dataesr/scientific-paragraphs-categorization huggingface dataset:
For the 4-parameter model, k is also the rate of change of the target AA concentration. The choice of the model was done on a per AA basis to compare the values of parameters using the same model between diets. First, the individual plasma concentrations of the 20 proteinogenic AA were fitted using the 4-parameter model using the NLS2 function (nls2 package  (Grothendieck, 2013 )) of R. Regardless of the diet, if C d differed from zero for at least half the pigs (!12 pigs) for a particular AA, the 4-parameter model was used. In the other cases, the plasma concentrations were refitted using the 3parameter model. The effect of the diet on each parameter was tested using the LMER function using the lmerTest package  (Kuznetsova et al. , 2017)  with diet as a fixed effect and pig as a random effect.
Acknowledgements The authors thank  F. Le-Gouevec ,  A. Chauvin ,  M. Genissel ,  J. Georges ,  G. Guillemois , and  J. F. Rouaud  from  UE3P ,  INRAE , 35590  Saint-Gilles , France for animal care, sample collection, and production of experimental diets,  N. Meziere ,  F. Thomas ,  R. Comte ,  A. Costentin ,  C. Mustière , and  C. Perrier  from  PEGASE ,  INRAE ,  Institut Agro ,  35590 ,  Saint-Gilles , France for laboratory analyses.
Financial support statement This work was supported by  Institut national de recherche pour l'agriculture , l'alimentation, et l'environnement (INRAE) and  BCF Life Sciences (Pleucadeuc, France)  as part of the  Convention industrielle de formation par la recherche PhD program  of  Association nationale de la recherche et de la technologie (ANRT)  granted to  FA Eugenio  (grant number:  2018/1771 ).
These results are further confirmed by comparisons of the LPT predictions performed this way to measurements in Vlasov simulations performed with the public code ColDICE. We also show that the component of the force orthogonal to the collapse direction preserves its single-stream nature -it does not change qualitatively before or after the collapse -allowing sufficiently high-order LPT acceleration to be used to approximate it accurately as long as the LPT series converges. As expected, solving the Poisson equation on the density field generated with LPT displacement provides a more accurate force than the LPT acceleration itself, as a direct consequence of the faster convergence of the LPT series for the positions than for the accelerations. This may provide a clue as to how we can improve standard LPT predictions.
This paper is organised as follows. In Sect. 2 we introduce the basic equations of motion in the Lagrangian description and the LPT framework. We also discuss important aspects of the gravitational force calculation. In Sect. 3 we provide a simple analytical formula for the force inside a pancake in a case where the caustic structure is seeded by a locally symmetric displacement. In Sect. 4 our analytical predictions are tested in systems with initial sine wave conditions, using high-order LPT as a proxy for the dynamics. The approximation of the force field is compared to the exact solution of the Poisson equation, in which the density field is generated by the LPT motion. This is followed in Sect. 5 by comparisons of the analytical predictions to the force field directly measured in Vlasov-Poisson simulations performed with the public code ColDICE  (Sousbie & Colombi 2016 ).
Comparison to simulations In the previous sections, we tested the accuracy of force field calculations by using LPT as a proxy of the dynamics. We now rely on actual measurements in Vlasov-Poisson simulations to test LPT itself, since it is used for computing the coefficients in Eqs. (  34 )-(  36 ) that lead to the asymptotic expression (51) as well as the gravitational acceleration as a second time derivative of the LPT displacement in Eqs. (  49 ) and (  15 ). The simulations we used to conduct the analyses were performed by  Colombi (2021)  and STC with the public Vlasov code ColDICE  (Sousbie & Colombi 2016). This solver directly follows the evolution of a self-gravitating 3D (2D) phase-space sheet in 6D (4D) phase-space with an adaptive tessellation of tetrahedra (triangles). The initial configuration uses a regular pattern of n s vertices with null velocities to construct the tessellation.
Acknowledgements. We thank  C. Rampf  for reading and commenting on the draft. S. S. is supported by  JSPS Overseas Research Fellowships. This work was supported in part by  MEXT/JSPS KAKENHI  Grant Numbers  JP20H05861  and  JP21H01081  (AT),  ANR  grant  ANR-13-MONU-0003  (SC), as well as  Programme National Cosmology et Galaxies (PNCG)  of  CNRS/INSU  with INP and  IN2P3 , co-funded by  CEA  and  CNES (SC). Numerical computation with ColDICE was carried out using the HPC resources of CINES  (Occigen supercomputer)  under the GENCI allocations  c2016047568 ,  2017-A0040407568  and  2018-A0040407568. Post-treatment of ColDICE data were performed on HORI-ZON cluster of  Institut d'Astrophysique de Paris.
