dataesr/scientific-paragraphs-categorization huggingface dataset:
The ER change between two iterations can now be written as: 𝛿𝐸𝑅 = 1 2 𝑙𝑜𝑔 2 (1 + ℎ′ 𝑇 𝐴 𝑖-1 ℎ ′ ) At each step, the channel that has the largest information content (measured as a reduction of the entropy of the corresponding Tskin retrieval when the candidate channel is used) is selected, given the information content of the previously selected channel(s). The channel selection starts with no channel selected, and sequentially chooses the channel with the highest information content in complement to the information from all the previously selected channels. The spectra and Jacobians used in this study were simulated using the last version of the Optimum Spectral Sampling (OSS) radiative transfer model  (Moncet et al. , 2008) , using the Thermodynamic Initial Guess Retrieval (TIGR3) database  (Chevallier et al.
(Good, 2016). To this end, a few dedicated ERA5 experiments were The neural network learns how to associate any set of radiances to a corresponding skin temperature. The feasibility of using ANN to Tskin retrieval has been shown for instance by  Aires et al. (2002)  for IASI, and has also been performed to tackle various problems in atmospheric remote sensing  (Blackwell and Chen, 2009; Hadji-Lazaro et al. , 1999; Whitburn et al. , 2016; Van Damme et al. , 2017). In the following "TANN" refers to the product developed in this study using artificial neural networks from IASI radiances. Figure  3  shows the training results when the TANN is compared with the TERA5 dataset is used for the training, and in Figure  4  when the TEUMETSAT is used for the training. We achieve a good agreement with a standard deviation of 2.
Results To validate the TANN product, the month of June 2016 is chosen. Since we train our neural network with 2018 data, 2016 is a good choice and data is readily available for this year. TANN is calculated from the two ANNs obtained in section 2 by applying it to each set of 100 radiances retrieved from IASI for all cloud-free observations in June 2016. Comparison with ERA5 also shows a correlation close to 1, and σ=2. 17 While this paper focuses on validating IASI TANN, inter-comparisons between the different products (ERA5 with EUMETSAT L2 or EUMETSAT L2 with SEVIRI, etc. ).
Validation of the TANN obtained from the ERA5 neural network are valuable for assessing their differences. Figure  6  shows the box plot of these inter-comparisons, with the absolute bias and standard deviation of the comparison between the products. We perform inter-comparisons for day-and night-times  Figure  6  shows that the night observations of TANN, TEUMETSAT and TERA5 seem to agree better with each other, an expected result and also detected for other satellite data  (August et al. , 2012; Martin et al. , 2019). Comparison with SEVIRI shows a consistent negative bias during the night when compared to TANN, TEUMETSAT and TERA5. Several studies (e. g. ,  Garand, 2003; Zheng et al. , 2012)  already reported cold biases between SEVIRI and other Tskin products. For the ECMWF model, the cold bias over land was identified for a previous version of the model by  Trigo and Viterbo (2003)  and for a more recent version by  Trigo et al.
IASI cloud-free data was co-localized in space and time (within 1 minute of the station data). The spatial matching is done around 0. 5 o of a validation site [15. 17 that has the largest bias. At night, TANN comparison with ground measurements is better, so is the comparison with other datasets, as also seen in Figure  7. Absolute biases mostly range between 0 and 2 K, which is similar to the Tskin spatial variability around Gobabeb station determined with detailed measurements carried by  Goettsche et al. (2013). Comparison with other satellite measurements shows a general bias between -2 and 5 kelvins in summer months  (Martin et al. , 2019). Finally, comparison with ground observation in Figure  10  shows a better performance of TANN than what was presented in Figure  6. Table  3  hereafter details the day and night biases where we can see that the TANN in this case agrees better with ground measurements that what we presented in Table  2.
radiances. The first challenge is to find the channels with access to surface information. To this end, we present a method based on entropy reduction, to find the channels with the highest information content in skin temperature. An efficient and fast IASI retrieval algorithm based on artificial neural networks is then used to calculate Tskin from the upwelling IASI radiances. While empirical methods using ANN can deal with hundreds to thousands of channels  (Aires et al. , 2002) , we show in this study how ANN and channel selection can be used to retrieve Tskin, making this method fast and reliable for near real-time application, as well as to reprocess more than 11 years of IASI data. In this study, we perform two ANN trainings in 2018 with IASI radiances as input and we use two distinct datasets for two separate trainings.
