dataesr/scientific-paragraphs-categorization huggingface dataset:
However, in our hands, this antibody gave unspecific results in the epidermis of human skin and still stained the dermis of TNX-deficient patients (Supplementary Fig. S3 ). Additionally, our GEO dataset analysis demonstrated that 7 over the 8 selected datasets presented a significant downregulation of TNXB during ovarian carcinoma and results for the remaining dataset were not significant (Supplementary data S1). Therefore, our analysis of curated datasets is definitely at odds with the published data. Though, it has to be noted that results obtained through TNX immunohistochemistry on TMA were less clearcut. Indeed, TNX protein expression was predominantly downregulated in most ovarian cancers (16 samples) compared to normal tissues (8 samples), whereas it was drastically upregulated in 3 tumoral samples (Fig.
TNX has been shown to be highly expressed in malignant mesothelioma compared to other cancers involving the serosal cavities  [51, 52]. However, in those studies, no healthy tissue has been analyzed. In contrast, in a very recent study, Nayakama and collaborators have demonstrated that TNXB gene expression was upregulated in malignant pleural mesothelioma compared to healthy adjacent tissue using the GSE51024 dataset from GEO database. However, in this study normal paired lung parenchyma was used as control tissue but does not correspond to equivalent healthy tissue to malignant pleural mesothelioma and is therefore in our view hardly comparable  [53]. In the GEO database, no dataset was available with sufficient number of equivalent normal tissues. However, we decided to analyze datasets with low number of proper normal tissue (pleura) but could not demonstrate a regulation of TNXB expression with certainty.
A very low number of normal and tumoral samples were also present in the MPNST datasets from GEO and no TMA was available for this specific cancer thus unabling us to conclude for this rare disease. In this report, we demonstrated TNXB mRNA and TNX protein levels were concomitantly and significantly downregulated in various types of cancer, which raises the question of the mechanism(s) leading to the downregulation of TNXB gene expression in this pathological context. Very few data are currently available regarding TNXB gene regulation. Unlike for the other tenascins, there are so far no report indicating that TNXB is regulated by growth factors or cytokines. However, like TNC and TNW, TNXB is subjected to negative regulation by glucocorticoids  [40, 46, 65]. Nevertheless, through GSE dataset analysis, we could not find an upregulation of glucocorticoid receptor (NR3C1) mRNA level concomitant with TNXB mRNA downregulation.
However, the physiological significance of hypoxiainduced expression of TNXB-S gene has not yet been understood. Hypoxia corresponds to a nonphysiological level of oxygen tension, a phenomenon common in a majority of malignant tumors. We could thus hypothesize that under hypoxic conditions the short TNXB (TNXB-S) protein is favoured at the expense of the long form and that HDAC1 is involved in this process. By analyzing the GEO datasets used for TNXB mRNA expression (GSE31210 for lung cancer and GSE37751 for breast cancer), we demonstrated that HDAC1 mRNA expression was significantly upregulated in lung (Fc = 0. 401; p = 1. 30•10 -4 ) and breast (Fc = 0. 457; p = 4. 90•10 -6 ) carcinomas compared to non-tumoral tissues. To definitely validate our hypothesis, it will be important to analyze the level of TNXB-S protein in tumor samples through the specific antibodies raised against the truncated TNXB (anti-h29 and anti-h30 antibodies developed by Endo et al.
[69] ). A number of long non-coding RNAs (lncRNAs) have been shown to play significant roles in the pathogenesis of several cancers including colorectal, kidney and breast cancers  [70] [71] [72]. Through bioinformatics analysis, Yan and colleagues demonstrated that the lncRNA LINC01305 was the most overexpressed lncRNAs in cervical cancer and that TNXB was a target gene of LINC01305. Additionally, an upregulation of LINC01305 in cervical cancer cell lines was observed concomitantly with a downregulation of TNX  [73]. In the pan-cancer TMA we used, we also detected lower TNX protein levels in cervical cancer samples compared to adjacent normal cervix tissues (Supplementary Fig. S5 ). The correlation between LINC01350 and TNXB expressions in cervical cancer prompted us to analyze LINC01350 expression in our selected GSE datasets where TNXB was downregulated, but this link could not be generalized to the other analyzed cancers (data not shown).
