dataesr/scientific-paragraphs-categorization huggingface dataset:
(  2 ) Finding ways to visualize and compare accessibility (i. e. , the analytical dimension of flows in urbanism that considers the time that it takes to make a certain trip and other physical constraints) between datasets, a dimension that was not considered in our current work, which adds a new layer of complexity to the data. (3) Using the system to extend our study to different sources of pervasive data, thus developing a better understanding of their differential biases, and to leverage human critical thinking in the task of finding ways of making cross-origin datasets complement each other. We think that this paper stresses the importance of visualization as a critical tool from a human-in-the-loop perspective, particularly in complex scenarios, where no benchmark or ground truth is available, such as urban mobility.
Guidelines on input sequences We highly recommend taking the amino acid level into account during the alignment of protein-encoding DNA/RNA. This will ensure nucleotides to maintain the correct reading frame for each sequence in the alignment. There are several tools available to generate a codon-based alignment as web servers, such as RevTrans  (17) , PAL2NAL  (18) , TranslatorX  (19)  and software suites as MACSE  (20, 21).
FUNDING French National Agency for Research against HIV and Hepatitis (ANRS)  [ CSS4 ECTZ25224 2017-19 ];  French Government , " Programme d'Investissement d'Avenir" (LABEX CALSIMLAB)  [ ANR-11-LABX-0037-01  and  ANR-11-IDEX-0004-02 ]. Funding for open access charge:  Agence Nationale de la Recherche  ( ANR ChromaLight Grant ).
) to select badges. Please be careful to choose the badges "Open Code" and "Open Data" only if appropriate (in addition to the "Open Access" and "Open Peer-Review" badges). 3-we suggest that you deposit a copy of your MS in zenodo. org and ask for its inclusion in the PCI community ("Communities" section in the deposit form). Indicate the current doi of your MS, if it already has one, in the "doi" section. I.
org/10. 24072/pci. mcb. 100002 1--XXXXthe "citeas" → Briand, S. , Dessimoz, C. , El-Mabrouk, N. and Nevers, Y. (2020) A linear time solution to -XXXXthe surname and name of the recommender → Céline Scornavacca • I suggest you move a paragraph (see attached document). Could you send me an informal response to these points and post a new version on bioarxiv so that I can make my recommendation?Thanks,CélineAdditional request from the managing boardTo format your final version of your MS Mandatory modifications 1-Please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e.
g. , data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc. ) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused. -Details on experimental procedures are available to readers in the text or as appendices. -Authors have no financial conflict of interest relating to the article. The article must contain a "Conflict of interest disclosure" paragraph before the reference section containing this sentence: "The authors of this preprint declare that they have no financial conflict of interest with the content of this article. " If appropriate, this disclosure may be completed by a sentence indicating that some of the authors are PCI recommenders: -In the acknowledgements, add this sentence → "Version 4 of this preprint has been peer-reviewed and recommended by Peer Community In Mathematical and Computational Biology (https://doi.
It is easy to see that the larger is N the smaller this additional factor gets. Essentially, the refined mean field takes not only the first moment (the mean) but also the second moment (variance) into consideration in the approximation. In  [12]  we have applied this discrete time refined mean field approximation on a number of examples ranging from the well-known epidemic model SEIR to wireless networks. Here we investigate its application to a novel model of the more complex gossip shuffle protocol. A proof-of-concept implementation of both the classical and the refined mean field techniques and a discrete event simulator has been developed by one of the authors of the present paper in F# using the DiffSharp package  [5]  for symbolic differentiation. The results in this paper have been obtained using this implementation which can be found at  [20].
Experimental site, soil treatments and measurements Simulations were based on a two-year (2016-2018) field experiment set up at the Muñovela experimental farm belonging to the Spanish Institute of Natural Resources and Agrobiology of Salamanca (40°54′ 15″N latitude and 5°46′26″W longitude). A detailed description of the Muñovela dataset is given by  Carpio et al. (2020) , therefore, only a short overview of the experimental site, soil treatments, and measurements is given here. Nine plots of 81 m 2 were treated as follows (three replicate plots per treatment): unamended control soil (S), soil amended with 140 t ha -1 (dry weight basis) of spent mushroom substrate (S + SMS), and soil amended with 85 t ha -1 (dry weight basis) of green compost (S + GC). The soil is an Eutric-Chromic Cambisol (IUSS Working Group WRB, 2015) with a predominant sandy-loam texture down to 1 m depth (Table  1 ).
