dataesr/scientific-paragraphs-categorization huggingface dataset:
The water masks used for the analysis are available from the Global Surface Water website (https:// global-surface-water. appspot. com/download), the  Landsat scenes via USGS EarthExplorer  (https:// earthexplorer. usgs. gov/),snow cover data from the NSIDC database (https:// nsidc. org/data/g02156),permafrost data from the  USGS ScienceBase Catalog at doi : 10. 5066/F7C53HX6 , and temperature data from the  NOAA Global Summary  of the Day website (https://data. nodc. noaa. gov/cgi-bin/iso?id=gov. noaa. ncdc: C00516 ).
7 mm, NEX 6, flip angle 180 , field of view (FOV) 5 cm, rare factor 8, imaging time: 11 0 17 00 minutes; and a T1w Fast Slow Angle Shot (FLASH) sequence; TR 398 ms, TE 2. 3 ms, matrix 212 × 180, 54 slices 0. 7 mm, NEX 20, flip angle 80 , FOV 5 cm; imaging time: 12'. Integrated correction of respiratory movements was performed with the Intragate technique. The volumetric analysis was performed using dedicated volume analysis software (Myrian; Intrasense, Paris, France). Whole liver and tumor margins were manually outlined on each slice of T2w sequences, taking advantage of the high natural contrast between liver (containing iron with high T2 relaxivity and CRC metastasis with slower T2 relaxation). All volumetric analyses were performed in a blinded fashion with respect to the experimental groups.
Image analysis and statistics Results were expressed as mean ± standard error of the mean (SEM). Differences between groups were evaluated with the unpaired Student's t test. Analysis of variance (ANOVA) was used to determine statistical differences. Overall statistical significance was set at p-value < 0. 05. Statistical analysis was performed using the software package SigmaStat (SPSS Inc. , Chicago). Fig. 2  summarizes the different steps of the protocol.
Metastastic tumor engraftment and general health condition At day 7 following the implantation of tumor cells, all animals showed microscopic foci of tumor in the caudate lobe and/or in the posterior lobe. A solitary tumor in the left liver lobe could also be observed for all animals (subcapsular tumor) (Fig. 3 ). There were no signs of extrahepatic disease. Animals were not affected by the tumor, showing normal feeding and cleaning habits. Weight gain during the study period did not differ between groups. Of the 48 operated animals, tumor volume could be analyzed at POD10 for 45, POD17 for 43 and at POD24 for 43 animals. Data were missing in 4 animals due to major injury of the portal vein during tumor cell injection (n = 1), major perihepatic adhesions limiting the feasibility of liver resection (n = 2) and portal vein ligation (n = 1).
Conclusion We have confirmed in a reproducible rat model of bilobar CLM, that contrary to portal vein occlusion, major hepatectomy decreases the growth of macroscopic CLM in the remnant liver as compared to laparotomy and minor hepatectomy. These results allow us to discuss the place of "Major (Right) hepatectomy first approach" in the TSH sequence. This approach may allow some patients to avoid embolization of the portal vein and limit the risk of tumor progression between the two stages. The arsenal of surgical approaches is important, and each strategy must be best adapted to the tumor disease, according to the principle of personalized therapy. The "Major Hepatectomy First-approach" should be evaluated in the future. French multicenter retrospective data are currently being collected to assess the feasibility of a prospective pilot study.
At the point of the study inclusion, several data were collected, namely demographic and clinical data, and APOE genotype (see Supplementary materials for more details). Exclusion criteria were a history of neurological or psychiatric diseases, including depressive disorders. The study was conducted in accordance with the tenets of the Declaration of Helsinki of 1975 and approved by the local Institutional Review Board at the participating center  (Ethical approval number: 2013-Fev-13150). All participants or their representatives gave written informed consent for the use of their clinical data for research purposes.
Blood sampling and immunoassays Ten milliliters (10 mL) of venous blood were collected in 1 BD Vacutainer tube (lithium heparin), which was employed for all subsequent immunological analyses. Blood samples were taken in the morning, after a 12-hour fast, handled in a standardized way, and centrifuged for 15 minutes at 2000 G-force at 4 °C. Per sample, plasma fraction was collected, homogenized, aliquoted into multiple 0. 5 mL cryovial-sterilized tubes, and finally stored at -80° within 2 hours from collection. Longitudinal data for plasma concentrations of YKL-40 were collected across 3 time-points, beginning at participants' enrollment ("baseline visit" or "M0") over a 3-year follow-up (1-year follow-up or "M12" and 3-year follow-up or "M36").