In the first, a dedicated ERA5 12-minute simulation is used as output, and in the second EUMETSAT L2 data is used as output. Each of the resulting neural networks is then applied for a different year (2016) and validated. Our results show the potential of ANN in mapping radiances globally and locally to skin temperature. We show how both neural networks perform similarly well when compared to other datasets, with the EUMETSAT-derived network performing better (in particular during nighttime) when it is compared to ground station Tskin. To compare the two products obtained from the two neural networks, we show in Figure  11    More generally, retrieval of Tskin from space measurements faces many challenges. First, the Tskin calculation from the radiance within the radiative transfer equation is an ill-posed problem.
The solution of the radiative transfer equation requires the simultaneous knowledge of two unknowns: Tskin and the surface emissivity. This is generally solved with the assumption of a good initial guess to constrain the solution  (Aires et al. , 2001; Paul et al. , 2012)  and a rapid and accurate direct transfer model  (Rodgers, 1976). Since the observed radiance spectra are affected by the surface properties, using it as input to the ANN takes emissivity knowledge into account. Second, infrared retrievals are only available under clear-sky conditions, reducing the amount of global data by roughly one third. This study has been performed with data from IASI on Metop A, and it implies that with IASI on Metop B and Metop C, the global coverage can be enhanced. Third, validation and inter-comparison between different products are challenges that not only bound to this study.
Using channel selection and artificial neural network, this work shows a Tskin retrieval method that can serve as a baseline for constructing the first homogeneous dataset of skin temperature from IASI, and can be extended to other infrared remote measurements. Future work will look at constructing a Tskin time series from IASI during 2007-present and using Metop A, B, and C for climate trends application. Regional and seasonal variations can be studied using the atlas for the surface skin temperature distributions. The daily/monthly/yearly variations will be studied in terms of the main climate drivers (solar, volcanic eruptions, aerosols and greenhouse gases) and modes of variability at the inter-annual and decadal timescales.
298performed at ECMWF at a 12-minute time-step (as opposed to the publicly released hourly Tskin product), each spanning a couple of days. The aim of these experiments is to increase the temporal resolution and therefore increase the performance of the neural network obtained. Four days in January and June 2018 are used for the training to represent seasonality. We interpolate ERA5 space/time grid to IASI's observations (at 9:30 AM and PM local time). We provide more information on the ERA5 reanalysis in section 3. The resulting training dataset is formed out of around 5. 9 x 10 5 scenes. In the second training, we use EUMETSAT L2 Tskin product as target. EUMETSAT Tskin is derived from Metop observations and the IASI instrument. They are therefore collocated in space and time. Since major and minor updates on the processing algorithms of the L1C and L2 EUMETSAT product took place in the past 10 years (EUMETSAT, 2017a; 2017b), the ANN training in this study uses a recent and coherent year, 2018.
To represent the seasonal variability, scenes from January 1 st , April 1 st , July 1 st , and October 1 st 2018 are used. The resulting training dataset is formed out of around 9 x 10 5 scenes for EUMETSAT. More information on the EUMETSAT Tskin product is provided in section 3. Since IASI has more frequent overpasses at the poles (given its polar orbit), a weighting function is applied to equally distribute the number of scenes around the globe. The training is done using mini-batches with a maximum of 10. 000 epochs to train. The ANN has 2 hidden layers with 4 nodes, and a network training function that updates weight and bias values according to Levenberg-Marquardt optimization.
For large viewing angles, in particular near the edge of the Meteosat disk (such as the Arabian Peninsula), the uncertainty of SEVIRI Tskin is high (Freitas et al. , 2010). A study byTrigo et al. (2015)  reported similar to larger cool biases in the rest of the domain between the ECMWF model data and SEVIRI, especially over semiarid regions, such as North Africa, Sahara, and Namibia. In the rest of the domain, the two datasets agree reasonably well.
the daily variation of the skin temperature in 2017, for the Northern Hemisphere in the left panel and the Southern Hemisphere in the right panel. Generally, all datasets agree well with one another, with TANN obtained from the ERA5 Tskin product closer to the latter (which is expected) same as TANN obtained from the EUMETSAT L2 Tskin product is closer to the actual EUMETSAT Tskin product. https://doi. org/10. 5194/amt-2019-185 Preprint. Discussion started: 5 June 2019 c Author(s) 2019. CC BY 4. 0 License.
