dataesr/scientific-paragraphs-categorization huggingface dataset:
25. The correlation coefficient (R) of 0. 65 reflects considerable correlation for both datasets. The MBE was calculated to analyse the bias between ground-based and satellite data. We obtained a value of +5 %, indicating an MWR overestimation with respect to the MLS. Unlike the average ozone mixing ratio at 27 km, the MBE at 37 km reflected an underestimation of the ozone mixing ratio of -11 % compared with MLS. Fiorucci et al. (2013)  also presented differences ranging between -8 % and -18 % in the 17-50 km vertical range, reaching ∼ -18 % at 37 km. The regression analysis presents a slope of 0. 96 and an intercept of 0. 44. Similarly, the correlation coefficient at this altitude was calculated (R = 0. 63) to evaluate the correlation between MWR and MLS at this altitude. The best correspondence was found at 65 km.
Description of the case study After analysing the correspondence of the MWR measurements to independent instruments, here we analyse a shortterm ozone variation for an atypical case study of the influence of the AOH over Río Gallegos during November 2014. Figure  7a  shows the time evolution of the MWR ozone mixing ratio at 27 (blue line) and 37 km (red line) by November 2014 with their respective zonal mean value (white line) and 1 standard deviation (SD) (light blue and light red, respectively). The statistical quantifiers were calculated from the MLS profile dataset, interpolated to the latitude of Río Gallegos from 2004 to 2016. Both altitudes present similar behaviours. We observe a rapid ozone decrease at both altitudes from 11 November at 19:30 local time (LT) to 15 November. The minimum value at 27 km is reached at 06:30 LT, while at 37 km it occurs at 05:30 LT, and both minimums are far more than 2 SD from the mean value (around less than 25 % and 20 % with respect to the climatology, respectively).
Therefore, the distance between the considered location of the MLS measurements and the location of the MWR could explain partly the difference between the ozone mixing ratios retrieved from these two instruments. Comparisons between DIAL and MLS were realized by  Sugita et al. (2017)  for an unusual case of persistence of the AOH over Río Gallegos that occurred during November 2009, and they also attributed part of the differences to the non-co-location of the measurements. Future studies analysing longer datasets will be interesting to determine the influence of the distance between measurements on the ozone mixing ratio differences between instruments in this region. It is important to note that the MWR and DIAL instruments retrieve ozone in different fundamental units. While the MWR provides the ozone mixing ratio, DIAL provides the ozone number density as a function of altitude.
Conclusions We have presented ozone mixing ratio measurements at 27, 37 and 65 km with a temporal resolution of ∼ 1 h from a millimetre-wave radiometer installed at Río Gallegos, Argentina, from October 2014 to December 2015. The MWR ozone mixing ratio retrieved was compared for the first time with ground-based measurements from the ozone DIAL instrument and satellite measurements from the MLS onboard the AURA in defined overlap altitudes. The comparison revealed good correspondence between independent instruments. The comparison with MLS measurements presents a positive bias of 5 % at 27 km and a negative bias of -11 % and -7 % at 37 and 65 km, respectively. The correlation between measurements at those altitudes was 0. 65, 0. 63 and 0. 88 at 27, 37 and 65 km, respectively. The comparison with the DIAL data at 27 km reflected a good corre-spondence, with a negative bias of -1 % with a correlation coefficient of 0.
The time evolution of the daily maximum UVI measurements during the analysed period clearly reflects the anti-correlation with the TOC. The MWR installed in the OAPA covers the lack of ground-based radiometer observations of ozone between Antarctic latitudes and mid-latitudes, allowing us to improve the understanding of the stratospheric and low-mesospheric dynamic using the ozone mixing ratio as a tracer and improving the validation of dynamical models. It is expected to join the MWR to the NDACC Network in future. Data availability. AURA/MLS satellite data have been downloaded from https://mls. jpl. nasa. gov/ (last access: January 2016), EOS MLS Science Team, MLS/Aura Near-Real-Time L2 Ozone (O3) Mixing Ratio V003, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC)  (Livesey et al.
