dataesr/scientific-paragraphs-categorization huggingface dataset:
One of the θ i is (or slightly deviates from) the main azimuth of objects and reflects organization due to interactions between fractures of the same set during their growth. The Oman data set has shown that also other directions are of interest to study interactions and chronology between objects of two different sets. In a nutshell, the proposed statistical method follows these five main steps: 1. Ensure the validity of the stationarity hypothesis. 2. Build marked points for both fractures and branches. 3. Study directionality: decide whether the pattern can be considered isotropic or determine main azimuthal directions and identify sets of objects. 4. If isotropy then use g(r). Otherwise determine the g(r, θ i ) for the sets with θ i the directions of main interest. 5. Discuss the form of g(r) or of the g(r, θ i ).
Data Availability Statement Data used in this study have been taken from the literature (see, e. g. ,  Odling, 1997;  Zeeb et al. , 2013). We used SKUA-Gocad Software and Geode-Solutions (2022a, 2022b) to convert these data into a realization of marked points which are provided as supplementary data. A plugin of the OpenGeode platform named OpenGeode-PointProcess has been used for statistical analysis of marked point process. This software belongs to ASGA (Association Scientifique pour la Geology et ses Applications), therefore is not publicly available. ASGA manages the RING Consortium (www. ring-team. org/consortium) and provides access to the research material only to its members/sponsors. Tools and parameters are presented in detail in the paper (see Appendix) and most of the analysis can be done using SpatStat R package with the guidance of  Baddeley et al.
25-m-initialradius spiral trajectory in which the robot EE was loaded with a 600 N deadweight-type force (Figure  14a ). The continuous trajectory had a length of 2. 7 m, the EE motion speed was 10 mm/s, and the raw laser tracking data were gathered at 50 Hz. The plane in which the spiral was located was 0. 2 m above the working area used for the ISF application (Section 4), resulting in 0 p z = 0. 3 m. As one can see in Figure  14b , the presented model confirms an accurate prediction of the elastic behavior over a given trajectory.
Hardware Implementation of the Experimental Tests A schematic view of the real-time pose control system, developed and proposed in this study, is shown in Figure  16. As can be seen, the tool-path trajectory is computed offline from the part CAD model via a CAM software. RoboDK software was then used to convert motion planning into instructions in the Stäubli-specific robot-controller-programming language VAL3. The robot program was sent offline to the robot CS8 internal controller via a TCP/IP file transfer protocol. The control algorithm was deployed in a National Instruments CompactRIO controller using the programming language LABVIEW. Two NI9215 modules collected data from the six channels of the ATI Omega-160 6-DoF load cell to determine the external wrench w acting on the forming tool.
The measurement sampling frequency was set at 100 Hz. In the LABVIEW environment, the data were processed by the external control loop (Section 2. 1) to reconstruct ŝreal based on the elasto-geometrical model (Equation (  16 )) and then compute the pose correction ξ. The resulting correction vector was sent directly to the robot internal controller through the TCP/IP protocol at a frequency of 100 Hz. This communication was compatible with the CS8 industrial robot controller, running at a fixed internal frequency of 250 Hz. Path corrections were handled online by the robot controller via the Alter function pack available in the VAL3 language. Alter functions allowed the robot trajectory to be modified from the external controller data at the frequency of 250 Hz. During the forming process, the real tool path was measured using the API Radian laser tracker, only to validate the control strategy.
Methods and findings We conducted a systematic review with meta-analysis (prospectively registered protocol CRD42020200752). Three databases (MEDLINE, Embase, and Web of Science) and information sources including grey literature (Google Scholar, OpenGrey, EThOS, Pro-Quest Dissertations & Theses) were searched. Eligible articles were published during 2009 to 2022 and reported the duration of the following intervals in adult patients diagnosed with primary symptomatic cancer: patient interval (from the onset of symptoms to first presentation to a healthcare professional), diagnostic interval (from first presentation to diagnosis), and treatment interval (from diagnosis to treatment start). Interval duration was recorded in days and study medians were combined in a pooled estimate with 95% confidence intervals (CIs).
