dataesr/scientific-paragraphs-categorization huggingface dataset:
07 dex, much less that the reported intrinsic error. Figure  3  confirms the reliability in the detected Si I lines, where the spectra of some Si-rich and Si-normal stars are compared in the relevant wavelength intervals. The Si-rich stars have remarkably stronger Si l lines which, in view of the similarity between the pairs of stars in all the other relevant parameters, can only mean that they have much higher silicon abundances. Figure  1  also indicates that the BACCHUS code recovers the [Fe/H] abundance ratios of these stars within ∼0. 12 dex for both the adopted photometric and spectroscopic temperatures. The adoption of a purely photometric temperature scale enables us to be somewhat independent of the ASPCAP pipeline, which provides important comparison data for future pipeline validation.
Stars in the Jurassic Structure We then label all stars with silicon over-abundances more than +3σ [Si/Fe] above the [Si/Fe] bin at fixed metallicity as Si-rich, which is the same as the selection in  Fernández-Trincado et al. (2019a). This returns 55 stars as potential Si-rich stars relative to the final data set, which we have called the Jurassic structure. Our first selection was simply based on ASPCAP abundances, however, for several other issues that might affect the abundance determinations in the metal-poor regime (see e. g. ,  Jönsson et al. 2018) , we have decided to adopt a detailed manual examination and visual inspection, in order to ensure that the spectral fit was adequate for those stars by using the Brussels Automatic Stellar Parameter (BACCHUS) code  (Masseron et al. 2016)   the chemical abundances of our sample, by adopting a simple line-by-line approach of selected atomic and molecule lines, under the assumption of local thermodynamic equilibrium (LTE), and using the standard iron ionization -excitation equilibrium technique.
Statistical Significance With our large sample, we revisit the statistical significance of the Jurasicc structure over a wide range of metallicities for an unprecedented homogeneous dataset, together with other previously identified Si-rich stars from the literature and highlighted as black filled "star" symbols in  All of them (with the exception of one star from Schiavon's sample and a few N-rich stars from the Fernández-Trincado's study) exhibit typical enhancement in Al (e. g. , [Al/Fe]> +0. 5), wellabove the typical Galactic levels. Note that in  Fernández-Trincado et al. (2019a)  we searched for Si-stars exclusively enriched in Al ([Al/Fe] +0. 5), as aluminum appears to be one of the most effective chemical tags for GC-like abundance patterns among metal-poor stars (such a large enrichment in Al has not been observed in dwarf galaxy stellar populations; see  Shetrone et al.
Orbits We used the GravPot16 1 code to study the Galactic orbits of the stars in the Jurassic structure. For this purpose, we combine accurate proper motions from Gaia DR2 (Gaia  Collaboration et al. 2018; Lindegren et al. 2018) , radial velocities from APOGEE-2+  (Nidever et al. 2015; Majewski et al. 2017; Ahumada et al. 2020) , and distances from StarHorse  (Anders et al. 2019; Queiroz et al. 2018 Queiroz et al. , 2020b,a) ,a)  as input data in our model. Only stars with a re-normalized unit weight error  (Lindegren et al. 2018) , RUWE< 1. 4 (stars with reliable proper motions) were considered in our orbital analysis. The Galactic potential assumed in these calculations is the non-axisymmetric MW-like potential, which considers perturbations due to a rotating boxy/peanut bar. This potential fits the structural and dynamical parameters of the Galaxy, based on recent knowledge of our MW.
Concluding Remarks Using the internal APOGEE-2+ data set, we confirm and reinforce the existence of a distinct stellar population in the inner halo of the MW-the Jurassic structure. Here, we report on the identification of 55 new Si-rich, mildly metal-poor stars, 45 of which exhibit high [Al/Fe] +0. 5, making it unlikely that dwarf galaxy satellites could have contributed the majority of these stars. We find some dynamical evidencethat a few of them, in particular the stars in the Jurassic structure with [Al/Fe] +0. 5, could be associated with the accreted GSE dwarf galaxy and/or GCs of the GSE progenitor(s). We also find that the large majority of the new stars in the Jurassic structure exhibit chemical and dynamical properties similar to that of in-situ GCs, supporting the picture that these peculiar stars have likely been dynamically ejected into the inner halo by dissolved GCs.
