be more cost-and time-effective, clinicians may also select a set of relevant posture indices according to a patient's condition to document change in posture over time.
Conclusion Our results indicate that the CPPAT is perceived to be useful and easy to use by clinicians. The CPPAT tool contributes to clinical practice by facilitating the quantitative analysis of posture and by enhancing the education of patients presenting with different musculoskeletal impairments. The principal barriers for the acceptance of CPPAT were the time to conduct a complete postural analysis and difficulties in interacting with the system. Adapting the software-human interface and automation for posture index calculation may facilitate the wider adoption of the tool.
AN INTEGRATED BIG DATA INFRASTRUCTURE FOR AGRICULTURAL AND FOOD RESEARCH AGINFRA+ aims to exploit core e-infrastructures such as EGI. eu, OpenAIRE, EUDAT and D4Science, towards the evolution of the AGINFRA data infrastructure, so as to provide a sustainable channel addressing adjacent but not fully connected user communities around Agriculture and Food. To this end, AGINFRA+ entails the development and provision of the necessary specifications and components for allowing the rapid and intuitive development of variegating data analysis workflows, where the functionalities for data storage and indexing, algorithm execution, results visualization and deployment are provided by specialized services utilizing cloud based infrastructure(s). Furthermore, AGINFRA+ aspires to establish a framework facilitating the transparent documentation and exploitation and publication of research assets (datasets, mathematical models, software components results and publications) within AGINFRA, in order to enable their reuse and repurposing from the wider research community.
In a high-level, conceptual view on the AGINFRA architecture (see Figure  1 ), the AGINFRA+ Data & Semantics Layer comprises the Chapter #13 functionalities that facilitate the discovery and consumption of data collections (annotation, description enrichment, linking / mapping, etc. ). These datasets and components enable researchers, innovators as well as businesses to compose customized data-driven services via the AGINFRA+ Presentation Layer, which builds upon the use of existing open source frameworks such as D3 (an open-source visualization library for data-driven real-time interactivity), Processing. js (a library that sits on top of the Processing visual programming language), Shiny (for interactive web data analysis) etc. Alternatively, researchers can create (advanced) data analysis and processing workflows which can be packaged as ready-to-deploy components and can be submitted for execution to the AGINFRA+ Data Analytics & Processing Layer.
EGI-Engage The EGI-Engage: Engaging the Research Community towards an Open Science Commons (https://www. egi. eu/about/egi-engage/) started in March 2015, as a collaborative effort involving more than 70 institutions in over 30 countries, coordinated by the European Grid Initiative (EGI) association. EGI-Engage aims to accelerate the implementation of the Open Science Commons by expanding the capabilities of a European backbone of federated services for compute, storage, data, communication, knowledge and expertise, complementing community-specific capabilities. Agriculture, food and marine sciences are included as use cases in EGI-Engage, providing requirements that shape the new Open Science Commons platform. AGINFRA is positioned as the thematic data infrastructure and is exploring use cases and workflows of storing, preserving and processing data in the generic e-infrastructure.
EUDAT EUDAT's vision is to enable European researchers and practitioners from any research discipline to preserve, find, access, and process data in a trusted environment, as part of a Collaborative Data Infrastructure (CDI) conceived as a network of collaborating, cooperating centres, combining the richness of numerous community-specific data repositories with the permanence and persistence of some of Europe's largest scientific data centres. EUDAT offers common data services, supporting multiple research communities as well as individuals, through a geographically distributed, resilient network of 35 European organisations. AGINFRA+ partners have been early and continuously participating in EUDAT user workshops, providing requirements and use cases on how various EUDAT services may be used by our scientific communities.
D4Science D4Science is a Hybrid Data Infrastructure servicing a number of Virtual Research Environments. Its development started in the context of the homonymous project cofounded by the European Commission and has been sustained and expanded by a number of EU-funded projects (ENVRI, EUBrazilOpenBio, iMarine). Currently, it serves as the backbone infrastructure for the BlueBRIDGE and ENVRIPlus projects. AGINFRA+ partners are technology leaders in D4Science, while also contributing use cases and requirements to D4Science, through their participation of the corresponding projects or project events. Agriculture, food and marine sciences are represented as communities in ENVRIPlus.
ELIXIR The goal of ELIXIR is to orchestrate the collection, quality control and archiving of large amounts of biological data produced by life science experiments. Some of these datasets are highly specialised and would previously only have been available to researchers within the country in which they were generated. For the first time, ELIXIR is creating an infrastructure -a kind of highway system -that integrates research data from all corners of Europe and ensures a seamless service provision that is easily accessible to all. In this way, open access to these rapidly expanding and critical datasets will facilitate discoveries that benefit humankind. In the currently running EXCELERATE project (https://www. elixireurope. org/excelerate) that develops further the ELIXIR infrastructure, a major use case is on Integrating Genomic and Phenotypic Data for Crop and Forest Plants.
