dataesr/scientific-paragraphs-categorization huggingface dataset:
showed a T1 lesion. We then decided to remove this fundic NET by endoscopy. Therapeutic endoscopy confirmed a 25-mm sessile lesion in the body of the stomach. The procedure was done with the patient under deep sedation using propofol. After the tumor had been removed with a 25-mm snare with a twisted wire (ERBE settings: Endocut Q effect 2, forced coag effect 3), bleeding from 3 different arterioles occurred. Spurting bleeding continued from 1 particular arteriole. The bleeding did not decrease despite copious washing of the lesion. This kind of bleeding could be compared with a Forrest 1a ulcer. Hemostasis could thus be performed by use of a clip or thermal coagulation. However, recent data suggest the superiority of thermal therapy for the treatment of bleeding ulcers. 1  We thus performed hemostasis using the Coagrasper forceps (Olympus Corporation, Tokyo, Japan), a monopolar hemostatic hot forceps (soft coagulation mode, effect 6.
Zhu and Mohanty (2006)   used Miller-Miller scaling  in combination with a one-dimensional infiltration equation based on the  Haverkamp et al. (1990)  model to derive effective hydraulic parameters at the grid scale of LSMs and global climate models (GCMs). The heterogeneity of soil hydraulic parameters was considered in the horizontal dimension, and the infiltration process was described using the concept of parallel stream tubes without lateral interaction. They found that the variability in Miller-Miller scaling factors had a stronger influence on the effective grid cell infiltration than the saturated water content and the ponding depth, which reflects microtopography. This approach was also implemented by  Montzka et al. (2017)  to generate an optimized soil hydraulic properties database at a coarse global grid resolution of 0.
Bayesian Upscaling Considered as a calibration method at the remote sensing footprint scale, the Markov chain Monte Carlo based upscaling algorithm introduced by  Das et al. (2008)  provides an alternative to derive the upscaled effective soil hydraulic parameters from a time series of soil moisture observations and stochastic information of the fine-scale soil hydraulic parameter variability. The Bayesian framework produces probability distributions of the effective (pixel-scale) soil hydraulic parameters, where preexisting knowledge about the local-scale soil parameters (e. g. , from the Soil Survey Geographic [SSURGO] database) can be combined with dynamic hydrologic observations and model outputs. Kim and Mohanty (2017)  proposed a more general approach by accounting for the effects of mixed (weighted) physical controls (covariates) as well as the interactions between the controls on the soil moisture distribution and subsurface flow including lateral flow between grid cells.
Sharma et al. (1980)  used two alternative approaches to realize the scaling of cumulative infiltration: either (i) by determination of two separate scaling factors for the sorptivity and steady-state infiltration, or (ii) by determination of a single optimized scaling factor. For the first approach, the two scaling factors a Sop and a AC (dimensionless), for sorptivity S op [L T -0. 5 ] and steady-state infiltration AC [L T -1 ], respectively, were calculated according to op, 2 op, op i i S S S ae ö ÷ ç ÷ ç ÷ a =ç ÷ ç ÷ ç ÷ ç è ø [22] and 1/2 AC, AC AC i i ae ö ÷ ç ÷ a =ç ÷ ç ÷ ÷ ç è ø [23] where áS op ñ and áACñ represent the respective mean values of those fitted infiltration parameters. The scaling of the infiltration data was subsequently performed by * Q Q =a [24] and 3 * t t =a [25] where Q* and t* identify the scaled cumulative infiltration [L] and time [T], respectively, and the scaling factor a is either a Sop or a AC.
We focus on infiltration of vegetated or bare land surfaces and exclude glaciers, lakes, and urban areas. An interesting example was provided by  Entekhabi and Eagleson (1989) , who used a Darcy-based approach to estimate the maximum infiltration rate or infiltrability for a heterogeneous grid cell in a LSM. They defined surface runoff, q s , as the sum of Hortonian infiltration excess (P x -f * for P x > f * and s < 1, i. e. , the first term on the right-hand side in Eq. [36]) and Dunne saturation excess (the second term on the right-hand side of Eq. [36]), where saturation excess is equal to P x when s ³ 1:  no yes no † Based on exponential pdf (see Table  3  and Appendix A, A2. 6). ‡ The International Geosphere-Biosphere Program soil dataset (Global Soil Data Task, 2014) of 4931 soil mapping units and their sand and clay content for each soil layer were used to create a mineral soil texture dataset  (Bonan et al.
