dataesr/scientific-paragraphs-categorization huggingface dataset:
How to Report and Present AID Data Internationally agreed standardization of CGM metrics, targets and a report to visualize them were published and endorsed by a wide range of diabetes associations and endocrine societies  (101; 119-121). The use of AID systems is based on CGM data, and its success may be measured in improved CGM outcomes such as TIR. As the use of AID grows, it is therefore important that clinical teams receive AID data reports with consistent and familiar data displays  (122). Data should be provided in a way that assists with appropriate modification of insulin delivery settings. An academic panel of experts in AID system development, research or clinical use collaborated to generate a template for an AID system data report. The standardized two-page "Automated Insulin Delivery Report" (AID Report) (Figure  1 ) template was arranged to make sure that the clinically most important glucose and insulin metrics are shown at the very top of the first page (upper panel).
Acknowledgements The consensus group participants wish to thank the ATTD congress for organizing and coordinating the meeting and  Rachel Naveh  (The Jesse Z and  Sara Lea Shafer Institute  for Endocrinology and  Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel)  for assistance in organizing the meeting. The authors also wish to thank  Christopher G Parkin ,  MS, CGParkin Communications, Inc. , for his assistance in preparing this manuscript.
Animals were genotyped by polymerase chain reaction (PCR) on tail genomic DNA  (Yoo et al. , 2004) ; primers for P23H knock-in detection: 5′-GCCTGTTTAGCTGAGAAAAC-3′ and 5′-GACCACGTAACAAACTTCTG-3′. The retinal status in the distinct genotype/age groups was assessed by morphological analysis using hematoxylin/eosin staining (Supplementary Figure  S1 ). The results are in agreement with what was previously described for mice carrying heterozygous and homozygous versions of the P23H mutation at various ages  (Sakami et al. , 2011). Whole retina circadian rhythms were assessed by PER2::Luciferase bioluminescence recordings carried out at Postanatal Days 36 (P36) and 70 (P70) as previously described  (Jaeger et al. , 2015). Oscillations were analyzed using the Lumicycle Analysis software (Actimetrics, USA).
Raw data (counts per second, cps) were baseline-subtracted (24-h running average). Periods and amplitudes (height of the three first peaks) were determined by using LMFit (damped sine). The relative rhythmic power was determined using Periodogram function of Lumicycle Analysis to evaluate robustness of rhythms  (Buonfiglio et al. , 2014; Herzog et al. , 2015; Ruan et al. , 2012). Analyses were performed on 3 complete cycles after exclusion of the first oscillation. Glial cell involvement was assessed using the metabolic inhibitor of glial cells, L-α aminoadipic acid (L-AAA), on retinal explants from 3-month-old mice  (Jablonski and Iannaccone, 2000; Thangaraj et al. , 2015). After three complete cycles of control recording (baseline), medium was replaced with a fresh medium containing L-AAA (0.
Although inexpensive measures of body fat are readily available in most clinical settings from lower-to-high income and even rural-to-urban, these measures are often not robust enough to differentiate between fat and muscle mass, nor visceral adiposity and peripheral adiposity; therefore, poorly predicting body shape as an index for obesity. Thus, there is a growing interest to evaluate body shape derived from the principal component analysis (PCA) of anthropometric traits in large diverse datasets of uncorrelated variables that are combined to denote body shape. Only a few studies have applied a PCA approach to investigate genetic loci associated with a complex trait like body shape, predominately in Europeans [6e8]. However, due to the history of steatopygia and its elevated prevalence in Africans, a PCA approach assessing body shape as an index of obesity is needed in continental Africans.
Results Overall, our study had 6407 Ugandans from the general population cohort (GPC) and 2598 South African Zulu cohort (SZC). The majority of our study participants were females in both the GPC (57. 1%) and SZC (73. 8%) datasets (Table  1 ). The median age of our study participants was 29. 8 years in the Ugandan GPC and 52. 0 years in the SZC, indicating SZC was predominately made up of older women. Moreover, we observed SZC had a higher median BMI, height, weight, WHR, HC, and WC compared with the Ugandans (Table  1 ).
