dataesr/scientific-paragraphs-categorization huggingface dataset:
A distance perturbation factor was quantified by normalizing this area with the area under the straight line within the same duration, such that data from different microtube sizes can be compared. To reduce bias, this factor was calculated for different pairs of parameters (r, δt) with r = 18. 6, 21. 1, 23. 6, 26. 0 μm and δt = 10, 20, 30 min, and all taken into account for statistics. Phase contrast movies were used with α-catenin KD and Snail-overexpressing cells in microtubes as these cells have no fluorescent markers. To better approximate the cell position and velocity components in the circumferential direction of tubes in phase contrast movies that only show a 2D projection of these values, the formula x circumference ¼ R sin À1 ðx=RÞ was used to make the positional conversion, where x is the position in the circumferential direction measured in phase contrast images, R is the microtube radius, with the coordinates taken to center around the midpoint of the tube axis taken as x=0.
Acknowledgements We thank  Dr. Fang Kong  from  SMART, Singapore  for helping to design and manufacture the precise stage for the microtube fabrication as well as to make Supplementary Fig. 1,  Wai Han Lau  and  Jun Liu  from the microscopy core of  Mechanobiology Institute Singapore (MBI)  for imaging support,  Hui Ting Ong  and  Dr. Hayri Emrah Balcioglu  from  MBI  for help in programming and  Dr. René-Marc Mège  and  Dr. Delphine Delacour  from  IJM, Paris , and  Dr. Sham Tlili  from  MBI  for helpful discussion. We thank  Song Hui Tan  and  Bee Leng Tan  from  MBI laboratory facility  for supporting in the lab work and  Chun Xi Wong  from  MBI Science Communications Unit  for help in making illustrations. We also thank  MBI students-Yejun Wang ,  Zhe Wen ,  Yue Zhang ,  Anh Phuong Le , and  Shreyansh Jain  for support in the experiment.
The MDCK cell lines were kindly provided by  Sham Tlili ,  Shigenobu Yonemura , and  W. James Nelson. The monoclonal antibody of anti-Gp135 developed by investigators was obtained from the  Developmental Studies Hybridoma Bank , created by the  NICHD  of the  NIH  and maintained at The  University of Iowa, Department of Biology, Iowa City ,  IA 52242. Financial supports from the  Mechanobiology Institute  seed grant,  Ministry of Education's Academic Research Fund (AcRF)   Tier 1 Grant  ( R-397-000-247-112 ), the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/2007-2013) / ERC  grant agreements no. 617233 , the  LABEX "Who am I?" and USPC-NUS  funding are gratefully acknowledged.
Most studies based on the Kinect sensor use the Microsoft Software Development Kit (SDK). Our system does not rely on the Microsoft SDK but instead uses an algorithm developed by Dubois and Charpillet  [25]. This is because, with our system, the parameters are extracted from the vertical displacement of the geometric center of the body, so that an accurate representation of the skeleton and the body segments is not necessary. Our approach has two main advantages. First, parameters can be extracted even if the feet of the walking person are occluded. Second, the performance of the analysis is relatively unaffected by the angle of view of the sensor, which also constitutes a major advantage for monitoring individuals in any place, and especially in furnished environments (e. g. , physician's office, patient's room).
Data Acquisition A standard chair with arms was used. A mark was placed on the ground 3 m in front of the chair to indicate to the participants the place where they had to turn around. At the beginning of the test, participants were sitting on the chair. After a signal given by the clinical staff, the participant had to get up from the chair, walk, turn around after passing the mark located on the ground, go back and sit on the chair (see Figure  1 ). The participants were instructed to walk at a normal and comfortable speed. The participants walked perpendicularly to and at a distance of 4 m from the Kinect sensor. This sensor recorded the movement pattern of the participants. Two clinicians timed with a stopwatch the execution of the test (for the 37 participants) and also completed an evaluation grid comprising different assessment criteria (for 34 participants only).
Data Analysis First, we assessed how reliably our system could automatically measure TUG test duration. TUG test sequences were recorded for 37 participants. We assessed the level of agreement between the 99 durations obtained by healthcare professionals and those extracted automatically with the Kinect sensor. The agreement was tested using Bland-Altman 95% bias and limits of agreement (LoA), percentage error (PE), Spearman's correlation, concordance correlation coefficient (CCC), intra-class correlation (ICC) and linear regression. The Bland-Altman analysis was adjusted for repeated measurements in a random-effects model  [27]. The PE was calculated by dividing the limits of agreement by the mean of the values obtained with the two methods of measurement  [28]. A bootstrap-method (with 2000 resamples) using bias corrected and accelerated (BCa) was used to estimate the 95% confidence interval of Spearman's correlation.
MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore's default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG.
