dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental setup. Movements were recorded at 100 Hz with 8 infrared optical cameras from the Vicon Motion Capture System (Vantage V5, lens 8. 5 mm, Oxford Metrics, UK). VICON Nexus 2 software was used to save time series of each marker. The experimenter placed markers on the target and the manubrium, and for each body side on the 1st metacarpal, the lateral epicondyle of the humerus, the acromion process, and the iliospinal. For each side, we corrected iliospinal marker position before data analysis to correspond at best to the anatomical centre of hips joints. Markers of both sides were needed to compute shoulder and trunk angles.
Third, we computed the trunk recruitment as a percentage of reach length. Reach length is the hand-to-target Euclidean distance at t 0. In order to know the normalised implication of the trunk to the reach, we computed trunk recruitment as the ratio between shoulder displacement and reach length. More precisely, trunk recruitment = (shoulder to target distance at t 0 -shoulder to target distance at t final )/(hand to target distance at t 0 -hand to target distance at t final ). Shoulder displacement is sensitive to both trunk anterior flexion and trunk rotation. Over the 5 trials, the trial with the median trunk recruitment was retained for data analysis. Fourth, antigravity shoulder torque was computed as the Euclidean norm of the 3D static torque against gravity at the final posture. In order to compute the static torque, the positions of centre of mass and the absolute weight of upper limbs were approximated from the height and weight of participants following De Leva's equations (1996).
Identification and quantification of carbonyl compounds Carbonyls were collected from aerosols into two silica cartridges coated with 2,4dinitrophenylhydrazine (DNPH) placed in series, as described previously  (Beauval et al. , 2019). Sep-Pak DNPH-Silica Plus Short Cartridges containing 350 or 750 mg of sorbent per cartridge (Waters, Guyancourt, France) were used for e-cig and iQOS aerosols or cigarette smoke, respectively. Each collection was replicated four times. Blank collections were performed using the smoking machine working without e-cig, HTP or conventional cigarette connected to and were taken into account for data analysis. DNPH cartridges were eluted with 3 or 6 mL of acetonitrile for short and long cartridges, respectively. Elutions from both cartridges placed in series were pooled and then injected into a Thermo Scientific Dionex UltiMate 3000 UHPLC System with UV/VIS Detector (Thermo Scientific, Waltham, MA).
The UHPLC instrument was operated at 28 C° at a constant flow rate of 0. 4 mL/min, under a gradient of acetonitrile and water during 15 min. Analysis was performed using Acclaim Carbonyl C18 RSCL 150 mm x 2. 1 mm x 5 µM (Thermo Fisher Scientific). Acquisition was performed at 360 nm wavelength. Instrument monitoring and data acquisition were done using Chromeleon 7. 0 Data Acquisition System for LC (Thermo Scientific, Waltham, MA). The method allows the detection and quantification of nineteen compounds (Table  1 ) with limits of quantification (LOQs) ranging from 6 to 15 ng/mL. Acrolein was not measured in this study due to the unsuitability of using the DNPH-coated solid sorbent cartridge for its collection  (Ho et al. , 2011). The number of puffs for each collection was determined in preliminary experiments in order to avoid saturation in the cartridges.
Finally, for e-cig, HTP and 3R4F analyses, 20, 4 and 1 puff(s) were respectively found to be the best compromise with satisfying efficiency without saturation of the cartridge (data not shown). Moreover, concerning e-cig, we measured carbonyl emissions in the twenty last puffs of one-hour-exposure session (100-120 puffs). To compare the different aerosol samples, the levels of the carbonyls were then expressed in mass of each compounds by puff.
Gene expression analysis The total RNA of BEAS-2B cells was extracted using the RNeasy plus mini kit (Qiagen, Courtaboeuf, France) following the manufacturer's instructions. Expression of target genes was measured by quantitative real-time PCR of corresponding reverse-transcribed mRNA. One μg of total RNA was reverse-transcribed into cDNAs using the High Capacity cDNA Reverse Transcription kit (Applied biosystems, CA, USA). qPCRs were carried out with the StepOnePlus thermocycler (Applied Biosystems), using the TaqMan Fast advanced Master Mix (Applied Biosystems) and the following TaqMan Assays: Hs99999901_s1, 18S; Hs01054797_g1, CYP1A1; Hs00164383_m1 CYP1B1; Hs01110250_m1, HMOX1; Hs01045993_g1, NQO1. Amplification curves were read with the StepOne software V2. 1 using the comparative cycle threshold method.