, 2002). Soil organic matter data are merged from two sources. The majority of the globe is from ISRIC-WISE  (Batjes, 2006). The high latitudes come from the 0. 25° version of the Northern Circumpolar Soil Carbon Database  (Hugelius et al. , 2013). Both datasets report C down to 1-m depth. ( ) ( ) ( ) ( ) ( ) ( ) 1 s s 0 * s 1 0 1 * d d d d x x x P x x f x P x x q P f f P P f s s P f P P f s s ¥ ¥ ¥ é = - ê ê k ë ù + ú ú û ò ò ò ò [36] VZJ | Advancing Critical Zone Science p. 20 of 53 where P x [M L 2 T -1 ] is the flux of water incident at the soil surface, f * is the infiltrability of the first soil layer, s is effective relative saturation, k is a scaling factor needed to redistribute the GCM grid-scale precipitation across the scale of precipitation events, and f represents the spatial pdf of the respective variables (rainfall and relative saturation).
5% when using bulk density, sand, silt, and clay percentages. Including soil moisture content at field capacity and the wilting point, the R 2 increased to 64. 7%. The HYPRES database of soil hydraulic properties for European soils developed by  Wösten et al. (1999) , which contains information on 1136 horizons for hydraulic conductivity, provided a PTF to estimate point-scale K s with an R 2 of 19%. This function uses silt and clay content, bulk density, organic matter percentage, and whether the soil sample used to derive the properties originated from the topsoil or subsoil. A similar R 2 value was obtained by  Vereecken et al. (1990)  using clay, sand, and carbon content, as well as bulk density, for 127 undisturbed point-scale samples. To further increase the prediction of point-scale K s , structural properties in addition to currently used soil properties need to be considered  (Vereecken et al.
VZJ | Advancing Critical Zone Science p. 25 of 53 Estimators for the Saturated Hydraulic Conductivity. In all LSMs, the estimation of grid-scale K s is typically based on soil textural information but using classical PTFs developed for the point scale (details on these PTFs are provided in Appendix A4). These estimations are often completed with specific estimation functions for organic-rich soils (e. g. , CLM 4. 5, ISBA, JULES, see details below and Table  3 ). Rahmati et al. (  2018 ) compared the K s values for the point scale provided by  Clapp and Hornberger (1978)  with K s estimates derived from field-scale infiltration experiments using the global soil infiltration database SWIG. The K s derived from these experiments did not show a dependence on soil textural properties. Only for sand and loamy sand were the values of  Clapp and Hornberger (1978)  and the SWIG data on the same order of magnitude.
For all other textural classes, SWIG values were larger by a factor of two to a factor of >1000. Weynants et al. (2009)  pointed out that improved estimates of point-scale K s may be obtained by including structure-related information such as pedologic data and information about land use and crop management, which strongly affect surface soil properties as a result of tillage, root growth, soil trampling by cattle, etc. (see below). A notable advance is offered by OLAM-SOIL  (Walko and Avissar, 2008; Walko et al. , 2000) , which uses the PTFs developed by  Weynants et al. (2009)  or de Boer (2016) to estimate K s. The PTFs of  Weynants et al. (2009)  are based on a dataset of 182 point-scale samples of Belgian soils  (Vereecken et al. , 1989 (Vereecken et al. , , 1990)). They estimated the texture-dependent K s value by extrapolating the unsaturated soil hydraulic conductivity data to saturation and then calculated the ratio between the textural-dependent K s and the measured K s value, which was also available.
Use of Soil Maps to Estimate the Saturated Hydraulic Conductivity. The previous section shows that the basic soil information that is used to derive the soil properties needed to estimate point-scale K s differs among the models. Importantly, each LSM, in particular when used for weather forecasting or GCM studies, can use different soil maps to inform the model on soil hydraulic parameters (so-called ancillary data). For example, CABLE uses the Zobler soil class information  (Zobler, 1986). The version used by  Decker (2015)  uses the soil texture from the Harmonized World Soil Database  (FAO, 2009) , and so does JULES. OLAM-SOIL uses the SoilGrids data published by  Hengl et al. (2014 Hengl et al. ( , 2017)). The SoilGrids databases provide information on basic soil properties at seven depths in the soil profile up to 2 m.