Experimental procedures In silico analyses Microarray datasets were selected from the NCBI GEO database (https://www. ncbi. nlm. nih. gov/gds). Results were filtered by date and number of samples. For all datasets, differentially expressed genes (D. E. G. ) between cancer and normal tissues were obtained thanks to GEO2R analysis tool which compares both groups using limma (Linear Models for Microarray Analysis) R package (Bayesian statistics). D. E. G. were determined by Benjamini & Hochberg (False discovery rate) adjusted pvalue b 0,05. Then, representative datasets were selected according to the criteria cited in the "Results" section. For those datasets, TNXB expression values were extracted from GEO and plotted. Comparisons between tumoral and non-tumoral tissues were performed using nonparametric paired (Mann-Whitney, when nontumoral samples were adjacent tissues) or unpaired (Wilcoxon, when non-tumoral samples were healthy tissues) statistical tests.
TCGA data were analyzed through UALCAN web-portal and results for TNXB gene expression were extracted as presented in the website. To study TNXB mRNA expression during tumor progression, patients were separated depending on their tumor stage or grade and the mean TNXB expression value of each group was plotted. Groups were then compared using non-parametric one-way ANOVA (Kruskal-Wallis test) followed by Dunn's multiple comparison test. Finally, patient survival was studied using datasets containing clinical data. Tumor samples were separated in 2 groups (TNXB low and TNXB high ) by median cut-off on the TNXB expression values (50% of patients with the lowest values = TNXB low , 50% with the highest values = TNXB high ). Cumulative survival proportions were estimated using the Kaplan-Meier method and TNXB low and TNXB high groups were compared thanks to Log-Rank (Mantel-Cox) statistical test.
Each imaged spot was extracted by QuPath software (https://qupath. github. io/) and analyzed using Fiji software (https:// fiji. sc/). Stromal and epithelial areas were separated thanks to Fiji Trainable Weka Segmentation plugin (https://imagej. net/Trainable_Weka_Segmentation). The segmentation training was newly performed for each tissue spot because of histological differences between the various analyzed organs and between tumoral and normal samples. Colors were then separated by "H DAB" Colour Deconvolution and DAB staining was quantified in stromal region (mean grey value). Reciprocal mean grey value (255 -mean grey value) was considered, as LookUp Table (LUT) is inverted in Fiji software. An example of the quantification process is shown in Supplementary Fig. S4. Tumoral and normal tissues were compared by nonparametric unpaired (Mann-Whitney) test.
Acknowledgments We acknowledge the contribution of Federative Structure of  Health Research Lyon-Est   CNRS UMS3453/INSERM US7  and particularly  Denis RESSNIKOFF  from the microscopy platform (CIQLE) for his advice regarding TMA scanning and imaging analysis. Additionally, we greatly thank members of the histology and imaging facility (PrImaTiss) at the  LBTI  institute for their advices regarding the setting for TNX immunodetection. We are very grateful to  Manuel KOCH  ( University of Cologne, Germany ) for kindly providing us the antihuman TNX antibodies used for antibody specificity validation. We also acknowledge  Sylvie RICARD-BLUM  for critical reading of the manuscript. SL and AA are recipients of PhD student fellowship from the  French government (NMRT/ENS of Lyon). This work was supported by the  "Ligue Nationale contre le Cancer, Comité du Rhône" and "Comité de l'Allier" , as well as by the  "Fondation ARC pour la Recherche sur le Cancer " (PJA 20141201790).
Data Source Data were collected in five public NHs around the city of Toulouse (France): Three voluntary Communal Centre of Social Action-attached NHs (CCAS-NHs) and two public Hospital-attached NHs (H-NHs). By choosing five different structures, we aimed to analyze different organizations and drug supply chains. Consent of participation was recorded automatically. Participants or guardians were informed by the physician care coordinator that their data, gathered and previously de-identified, would be used for scientific purposes.