DDS is a population-based cross-sectional study of individuals aged >18 years residing in the urban black communities in Durban, KwaZulu-Natal, South Africa. DCC is a caseecontrol study of individuals aged >40 years with diabetes recruited from tertiary hospitals in Durban. Data collection was conducted from 2009 to 2013 for the DCC and from 2013 to 2014 for the DDS. The survey questionnaire included socioeconomic factors, health information, lifestyle factors, anthropometric measurements (including height, weight, systolic blood pressure, diastolic blood pressure, and hip and waist circumferences), biomarkers for communicable and non-communicable diseases, and genetic data. Of the 2804 individuals surveyed, 1204 were from the DDS and 1600 were from the DCC; more detailed information on the study design and quality controls have been published previously  [21].
Genotype data Information on sample collection, genotyping, imputation and quality control procedures were described in detail by previous studies  [8, 19]  Participant samples from the Uganda GPC were genotyped on the Illumina Human Omni 2. 5M Bead Chip array at the Wellcome Trust Sanger Institute (WTSI). The samples were chosen as a subset of the survey population with the most complete phenotype data on the traits measured. Samples from the South African Zulu study were genotyped on the consortium-driven Illumina HumanOmni Multi-Ethnic GWAS/Exome Array (MEGA precommercial v1) using the Infinium Assay. Quality control was carried out collectively, with sample QC including filtering for called proportion (<97%), heterozygosity (>4SD from mean), sex check fails (F statistic <0. 8 for men, and >0.
Body shape and cardiometabolic traits Based on the Kaiser rule, we chose to retain the first and second PCs after PCA. However, in our analysis, we focused on the first PC in Ugandans as it was similar to the first PC in the South African Zulu dataset. We further evaluated whether our PC1 in both cohorts are associated with cardiometabolic traits, we then performed a linear regression analysis in both Ugandans and South African Zulu. In both cohorts, we adjusted for age and sex. We further adjusted for T2D in the South African Zulu dataset. In this analysis, our cardiometabolic traits were blood pressure (systolic and diastolic) and blood lipids (low-density lipoprotein cholesterol, triglycerides, total cholesterol, and highdensity lipoprotein cholesterol) traits. The linear regression analysis was performed using the generalised linear model (GLM) and all statistical analyses were performed using the R language  [25].
Acknowledgements SF is an international Intermediate fellow funded by the  Wellcome Trust  grant ( 220740/Z/20/Z ) at the  MRC/UVRI  and  LSHTM. TC is an international training fellow supported by the  Wellcome Trust  grant ( 214205/Z/18/Z ). MN is a  Makerere University Non-Communicable Diseases (MakNCD) Fellow. MN acknowledges the support of the  Makerere University Non-Communicable Diseases (MakNCD)   Research Training Program  supported by the  Fogarty International Center (FIC)  of the  National Institutes of Health (NIH)  under award number  1D43TW011401-01. MN and SF received previous support from the  Makerere University-Uganda Virus Research Institute Centre of Excellence for Infection and Immunity Research and Training (MUII). MUII is supported through The Developing Excellence in Leadership, Training and  Science (DELTAS) Africa Initiative  (grant  107743 ).
Study Population We investigated data from three French and Belgian prospective cohorts designed to investigate biochemical and genetic determinants of DKD in people with type 1 diabetes  [23] [24] [25]. This investigation was conducted according to the principles expressed in the Declaration of Helsinki. Study protocols were approved by the ethics committee of Angers University Hospital (Angers, France), and all participants from the three cohorts gave written informed consent. As previously published, SURGENE was a single center study of all volunteer individuals with type 1 diabetes attending the clinic of diabetology at Angers university hospital in France  [24]. From 1989 to 1996, participants were selected on the basis of a diagnosis of type 1 diabetes before the age of 40 years, and duration of diabetes more than three years.
Study Flow-Chart Among 1347 patients included in the three cohorts, we excluded participants with a baseline history of ESKD (hemodialysis, peritoneal dialysis, or kidney transplantation, n = 82), those for whom no genotyping was available (n = 51) and those without followup data regarding ESKD (59 deaths and 143 loss of follow-up, Supplemental Figure  S1 ). Hence, 1012 participants were included in the present study. We checked that minor allele frequencies of the investigated SNPs were not different in excluded participants versus those who remained in the analysis (Supplemental Table  S1 ).