Literature search Following published recommendations for optimal database selection  [29]  and in close collaboration with the first author, a librarian designed and implemented a search strategy in MED-LINE (via Ovid), Embase, and Web of Science (WOS)-Core Collection. The strategy was initially designed for MEDLINE (Ovid), which combined MeSH terms and keywords, and subsequently adapted for the rest of bibliographic databases including the use of EMTREE controlled vocabulary in Embase database. Other sources of information were also explored to identify grey literature (Google Scholar, OpenGrey, EThOS, and ProQuest Dissertations & Theses). The full search strategy, informed by the PRISMA-S extension  [30] , is available in S1 Text. The period searched was initially from January 1, 2009 to September 1, 2020 and was then updated until May 19, 2022, following initial peer review.
Inclusion criteria Studies reporting data on the length of any of the 3 intervals of interest for any cancer site in adult patients with cancer presenting with primary cancers were included. The intervals were defined according to the Aarhus statement  [4]. The patient interval was defined as time from the date of first symptom to the date of first presentation, i. e. , first contact with a healthcare professional. The diagnostic interval was defined as time from the date of first contact with a healthcare professional to the date of diagnosis. Finally, the treatment interval was defined as time from the date of diagnosis to the date of start of the first treatment. In the case of the patient and diagnostic intervals, only studies of symptomatic patients were considered (i. e. , excluding screening or accidentally detected cancers).
Data extraction This was performed in the Covidence tool (study and population characteristics) and in a spreadsheet (statistical results) by 2 reviewers. Disagreements were resolved through discussion or a third reviewer (except for articles considered after the literature search update, where the first author acted as an arbiter in case of disagreement). For each study, we recorded year of publication, country, total number of patients, study setting, data sources, study design, inclusion and exclusion criteria, cancer site, type of interval studied, and participant characteristics. For each interval, the following statistical information was recorded if available (in days): median, interquartile range, minimum, maximum, mean, standard deviation (SD), sample size (N), country, year of start and end of data collection (data were recorded separately for different years if reported per year), cancer site, specific diagnosis, mode of diagnosis confirmation, and type of first treatment if specified (relative to the treatment interval).
Country socioeconomic indicators To separate countries into high-and lower-income economies, 2 socioeconomic country indicators were extracted for each study by an expert health economist: the gross national income (GNI) and the Human Development Index (HDI) (see S2 Text for details). Following a previous meta-analysis  [32] , the indicators were extracted for each study according to the respective country and year in which data were collected, to represent the country's development during the time of diagnosis and treatment. In addition, to further explore variability within high-income countries only, we extracted the Index of Cancer Preparedness (ICP): Policy and Planning  [33]. This index offers a quantitative measure of the quality of policies aimed to control cancer based on multiple indicators such as the existence and comprehensiveness of a national cancer plan, cancer registries, policies regarding tobacco control, lifestyle and diet, and cancer research, among others (see S2 Text for details).
Statistical analysis As expected, the most often reported statistic for the duration of the intervals was the median, and meta-analysis was conducted with the "metamedian" package (v. 0. 1. 5) in R (v. 4. 1. 1) and following McGrath and colleagues  [27]. Specific study medians (or means in a minority of occasions, when medians were not reported, as per McGrath and colleagues  [27] ) were combined in a pooled median, and 95% confidence intervals (CIs) were calculated  [27]. The metaanalytical methods available for medians do not provide an estimate of heterogeneity; however, we used the "median of medians" method, which is more suitable for heterogenous data  [27]. Specific cancer sites were further grouped following the categorization of the National Cancer Institute  [34]  into the following main cancer groups: acquired immunodeficiency syndrome (AIDS)-related, breast, digestive/gastrointestinal, endocrine/neuroendocrine, genitourinary, gynecologic, head and neck, hematologic/blood, musculoskeletal, neurologic, respiratory/thoracic, skin, and unknown primary.