Participants Residents aged over 75 years and having lived in the NH for more than three consecutive months were included in the study. This minimum length of stay was chosen to focus on long-stay residents. Palliative care residents were excluded (in accordance with the physician care coordinator recommendation). We collected all the data from eligible NHR who were living in the CCAS-NH, but for feasibility we could not analyze all patient files from NHRs living in the H-NH-thereby we randomly selected a subset sample. Indeed, the capacity of the H-NH was much greater than the CCAS-NH, so we collected data as we went through the resident list until the end of the collection period (3 weeks).
Procedures Data regarding all prescriptions in progress at the time of collection were collected by one pharmacist. Information was collected from computerized (H-NH) or not computerized (CCAS-NH) patient records. The information was transcribed into an anonymous paper file to facilitate collection on site. Then we established an Access ® spreadsheet (Microsoft Corporation, Redmond, Washington, DC, USA) in order to have all information on an adapted page layout and facilitate the analysis. Drugs were coded according to the anatomical therapeutic and chemical (ATC) classification system  [22]. Drugs information included the non-proprietary name, forms, dosage and frequency of administration. For each prescription, identification of PIDP was done, based on explicit and implicit criteria. The explicit criteria were: (i) the French Laroche list  [23] , which contains drugs that should be avoided in the elderly aged over 75 years; (ii) the STOPP and START criteria  [9, [24] [25] [26] ; (iii) the Summaries of the Characteristics of Products (RCP) to detect overdosing, underdosing, contraindications, drug-disease interactions and drug-drug interactions.
Resident Characteristics We collected demographic data (age, gender) and health status data (medical comorbidities, falls and hospitalizations during the last twelve months), as well as the consensual French version of the Mini Mental State Examination (MMSE) scores for screening for states of mental confusion  [28]. Biological data were collected according to drugs prescribed (for example, the clearance of creatinine, or International Normalized Ratio (INR)). Modalities of administration related to deglutition disorders were collected among nurses. All systematic or conditional "as needed" (PRN medicines) prescriptions were recorded.
Statistical Analysis The qualitative variables were presented by the number and percentage of each category. Distributions of Gaussian quantitative variables were represented by the mean and standard deviation (SD). Non-Gaussian quantitative variables were expressed by their median and interquartile ranges (IQR 25th-75th percentiles). The prevalence of PIDP was obtained by bringing back the full number of residents presenting a PIDP to the full number of residents included. All analyses were performed using SAS 9. 3 software (SAS Institute Inc, Cary, North Carolina, NC, USA).
Characteristics of NHs and Study Population (Residents) The number of general practitioners intervening in the NH varied from 1 to 25 per hundred beds. All NHs were public, located in urban area and are entitled to accept social assistance. The NHs structural and organizational characteristics are presented in Table  1. A flow chart is presented in Figure  1. Pharmacy 2020, 8, x FOR PEER REVIEW 4 of 14 back the full number of residents presenting a PIDP to the full number of residents included. All analyses were performed using SAS 9. 3 software (SAS Institute Inc, Cary, North Carolina, NC, USA).
In addition, nurses responsible for crushing consequently incur several chemical and biological risks (powder inhalation). Nurses and nurses' aides should systematically contact the pharmacist and the GP when facing difficulties related to administration or doubts about the presence of tolerance problems. The detection of PIDP would thus be facilitated with more interaction and reciprocal recognition. Some limitations of the present study need to be recognized. First, we recorded all conditional prescriptions and some data may be missing from medical records. This could have determined an overestimation of the overall PIDP prevalence and some prescriptions may have been justified if the prescriber had been interviewed. Second, we used heterogeneous criteria to define our principal outcome.
