dataesr/scientific-paragraphs-categorization huggingface dataset:
1% SAE : 4. 8% group, 2. 6% placebo SAE : 8. 6% memantine group Memantine 2013 Moreau C, NCT01108029 25 65 3 Capacity No France Yes Parkinson No adverse 2013 [47] of mobility desease, events were measure UPDRS part reported III item 29 >= 2 Duco et al. Trends Geriatr Healthc 2016, 1(1):1-13 ISSN: 2689-9019 | Duco et al. Trends Geriatr Healthc 2016, 1(1):1-13 ISSN: 2689-9019 | Duco et al. Trends Geriatr Healthc 2016, 1(1):1-13 ISSN: 2689-9019 |.
Data have been collected prospectively about socioeconomic characteristics, lifestyle, medical, history of mothers, events at delivery, and complications in neonates. Pre-pregnancy weight was collected. During the medical visit of the first trimester, pregnant women were measured for height and weighed. During the medical visits of the second and third trimesters, pregnant women were weighed. Exclusion criteria for this study were pregnant women without information on ppBMI or weight during pregnancy, miscarriage or fetal death before 22 weeks of gestation, pregnancy termination (elective abortion or medical interruption of pregnancy), multiple pregnancies, and lost to follow up (Figure  1 ). Women were classified by their ppBMI according to WHO categories (BMI calculated as weight in kilograms divided by height in meters squared; BMI for underweight, <18.
Statistical Analysis All analyses were performed using Stata software (Version 13, StataCorp, College Station, TX) and R software  [20]  for a two-sided Type I error of 5%. Patient's characteristics were expressed as mean ± standard-deviation (SD) or median (interquartile range, minimum and maximum) for continuous data (assumption of normality assessed by using the Shapiro-Wilk test) and as numbers and associated percentages for categorical parameters. To study the association of maternal and neonatal outcomes with ppBMI and IOM recommendations on gestational weight gain, univariate and multivariable analyses were performed using robust (standard-errors) Poisson generalized-linear-model regression (package gllamm). Results were expressed as relative-risk ratios (RR) and 95% confidence intervals (95% CI).
e. , identify groups of distinctive trajectories which are summarized by a finite set of different polynomial functions of time  [22, 23]. Rather than assuming a priori, i. e. , the existence of trajectories of a specific form, the method allows the trajectories to emerge from the data. This model allows for data grouping using different parameter values for each group distribution. Groupings may identify distinct subpopulations. Maximum likelihood is used for the estimation of the model parameters. The maximization is performed using a general quasi-Newton procedure. The fundamental concept of GBTM is the distribution of weight conditional on time. That is, the distribution of weight trajectories denoted by P(Y i |Time i ) where the random vector Y i represents individual i's longitudinal sequence of weight and the vector Time i represents individual i's time when each of those measurements is recorded.
In vitro imaging of cell lines Cell lines were plated at 1x10 5 cells per well in black 96-well plate overnight. Luciferase substrate, D-luciferin, was added at 0. 3 mg/well in phosphate saline buffer (PBS), 5 min before imaging with a highly sensitive cooled charge-coupled device camera (IVIS Spectrum Lumina II; Perkin Elmer). Wells were then rinsed with PBS and fluorescent acquisition was performed for Tomato fluorescence emission (excitation: 535 nm, emission: 580 nm) with the IVIS camera (Supporting Information Fig. 1 ). Quantity of bioluminescent or fluorescent photons emitted by cells was measured in well-surrounding region of interest (ROI) in plates, using Living Image ® 4. 4 software (Perkin Elmer). Bioluminescence was quantified in photons (ph)/sec(s)/cm 2 /steradian (sr) and fluorescence was quantified in [ph/cm 2 /s/sr]/ [υW/cm 2 ] using the Living Images version 4.