Acknowledgements. The author is grateful for the enlightening feedback from the anonymous referee. J. G. F-T is supported by  FONDECYT  No. 3180210. T. C. B. acknowledges partial support for this work from grant  PHY 14-30152 :  Physics Frontier Center / JINA Center for the Evolution of the Elements (JINA-CEE ), awarded by the  US National Science Foundation. D. M. is supported by the  BASAL Center for Astrophysics and Associated Technologies (CATA)  through grant  AFB 170002 , and by project  FONDECYT  Regular No. 1170121. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Argo is a real-time global ocean in situ observing system. It provides thousands of highly accurate ocean measurements every day. The Argo dataset has now accumulated more than 2. 3 million vertical ocean profiles and accessing it for scientific analysis remains a challenge. The Argo expert community, focused on delivering a curated dataset of the best scientific quality possible, has never provided its user base with a Python software package to easily access and manipulate Argo measurements: the argopy software aims to fill this gap. The argopy software can be used to easily fetch and manipulate measurements from Argo floats. It is dedicated to scientists without knowledge of the Argo data management system but is also designed to accommodate expert requirements.
Introduction The ocean is a key component of the Earth's climate system. It therefore needs continuous real-time monitoring to help scientists better understand its dynamics and to predict its evolution. All around the world, oceanographers have managed to join their efforts and set up a Global Ocean Observing System among which Argo is a key component. Argo is a global network of nearly 4000 autonomous probes measuring pressure, temperature and salinity from the surface to 2000m depth every 10 days. The localisation of these probes is nearly random between the 60 o parallels (see live coverage here). Data from the probes are collected by satellite in real-time, processed by several data centers, merged in a single dataset (comprising of more than 2. 3 million vertical profiles as of June 2020) and made freely available to anyone through an ftp server or monthly zip snapshots.
The Argo international observation array was initiated in 1999 and soon revolutionised our perspective on the large scale structure and variability of the ocean by providing seasonally and regionally unbiased in situ temperature/salinity measurements of the ocean interior, key information that satellites can't provide  (Riser et al. , 2016). The Argo array reached its full global coverage (of 1 profile per month and per 3x3 degree horizontal area) in 2007, and pursues its evolution to fulfil new scientific requirements  (Roemmich et al. , 2019). Argo data have been used in more than 4000 scientific publications. This online figure shows the current coverage of the network. It now extends to higher latitudes than the original ±60 o and some of the floats are able to profile down to 4000m and 6000m.
New floats are also equipped with biogeochemical sensors, measuring oxygen and chlorophyll for instance. All these evolutions of the network increase the total number of floats to nearly 4000. Argo is thus providing a deluge of in situ data: more than 400 profiles per day. Each Argo probe is an autonomous, free drifting, profiling float, i. e. a probe that can't control its trajectory but is able to control its buoyancy and thus to move up and down the water column as it wishes. Argo floats continuously operate the same program, or cycle, illustrated Figure  1. After 9 to 10 days of free drift at a parking depth of about 1000m, a typical Argo float dives down to 2000m and then rises back to the surface while profiling -measuring pressure, temperature and salinity. Once it reaches the surface, the float sends by satellite its measurements to a data center, where they are processed in real time and made freely available on the web in less than 24 hours.
Why argopy ? For non-expert users of the Argo dataset, it is rather complicated to get access to Argo measurements. Even though data are made freely available on the web, the Argo dataset consists of thousands of files organised using jargon, tens of different variables and many reference tables. The exhaustive Argo user manual is more than 100 pages long, which can be rather intimidating to go through for new users. This complexity arises from the fact that Argo operates many different models of floats and sensors, quality control of in situ measurements from autonomous platforms requires a lot of complementary information (meta-data), and the Argo data management workflow is distributed between more than 10 Data Assembly Centers all around the world. The Argo data management is a model for other ocean observing systems and constantly ensures the highest quality of scientific measurements for the community  (Wong & coauthors, 2020).
The result of this tremendous success in data management -in developing good practices and well calibrated procedures (see all the Argo Data Management Team documentation here)is a very complex Argo dataset: the argopy software aims to help users navigate this complex realm. Since the Argo community focuses on delivering a curated dataset for science, software packages exist for Argo data operators to decode and quality control the data (e. g. Detoc, Thepault, Carval, Mahoudo, & Garo, 2017). However, no open source softwares are available for scientists, who therefore must develop their own machinery to download and manipulate the data. Python is becoming widely used by the scientific community and beyond: worldwide, and is the most popular and fastest growing language in the last 5 years (20%, source: http: //pypl.
Data fetching argopy provides a trivial fetching API of Argo data through a simple call to one of the 3 different ways to look at Argo data: over a space/time domain (with the region access point), for one or a list of specific floats (given their unique WMO number with the float access point) or for one or a list of float profiles (with the profile access point). This is as simple as: from argopy import DataFetcher as ArgoDataFetcher fetcher = ArgoDataFetcher(). region ([-75, -45, 20, 30, 0, 100, '2011', '2012'] ) ds = fetcher. to_xarray() Here we used argopy to fetch data between 75/45W, 20/30N, from 0 to 100db and for the entire year 2011. Once the user has defined what they need (the fetcher class instance in the example above), argopy will fetch data online and manage internally all the complicated processing of formatting the web request and creating a workable in memory data structure (the to_xarray() call above).