They also provide estimates of the depth to bedrock and the distribution of soil classes. The gridded predictions were based on ?150,000 soil profiles and remotely sensed data. The input data used to estimate the soil hydraulic properties by CLSM or ISBA-SURFEX are obtained from the Harmonized World Soil Database version 1. 21 (HWSD1. 21) and the State Soil Geographic (STATSGO2) project  (De Lannoy et al. , 2014). PARFLOW-CLM uses the UNESCO soil map of the world  (FAO, 1988)  to provide a global coverage of K s values. The default soil texture map used in ORCHIDEE is the map presented by  Zobler (1986)  at 1°, and it is used in the currently ongoing CMIP6 simulations. CMIP6 refers to the Coupled Model Intercomparison Project Phase 6 that aims to address (i) how the Earth system is responding to forcing, (ii) the origins and consequences of systematic model biases, and (iii) the assessment of future climate change in the context of internal climate variability, predictability, and scenario uncertainty  (Eyring et al.
, 2016). ORCHIDEE can also use two other soil texture maps. The first one is the map developed by  Reynolds et al. (2000)  at 1/12° and the second is the surface SoilGrids maps at 1 km  (Hengl et al. , 2014). All these maps are used to define the dominant texture at the model resolution using the USDA texture classification. CLM 4. 5 uses the soil dataset produced by the International Geosphere-Biosphere Program (Global Soil Data Task, 2014) that comprises 4931 soil mapping units and contains the textural composition (sand and clay content). These data were used by  Bonan et al. (2002)  to create a global soil map of mineral soil textural data. The global data on soil carbon content are obtained from ISRIC-WISE  (Batjes, 2006) , whereas the soil carbon content for the higher latitudes are obtained from the Northern Circumpolar Soil Carbon Database  (Hugelius et al.
(1984)  and given by ( ) 0. 884 0. 0153 %sand s,min, 0. 0070556 10 i i K - + = ´ [49] Values for K s,sc were given by  Letts et al. (2000). In JULES and ISBA-SURFEX, a geometric average of the point-scale K s is used to account for the effect of soil C following the approach of  Chadburn et al. (2015b)   high latitudes, which come from the Northern Circumpolar Soil Carbon Database  (Hugelius et al. , 2013). In ISBA, the soil organic C content is determined from two soil horizons (0-30 and 30-100 cm) of the HWSD1. 21. In JSBACH, three additional classes are added to the 11 soil textural classes, namely peat, moss, and lichen, each with an average value of K s according to  Beringer et al. (2001). In CLSM, soils are stratified into four levels of organic C content  (De Lannoy et al. , 2014) , with the highest one (>8.
Specific attention was given to the underlying physical principles and concepts used to predict infiltration at the point and grid scales and the approaches used to describe spatial heterogeneity and upscaling of key parameters controlling the infiltration process in LSMs. We identified several topics and processes that warrant further attention in advancing the prediction of infiltration processes. First, there is the prediction of K s , a key parameter in describing infiltration. Currently, K s estimates in LSMs are derived from PTFs that are typically based on the textural composition of soils but do not consider the impact of soil structure on the infiltration process in general. Recently,  Rahmati et al. (2018)  published a global database of infiltration measurements that clearly shows that K s derived from field experiments cannot be predicted from soil texture alone.
A3. 7 JULES There are two options in JULES to account for the spatial heterogeneity of soil water content and thus determine F sat. The first option is based on a modified form of TOPMODEL  (Beven and Kirkby, 1979) , described by  Gedney and Cox (2003) , where the assumption of an exponential decay of K s with depth (leading to Eq. [A24-A25]) is relaxed. Grid box mean water table depth, z Ñ , is calculated using the approach described for A3. 0 TOPMODEL (Eq. [A27-A29]). This is used to estimate a critical topographic index l crit using the relation The value of F sat is then calculated as the fraction of the grid box for which l > l crit , assuming l follows a g distribution with mean (l m ) and standard deviation read in from observational datasets. To speed up the computation, the integral over the probability distribution of l is calculated for a range of mean water table depths during model initialization and approximated by an exponential function as ( ) sat s s crit exp F a c = -l [A48] where a s and c s are fitted to approximate the full integral (i.
