dataesr/scientific-paragraphs-categorization huggingface dataset:
Post-weaning animals are often used as tissue donors because of their high sensitivity to mycotoxins  [26]. To avoid unspecific molecular responses (i. e. event related with apoptosis or oxidative stress) able to mask ZEN-specific toxic events  [27]  we selected non-cytotoxic exposure conditions (4h at 100µM) for pig intestinal epithelial cells (Lahjouji T. ; unpublished results). Proteomic analysis and verification was performed in three paired control and treated samples (analytical sample set), whereas a second set of samples (six paired control and treated samples; validation sample set) obtained in an independent experiment under the same conditions were employed for functional analysis validation. Paired t-tests (GraphPad Software, San Diego, CA, USA) and paired ratios were employed to analyze differences in protein abundance in all cases.
The OrbiTrap QHF-X was operated in Data Dependent Acquisition mode to automatically switch between full scan MS and MS/MS acquisition using Xcalibur software (Thermo Fisher Scientific). Survey scan MS was acquired in the Orbitrap over the m/z 350-1400 range, with the resolution set to a value of 60 000 (m/z 400). The 12 most intense ions per survey scan were selected for higher-energy collisional dissociation, and the resulting fragments were analysed in the linear ion trap. Dynamic exclusion was employed within 60s to prevent repetitive selection of the same peptide.
Bioinformatics analysis of mass spectrometry raw files and functional analysis of results Mass spectrometry raw files were analysed using the Proline software version 1. 6  [30]. MS/MS spectra were searched in the Mascot search engine (Matrix Science Inc. , Boston, MA, USA) against the forward and reverse UniprotKB Sus scrofa database combined with a commonly observed contaminants list. The digestion enzyme was set to trypsin with up to two missed cleavages. Methionine oxidation and N-terminal acetylation were searched as variable modifications and carbamidomethyl of cysteine as fixed modification. Parent peptide masses and fragment masses were searched with maximal mass deviation of 10 ppm. A first level of false discovery rate (FDR) filtration was done on the peptide-spectrum match level, and this was followed by a second level of FDR control on the protein level.
Both filtrations were performed at a 1% FDR. These filtrations were done using a standard target-decoy database approach. For label-free relative quantification of the samples, the "match between runs" option of Proline was enabled to allow cross-assignment of MS features. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD017845. For an overview of the Gene Ontology (GO) terms regarding cellular compartment, molecular function, and biological processes in which proteins with differential abundance are involved (fold change ≥1. 5 and paired t-test p-values ≤0. 05), we used the DAVID Bioinformatic database v6. 8 software  [31]. The list of proteins showing differential abundance was also uploaded onto Ingenuity Pathway Analysis (IPA) (Qiagen Bioinformatics, Hilden, Germany) and GeneAnalytics (LifeMap Sciences, Alameda, CA; USA) software, and mapped to the respective databases of each tool.
IPA uses networks, diseases, molecular and cellular functions. generated from previous publications and public protein interaction databases using the Ingenuity Knowledge Base as a reference. GeneAnalytics analysis employs and GO terms, pathways, phenotypes, and drug/compounds from GeneCards as reference. The functional analysis provided by IPA using significantly different proteins with fold change abundance ≥1. 5 was not informative, so we decided to include also significantly different proteins with fold change abundance ≥1. 2.
Loading control consisted in total protein staining of membranes using SyproRuby blot stain after blotting (Thermo Fisher Scientific). Images were obtained by scanning Chemidoc (SyproRuby-stained membranes; Bio-Rad) or a Li-Cor Odyssey Infrared Imager (Fluorescent immunoblots; Li-Cor Biosciences, Lincoln, NE, USA). Image Studio Lite Software (Li-Cor Biosciences) was used for image analysis. Band intensity values were normalized onto the respective total protein staining. Levels of phosphoproteins (phosphor-ERK1/2 and NF-κB) were normalized against the corresponding non-phosphorylated protein.
