dataesr/scientific-paragraphs-categorization huggingface dataset:
Improved constraints could also stem from flood-specific metrics accounting for multi-frequency signatures. In terms of perspectives, future comparisons of hydrological models of different complexities should study large samples with rich datasets, including high-resolution satellite data of soil moisture, which are particularly interesting for distributed models, as shown in  [23]  for French Mediterranean catchments. One could assess/discriminate internal model behaviors, given multiple plausible parameter sets potentially corresponding to contrasted functioning points, hence model components' activation/interplay for a given model structure, for instance. Finally, improved calibration-regionalization methods, in a flexible multi-model framework, seem highly needed and will be developed in SMASH with hybrid methods.
R E S E A R C H A R T I C L E JCI Insight 2022;7(7):e141213 https://doi. org/10. 1172/jci. insight. 141213 differentially regulated as compared with control mice, 158 upregulated and 271 downregulated. We focused the analysis on identifying alterations in transcriptional pathways that are known to play a role in left-right asymmetry, since we had noticed that several genes, including Gja5 and Kcnj2, appeared to be differentially affected by AMPK depletion in the left compared with the right atrium. Microarray data (Supplemental Table  1 ) revealed an interesting reduction in Pitx2, which encodes a homeobox transcription factor that plays a pivotal role in left-right determination during embryonic development  (24). We then performed real-time quantitative PCR (RT-qPCR), which demonstrated a 160-fold higher expression of Pitx2c in left atria compared with right atria in control mice, consistent with its predominant left atrial expression pattern after development.
Atrioventricular capture was confirmed both electrically and optically. Analysis of periodic optical signals. Raw data were collected, frame-selected, and processed in CardioPlex (RedShirt Imaging) and a custom-made software written in MATLAB (MathWorks). To improve signalto-noise ratio, frames were temporally averaged over 8 consecutive beats. Images were further spatially averaged using a 5 × 5 uniform kernel at each pixel, normalized, baseline subtracted, and inverted. Activation delays were determined across left atrial and left ventricular mapping fields during steady-state pacing. Action potential duration was calculated for each pixel as the time difference between the action potential upstroke and the point at 75% repolarization from the peak (APD 75 ). The values for the left atrium and ventricle represent the average of all action potential durations within the region of interest.
Atrial myocyte isolation. Neonatal atrial cardiomyocytes were isolated from rats (P1-P3) using collagenase/dispase-containing L15 medium  (53). Suspended cardiomyocytes were plated into cell culture dishes precoated with collagen and were left undisturbed in the incubator for 18 hours to allow for adherence and spreading of cardiomyocytes. Cell transfection assays. Mouse atrial cardiomyocytes underwent knockdown of Prkaa1, Prkaa2, Pitx2c, and Mef2c by siRNA treatment (Qiagen). Transfection of cells with siRNA was performed using a standard protocol with Lipofectamine RNAiMAX (Invitrogen). Data availability. The data sets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Gene expression data files are available (National Center for Biotechnology Information's Gene Expression Omnibus GSE200144).
Acknowledgments This research was funded in part by grants from the  NIH :  R01HL148008 ,  R01HL128069 ,  R01HL137259 , and  S10 OD021845. Address correspondence to:  Lawrence H. Young ,  Yale Cardiovascular Research Center, 300 George Street, New Haven, Connecticut 06511 ,  USA. 203. 785. 4102 ;Email: lawrence. young@yale. edu.
The discovery of super-Earth and mini-Neptune exoplanets means that atmospheric signals from low-mass, temperate exoplanets are being increasingly studied. The signal acquired as the planet transits its host star, known as the transit depth, is smaller for these planets and, as such, more difficult to analyze. The launch of the space telescopes James Webb (JWST) & Ariel will give rise to an explosion in the quality and quantity of spectroscopic data available for an unprecedented number of exoplanets in our galaxy. Accurately extracting the information content, thereby permitting atmospheric science, of such data-sets will require robust models and techniques. We present here the analysis of simulated transmission spectra for water-rich atmospheres, giving evidence for non-negligible differences in simulated transit depths when self-broadening of H 2 O is correctly accounted for, compared with the currently typically accepted standard of using H 2 and He-broadened cross-sections.
For the present study we use the Voigt profile of Eq. 5. The self-broadened H 2 O cross-sections used in this study were computed using ExoCross  [43] , as were the H 2 /He-broadened cross-sections for H 2 O. The latter are similar to those from the ExoMolOP database  [21]  but with J-dependent broadening parameters (where J is the rotational angular momentum quantum number) from the ExoMol website 1  used for H 2 and He broadening  [44, 45]. All H 2 O cross-sections presented here use the ExoMol POKAZATEL line list  [46]. We computed the line wings out to 500 Voigt widths in all cases, out to a maximum of 25 cm -1. The values of γ and n for the self-broadening of H 2 O used in the present study are detailed below.