Finally, it should be underlined that the weak mobilization of the GP was a barrier to therapeutic optimization and having a General Practitioner-Pharmacist collaboration would have allowed better and more detailed prescription analysis, as mentioned by Bryant and al. [51]. In spite of this, our study also has strengths. Data were collected by the same health professionals throughout the whole study. We combined explicit and implicit criteria to optimize pharmaceutical analysis, as well inadequate administration practices. We also took the time to heighten awareness measures among the NH staff during the restitutions of the results so it may improve their medical practice and thereby spread our methodology for other residents.
Data Model The data model describes the structure of the telemonitoring system database for patients with T2DM, which is established in the PostgreSQL database manager (see Figure  7 ). The database of the proposed system is composed of 35 tables that include primary keys to identify each entity (or table), as well as foreign keys that represent the relationship between tables. The database is prepared in order to be adapted to system updates, i. e. , if it is required to add tables or attributes in the existing ones, it is also prepared for the development of food menus by the nutritionist and the modification of the reports about the food intake frequency questionnaire. This data model also shows the relationships between tables that guarantee the integrity of the stored information. The main tables of the data model of the proposed system are described below.
The balloon flight was terminated after 4 hrs of dark time, with less than one hour spent above the minimum science altitude of 32 km. Upon landing, the payload suffered some structural damage that the team is working to repair. We expect a second launch of the refurbished payload in 2021, due to delays from COVID-19. This paper presents an overview of the FIREBall-2 mission. In particular, we describe changes from the previous version of the mission, FIREBall-1, and both the motivation and results of those changes. We provide performance details about all subsystems, and references to more specific papers about those subsystems in the case of the detector and CNES guidance systems. We give an overview of the analysis of the data collected during flight, which was severely limited by the low altitude and high background.
In flight performance The instrument in-flight performance is described in more detail in  Picouet (2019)  but briefly summarized here. Post flight processing of the data from Field 2 was used to determine the in-flight performance of the spectrograph. Due to the degraded conditions described in Sections 3. 1 and 3. 2 the performance of some systems were sub-optimal. Several objects are detected in the co-added data from Field 2. The brightest was continuum emission from a UV bright horizontal branch star with M F U V =17. 8, which was detected by FB-2 with SNR>15 (ID: 2MASS J16515894+3459322, known as Bright Star 1 hereafter). Two fainter UV bright stars were also detected. Bright Star 1 was used to measure the system efficiency, finding 30% degradation compared to the ground calibrations with atmospheric losses.
We were able to re-create much of the signature of the scattered light after the fact using pseudo-dome flats and other methods to fully illuminate the entrance aperture to the spectrograph tank, beyond just the pupil. In addition to large scale scattered light there were occasional flashes that are visible in the flight data, shown in Figure  14. These flashes are typically much brighter (∼2. 5 times) than the background level of scatter and have a characteristic spatial signature. We have also been able to re-create them in the lab post-flight and believe, due in part to their transient nature, brightness, and distinctive presentation, that they are the result of light reflecting off of a metal surface on the detector PCB mount. We are currently running a comprehensive scattering analysis of the as-built 2018 spectrograph and gondola using the FRED software package.
Additional technology, such as the anamorphic grating, guidance system, and use of a multi-object spectrograph on a balloon payload have also been significant improvements over the existing state of the art. Finally, FIREBall has always been a pathfinder for a larger program to observe and map the faint circumgalactic and intergalactic media. While the observations in the visible bandpass have vindicated the concept of directly observing the diffuse gas from the CGM, we have not yet realized the potential of doing these observations in the UV. The 2021 flight of FIREBall-2 will detect this emission at a redshift of 0. 7, but a future mission concept to explore the CGM in the UV from redshifts of z<2 is essential in our understanding of galaxy evolution. While the data gathered from the 2018 flight of FIREBall-2 may not provide a detection of the CGM at z=0.
ACKNOWLEDGMENTS The authors wish to thank the helpful reviewer for their insightful comments. In addition, we sincerely thank the many and varied supporters over the years of development, design, integration, and flight. Many people have helped out the team in moments of need and we are grateful. We especially thank the many hardware and component vendors who were patient and helpful in all of our questions. The FIREBall mission is a joint venture of  NASA  and  CNES , and is funded on the US side via the  NASA APRA program. The FIREBALL collaboration also acknowledges considerable support from  CNES ,  LAM , and  CNRS. The authors wish to thank in particular  Mike Garcia , the  NASA   program  scientist for APRA, and  Mario Perez , also of  NASA , for their continued support for this mission.