To validate these data in human tissues, we monitored the expression of RIP140 and HES1 proteins by immunohistochemistry in a cohort of human CRC biopsies. This cohort comprised 45 primary tumors of different stages (7 stage 0, 9 stage I, 10 stage II, 12 stage III, 7 stage IV) and 9 metastases (see Table  1  for patient characteristics). The expression of RIP140 and HES1 were quantified both in the non-tumoral mucosa and in the adenocarcinoma, as described in the Material and Methods section. As shown in Figure  5E  and F, the nuclear staining of the two markers was significantly correlated in both normal and tumoral intestinal tissues.
Using the Kaplan-Meier Plotter database, we reanalyzed RNAseq data obtained on colon adenocarcinoma from 452 patients. This cohort was separated into two groups of 226 patients with low and high RIP140 expression in the corresponding tumors using the median as a cutoff value (Supplementary Figure  S5B  and C , respectively). As observed by IHC, we confirmed a statistically significant association of high expression of HES1 with a decreased risk of death in colon cancer patients, only when their tumor express high RIP140 expression (Supplementary Figure  S5C , p=0. 048). Altogether, the data strongly suggested that RIP140 influences the biological activity of the HES1 protein, in accordance with our data demonstrating a strong effect of RIP140 on HES1 expression and transcriptional activity.
with FFPE tumor samples collected in the frame of the Clinical and Biological Database BCBCOLON (Institut du Cancer de Montpellier -Val d'Aurelle, France, Clinical trial Identifier #NCT03976960). Adenomas, primary adenocarcinomas, and metastatic lesions were sampled as two cores of 1 mm diameter. All samples were chemonaive. Tumor samples were collected following French laws under the supervision of an investigator and declared to the French Ministry of Higher Education and Research (declaration number DC-2008-695). Study protocol has been approved by the French Ethics Committee: CPP (Comité de Protection des Personnes) Sud Méditerrannée III (Ref#2014. 02. 04) and by the local translational research committee (ICM-CORT-2018-28). We have complied with all relevant ethical regulations for work with human participants, and informed written consent was obtained for all patients.
MD simulation Initially, we constructed a 79. 1 × 64. 5 × 8. 8 Å 3 magnetite slab and an α-PVDF layer according to the procedure described in our previous work  [6]. The surface of the magnetite slab was treated by replacing the single bonded oxygen (O) atoms with OH groups. Then, 20% of the OH groups were replaced with oleic acid groups. The chemical structures of water and oleic acid were optimized at the level of B3LYP/6-31g** using Gaussian 16 quantum chemistry software (USA)  [32] , before modification of the magnetite surface. The α-PVDF layer was placed on top of the magnetite slab and their distance was set to about 5. 0 Å. MD simulation calculations were carried out using the simulation package GROMACS version 5. 0. 7  [33, 34]. The details of the 10 ns MD simulation procedure and force field parameters of all the components are presented in the supporting information (SI-1).
We can derive from our results that the need to have oleic acid chains grafted on to the nanoparticle surface for stability and good dispersion purposes can impede the interaction of PVDF chains with the nanoparticle surface, diminishing the booster effect of the nano-fillers on the electric properties of PVDF nanocomposites. Tuning the grafted-chain conformation is another parameter that might enhance the macroscopic properties of nanocomposites. relationships which may be considered as potential competing interests: Fahmi BEDOUI reports financial support was provided by Région Hauts de France and Labex MS2T.
Acknowledgement This work was funded by the  Région Hauts-de-France  and the  French Government , through the program "Investments for the future" managed by the  National Agency for Research  (reference  ANR-11-IDEX-0004-02 ) in the framework of the Labex  MS2T. Fahmi Bedoui  acknowledges the support from the  Alliance Sorbonne University  through the  Emergence Funding Program  and  GDR-Polynano  for nanoparticles supply. Andres Jaramillo-Botero  and  William A. GoddardIII  acknowledge funding from the  National Science Foundation  ( CBET-2005250 ).