Data formatting argopy aims to thrive in providing Argo data to non-experts. One key feature of argopy is the option for selecting a user mode that is either standard or expert. Standard users are those who want to focus on the measurements for scientific analysis; those who do not know, or don't want to be bothered with all the Argo jargon and multitude of variables and parameters. For standard users (the default mode), argopy internally runs a series of processes that curate the raw data and provide a simplified and science focused dataset. For expert users, argopy will apply its data model to raw fetched data and return Argo variables that experts users are already used to. And more argopy has features to manipulate Argo data, for instance: • the possibility to transform data from a collection of measurements to a collection of vertical profiles, and vice-versa; • the possibility to interpolate irregularly sampled measurements onto standard pressure levels.
Another feature is the ability to cache fetched data, so that requests provide users with data much more rapidly, saving bandwidth and time. Two last important features of argopy to mention here are: • the possibility to fetch data locally, from a user copy of the entire or subset of the Argo database, • the possibility to fetch only meta data (organised in index lookup tables), which allows the user to determine the regional Argo sampling, for instance. These more advanced features may be more of interest for expert users, since they require more knowledge of the Argo dataset.
Bioethanol production Flaks of Czapek -OMW mineral medium were inoculated with F. oxysporum (76) and F. solani (102) and statically incubated at 25 C for 7 days. Culture broths were recovered, centrifuged at 10 000 rpm for 10 min, 0. 2 mm aseptically-filtered and supernatants analyzed with High-Performance Liquid Chromatography (HPLC) system for ethanol, sugars, and carboxylic acids produced during fermentation. All samples were triplicated. F. oxysporum (76) was selected for one-week batch fermentation in a bioreactor using OMW as substrate and cellulose as control. 5 L Stirred-Tank Bioreactor (STR) (Global Process Concept BIO, France) was connected to a GX control station and monitored online using C-BIO software. It was inoculated at 6. 25 % v/v inoculation ratio from one-week mycelial culture, temperature maintained at 25 C (online probe control).
Acknowledgments Hasna Nait M'Barek  thanks  Angéla La  and  Barbara Malinowaska  for their guidance and support during the CHNS-O elemental analysis and  Cédric Guerin  for the HPLC analysis. OLEA  FOOD Company  is acknowledged for supplementing the Olive Mill Waste used in this study. The  IUC  project financed by  VLIR-UOS ,  Moulay Ismail University, and Laboratoire Génie des Procédés et Matériaux (LGPM) ,  Ecole CentraleSupélec , are both acknowledged for their financial support. This study was in part carried out in the  Centre Européen de Biotechnologie et de Bioéconomie (CEBB) , supported by  Région Grand Est, Département de la Marne ,  Grand Reims  and the  European Union.
Study design We used the database constructed for the ECOGEN study, a French, nationwide, multicentre, cross-sectional study conducted in general practice that described the distribution of health problems managed in general practice and the associated processes of care, as a source for secondary analyses  [13]. The investigators were 54 interns from 27 medical schools who acted as passive observers on the days of data collection and reported the regular practice of their 128 GP supervisors. They had been trained to identify health problems and associated care processes, and to use the International Classification of Primary Care (ICPC-2)  [14].
Data collection Over 20 days distributed between December 2011 and April 2012, the interns collected data for each consultation, irrespective of the reason(s) for the encounter. The limited time frame was imposed by the rotation period of the interns in the office of supervising GPs. The following data were collected: consultation locations (office or home visit) and duration; patient age, gender, medical fee exemption status for low income (full financial coverage by the national public healthcare insurance for individuals on low income) as a marker of social inequalities in health; health problem assessments and the care processes (performed or ordered during the consultation) associated with each of these, along with a free-text description. The ECOGEN database recorded only the health problems managed, provided that the patient encounter involved its management through at least one care process.
The care processes included various preventive, screening, diagnostic, curative, administrative, and coordinative tasks. Data were collected on a paper BOX 1. Cancer screening and management in the French healthcare system. Organised screening  [5]  Breast cancer: mammography is recommended every two years in women aged 50-74 years. A prescription is sent to the patient's home. The general practitioner (GP) usually receives the result. The awareness month is Pink October. Colorectal cancer: a faecal immunological test is recommended every two years in patients aged 50-74 years. An invitation to consult the GP is sent to the patient's home. The GP explains the screening procedure and hands over the equipment. The awareness month is Blue March. Opportunistic screening Cervical cancer: a smear is recommended every three years in women aged 25-65 years.
Cancer treatment and follow-up Cancer care is 100% covered by public health insurance through exemption status for the care of chronic severe diseases ('long-term disease status'). Cancer treatments such as surgery, chemotherapy, radiotherapy, or immunotherapy, are delivered in hospitals. GPs work in private practices. Each patient has a regular physician of his or her choosing. This physician can be involved in cancer diagnosis, follow-up of cancer and its comorbidities during the treatment phase and after, in collaboration with cancer specialists. In particular, the GP can prescribe sick leave and hormone therapy, and provide psycho-social care. case report form (CRF) filled out by the intern at the end of each consultation, and the data collected were later entered into a central database via a dedicated website.