One of the most widely used approaches to explore and understand non-random structure in data in a largely assumption-free manner is clustering. In this paper, we detail two original Shiny apps written in R, openly developed at Github, and archived at Zenodo, for exploring and comparing major unsupervised algorithms for clustering applications: k-means and Gaussian mixture models via Expectation-Maximization. The first app leverages simulated data and the second uses Fisher's Iris data set to visually and numerically compare the clustering algorithms using data familiar to many applied researchers. In addition to being valuable tools for comparing these clustering techniques, the open source architecture of our Shiny apps allows for wide engagement and extension by the broader open science community, such as including different data sets and algorithms.
Once each of the components in the mixed space are identified, clustering is carried out by assigning each observation to the most likely component in the mixture, which is determined by maximizing the posterior probabilities of each observation belonging to each component. In this paper, we present two original Shiny apps written in the R language  [4] , that allow researchers in any field to explore, understand, and directly compare clustering results from these widely used clustering algorithms. The first Shiny app is based on simulated data to ease the user into the logic of clustering, and the second leverages the Iris data, and is expanded to also include observation-level numeric output (e. g. , cluster assignments) in addition visual output. Both apps leverage ggplot2 graphics  [5] , which contribute to clean, simple user interfaces to contribute to deeper understanding.
Implementation and architecture The software for each app was implemented by programming a user interface (UI) and a server, as necessary component parts of a Shiny app. Upon creation of the UI and server, each app was consolidated into a single script, and then deployed at the corresponding author's Shiny host site, pdwaggoner. shinyapps. io. More details on deployment are available in the following "Availability" section. To get a clearer sense of the architecture of each app, consider the screenshots of the launched apps below in Figures  1 2 3. Figure  1  shows the first clustering app using simulated data. Figure  2  shows the visual component of the second clustering app using the Iris data. Figure  3  shows the numeric output from the second clustering app also using the Iris data. In Figure  1 , the raw data densities are displayed along with an optional histogram for another view, as well as the computed cluster densities.
Quality control The apps have been thoroughly quality checked and tested locally. First, the UI's have been tested on the several widely used internet platforms including Firefox Quantum 65. 0. 2, Chrome 72. 0. 3626. 121, Safari 12. 0. 3, and Opera 58. 0. 3135. 107. No errors or problems occurred on any browser or system. The apps worked efficiently and as expected on all platforms. Second, the code was locally tested using the shinytest package, which is a unit testing package for Shiny apps. This package tests for the following, which was copied and pasted from RStudio for consistency: 2   1. "First is the test driver. This is the R process that coordinates the testing and controls the web browser. When working on creating tests interactively, this is the R process that you use. 2. Next is the Shiny process, also known as the server.
This is the R process that runs the target Shiny application. 3. Finally, there is the web browser, also known as the client, which connects to the server. This is a headless web browser -one which renders the web page internally, but doesn't display the content to the screen (PhantomJS). " Across all of these local tests for both apps, which are catalogued at the Github repository under the "tests" folder for the simulated data app (1) and "tests_iris" for the Iris data app (2), there were zero errors thrown, and all code and apps behaved as expected. Finally, at the Github repository listed below, users can inspect all source code, unit testing results, as well as inspect the README for more details on each app as well as how to diagnose the results both visually and numerically (see the "Understanding the Output" subsection in the README).
2) Availability The software includes two Shiny apps that are hosted separately at unique Shiny app pages. The first app is hosted at https://pdwaggoner. shinyapps. io/App1-Simulated_Data/ and the second app is hosted at https:// pdwaggoner. shinyapps. io/App2-Iris_Data/. The code is openly developed at a single Github repository: https:// github. com/pdwaggoner/clustering_Shiny.