dataesr/scientific-paragraphs-categorization huggingface dataset:
The mean values for all wheel activity traits on days 4, 5 and 6 were used for data analyses. At ~6 weeks of age, male TgACTA1 D286G mice treated from pre-conception were acclimatised to the rotarod on the day prior to testing by placing mice onto the rotarod at a constant slow speed of 4 rpm for 2 minutes. The following day, mice were tested with placement on the rotarod at 4 rpm, with the rotarod gradually increased speed over 3 minutes until it reached a maximum value of 60 rpm. The test concluded after the mice had fallen off the rotarod. Each mouse was assessed three times on the same day, being allowed at least 10 minutes to rest in between assessments. The latency (time to fall from the rod) and the speed of the rotarod when the mice fell off were recorded for each test. Data were expressed as the averaged value across the three tests.
Regarding the fatigue protocol, the corresponding tetanic force was averaged every 5 contractions. A fatigue index corresponding to the ratio between the last five and the first five contractions was determined. The resulting force was divided by the volume of the corresponding hindlimb muscles (see above) in order to obtain specific force (in mN. mm -3 ). MRS evaluation and metabolic analyses of skeletal muscles from TgACTA1 D286G and KIActa1 H40Y mice. Metabolic changes were investigated using  31  P-MRS at rest and during the fatiguing protocol. Spectra from the gastrocnemius region were continuously acquired at rest and throughout the fatigue protocol. A total of 497 free induction decays (FID) were acquired (TR = 2 s). Data were processed using proprietary software developed using IDL (Interactive Data Language, Research System, Inc.
, Boulder, CO, USA). The first 180 FID were acquired at rest and summed together. The next 317 FID were acquired during the stimulation period and summed together. Relative concentrations of high-energy phosphate metabolites (phosphocreatine (PCr), inorganic phosphate (Pi) and ATP) were obtained by a time-domain fitting routine using the AMARES-MRUI Fortran code and appropriate prior knowledge of the ATP multiplets. Intracellular pH (pH) was calculated from the chemical shift of the Pi signal relative to PCr  38.
Statistics. Statistical analyses for experiments in TgACTA1 D286G mice and zebrafish were performed using GraphPad Prism 7. All phenotypic traits measured were tested using a nonparametric t-test (Mann-Whitney) or a two-way ANOVA. Unequal variances were assumed and all data were tested for normal distribution and passed using D' Agnostino and Perron's test for Gaussian distribution. All reported sample sizes were powered to detect statistically significant differences in all parameters measured. Statistical analyses for experiments with 6-7 month-old TgACTA1 D286G and KIActa1 H40Y mice were performed with the Statistica software version 9 (StatSoft, Tulsa, OK, USA). Normality was checked using a Kolmogorov-Smirnov test. Two-factor (group x contraction number) analysis of variance (ANOVA) with repeated measures on contraction number were used to compare force production.
Acknowledgements We thank the staff at the  Animal Resources Centre. Some analysis for this study was performed by the  Centre for Microscopy, Characterisation and Analysis (Metabolomics Australia ) and was supported by infrastructure funding from the  Western Australian State Government  in partnership with the  Australian Federal Government , through the  National Collaborative Research Infrastructure Strategy (NCRIS). This study was funded by the  Association Française contre les Myopathies  (trampoline grant # 18207 , JG), and by the  Auism Foundation  (NL). KN was supported by an  Australian Research Council   Future Fellowship  ( FT100100734 ), NL a  National Health and Medical Research Council  ( NHMRC) Principal Research Fellowship  ( APP1002147 ), AM an  Australian Postgraduate Award , and TS an  MDA Development Grant  ( APP381325 ) and an  AFM Postdoctoral Fellowship  ( APP19853 ).
Experiments In the experiments we evaluated three different algorithms for DCS with the criteria presented in section 3. Source code and data sets are publicly available 1. Data Sets. The data set consists of 140,341 scientific papers from the economic domain available in the digital collection EconStor 2. The data set contains information about author, paper abstract, paper type, publication year, venue and a set of JEL-classification codes 3  in meta-data fields. For our experiments we selected those papers that have an abstract and at least one JEL code assigned resulting in 67,813 documents. We annotated phrase candidates of at most 3 terms using the phrase collocation detection described by Mikolov et al. [7]. We constructed artificial user collections D containing k documents with the following property: All documents in the collection must have at least n JEL codes in common, where n ∈ {1,.