NMR experiments for assignment of the peptide Initial peak assignments of the YadAO:9 N-terminal peptide was performed using pep3. The peptide was resuspended in NMR buffer at a concentration of 1mM. The concentration was determined spectrophotometrically at 280 nm. A full set of NMR spectra was acquired on an 800 MHz Bruker Avance III spectrometer equipped with a 5 mm TCI cryo probe ( 1 H, 13 C, 15 N) at a temperature of 298. 15 K: homonuclear 1 H-1 H 2D TOCSY (80 ms mixing), NOESY (100, 200, 300, 400, 600 ms mixing), DQCOSY, 1 H- 15 N HSQC, 1 H- 13 C HSQC, and 13 C-1 H HMBC. In homonuclear spectra, water suppression was obtained using excitation sculpting  [54]. The data was processed using Topspin 3. 5 pl6 (Bruker Biospin). DSS was used as a chemical shift standard, and 15 N and 13 C data were referenced using frequency ratios  [55].
Chemical shift analysis Predictions for secondary structure populations (β-strand, α-helix, PPII and coils) were obtained from 15 N, 13 C and 1 H backbone chemical shifts using δ2D software  [27]. Backbone dihedral angles were predicted with TALOS-N  [25]  from HN, Hα, Cα, Cβ, C' and N chemical shifts. Predictions classified as "Bad" were not considered for further modelling analysis.
Alphafold2 modelling of trimeric YadAO:9 head domain The YadAO:9 head domain (residue 1-487, excluding the signal peptide) structure was predicted using Alphafold2  [32]  Multimer via the Colabfold Multimer pipeline  [36]  with default parameters, 20 recycles and final amber relaxation. The best ranked model was used for analysis and figure preparation. pLDDT scores were plotted against the residue number using the generated. json file extracted using Alphapickle (https://github. com/mattarnoldbio/alphapickle/tree/v1. 4. 0).
Acknowledgements The authors thank  Annika Teresa Lehmann  for support in protein production during her Erasmus+ internship. We furthermore thank  Régine Dazzoni ,  Theis Jacobsen ,  Maylis Lejeune , and  Sébastien Cardon  at the  Institute Pasteur  and  Athanasios Saragliadis  at the  University of Oslo  for useful discussions and technical support. Finally, we thank the  NMR facility  as well as the  Mass Spectrometry facility  at the  University of Oslo  for access to excellent equipment, and  Bernd Thiede  for mass spectrometry analysis of the peptides. We are thankful for the funding received in form of a  Horizon 2020 Innovative Training Network "ViBrANT"  (grant ID:  765042 ) to D. L. and N. I. P and a  FEMS mobility grant  (grant -ID :  FEMS-GO-2019-608 ) to I. M.
0 for all calculations. The kinematic viscosity at oil temperature was calculated acc. to DIN 51563  [5]. The oil density at oil temperature was calculated acc. to DIN 51757  [4]. The kinematic viscosity at 40 °C and 100 °C and the density at 15 °C were taken from the oil database of the calculation program 'WTplus' which is developed by FZG  [20, 21]. The values in the database are similar to the values documented in the FVA reference oil catalogue  [17]. Surface roughness, rotational speed and the lubricant temperature were varied to investigate the influence on the calculated values of the mean gear coefficient of friction. By varying the oil temperature, the operating viscosity of the oil is changed indirectly. It should be added that the varied parameters and operation conditions can partly lead to unrealistic combinations of operating parameters.
Data analyses and reporting All data analyses will be performed using SAS V. 9. 2 or higher (SAS Institute, Cary, North Carolina, USA). Descriptive analyses will be performed. Statistical analyses will be based on observed cases. Multiple imputations will be performed as a sensitivity analysis. All AE verbatim terms will be coded using the Medical Dictionary for Regulatory Activities. Medication will be coded using WHO Drug Dictionary. Final analyses will be performed once the data from all patients have been collected in the database, cleaned, and database lock has occurred. Analyses will be performed overall and per country. Changes in WHO-5 (to assess the primary objective) and changes in DLQI-R, PBI-S-10, TSQM-9, patient's and physician's and NRS scores will be calculated as the difference between baseline and follow-up visits (up to 24 months) and will be compared using paired t-test (or Wilcoxon signed-rank test).
Acknowledgements Editorial assistance and writing support were provided by  Paula Casajust, MSc  and  Stefania Ippati, PhD  of  TFS HealthScience. Contributors MA, RS, ED, PL, EdJ, GF, LN, AAN, JL, ZR, SG, EM, TO, IK and UM were involved in conception and study design. MA, RS, ED, PL, EdJ, GF, LN, AAN, JL, ZR, SG, EM, TO, IK and UM provided edits, critiqued the manuscript for intellectual content and approved the final manuscript. Funding This work was supported by  Almirall ,  Barcelona, Spain. Award/grant number: N/A. Competing interests MA has served as consultant for, or has been a paid speaker for clinical trials sponsored by companies that manufacture drugs used for the treatment of psoriasis, including Abbvie,  Almirall ,  Amgen ,  Biogen ,  Boehringer Ingelheim ,  Celgene ,  Centocor, Eli-Lilly ,  GSK ,  Janssen-Cilag ,  Leo Pharma ,  Medac ,  Merck ,  MSD ,  Novartis ,  Pfizer ,  UCB  and Xenoport.