7 μL of Hi-Di formamide (Applied Biosystems) and 0. 3 μL of GS-500-ROX (DNA Size Marker with fragments ranging from 35 to 500 bp used to size PCR products, ThermoFisher Scientific). Analysis were performed with Peak Scanner 2 ® software. FAK mRNA variants were independently detected as single peak at the corresponding product size (see Supporting Information Fig. 2  as an example): for F3-FAMR2: FAK 0 = 178 bp and FAK  7  = 199 bp, F4-FAMR2: FAK 28 = 178 bp and FAK  7, 28  = 199 bp, F5-FAMR2: FAK 6 = 196 bp and FAK  6, 7  = 217 bp, F6-FAMR2: FAK 6,28 = 196 bp and FAK 6,7,28 = 217 bp.
Quantitative real-time PCR Quantitative (q)RT-PCR was performed on the capillary electrophoresis-detected FAK mRNA variants using LightCycler 480 SYBR Green I Master (Roche, Boulogne-Billancourt, France). Briefly, per sample 45 ng of cDNA was mixed with 5 μL of Syber Green Mix and 0. 5 μM of the following primers: hF3-R2 (for FAK 0 ), hF4-R2 (5 0 ATTTCTCTCTCACGCTGTCC3 0 ) (for FAK  28  ), hF5-R2 (for FAK  6  ) and forward hHPRT primer (CC TGGCGTCGTGATTAGTGA) and reverse hHPRT primer (CGAGCAAGACGTTCAGTCCT) (for housekeeping gene). hHPRT has been validated as specific for Homo sapiens that does not cross-react with Mus musculus (data not shown). qPCR reaction was performed for 45 cycles in a LightCycler 480 (Roche) and analyzed using LightCycler 480 SW1. 5. 1 software.
No TdTomato fluorescence was detected in the liver and lungs of the SW480-IC-injected mice. In the SW620-IC-engrafted mice, liver metastases were detected from day 80. In HT29-IC-engrafted mice, TdTomato fluorescence was detected from day 35 in the liver but only traces of metastases could be detected in the lungs from day 45. Finally, regarding HCT116 cells, liver and lung metastases were clearly detected as early as day 35 highlighting the high aggressiveness capacities of this CRC cell line. By comparison with HCT116, only a small amount of liver metastases was detected at days 45 and 80 for HT29 and SW620, respectively (Fig. 3 ). Thus, our data obtained from the orthotopic CRC model in SCID beige mice demonstrate that HCT116 and HT29 cells are more aggressive, with higher proliferative and invasive properties, compared to SW620 and SW480 cells.
Acknowledgements The authors thank the patients who agree on supporting our research by giving their consent,  Jean-Antoine Girault  and  Gilles Dietrich  for helpful discussion and advices on the study. This study was supported by  European Research Council  funds (ERC-310973 PIPE to NV), The Ligue régionale contre le cancer (Grant to CRS), and The  Région Midi-Pyrénées/Occitanie  and  Urosphère  supported the post-doctoral salary of CD (Grant to AF). IVIS spectrum used in our study was supported by  Equipex  " Investissements d'avenir" funds  ( ANR-11-EQPX-0003 ).
European Research Council ; Grant numbers:  ERC-310973 PIPE , ERC-310973 ; Grant sponsor:  Ligue Régionale  contre le Cancer; Grant sponsor:  Région Midi-Pyrénées / Occitanie ; Grant sponsor:  Equipex "Investissements d'avenir " funds; Grant number:  ANR-11-EQPX-0003 ; Grant sponsor: Urosphère DOI: 10. 1002/ijc. 32120.
e sequences obtained were aligned with the software BioNumerics 7. 5 (Applied Maths, Belgium) and corrected manually to resolve discrepancies between the sense and antisense strands. Sequences were compared with homologous sequences contained in the sequence databanks through NCBI (National Center for Biotechnology Information (http://www. ncbi. gov/Blast. cgi) using the BLASTn program based on the identification criterion published by Drancourt  [20]. All sequences have been stored in NCBI GenBank data.