, 2011). OMI-ERS2 overpass satellite data are available at https://avdc. gsfc. nasa. gov/index. php?site=2045907950, last access: July 2019  (OMI, 2019). APV data assimilated with the MI-MOSA model can be solicited directly by email to Nelson Bègue (nelson. begue@univ-reunion. fr). Ozone and UV Index groundbased data (MWR and YES UVB-1) are not available on international networks yet. For these datasets and further information, contact the corresponding author Pablo Facundo Orte by email (porte@citedef. gob. ar). Author contributions. PFO, EW, AM, NB and HB conceived and designed the analysis. PFO and EW performed the experiment and analysed the data. JLB contributed with the statistical data analysis. AM was the PI of the Saver-Net Project and he was responsible for the MWR instrument. AM and HO contribute with the MWR description.
RD was responsible for the YES UVB-1 dataset and contributed with the data analysis. NB performed the MIMOSA model runs together with HB, and they contributed with the PV data interpretation. AP and SGB also assisted with the PV data analysis and with the MIMOSA model information. HO, AM and JS as-sisted with the MWR ozone data inversion. JS and JQ performed the DIAL measurements and ozone profile inversion. PFO wrote the paper. All the authors contributed with the preparation and the improvement of the paper.
sections and daily air mass factor to convert measured slant column densities (SCDs) into vertical column densities (VCDs). In the case of ozone, look-up tables (LuTs) of air mass factors are used(Hendrick et al. , 2011). The LuTs were obtained from the UVSPEC/DISORT radiative transfer model using the TOMS V8 ozone and temperature profiles' climatology. This SAOZ joined the NDACC network in 2009 and the dataset can be downloaded from the SAOZ webpage (http://saoz. obs. uvsq. fr/SAOZ-RT. html, last access: March 2018) and NDACC webpage (http://www. ndaccdemo. org/, last access: March 2018).
Acknowledgements. The authors would like to thank  JICA  and  JST  for the financial support of the  SAVER-Net  network and OAPA facilities in the south of Argentina. The  French-Argentine ECOS-Sud  ( A16U01  project) programme is thanked for support research visits between France and Argentina which facilitated the research carried out. JSPS KAKENHI  (grant no. JP 18KK0289 ) is also thanked for supporting the cost of the publication. The authors acknowledge  NASA  for the availability of the data from AURA/MLS and  OMI. The University of Reunion Island  is thanked for the access to the MIMOSA model and the  CCUR team  for the use of the TITAN supercomputer. The authors also want to thank the referees for their valuable comments which helped to improve the manuscript. Financial support. This research has been supported by  JICA  and  JST , the  French-Argentine ECOS-Sud  ( A16U01  project), and  JSPS KAKENHI  (grant no.
sections and daily air mass factor to convert measured slant column densities (SCDs) into vertical column densities (VCDs). In the case of ozone, look-up tables (LuTs) of air mass factors are used(Hendrick et al. , 2011). The LuTs were obtained from the UVSPEC/DISORT radiative transfer model using the TOMS V8 ozone and temperature profiles' climatology. This SAOZ joined the NDACC network in 2009 and the dataset can be downloaded from the SAOZ webpage (http://saoz. obs. uvsq. fr/SAOZ-RT. html, last access: March 2018) and NDACC webpage (http://www. ndaccdemo. org/, last access: March 2018). used in this work was installed in the OAPA observatory on 11 March 2008, and it belongs to LATMOS/CNRS. SAOZ measures the sunlight scattered from the zenith sky. The dif- ferential optical absorption spectroscopy (DOAS) method is applied to retrieve total ozone and nitrous dioxide columns twice a day at high solar zenith angles between 86 and 91 • at sunrise and sunset.
Background: At sexual maturity, the liver of laying hens undergoes many metabolic changes to support vitellogenesis. In published transcriptomic approaches, hundreds of genes were reported to be overexpressed in laying hens and functional gene annotation using gene ontology tools have essentially revealed an enrichment in lipid and protein metabolisms. We reanalyzed some data from a previously published article comparing 38-week old versus 10-week old hens to give a more integrative view of the functions stimulated in the liver at sexual maturity and to move beyond current physiological knowledge. Functions were defined based on information available in Uniprot database and published literature. Results: Of the 516 genes previously shown to be overexpressed in the liver of laying hens, 475 were intracellular (1.