ZEN-induced accumulation of ERα mediates an activation of NF-κB signaling without initiating an inflammatory response The list of differentially abundant proteins (fold change ≥1. 5 and paired t-test p-values ≤0. 05) was analyzed using DAVID database in order to identify significantly enriched GO terms (Table  2 ). Overall, enriched GO terms were associated with three general three functions: (i) translational/posttranslational responses, (ii) metabolic changes and (iii) NF-κB signaling. The first function, translational/post-translational responses, was linked with the over-representation of proteins related with endoplasmic reticulum and the ATPase complex that links the endoplasmic reticulum with the proteasome system, as well as changes in rRNA processing-related proteins. Changes in the second function, cell metabolism, was indicated by the enrichment in mitochondrial proteins, isomerase activity, glycerol transport and water channel proteins.
Funding: This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 722634 as well as by the  Agence Nationale de la Recherche (ANR)  grant  ExpoMycoPig  ( ANR-17-Carn012 ). The work was also supported in part by the  Région Occitanie, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , and the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ).
Flight phases including ground phase, climb, descent, cruise and levelling  [9]  Hovering aircraft registration (helicopters or drones) External sources of information are essential to properly label trajectories: registration databases validate the identified missions as not all aircraft have the required equipment for those. Navigational events also heavily rely on the structure of the airspace where aircraft evolve. Section 3 presents the most basic methods to leverage these sources of information to identify missions, navigational events and flight phases.
Identification of Missions Based on Tail Numbers The most basic method to select trajectories for specific missions is to make use of aircraft trajectory databases like the OpenSky Aircraft database. For instance, firefighting operations are mostly conducted by aircraft and helicopters owned by specific institutions and agencies. Rescuing operations, aerial surveys or flight inspections are also usually conducted by the same fleet of aircraft (including helicopters). Figure  1  shows how aircraft trajectories flown by the California Department of Forestry and Fire Protection during a severe period of wildfires are consistent with the location of wildfires. A simple selection of pieces of circling segments can produce a trajectory-based heat map of wildfires in a given region. This approach has three main limitations.
Detection of Take-Off and Landing Among the navigational events listed in Table  1 , some can be directly specified and implemented on ADS-B data, in spite of some tricky corner cases to keep in mind, as they can become limitations in specific contexts. Aircraft broadcast specific type codes (between 5 and 8) in ADS-B messages (DF17) when on the ground. This piece of information is commonly used to determine whether an aircraft has landed or not taken off yet. The OpenSky Impala database stores this piece of information as an onground boolean. This bit is usually sufficient to determine whether an aircraft is really on the ground. However, in some situations, aircraft, possibly subject to faulty sensors, continue to broadcast airborne positional messages while on the ground. Vertical rate information can be a good way to cross-check the validity of the onground flag.
The ROXAS software (von Arx and Carrer 2014) combined with Image-Pro Plus (Media Cybernetics, Rockville, MD, USA) allowed us to detect fibers and vessels automatically and manually from the images  (von Arx et al. 2016). Cell lumen area (A in μm 2 ), mean cell wall thickness (WT in μm) and cell radius of the long (α) and short (β) axis were measured for each detected fiber (A <150 μm 2 ) and vessel (A ≥150 μm 2 ), together with positional information relative to the ring boundary (see  Peters et al. 2018). A density profile was established by dividing each tree ring in the processed image into 100-μmwide sectors (s) parallel to the ring boundary and calculating the mean density per sector (ρ s , assuming a fixed density of wall material expressed with γ = 1. 504 g cm -3 ; Kellogg et al. Tree Physiology Volume 40, 2020 1969) based on Eq.
The resulting intercepts and slopes provided a metric of how strong the axial changes in RW were in a given year. In a second step, we compared the strength of these axial changes to radial growth at breast height across all trees and years. For this, we constructed a linear mixedeffects model, in which the annual slopes were fitted against annual RW at breast height from the corresponding trees, with 'tree' included in the model as a random effect. To constrain the uncertainties associated with the fitted linear regression parameters, we performed a bootstrapped resampling analysis (1000 iterations with replacement). This analysis was performed using the 'lme4' package in R  (Bates et al. 2014 ) and accounted Tree Physiology Online at http://www. treephys. oxfordjournals. org for linear model assumptions (e.