These problems are common in genome-scale models that include an initial auto-generation of the reaction network from databases that can include incomplete or incorrect annotation. These issues are particularly acute for modelling mitochondrial metabolism and metabolite transport, as all the current genome-scale models neglect the electrical gradient component (ΔΨ) of the proton motive force (PMF), and the correct proton cost of making ATP by the mitochondrial ATP synthase in animals  [14]. It is also sometimes questionable whether the enormous size and complexity of these genome-scale models benefits simulations where only subsystems of cellular metabolism are of interest, such as central metabolism. Furthermore, the scale of genome-scale models can make some techniques computationally infeasible, such as elementary mode analysis  [15] , and the longer runtimes for their simulations and the complexity of the results can hinder exploratory analyses and hypothesis testing.
These simulation results were used to generate detailed mechanistic hypotheses for data interpretation and to design further experiments. However, we recognised that this model could be improved upon by constructing a new model encompassing more of central metabolism and explicitly modelling physiochemical features such as the mitochondrial proton motive force. In particular, ease-of-use could be improved by providing extensive annotation of reactions and their parameters. Here we present MitoCore, a new constraint-based model of central metabolism that addresses these issues and comprehensively expands upon and refines our previous mitochondrial models. This model has been designed to be easy-to-use, includes extensive annotation, has default parameters to simulate human cardiomyocyte metabolism, and is encoded in the widely used SBML format  [22].
Building the MitoCore model of human central metabolism We designed MitoCore as a constraint-based model of central metabolism with two compartments; one representing the cytosol, outer mitochondrial membrane, inter-membrane space and cytosolic side of the inner mitochondrial membrane, and the other the mitochondrial side of the inner membrane and the mitochondrial matrix. The accuracy and utility of a metabolic model is dependent upon the reactions included and the correct partitioning of these reactions between compartments. Thus to create MitoCore, we built a list of candidate reactions to include by considering human reactions in the KEGG  [23] , HumanCyc  [24]  and BRENDA  [25]  databases that use any metabolites involved in central metabolism, and assigned each reaction to the appropriate cellular compartment(s) by assessing the localisation evidence collated in the MitoMiner database  [26]  for its catalysing protein.
For reactions catalysed by enzymes with a large amount of evidence for mitochondrial localisation but lacking specific evidence for being in the mitochondrial matrix or matrix side of the inner membrane, we applied the principle of metabolite availability  [18]. A summary of this localisation evidence is provided in the mitochondrial evidence section of the supplementary annotation file (Additional file 1, 'Reaction & Fluxes' worksheet) and consists of confidence scores from the MitoCarta 2  [27]  inventory of genes that encode mitochondrial proteins. Reaction directionality was assigned by taking the consensus from annotation in metabolic databases, estimates of Gibbs free energy  [28, 29] , and general rules of irreversibility  [30]. For each reaction extensive additional annotation was recorded including the original KEGG identifiers, EC number, description, gene mappings (both HUGO gene symbol and Ensembl identifiers), and evidence for the gene's expression in heart and the protein's mitochondrial localisation.
g. to the reduction of ubiquinone to ubiquinol. For convenience in setting up simulations, we created four 'pseudo' reactions that summarise aspects of MitoCore's biological activity and can be used by flux balance analysis as objective functions: ATP hydrolysis (representing cellular ATP demand), and the biosynthesis of heme, lipids and amino acids. To enable comparison of results from MitoCore to those of genome-scale models such as Recon 2  [5] , we re-used identifiers for metabolites and reactions present in the Virtual Metabolic Human database (https://vmh. uni. lu) for MitoCore where possible. However, it was necessary to create 105 new reactions for MitoCore (Additional file 3) that were either absent from the Virtual Metabolic Human database and Recon 2. 2  [10]  (such as transport steps or compartment specific versions), inaccurately described (such as specifying prosthetic FAD as a free co-factor), or needed to represent new features (such as the proton motive force).
For example, the directionality evidence section describes why a constraint has been set; a manually evaluated consensus of information from the KEGG  [23] , HumanCyc  [24]  and BRENDA  [25]  databases, general rules of irreversibility  [30] , large ΔG values from eQuilibrator  [29]  or estimated using a group contribution method  [28] , and information from the literature. In cases for which reaction directionality was unclear, it was kept reversible. To address why a reaction has been included in our cardiomyocyte model, we included a heart expression section consisting of RNAseq and immunochemistry expression levels of genes taken from the Human Protein Atlas (version 14)  [9]. The spreadsheet also includes gene mappings, identifiers from Recon 2 and KEGG, mitochondrial localisation evidence (as described above) and baseline reaction fluxes when the objective function was maximum ATP production under normal conditions.