Acknowledgments The authors thank  J. Hadji-Lazaro ,  J. M. Sabater ,  C. Jimenez ,  I. Trigo ,  C. F. Barroso , and  P. Kasibhatla  for useful discussions. This work was supported by the  CNES. It is based on observations with IASI embarked on Metop. The authors acknowledge the Aeris data infrastructure (https://www. aeris-data. fr/)for providing access to the IASI Level 1C data and Level 2 temperature data used in this study. The LST validation site is supported by the  Satellite Application Facility (SAF) on Land Surface Analysis (LSA) , a European project initiated and financed by  EUMETSAT. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 and innovation programme  (grant agreement No  742909 ).
Data availability The IASI Level 1C data for 2018 are distributed in near real time by Eumetsat through the EumetCast system distribution. The reprocessed Metop-A L1C data used in this study for June 2016 will be available in summer 2019 (doi: 10. 15770/EUM_SEC_CLM_0014). The EUMETSAT L2 data used in this study can be retrieved from the Aeris data infrastructure (https://www. aeris-data. fr/). ERA5 data is provided by ECMWF and can be retrieved at http://www. ecmwf. int or https://cds. climate. copernicus. eu/. The 12-minute simulation output used in this work can be obtained by contacting the lead author (sarah. safieddine@latmos. ipsl. fr). The hourly LST data derived from SEVIRI/Meteosat are freely available from http://landsaf. ipma. pt within the context of the LSA SAF project funded by EUMETSAT.
Both datasets confirmed that male livers were feminized in the absence of microbiota. We also compared these sexually dimorphic gene expression signatures with additional conditions known to alter gut microbiota. When examining mice that grew up on an HFD  (Cox et al. , 2014)  and were then subjected to vertical sleeve gastrectomy (VSG)  (Myronovych et al. , 2014) , or fed with resistant starch  (Kieffer et al. , 2016) , we found that HFD  (Ley et al. , 2005)  led to a feminization of hepatic gene expression  (Figures S2C and S2F). Both VSG and resistant starch feeding change gut microbiota composition  (Kieffer et al. , 2016; Tremaroli et al. , 2015)  and reverse the HFD-induced feminization of obese animals (Figures  S2D-S2F ). The observed feminization was not specific to the liver, as the sexually dimorphic WAT  (Yang et al.
In addition, microbiota is able to metabolize and synthesize carnitine  (Reuter and Evans, 2012)  and plays a role in its absorption and availability. Consistent with these reports, we detected low levels of carnitine in GF mice. Interestingly, ConvR females showed high levels of the atherosclerosis-associated metabolite trimethylamine N-oxide (TMAO), likely produced by the femalespecific Flavin-containing Monooxygenase 3 (FMO3) from carnitine. Altogether, our data corroborated the reported sexual dimorphism in energy homeostasis (Mauvais-Jarvis, 2015) and highlighted the role of microbiota in this pathway. Finally, a cluster containing glutathione (GSH), AA, phospholipid (PL), and BA metabolism was profoundly sexually dimorphic and modulated by microbiota. GH affects methionine and GSH metabolism  (Brown-Borg et al.
DATA AND SOFTWARE AVAILABILITY The accession number for the entire RNA-seq dataset reported in this manuscript is GSE77221. To ease accessibility, we divided the data into the following subseries: (1) Temporal profile of GF and ConvR male and female mice (duodenum and ileum -GSE114399, liver -GSE114400, WAT -GSE114401). (2) Temporal profile of Liver of Cry1/Cry2 KO mice (GSE114402). (3) GF male mice liver injected with GH (GSE114610). (  4 ) GF male mice liver injected with Ghrelin (GSE114611). (  5 ) Testes and ovaries of ConvR and GF female mice (GSE114612). (  6 ) GH and mouse serum treatment on primary hepatocytes (GSE114613). 16s rRNA data have been deposited on the SRA database under the reference SRP151275.
QUANTIFICATION AND STATISTICAL ANALYSIS If not otherwise stated, statistical analysis comparing the means between experimental groups (ConvR male, GF male, ConvR female and GF female) was performed with one-way analysis of variance (ANOVA) followed by the Holm multiple comparison test comparing selected comparisons (ConvR male vs. GF male, ConvR male vs. ConvR female, ConvR female vs. GF female and GF male vs. GF female) using R (R  Core Team, 2018). Intersecting gene sets were visualized using the R-package UpSetR  (Lex et al. , 2014). All data are expressed as mean ± standard error of the mean (SEM) if not otherwise stated.