Immunoassay for plasma YKL-40 Plasma YKL-40 concentration was measured using a commercially available ELISA kit (R&D Systems, Minneapolis, MN), according to manufacturer's instructions. Repeatability was 6. 6% and 6. 9% and intermediate precision 6. 6% and 6. 9%, for 2 internal quality control plasma samples with concentrations of 14. 1 and 108. 0 pg/mL. All samples were analyzed using one batch of reagents by board-certified laboratory technicians who were blinded to clinical data  (Vergallo et al. , 2019).
PET data acquisition and processing Aβ and 18F-FDG-PET investigations were performed at the baseline visit ("M0")-as mandatory inclusion criterion-and at 2-year follow-up ("M24"). Brain Aβ-PET scans were acquired 50 minutes after injection of 370 MBq (10 mCi) of 18F-Florbetapir, which has high affinity for amyloid plaques. Brain 18F-FDG scans were obtained 30 minutes after injection of 2 MBq/kg of 2-deoxy-2-(18F)fluoro-d-glucose (18F-FDG). All acquisitions were performed in a single session on a Philips Gemini GXL scanner and consisted of 3 × 5 minutes frames with a voxel size of 2 × 2 × 2 mm3. Images were then reconstructed using iterative LOR-RAMLA algorithm (10 iterations), with a "smooth" post-reconstruction filter. All corrections (attenuation, scatter, and random coincidence) were integrated into the reconstruction.
HP, entorhinal, and BF volumes For the automated calculation of individual HP and BF volumes, the 3D-T1 MRI data were processed using statistical parametric mapping (SPM8, Wellcome Trust Center for Neuroimaging) and the VBM8 toolbox (available at http://dbm. neuro. uni-jena. de/vbm). EC volume was defined using the probabilistic maps present in the SPM Anatomy Toolbox. The technical details of these procedures are described in the Supplementary material. The delineation of the HP follows the consensual standard space harmonized protocol labels as described in detail by  Wolf et al. (2017). Modulated white matter voxel values were included in the HP volume calculation because the harmonized protocol explicitly specifies to include small white matter regions (alveus and fimbria) in HP segmentation  (Cavedo et al.
, 2018). The delineation and localization of the cholinergic BF followed the Mesulam's nomenclature based on the histological serial coronal sections and postmortem MRI scan of a brain from a 56-year-old man, as previously described  (Cavedo et al. , 2018). HP and BF volumes were corrected to the total intracranial volume (TIV) using the residuals method. First, a linear regression of the volume of a neuroanatomical structure on the TIV was fitted to the entire dataset. From the fitted model, the residuals, which are differences between actual volume and fitted volume based on a subject's TIV, were calculated  (Cavedo et al. , 2018). The TIV-corrected measurements were expressed as Vol_adji = Voli -b (TIVi -meanTIV)  (Cavedo et al. , 2018). Vol_adji is the TIV-adjusted volume of the subject i, Voli is the original uncorrected volume of the subject i, b is the slope from the linear regression of Vol on TIV, TIVi is the TIV for the subject i, and meanTIV is the mean TIV across all subjects.
Statistical analysis The analyses were performed excluding those individuals without YKL-40 concentrations at baseline, leading to a sample of 314 participants. In order to have a normal distribution, we used the natural logarithm of YKL-40 plasma concentration. First, we studied the univariate association between plasma YKL-40 concentrations and age, sex, and APOE ε4 allele. Linear models (LM) were used for baseline exploration and linear mixed models (LMMs) for longitudinal analysis over a 3-year follow-up (M0, M12, and M36). To investigate the effect of the variable of interest on longitudinal changes of YKL-40, LMMs with a random intercept were built including a time interaction. LMM is especially suitable for repeated measures with a considerable number of missing data across time points and to confirm neuroimaging baseline data calculated with a longitudinal pipeline.
Acknowledgements The research and this manuscript was part of the translational research program "PHOENIX," awarded to HH, and administered by the  Sorbonne University Foundation  and sponsored by la  Fondation pour la Recherche sur Alzheimer. The study was promoted by  INSERM  in collaboration with  ICM, IHU-A-ICM , and  Pfizer  and has received support within the  "Investissement d'Avenir"  ( ANR-10-AIHU-06 )  French program. The study was promoted in collaboration with the " CHU de Bordeaux " (coordination CIC  EC7 ), the promoter of Memento cohort, funded by the  Foundation Plan-Alzheimer. The study was further supported by  AVID/Lilly. CATI is a French neuroimaging platform funded by the  French Plan Alzheimer  (available at http://catineuroimaging. com).