2014b ) to be able to assess the climate response of ρ over a longer time period starting in 1930. We calculated Pearson's correlation coefficients between the resulting RW and ρ chronologies and monthly mean temperature (in • C) and precipitation sum (in mm) derived from the CRU 3. 23 gridded dataset  (Harris et al. 2014 ). This climate response was assessed for two separate periods starting (i) in 1930 (maximum length of RW and X-ray ρ series with ≥10 individuals) to identify the overall temperature and water limitations on tree growth at our site and (ii) in 1996 (covering RW and ρ derived from wood anatomical data) to assess hypothesis H2 in more detail. Additionally, we performed an uncertainty analysis on the climate-growth correlations to confirm that the shorter records from the axial trees (starting in 1996) matched with the variability of the longer time series (starting in 1930) and showed a similar climatic response (see Figure  S3  available as Supplementary Data at Tree Physiology Online).
Funding This work was part of the project ' Inside out ' (# POIR. 04. 04. 00-00-5F85/18-00 ) funded by the  HOMING program  of the Foundation for Polish Science co-financed by the  European Union  under the  European Regional Development Fund. The work was also supported by the  EU-H2020 program  (grant  640176 , ' BACI ') and by the  Swiss National Science Foundation Early Postdoc. Mobility  (grant  P2BSP3_184475 ).
Conclusion In this study, a promising technique, derivate from the Laser Shock Adhesion Test, was successfully experienced to delaminate at very high strain rate a Ultra High Molecular Weight PolyEthylene composite material that is commonly used for the development of light weight armors and of ballistic protection. It was assessed that the technique is able to generate pressure loading comparable to those exerted by a ballistic impact. The duration of the laser induced shock loading is however of the order of thousand times shorter than the one of a ballistic impact. This allows tests at the laboratory scale and also its use for reduced scale samples, which presents economic interest. In addition, a spatial shape of the laser induced shock has been determined. It remains a key parameter as input data in the Finite Element Model.
Calculation process of fouling resistance The study was done on the level of heat exchanger of the phosphoric acid concentration process. The experimental data was collected out during one year. The method that we used to monitor the fouling evolution consists on carrying out a heat balance at the boundaries of the heat exchanger through the measurements of inlet and outlet temperatures, suction and discharge pressure and acid density. The latter were taken every 2 h throughout all the day. This method, although indirect, allow to detect the necessary moment to shut down the installation for cleaning. In the present study, the temporal evolution of the fouling resistance of the phosphoric acid was studied. The calculation of the fouling resistance (Rf) was carried out using the following relation: 1 1 1 1 ( ) ( ) ( 0) s p Rf t U U U t U t      (1) The overall heat transfer coefficient at the dirty state (Us= U(t)) was given in the time course, via the expression:   , , , ( ) ac cir ac ac out ac in s ml t m Cp T T U A F T                (2) This relation is drawn from the evaluation of energy on the heat exchanger assuming the isolated system and the physical properties of the two fluids remaining constant along the exchanger.
Conclusion The monitoring of heat exchanger allows to fully knowing the fouling evolution under specific conditions of the process. The deposit formation is a thermal resistance that causes significant economic and ecological penalties  [15]. The objective of this work was the study of the heat exchanger fouling phenomenon in the concentration process. A large number of plant operating data of the graphite blocks heat exchanger of the phosphoric acid concentration unit were collected every 2 hours for 7 runs, each run is about 3-5 days. These operating parameters allow evaluating the temporal evolution of the fouling resistance, the volume flow rates, the temperature difference of phosphoric acid, the steam temperature and the effect of the phosphoric acid density. Results indicate that the fouling resistance follows an exponential evolution in conformity with the model of Kern and Seaton with the absence of the induction period, which is explained by a poor cleaning, or a deviation between the present study and the beginning of the functioning of the heat exchanger after the last stop.