Standard Biological Assessments, Clinical Conditions and Study Outcome Urinary albumin concentration (UAC) and serum creatinine were measured centrally at baseline using nephelometry and derivation of Jaffé's (with adjustment to the enzymatic method when it was introduced in routine practice) methods, respectively. Estimated glomerular filtration rate (eGFR) was computed using the Chronic Kidney Disease Epidemiology Collaboration equation. UAC was categorized at baseline as normoalbuminuria (<30 mg/L), microalbuminuria (30 to <300 mg/L) and macroalbuminuria (≥300 mg/L). eGFR data was categorized at baseline as < and ≥60 mL/min/1. 73 m 2. Diabetic retinopathy was staged at baseline as absent, non-proliferative and proliferative. The definitions of other conditions were previously reported  [26].
Dataset The data used for this research have been collected from the cryptomarket 'White House Market' (WHM). This cryptomarket, online from February 2019 to October 2021, was one of the major cryptomarkets in the Dark Web at the end of the study. The sections presented here have subsections. The subsections "Fake Document (Digital)" and "Fake Document (Physical)" are included in the section "Services". As the focus of this study is on fake documents, those two subsections are treated separately from the rest, for a total of 1103 advertisements (1. 3% of all ads) and 86 vendors (3. 4% of all vendors).
Pretreatment This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4180769 P r e p r i n t n o t p e e r r e v i e w e d To carry out the textometric analysis, we chose to use the software IRaMuTeQ 2 , which is a free software based on Python and R. It allows multiple statistical analysis and produce visualizations. It has been chosen for its ease of use and the available textometric methods. To integrate the data into the software as corpus (i. e. a set of text units to be analysed), they have to fit with a particular format, called "Alceste"  (Marpsat, 2010). First for the ads, each category is separated from the others and converted into a. txt document (UTF-8 encoding) containing the ad title, category and vendor's name. Every new text is introduced with four asterisks "****".
These are followed by the first information, here the name of the vendor, like "*_name1" and then the name of the corresponding category in the same format. These variables are called "illustrative variables", which means that they are not part of the text analysed but used to filter the dataset. The text submitted to textometric analysis is the title of the ad. The descriptions of the products in the ads have been tested in several analyses but didn't give sufficient results to be considered relevant and thus are excluded. The same process is used to prepare the corpus composed of the 86 vendors of fake documents, with their names and date of admission to White House Market as illustrative variable and their profile for the textometric analysis. In Table  2 , it is possible to see that only 69 vendors are taken into account for the "vendor_fakedoc".
Corpus Description listing_defense « Defense » section of the cryptomarket listing_drugs « Drugs » section of the cryptomarket listing_forgeries « Forgeries » section of the cryptomarket listing_onlinebusiness « Online business » section of the cryptomarket listing_services « Services » section of the cryptomarket listing_software « Software» section of the cryptomarket listing_fakedoc « Fake Document Digital/Physical» subsections listing_all_without_drugs All listings except the drugs section Section specific listing_all All listings listing_fakedoc/drugs Combination of the "fakedoc" and "drugs" corpora listing_fakedoc/forgeries Combination of the "fakedoc" and "forgeries" corpora listing_fakedoc/onlinebusiness Combination of the "fakedoc" and "online business" corpora listing_fakedoc/services Combination of the "fakedoc" and "services" corpora listing_fakedoc/software Combination of the "fakedoc" and "software" corpora Mixed listing_fakedoc/defense Combination of the "fakedoc" and "defense" corpora vendor_fakedoc Fake documents vendors with a written profile on the cryptomarket Table  2  -Description of all the corpora created from the data and integrated in IRaMuTeQ 2 http://iramuteq.
Descending Hierarchical analysis (DHA) Marpsat describes DHA as a method that allows to "give an account of the internal organization of a discourse"  (Marpsat, 2010, p. 1). After separating the forms thus obtained into two categories, the "analysable forms" (i. e. terms of the text taken into account during the analysis) and the "illustrative forms"  (Marpsat, 2010, p. 2) that having a purely descriptive value for the classes obtained from the analysable forms, the text is cut into segments. These are parts of the text of fixed size often delimited by punctuation or special characters. These text segments are then grouped together so that they contain enough analysable forms for analysis. They constitute the context of the words. They are created automatically by the software (three lines)  (Camargo et al.