Patients and setting This study was a retrospective study performed on data collected prospectively in a mixed 21-bed ICU university. The database regarding ARDS patients admitted to our ICU was initiated in 2005  [15]. The study was approved by the hospital's ethics committee (number 16-117). We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations for cohort studies (supplemental data). We included all patients aged older than 18 years who were admitted between October 1, 2009, and March 1, 2020, for ARDS (according to the American-European Consensus Conference criteria) with an arterial oxygen tension (P aO 2 )/inspiratory oxygen fraction (F IO 2 ) ratio ⩽150 mmHg  [15, 19, 24]  after at least 12 h of lung-protective mechanical ventilation (MV) with an F IO 2 ⩾50% and a positive end-expiratory pressure (PEEP) level ⩾5 cmH 2 O  [25].
Prospective data collection ARDS diagnosis and severity When IV alone was isolated in respiratory samples obtained at ICU admission, ARDS was classified as IV ARDS alone. Aetiological causes of non-IV ARDS alone were listed as IV associated with a co-pathogen, non-IV pneumonia, nonpulmonary sepsis, aspiration and miscellaneous  [1, [19] [20] [21]. Trauma patients were admitted to another ICU in the hospital (surgical ICU). Based on the Berlin criteria  [27] , patients were retrospectively categorised according to whether they had severe ARDS (P aO 2 /F IO 2 ⩽100 mmHg) or moderate ARDS (P aO 2 /F IO 2 >100 mmHg and ⩽150 mmHg) on the first day of ARDS diagnosis. Consequently, patients were prospectively selected based on the 150 mmHg P aO 2 /F IO 2 threshold used in 2005 and were classified retrospectively for severity based on the Berlin criteria.
Baseline characteristics of patients and causes of death In addition to the diagnosis of IV infection, the following variables recorded upon ICU admission and during the ICU stay were included as control variables because they are potentially associated with ARDS and influenza prognosis  [1, 4, 11, 28, 29]. Data collected for all patients were as follows: age, sex, the Simplified Acute Physiology Score (SAPS) II  [30]  (calculated within 24 h after admission) and the daily Sequential Organ Failure Assessment (SOFA) score  [31]  (calculated on the first 3 days following ARDS diagnosis). Comorbidities included in the analysis were liver cirrhosis, obesity, diabetes mellitus, aplasia and/or recent chemotherapy for a solid tumour or haematological disease, and previous coronary artery and/or valvular disease with treatment.
One patient with ARDS due to IV alone was matched with a patient with ARDS due to another cause with the closest absolute PS, and the maximum distance allowed between two matched patients was set at 0. 2 (i. e. calliper restriction). Tests were two-sided, and we considered p<0. 05 as significant. Statistical analyses were performed using Statview 5. 0 (SAS Institute Inc. , Cary, NC, USA) and the Statistical Package for Social Sciences, version 20 (SPSS, IBM, Chicago, IL, USA). The PS analysis was performed using R 3. 3. 3 (R Foundation for Statistical Computing, Vienna, Austria) with the MatchIt package.
Methods The E3N study E3N (Etude Epidémiologique auprès de femmes de l'Education Nationale) is a prospective cohort study involving 98,995 French women, aged 40-65 years at inclusion in 1990 and insured by a national health insurance covering workers from the French National Education System (Mutuelle Générale de l'Education Nationale, MGEN)  [21]. Participants were enrolled after completing a baseline self-administered questionnaire and returning a signed informed consent. Follow-up questionnaires were sent every 2-3 years thereafter. Detailed cancer risk factor data were collected through questionnaires at different time points during follow-up, including reproductive history, use of hormonal treatments, anthropometric characteristics, smoking habits, alcohol consumption, diet, and physical activity.