Third, these analyses were complemented with a random-effects metaregression analysis in the "metafor" (v. 3. 0. 2) package in R  [35]. In this analysis, the study-specific medians were declared as a "GEN" measure, and studies were weighed analogous to weighting in the "metamedian" package (proportional to the number of subjects and normalized). The GNI group (high versus lower) and the HDI score (continuous and centered at the mean) were individually tested as moderators. We extracted p-values for the moderator tests and the percentage of variance explained by the moderator (R 2 ). To further explore variability within high-income countries only, we conducted analogous analyses using the ICP: Policy and Planning index only considering studies conducted in high-income countries (based on GNI).
To compare and estimate the relative contribution of the different intervals, following previous studies  [19] , we calculated the ratios between the different intervals. Specifically, because the diagnostic interval was the longest interval for the majority of cancers, following methods by Bonett and Price  [36] , we calculated the ratios of the diagnostic to the patient interval (DI/ PI), the diagnostic to the treatment interval (DI/TI), and the patient to the treatment interval (PI/TI) with their respective 95% CI. This was only done for studies that reported the duration of all 3 intervals in the same sample of patients and intervals were considered to be significantly different when the 95% CI for their ratio excluded 1. Sensitivity analysis included repeating the main analysis after excluding studies with high risk of bias according to the Aarhus checklist and after excluding studies that did not report the median and the mean was therefore imputed as median (even though the "metamedian" package can reliably estimate a pooled median when the mean is reported instead of the median for a small proportion of studies, in our case, this was n = 52 (14%), n = 15 (5%), and n = 70 (15%) for the patient, diagnostic, and treatment interval, respectively).
Results Initially, 12,140 records were retrieved and 410 articles were finally included in the review. ), the most frequently represented being the United States (22%), United Kingdom (8%), Canada (7%), and the Netherlands, Denmark, Australia, and Spain (4% each). The majority of articles reported data from high-income countries (294 or 72%), with 116 (28%) reporting on lower-income countries: 48 (12%) reporting data on upper-middle, 43 (10%) on lower-middle, and 24 (6%) on low-income countries. The majority of studies (88%) were cross-sectional studies. S4 Table contains detailed information about each included study, including specific inclusion and exclusion criteria, study setting, number of participants, design, and data sources. The studies included a total of 5,537,594 patients: 85,609 with data on the patient, 266,331 on the diagnostic, and 5,166,938 on the treatment interval, respectively.
In particular, 146 (36%) used questionnaires or interviews (with patients or health professionals), 215 (52%) used medical records, and 141 (34%) used large databases (e. g. , population-based cancer registry data, the USA National Cancer Database (NCDB), Surveillance, Epidemiology, and End Results (SEER), or similar). Use of questionnaires or interviews was more frequent in studies from lower-income countries (n = 77, 66% versus n = 69, 23% of studies from highincome countries), whereas use of large databases was more frequent in studies from highincome countries (n = 137, 47% versus n = 4, 3% of studies from lower-income countries). The use of medical records was relatively similar (n = 150, 51% for high-income, and n = 65, 56% for studies from lower-income countries, respectively). The mean risk of bias score for the sample was 51% (SD = 22%).
Fewer studies were available from lower-income countries, especially reporting on the diagnostic and treatment intervals. Information on these intervals came mostly from high-income countries that have powerful health information systems in place to record, monitor, and analyze such information (e. g. , population-based cancer registries, national healthcare databases, and complete and often easily accessible medical records). For example, studies reporting treatment intervals were mostly based on such information systems and frequently had very large sample sizes (in the thousands), offering representative data. The expansion and creation of cancer registries or large cancer epidemiological databases in lower-income countries as an investment in national cancer control planning is one of the priorities suggested to reduce cancer care disparities worldwide  [22].