These devices are often referred to as "heat-not-burn" tobacco products with a reduction of the user's exposure to carcinogenic chemicals usually produced by the combustion of tobacco  (Schaller et al. , 2016a). We effectively observed a reduction of about 90 % in carbonyl emissions in accordance with data from the IQOS manufacturer  (Schaller et al. , 2016)  and two independent studies  (Farsalinos et al. , 2018b; Li et al. , 2019). However, several harmful carbonyls were still measured in HTP aerosol, such as acetaldehyde and formaldehyde, which are carcinogenic compounds. The presence of high levels of acetaldehyde is the mark of pyrolysis and thermogenic degradation of tobacco  (Auer et al. , 2017). By contrast, the main source of carbonyls in e-cig emissions is the thermal degradation of glycerol and propylene glycol contained in the e-liquid (Uchiyama et al.
Cell viability Cell viability was assessed 24 h after aerosol exposure by measuring intracellular ATP content, which is directly proportional to the number of living cells. Data displayed in figure  4 (A-E ) show that e-cig vapours had no significant (Modbox model) or low (Lounge model: > 75 % cell viability) cytotoxic effects up to a 120-puff-exposure. In comparison, HTP emissions caused intracellular ATP changes from 12 puffs (89 % viability) to a strong reduction of cell viability that reached 2 % after 120 puffs. Cigarette smoke demonstrated also a full doseresponse curve, but its cytotoxicity appeared within fewer puffs (< 10 puffs). In order to better compare the different devices, the effective dose of aerosol which results in a 50 % reduction of cell viability (ED50, expressed here in puff number) was calculated.
Acknowledgements. We thank the  Institut des Régions Arides (IRA)  for providing logistics and support during the experiment. Special thanks are expressed to  Thierry Henry Des Tureaux  for his invaluable help during sampling in Tunisia and to  Elisabeth Bon Nguyen ,  Mickael Tharaud ,  Anais Feron ,  Jessica Chane Teng , and  Zihan Qu  for their advice and help during the analyses. Thanks are given to  Carmela Chateau-Smith , who reviewed the English, and to the two anonymous reviewers, who helped us to improve this paper greatly. Financial support. This work was supported by  NILDAE (Nutrients Inputs and Losses Due  to  Aeolian Erosion in the Sahel) , an  IdEx Université Sorbonne Paris Cité  (now  Université de Paris)  programme. Review statement. This paper was edited by  Pierre Herckes  and reviewed by two anonymous referees.
Flow cytometric analysis Cells were stained with the following antibody conjugates: anti-CD14-APC, anti-CD14-V500, anti-HLA-DR-V450, anti-CD3-APC-H7, anti-CD4-V450, anti-CD4-PerCP, anti-CD80-fluorescein isothiocyanate (FITC), anti-CD83-PE, anti-CD86-PE, anti-IL-17A-APC, anti-IL-17A-alexa fluor (AF) 647, anti-IFN-γ-FITC, anti-IFN-γ-V450, and anti-NF-κB p65-phosphoethanolamine N-carboxyfluorescein (PE-CF) 594. Antibodies were obtained from Becton Dickinson (BD) Biosciences or eBioscience. Intracellular stainings for IFN-γ and IL-17A were performed using the Cytofix/ Cytoperm kit from BD Biosciences. Stained cells were measured by FACSCanto II (BD Biosciences) and analyzed with FlowJo software (Tree Star).