The average follow-up rate per questionnaire cycle has been of 83%, and to date, the total loss to follow-up since 1990 has been < 3%. The study was approved by the French National Commission for Data Protection and Privacy. Between 1994 and 1999, E3N participants were invited to donate blood, resulting in the collection of blood samples from approximately 25,000 participants. Each sample was separated into 28 aliquots (i. e. plasma, serum, buffy-coat, leukocytes, and erythrocytes) that were stored in plastic straws in liquid nitrogen containers (-196 °C) in a biobank. Breast cancer cases were identified through selfreport in the questionnaires, from the MGEN files, or through information from death certificates. Deaths were reported by family members and by searches in the MGEN files, and causes of death were obtained from the National Death Index.
below the median), both derived using principal component analysis, as previously described  [28]. The selection of confounders was done a priori, based on the known breast cancer risk factors available in our dataset that are potentially associated with exposure to PBDEs and PBB-153. Using multinomial regression, stratified analyses were performed based on tumor expression of estrogen (ERvs. ER+) and progesterone (PR-vs. PR+) receptors including all variables used in Model 2. Since multinomial regression models do not consider the case-control matching, multinomial models were additionally adjusted for the matching criteria: age at blood draw (continuous), BMI at blood draw (continuous), and year of blood draw (continuous), except for menopausal status at blood draw, in order to avoid over-adjustment considering the a priori inclusion of age at menopause in the model (premenopausal, postmenopausal and age at menopause < 51 years, postmenopausal and age at menopause ≥51 years).
Using the PROC TRAJ of SAS 9. 3, we highlighted three trajectories of evolution of the body size of each woman using age as time scale: normal body size from ages 8 to 35 years; normal body size until puberty, followed by a large body size afterwards; and large body size from ages 8 to 35 years. All women were included in these trajectories. Then, Model 2 was adjusted for BMI variation over the 5 years preceding blood sampling (BMI decrease of more than 2 points; stable BMI; and BMI increase of more than 2 points). We conducted a stratified analysis based on BMI, considering groups with BMI < 25 and ≥25 kg/m 2. In our study population, missing values were < 5% for all variables and were imputed to the median (continuous variables) or modal value (categorical variables). Statistical analyses were performed using the SAS software (version 9.
Purpose: The primary aim of this study was to assess whether there was a clinically significant difference in the mean Oxford knee score (OKS) between 6 and 12 months after total knee arthroplasty (TKA). The secondary aim was to identify variables associated with a clinically significant change in the OKS between 6 and 12 months. Methods: A retrospective cohort study was undertaken using an established arthroplasty database of 1574 primary TKA procedures. Patient demographics, body mass index (BMI), comorbidities, OKS and EuroQoL 5domain (EQ-5D) score were collected preoperatively and at 6 and 12 months postoperatively. A clinically significant change in the OKS was defined as 5 points or more. Results: There was a 1. 1-point increase in the OKS between 6 and 12 months postoperatively, which was statistically significant (95% confidence (CI) 0.
Patients and methods Patients for this study were identified retrospectively from a prospectively compiled arthroplasty database held at the study centre. During a 57-month period (January 2013 to September 2017) 2550 TKA were performed at the study centre and patients were asked to complete a preoperative patient questionnaire at their preassessment clinic. Medical staff were on hand to assist and answer any questions. The patient demographics, body mass index (BMI) and comorbidities were recorded at the preoperative assessment. Categories of comorbidity included were myocardial infarction (MI), heart failure, peripheral vascular disease (PVD), stroke, dementia, chronic obstructive airways disease (COPD), diabetes mellitus, back pain and pain in other joints, which were recorded as dichotomous variables.
This index is on a scale of -0. 594 to 1, where 1 represents perfect health, and less than 0 represents a state worse than death. During the study period two type of implants were used: Triathlon (Stryker, Marwah, NJ, USA) and the PFC Sigma (DePuy, Johnson & Johnson Professional Inc. , Raynham, MA, USA). A measured resection technique was used. All patients were reviewed at a preassessment clinic. A standardised rehabilitation protocol was used for all patients, with active mobilisation on the first postoperative day. Patients were then reviewed at 6 weeks, 6 months and 12 months postoperatively. Statistical analysis was performed using the Statistical Package for Social Sciences version 17. 0 (SPSS Inc. , Chicago, IL, USA). Student's paired or unpaired t test was used to compare linear variables between groups.