We used the TextRank implementation of the Python summa library. For ∆Rake, we used Rake  [8]  from the Python library rake nltk. All the algorithms create a set of keywords for a single document. The keywords for a collection were derived using the set union operator for all documents in a collection. The commonalities C(D, d) were calculated as the set intersection between the keywords of the collection D and the document d. Novelties N (D, d) were calculated by subtracting the set of keywords of the collection D from the set of keywords from the document d. Evaluation Measures. We measure Comparability and Differentiability as the size of the keyword overlap: kwm(dc)∩kwm(D) kwm(dc) , where, in the case of comparability d c = d x and in the case of differentiability d c = d y (cf. figure  1 ).
kw m (d) is the number of keywords extracted by method m on the document d. For Diversity we construct a binary JEL code-keyword matrix (M ) for each keyword extraction algorithm on the entire data set. Each entry m ij in M indicates whether a specific JEL code i occurs in at least t documents for which keyword i has also been extracted. The parameter t is set to 10 for Rake, 20 for TF-IDF and 5 for TextRank in the experiments. These values were obtained by manual optimization. Thus, the columns of M contain representations of keywords in terms of JEL codes. In a second step, given a candidate document, keywords are extracted and their respective columns of M are combined by logical OR yielding a vector v. The ground-truth JEL codes for the document are compared to the candidate vector v using Jaccard similarity.
To obtain more robust evidence, we examined the association between long working hours and clinically assessed risk markers for chronic diseases, using a large dataset of more than 75 000 French men and women. By carefully assessing exposure, its frequency and duration, we were able to differentiate those never exposed from those formerly or currently exposed and examine potential doseresponse patterns in the association. In addition, we assessed associations with several risk factors that have not been examined in relation to long working hours, such as lung function, indicators of liver and kidney function, white and red cell count, and blood clotting (platelets). We also examined.
Of the 99 924 participants in 2012-2016, 90 607 (91%) provided data on working hours, and of them, 88 009 (97%) also provided data on SEP. A total of 76 486 (87% of those) had data on all other covariates (smoking, alcohol use, physical activity, depressive symptoms and chronic disease), and of those, a maximum of 75 709 (96%) provided data on clinical measurements. The CONSTANCES Cohort project has been approved by the authorisation of the National Data Protection Authority (Commission nationale de l'informatique et des libertés-CNIL). CNIL verified that before inclusion, clear information is provided to the eligible subjects (presentation of CONSTANCES, type of data to be collected, ability to refuse to participate, informed consent, etc).
Measures Long working hours were elicited by the following survey question: "Do you have or have you had a daily working time (excluding travel) of more than 10 hours on at least 50 days per year?" with yes/no response options. Of those responding yes, the timing of exposure was requested (starting and ending years of participant's exposure). We were able to record up to three episodes, and from this information, we determined a total dose of exposure (in years) and whether the participant was formerly (yes/no) or currently (yes/no) exposed. Risk markers were assessed during a health examination, which was standardised according to standard operating procedures in order to guarantee high-quality physiological data despite unequal conditions (see details  23  ). We calculated BMI from weight and height measurements (weight kg/height m 2 ).
We measured waist circumference and hip circumference and calculated waist:hip ratio. Systolic and diastolic blood pressure was measured after 5 min rest with a 2 min break between measurements. The highest value was used for analyses. We calculated pulse pressure as systolic minus diastolic blood pressure. Lung function was assessed by spirometry, which was performed with three measures each of FVC and FEV. For both of these, we used the highest of the three measurements. 24 articipants were instructed to fast for 12 hours before the blood test, which was taken between 8:00 and 10:00. Valid lipid and glucose values require a minimum of 8 hours of fasting, which 71 375 (94%) participants with clinical data adhered to. Laboratory tests included blood glucose, lipids (total cholesterol (TC), high-density lipoprotein (HDL) and triglycerides (TG)).