Cytokine and chemokine assays To stimulate the secretion of cytokines and chemokines, the CD14 + monocytes were incubated with 1 μg/mL lipopolysaccharide (LPS) for 24 hours. Thereafter, the supernatant was collected to determine the levels of cytokines and chemokines produced. Cytokine concentrations were measured with the FlowCytomix (eBioscience) Human Th1/Th2/Th9/Th17/ Th22 13plex kit (IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 p70, IL-13, IL-17A, IL-22, and TNF-α). Chemokine concentrations were measured with the FlowCytomix Human Chemokine 6plex kit (G-CSF, IL-8, monocyte chemoattractant protein (MCP)-1, MIG, macrophage inflammatory protein (MIP)-1α, and MIP-1β). Data were analyzed with Flow-Cytomix Pro 2. 4. The experimental results represent the fold induction of protein secretion after stimulation with LPS.
Data collection and ethical considerations Data were collected from patient's medical files and from the microbiological department database. We collected pre-morbid and demographic conditions, characteristics of V-A ECMO support, clinical and biological parameters, and ICU/hospital course and outcomes. For each BC, body temperature, site of sampling, and sampling conditions were analyzed. According to the French law, patients were informed of the anonymous data extraction and analysis from medical files  [23]. This study was approved by the Comité d'Ethique pour la Recherche en Anesthésie-Réanimation (CERAR, IRB 00010254-2019-027).
After multivariable logistic regression, we assessed the test performance (sensitivity, specificity, positive predictive value, and negative predictive value) of risk factors identified in the multivariable model. A sensitivity analysis restricted to routine BCs was performed. As a sensitivity analysis, we performed a multilevel logistic modeling, considering blood samples as level 1 (with "level 1 variables" including collection on V-A ECMO, routine sampling, ongoing microbial therapy, and ongoing RRT) and patients as level 2 (with BMI, KDIGO, lactate level, and bilirubin level as "level 2 variables"). All tests were two-sided, with p < 0. 05 considered statistically significant. STATA/SE 15. 0 software (College Station, TX, USA) was used for statistical analysis.
Introduction: Frenectomy is an oral surgical procedure usually performed with a scalpel. Several authors recently reported the interest of laser, as a minimally invasive and efficient alternative tool to perform frenectomy. This study aimed to compare the perioperative management of patients requiring a frenectomy using scalpel versus laser. Materials and methods: A systematic review of the literature has been carried out from Pubmed and Scopus databases following PRISMA guidelines. PICO method was used to select the relevant articles. Clinical studies comparing the perioperative outcomes of patients requiring frenectomies using scalpel versus laser were included. Results: Ten articles involving 375 patients were included. Pre-operative parameter (anxiety before surgery) and per-operative data such as anesthesia, surgery duration, bleeding, suture and difficulty were assessed.
Research strategy and selection criteria An electronic search of the MEDLINE À Pubmed and Scopus databases was carried out. We searched for articles published in English up to and including January 2020, using the following search combination: « (frenectom*) AND ("surgical procedures, operative" [MeSH terms] OR "lasers" [MeSH terms]) ». Additional articles were also selected after manually screening the list of references of all publications included by the search. The following eligibility criteria have been selected: Studies published in English and conducted on human subjects were included. Studies comparing the benefit of laser versus blade for oral frenectomy were considered. Only randomized or quasi-randomized prospective clinical trials were included.
Screening of studies and data collection Following the eligibility criteria defined beforehand, the article selection and the data extraction were independently performed by two independent reviewers (F. M and A. P. M). In case of disagreement, articles were discussed with a third reviewer (C. S) to decide the final outcome. First, the titles were studied, and duplicates were removed. Then titles and abstracts were screened in the first time according to the defined question "Does laser improve the perioperative management of patients benefiting a frenectomy compared to the conventional technique?". If an article was prone to be selected, the full-text was read. Additional manual search was performed notably by screening the bibliographic references of the selected articles. Data were finally extracted using structured generated tables.
Collection of data such as the type of study, effective, study duration, laser properties, type of frenum and classification, frenectomy indication, judgment criteria and results were reported. Quality of the included studies was evaluated and ranked relying on the National Health and Medical Research classification (NHMRC)  [12]. The methodological quality of the included studies was scored according to the Newcastle-Ottawa Scale (NOS)  [13]. The NOS is a star rating system that allocates a maximum of nine stars across three categories: participant selection (four stars), comparability (two stars) and measurement of outcome in cohort (four stars) (Tab. I).