Statistical Analysis. Principal component analysis (PCA) was used to investigate possible correlations between growth after 2 to 8 and 11 days or heavy trace metals and (1) strains assemblages or (2) strains assemblages and Consortium of Gram-negative/Gram-positive bacteria groups. Prior to ordination, strains abundance data were transformed to better meet the assumptions of normality  [22]  using ln (x + 1). In addition, a PCA of cytochrome oxidase enzymatic activity was conducted in order to see the effect of metals on enzymatic activity. All analyses were performed using CANOCO (Canonical Community Ordination, version 4. 5)  [23].
66. 66% (48) of Bacillaceae are the most dominant genera in the digestive tract of P. reticulata isolated from Brazzaville wastewater, 8. 33 % (6) of Staphylococcaceae and 25 % (18) Enterobacteriaceae. e identification of Shigella spp. and Salmonella spp. has been confirmed using direct PCR of targeted specific genes such as icsB and invG, respectively. Using 16S rRNA gene, Acinetobacter haemolyticus (GenBank: MK099885. 1), Bacillus spp. including Bacillus subtilis (GenBank: MK099888. 1), Bacillus cereus (GenBank: MK099886. 1, MK099887. 1, and MK099891. 1), Bacillus amyloliquefaciens (GenBank: MK156314. 1) and Bacillus licheniformis, Bacillus altitudinis (GenBank: MK099889. 1 and MK156313. 1) and Bacillus megaterium (GenBank: MK099890. 1, MK391976. 1, MK391968. 1, MK391975. 1, and MK391970.
Effect of Consortium in the Degradation of Hydrocarbons and the Secretion of Biosurfactant. To better understand the cell-cell interaction roles in guppy divergence and adaptation, Gram-positive and Gram-negative bacteria were pooled together. We first assessed for consortium to degrade and/or tolerate hydrocarbons by using the BH medium supplemented with hydrocarbons from gasoline or diesel fuel. Growth was done after 2 and 4 days for the group of Gramnegative bacteria and 1 and 3 days for the Gram-positive bacteria group (data not shown). In addition, the evaluation of E24 also found that each group is able to secrete biosurfactant ranging between 97 and 100% (Figure  4 ).
Acknowledgments We are grateful to  Prof. Anne Botteaux  ( Laboratoire de Bactériologie Moléculaire, Batiment GE, 5ème Etage, Campus ERASME-Université Libre de Bruxelles ),  Prof. Clobite Bouka Biona  ( Université Marien NGOUABI ),  Prof. Arsene Lenga , and  Dr. Bienvenu Dinga  ( Université Marien NGOUABI ) for their continuous encouragements and material support. is work was supported in part by  Institut National de Recherche en Sciences Exactes et Naturelles (IRSEN).
Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational longrange prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods.
From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.
INTRODUCTION Sustained high-quality observations are essential for improving our understanding of the ocean and its interactions with the atmosphere and the overall Earth system. An important tool to study the Earth system is the production of historically accurate four-dimensional reconstructions of quantities that characterize the ocean state (such as temperature, salinity, and currents). Mathematical methods from the field of Data Assimilation (DA) allow information provided from observations to be propagated in time and space to unobserved areas using the dynamical and physical constraints imposed by numerical models. When these methods are applied to form the aforementioned historical reconstructions, this procedure is called a retrospective analysis, or "reanalysis"  (Kalnay et al. , 1996; Dee et al.
METHODOLOGICAL DEVELOPMENTS IN OCEAN DATA ASSIMILATION Data assimilation is essentially an automation of the scientific method. A hypothesis is made and encoded in a numerical model. This model is then used to make predictions that can be tested against new observations. Prediction accuracy is then examined and provided as feedback to modify the model and methods, and the process repeats. The development and application of DA serves fundamental Earth science goals such as to: (1) fill gaps between sparse measurements to form a complete picture of the Earth system, (2) utilize the observing network to initialize forecast models, (3) characterize errors in the modeling and observing systems, and (4) identify areas of high uncertainty where observations can illuminate poorly understood phenomena, help target observing campaigns, and improve numerical models and forecasts.