Thermal cycling conditions were as follows: denaturation of the target DNA at 98 °C for 3 min, followed by 30 cycles at 94 °C for 1 min, primer annealing at 52 °C for 1 min and primer extension at 72 °C for 2 min. At the end of the amplification process, the reaction mixture was maintained at 72 °C for 10 min and then cooled to 4 °C. The PCR product was detected by electrophoresis on agarose gel (Sigma-Aldrich, Darmstadt, Germany), using ethidium bromide (Sigma-Aldrich, Darmstadt, Germany). The sequencing was performed by the Coulter Genomics company (Paris, France) using the same primers described above. The 16S rRNA gene sequences of all the strains have been deposited in the GenBank database (c. f.
Zhang et al. , 2000) against 16S ribosomal RNA sequences (Bacteria and Archaea) database, targeting sequences from type material only (Agarwala et al. , 2018). Sequences with the highest scores (E-values) were selected for the phylogenetic analysis using MEGA 7 package (Kumar et al. , 2016). The 16S rRNA gene sequences of each strain were aligned using MUSCLE (Edgar, 2004) with corresponding nucleotide sequences to the closest taxa recovered from the BLASTn results. The evolutionary distances were computed using the Maximum Composite Likelihood method (Tamura et al. , 2004) and the evolutionary history was inferred using the Minimum Evolution method (Rzhetsky & Nei, 1992). The topology of the tree was assessed through the bootstrap analysis (Felsenstein, 2005) using 100 resampling. The final tree figure was processed using Adobe Illustrator CC 18.
, clade I). Given the current 16S rRNA gene similarity threshold (98. 65 %) for species delineation (Kim et al. , 2014) , the strain Bacillus sp. BH1 is likely to be a novel species of the genus Bacillus. The 16S rRNA gene sequences of the five strains have been deposited in the GenBank database and assigned the following accession numbers: KY910253 (Bacillus sp. BH1), KY910252 (Bacillus sp. BH39), MT704963 (Bacillus sp. BH40), KY910255 (Bacillus sp. BH49) and MT704962 (Bacillus sp. BH50).
( Zhang et al. , 2000) against 16S ribosomal RNA sequences (Bacteria and Archaea) database, targeting sequences from type material only (Agarwala et al. , 2018). Sequences with the highest scores (E-values) were selected for the phylogenetic analysis using MEGA 7 package (Kumar et al. , 2016). The 16S rRNA gene sequences of each strain were aligned using MUSCLE (Edgar, 2004) with corresponding nucleotide sequences to the closest taxa recovered from the BLASTn results. The evolutionary distances were computed using the Maximum Composite Likelihood method (Tamura et al. , 2004) and the evolutionary history was inferred using the Minimum Evolution method (Rzhetsky & Nei, 1992). The topology of the tree was assessed through the bootstrap analysis (Felsenstein, 2005) using 100 resampling. The final tree figure was processed using Adobe Illustrator CC 18.
An efficient methodology for simulating nonlinear irregular waves in a Computational Fluid Dynamics (CFD) solver is proposed. The High Order Spectral (HOS) method is used to generate nonlinear irregular waves in an open ocean and a numerical wave tank. The inverse Fast Fourier Transforms (FFTs) and multi-dimensional interpolation from the HOS simulation results are used for the efficient reconstruction of nonlinear waves in the CFD solver. The proposed procedure is published as an open-source project called Grid2Grid, which is developed to interface with a generic CFD solver. It provides the function Application Programming Interface (API), which can communicate with different programming languages. Extreme wave events were used to validate the proposed procedure. The predicted wave breaking events are reported both in OpenFOAM CFD and HOS simulations, and the wave elevations of CFD during simulations show good agreement with experiments and with HOS simulation.
Then, the API (Application Programming Interface) function, which follows the ISO_C_BINDING rule, transfers the time and space variables and physical quantities. This way, Grid2Grid can be connected to any CFD solver whose API functions follow the ISO_C_BINDING rule. Some important API functions of Grid2Grid are summarized in Table  1. By using these API functions, the Grid2Grid library can be initialized and updated to simulate nonlinear waves in the CFD solver. Some examples written in Fortran and OpenFOAM (Cþþ) are given in the Grid2Grid package to provide a straightforward implementation example for CFD developers.