For this purpose, we selected and used the data obtained from a layer line that is used worldwide  [11]. The originality and the added-value of the present work is that the functional annotation of overexpressed genes was achieved using a manual approach by retrieving information from Uniprot database that is currently available, but also by considering known physiological changes associated with sexual maturity in hens. The reason for such an approach instead of using classical gene ontology analysis is that most gene ontology tools are highly efficient to decipher the biological functions of proteins and molecules responsible for physiopathological situations in mammals. In contrast, these approaches are clearly less relevant when using oviparous models. This comment is particularly true for the chicken liver of females knowing that this organ expresses and secretes in blood most precursors of yolk proteins that lack homologous genes in mammals.
Number of overexpressed genes Using a 20 K chicken oligoarray corresponding to 12,595 different chicken probes, a total of 582 probes had been shown to be over-expressed in the liver at sexual maturity of hens  [11]  (Additional file 1, column A-C). It is noteworthy that these genes have a basal expression in the liver of immature pullets. Further to this publication, the Gallus_gallus-4. 0 assembly was released in April 2013 by the International Chicken Genome Consortium followed by the Gallus_gallus-5. 0 assembly (released in Oct 2016) and GRCg6a (GCA_000002315. 5) assembly, submitted by the Genome Reference Consortium on April 2018. Using this last genome annotation, we reanalyzed the full list of genes shown to be overexpressed in the liver of mature hens to remove redundancies and to update accession numbers.
The antioxidant capacity of the liver constitutes an important factor that influences its function. A balance between ROS generation and the anti-oxidant system is essential to scavenge ROS and thus attenuate oxidative stress damage. In the present study, 6 members of the antioxidant system are over-expressed at sexual maturity (CAT, CYB5A, MSRB3, GLRX, STK25 and TXNDC11, Additional file 2) and could represent a signature of the increased metabolic rate of the liver. Moreover, although there is currently a lack of information related to CRELD2 gene in Uniprot database, many publications report that this gene is overexpressed upon stress and may constitute a novel mediator in regulating the onset and progression of ER-stress associated dysfunctions (which may occur during hyperstimulation of liver in laying hens)  [55, 56].
Methods Data analyses and integration used to write the article are available as additional files 1 and 2. In the experiment published by  [11] , the diet and the light/dark cycle as well as the rearing environment were the same for pullets and laying hens. Animals had free access to water and food. Samplings of liver were performed between 2: 30 PM and 4:00 PM, about 1 h30 to 3 h after light extinction for both pullets and laying hens.
Data update The list of overexpressed genes in the liver of laying hens were retrieved from NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE35595  [11]. The accuracy of gene ID and names and uniprot accession numbers were checked using NCBI database (https://www. ncbi. nlm. nih. gov/gene/) and Ensembl (http://www. ensembl. org/Gallus_gallus/Info/Index) (Additional file 1; GRCg6a (GCA_000002315. 5) assembly).
Acknowledgements The  PhD project of Audrey Gloux  was supported by  Neovia , within the framework of " Institut Carnot France Futur Elevage "(http://www. francefuturelevage. com/fr/). We want to thank  Joëlle Dupont  ( PRC, INRA, CNRS, IFCE, Université de Tours, 37380, Nouzilly, France ) for her critical reading of the manuscript.
2001; Meehl et al. 2007; Church et al. 2013). N20 Sterodynamic sea-level change DZ: Relative sea-level change due to changes in ocean density and circulation, with inverse barometer (IB) correction. This term is the sum of ocean dynamic sea-level change (which includes the IB correction) and global-mean thermosteric sea-level rise, DZðrÞ ¼ DfðrÞ þ h h : ð38Þ It can be diagnosed from ocean models (even those that do not conserve mass as per the commonly used Boussinesq models) as the sum of the changes in zos and zostoga. Sterodynamic sea-level change is the part of relative sea-level change that can be simulated with such models. (As discussed above for ocean dynamic sea-level change Df, the change in zos calculated from CMIP data should have zero global mean. ) ''Sterodynamic'' is a term which is newly introduced in this paper.