ACKNOWLEDGMENTS We thank  Mathieu Membrez ,  Florian Atger , and  Meltem Weger  for scientific discussions;  Julie Deuquet  for project management;  Alexandra Pinto  and  Nuno Silvano  for taking care of GF animals; and  Michele Delley  for technical assistance. We also thank  Kathleen McCoy  for her expertise in GF animals,  Xavier Bonnefont  for advice, and  Sylvie Rabot  and  Ueli Schibler  for advice and critical reading of the manuscript. Histology was performed at the  EPFL Histology Core Facility. This research was supported by the  European Research Council  through individual Starting Grants  ERC-2010-StG-260988  to F. G. F. N. 's laboratory was supported by the  Swiss National Science Foundation  grant  310030_173079. J. Y. benefits from the  Natural Sciences and Engineering Research Council of Canada   Postgraduate Studies Doctoral Scholarship.
RNA-Seq Data Processing Paired-end reads were mapped onto the Ensembl Mus musculus genome annotation (GRCm38/mm10) using STAR 2. 3. 8  (Dobin et al. , 2013). Uniquely mapped reads were counted for each gene locus as described  (Atger et al. , 2015). Specifically, using a custom Perl script we applied SAMtools view  (Li et al. , 2009)  to assigned unique mapped paired-end reads to exons considering read orientation. The count data were normalized by size factor and a variance stabilizing transformation was applied for downstream analysis. Differential gene expression was assessed by DESEq2  (Love et al. , 2014). Data from other RNA-seq based studies that were used for subsequent gene set enrichment analysis were retrieved from indicated sources in Key Ressources Table and analyzed as described above.
Functional and Gene Set Enrichment Analysis Analysis for functional enrichment were performed with Ingenuity Pathway Analysis (Qiagen), Enrichr  (Kuleshov et al. , 2016) , and RDAVIDWebService  (Jiao et al. , 2012). For the latter, we calculated enrichment for GO terms for biological processes and KEGG pathway annotations. p values were adjusted for multiple testing  (Benjamini and Hochberg, 1995). To perform gene set enrichment analysis for sex-biased genes and TFs target genes, we employed the geneSetTest function of the limma package on a pre-ranked gene lists defined according to the DESeq2 or limma results for RNA-seq-derived or microarray-derived datasets, respectively. Gene sets were derived from several sources: gene sets for Ahr, Fxr, and Pxr KO regulated genes are derived from the list of differentially expressed genes from the indicated datasets (Key Resources Table ).
The gene set list of sexually dimorphic liver genes is based on the comparison between ConvR males and ConvR females. Sexually dimorphic genes for WAT were derived from the indicated reanalyzed microarray dataset (Key Resources Table ). Genes that show significantly different expression patterns (adjusted p value % 0. 05 and log2 fold change > 0. 5) between males and females were considered as male and female-biased, respectively. Public available ChIP-seq data were used to define downstream target gene sets of BCL6, STAT5, FOXA1/2, and CUX2 (Key Resources Table ). We tested whether these gene sets are enriched in the up-or downregulated genes of the indicated comparisons. If neither of the alternatives showed a significant tendency, we tested whether the genes in the set tended to be differentially expressed without regard for direction (mixed).
Ground-based MRR observations The APRES3 and HYDRANT-AEROCLOUD observatories are both equipped with a K-band vertically pointing MRR-2 manufactured by METEK, deployed with the aim of longterm monitoring of precipitation at DDU and PE, respectively. Measurements at DDU have been continuously collected since November 2015  (Grazioli et al. , 2017a) , while at PE in situ observations are carried out mainly during summer campaigns between 2010 and 2016, including 1 full year of measurements in 2012  (Gorodetskaya et al. , 2015; Souverijns et al. , 2017). In this study, 2 full years of data at DDU and all the observations at PE are used for the analysis. MRR is a frequency-modulated continuous-wave (FMCW) system with a low requirement of energy (small transmitter power: 50 mW) and of supervision during operation time  (Peters et al.