Modelling strategy Simulations were run from 29 November 2016 to 30 November 2018 according to field dataset. One period out of the two experimental periods was used for models calibration (between 29 November 2016 and 12 November 2017), and another one to test the performance of the models (from 13 November 2017 to 30 November 2018). Therefore, the strategy of simulation was first to calibrate the models against the field measurements of water, Br -, chlorotoluron and flufenacet contents in the control and amended soil profiles over the calibration period. Then soil water contents and chemicals transport were simulated without any further models calibration, and the results were compared to soil moisture, Br -, chlorotoluron and flufenacet concentrations measured in the soils over the test period.
Soil temperature in deep soil layers was assumed to be 1 °C below those observed on the topsoil  (Marín-Benito et al. , 2014b). For MACRO, a constant hydraulic gradient equal to 1 was assumed as bottom boundary condition. Climatic data (rainfall, maximum, minimum and average air temperature) were daily monitored using a meteorological station located at the field site. Measured daily and cumulative rainfall over the calibration and test periods are shown in Fig. S1  (Supplementary Material). Solar radiation, evapotranspiration and wind speed data were obtained from the station of Matacan airport (23 km away from Muñovela farm). Both meteorological stations are operated by the AEMET (Spanish Agency of Meteorology).
g. formation of herbicide-DOC mobile complexes), however they are not considered in the models. PRZM and MACRO could be successfully used as predictive tools against groundwater contamination by herbicides in amended soils, at least at a bi-annual scale, after calibrating DT 50 and/or K d. The results also emphasize the need of modelling the pesticide fate in amended soil scenarios in the regulatory context. However, as the calibration of pesticide parameters is not possible when these models are used for risk assessment for European pesticides registration, a module allowing to correct the DT 50 and K d values according to the DOC of the amended soils should be added. Finally, due to the decrease in models efficiency with simulation time (mainly for PRZM) to predict the herbicides mobility in the amended soils, they should be tested for longer periods.
Acknowledgements This work was financially supported by  MINECO/FEDER UE  (Project  AGL2015-69485-R ) and  MCIU/AEI/FEDER ,  UE  (Project  RTI2018-101587-J-I00 ). M. J. Carpio  thanks for her predoctoral contract co-funded by  European Social Fund (ESF)  and the  Consejería de Educación (Junta de Castilla y León Government). J. M. Marín-Benito  thanks  MINECO  for his  Juan de la Cierva-Incorporación  ( IJCI-2014-19538 ) contract.
With the cards and a flipchart as props, facilitators teach core skills and competencies of outbreak investigations in an interactive group exercise. We modeled a hypothetical Salmonella species outbreak investigation with a cohort design in Excel 2010 (Microsoft, https://www. microsoft. com). We took symptoms and incubation periods from our own unpublished outbreak investigations and literature research  (4, 5). We reverse-engineered relative risks for univariable and stratified analysis with their 2-by-2 tables into a line list with the desired properties and transcribed onto playing cards in which each card represents 1 person attending the exposure event. Dynamic illustrations of gastroenteric symptoms and food items were hand drawn with pen and ink on Bristol board, scanned, and colored by using the GNU Image Manipulation Program (https://www.
gimp. org). The illustrations depict characters from our earlier work, the disease detectives  (6). We used OpenOffice software (https://www. openoffice. org) for layout of the cards. The teaching script is a restructured and simplified adaptation of outbreak case studies used during European Programme for Intervention Epidemiology Training introductory courses  (2, 3)  The exercise is for 16-26 students who play roles of participants in the outbreak event and investigate their own outbreak. Each participant draws 1 card that illustrates their fictional health status, food eaten, symptoms if ill, and time of onset  (Figure). Facilitators subsequently collect the information from the cards and summarize them on a board during the 1-2-hour teaching activity as the outbreak investigation unfolds.
