dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The work was supported by grants from  Ligue Nationale Contre le Cancer : (H. N-K. , J. F. , C. G. , X-Y. M. , L. C. , F. D. , C. K. , Y. A. , F. R. , I. B-P. ) as an associated team (Equipe labellisée), the " Carte d'Identité des Tumeurs" program  initiated, developed and funded by  Ligue Nationale Contre le Cancer , and a post-doctoral fellowship supporting H. N-K. J. F. was supported by the  Fondation ARC  pour la recherche sur le cancer, L. C. by  FRM  ( Fondation Recherche Médicale ) and X-Y. M. was supported by a fellowship from  ITMO Cancer AVIESAN , within the framework of  Cancer Plan. The work was also supported by a " PL-Bio " project funded by  INCa  ( 2016-146 ), the  French Ministry of Education and Research , the  CNRS , and the  Institut Curie. ChIPsequencing was performed by the GenomEast platform, a member of the ' France Genomique' consortium  ( ANR-10-INSB-0009 ).
Theoretical background The goal of the study presented in this paper is to evaluate the impact of atmospheric conditions on the solar performances of low-e or solar control coatings. Firstly, we will evaluate the impact of the main atmospheric variables. Secondly, realistic simulations shall be carried out based on local atmospheric data, for 12 sites located around the world, and for a period of at least one year (see Table  1 ). SMARTS 2. 9. 8 is used to model Global Tilted Irradiance (GTI) at 37 • from a solar spectra based on local atmospheric conditions  [19]. The local atmospheric conditions come from the AERONET meteorological database  [30, 31]. Finally, we will compare the longtime solar performances obtained with the local atmospheric data to the ASTM G173-03 solar spectrum, currently used as a reference worldwide  [8, 32].
Atmospheric turbidity Aerosol Optical Depth (AOD) quantifies atmospheric turbidity. Turbidity is the relative transparency provided by tiny solid and liquid particles such as pollen, ash, urban haze, or smoke particles suspended in the air, which constitute aerosols. Remember that clouds do not impact Aerosol Optical Depth. Other expressions of AOD such as the Angstrom or Schuepp Turbidity coefficients, or even meteorological range or airport visibility reports are available, but these are not relevant to our study. We set the AOD at 500 nm in our SMARTS calculation because this data is provided by the AERONET database but also because a similar choice has been made in previous studies  [27, 37, 38]. A sole AOD measurement, at 500 nm, does not constitute a spectral characterization, especially when compared to the full spectral domain of the solar spectrum (280-4000 nm).
To achieve this goal, the SMARTS model needs the parameters listed above. All the details can be found in the literature  [33, 34]. To put it simply, SMARTS inputs information from text files and outputs its results into two text files. This model is both user-friendly and efficient but fails to generate a huge quantity of solar spectra. For this study, we coded a script on Scilab (v5. 5)  [41]  in order to read the data.
AERONET database and modeling To obtain the atmospheric variables of different sites, we used AERONET. AERONET, which stands for AErosol RObotic NETwork, is a free and public database which provides data from atmospheric conditions all around the world. This ground-based remote sensing aerosol network was established by NASA and PHOTON  [30]. Its website is available at: https://aeronet. gsfc. nasa. gov [31]. The AERONET federation has imposed the standardization of instruments, calibration and processing and provided researchers with high-quality data for decades. In November 2020, over one thousand stations were available worldwide. We only used the 2. 0 quality assurance data, which is the highest level available after cloud screening, calibration, and degradation correction. All the atmospheric parameters previously described, such as AOD, PW, Angström coefficients (α 1 and α 2 ), ozone layer and solar zenith angle were available.
This allowed us to model solar spectra with SMARTS without using other data  [34]. Even if several preconfigured atmospheric models are available in SMARTS, we used the two Angström coefficients (α 1 and α 2 ), also provided by AERONET, to model the aerosol. This way, SMARTS could estimate the size distribution of the particles. Global Tilted Irradiance (GTI) solar spectra are calculated thanks to the SMARTS radiative model  [12]. Even if the AM values can be calculated directlyfrom the solar zenith angle (SZA, equ. (1)), we used the value proposed in the AERO-NET database.
