dataesr/scientific-paragraphs-categorization huggingface dataset:
A similar approach can be used, with the INT solution as a reference instead of the SW + PF. Instead of calibrating the original EO datasets using basin-scale data, we propose here to calibrate the SW solution towards the INT solution at the pixel scale. This calibration of the SW allows a long-term dataset at the pixel scale to be obtained (see Table  3 ) with WC budget closure statistics closer to the INT solution. In our tests (not shown), the linear regression is quite satisfactory for the calibration, and it is not necessary to use a more complex statistical regression tool such as a neural network. The merging/integration techniques used in this study are described in Table  3.
Evaluation of the integrated datasets In this section, the obtained integrated datasets are first evaluated in terms of WC budget closure. Our EO dataset integration technique is based on the closure of the WC budget. This is a physical constraint, but in some cases (e. g. , missing important water component), this constraint could result in a degraded estimation of the components. Therefore, available in situ data (precipitation, evapotranspiration and seawater level) are used to validate some of the water components of the integrated dataset. This evaluation is performed Table  4. rms of the WC budget residual (in mm month -1 ) over the sub-basin using OS, SW, SW + PF, INT and CAL solutions and for the period 2004-2009. Percentage of improvement of the rms of the residuals from the SW solution to constrained methods is also shown.
Only one dataset is available for the moisture divergence  (Pellet et al. , 2017). As shown in  Aires (2014) ,  Munier et al. (2014)  and  Pellet et al. (2017) , the SW merging procedure reduces the WC budget residuals at the sub-basin scale, by reducing the random errors of the EO data. The SW product outperforms the ERA-I reanalysis and the OS product. However, the full closure is generally still not satisfactory with this technique. The SW + PF procedure closes the water budget over all the sub-basins, and over the surface and in the atmosphere, with an rms of the residual of about 4 mm month -1. The surface budget residuals are drastically reduced: from 72 % over the GR-TR-IL sub-basin and up to 94 % for the Mediterranean Sea. This shows the necessity to use a WC budget closure constraint that links the six water components.
Evaluation at the sub-basin scale Since the WC budget closure constraint was imposed at the sub-basin scale (see Sect. 3), the evaluation of the integrated product is done at this scale too. Two metrics are used here, the rms of the difference (rmsd) with in situ measurements and the CORRelation (CORR). Only multiple-EO integrated datasets are compared in the two following sections. Terrestrial precipitation -Table  5  provides the comparison of the EOBS gridded gauge precipitation dataset (Sect. 2. 2) with the SW, SW + PF, INT and CAL solutions, in terms of temporal correlation (at the monthly and sub-basin scales), and rmsd, for each sub-basin and for the continental scale (land included in Fig. 1 ). Since the SW + PF product is defined only on the Mediterranean drainage sub-basins, no statistics are shown for this approach over the continental region (last column).
INT period. Seawater-level change -the seawater storage (related to the seawater level) change over the Mediterranean Sea (excluding the Black Sea) is tested using altimetry and thermal datasets over the 2004-2009 period. First, the thermal content estimates of the four datasets presented in Sect. 2. 2 are merged into one single estimate. The merged thermal content estimate is then subtracted from the AVISO altimetry seawater level. The monthly change is then computed using the same derivative filter as the one used for GRACE: [5/24 3/8 -3/8 -5/24]. Figure  3  shows the altimetry estimate and the various methodologies estimates. Since the Mediterranean Sea is considered without the Black Sea for this evaluation, there is no SW + PF estimate (that added the Mediterranean Sea and the Black Sea).
Evaluation at the pixel scale The INT and CAL estimates are here evaluated at the pixel scale, for precipitation and evapotranspiration. Improvements of SW by INT and CAL are measured using in situ measurements of precipitation and evapotranspiration from the FLUXnet database, available over the Mediterranean region, for the 2004-2009 period (Sect. 2. 2). Figure  4  presents the scatterplots of the rmsd between the SW estimate (E SW ) and INT or CAL (E cor for "corrected") datasets with the FLUXnet evapotranspiration data (E FLUX ), for each station. The 1 : 1 line is also shown. Each dot under the 1 : 1 line represents an improvement at the corresponding station from SW solution to INT and/or CAL. INT and CAL improve evapotranspiration estimates for more than 53 % of the sites. The distribution of the differences in the encapsulated figure is slightly narrowed by the INT and CAL compared to the SW solution.