Acknowledgements We thank  Cathy Raphael  from  NOAA/GFDL  for kindly drafting the figures, and  John Krasting ,  Jianjun Yin ,  Sophie Nowicki ,  Angelique Melet, Carl Wunsch, Unnikrishnan, Chris Merchant and Deborah Idier  for useful comments. We are grateful to  Don Chambers  and the other (anonymous) reviewer for their encouraging remarks and helpful suggestions for improvement. Rui Ponte  acknowledges support from  NASA  Grant  NNH16CT01C. John Church  was partially supported by the  Centre for Southern Hemisphere Oceans Research , a joint research centre between QNLM and  CSIRO. Jonathan Gregory 's work was supported by Grant  NE/R000727/1  from the  UK Natural Environment Research Council  on.
Statistical analyses All analyses will be detailed in and performed according to a statistical analysis plan written before database lock. Populations will be defined as: intention-to-treat (ITT) patients, i. e. all randomized patients; per-protocol (PP) population, i. e. all eligible and evaluable  patients (patients randomized and treated, with the two evaluations performed); safety population, i. e. all patients who received at least one dose of treatment. All statistical analyses will be performed on the ITT population, efficacy analyses on the PP population, and safety analyses on the safety population. Descriptive analyses will be performed using medians and ranges for continuous parameters, and frequencies and percentages for categorical variables. Baseline characteristics of randomized patients in each arm will be compared using the Kruskal-Wallis or Wilcoxon tests for continuous variables, or the [2 or Fisher exact tests for categorical variables.
Data collection and management Data collected will include the following: patient demographics and baseline characteristics, and medical preoperative history (age, weight, BMI, ASA, ECOG performance status cancer treatment, surgical history, comorbid conditions, indication for surgery, baseline quality of life), clinical examination (ECOG and vital signs, i. e. heart rate, blood pressure and weight), surgical data (accessibility, presence of adhesions, trocar placement, intraoperative incidents, operative time, presence and quantity of ascites at each laparoscopy, localization and number of biopsies), concomitant medications at baseline, postoperative assessments (toxicities, postoperative complications) and followup assessments (histological response, OS, PFS, recurrence and recurrence type, extra-peritoneal metastatic disease, feasibility of CRS, intraoperative and postoperative outcomes of CRS, quality of life).
Independent data and safety monitoring board: The independent data and safety monitoring board (DSMB) will have an advisory role and provide its opinion concerning all safety issues related to the study or study treatment. He will advise the sponsor to continue, modify or stop the study. The DSMB will be composed of at least 3 experts in clinical research and/or in the study indication (a methodologist, a medical oncologist and a surgical oncologist), will not be implicated in the study protocol, and are not allowed to have any financial interests in the study. They will sign a financial disclosure form and a confidentiality agreement form before attending the board. The members will meet at 6 months after inclusion of the 12th patient of the experimental arm; an annual meeting to evaluate safety and reliability of the trial is also scheduled.
Although the combination Cisplatin 10. 5 mg/m² and Doxorubicin 2. 1 mg/m² was shown to have limited toxicities  [24] , larger cohort data were not published yet. The outcomes will be evaluated in a standardized manner. All toxicities as well as surgical complications will be defined according to the CTCAE v5. 0. A recent publication has already shown that 90 days morbidity is better evaluated with CTCAE v5. 0 than with the Clavien Dindo classification in patients who underwent CRS and hyperthermic intraperitoneal chemotherapy  [29]. As PIPAC is a drug delivery method, it can be inferred that this classification is also superior to the Clavien Dindo classification  [28]. However, the latter will also be present in the CRF for comparative purposes. The results of this study will impact a population of patients affected by a rare disease and considered initially unresectable who are currently treated with a combination of drugs that has not seen any progress in the last fifteen years.
The cells in each image were counted and analysed in comparison with control-transfected cells. Migration in the absence of FBS (random migration) was taken as 100% for each siRNA treatment, to control for variations in final cell number. For scratch wound assays cells were seeded 24 hrs after transfection in 24-well plates at confluency in growth medium; the confluent monolayer was wounded after 24 hrs by scraping with a pipette tip and then imaged by time-lapse microscopy. A phase-contrast image was acquired every 10 minutes for 18 hrs on a fully motorized Leica DMI6000 microscope with a Plan APO 10× objective. The area occupied by cells in scratch wounds was determined from time-lapse movie images taken at different time after wounding, using ImageJ analysis software. For polarity analysis, cell were plated at confluency on coverslip coated with Collagen I, wounded and fixed 4-6 hrs after wounding for antigiantin immunostaining.