Tsimane Health and Life History Project Since 2002, a mobile team of trained Bolivian physicians, laboratory technicians, and anthropologists has visited 18 to 90 villages per wave (each wave is 1 to 2 years) and conducted systematic clinical evaluations with no exclusion criteria. Certain clinical evaluation protocols were limited to adults aged 40+ years given the project's focus on aging  (8). As part of the evaluation, the team collected data for each participant on medical history, current symptoms and diagnoses (ICD-10), anthropometry, hematology, and health biomarkers from fecal and urine analysis. The use of an upper threshold of 39°C to eliminate cases of acute fever is arbitrary and greater than some standard clinical cutoffs (i. e. , the American College of Critical Medicine and Infectious Disease Society classify fever as BT ≥38.
3°C). To investigate the effect of different upper thresholds, we reran our analyses and that of Protsiv et al. (1) , removing any data ≥38°C (tables S11 and S12). This change did not affect the results of either study. Other THLHP data reveal changes in environment and behavior over the two-decade study period (Fig. 3 ). Anthropometric indices include weight (kg) and body fat (%) as measured by bioelectric impedance using a Tanita scale (BF series) and BMI (kg/m 2 ). Height was measured using a portable Seca 213 stadiometer. In-field blood analysis of fasting venous samples provided estimates of ESR (Westergren method) and leukocyte [white blood cell (WBC)] count. WBC count is a biomarker of immune activation and possibly infection, while ESR is a nonspecific indicator of inflammation  (46).
Leukocytosis was defined as WBC > 10. 0 × 10 Physical activity was measured as daily step count based on ~3-day samples using a waist-worn triaxial ActiGraph GT3X accelerometer  (47). Accelerometers were initialized using a participant's ID, weight, age, sex, and a 30-Hz resampling rate using ActiGraph's ActiLife software. Participants were instructed as to the proper positioning of the accelerometers on the right hip and asked to only remove the accelerometers before submersion in water (e. g. , bathing in the river) and to reapply them directly after leaving the water. Raw data were downloaded and converted to 1-min epochs using ActiLife version 6. 13. 4. We used the "low-frequency" extension filter from ActiLife to convert raw accelerations to steps. Non-wear periods, considered any period of 60-min or longer with accelerometry counts at zero, were excluded.
2°C. Nevertheless, we included a variable in models for type of instrument used. Additional models explore other indicators of infection or compromised health (table  S5 ), changes in these indicators over time (table  S7 ), and the effects of body composition (table  S10 ). The sample sizes for these models vary based on missing data due to selective sampling by the THLHP  (8). In general, our sampling scheme here focused on all adults age 40+ years and a stratified random sample of those age 15 to 39 years. Indicators include ESR, leukocyte count (WBC), and Hb. Elevated ESR indicates nonspecific, systemic inflammation, whereas elevated WBC suggests immune activation. Low Hb is an indicator of anemia status, which acts as an additional proxy of nutritional status and condition. For a smaller subset (n = 5821 observations on 2221 individuals), microscopy of eggs or larvae from fecal smears on wet mounts was conducted to identify the presence or absence of four intestinal helminth species: hookworm (Necator americanus), large roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura), and threadworm (Strongyloides stercoralis) [see  (6)  for methodological details].
BT data (n = 17,958 observations) exist for 5481 Tsimane age 15+ years measured from 2002 to 2018, covering birth years 1914-2003 (fig. S1). An oral mercury thermometer was used from the beginning of the study until mid-2006, while tympanic Braun digital thermometers (Thermoscan 5) were used thereafter. As in Protsiv et al. (1), we eliminated extreme temperature values [i. e. , <35°C (n = 41) and >39°C (n = 16)]. The final dataset including complete data contains 16,830 observations on 5370 individuals (tables S1 and S2).