Location of the station where the closure improves the rmsd with the flux measurement is shown in green if INT and CAL both improve the estimate, blue when only CAL improves, and magenta when only INT improves. Red dots represent stations where there is a degradation in both INT and CAL. The evaluation of the EO estimate at 0. 25 • spatial resolution using tower sites should be made with caution. The poor performance of satellite estimates over particularly complex topography (mountainous rainfall) or coastal pixels with land-sea contamination could explain the difference between the INT estimate and the FLUXNET measurement on these particular locations. Figure  5  presents the scatterplots of the rmsd between the SW estimate (P SW ) and the INT or CAL (P cor ) datasets with the FLUXnet precipitation data (P FLUX ), for each station.
Comparison of the Mediterranean flux estimates with the literature Table  6  summarizes the comparison of the various estimates of the water fluxes in the current analysis with what can be found in the literature. The various annual mean estimates are based on different time periods and comparison must be taken with caution since some interannual variability is likely to be due to the change in hydrologic regime. Sanchez-Gomez et al. (2011)  focused on the Mediterranean Sea heat and water budget using an ensemble of ERA-40-driven high resolution Regional Climate Models (RCMs) from the FP6-EU ENSEMBLE database. The atmospheric budget was not considered in  Sanchez-Gomez et al. (2011)  and no moisture divergence estimate was provided. For comparison purposes, we decided to select the RCM ensemble-mean estimate and two particular models: the Danish HIRHAM (Hesselbjerg Christensen and Meteorologisk Institut, 1996) and the Canadian CRCM  (Plummer et al.
, 2006). These two models have been selected since their E -P estimates are the extremes of the RCMs ensemble. In  Sanchez-Gomez et al. (2011) , the netflow at Gibraltar was estimated as the deficit term of the WC: Gib = E -P -R -Bos. Mariotti et al. (2002)  analyzed the WC over the Mediterranean region in the context of the NAO teleconnection over the 1979-1993 period using two reanalyses (ERA-40 and NCEP-NCAR) for precipitation, evaporation and moisture divergence. They used the discharge data from the monitored rivers through the Mediterranean Hydrological Cycle Observing System (MED-HYCOS) and GRDC. Their estimate includes a total Mediterranean input of 100 mm yr -1 from MED-HYCOS and the Bosporus input of 75 mm yr -1 from the literature  (Lacombe and Tchernia, 1972). Mariotti et al. (2002)  estimated the netflow at Gibraltar as the balance of the Mediterranean water deficit using the equation Gib = Div -R -Bos coming from the oceanic and atmospheric budgets and the null assumptions about the storage change.
(2017). Table  6  also shows the results from  Rodell et al. (2015)  before and after their satellite data optimization based on a vari- ational assimilation at the annual scale. The constraints of the fluxes over the Mediterranean Sea and the Black Sea were made independently (considering no netflow at the Bosporus Strait). The Mediterranean Sea was closed with no exchange to the Atlantic at Gibraltar (no netflow). Rodell et al. (2015)  provided no explicit discharge for the Mediterranean Sea, but only for the Eurasian continent. For the four mentioned articles, only the Mediterranean Sea without the Black Sea is considered. No estimate from SW + PF methodology is provided in Table  6. Our integrated dataset is the only one to use direct observations for the Gibraltar netflow and to compute the Bosporus netflow via a WC budget.
Sanchez-Gomez et al. (  2011 ) had already noted that gauges-calibrated satellite datasets such as GPCP and TMPA tend to have higher precipitation values than what was simulated in the RCMs. Precipitation over the Sea is a sensitive variable and its validation is difficult due to the lack of buoys. The ERA reanal-yses value in  Mariotti et al. (2002)  was low compared with the NCEP estimate. Evaporation minus precipitation -Sanchez-Gomez et al. (2011) focused on E -P to assess the physical consistency in the RCMs. They assumed that a model with high evaporation tends to have higher precipitation. The averaged E -P budget among the RCMs was 812 ± 180 mm yr -1 and the range was between 952 ± 80 (HIRHAM model) and 602 ± 107 mm yr -1 (CRCM model). The inter-model spread was high for the E -P budget, stressing the difficulties in providing realistic water budget evaluation.