Herein, through (1) in silico analyses of the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and (2) immunohistochemistry staining of Tissue MicroArrays (TMA), we performed a large and extensive study of TNX expression at both mRNA and protein levels (1) in the 6 cancers with the highest incidence and mortality in the world (i. e. lung, breast, colorectal, prostate, stomach and liver) and (2) in the cancers for which sparse data regarding TNX expression already exist in the literature. We thus demonstrated that, in most cancers, TNX expression is significantly downregulated during cancer progression and we also highlighted, when data were available, that high TNXB mRNA expression in cancer is correlated with a good survival prognosis.
Herein, we thus decided to perform a large metaanalysis to evaluate TNX expression during tumor progression and chose to focus on the 6 cancers with the highest incidence and mortality in the world, i. e. lung, breast, colorectal, prostate, stomach and liver carcinoma (Table  1  -Globocan 2018 -http:// gco. iarc. fr). Additionally, we included in our metaanalysis the 7 specific types of cancer already described in the literature to further validate our study and clarify previously published data performed with a low number of samples or restrictive datasets. We thus analyzed TNX expression in 13 types of cancer compared to normal equivalent tissues at the mRNA and protein levels (Table  1 ). TNXB mRNA expression was evaluated (1) by dissecting 90 GSE datasets from the Gene Expression Omnibus (GEO) database and (2) by using the UALCAN interactive web-portal to perform in-depth analyses of TCGA gene expression data  [55].
TNXB mRNA expression is mainly downregulated during carcinogenesis The aim of our study was to determine whether TNX mRNA and protein levels were significantly dysregulated in various cancer types compared to normal tissues (Table  1 ). For gene expression analysis, we focused on two databases: (1) The Gene Expression Omnibus (GEO) database, which is a public functional genomics data repository, and (2) The Cancer Genome Atlas (TCGA), this latter being analyzed through UALCAN, an integrated data-mining platform to harness the potential of cancer transcriptome  [55]. For the GEO database analyses, we performed a pre-screening of all the published datasets and only listed the most recent ones (from 2009) by excluding the datasets without healthy controls. To avoid interpatient variability, when possible, we chose the datasets with adjacent control tissues except for the prostate carcinoma, for which it is clearly established that the gene expression pattern in tissues adjacent to prostate cancer is so substantially altered that it resembles a cancer field effect  [56].
For malignant mesothelioma, we only focused on malignant pleural mesothelioma, since this specific neoplasia (1) accounts for 80-90% of all diagnosed cases and (2) was the subject of a previously published study  [53]. Furthermore, we only selected the datasets with pleura as normal samples to perform a proper comparison between normal and tumoral tissues. Finally, for dataset selection, we excluded datasets with low number of samples (b20 normal or tumoral cases) except for rare carcinomas, for which datasets with high number of normal samples were not available, i. e. MPNST, skin melanoma, ovarian cancer and malignant mesothelioma (Supplementary data S1). Therefore, we investigated 90 GSE datasets for the 13 selected types of cancer (Table  1 ) and an overall analysis showed TNXB gene was   significantly downregulated for 67 of them (74.
4%), upregulated for 3 of them (3. 3%) and not significantly altered in 20 of them (22. 2%) (Table  2  and Supplementary data S1). For each cancer, we next analyzed the pre-screened datasets and evaluated the number of datasets for which TNXB mRNA expression was significantly up-or down-regulated. We then established the regulation trend for TNXB mRNA expression and chose the most representative dataset for each cancer with the highest number of samples by selecting the dataset with clinical data when available (Supplementary data S1). In all the selected cancers with the highest incidence and mortality worldwide, we thus clearly observed a significant downregulation of TNXB gene expression. Indeed, compared to normal tissues, we found a 78. 5% decrease of TNXB mRNA expression in lung cancer, 15.