Selected locations Twelve different locations were selected according to the following criteria:  1. They should provide level 2. 0 quality data (the highest quality provided by AERONET) for several years, without important discontinuity. This first criterion was the most difficult to meet, and most of the locations proposed in AERONET do not meet this criterion  [28, 49, 50 ]. 2. They must represent the different weather conditions found around the world as closely as possible. 3. Specific atmospheric conditions must be represented if possible, for example: sites with low and high ozone, PW or AOD. In related bibliography, W. Jessen et al. [27]  used 5 sites in their study about thermal solar materials, while Vossier et al. [26]  used 6 sites in their study on CPV solar cells  [28]. In addition, our solar spectra data bank was used to calculate the longtime solar performances of smart windows.
The necessity of low-e or solar control glass was not considered in the selection of the locations. A selected site could be chosen for several reasons, such as geographical interest (high altitude, high or low latitude), specific atmospheric conditions (low or high PW, AOD … etc. ) or specific location (large cities, near the ocean). Table  1  presents the different sites according to their name in the AERONET software and their location (latitude and longitude). As our aim was to analyse a complete year without an important discontinuous period, the simulated years (not specified in the table) could be different for each location but do not have any impact on the overall conclusion. GHI (Global Horizontal Irradiance, in kWh/m 2 /y) was given as complementary information, from the SolarGis interactive web map  [51].
The selected locations cover a large part of the latitude and longitude of the Earth, with stations on both sides of the equator. Fig. 8  shows the selected locations on a GHI (Global Horizontal Irradiation, kWh/m 2 ) world solar map. As for geographical distribution, five sites are located in the Northern hemisphere, three in the Southern hemisphere and four near the equator. One site (Casleo n • 2) is situated at high altitude (up to 2, 000 m), in the Argentinian desert. The city of Helsinki was added because it is the northernmost station in the AERONET database (latitude: 60. 204 • ). Likewise, Singapore is an interesting site for a dual reason: it is situated near the equator (1. 298 • ) while having tropical weather (see Table  2 ). Dakar (n • 4), La Parguera (n • 7) and American Samoa (n • 10) are close to the equator and located near the ocean.
Birdsville (n • 2) is a unique site located in the middle of the Australian desert. Casleo (n • 3) and Mezaira (n • 8 near Abu Dhabi), present the most abundant solar resource in the world, with GHI up to 2400 kWh/m 2 /y. For each sites, we selected atmospheric data for five years. The years selected years are between 2015 and 2020, when possible. Two sites are exceptions: Casleo and Birdsville where coverage over this period is not available, probably due to the shutdown of the station. As far as possible and according to the data available, this five-years has been respected. Table  2  presents the atmospheric parameters of values such as turbidity (AOD), precipitable water (PW) etc. For each selected location. With geographical location, atmospheric conditions are the second criterion selected.
A site can be chosen for specific atmospheric conditions despite a less interesting geographical situation. As previously mentioned, the values presented are time-weighted. Weighted values are necessary because the AERONET database is rich in points close to the sunrise or sunset, which are not representative of the conditions expected in the daytime. Atmospheric conditions used to model the ASTM reference solar spectrum are given as a reference by literature  [18]. Most sites, except Birdsville, Casleo, Helsinki and American Samoa, have a higher AOD than the ASTM G173 (AOD = 0. 084). The AOD variation can be important, as in Mezaira located in the UAE desert (four times the ASTM G173 AOD value), in Dakar, or in Beijing or Seoul, two megalopolises. In comparison, Casleo is situated at a high altitude (2,485 m) and has the purest atmosphere (AOD = 0.
Longtime optical performances The complete collection of thousands of solar spectra per site allows us to calculate the longtime solar performance of each sample, noted A s,lt for the solar absorptance, and T s,lt for the solar transmittance. Then, similarly to solar transmittance (T s ) or solar absorptance (A S ), we defined the ratio of the total transmitted or absorbed energy, divided by the total energy available for several years,  [8, 22]. As the solar spectrum changes over time, we added the subscript (n) so as to point out that two solar performances can be different, depending on their solar spectrum. Since the difference in time between two measurements in the AERO-NET database is not fixed, we calculated the time gap Δt n , between a given solar spectrum (n) and the following one (n+1).