Conclusions The main goal of this work was to build a multi-component dataset describing the Mediterranean hydrology by constraining the WC closure. Various methodologies have been presented and particular attention has been placed on the INTegration method. This approach fulfills the previously stated objectives: being a pixel-wise dataset but in which the WC closure is controlled. INT is an integrated dataset that shows several benefits compared to previous studies. The INT product allows reduction of the rms of the WC budget residual down to 3. 55 mm month -1 over land and 5. 27 mm month -1 in the atmosphere. These reductions represent an improvement of, respectively, 78 % and 80 % compared to the best unconstrained satellite combination dataset. The temporal coverage of INT is limited by the common coverage period 2004-2009 of all the satellite estimates used in this study (see Table  A2 ).
The INT dataset has been evaluated at various scales. Even if the evaluation is a difficult task and the presented work is not exhaustive, our results show that the consideration of the WC closure allows one to reduce differences with the available in situ measurements. At the sub-basin scale, the overall precipitation is closer to the in situ gridded EOBS dataset after being constrained. The seawater-level estimate is also improved compared to the altimetry estimate. At the pixel scale, the INT estimate shows a better agreement with in situ tower measurements from the FLUXnet2015 database. The WC has been analyzed in terms of long-term means over the 2004-2009 period and compared with the previous literature. The INT methodology has improved estimates of the Mediterranean water components.
(2) New WC inputs could be considered (e. g. , groundwater exchange or horizontal exchange at the oceanic sub-basin scale) to better characterize the flux and stock terms in the WC. (3) The use of other sources of EO estimates should be considered. For example, the evapotranspiration estimate based on the closure of the energy cycle  (Su, 2002; Chen et al. , 2013)  could be tested. This dataset could be an opportunity to (4) close simultaneously the water and the energy cycles and should lead to a better estimate of the evapotranspiration over land. Multiple-component dataset INT shows promising aspects for forcing, calibrating or constraining regional models with a water conservation constraint. Some developments and evaluations are still required before the production of a climate data record  (Su et al.
Acknowledgements. We would like to thank the  ES (European Space Agency)  and its  Support To Science Element program  for funding the " Water Cycle Observation Multi-mission Strategy For Mediterranean region " project (ESRIN contract no. 4000114770/15 /I-SBO; wacmosmed. estellus. fr). We are grateful for the E-OBS dataset from  EU - FP6  project  ENSEMBLES  (http://ensembles-eu. metoffice. com,last access:  24 January 2019 ), and the data providers of the ECA & D project (http://www. ecad. eu,last access:  24 January 2019 ). We would like to thank the WACMOS-Med partners for the interesting related discussions and  Philippe Drobinski  and  Véronique Ducrocq  for their support to  WACMOS-Med  and his role in the  HYMEX  project. Edited by: Eric Martin Reviewed by:  Bob Su  and two anonymous referees.
Material and Methods study design, context. This retrospective study was carried out in six French tertiary care referral centers. Three centers had an inpatient care policy (ICP) (the maternity units of the Robert Debré, Intercommunal de Créteil and Angers Hospitals) and three centers had an outpatient care policy (OCP) (the maternity units of the Antoine Béclère, Bicêtre and Louis Mourier Hospitals). The study was conducted over a 4-year period between January 1, 2009 and December 31, 2012. Data sources. Women were identified through the electronic database of each center. Data were then collected from obstetric and neonatal hospitalization reports. Inclusion/exclusion criteria. Inclusion criteria were all patients admitted to hospital with a diagnosis of PPROM between 24 +0 and 33 +6 WG not delivered within the 48 hours following PPROM.
In the ICP group, the same protocol was applied, but the patient was not discharged until delivery. All patients received betamethasone (two intramuscular doses of 12 mg 24 hours apart) for fetal lung maturation and antibiotic prophylaxis at PPROM (ampicillin or cefixime) according to the unit's protocol. Antibiotic therapy was amoxicillin in five centers and cefotaxime in one center. The administration of tocolysis for a duration of 48 hours was not systematic and depended on the center's practices. All patients were managed expectantly until spontaneous labor, signs of chorioamnionitis, fetal heart rate anomalies, or acute complications (placental abruption, cord prolapse). If not delivered at 37 WG, induction of labor or cesarean section was performed. Maternal demographic data were collected: maternal age, weight, size, parity, history of PPROM, history of late miscarriage (abortion between 14 +6 and 23 +6 WG) or prematurity, previous cesarean section, cervical cerclage, and pregnancy pathology (abnormal placental insertion such as placenta previa, intrauterine growth restriction or gestational diabetes mellitus).