Data retrieval and statistical analyses Data were managed and analysed using SAS software (version 9. 4, SAS Institute Inc. , Cary, NC, USA). The relational database underwent quality control to detect missing or inconsistent values. Since each procedure code was linked to a health problem assessment code (according to the ICPC-2 classification), we could identify screening procedures as those linked to health problem assessments coded A98 (Health maintenance/ prevention). As ICPC-2 codes are sometimes not sufficiently granular, we complemented code retrieval by using the associated free-text description to improve the specificity of the screening procedures and cancer diagnoses. The search algorithms for identifying cancer screening and follow-up situations are presented in Supplementary Appendix 1.
Among other health problem assessments managed during the consultation, chronic conditions were determined based on a subclassification of ICPC-2. When the reported consultation duration was longer than one hour, we considered this variable as missing data. We compared categorical data using Pearson's Chisquare test, and numerical data using Student's t-test. Also, we compared the number of problem assessments and chronic conditions as well as the duration of the consultation, after adjustment on patient age and gender, using a multivariate, hierarchical, linear mixed-effects model. This model considers the data structure and controls any confounding effect of age and gender. All tests with p-values less than 0. 05 were considered statistically significant.
Strengths and limitations To the best of our knowledge, this is the first quantitative study of the overall cancer-related activity of GPs based on detailed consultation data. As reported elsewhere, the quality of data entry in the database used herein was assessed through double entry in the database of a subsample of 987 CRFs (4. 7%); there was no significant difference in the mean number of problems managed (mean difference: 0. 002; p ¼ 0. 69) but a different code was recorded for 3. 2% of the problems  [13]. The practices involved were well distributed across metropolitan France, apart from the south-western part of the territory, and this could influence screening and follow-up of skin cancers  [13]. It is also of note that the data were collected in the early part of the present decade, but the French screening system has been stable during the last eight years  [16].
Study Population, Sample Selection, and Sample Size. For objective 1, we used data of all TB patients diagnosed in the city of Harare disaggregated by age group from 2009 to 2018. For objective 2, all children (0-14 years) diagnosed with any form of TB in the city of Harare from January 2015-December 2017 were included in this study (without any exclusion). e study included children with drugsensitive TB as well as drug-resistant TB.
Data Variables, Sources of Data, and Data Collection Form. For objective 1, the data variables included yearwise total number of all TB patients and number of childhood TB patients registered (disaggregated by age (0-4 years, 5-14 years, and gender), and corresponding estimated midyear population of 0-14 years). e source of data was province annual/quarterly TB case finding reports obtained from the provincial TB office and the estimated midyear population of 0-14 years from city of Harare population records. e data were collected on a structured proforma by the principal investigator. For objective 2, the data variables included: TB number, age, gender, type of residential area, HIV status, ART status (if HIV positive), type of TB (new, retreatment, and MDR), disease classification (pulmonary/extra-pulmonary), treatment regimen, and TB treatment outcome (treatment completed/cured, lost to follow-up, death, failure, transferred out, and not evaluated).
All TB-related definitions were according to the WHO definitions and reporting framework for tuberculosis-2013 revision)  [18]. e source of data was from the district TB register and facility treatment registers. Data were collected into a paper-based structured data collection form. For objective 1, we conducted a trend analysis of the following childhood TB notification measures: annual number of childhood TB patients notified; proportion of childhood TB patients among all TB patients notified; and annual childhood TB notification rates (number of TB patients per 100,000 persons aged 0-14 years). Linear and nonlinear regression models were used to assess the trends. We assessed for model fit using goodness of fit (R 2 ) values, and the best fit model (linear or nonlinear) was used for making inferences on the trends.
Acknowledgments e authors are grateful to the  City of Harare  staff members and nurses at the city clinics for their assistance in data collection. is research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership led by the  Special Programme for Research and Training in Tropical Diseases at the World Health Organization (WHO/TDR ). e training model is based on a course developed jointly by the  International Union against Tuberculosis and Lung Disease ( e Union)  and  Medécins sans Frontières (MSF). e specific SORT IT programme resulted in this publication was implemented by the  Centre for Operational Research, e Union, Paris, France. Mentorship and the coordination/facilitation of this particular SORT IT workshop was provided through the  Centre for Operational Research, e Union, Paris, France ; the  Department of Tuberculosis and HIV, e Union, Paris, France ; e Union, Zimbabwe Office; e Union, South East Asia Office; and  AIDS & TB Department ,  Ministry of Health & Child Care, Harare, Zimbabwe.