Self-broadened half-width, γ H 2 O There are many literature sources with broadening parameters for selfbroadening of H 2 O. For example, Gamache and Hartmann  [47]  compiled and compared various parameters related to H 2 O line shape, including values for the half-width γ for self-H 2 O broadening. There are over 47,000 lines in their database, with values of γ H 2 O ranging from 0. 108 -0. 805 cm -1 atm -1. The 2020 release of the HITRAN database  [48] , who follow a "diet" procedure  [49] , provide an update of this 2004 broadening measurement database of  [47]. A simple average of all values of the main isotopologue of H 2 O (with no weighting) from HITRAN2020 for γ H 2 O yields a value of 0. 35 cm -1 atm -1. There are many other works with available broadening values: for example,  [50]  and  [51]  both present a number of values for γ H 2 O for a few thousand lines each.
They report average values of γ H 2 O = 0. 4 and between 0. 1 and 0. 5 cm -1 atm -1 , respectively. As noted above, for the present study we extract the broadening parameters from the HITRAN2020  [48]  database as a function of rotational angular momentum quantum number J, computing an average value of γ for each value of J. The data extends up to a maximum of J=26 and varies between 0. 1 cm -1 atm -1 for high J to 0. 5 cm -1 atm -1 for low J.
2. 2. 2. Temperature exponent n H 2 O for self-broadened half-width HITRAN2020  [48]  currently only include γ self and not n self values for H 2 O, due to the large effort required to validate and populate such parameters into their database. For now we use averaged J-dependent values for γ self from HITRAN2020  [48]  and an apparently typical value of n self =0. 7 for H 2 O. The focus of some ongoing and future work is to update the ExoMol  [39]  and ExoMolOP  [21]  databases to include broadening parameters in a more comprehensive way. This is not a simple undertaking: the H 2 O line list used in this work, for example, ExoMol POKAZATEL  [46] , contains 6 billion transitions between 800,000 energy levels, with even larger line lists required to describe larger species, see Table  13  of  [21].
0003 -0. 014 bar. The J-and T -dependent values of γ were found to vary between 0. 67 -1. 07 cm -1 atm -1. They found J-dependent values of n self between 0. 56 and 0. 81. Table  2  gives some average values extracted from the HITRAN 2020  [48]  database for various molecules and broadeners. The H 2 -and He-broadened average values used in the ExoMolOP database  [21]  (a database tailored for modelling "hot Jupiter"-type exoplanet atmospheres) are also included for reference. It can be seen that in general that the largest γ and n values occur when H 2 O is the broadener, which for γ H 2 O is an order or magni- Figure  1 : Cross-sections computed using the self-broadening parameters for H 2 O, for the abundances of water vapor given in the legend. We compare the self-broadened crosssections (orange, pink) with the H 2 /He-only broadened cross-sections (blue).
The next generation of telescopes will widen and deepen our spectral view, both in terms of signal-to-noise and resolution, but also in wavelength coverage; hence these problems should be easily tackled with Ariel and JWST. It is therefore especially imperative for the accuracy of cross-section data to compete with the level of precision obtainable with these future facilities. Standard H 2 /He-broadened cross-sections which are in widespread use by the exoplanet community are simply non-optimal for the study of secondarytype, heavy atmospheres dominated by non-H 2 /He species due to the fact that these opacities are calculated with respect to a nominal atmosphere which is dominated by H 2 and He. At present, due to the computationallyintensive nature of numerically evaluating absorption strengths for molecular transitions and interactions for a given atmospheric species, specifying a grid of temperature and pressure values is necessary obtain computable opacities for input into forward models and atmospheric retrieval algorithms.
Acknowledgements Funding: We acknowledge funding from the  ERC  through  Consolidator grant ExoAI  ( GA 758892 ) and  Advanced Grant ExoMolHD  ( GA 883830 ), as well as from  STFC  grants  ST/P000282/1 ,  ST/P002153/1 ,  ST/S002634/1  and  ST/T001836/1. BE is a Laureate of the  Paris Region fellowship  programme which is supported by the  Ile-de-France Region  and has received funding under the  Horizon 2020 innovation framework programme  and the  Marie Sklodowska-Curie  grant agreement no. 945298.
Cadmium is found in the earth's crust at an average concentration of 0. 1 mg kg -1  [6]. The average level of cadmium in agricultural soil fluctuates between 0. 07 and 1. 1 µg g -1 , with a natural base level of 0. 5 µg g -1  [7]. The production of cocoa beans in Colombia has been evaluated on numerous occasions for its high levels of cadmium. A study by Echeverri & Reyes (2016)  [8]  showed that the cadmium concentration in chocolate with more than 30% content of Colombian cocoa yielded an average value of 4. 0477 mg kg -1 of cadmium. This data puts the health of local producers and consumers at risk and pose a threat to the productive chain of cocoa derivatives because it exceeds the maximum limits established by the recent EU provision (Regulation No. 488/2014 with tolerable values between 0, 1, and 0.