Search outcomes The research in the PubMed and Scopus database generated 155 potentially relevant articles, and 92 of them were selected after duplicates removal. After screening title and abstracts, eleven studies were retained for further investigation. Amongst these articles, six were included  [14] [15] [16] [17] [18] [19]. After a manual screening of the references of the articles included, four other articles were added  [20] [21] [22] [23]. Finally, ten articles met the eligibility criteria and they were included in this systematic review. The selection process is detailed in Figure  2. Among these ten studies, five were performed in Turkey  [14] [15] [16] 18, 19] , two in India  [22, 23] , one in Brazil  [17] , one in Nepal  [20]  and one in Macedonia  [21]. They all have been published since 2006.
On-demand labor On-demand labor is probably best epitomized by its most prominent platform, Uber. Despite the ebbs and flows of the company's performance, its business model is a bona fide cultural success, which gave birth to the word "uberization", which now stands from disrupting a market by introducing algorithmic processes. The technological and organizational innovations introduced by Uber are also paradigmatic of a specific style of labor governance. Since the second half of the 2010s, numerous court cases have rejected the classification of Uber drivers as self-employed partners. In 2017 the European court of justice ruled that Uber should be regulated as a transport services company. Yet, Uber displays several features of a technology business whose drivers are, first and foremost, data providers.
Despite the changes in the service over time, almost 2/3 of drivers' workday is actually spent on producing data on the company's app while driving around or waiting for new gigs  (Cassano, 2016). The data produced by Uber drivers are in some cases personal information, such as their names, likeliness, address and payment details, that are entered in their profile. Subsequently, Uber drivers produce data whenever they enroll in specific time slots. Drivers send and reply to messages and calls, both from the platforms and from customers, thus generating more information. Location data are very important too, as drivers use several apps in order to negotiate the best route. They sometimes have to use several apps on two or more devices, to compensate for possible gaps in the information provided by Uber's GPS, thus turning their everyday activity into a hybrid between urban transportation and multi-device data management.
Their reputation score is probably the single most important data point drivers have to take into account. The evaluations and reviews left by riders are used to compute it, based on standard criteria such as hygiene and punctuality, or more elusive indicators such as "chattiness" and driving style. This very subjective, and in some cases arbitrary way of assessing workers' performance can have adverse consequences. If their score falls below a given threshold, drivers face deactivation of their profile and exclusion from the platform. The data produced by drivers operationalize Uber's algorithmic matching models, that put in place economic incentives for drivers based on real-time estimates of customers' demand for the service. The patented surge pricing algorithm that is used by Uber to match supply and demand has been described by Uber's R&D researchers.
Of course, not all consumers are ready to pay a higher price. They either wait for the price to return to normal or move to another area where the coefficient is 1. In both cases they continue using the app, thus producing data themselves. Data generated both by drivers and by riders is an important revenue source for the company that monetizes this information by allowing access to municipalities, brands, and advertisers. Another use for the same data, is mainly automation. Geolocation, transaction, and personal data are used to train Uber autonomous vehicles. Despite the recent transfer of their Advanced Technologies unit to Amazon-backed startup Aurora, Uber continues to use data to develop "driverless" cars. Since several decades, autonomous vehicles have been imagined as sci-fi boudoirs on wheels, where passengers can read or chat, while the actual driving is fully automated.
Apart from single human workers that are still needed inside an autonomous car, there is also a multitude of other invisible workers that operate around it. This new category of workers allows us to transition to the second type of digital labor -microwork. Uber self-driving cars are basically mobile communicating devices that exchange data with the platform and with other connected vehicles. But they also capture huge amounts of information from the environment surrounding them. If a pedestrian crosses the street, the car's lidars and cameras are supposed to record that and then process it to act consequently. In order for the car to autonomously determine the best course of action, recorded images need to be annotated, filtered, documented. Raw data need to be transformed into enriched quality data that Uber cars' computer vision systems can use.