Both a minor time difference of less than 15 min, and a larger one superior to several hours, are excluded, in order to avoid miscalculation  [53]. A s,lt = ∑ n=1 S n ⋅A s,n ⋅Δt n ∑ n=1 S n ⋅Δt n (4a) T s,lt = ∑ n=1 S n ⋅T s,n ⋅Δt n ∑ n=1 S n ⋅Δt n (4b) It should be noted that the longtime irradiance (I n ⋅Δt n ) calculated thanks to this method, cannot substitute for the annual irradiance (GTI in our case) found in a database. The values given here are only for 4-5 years, while sites like SolarGis provide average solar potential values calculated for many years  [51].
Discussion & conclusion With the growing concern of energy consumption in the construction industry, it has become crucial to understand the role of solar flux. The use of low-e and solar control glass in buildings plays an important energy-saving role. In order to increase our understanding of the Solar Heat Gain Coefficient, in this paper we endeavoured to calculate the solar transmittance and absorptance of a panel of low-e glass samples in correlation with local atmospheric variables, as opposed to using the default calculations of the ASTM G173-03 solar spectrum. We have shown that the Air-Mass factor, Aerosol Optical Depth and Precipitable Water play an important part and can modify solar flux, whether it is transmitted or absorbed by smart windows. Using 12 locations, we performed realistic modeling to calculate the longtime solar transmittance and absorptance using a databank of thousands of solar spectrum models according to local atmospheric conditions.
This modeling was performed thanks to the AERONET database, using SMARTS software. We have demonstrated that in specific conditions, such as high Air-Mass and Aerosol Optical Depth, as observed in the cities of Beijing and Seoul, the longtime transmitted flux could be reduced up to 5%. Tropical climates, such as La Parguera or American Samoa, where Precipitable Water in the atmosphere is high (>3 cm), increase the longtime transmitted flux (≈1%). Such specific sites are now well-identified and require further study. As opposed to longtime transmitted flux, longtime solar absorptance at the local sites fluctuates less for the most part, showing variations below 1. 2%. The variation between the solar transmittance or absorptance values, using the ASTM G173 solar spectrum and the local sites, is different for each sample.
3g, h ). These data indicate that GLP-1RA/tesaglitazar possesses, particularly at low concentrations, a substantially enhanced potency to acutely improve glucose tolerance relative to the GLP-1RA monotherapy. Emphasizing the value of this finding, glucose tolerance was even substantially improved by GLP-1RA/tesaglitazar, but not by any other treatment group, at a dose of 0. 5 nmol kg -1 (Fig. 3i, j ). When compared at doses of 0. 5, 5 and 50 nmol kg -1 in the same -week-old male C57BL/6J DIO mice treated for 14 days with 50 nmol kg -1 per day. e,f, Fasting levels of blood glucose (e) and insulin (f) at study day 7. g,h, i. p. insulin tolerance (ipITT) assessed at day 14: blood glucose (g) and AUC (h). i, In vivo glucose-stimulated insulin secretion (GSIS) in 47-week-old male DIO mice treated i.
Glucose-and insulin-tolerance tests (GTT/ITT). Glucose tolerance was assessed in 6 h fasted mice after i. p injection of 1. 75 g glucose per kg body weight (DIO mice). Insulin tolerance was assessed in 6 h fasted mice after i. p. injection of either 0. 75 U Insulin per gram body weight (DIO mice) or 1 U Insulin per gram body weight (db/db mice) (Humalog, Eli Lilly). Tail vein blood glucose was subsequently measured using a handheld glucometer (TheraSense Freestyle) at baseline and after 15, 30, 60 and 120 min. Data were subsequently graphed as baseline-corrected AOC as previously suggested  36. Plasma creatinine. Plasma creatinine was measured by a fluorometric creatinine assay kit (catalogue no. ab65340, Abcam) based on the manufacturer's instructions. Urinary albumin and creatinine measurement.
The images were obtained by using a Leica microscope of the type DMI 6000 and Leica's LAS AF software. ImageJ and/or LAS AF were used to analyse the images. Mean signal intensity per islet area of confocal images were used to quantify the hormone contents. Nine to 12 islets were quantified for each animal. Measurement of islet size area. Dissected pancreata were fixed in Formalin (Formalin 10% neutral buffered, HT501128, Sigma-Aldrich) for 24 h at room temperature and standardly processed for paraffin embedding (Tissue Tec VIP. 6, Sakura Europe) Paraffinized pancreata were exhaustively cross-sectioned into four parallel, equidistant slices per case. Maintaining their orientation, the tissue slices were vertically embedded in paraffin. After a costaining for insulin (monoclonal rabbit anti-insulin, no.