CONNECTING OCEAN DATA ASSIMILATION WITH OCEAN OBSERVING EFFORTS Although the growing constellation of satellite observing platforms continues to provide a much more coherent view of the ocean surface, there are limitations that remain in the integrated ocean-observing system that prevent the accurate estimation of the full state of the ocean based on observations alone. In situ measurements are quite sparse, while small-scale processes important to air-sea interaction and the deep ocean remain largely unobserved. In order to acquire a complete picture of the ocean state while appropriately characterizing our uncertainty of this picture, the gaps in coverage must be bridged in space and time using rigorous mathematical methods. This is a primary activity of the DA community and requires close collaborations between theorists in academia and practitioners at operational centers.
g. ,  Oke et al. , 2015a,b). Advances in DA methods have been and will continue to be driven by new observing technologies. We mention two notable features of upcoming observing technologies that deserve attention. First, amongst recent and planned satellite missions are increasingly highresolution datasets covering the ocean surface. The development of instrumentation such as VIIRS, SLSTR on board Sentinel-3 (A and B), and platforms such as the Surface Water and Ocean Topography (SWOT) mission indicate there will be large volumes of data available for assimilation. At present, the fidelity of these data products is far higher than many operational ocean models are capable of resolving. Ocean DA faces a challenge due to computational limitations: there is a need to either increase the resolution of ocean models in order to take full advantage of new data sources using conventional DA approaches or design new methods to extract more information from these observations without resorting to highresolution modeling (e.
, 2018). A common assumption is that errors are Gaussian-distributed and that the time evolution of the errors is linear. As such, there are common limitations to all currently used DA methods and a primary goal for improving the accuracy and applicability of DA in the coming decade will be to relax these limiting constraints (see  Martin et al. , 2019; Moore et al. , 2019, this issue). This has relevance to future ocean-observing system design, as it may change requirements on the observing system either to test and design new methods or to take advantage of new capabilities afforded by the methods. In recent years, the Global Ocean Data Assimilation Experiment (GODAE) and its offshoot, GODAE OceanView (GOV) have been active in galvanizing ocean DA activities by providing a platform for promoting ocean DA and forging international collaborations  (Bell et al.
THE ADVENT OF COUPLED DATA ASSIMILATION The components of the Earth system have traditionally been analyzed independently. However, modeling improvements and increases in computing power are now enabling the analysis of the Earth system as a whole  (Saha et al. , 2010 (Saha et al. , , 2014;; Lea et al. , 2015). Observation-model synthesis activities that incorporate observational data into coupled Earth system models have led to the emergence of a new research area called Coupled Data Assimilation (CDA;  Penny et al. , 2017). While traditional methods have generally focused on a single scale of motion within any given DA system, an essential characteristic of CDA is the need to account for the multiple spatiotemporal scales present in the error dynamics of the coupled system. The most basic application of DA to coupled models has been the application of legacy DA systems to each component separately, which is called weakly coupled data assimilation (WCDA).
) are also crucial for assessing uncertainty in reanalyses and improving process representation in models. Within historical data records, the accuracy of the observations assimilated is often unknown or underestimated due to lack of metadata. This also prevents effective bias-correction procedures from being implemented and may lead to the erroneous specification of instrumental errors. For the historical ocean subsurface temperature record, the situation is improving through an internationally coordinated community effort  (Domingues and Palmer, 2015)  1 , focusing on recovery of data and metadata, development of intelligent metadata, coordinated quality control (automated and expert), and assignment of uncertainties. Their overall goal is to produce a long-term climate quality global ocean subsurface database that can be used with greater confidence by the ocean reanalysis community and other users.
The first interim IQuOD database product is available from The IQuOD Team (2018). Reanalyses will continue to extend further backward in time to cover longer historical periods, following the trend set by  Compo et al. (2011)  and ECMWF's ERA-20C, and later followed by comparable century-long ocean reanalyses  (Giese et al. , 2016; Yang et al. , 2017)  and the coupled reanalysis CERA-20C  (Laloyaux et al. , 2018a ). This will require improved methods to handle sparse observations, discontinuities in the observation network, and correction of large-scale biases, as well as continuous efforts on data rescue. With the recent emergence of coupled Earth system reanalyses, non-oceanic data will also play an important role, particularly in time periods where ocean observations are extremely sparse or non-existent.