Mean differences and their 95% CIs were computed using the SAS V. 9. 4 general linear model (genmod) procedure. We analysed men and women separately and adjusted the models for age, socioeconomic position, smoking, alcohol use, physical activity, depressive symptoms and chronic disease. We performed analyses of spirometry both with and without adjustment for smoking, and analyses of ALT and gamma GT both with and without adjustment for alcohol use. In other sensitivity analyses, we excluded participants with chronic disease and stratified the data by socioeconomic group.
Reasons behind these findings and discrepancies between men and women are unclear but might relate to different selection processes in increasing or decreasing working hours, more excessive working hours among men, as well as women's resilience towards cardiometabolic risk factors during working age. A major strength of our study is the large population-based sample, which allowed a precise estimation of the timing of exposure to long working hours, which in turn enabled the assessment of current versus former exposure and dose-response relationships. Such a large study with wide-ranging clinical data has not been carried out in this research field before. The study population included people from across the country, and men and women were equally represented, with a broad range of socioeconomic positions, which supports the generalisability of our findings, although only to the population studied.
Funding This study was supported by  Nordforsk  ( 75021 ). The CONSTANCES cohort is supported by the  French National Research Agency  ( ANR-11-INBS-0002 ) and also partly funded by  MSD ,  AstraZeneca  and  Lundbeck. Competing interests None declared. Patient consent Not required. ethics approval The CONSTANCES Cohort project has been approved by the authorisation of the National Data Protection Authority (Commission nationale de l'informatique et des libertés-CNIL). Provenance and peer review Not commissioned; externally peer reviewed.
1 software (Bruker Daltonik) and analyzed using Mascot Server 2. 5. 1 (Matrix science, London, UK); for details see Protocol 4 (Supplementary Material). The hypothetical identity of molecular components was established via assignment of m/z values of peptide and lipid species detected by MALDI MSI to masses of species with a known identity. M/z values of peptide components were attributed to measured masses of peptides identified in LC-MALDI-MS/MS experiment described above. The assignment was performed allowing ± 0. 05% mass tolerance. M/z values of lipid components were annotated using SimLipid (PREMIER Biosoft) software. Search parameters were set according to MALDI-MSI experiment conditions, specifying positive ion mode with protonated ion [M+H] + and two cation adducts: [M+Na] + and [M+K] +.
prote inatl as. org]. ANHAK is involved in migration and invasion of cancer cells  (Sudo et al. 2014 ), MYH9 could be a tumor suppressor via regulation of p53  (Schramek et al. 2014) , while ENO1 is involved in Warburg effect and its targeting sensitizes cancer cells  (Capello et al. 2016). Moreover, A2M putatively synthesized by macrophages present in cancer microenvironment could activate cancer cells  (Misra and Pizzo 2015). The hypothetical identity of discriminatory lipid components was attributed by annotation of MSI components at the SimLipid database. There were 113 such MSI components with at least one specific compound associated; the most probable hit was attributed to each component based on mass tolerance and expected abundance in a tissue (Supplementary Table  S4 ). There were 56 phosphatidylcholines, 34 sphingolipids, and 16 acylglycerols among compounds hypothetically attributed to lipid components discriminating cancerous and normal epithelium.
datasets. The fact that 97% of the variability in the motif fingerprints can be explained by just two principal components and that the motif PCs correlate with global topological metrics suggests that the networks lie in a low-dimensional topological space. This result is in line with prior research suggesting that very few parameters are needed to reproduce key global topological features of macroscopic brain networks, for example results from generative models  (Betzel et al. , 2016; Klimm et al. , 2013; Vértes et al. , 2012). Our identification of PC1 as the smallworldness axis raises the question of which aspects of the motif fingerprints and morphospace a simple generative model that takes into account the economic trade-off between long-distance edges' cost and their useful topological properties can reproduce.