1B ). As mentioned previously, when available, we selected datasets with adjacent normal tissues as controls to exclude inter-individual variability. We were thus able to demonstrate a downregulation of TNXB mRNA expression in tumoral sample compared to adjacent normal tissue for patients suffering of breast cancer (in 93. 3% of analyzed patients), stomach adenocarcinoma (74. 6%), liver hepatocellular carcinoma (93%) and kidney clear cell carcinoma (61. 4%) (Fig. 1A  and B , lower bar charts). For datasets for which clinical information were available, we have also analyzed more precisely TNXB expression status in various clinical subgroups referring to (1) classical mutation status (ALK fusion, EGFR and KRAS mutations) or the Myc oncogene expression status in lung adenocarcinoma, (2) the breast cancer subtypes (Estrogen Receptor positive or negative carcinoma; Triple negative carcinoma or other subtypes), (3) p53 mutation status in colon adenocarcinoma or to (4) the various subtypes of glioma (astrocytoma, glioblastoma multiforme and oligodendroglioma) (Supplementary Fig.
S2A ). We thus demonstrated that TNXB expression was downregulated in all subgroups of lung, breast and colon carcinomas compared to the group composed of equivalent healthy tissues and we definitively showed that TNXB mRNA level was upregulated in all glioma subtypes compared to equivalent normal samples (Supplementary Fig. S2A ). To validate our results obtained through GEO database analyses, we also studied The Cancer Genome Atlas database through the UALCAN webportal  [55]. TCGA only focuses on 33 selected types of cancer, explaining why information were absent for leiomyoma and MPNST. Additionally, we could not conclude for ovarian carcinoma, malignant mesothelioma and skin melanoma because, in TCGA database, no or only one normal sample was available. However, this database allowed us to show that TNXB mRNA expression was downregulated in lung (-89,9%), breast (-96,4%), colon (-94,8%), stomach (-56,1%), liver (-46%) and renal (-48,7%) carcinomas (Fig.
1C  and D ), thus confirming the results obtained from the GEO database. Nevertheless, TNXB mRNA level was not significantly regulated in prostate adenocarcinoma compared to normal tissue (Fig. 1C ). Once again, TNXB expression was upregulated (+21,3%) in glioblastoma multiforme compared to normal samples (Fig. 1D ), in contrast to previously published data  [47]. Altogether, this extensive in silico analysis enabled us to demonstrate that TNXB expression was downregulated in most analyzed cancers except in brain tumors where it was upregulated.
TNXB downregulation in cancer is confirmed at the protein level We next studied TNX protein level through analysis of a pan-cancer TMA following Fig. 1. Variation of TNXB mRNA expression in tumors versus normal or adjacent tissues. Cancers of high incidence/mortality were studied (A and C), as well as cancers for which TNX status had been previously studied (B and D). A and B: Expression values of TNXB mRNA obtained from the selected datasets extracted from the GEO database. Non-parametric paired (breast, stomach, liver, and kidney cancers) and unpaired (lung, prostate, colon, glioma, ovary, melanoma and leiomyoma cancers) tests were performed comparing tumoral to normal tissues. For datasets comparing TNXB mRNA expression in tumoral sample versus adjacent normal tissue, a bar chart is presented just below the first graphical representation.
A stroma-specific quantification of TNX labelling using Fiji software was performed (Supplementary Fig. S4 ). In order to overcome the histological heterogeneity of cancers and origin of tissues, we repeated the segmentation training for each tissue spot. We thus demonstrated that TNX protein level was significantly decreased in the stroma of lung, breast, prostate, colon, liver, kidney and uterus carcinomas as well as in skin melanoma, compared to equivalent normal tissues (Fig. 2A  and B ). The downregulation was not significant for ovary carcinoma due to 3 highly positive tumor samples, whereas the others were definitely negative for TNX labelling (Fig. 2A  and B  and Supplementary Fig. S5 ). For malignant pleura mesothelioma, a decrease in TNX level was also observed, however statistical analysis could not be performed due to the very low number of samples available.