Not only this startup would need a database containing billions of examples of human voices pronouncing song titles (in different languages, with different accents and speech patterns…), but would also require that the data be arranged so to differentiate music genres, discriminate songs with the same title, associate similar songs etc. Similarly, companies who want to automate business processes turn to Amazon Mechanical Turk or to other microwork platforms to hire persons who will perform these data annotation tasks. They include, for instance, recognizing languages or types of conversations, transcribing text, providing keywords. Generally, neither platforms nor client companies formally hire the workers. They are a disposable workforce, with no actual employment contract and paid by the piece.
Instead of training it on a dataset of examples of conversations previous to its launch, Microsoft decided to train it live on Twitter. A few hours in, a coordinated campaign from the members of an online forum succeeded in "teaching" Tay all sorts of profanity, homophobic and racist slur, and problematic political opinions. Microsoft immediately discontinued the experiment. Its failure contained a valuable lesson on the importance of quality training for AI solutions. It also was evidence to the tendency of Big Tech platforms to rely on unpaid consumer work to produce them. In the same way, Alphabet recruits paid microworkers on its proprietary platform Rater Hub to refine and improve the results of Google Search engine. Rater Hub was introduced around 2007 and allows microworkers to verify the quality of the results of sampled search engine queries.
ReCaptcha is probably the most prominent case of user contributions being harvested to produce value in the form of distributed and unrecognized digital labor. For users, ReCaptcha is a page or a pop-up that appears whenever a website prompts them to show that "they are not bots". In the early 2010s, users were urged to transcribe distorted words. These words were too difficult to read for a text-recognition software, but easily interpretable for an individual with basic literacy skills. While transcribing these words, users were actually training optical character recognition (OCR) algorithms to read scanned texts hosted on the Google Books platform. Books were largely digitized by hand, which sometimes skewed or distorted scanned words. By transcribing these words, initial reCaptcha users were actually annotating the scanned document by adding a tag corresponding to the illegible terms.
What is the difference between micropaid data annotation work and user-provided unpaid data annotation work? In both cases, platformmediated human agents are producing data and value. In the case of ReCaptcha, it is common sense to say that users' activity cannot be construed as work because they receive a service in exchange for their contribution. But sometimes the service is not even an actual product available on the market. Translation tools, for instance, can be eternal prototypes, trial versions of actual software. They attempt to translate based on masses of examples suggested or rated by users. Facebook provides automatic translations of posts and messages. Each transaction attempt comes with a button prompting users to come up with a better translation. This usergenerated edit is included in a training database for an algorithm, which the users unknowingly improve.
Phylogeny (use of 'common ancestry' to catégorise transfer data) and allometry (mass dependence) have been suggested as approaches to extrapolate data across species  (Beresford et al. 2016) ). Initial testing has shown that these techniques are promising but need further development (e. g. Beresford et al. 2020c). Bayesian statistics allow a low number of empirical observations to be supported by inferences from more comprehensive, larger datasets  (Brown et al. , 2016). The data for model parameterisation will require dedicated laboratory-based work and field studies, as well as on-going reviews of published information from the wider scientific community. Long-term data series obtained along routine surveillance programs can also provide information for transfer modelling  (Brimo et al.
Challenge for Infrastructures and In the recent past, several EURATOM funded projects have performed activities to drive the improvement of the awareness and use of radioecology infrastructures in Europe. The Network of Excellence on Radioecology STAR created an inventory of infrastructure, including databases and sample archives (STAR Deliverable 2. 2). Within EJP-CONCERT efforts were subsequently made to increase visibility of radiation protection infrastructures including those of ALLIANCE members (see the AIR2D2 database8 and AIR2 bulletin9 ). The approaches used to study and evaluate the behaviour and impacts of radiation and radionuclides on the living world are changing. Consequently, the required infrastructures and capabilities are also changing. A robust longterm vision is essential to successfully and sustainably develop, construct and operate radioecological (and radiation protection) infrastructures and capabilities.