Overall statistics The statistics of all the MRR profiles for the complete observation period at both stations were carried out, prior to the classification of the precipitation profiles and the seasonal analysis, to obtain a general picture of the distribution of the variables of interest. A total of 5331 and 5058 hourly vertical profiles were obtained at DDU and PE during all respective data periods. Figure  3  shows the distribution of Ze, W , and σ in the vertical profile extension as a percentage, normalized by the total number of the range gates containing precipitation. In the following subsections, the statistics for vertical profiles of the three Doppler radar moments are presented.
The present study explores unique datasets of MRR measurements, which demonstrate a great potential for long-term monitoring of the vertical structure of precipitation in a rewww. the-cryosphere. net/13/247/2019/ The Cryosphere, 13, 247-264, 2019 mote region like Antarctica. Nevertheless, it is necessary to extend this analysis to additional locations across the continent (once large enough datasets become available), in order to improve the characterization of different climatological patterns of precipitation. Moreover, new intensive field campaigns to collect more detailed information on microphysics of hydrometeors are also important to improve the interpretation of the results. Current statistics and future measurements will contribute significantly to better understanding the Antarctic precipitation and to evaluating satellite products and verifying numerical precipitation models.
Acknowledgements. We thank the  French Polar Institute , which logistically supports the APRES3 measurement campaigns at  DDU. We thank  Alexandre Flouttard ,  Vincent Terol ,  Philippe Rouault , and  Romain Vérité  from  Météo-France  for taking care of the AWS at  DDU. We thank the logistical support in the maintenance of the instruments at PE. We also acknowledge the support of the  French National Research Agency (ANR ; grant number  ANR-15-CE01-0003 ) to the  APRES3  project. The project Expecting Earth-Care, Learning from the  ATrain (EECLAT)  funded by the  Centre National d'Etudes Spatiales (CNES)  also supported this work. The Swiss National Science foundation SNF is acknowledged for grant  200021_163287 , financing the Swiss participation to the project. KUL members and  Irina V.
Data availability. Data from the micro rain radar at the Princess Elisabeth station can be obtained from the database at https://ees. kuleuven. be/hydrant/aerocloud/database/quicklooks. html?instrument=mrr  (Souverijns et al. , 2019). The micro rain radar database at the Dumont D'Urville station is available at https://doi. pangaea. de/10. 1594/PANGAEA. 897614 (Durán-Alarcón et al. , 2019).
The bond distances so obtained are given in Figure  1. Hence, both HC 2 NC and HNC 3 were considered as rigid linear rotor in the PES calculations. The Molpro electronic structure package was used to perform all of the calculations reported here. 19 4D PESs were constructed for the two systems by fitting automaticallygenerated points at the CCSD(T)-F12b/VTZ-F12 level. For HC 2 NC, two wells with similar depths are noted. First, a collinear structure with the H 2 molecule aligned end-on to the C-atom of HC 2 NC, is found with a center-of-mass distance of 5. 303 Å and a depth of 174. 1 cm -1 relative to separated molecules. Second, a nearly parallel side-by-side structure is located with a slightly deeper well-depth of 178. 0 cm -1. The center-of-mass separation for this global minimum structure is 3.
Acknowledgement RD and EQS are supported by the  US National Science Foundation  (No. CHE-1566246 ). F. L. acknowledges financial support from the  Institut Universitaire de France  and from the  French National Research Agency (ANR)  through a grant to the Anion Cos  Chem  project ( ANR-14-CE33-0013 ). We acknowledge the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES.
Flow cytometry Tumor samples were cut into small fragments, digested with 0. 1 mg/ml of Liberase TL (Roche) in the presence of 0. 1 mg/ mL DNase (Roche) for 20 min, before the addition of 10 mM EDTA. Cells were then filtered through a 40 mm cell strainer (BD Falcon) and washed. Staining was performed using the following antibodies: anti-CD45 APC-Cy7 (BD), anti-CD14 Qdot 605 (Thermo), anti-CD3 Alexa700 (Biolegend), anti-CD 19 Alexa700 (Biolegend), anti-CD56 Alexa700 (Biolegend), anti-HLA-DR BV711 (Biolegend), anti-CD11c PC5 (Beckman Coulter), anti-BDCA-1 PE (BD), anti-CD123 PCy7 (BD) and anti-C-SIGN FITC (BD). Cells were analyzed by a LSRFORTESSA X-20 instrument (BD Biosciences). Data was analyzed with FlowJo (Tree Star). The FACS files are available and can be requested to the corresponding author.