Generation of the Morphospace From Motif Fingerprints We begin by calculating undirected fMRI networks for 100 healthy subjects from the Human Connectome Project by correlating the regional fMRI wavelet time series and thresholding the resulting correlation matrices. Details of the data and the preprocessing are given in the Methods section. Unless otherwise stated, we take a threshold of 800 edges (a connection density of ≈ 20%). We then characterize the networks in terms of their motifs. G = (V , E ) is a motif of the graph G = (V, E) with vertices V and edges E, if V ⊆ V and E is a subset of E such that the vertices of each edge in E are in V. In this work we consider induced motifs, for which E includes all edges of G that end on vertices V  (Diestel, 2006). We focus on the six possible four-node, undirected motifs, which are shown in Figure  1A.
We note that the correlations of PC1 with the efficiency and transitivity of the networks and PC2 with the assortativity described above are extremely robust. They can be replicated with 400 or 1,200 edges instead of 800, as shown in section 2 of the Supplementary Information  (Morgan et al. , 2018). The results are robust to changing the parcellation to the AICHA parcellation  (Joliot et al. , 2015) , which has 384 regions rather than 90; see section 3 of the Supplementary Information  (Morgan et al. , 2018). The results can also be reproduced both in the retest HCP dataset and the independent Cambridge dataset described in the Methods section. For details, see sections 4 and 5 of the Supplementary Information  (Morgan et al. , 2018). Finally, if the global metrics (efficiency, transitivity, and assortativity) are included directly in the PCA in addition to the motif fingerprints, the directions of the efficiency, transitivity, and assortativity vectors in the resulting biplot are broadly the same as the directions of the corresponding correlations shown in Figure  2.
To explore this finding further in the whole dataset, we count the number of the longest 5% of edges in each subject that are interhemispheric. The results correlate negatively with PC1 (r = -0. 44, p < 0. 001); see Supplementary Figure  12   (Morgan et al. , 2018). In other words, the longest edges in networks with high PC1 scores (which tend to have high global efficiency) are more likely to be intrahemispheric than the longest edges in networks with low PC1 scores. These edges tend to connect the front and the back of the brain and could therefore be particularly important for brain integration. Importantly, there is no correlation between PC1 and the total number of interhemispheric edges in the network, hence on average networks with low or high PC1 scores have the same number of inter (and therefore intra) hemispheric edges in total.
The significant correlation between PC1 and the length of the longest edges is robust to changing the percentage of the longest edges considered from 1% to 10% and to considering 400 or 1,200 edges instead of 800 (see sections 2 and 9 of the Supplementary Information  (Morgan et al. , 2018). We also reproduce the result in the retest HCP dataset and obtain r = 0. 43 (p < 0. 001). Interestingly, the result is not significant (r = 0. 30, p = 0. 14) in an independent dataset with 26 subjects (the Cambridge dataset) as discussed in section 5 of the Supplementary Information  (Morgan et al. , 2018 ; note that the kurtosis of the distributions also does not significantly correlate with the PCs in this case). This may be due to the reduced number of subjects available, however further work is required to clarify the reason for this difference.
However, the generative models require relatively sparse networks, therefore we use a threshold of 400 edges in this section. We also use a single hemisphere (left) rather than both hemispheres, as in  Vértes et al. (2012)  and  Betzel et al. (2016). We use an independent dataset and an identical approach to generating the networks as  Vértes et al. (2012). We determine the parameters (γ and η) by exploring the parameter space manually to obtain values of γ and η that optimize the energy function used by  Vértes et al. (2012) , which is calculated from the global efficiency, modularity, mean clustering coefficient, and degree distribution of the generated networks in comparison to the real HCP fMRI brain networks. For our networks, which have fewer nodes than those used in  Vértes et al. (2012) , we find that η = -3 and γ = 2.
Note that other two parameter models, for example the economical preferential attachment generative model also discussed by  Vértes et al. ,  are not able to reproduce the global topology of the fMRI brain networks. Vértes et al. (2012)  showed that this was the case in their dataset, based on the energy function that gave higher values for the preferential attachment model than the clustering model, indicating a worse fit to the real data. The same result holds in our dataset. Moreover, the preferential attachment model is unable to reproduce the motif fingerprint of the real fMRI brain networks, as demonstrated in section 10. 2 of the Supplementary Information  (Morgan et al. , 2018). For a discussion of the ability of other network similarity measures to distinguish between the two models, see section 10.