15 ± 1. 1 W/m 2 ) and ocean temperature increments (-0. 11 ± 1. 9 W/m 2 ) averaging close to zero over the century, compared to the forced ocean reanalysis ORA-20C (-1. 62 ± 1. 89 W/m 2 and 1. 66 ± 2. 32 W/m 2 ). However, given that the SST in the ocean component of CERA-20C was nudged toward an external data product, this suggests that there is further room for improvement. While midlatitude storms, heat waves, or cold-air outbreaks are often well-represented in regions with dense observational coverage, this is not always the case for tropical cyclones, which are difficult to model and not well-constrained by observations. CERA-20C struggles to correctly represent several tropical cyclones at the beginning of the 20th century  (Laloyaux et al. , 2018a). More work is needed to quality control observations from the International Best Track Archive for Climate Stewardship (IBTrACS).
These reforecasts are initialized by ocean or coupled reanalyses  (Balmaseda, 2017). The length of the reforecast record adds value to the forecast. For this purpose, sustained data rescue activities are recommended as well as maintaining stability of the existing observing system. Recently, ocean reanalyses used to initialize seasonal prediction systems (reforecasts and near real-time reanalyses) have become publicly available via the EU-funded Copernicus Programme and are being used to evaluate subsequent forecasts  (Juricke et al. , 2018). Measurements from observing platforms such as satellites, moored surface and subsurface buoys, drifters, floats, dedicated manned and unmanned vehicles, research ships, and vessels of opportunity are collected and distributed with various time lags. Operational predictions rely on observational platforms equipped with the capability for distribution in real-time or near real-time.
Some observation types are used primarily as independent measurements for evaluation because they cannot be assimilated due to time delays or other technical complications. These include ocean current profilers, satellite-derived ocean surface currents, and a suite of biogeochemical observations such as carbon, oxygen, nutrients, ocean color, and phytoplankton. Overall, a lack of uniformity in data management infrastructures imposes problems for the effective and efficient use of the global observing system in prediction efforts. These issues include, but are not limited to, delayed and duplicate data receipts, versioning issues, missing data and metadata, and non-documented data processing procedures. In order to advance the deployment of effective ocean-observing systems, modern data management infrastructures are needed such that all activities along the data flow pipeline, from data collection through assembly and preservation, are more automated and fault-tolerant and progressively advance the systems toward interoperability.
Building strong collaboration amongst the observing networks, data managers, and decadal forecasting centers will lead to improved access and uptake of data and to efficiencies that will eventually lead to improvements both in the observing networks and the decadal prediction system. The future ocean observational requirements for the decadal prediction system include sustained and reliable data streams that have global sampling and are continuous in time, subject to regular quality control and calibration procedures, and encompass several spatial and temporal scales (e. g. , National Academies of Sciences, Engineering, and Medicine [NASEM], 2017). To this end, there is great value to centralized data centers that collate observations from individual observing platforms in order to provide timely access to data and a consistent data format for ease of integration into DA systems.
Self-sailing boats currently exist that can autonomously gather ocean and atmospheric observations over large areas of the ocean surface. Such technologies have the potential to precipitously drop costs of collecting observations of quantities such as wind, temperature, humidity, salinity, dissolved oxygen, and fluorescence near the ocean surface. These technologies are promising for constraining surface flux estimates in CDA, leading to improved modeling of air-sea interaction and improved initialization of coupled model forecasts. S2S forecasts for high latitudes and midlatitudes can be improved with more numerous and accurate ocean and sea-ice observations in data-sparse regions. A redesign of the TAO/TRITON array is currently underway by the Tropical Pacific Observing System (TPOS-2020) working group 3  that is largely influenced by the volume of new complementary data provided by a number of new observing platforms.