This allows the simulation results to be generalisable, without having features that are particularly cell specific, such as those found only in hepatocytes. However, we have included reactions that are inactivated in the default cardiac model, but can be activated to represent the metabolic capabilities of other cell types, e. g. gluconeogenesis, ketogenesis, β-alanine synthesis and folate degradation. Thus, the model allows biologically relevant flux distributions to be generated 'out of the box' without altering the model, while allowing for easy modification to represent metabolism in other cell types. MitoCore has several unique features. The first is the more accurate partitioning of metabolism between the mitochondrion and the cytosol by using extensive localisation data and annotation.
This partitioning has an important impact on model behaviour as the limited transport steps into the mitochondrial matrix result in dramatic differences in metabolite availability in the matrix compared to the cytosol. Therefore, it is important that reactions are assigned to the correct compartment. We achieved this by manually evaluating the subcellular localisation of each reaction's catalysing protein by using the mitochondrial localisation evidence in the MitoMiner database  [26]. To model oxidative phosphorylation in MitoCore, we devised a new representation of the PMF and mitochondrial respiratory chain-the second unique feature of MitoCore. MitoCore's representation of the respiratory chain differs in many key aspects to other metabolic models, due to how it models vectorial protons and accounts for both components of the PMF.
This problem is endemic in largescale models that auto-generate reaction networks directly from metabolic databases without manual curation of reactions. Other improvements to MitoCore included modelling protein complexes as one reaction rather than a series of Fig. 3  The effect of increasing proton leak through UCP2 during simulations of the MitoCore model on: maximum ATP production, flux through mitochondrial ATP synthase, flux through the mitochondrial ATP/ADP transporter, and flux through the mitochondrial phosphate transporter. The flux through ATP synthase is distinct from all the other fluxes that are superimposed, because there is also synthesis of mitochondrial ATP from the TCA cycle linked reactions, as can often be found in metabolic pathway databases and other models. This is particularly important for simulations of gene knockouts, as a gene deficiency will disrupt the activity of the whole protein complex rather than just one of its subunit's reactions.
Conclusions MitoCore is a constraint-based model of human central metabolism, provided with default parameters that provide physiologically realistic reaction fluxes for cardiomyocytes in FBA simulations. The model has several innovations including a new representation of the respiratory chain and proton motive force, and partitioning of reactions to subcellular compartments based on the latest localisation evidence. To achieve an accurate depiction of central metabolism, each of MitoCore's 407 reactions and transport steps was manually evaluated for directionality, expression of its gene in heart, and subcellular localisation of its protein. To allow MitoCore to be easily used and to make the results comparable with other models and compatible with other types of analyses, we used identifiers from the Virtual Metabolic Human database for both reactions and metabolites where possible, and recorded KEGG identifiers in the annotation.
Partitioning reactions between the cytosol and mitochondrion in MitoCore To partition the reactions into either the cytosol or mitochondrion, each enzyme was manually evaluated by using the mitochondrial localisation evidence in the Mito-Miner database  [26]. MitoMiner collates GFP tagging, large-scale mass-spectrometry mitochondrial proteomics studies and mitochondrial targeting sequence predictions, with detailed annotation from the Gene Ontology and metabolic pathway data from KEGG. MitoMiner also contains homology information allowing localisation evidence to be shared species. All available experimental localisation evidence, mitochondrial targeting sequence predictions and annotation were considered, including from homologs from mouse, rat and yeasts. For reactions with strong evidence for mitochondrial localisation, but where matrix localisation is unclear, the principal of metabolite availability was applied -a reaction can only be present in a compartment if all its substrates are available and its products can be used by reactions the same compartment  [18].
Assigning reaction directionality in MitoCore Reaction directionality was manually evaluated for each reaction in MitoCore. The KEGG  [23] , Human-Cyc  [24]  and BRENDA  [25]  databases were consulted, and general rules of irreversibility were taken into account (such as most reactions consume ATP rather than produce it and carbon dioxide is normally produced not consumed)  [30]. The ΔG values for reactions were also considered, both calculated by eQuilibrator  [29]  and estimated by using the group contribution method  [28] , and large changes noted. If reaction directionality was conflicted or unclear, then the literature was consulted. If support for irreversibility was poor or a consensus could not be found, the reaction was assigned reversible. The information used to make each assessment was recorded in the directionality evidence column of the annotation spreadsheet (Additional file 1).
In some cases a reaction's subcellular localisation was changed by defining mitochondrial-specific metabolite species, or new reactions were written using existing metabolite species. To highlight where there are differences between a MitoCore reaction and the corresponding reaction in the Virtual Metabolic Human database, the MitoCore identifier used the Virtual Metabolic Human database identifier with the suffix 'MitoCore'. (N. B. Recon 2 also uses The Virtual Metabolic Human database identifiers. ).