We make use of sensitive (9. 3µJy beam -1 RMS) 1. 2 mm-continuum observations from the ASPECS ALMA large program of the Hubble Ultra Deep Field (HUDF) to probe dust-enshrouded star formation from 1362 Lyman-break galaxies spanning the redshift range z = 1. 5-10 (to ∼7-28 M ⊙ yr -1 at 4σ over the entire range). We find that the fraction of ALMA-detected galaxies in our z = 1. 5-10 samples increases steeply with stellar mass, with the detection fraction rising from 0% at 10 9. 0 M ⊙ to 85 +9 -18 % at >10 10 M ⊙. Moreover, stacking all 1253 low-mass (<10 9. 25 M ⊙ ) galaxies over the ASPECS footprint, we find a mean continuum flux of -0. 1±0. 4µJy beam -1 , implying a hard upper limit on the obscured SFR of <0. 6 M ⊙ yr -1 (4σ) in a typical low-mass galaxy. The correlation between the infrared excess IRX of U V -selected galaxies (L IR /L UV ) and the U V -continuum slope is also seen in our ASPECS data and shows consistency with a Calzetti-like relation at >10 9.
2020). The region chosen for targeting by ASPECS is that region of the HUDF containing the deepest near-IR, optical, Xray, and radio observations available anywhere on the sky  (Beckwith et al. 2006; Bouwens et al. 2011; Ellis et al. 2013; Illingworth et al. 2013; Teplitz et al. 2013; Rujopakarn et al. 2016). These deep, multi-band photometric observations have made it possible to identify 1362 U V -selected star-forming galaxies at z ∼ 1. 5-10 and to systematically quantify their obscured SFRs as a function of a wide variety of physical properties. The new 1-mm continuum ASPECS observations are sufficiently sensitive to probe dust-obscured SFRs of 7-28 M ⊙ yr -1 at 4σ over a ∼5×10 4 Mpc 3 comoving volume in the dis-tant universe. The 4. 2 arcmin 2 targeted with our large program is ∼4× wider than in our ASPECS pilot program  (Walter et al.
OBSERVATIONS AND SAMPLE 2. 1. ASPECS Band 6, HST, and Spitzer Data The principal data used are the band-6 ALMA observations from the 2016. 1. 00324. L program over the HUDF. Those observations were obtained through a full frequency scan in band 6 (212 -272 GHz) with ALMA in its most compact configuration. The observations are distributed over 85 pointings separated by 11 ′′ and cover an approximate area of ∼4. 2 arcmin 2 to near uniform depth. Our construction of a continuum mosaic from ALMA data is described in  González-López et al. (2020). The peak sensitivity in our 1. 2 mm continuum observations is 9. 3µJy (1σ) per synthesized beam (1. 53 ′′ × 1. 08 ′′ :  González-López et al. 2020). For HST optical ACS/WFC and near-infrared WFC3/IR observations, we make use of the XDF reductions  (Illingworth et al.
22  For the 0. 2-0. 4µm WFC3/UVIS data over the ASPECS field, we made use of the v2 release of the UVUDF epoch 3 data  (Teplitz et al. 2013; Rafelski et al. 2015)  which included imaging data in the F225W, F275W, and F336W bands. The Spitzer/IRAC observations we utilize are from the ∼200-hour stacks of the IRAC observations over the HUDF from the GREATS program (M. Stefanon et al. 2020 : PI: Labbé).
Flux Measurements Photometry for sources in our samples is performed in the same way as in the  Bouwens et al. (2016)  analysis from the ASPECS pilot program. HST fluxes are derived using our own modified version of the SExtractor  (Bertin & Arnouts 1996)  software. Source detection is performed on the square-root of χ 2 image  (Szalay et al. 1999 : similar to a coadded image) constructed from the V 606 , i 775 , Y 105 , J 125 , JH 140 , and H 160 images. After PSF-correcting fluxes to match the H 160 -band image, color measurements are made in  Kron-style (1980)  scalable apertures with a Kron factor of 1. 6. "Total magnitude" fluxes are derived by (1) correcting up the fluxes in 22 We do nevertheless note the existence of a new Hubble Legacy Field data release  (Illingworth et al. 2016; Whitaker et al.
2013; Rafelski et al. 2015). Eighteen of the z > 1. 5 galaxies within our ASPECS footprint are detected at >4σ in our 1. 2-mm continuum observations. 12 of the 18 >4σ detections were previously identified as part of the ASPECS pilot program  (Aravena et al. 2016; Bouwens et al. 2016)  or the  Dunlop et al. (2017)  program. Six of the reported continuum detections are new discoveries from the ASPECS large program (see  González-López et al. 2020; Aravena et al. 2020). The observed number of continuum detections is in agreement with the predictions obtained by applying a consensus low-redshift IRX-β relationship derived here (Appendix B) to the highest-mass z = 1. 5-10 galaxies found over ASPECS suggests a likely sample of 28 continuum detections, while only 16 continuum detections is predicted if only sources with stellar masses in excess of 10 9.