For stomach adenocarcinoma, a nonsignificant upregulation was observed compared to equivalent normal samples, which was at odds with the results obtained through the GEO and TCGA database analyses. Furthermore, and as already deduced through GEO and TCGA database analyses, TNX protein level was significantly upregulated in gliomas compared to equivalent healthy samples (Fig. 2A  and B ). Finally, we confirmed that TNX protein level in tumoral samples was observed regardless of the subtype of analyzed cancer (Supplementary Fig. S2B ). All tissue core quantifications were confirmed through blind scoring by a clinical anatomopathologist (Fig. 2C ), thus definitely demonstrating that TNX protein level is markedly diminished in most of the analyzed cancers.
TNXB mRNA expression is correlated with tumor progression and should be considered as a new prognosis marker in cancer As mentioned above, for the GEO dataset selection, we chose, when available, datasets associated with clinical data, i. e. grade/stage and survival. We first studied TNXB expression levels in the various clinical stages or grades of cancer development. Therefore, we demonstrated that the decrease in TNXB expression observed in lung and breast carcinomas is all the more important that the stage of tumor development is advanced (Fig. 3A  and B ). Inversely, TNXB expression levels increased concomitantly with the grade of glioma (Fig. 3C ). Datasets for lung and breast carcinomas also presented clinical survival information. We thus analyzed the expression of TNXB mRNA in tumoral samples and established TNXB low and TNXB high expression subgroups by median cut-off on TNXB expression values, meaning that the 50% of patients with lowest expression values were considered as "TNXB low " and the 50% of patients with highest expression values were considered as "TNXB high ".
S6  and S7 ). This discovery could be of great importance since even MMPs that are known to be regulated during tumor progression can hardly be considered as pan-cancer markers  [58]. Our results are reinforced by the data recently published by Chakravarthy et al. , in which they examined TCGA database and analyzed the pan-cancer landscape of ECM gene dysregulation in 8043 malignant tissues from 15 different tumor types and 704 corresponding normal tissues. In this study, 58 out of the 249 ECM genes represented in the RNA-seq dataset were significantly dysregulated; 30 of them were upregulated and 28 of them were downregulated. Interestingly, the gene which was the most significantly downregulated in cancers, was TNXB  [59]. Obviously, this large study also leads to many questions and notably regarding the relevance of TNX regulation in all subtypes of cancers but we demonstrate when data were available that, except for gliomas in which TNX was inversely regulated, all subtypes of analyzed cancers presented a TNXB  repression (Supplementary Fig.
S2 ). However, a fine analysis of TNX expression in all subtypes of all cancers has to be realized. Moreover, whereas the downregulation of TNX mRNA and protein levels was robustly observed in most cancers, we also noted some discrepancies between gene and protein expressions in our analyses, as well as with results reported in the literature, as discussed hereafter (Table  3 ). Indeed, when analyzed in detail, we could observe that for the in silico analysis of prostate cancer dataset from TCGA using UALCAN webportal, TNXB mRNA expression was not significantly regulated. However, as previously described for the prostate carcinoma, the gene expression pattern in tissues adjacent to prostate cancer is so substantially altered that it resembles a neoplastic tissue  [56]. And in fact, control tissues for prostate cancer in TCGA dataset included tumor-adjacent tissues which could lead to misinterpretation  [60].
Our hypothesis that TNX is diminished in prostate cancer is strengthened by the fact that we observed (1) a significant TNXB downregulation in prostate carcinoma through the 2 GEO dataset analyzed and (2) a robust decrease of TNX immunostaining in 20 tumors compared to 8 normal samples. Additionally, we are particularly confident in the non-tumoral origin of the "normal tissues" in our pan-cancer TMA since they were collected from healthy patients between 30 and 40-year-old and the average age at which prostate cancer is diagnosed is 73-year-old (http:// gco. iarc. fr/). For stomach adenocarcinoma, our results showed TNXB mRNA expression was significantly downregulated, whereas TNX protein detection by immunohistochemistry revealed an upregulation trend even if it was not significant. The stomach is composed of three anatomical regions: (1) the cardiac, which contains mucous secreting glands (called cardiac glands) and is closest to the oesophagus, (2) the fundus (corpus), the body or largest part of the stomach which contains the gastric (fundic) glands and (3) the pyloric, which secretes the gastrin hormone and two types of mucus.