Documentation of informed consent was waived because the data were collected and analyzed anonymously from web-based surveys. Pretest. An online survey was fielded between January 31 and February 10, 2020 (not preregistered). At this time, the coronavirus was spreading in Asia but had not spread yet in the United States. The purpose of this original survey was to examine the physical, social, and cognitive correlates of FFM personality traits and well-being across adulthood. We contracted with Dynata (www. dynata. com) to recruit participants to complete a Qualtrics survey administered by the Florida State University College of Medicine. Participants were sampled from across the United States and stratified in equal numbers (n = 500) across seven age bands: 18-19, 20-29, 30-39, 40-49, 50-59, 60-69, and 70 and older.
The adolescent age band was smaller than the other 10-year age bands because it was restricted to participants who could consent legally to participate without consent from a parent or legal guardian. The sample was also stratified by gender (50%/50% male/female) and race (20% African American). The stratification did not go as intended in the original data collection and there was an oversample of participants between 30-59 years old and few participants who were 18 or 19. More participants in the 18-19 year-old age band were recruited and tested to improve representation in this age group. As such, in the final sample, participants between 30 and 59 years old were oversampled relative to the other age groups, and the overall sample size was larger than originally planned. A total of 3,963 participants had valid personality data at pretest (See S1 Fig for a flowchart of participant inclusion at pretest).
The personality measure was embedded in a larger survey block that included questions about other aspects of psychological functioning, health, and health-related behavior (see preregistration for full questionnaire). The survey also included a block of questionnaires about COVID-19. These two blocks were randomly counterbalanced across participants. There was no effect of order on any of the results reported below. A total of 2,137 participants had valid data on personality traits at both pretest and posttest (See S1 Fig for a flowchart of participant inclusion at posttest). Participants in the final analytic sample were from all 50 states and Washington, DC and Puerto Rico and participated in numbers roughly proportional to the population of the state/DC/PR (i. e. , sample sizes were larger in populous states, such as California and New York, and smaller in less populous states, such as North Dakota and Wyoming).
With the pandemic, the study was reconceptualized to take advantage of the data collected on psychological functioning just prior to the pandemic. Fortunately, many participants were willing to fill out a second survey, but given that the original study was not meant to be longitudinal, there was no expectation that participants would continue to participate. Second, we tested for trait change in the acute phase of the pandemic. Although the purpose of this measurement was to address whether trait psychological functioning was responsive to an acute health-threatening crisis, it is also possible that the effects of the crisis could take longer to consolidate into substantial changes in personality. Future work will need to address whether there are long-term changes in personality in response to the coronavirus pandemic.
Shellfish Samples Among samples received in the laboratory between January and May 2016 for suspicion of NoV contamination in the frame of epidemiological investigations following NoV outbreaks in consumers, we selected 10 samples with a mean Ct value for NoV GII below 36 after triplicate qualitative qRT-PCR analysis (see below), and with enough available material for our study. These 10 samples (one clam, two mussel and seven oysters samples) were linked to eight NoV outbreaks involving a total of 26 human cases (Table  1 ). For two outbreaks, matched stool samples from patients were analyzed and NoV GII. 17 was detected (data from the National Reference Center for gastroenteritis viruses, Dijon, France). These samples were not available for other outbreaks.
Detection of Viral Genomes by rRT-PCR Primers and probes previously described were used for MgV and NoV detection by real-time RT-PCR (ISO 15216-1:2017). Specifically, NoV GII was detected using QNIF2 and COG2R primers, and QNIFs probe  [26, 27]. The rRT-PCR was carried out with the Ultrasense One-step quantitative RT-PCR system (Fisher Scientific, Illkirch, France) using 5 µL of NA per well (final volume of 25 µL) on a Mx3000P QPCR System (Agilent Technologies, Les Ulis, France). Undiluted and 10 -1 dilutions of NA were processed in triplicates. Negative controls (H 2 O and negative DT extract) and a positive control based on in vitro transcription of plasmids containing nucleotides 4191-5863 of the GII. 4 Houston virus (Genbank EU310927) were included in each run. The cycle threshold (Ct) value enabling quantification was defined as the cycle that showed a significant increase in fluorescence.