Data upon admission were collected: gestational age at PPROM, vaginal bacterial culture, maternal serum levels of inflammatory markers (C-reactive protein and white blood cell count  21  ), fetal presentation, and amount of amniotic fluid evaluated by the single deepest pocket. The cervix was not systematically measured by ultrasound and this was left to the clinician's discretion. In centers reporting an OCP, inpatient or outpatient care following the initial period in hospital and duration of hospitalization were collected. Outcomes. The main variable of interest was a composite measure of neonatal outcome, including one or more of the following criteria: perinatal death, early neonatal infection with sepsis (defined as sepsis in a neonate with a positive blood or cerebrospinal culture in the first 48 hours of life), respiratory distress syndrome, bronchopulmonary dysplasia at discharge (oxygen requirement for a minimum of 28 days)  22  , grade 3 or 4 intraventricular hemorrhage  23  , and necrotizing enterocolitis  24.
ethics. The study was approved by the Institutional Review Board (IRB, CEROG Comité d'éthique de la recherche en Obstétrique et Gynécologie) (protocol number 2018-OBS-0207). The need to obtain informed consent was waived by the ethics committee. An information sheet explained the purpose and the design of the study to the patients. The original identification of each patient in the database has been encrypted and replaced with surrogate identification. All methods were performed in accordance with the relevant guidelines and regulations.
DATA AND METHODOLOGY The goal of this paper is to demonstrate how unsupervised machine learning can be used to understand and optimize model fitting. To do that we set out a road map based on a demonstration data set. Specific problems will require variations of the method described here. In this section we begin by describing the data we used for the demonstration from the COSMOS survey in 2. 1 and the model library we have chosen to fit to the data in 2. 2. We then give a brief overview of the SOM algorithm that we have chosen for this demonstration in 2. 3 and note some alternative algorithms that could be used. (u -B, B -V, V -r, r -i + , i + -z ++ , z ++ -Y, Y - J, J -H, H -Ks, Ks -ch1, ch1 -ch2 ) are used to train the SOM. The median of the physical properties of the models mapped to each cell are used to color the grids with stellar age, specific star formation rate (sSFR), dust reddening, and mass to light ratio.
We refer the interested reader to those work for more details about the methodology. For this work we use the sompy package, a python library for self organizing maps and adopt an 80 × 60 rectangular grid. The size is chosen to represent the data distribution well  (Hemmati et al. 2018). The consecutive colors of galaxies as measured in the filters listed in §2. 1 define the multi-dimensional (11D) color-space to be learned. Hence, each cell on the constructed grid has an eleven dimensional weight vector whose values (initially assigned by principle component analysis) change until they fully represent the training data. Having a large number of physical parameters (dimensions) in a model SED library is valuable for understanding the galaxy population. However with large numbers of parameters traditional model fitting analysis becomes computationally unfeasible even with advanced techniques such as Monte-Carlo-Markov-Chain (MCMC) fitting (e.
We note that a better weighting scheme could easily be devised, but is beyond the scope of the demonstration here. In Figure  3 , we compare best fit stellar mass measured from the SOM to that from the COSMOS catalog estimated using the LePhare SED fitting code  (Arnouts et al. 1999; Ilbert et al. 2006). In this comparison, we use statistics similar to those defined in  Nayyeri et al. (2017) , where σ F = rms(log(M SOM ) -log(M COSMOS )), σ NMAD = 1. 48 × median(|log(M SOM ) -log(M COSMOS )|) and σ O = rms(log(M SOM ) -log(M COSMOS )) after removing the outliers with the outlier fraction(OLF) defined as the fraction of objects with ∆log(M ) = (log(M SOM ) -log(M COSMOS )) > 0. 5. The scatter of ∼ 0. 2 and 3% OLF is comparable with the values reported in the literature when various SED fitting codes are compared (e.
COMPUTATIONAL SPEED Measuring galaxy physical properties using SOMs is also computationally efficient. Once the SOM is trained with a library of model SEDs, there is no need to keep the large number of models in the memory and any set of observations can be mapped to the trained SOM rapidly. In addition, the size of the SOM is determined by the data space, not the model space, so the required computational time to fit a set of models is largely invariant to the complexity and size of model grids and is instead determined by the dimensionality of the data set. So its possible to fit multiple very complex models to the data. This is a significant advantage for future large area surveys with billions of galaxies such as Euclid, LSST, and WFIRST because it will allow for rapid re-fitting of multiple SED model libraries.