EOSC As part of the European Digital Single Market strategy, the European Open Science Cloud (EOSC) will raise research to the next level. It promotes not only scientific excellence and data reuse but also job growth and increased competitiveness in Europe, and drives Europe-wide cost efficiencies in scientific infrastructure through the promotion of interoperability on an unprecedented scale. It aims to bring together and align the existing core e-infrastructure services in order to smoothly support and integrate thematic and domain specific data infrastructures and VREs. AGINFRA+ partners have already initiated a strategic discussion with the key players trying to shape the EOSC and have participated in the various workshops and brainstorming meetings.
The AGINFRA+ services will enable executable workflows for ecophysiological model intercomparisons driven by historical climate conditions using site-specific data on soils, management, socioeconomic drivers, and crop responses to parisons are the basis for the future climate impact and adaptation scenarios: instead of relying on single model outputs, modelbased uncertainties will be quantified using multi-model ensembles. The close interactions and the linkages between disciplinary models and scenarios, including climate, ecophysiology and socio-economics will allow researchers to prioritize necessary model improvements across the model crop and multi-location simulation ensembles will be Examples of various data types and sources used to support impact assessment #13. Big Data in Agricultural and Food Research: Challenges and Opportunities of an integrated Big Data e-Infrastructure 9 linked to multi-climate scenarios to perform consistent simulations of future climate change effects on local, regional, national, and global food production, food security, and poverty.
The data sources that can feed such bases required for this work are developed by different community members, are processed using different systems, and are shared among the community members. This creates several challenges that are connected to multiple factors: different platforms, diverse data management activities, distributed data processing and storage, heterogeneous data exchange, etc. and distributed model runs, data storage, scenario analysis, and visualization activities that take place. Thus, AGINFRA+ will also develop a reactive intensive data analysis layer over existing federations that will help the discovery, reuse and exploitation of heterogeneous data sources created in isolation, in very different and unforeseen ways in the rest of the communities' systems.
Landscape The AGINFRA+ infrastructure will be used to alleviate the big data challenges pertaining to specific problems on food security, namely the need to efficiently analyse data produced by plant phenotyping and its correlation with crop yield, resource usage, local climates etc. It will particularly explore how high throughput phenotyping can be supported in order to: a) Determine adaptation and tolerance to climate changes, a high priority is to design high-yielding varieties adapted to contrasting environmental conditions including those related to climate change and new agricultural management. It requires identifying of allelic variants with favourable traits amongst the thousands of varieties and natural accessions existing in genebanks. Genotyping (i. e. densely characterizing the genome of breeding lines with markers) has been industrialized and can now be performed at affordable cost and be able to link and analyse phenotyping and genotyping data is strategic for agriculture.
Data are produced at high frequencies and hundreds of thousands of images can be gather and be analysed each day. Such volumes require automatic data validation tools. On of major challenges of plant phenotyping is the Semantic Interoperability. This AGINFRA+ use case will assess the effectiveness of the proposed framework in data intensive experimental sessions, where the distinct processing steps operate over different datasets, require synchronization of results from various partial computations, and use very large and/or streaming raw or processed data.
Which data should be standardized, which not? Standardization with common ontologies is crucial. Elaborate a consensus in the phenotyping community in Europe with high degree of standardization for environmental variables (types of sensors, units) and basic phenotypic data (dates, phenology) and higher flexibility for elaborate traits that need to reach maturity before being standardized. The relation with generalist databases (plant/crop ontologies) need to be established. How to manage and process increasing data volume with distributed and reproducible data workflows? How to share data / how to access data from various experiments? How to combine large and heterogeneous datasets in an open data perspective? The most recent methods of information technologies will be used for smart data exchanges namely key discovery, matching and alignment methods for accessing and visualizing complex data sets in an integrative way.
Landscape In the context of the Food Safety Risk Assessment use case, the AGINFRA+ project will assess the usefulness of AGINFRA+ components and APIs to support data-intensive applications powered by the FoodRisk-Labs suite of software tools (https://foodrisklabs. bfr. bund. de). This includes the extension of FoodRisk-Labs' capabilities to handle large-scale datasets, to visualize complex data, mathematical models as well as simulation results and to deploy generated data processing workflows as web-based services. More specifically, FoodRisk-Labs will be extended such that it can use and access AGINFRA Services. This will allow to design new performance food safety data processing workflows facilitating e. g. the efficient extraction of data and models from the rich corpus of scientific literature.
The specific food safety analysis and modelling functionalities were implemented as extensions to the professional opensource data analysis and machine learning platform KNIME (www. knime. org). The KNIME visual workflow composition user interface enables users to apply or adapt preconfigured data analysis workflows or to create new ones from the large number of available data processing modules ("nodes"). The selection of KNIME as the technical implementation framework guarantees important features like modularity, flexibility, scalability and extensibility. All FoodRisk-Labs KNIME extensions also provide preconfigured (empty) databases allowing users to easily manage their domain-specific data and/or establish model-based knowledge repositories.