Penicilli, in general, are biverticillate, but certain species can have both biverticillate and monoverticillate with acerose or ampulliform phialides. Conidia are usually described as ellipsoidal to fusiform and globose with a lesser proportion. Its taxonomy has been improved using sequence-based approaches  [27]. To date, Talaromyces contains more than 250 described species in the Mycobank database  [28]  and Talaromyces section (sect. ). Talaromyces contains roughly 86 species  [29]. Additionally, inside the Talaromyces sect. Talaromyces is located the informal group Talaromyces pinophilus complex, which harbors T. lentulus, T. mae, T. soli, T. malicola, T. adpressus, T. annesophiae, and T. pratensis  [29, 30]. However, this complex requires improved phylogenetic elucidation to further support this subgroup species' evolutionary relationships.
In the present study, we performed a collection of an agricultural soil sample from the Santander region in Colombia. After the isolation in multiple media and morphological identification, the Talaromyces sample could not be assigned to any known species. Thus, we performed a polyphasic identification approach by adding phylogenetic analysis of partial ITS, β-tubulin (BenA), calmodulin (CaM), and RNA polymerase II second largest subunit (RPB2) gene sequences, and the use of macro-and micro-morphological data to delimitate the new species in the informal group and genus.
Microscopic Identification The microscopic slides were prepared from 7-day-old cultures grown on Malt Extract Agar (MEA, Merck KGaA ® , Darmstadt, Germany). The conidia were suspended in 1 mL of 60% lactic acid  [26]. Phenotypic characteristics of conidia (shape, ornamentation of the cell wall) and conidiophores (number of branching points between stipe and phialides, and shape/texture of phialides) were recorded. Microscopic examinations were done using a microscope Nikon (model Eclipse Ni-u). Two diameters (length and width) were measured for 100 conidia of each isolate in the Quick Photo Camera software program (Imagen Pro).
Molecular Characterization and Phylogeny For the phylogenetic reconstruction, a four-gene phylogeny from 80 species described for Talaromyces section Talaromyces was downloaded from NCBI. Strain codes and database accession numbers are indicated in Supplemental Table  S1. Phylogenetic analyses were performed with BenA, CaM, RPB2, and ITS sequences individually and concatenated, with T. dendriticus CBS 660. 80 of sect. Purpurei as the outgroup. Sequences were aligned with MAFFT v 7. 453  [39]. For evolution model determination and Maximum Likelihood (ML) analysis, IQTREE  [40]  was implemented with UltraFast Bootstrap. Bayesian Inference (BI) analysis was performed with BEAST2 with 10. 000. 000 generations; molecular clock was set as default; consensus tree was calculated with Treeannotator  [41].
Description of New Taxa Talaromyces santanderensis sp. nov. Guerra-Sierra B. and Arteaga-Figueroa LA. (Figures  1 2 3 ) MycoBank: MB845323 Etymology: The specific epithet refers to the Santander department in Colombia from where the type of strain was collected. Typification: Colombia, Santander, San Vicente de Chucurí, Finca Monserrate, from rhizosphere soil from Theobroma cacao, 15 February 2019, Guerra-Sierra B. , HF05 (holotype strain CBUDES:UDES:3068). GenBank accessions: BenA = OP067657, CaM = OP067656, ITS = OP082331, RPB2 = OP067655. In: Talaromyces sect. Talaromyces. Colony diameter (7 days at 25  C 30-32; CYA 10 • C 5-9. Colony characteristics (texture): CYA: floccose at the margin, powdery at center, YES: floccose, MEA and OA: floccose to powdery. Further details about coloring are available in Section 3.
Molecular Identification and Sequence Analysis Sequences were reported to Genbank under the code accesses: OP082331, ITS; OP067657, BenA; OP067656, CaM and OP067655, RPB2. PCR amplification generated amplicons of BenA about 1447 bp, CaM about 449 bp, RPB2 about 1033 bp and ITS about 569.