CFD solver foamStar is an in-house code co-developed by Bureau Veritas and Ecole Centrale de Nantes within the framework of OpenFOAM to solve wave-structure interaction problems. It is based on the multi-phase flow solver interDymFoam  (Rusche, 2002; Dami an, 2013)  in the OpenFOAM package  (Weller et al. , 1998; Jasak, 2009). The wave generation module is based on the open-source project waves2Foam  (Jacobsen et al. , 2012; Jacobsen, 2017). The mechanical solver is developed to simulate the dynamics of rigid or elastic marine structures  (Seng et al. , 2014). The free-surface is captured by the Volume of Fluid (VOF) method  (Hirt and Nichols, 1981). The governing equations describing multi-phase flow are given as follows: V,u ¼ 0 (15a) vðruÞ vt þ V,ðruu T Þ À V, h m Vu þ Vu T i ¼ ÀVp d À ðg,xÞVr (15b) va vt þ V,ðauÞ þ c a V, h u f n að1 À aÞ i ¼ 0 (15c) where r, u, p d and g ¼ (0, 0, À g) are the density, the velocity, and the dynamic pressure of the air-water mixture and the gravitational acceleration vector, respectively.
The Pearson correlation coefficient (r) measures a linear correlation between two data sets which always have a value between r ¼ À1 and r ¼ 1. When 0 < r 1, two sets of data have a positive correlation, and when 0 < r 1, they have a negative correlation. When r ¼ 0, two data sets have no relationship. The computed Pearson correlation coefficient(r) for different simulations is given in Table  3  over the whole CFD simulation time. The waves simulated by the CFD solver show a good correlation with reference HOS simulations.
3 m as shown in Fig. 11. A parametric study on the refinement of the computational grid of the CFD solver was conducted to obtain converged results. The spatial and temporal discretization are determined to have similar     values as in the previous studies  (Bouscasse et al. , 2021; Choi et al. , 2020). The level of refinement is given in Table  5. The wave elevations measured at the middle of the CFD computational domain are compared with the wave elevation obtained from the HOS simulation in Fig. 13. The results of the CFD simulations with different refinements agree well with the HOS reference, even if the wave breaking model in HOS cannot reflect the exact mechanism of wave breaking. The Pearson correlation coefficients of two time-series data can be used to indicate the global agreement between two datasets, but a timevarying Pearson correlation coefficient can be useful to observe the evolution of discrepancy as discussed in  Yule (1926).
Multimodal deep learning, in the context of biometrics, encounters significant challenges due to the dependence on long speech utterances and RGB images, which are often impractical in certain situations. This paper presents a novel solution addressing these issues by leveraging ultrashort voice utterances and depth videos of the lip for person identification. The proposed method utilizes an amalgamation of residual neural networks to encode depth videos and a Time Delay Neural Network architecture to encode voice signals. In an effort to fuse information from these different modalities, we integrate self-attention and engineer a noise-resistant model that effectively manages diverse types of noise. Through rigorous testing on a benchmark dataset, our approach exhibits superior performance over existing methods, resulting in an average improvement of 10%.
This was achieved by projecting the speaker's video (3D spatiotemporal data) onto 2D planes (XT and YT planes). The resulting visual features were found to enhance identification accuracy on the XM2VTS database  [25] , particularly when combined with audio features. This demonstrates the potential for improved performance by fusing different modalities in speaker identification tasks and the contribution that can be added by encoding lip movements (temporal information) with our identification system. The combination of audio and visual information has been shown to positively impact biometric recognition  [2, 3]. These two modalities can complement one another with audioonly and depth identification being resilient against static images, such as photographs. In a study by  [2] , the dynamic visual RGB information from the mouth region was combined with voice data.
Voice Speaker Identification To identify a speaker from brief speech segments, we employed pretrained models from SpeechBrain  [27]  and Hugging Face [28] that were initially trained on the TIMIT  [29]  and VoxCeleb datasets  [17]. We evaluated various models, such as SincNet  [14] , x-vectors  [20] , and ECAPA-TDNN  [19] , to find the most appropriate architecture for our KinectsDigits  [3]  and TCD-TIMIT  [30]  data. Although the article does not discuss the performance differences between these models, x-vectors  [20]  were chosen due to their reduced overfitting issues. Furthermore, for the VoxCeleb2 dataset  [17] , we fine-tuned the x-vectors  [20]  architecture using ultrashort utterances (<1 s) after initially conducting transfer learning with x-vectors  [20]  pretrained on both the VoxCeleb1 and VoxCeleb2  [17, 28]  datasets.