43 of 53 In JSBACH, K s values are assigned to 11 textural classes based on data presented by  Beringer et al. (2001). However, the origin of the tabulated K s values, which appear to be mean measured values for a specific textural class, is not clear. Most likely, they were derived from the dataset of  Clapp and Hornberger (1978). JULES uses sand and clay content to estimate the K s for the main soil column. Below the soil column is an additional "deep water store," within which K s decreases exponentially with depth, with a dampening factor set equal to 3 as proposed by  Clark and Gedney (2008). The K s values can also be defined for each soil layer and can account for the presence of soil organic matter  (Chadburn et al. , 2015a). Rahman and Rosolem (2017)  incorporated the effect of preferential flow into JULES (but note that this has not yet been adopted in the current "official" UKMO version of JULES) to allow simulation of highly fractured unsaturated chalk soils.
Their bulk hydraulic conductivity (BC) model introduces only two additional parameters (namely the macroporosity factor and the soil wetness threshold parameter for fracture flow activation) and uses the K s from the chalk matrix. The BC model was implemented into JULES and applied to a study area encompassing the Kennet catchment in the southern UK, and the model performance at the catchment scale was evaluated against independent datasets (e. g. , runoff and latent heat flux). The results demonstrated that the inclusion of the BC model in JULES improved the simulations of land surface water and energy fluxes across the chalk-dominated Kennet catchment. This simple approach to account for soil structure has potential for large-scale land surface modeling applications. ORCHIDEE uses van Genuchten soil parameters (K s , n, a, q r , and q s ) for each USDA class.
Campbell (1974)  or Wösten et al. (2001) Harmonized World Soil Database version 1. 21 (FAO, 2009) and the State Soil Geographic (STATSGO2) database (De Lannoy et al. 2014) no yes yes ISBA-SURFEX from Clapp and Hornberger (1978) or Cosby et al. (1984) (optional); for organic soils (Decharme et al. , 2016) Harmonized World Soil Database (FAO, 2009) at 1-km resolution no yes yes Noah-MP version 3. 0 from Clapp and Hornberger (1978) Harmonized World Soil Database (FAO, 2009) or Milovac et al. (2014) no yes no JULES version 4. 6 from Clark and Gedney (2008); for organic soils, Chadburn et al. (2015b) default configurations of JULES uses soil ancillaries based on sand, silt, clay fractions from the Harmonized World Soil Database using the function of Cosby et al. (1984) no yes yes CLM4. 5 from Clapp and Hornberger (1978) and Cosby et al.
(1984); for organic soils, Lawrence and Slater (2008) based on IGBP soil dataset for mineral soils (IGBP, 2000), with organic soil described from ISRIC-WISE (Batjes, 2006) and the Northern Circumpolar Soil Carbon Database (Hugelius et al. , 2013) ‡ no yes yes CABLE same as horizontal CABLE rev 2. 0 uses Zobler soil class information (Zobler, 1986, 1999); Decker (2015) uses sand, clay, silt fractions from the Harmonized World Soil Database (FAO, 2009), calculating soil properties from the textures as in CABLE rev 2. 0 from Zobler (1986) no yes no SSiB from Clapp and Hornberger (1978) and Sellers et al. (1986) Harmonized World Soil Database v. 1. 1 (FAO, 2009) no yes no CH-TESSEL based on tabulated values for 7 textural classes including organic soils (Balsamo et al. , 2009) FAO (2003) soil texture map no yes yes JSBACH version 3.
2b. Good agreements were obtained by Roger & Moreau  [6]  when applying the mode-matching technique with cambered vanes compared to Hixon's  [7]  results with the NASA Glenn Research Center BASS code which solves the fully nonlinear Euler equations. Roger et al. [2]  and Roger & François  [5]  then used Ovenden's  [8]  solution for the velocity potential to highlight the effect of cut-on/cut-off transition on sound propagation but did not implement this mechanism in the mode-matching technique. To derive the solution of the velocity potentials inside the cambered part of the inter-vane channels (from the BA' interface to the channel outlet CC' in Fig. 2b ), two effects need to be addressed : the curvature of the channel centerline and the stream-wise variation of the cross-section (diffuser effect).