Future reviews should consider the subintervals that compose the patient (e. g. , appraisal versus help-seeking interval) and diagnostic (e. g. , primary care interval versus referral to diagnosis interval) intervals to offer a more comprehensive understanding of the patient journey to diagnosis in different contexts. Finally, we did not differentiate between middle-, lower-middle, and low-income countries and grouped them together as "lower-income" economies due to the relatively small number of studies available. However, previous reviews show that there may be important differences in interval duration within this group  [20]. In an effort to reduce publication bias, we searched several databases that contain grey literature and considered publications in multiple languages. However, because the data pooled into meta-analysis is descriptive and not based on significance testing, formal tests for publication bias (e.
Acknowledgments We thank  Dr. Yasmina Okan  for her participation in the formulation of the review protocol and the shortened form of the Aarhus checklist. We thank  Dr. Elena Salamanca-Ferna  ´ndez for her help with abstract screening. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization.
Indeed, the putative macromolecular target(s) of a small chemical compound can be predicted by identify-ing ligands that are known experimentally to bind to some targets and that are structurally similar to the input query chemical compound. Therefore, this type of target fishing approach relies on a large collection of experimentally validated macromolecule-chemical compound binding data. The small chemical compounds can be described as molecular fingerprints encoding their structural characteristics as a vector. The published version of FastTargetPred used ligandtarget binding data extracted from the release 25 (2019) of the ChEMBL database. Here we provide a new dataset for FastTargetPred extracted from the last ChEMBL release, namely, at the time of writing, ChEMBL29 (2021). Four fingerprints were computed (ECFP4, ECFP6, MACCS and PL) for the extracted compound dataset (714,780 unique ChEMBL29 compounds while the entire ChEMBL29 database contained about 2.
1 million compounds). However, it was not possible to compute fingerprints for 19 molecules because of their unusual chemistry (complex macrocycles). These data files were then prepared so as to be compatible with FastTar-getPred requirements. The 714,761 ChEMBL chemical compounds with computed fingerprints hit 6,477 macromolecular targets based on the selected criteria. For these ChEMBL compounds a ChEMBL target ID is reported and these target IDs were matched with the corresponding UniProt IDs. Thus, when available, the UniProt ID is provided, the protein UniProt name, the gene name, the organism as well as annotated involvement in diseases, gene ontology data, and cross-references to the Reactome pathway database. As short peptides can be of interest for drug discovery and chemical biology endeavours, we were interested in attempting to predict putative macromolecular targets for a previously reported exhaustive combination of peptides containing four natural amino acids (i.
e. , 20 × 20 × 20 × 20 = 160,0 0 0 linear tetrapeptides) using FastTargetPred and the presently generated ChEMBL29 dataset. With the parameters used, putative targets are reported for 63,944 unique query peptides. These target predictions are provided in two different searchable files with hyperlinks to the ChEMBL, UniProt and Reactome databases. © 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Type of data Text files (CSV, TXT) DataWarrior files How the data were acquired Chemical compounds and corresponding macromolecular targets were extracted from the ChEMBL29 database  [1]. ChEMBL target IDs were mapped onto the UniProt target IDs  [2]  so as to collect UniProt protein names, gene names, possible involvements in diseases, gene ontology data  [3]  and pathway identifiers present in the Reactome knowledgebase  [4]. ( continued on next page ) 160,0 0 0 linear tetrapeptides (SMILES strings) were downloaded from  [5]  and merged into a single file. Putative targets for these peptides were predicted using FastTargetPred  [6]  and the newly generated ChEMBL29 dataset. The DataWarrior software  [7]  was used for data-visualization. Chemical file curation and computations of fingerprints were performed with MayaChemTools  [8].