3014, Cell Signaling 1:300; AlexaFluor750-conjugated goat antirabbit, A21039, Invitrogen 1:100) and for glucagon (polyclonal guinea pig anti-glucagon, M182, Takara 1:3,500; goat antiguinea pig AlexaFluor555, A21435, Invitrogen 1:200) nuclei were labelled with Hoechst33342 (H1399, Thermo Fischer, 7. 5 μg ml -1 ). The stained tissue sections were scanned with an AxioScan. Z1 digital slide scanner (Zeiss, ZEN Blue v. 3. 5) equipped with a ×20 magnification objective. Insulin expression cells were performed on the entire tissue sections by using image analysis software Definiens Developer XD2 (Definiens AG, v. 2. 7. 0). The insulin expressing cells were classified automatically using the fluorescence intensity. β cell volume (mg) was calculated by multiplying the detected relative insulin-positive cell area by total pancreatic weight.
A data-independent (DIA) acquisition method was used, in which one full scan (300-1,650 m/z, maximum ion fill time of 45 ms, normalized AGC target 300%, R = 120. 000 at 200 m/z) was followed by 66 tMS2 fragment scans of unequally spaced windows with 1 Th overlap, covering the same m/z range (fill time 22 ms, normalized AGC target 1,000%, normalized high-collision density energy of 30%, R = 15. 000). Pharmacokinetic analysis. Plasma concentration-time profiles were analysed in C57BL6J mice and Sprague Dawley rats by a non-compartmental method using sparse sampling in Pharsight Phoenix WinNonLin v. 6. 4 software, and the resulting terminal half-life (T 1/2 ), maximum plasma concentration (C max ), time for maximum plasma concentration (T max ), and AUC from zero to last valid measurable concentration-time point (AUC 0-t ) were determined.
Acknowledgements This work was funded by  Novo Nordisk. M. H. T. further received funding from the  Alexander von Humboldt Foundation , the  Helmholtz Alliance ICEMED  and the  Helmholtz Initiative on Personalized Medicine iMed by Helmholtz Association , the Helmholtz cross-programme topic Metabolic Dysfunction and the  European Research Council (ERC)  AdG HypoFlam no. 695054. T. D. M. received research funding from the  German Research Foundation (DFG)  grant nos. GRK 2816/1 ,  TRR152 ,  TRR296  and  SFB1123 , and the  German Center for Diabetes Research  (DZD e. V. ). S. M. H. received funding from the  German Research Foundation (DFG)  grant no. SFB1123. D. J. D. is supported by  CIHR  grant no. 154321 , a  Banting and Best Diabetes Centre Novo Nordisk Chair in Incretin biology  and the  Sinai Health Novo Nordisk Foundation Fund  in regulatory peptides.
C. Q. acknowledges support by  INSERM ,  Agence Nationale de la Recherche  (neuroIDobese, grant no. ANR-20-CE14-0046 ),  Société Française d'Endocrinologie  ( Pfizer - SFE Prix de Recherche en Endocrinologie ),  Société Française de Nutrition and Société Francophone du Diabète. C. G. -C. received funding from the  ERC STG  (AstroNeuroCrosstalk no. 757393 ). B. S. , E. B. and N. H. acknowledge support by the  Agence Nationale de la Recherche  (grant no. ANR-10-LABX-0046 )  European Genomic Institute for Diabetes.
Data availability The data used for the statistical analysis are available in the data source file, along with the GraphPad Prizm-derived report on the statistical analysis. The statistical report contains the mean difference between the treatment groups, the 95% confidence intervals, the significance summary, and the exact P values (unless P < 0. 0001). Additional raw data are available from the corresponding author upon reasonable request. Proteomic data shown Fig. 7c -h are available via ProteomeXchange under the identifier PXD033653. Source data are provided with this paper.
This information is particularly challenging to obtain, as it is not collected in routine programmatic data. Second, data on some primary contacts' demographic characteristics, such as age or region, are consistent with the distribution figures reported in programmatic data, showing the potential of the survey to collect reliable data. Finally, few people were recruited via the national HIV hotline, consistent with the fact that calls to the national hotline are limited and consistent with the pilot study results (admittedly from a very small sample) indicating that HIVST users encounter few difficulties in using the test. The leaflet and the demonstration video are therefore often sufficient to obtain the necessary information. This finding is in line with the feedback from the field agents.