III. Comparison With Numerical Simulations A. Methodology A couple of test cases have been defined to test the present analytical solution against numerical simulations computed with the commercial software Simcenter 3D Acoustics  [11]. The two-dimensional Helmholtz problem is addressed using a high-order adaptive Finite Element Method (FEM)  [12, 13]. Periodic boundary conditions are enforced and the far-field reflections are avoided using Perfectly Matched Layers (PML)  [14]  as shown in Fig. 3. Qualitative and quantitative comparisons in terms of real values of fluctuating pressure between FEM results and the mode-matching technique are presented below.
Vane Camber Effect Four vanes (V = 4) are taken at a duct radius of R 0 = 38 mm, with a leading-edge camber angle Ψ = 30 • and an axial chord length c x = 60 mm. No mean flow is considered (M = 0) and the mean density and sound speed are assumed constant and set to ρ 0 = 1. 225 kg/m 3 and c 0 = 340 m/s. Incident acoustic waves of unit amplitude are scattered by the OGV. Computations have been performed for two distinct incident mode orders, j = 1 at kc x = 2. 44 ( f 2200 Hz) and j = 5 at kc x = 12. 2 ( f 11000 Hz), with corresponding angles of incidence from the x-direction given by θ j = arcsin( j/k R 0 ) to ensure periodicity in the y-direction. Figure  4  shows the instantaneous pressure maps computed with the FEM code and the mode-matching technique at j = 1 and kc x = 2. 44. The pressure patterns are in good agreement upstream and downstream the OGV row, as well as in the inter-vane channels, for both analytical models with and without camber effects.
6  and Fig. 7 ). The new model proves to be efficient to predict the pressure pattern evolution inside the inter-vane channels but there are still some differences with FEM results when looking at the downstream pressure profile in Fig. 7c. Work is still in progress to understand the residual errors at high frequencies. The new algorithm already shows promising capabilities at the cost of a reasonably higher computation time. The mode-matching code without camber effects runs in less than 2 seconds for a given frequency while it takes around 17 seconds accounting for vane camber, with a not yet optimized MATLAB code.
V. Conclusion An improved mode-matching model including vane camber for OGV aeroacoustics studies has been developed in this paper. In particular, comparisons with numerical simulations have shown the importance of accounting explicitly for vane camber when modeling sound transmission through vanes row at high frequencies. The improved model still shows residual discrepancies with numerical simulations in the transmitted field. This is under investigation to further improve the current model, looking at possible curvature-gradient effects. Only a reasonably higher computation time is needed to account for vane camber with a non optimized MATLAB code, which makes the model attractive for broadband-noise predictions. However, the simplified model without camber should be preferred for predictions at low frequencies since it has proved accuracy in this range.
Encouraging results have been obtained with the mode-matching model compared to numerical simulations using a more realistic vane geometry. Even if more discrepancies can be expected at higher frequencies than presented in this paper, the model proves to give accurate results hence a more refined vane geometry description is not necessary in this range. Results with a mean potential flow have also been performed in Moreau et al. [16] , using the simplified mode-matching model, and give a reasonably good match with numerical simulations carried out using the Simcenter 3D Acoustics software. The formalism used in the mode-matching technique is also well suited to implement cut-off/cut-on and cut-on/cut-off transitions in the inter-vane channels. These transitions have already been implemented in the mode-matching technique without camber, with a uniformly valid solution derived from Rienstra  [15]  and Ovenden's work  [8].
Acknowledgements This work was performed within the framework of the  Labex CeLyA of the Université de Lyon , within the  programme 'Investissements d'Avenir'  ( ANR-10-LABX-0060/ANR-16-IDEX-0005 ) operated by the  French National Research Agency (ANR) , and is also partially supported by the industrial Chair ADOPSYS co-financed by  Safran Aircraft Engines  and the  ANR  ( ANR-13-CHIN-0001-01 ).
Following their planktonic phase, the larvae of benthic marine organisms must locate a suitable habitat to settle and metamorphose. For oysters, larval adhesion occurs at the pediveliger stage with the secretion of a proteinaceous bioadhesive produced by the foot, a specialized and ephemeral organ. Oyster bioadhesive is highly resistant to proteomic extraction and is only produced in very low quantities, which explains why it has been very little examined in larvae to date. In silico analysis of nucleic acid databases could help to identify genes of interest implicated in settlement. In this work, the publicly available transcriptome of Pacific oyster Crassostrea gigas over its developmental stages was mined to select genes highly expressed at the pediveliger stage. Our analysis revealed 59 sequences potentially implicated in adhesion of C.