Description of data collection The SQLite version of the ChEMBL29 database was downloaded. SQLite terminal shell commands were applied to extract an initial data file (this raw data file is provided in the extra_data folder). Canonical SMILES strings were extracted for small molecules associated with a biological assay involving a single protein or a protein complex, and the selected assay type was "binding". Only molecules with experimental potency/affinity/activity data (pChEMBL_value) corresponding to 20 micro-molar or less were selected. This initial raw file contained 1,412,822 compounds. Additional filtering involved selecting molecules with a ChEMBL confidence_score of 6 or above, manual curation of mixtures and removal of compounds with obvious errors. Salts were removed with MayaChemTools.
Value of the Data • This dataset should be valuable to research groups interested in carrying out macromolecular target predictions or in investigating bioactive molecules. • These data could be integrated into other tools and manipulated by different types of algorithms. • These data used together with FastTargetPred or related tools could be useful for understanding the polypharmacology and safety profile of existing or virtual small molecules. • Data generated on tetrapeptides could be useful as a starting point for drug discovery projects or chemical biology studies. • Users can enrich these data by performing in-vitro screening campaigns or by mining other repositories.
Data Description To be able to carry out target prediction via ligand-based similarity search, a large collection of experimentally validated ligand-target pairs must be generated  [10] [11] [12]. We extracted such annotated data from the last version (at the time of writing) of the ChEMBL database (release 29)  [1]. A first initial file (raw data) was thus generated containing 1,412,822 compounds (file located in the extra_data folder). Further filtering of the compounds was performed to remove annotation errors, molecules that did not have a high ChEMBL confidence_score, and a pChEMBL value. Salts and duplicates were also removed using Unix text mining commands, Open Babel  [9]  , and MayaChemTools  [8]  with visualization in DataWarrior  [7]. These steps led to the generation of a filtered file with 714,780 compounds in SMILES format with the associated ChEMBL compound IDs (file located in the folder named "extra_data").
Additional information were extracted via the UniProt server such as gene name, gene ontology data, possible involvement in diseases for each target (if known) and Reactome pathway identifiers. This file is required and is named uniprot_database_ChEMBL. csv. For a user, upon installation of FastTargetPred ( https://github. com/ludovicchaput/FastTargetPred) and MayaChem-Tools ( http://www. mayachemtools. org/) , it will be necessary to go to the main FastTargetPred folder, and then launch a basic command to search targets for a ligand in SDF format, for instance: python3 FastTargetPred. py rivaroxaban. sdf (the file containing the ligand rivaroxaban is provided in the extra_data folder). This command uses the previously generated data for FastTargetPred extracted from a previous release of ChEMBL (release 25) (the ChEMBL25 files are in the "db" folder).
Now, the user can download the newly generated files extracted from ChEMBL (release 29) and put the folder "dbchembl29" at the same level than the "db" folder and run the following command: python3 FastTargetPred. py rivaroxaban. sdf -fp ECFP4 -tc 0. 7db dbchembl29/chembl29_active. This will allow the package to search for the new data present in the "dbchembl29" folder using all the files that start with chembl29_active and the CSV file. In this example the Tanimoto coefficient is set to 0. 7 (i. e. , with such a value and with this fingerprint one is looking for molecules, and their corresponding targets, that should be very similar to rivaroxaban as well as rivaroxaban itself as it is present in the ChEMBL29 dataset). Note that several fingerprints can be used and that a consensus score can be generated (please see the FastTargetPred user guide presents in the package).
Applying ligand-based similarity search to identify targets for peptides (e. g. , short peptides, chemically modified peptides that are not easy to investigate using traditional amino acid sequence search, etc. ) is still underexploited. We decided to apply the newly extracted data and FastTargetPred to 160,0 0 0 linear tetrapeptides previously released in SMILES format (available here: https://data. mendeley. com/datasets/z8zh5rpthg/1) [5]. Putative targets were found for 63,944 query peptides using ECFP4 fingerprints and a Tanimoto coefficient of 0. 6 (that we evaluated to be reasonable for this type of investigation with FastTargetPred  [6]  ). Clearly, additional investigations will be needed but for now, some of these predictions can be used to generate novel hypotheses. All the results are reported in two DataWarrior files present in the "tetrapeptides" folder.