So far, unstable surfaces have been chosen in all oncology studies, but the smaller amplitudes of disturbances generated by vibrations make this system appropriate on individuals with postural control disorders. The consequences of performing a common daily dual task on posture will also be investigated in this study to determine whether using a common cognitive and fine motor task, such as texting (potentially impaired by CIPN), may disrupt postural control and increase the risk of falling. Finally, this study will assess dynamic stability to complement the limited data in the literature on postchemotherapy patients who appear to have impaired dynamic balance. 12 19 An improved understanding of the alterations in postural control may help establish the need for a systematic evaluation of postural control after chemotherapy, specifying the appropriate evaluation.
A maximum of five attempts will be allowed. The mean of the successful trials will be recorded for the data analysis in all postural control tests. Data collection will start 5 s after participants are positioned to avoid perturbations linked to the trial initiation. 13 articipants will be asked to maintain balance under the conditions of sensory disturbance described in table  3. The various conditions will be performed randomly according to a previously defined randomised list to avoid fatigue and learning bias. Validity  51  Intra-rater reliability  36  Validity  52  Test-retest reliability  53  Validity  41  Intra-rater reliability  54  Open access.
Sample size calculation Sample size calculation was performed using the G*Power V. 3. 1 software package and a two-sided t-test for paired data (HHU Düsseldorf, Germany, 2017). 46 With the significance level set at 0. 05, a power of 90%, and an effect size of 0. 61, the number of participants required for this study is 31. The assumption of the effect size is based on a previous study that evaluated the effect of chemotherapy on postural control, which found mediolateral RMS CP values of 3. 3 ± 1. 1 mm at the baseline and 4. 3 ± 1. 9 mm at the last follow-up after chemotherapy. 16 We assume a correlation of 0. 5 between the baseline and the last follow-up. With a conservative assumption that this correlation is null, the power is still 89% for detecting a mean difference of 1. 3 mm in CP RMS (3.
Patients undergoing invasive mechanical ventilation or breathing spontaneously were included. The non-inclusion criteria were limited to patient or healthy volunteer under legal protection or who refused participation. All participants gave informed consent to participate in the study. Participant blinding was not possible due to the very nature of the intervention. The study protocol was approved by the regional ethics committee (Espace de reflexion éthique région centre val de Loire, Tours, France, no. 2018_090) in accordance with national regulations. The protocol was registered (clinicaltrials. gov: NCT05078632, 2021/10/14, retrospectively registered).
Calibration (measuring characteristics of the user's eyes to derive gaze calculation based on a physiological 3D eye model) was performed on five gaze positions on the screen  [25]. The eye tracker frequency was 60 Hz, enabling the sensor to catch 60 corneal reflections per second. The eye tracker used in our study was the Tobii X2-30 compact (Tobii Pro, Danderyd, Sweden), and data were analyzed with Tobii Pro Studio (Tobii Pro).
Data collection For all participants, demographic information was gathered (age, gender, right or left-handed, education level), medical history (hearing, visual or neurological impairment), and current medication. For patients, additional information was collected: CAM-ICU scale (Confusion Assessment Method for the ICU)  [11, 12] , Simplified Acute Physiology Score II (SAPSII), primary diagnosis  [27, 28]  and organ support during ICU stay and at the time of performing the test (ventilation, dialysis, norepinephrine, etc. ).
Acknowledgements The authors would like to thank all the persons who have contributed to the study, especially the patients who participated, caregivers and research nurses of the intensive care units of  Orléans and Tours, and Centre d'investigation Clinique , and speech language students:  Tessa Charuel ,  Anne-Cécile Capel ,  Claire Lechevalier ,  Antoine Papazian  and  Pauline Tiquant. We would like also to thank  Mr Nespoulous  for his advice and the  SRLF (Société de Réanimation de Langue Française)  for the financial support.
Funding The equipment (eye tracker and computer) and the software were funded by  Tours University hospital. The study was funded by a research grant from  SRLF  ( Société de Réanimation de Langue Française ) and Tours University hospital. The eye-tracking system (eye tracker and software) was provided by the company  Tobii Dynavox  at a preferential rate. Those companies were not involved in study design, conduct, data interpretation or manuscript drafting.