Crucially, the space we obtain is low-dimensional, suggesting that the global topology of fMRI brain connectivity networks can be explained by few parameters. The PCs of the morphospace represent the two main degrees of variability in the network motifs. The first PC (which alone explains over 80% of the variability) is negatively correlated with transitivity and positively correlated with global efficiency and small-worldness. There is also some preliminary evidence that the average length of the longest 5% of edges is positively correlated with PC1, although this result was not reproduced in the independent dataset and needs to be checked in other large datasets. Hence the main axis of variability in the structure of healthy fMRI networks appears to represent the difference between networks with high numbers of long-distance edges and high global efficiency, versus those with fewer longdistance edges, low global efficiency, and high transitivity, in line with many reports in the literature that small-worldness is a key factor in fMRI brain networks and encodes a trade-off between the topological benefits of long-distance edges and their cost  (Bassett & Bullmore, 2006 , 2016; Stam & van Straaten, 2012).
Another limitation of the approach is that in order to compare networks' motif fingerprints directly, we thresholded and binarized the networks with equal edge densities. Our results are robust to changing this fixed density, as discussed previously and shown in the Supplementary Information  (Morgan et al. , 2018) , although fixing the edge density will result in a different threshold for different individuals. The binarization also means that the absolute values of the edges' strengths are no longer taken into account. Finally, as discussed already, our result showing that the Euclidean distance of the longest edges in the networks correlates with PC1 was not reproducible in our smaller Cambridge dataset and needs to be checked in other larger datasets.
METHODS We use 100 fMRI scans of healthy individuals from the Human Connectome Project. These data were already used in  Termenon, Jaillard, Delon-Martin, & Achard (2016)  to assess the reproducibility of the graph metrics. In this context, we downloaded the resting-state fMRI dataset publicly released as part of the Human Connectome Project (HCP), WU-Minn Consortium (for detailed parameters see  Smith et al. , 2013). The functional images were acquired on a 3T Siemens Connectome Skyra MRI scanner with a 32-channel head coil, using a multiband gradient-echo EPI imaging sequence with the following parameters: 2 mm isotropic voxels, 72 axial slices, TR = 720 ms, TE = 33. 1 ms, flip angle = 52, field of view = 208 × 180 mm 2 , matrix size = 104 × 90 and a multiband factor of 8. A total of 1, 200 images was acquired for a scan duration of 14 min and 24 s.
The data used from the HCP include a second dataset with the same 100 subjects scanned a second time on different days with identical acquisition parameters. This retest dataset allowed us to confirm and validate our results. In order to extract brain connectivity graphs, the structural and functional data were preprocessed according to the pipeline described by  (Glasser et al. , 2013). Finally, the functional data were registered to the individual structural image and further to the MNI152 atlas space using the transforms applied to the structural image (see  Termenon et al. , 2016 for details). In the present study, each brain image is parcellated using the classical automated anatomical labeling (AAL;  Tzourio-Mazoyer et al. , 2002)  composed of 90 regions. The results were also robust to using a different parcellation (the AICHA parcellation, with 384 regions), as shown in section 3 of the Supplementary Information  (Morgan et al.
We use the software FANMOD  (Wernicke & Rasche, 2006)  and Orca  (Hocevar & Demsar, 2014)  to count the number of motifs in each of the 100 networks (other software can also be used, for example mfinder;  Milo et al. , 2002). Unless otherwise stated, we focus on the six possible four-node undirected induced motifs shown in Figure  1A , which we denote as m i , where i ∈ 1, 6. We count the number of each of these motifs within each network, N i , and then normalize the number of motif i with respect to the total number of motifs in the network, to obtain the motif fingerprint for each network: p i = N i ∑ i N i. We then perform a principal component analysis on the motif fingerprints for all of the 100 HCP networks. Here we have 100 observations (the subjects) and six variables (the fingerprint scores for each of the six possible four-node undirected induced motifs).