DHA is performed automatically with IRaMuTeQ, and has been applied to the product's corpus "listing_fakedoc". The program took into account 1187 texts over 1321. One hypothesis that could explain this exclusion of certain texts is that the software performs a pre-arbitration in the texts, if some are too heterogeneous compared to the rest of the corpus and are therefore excluded before the analysis. It has been applied to the mixed corpora too (see Table  2 ), in order to see which categories of products can be found with this method. This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn. com/abstract=4180769 P r e p r i n t n o t p e e r r e v i e w e d Once the DHA is done, several statistics are automatically performed. The number of occurrences of every studied form (i.
She suggests that the software does not take every form as "full forms". The major problem is thus that the operator has no control of the forms or texts analysed, which is a real issue from a forensic point of view. In order to test the hypothesis, the term "id" was replaced with "identity" in the corpus. After another DHA, the term "identity" appeared in the class associated with driver licenses, with a higher number of occurrences. This finding raises the hypothesis of small words being excluded just like stop words. They may not be taken into account because of their size. However, terms such as "hq" and "dl" were taken into account in the analysis. This observation led to the fundamental methodological proposition recall by  Pincemin (2020) : "back to the text". As it helps to identify these gaps induced by analyses over which the operator's control is limited, it compensates for the "black box" effect inherent in some algorithms.
Globally, several steps of the methodology used required manual work, which leads to a certain risk of error. In the concordance table analysis, for example, it would have been difficult to estimate the number of products listed outside the fake document categories for each term studied, due to the high proportion of occurrence of each word. IRaMuTeQ did not allow for an automatic numerical estimate. The size of the corpus is also a limitation for some analyses, such as the product classification. This method requires the assess the construction of the corpus itself, in order to ensure that all forms are taken into account. Finally, some limitations come from the software used. Indeed, IRaMuTeQ is an easy-to-use software that allows to obtain good results for an exploratory analysis and provides very relevant global information.
Corpus Number of classes Top 10 words in each class (by number of occurrences) account ; warranty ; premium ; porn ; lifetime ; extra ; market ; cheap ; bonus ; month hq ; psd ; template ; card ; id ; scan ; license ; driver ; passport ; dl hq ; usa ; card ; bank ; cc ; fullz ; fresh ; balance ; email ; verify Fakedoc_onlinebusiness (N = 9813) 4 database ; record ; hack ; leacked ; plaintext ; million ; dtabase ; leak ; voter ; log psd ; template ; id ; driver ; license ; passport ; scan ; hq ; card ; statement replica ; perfect ; shoe ; vuitton ; louis ; lv ; gucci ; black ; bag ; dior series ; gold ; black ; watch ; rolex ; box ; pro ; counterfeit ; clone ; max Fakedoc_forgeries (N = 2812) 4 fakemoney ; series ; eur ; test ; pen ; pass ; uv ; usd ; version ; stripe Fakedoc_services (N = 3436) 6 book ; video ; mastery ; academy ; market ; figure ; facebook; amazon ; trade ; dan id ; driver ; license ; fake ; licence ; drive ; quality ; track ; high ; australia psd ; template ; passport ; hq ; card ; statement ; utility ; editable ; bank ; fully Fakedoc_defense (N = 1173) 3 passport ; scan ; card ; utility ; custom ; bill ; verification ; usa ; uk ; selfie gram ; quality ; ship ; free ; mdma ; cocaine ; pure ; high ; 5g ; ketamine quality ; pill ; mdma ; high ; top ; xtc ; mg ; europe ; dutch ; import ship ; pill ; mg ; xanax ; sale ; usa ; duplicate ; 10mg ; bar ; price free ; 5g ; uk ; top ; thc ; indoor ; sale ; aaa ; grade ; haze Fakedoc_drugs (N = 78725) ship ; free ; thc ; 1g ; new ; premium ; fast ; cannabis ; g ; day hq ; card ; usa ; cc ; full ; bank ; fullz ; fresh ; scan ; email account ; premium ; warranty ; hq ; extra ; market ; cheap ; bonus ; month ; access account ; premium ; warranty ; porn ; lifetime ; extra ; bonus ; video ; movie ; include hq ; psd ; template ; perfect ; full ; bank ; id ; scan ; license ; driver hq ; card ; egift ; pdf ; gift ; money ; save ; lot ; checker ; code full ; pro ; pack ; crack ; complete ; vpn ; security ; program ; gold ; adobe database ; record ; hack ; leacked ; plaintext ; million ; dtabase ; leak ; voter ; log All_sauf_drugs (N = 15844) 8 perfect ; replica ; shoe ; high ; quality ; vuitton ; louis ; lv ; gold ; gucci pro ; full ; crack ; program ; macos ; adobe ; x64 ; window ; pack ; hack full ; software ; mac ; source ; tool ; code ; bitcoin ; rat ; android ; stealer premium ; porn ; video ; account ; lifetime ; movie ; book ; private ; spotify ; proxifier Fakedoc_software (N = 3489) 4 psd ; template ; id ; driver ; license ; passport ; scan; card ; hq ; statement psd ; template ; id ; driver ; license ; passport ; statement ; fake ; utility ; usa id ; scan ; passport ; utility ; custom ; usa ; quality ; dl ; high ; bill complet ; credit ; full ; uk ; pack ; list ; delivery ; real ; utter ; service card ; hq ; egift ; pdf ; restaurant ; grill ; pizza ; italian ; group ; bar account ; lifetime ; premium ; warranty ; porn ; quality; vpn ; high ; instagram ; guarantee.
Thus, the use cases such as the detection of radicalisation can employ this tool. key-phrases are extracted from sequences of one or more words (or n-grams). The key phrase extraction algorithms consist mainly of several steps  [22] : (a) pre-treatment of the text (e. g. , parsing, POS tagging, stopword removal, stemming or lemmatizing), (b) candidate key-phrase extraction where a candidate for key-phrases are extracted from the texts, and (c) candidate key-phrase scoring and ranking for selection. The key phrase extraction algorithms differ from how they implement this essential steps  [21] [22] [23] 42]. The candidate key-phrases are represented with features such as their frequency, positions in the text, and word vectors using language models trained on open data sets such as Wikipedia to get the common context of terms.
3. Word w in the document m is probabilistically assigned a topic based on two things: • what topics are in document m • how many times word w has been assigned a particular topic across all of the document. This procedure is repeated a number of times for each document in the corpus. The number of topics depends on the size of the dataset as well as how focused it is on the subjects it presents. The number must be at least 2. As a rule of thumb, for very small datasets focused on a very specific subject (e. g. , survey experiments) 3-10 topics should suffice; small datasets (size from a few hundred to a few thousand documents) 5-50 topics should be enough; for medium-sized datasets (10,000 to 100,000 documents) 60-100 topics should work well; for larger datasets 100 topics is a common default size  [51].
Dataset and Example Use Case The data used in an analysis can come from various different sources. These can be texts from propaganda sites on the Internet (WWW or Darknet) but also posts in social media or emails (see also Section 5. 5. 2). All of these text files can contain a number of keywords or phrases that allow conclusions to be drawn about certain radicalisation processes. In the example use case that we will use to illustrate the functionalities of the software components we have developed, we consider a use-case in which "0049855" is the ID of an anonymous suspect. For this suspect, the data collected from different sources include 12 tweets from Twitter, two emails, and two articles from propaganda sites. These text data will be analysed by different tools that we developed to estimate the level of radicalisation of the suspect.
A lexicon is selected and then one or more text files (see Table  3 ) are selected. These can be stored in the database or a file system. The tweet_9. txt in the Table  3  is the sample text from the point 2 Datasets. It contains the corresponding weighted keywords from the created Lexicon. This tweet has a score of 0. 7 with a share of extremist content of 28. 91% (See Figure  6 ). It is a tweet with strongly radical content.
Text Visualisation Tool-Application of Visual Analytics for Analysis of Radicalised Content Figure  7  shows the topic model extracted from the dataset, which was introduced in Section 4. It combines artificially made tweets that represent a use case in radicalisation analysis. We also demonstrate here the application of visual analytics techniques, realised in TVT, for radical content exploration. The representation of a word co-occurrence network is presented in Figure  8. Semantically related word clusters are introduced there, which could be used for complementing specialised lexicons. In this way, analysing word cluster by cluster, useful words and word sequences can be added to the specialised lexicon for further analysis. Topic modelling results are presented in Table  4 , Figures  7  and 9.