1 M sodium citrate in 50% ethanol, pH 4. 2, and the absorbance at 595 nm was measured in a spectrophotometer. Alternatively, cells were fixed with 4% paraformaldehyde and counted under the microscope to corroborate the plate reader results. For cell spread area measurements, cells were allowed to spread on the indicated substrates for 20 min and fixed with paraformaldehyde without washing. Cells were then stained for F-actin, and area and perimeter quantified with ImageJ software.
All quantitative analyses were performed separately, considering FAs positive for anti-PXN (>100 cells), anti-P-PXN (>40 cells) or anti-Vcl (>60 cells). The quantitative analysis was carried out by Cell Profiler software (mean ± s. d. from three different experiments) (scale bars, 25µm). Values are shown relative to NT control cells (*p < 0. 05; **p < 0. 01). (D) Confocal images of immunofluorescence staining of cells seeded on ECM proteins showing FAs and focal complexes. Nuclei were stained with DAPI. (scale bars: 25µm).
In general, additional experiments are needed to measure both the number of receptors per neuron and the number of neurons for each receptor type. Robustness in the prediction is measured as the Pearson correlation between the predicted OSN numbers with complete information, and after subsampling. (a) Robustness of OSN abundances as a function of the fraction of receptors removed from the sensing matrix. Given a full sensing matrix (in this case a 24 × 110 matrix based on Drosophila data  [40] ), the abundances of a subset of OSN types were calculated in two ways. First, the optimization problem from eq. (  7 ) was solved including all the OSN types and an environment with a random covariance matrix (see Figure  5 ). Then a second optimization problem was run in which a fraction of the OSN types were removed.
Existing experiments, e. g. ,  [22] , do not record necessary details regarding the odor environment of the control group and the magnitude of the perturbation experienced by the exposed group. However, such data can be collected using available experimental techniques. Anticipating future experiments, we provide a Matlab (RRID:SCR_001622) script on GitHub (RRID:SCR_002630) to calculate predicted OSN numbers from our model given experimentally-measured sensing parameters and environment covariance matrix elements (https://github. com/ttesileanu/OlfactoryReceptorDistribution). Given the huge number of possible odorants  [51] , the sensing matrix of affinities between all receptor types in a species and all environmentally relevant odorants is difficult to measure. One might worry that this poses a challenge for our modeling framework.
Likewise, the model broadly predicts a monotonic relationship between the number of receptor types found in the epithelium and the total number of olfactory sensory neurons, all else being equal. A detailed test of our model requires more comprehensive measurements of olfactory environments than are currently available. Our hope is that studies such as ours will spur interest in measuring the natural statistics of odors, opening the door for a variety of theoretical advances in olfaction, similar to what was done for vision and audition. Such measurements could for instance be performed by using mass spectrometry to measure the chemical composition of typical odor scenes. Given such data, and a library of receptor affinities, our GitHub (RRID:SCR_002630) online repository provides an easy-to-use script that uses our model to predict OSN abundances.
Software and data The code (written in Matlab, RRID:SCR_001622) and data that we used to generate all the results and figures in the paper is available on GitHub (RRID:SCR_002630), at https://github. com/ttesileanu/ OlfactoryReceptorDistribution. supported by the Swartz Foundation. This work was also supported by NSF grant PHY-1734030 (Center for the Physics of Biological Function).
A Choice of sensing matrices and receptor noise variances We used three types of sensing matrices in this study. Two were based on experimental data, one using fly receptors  [40] , and one using mouse and human receptors  [41] ; and another sensing matrix was based on randomly-generated receptor affinity profiles. These can all be either directly downloaded from our repository on GitHub (RRID:SCR_002630), https://github. com/ttesileanu/OlfactoryReceptorDistribution, or generated using the code available there. Fly sensing matrix Some of our simulations used a sensing matrix based on Drosophila receptor affinities, as measured by  Hallem and Carlson [40]. This includes the responses of 24 of the 60 receptor types in the fly against a panel of 110 odorants, measured using single-unit electrophysiology in a mutant antennal neuron.