For simulations of the effect of proton leak on maximum ATP production, the lower bound of the reaction representing the gene UCP2 (MitoCore reaction ID: HtmB_MitoCore) was increased over a series of simulations, thus forcing a minimum flux-representing proton leak-through the reaction. FBA simulations were performed by using MATLAB (Math Works, Inc. , Natick, MA) and the COBRA Toolbox  [45] , with the linear programming solver GLPK (http:// www. gnu. org/software/glpk).
For this, archaeological indications could be the spatial distribution of sites in relation to features of the floodplain landscape (e. g. settlement spread); the system of irrigation, or the date when certain changes in land form, land cover, or land use occurred. The importance of spatial data for modeling the modern and ancient landscape with the help of remote sensing and near surface geophysics has in the most recent years also come to the fore. But ancient Egyptian written and iconographic reflections on the landscape can be equally important. Specialists in these areas have been less prone to look at scientific evidence, and their work is often less accessible to the natural scientists. Another issue is that Egyptology, while covering a time range that historians would consider very wide, is concerned with what to earth scientists is a very short period of time.
Statistical methods used for the analysis Because of the large amount of data, P. Wilbour was the document used to construct a model of the Nile Valley landscape. In a second step, this model was checked against the two other documents. Handling a land register such as P. Wilbour requires statistical methods appropriate for identifying complex interrelationship and free from any preconceived point of view to obtain an unbiased insight of the structure. These methods must also provide a synthetic, but not simplistic understanding of complexity. Here we used two such complementary methods. The first is a multiple correspondence analysis (MCA). 25 This global exploratory and descriptive method projects the relationship between data (appearing as a point cloud) on a two-dimensional graphic space in an easily and intuitively understandable manner, the proximity of two points positively correlating to their interrelatedness.
The second method, namely multivariate logistic regression, identifies and measures the strength of factors that specifically distinguish two variables. 26 The strength of the association is measured by the odds ratio (OR). An OR above 1 indicates a positive association, while an OR below 1 indicates a negative one; the greater the OR is above 1 or the lower below 1, the stronger the association. Univariate analysis was performed with the ANNOVA test for quantitative and with the chi-2 test for categorical variables. A p value <0. 05 was considered significant. Statistics were performed with IBM-SPSS 20™ software. 24 Details concerning this method can be found in antoine, 2011. 25 Benzecri, 1973 and le roux/rouanet, 2010. 26 Each component of a multivariable category is transformed into a new category coded 0 or 1 depending on whether or not the condition is fulfilled.
Thus, the MCA shows how the Nile Valley landscape in P. Wilbour was closely interconnected with the organisation of human settlements and marked by social, fiscal and economic considerations. However, this preliminary and global pattern requires further investigations and confirmation. I propose focusing on four aspects: 1) the organisation of the geographical features in the floodplain, 2) the specificity of idb, pat, iw, iw-n-mAwt, mAwt and qAyt categories of soil, 3) the irrigation system in the agricultural landscape and 4) the sociogeographical organisation of the land. Figure  1. Results of the multiple correspondence analyses. For easier reading, a projection of the data is shown on two different screens. Ellipses encompass the two groups of data identified by the analysis. The scale of the twodimensional space is arbitrary and was calculated by the software.
Ground Penetrating Radar (GPR) survey of the ramps A Ground Penetrating Radar (GPR) area survey (20 x 20 m) was conducted using a GSSI 200 Mhz antenna at the bottom of the ramps in February 2013 in order to determine whether they extended further west below the excavated ground  61 Benson/gourlay, 1899, p. 258, 378 and pl. XXII (5); perDu, 2010; perDu, 2011. 62 BoraiK, 2010b , p. 70, 72. 63 lauffray, 1971 , p. 101, fig. 17. 64 Ibid. , p. 101. 65 traunecKer, 1972, p. 234-235. level. The survey was carried out using traverses spaced at 0. 5 m intervals. The GPR data was processed in Reflex2DQuick and GPR Slice software. All profiles were processed to remove background noise, and a regain function was applied to strengthen the deeper responses to the radar signal. All data was then sliced and resampled to produce a series of timeslices through the site (key plan see figure  23 ).
Conclusion Kom Qamha provides a good example of the complementarity of all of these datasets to hypothesize about former site extent while contextualizing this information within a broader study of the region's cultural and natural landscape. Simply put, much can be learned from sites in Egypt that would otherwise be considered "destroyed" or "lost. " EES Delta Survey and SRTM imagery provided the basis for the first phase of hypothesizing and information collection. Comparing SEGY maps, Corona photos, and Quickbird-2 imagery proved an efficient second phase for collating historical changes to the landscape. In this instance, one could see on the Corona that the kom was probably once much larger than it is today. Surface collection and subsurface coring in the third phase corroborated this observation, suggesting that relative sherd densities of CUs within the Corona-derived mound extent were much higher than outside of it.