Furthermore, the mapping of galaxies to the SOM is inherently parallel and hence is easily applicable on a GPU or a large cluster. To verify the speed up we converted the mapping phase (i. e. finding the best matching unit of the SOM with least χ 2 ) to a vectorized GPU friendly code using PyTorch  (Paszke et al. 2017)  and Numba  (Lam et al. 2015). Figure  5  shows the processing time of mea-suring physical properties of galaxies using the LePhare SED fitting code (REF) compared to mapping the galaxies on to the SOM using both a CPU on a Core-i7 machine and an NVidia Titan V GPU. The SOM is a ∼ 3-4 orders of magnitude faster than LePhare on a CPU and ∼ 5-6 orders of magnitude faster on a GPU. This means even a single threaded CPU could measure the physical parameters of over a million galaxies per minute and a single GPU could re-compute stellar masses for the 30 billion objects expected from LSST in under nine hours compared with several years for LePhare.
). This work used SOMPY, a python package for self organizing maps (main contributers: Vahid Moosavi @sevamoo, Sebastian Packmann @sebastiandev, Iván Vallás @ivallesp). We are thankful to NVIDIA for the GPU granted as their academic grant program. Parts of this research were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
(D) The distribution of CD163 and MHCII expression among mTAMs; CD163 + TAM (left) were gated based on the fluorescence minus one (FMO) control for CD163 staining (right). (E) Relative proportion of CD163 + TAMs among mTAMs in YUMM1. 7 tumors at the indicated time points; data are represented as mean ± SEM of n = 4. (F) Immunofluorescence staining for CD163 (green) and CD146 (indicating blood vessels, red) and nuclei (Hoechst, white) in a 200-µm-thick vibratome cross section of an entire tumor. Image was acquired as a tile-scan with Z-stacks using 10× objective and subsequent processed in 3D (left). High-resolution images was acquired as single Z-stack and processed in 3D (right). Scale bar, 1,000 µm. (G) High-throughput gene expression analysis of MN, iTAM, and mTAM subsets: (1) MHCII -CD163 -, (2) MHCII + CD163 -, (3) MHCII -CD163 lo , and (4) MHCII -CD163 hi (see Fig.
Importantly, iTAMs from αCD163-dxrLNP-treated mice showed a significant decrease in expression of genes normally associated with patrolling or nonclassical loaded with dxr; mice were injected with dxr-loaded αCD163-LNP (αCD163-dxr), IgG control-LNP (ctrl-IgG-dxr), empty αCD163-LNP (αCD163-ctrl), or PBS alone (vehicle). After 24 h, RPMs were analyzed in spleen by flow cytometry and the proportion of CD163 + RPMs determined. (I) Mice bearing palpable tumors were randomized into groups and treated as in H every second day for 2 wk and tumor volume measured (red arrow indicates initiation of treatment). (J) At endpoint, total TAM and CD163 + TAMs were analyzed by flow cytometry and frequency of live cells calculated. Data in graphs are represented as mean ± SEM of n = 4-6, and results are representative of three independent experiments.
Network analysis connects k nearest neighbors (k = 5) based on similarity calculated by Pearson correlation (see Fig. S4  D for a heatmap of differentially expressed genes). (H) Relative expression of specific genes in iTAMs (2 -ΔCT ) with and without CD163 + TAM depletion; data are represented as mean ± SEM of n = 5. Statistically significant differences were calculated using a Mann-Whitney U test; *, P < 0. 05; **, P < 0. 01. (I) Depletion of CD163 + TAM in WT or Ccr2 -/-mice grafted with YUMM1. 7 tumors; cohorts of mice bearing palpable tumors were treated with either αCD163-dxr or vehicle alone, every second day for 2 wk (n = 6). Statistically significant difference was calculated using two-way ANOVA followed by Tukey post hoc test; ***, P < 0. 001; ****, P < 0. 0001. (J-L) Flow cytometry analysis of MN, iTAM, and mTAM in tumors from WT and Ccr2 -/-mice at endpoint (J); total numbers of iTAM (K) and CD163 + TAM (L) were calculated and expressed per gram of tissue.