Molecular Identification and Sequence Analysis Sequences were reported to Genbank under the code accesses: OP082331, ITS; OP067657, BenA; OP067656, CaM and OP067655, RPB2. PCR amplification generated amplicons of BenA about 1447 bp, CaM about 449 bp, RPB2 about 1033 bp and ITS about 569 bp. The trimmed alignments of BenA, CaM, RPB2, ITS and the combined BenA-CaM-RPB2-ITS sequences were 520, 639, 1008, 692 and 2859 characters with gaps, respectively.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/ 10. 3390/jof8101042/s1. Table  S1 : Physical-chemical analysis of the cocoa soil. Table  S2 : Genbank codes of the strains used for the phylogenetic analysis. Figure  S1 : BI phylogram inferred from partial CaM sequences. Nodes indicate ultrafast-bootstrap percentages greater than 90% derived from 10,000 replicates and posterior probabilities greater than 0. 9 posterior support. T indicates ex-type strains, and strains belonging to new species are indicated in bold. Scale bars: number of substitutions per nucleotide position. Figure  S2. BI phylogram inferred from partial RPB2 sequences. Nodes indicate ultrafast-bootstrap percentages greater than 90% derived from 10,000 replicates and posterior probabilities greater than 0.
Acknowledgments: This study acknowledges the Mycology/Microscopy section from the  Agroenvironmental Biotechnology Laboratory of the University of Santander  and  EAFIT University. The authors gratefully acknowledge the supercomputing resources made available by the  Apollo Scientific Computing Center of EAFIT University  (http://www. eafit. edu. co/apolo,accessed on 28 July 2022) to carry out the research reported in this scientific product and also thank the  Federation of Cocoa Growers of Colombia (FEDECACAO)  for assistance in obtaining the soil sample.
Atmospheric chemistry The Tracer Model version 5 (TM5) is the atmospheric composition model of  EC-Earth (van Noije et al. , 2014)  used in the EC-Earth3-AerChem and EC-Earth3-CC configuration. It can be used for the interactive simulation of carbon dioxide (CO 2 ), methane (CH 4 ), ozone (O 3 ), tropospheric aerosols, and other trace gases. These components are prescribed in IFS from forcing datasets (see Sect. 3. 1) if not provided interactively by TM5. Other well-mixed greenhouse gases and stratospheric aerosols are prescribed in all configurations. This section briefly describes how the various components are configured. As an alternative to the scaling approach for WMGHGs presented in Sect. 3. 1, the 3D distributions of CO 2 and CH 4 can be calculated online by TM5. In the EC-Earth-CC configuration a single-tracer version of TM5 is used for simulating the transport of CO 2 through the atmosphere.
Anthropogenic emissions of CO 2 are prescribed following the CMIP6 historical inventory  (Hoesly et al. , 2018)  or future scenarios  (Gidden et al. , 2019). Exchange of CO 2 with the ocean and terrestrial biosphere is included by coupling TM5 to PISCES and LPJ-GUESS, respectively (see  Sect. 3. 3 ). An important feature of the model is that the transport in TM5 is mass-conserving  (Krol et al. , 2005). For the simulation of CH 4 , a version of TM5 that includes atmospheric chemistry and aerosols is used  (van Noije et al. , 2021). A recent description of the chemistry scheme applied in EC-Earth has been presented by  Williams et al. (2017). Emissions of aerosols and chemically reactive gases are taken from the CMIP6 historical datasets for anthropogenic sources  (Hoesly et al. , 2018)  and biomass burning  (van Marle et al.
, 2017)  or the corresponding CMIP6 scenario datasets  (Gidden et al. , 2019). To force the CH 4 simulation to follow the pathway provided by CMIP6, its surface mixing ratios are nudged towards the monthly zonal means from CMIP6 interpolated to daily values. Moreover, because TM5 lacks a comprehensive stratospheric chemistry scheme, the CH 4 mixing ratios in the stratosphere are nudged towards a monthly zonal mean observational climatology representative for the 1990s (interpolated to daily values), scaled by a global factor based on the CMIP6 time series of global annual mean surface values. To calculate the scale factor, we assume a 1-year delay between the mixing ratios at the surface and in the stratosphere  (Meinshausen et al. , 2017)  and a reference value based on a 10-year average.
As for CH 4 , TM5 applies a nudging scheme for O 3 in the stratosphere. In EC-Earth3, the mixing ratios are nudged towards daily zonal means obtained from the CMIP6 dataset. For aqueous-phase chemistry in the troposphere, the acidity of cloud droplets is calculated assuming a uniform CO 2 mixing ratio following the CMIP6 time series of annual global mean surface values. TM5 simulates tropospheric aerosols, namely sulfate, black carbon, primary and secondary organic aerosol, sea salt, and mineral dust, in four size ranges describing nucleation, Aitken, accumulation, and coarse modes using the M7 aerosol microphysical model  (Vignati et al. , 2004). In addition, it simulates the total mass of ammonium, nitrate, and methane sulfonic acid (MSA). Optical properties of the aerosol mixture are calculated based on Mie theory in combination with the mixing assumptions described by  van Noije et al.
3: the root mean square error (RMSE) of EC-Earth3 is 0. 64 compared with 0. 52 for EC-Earth2. 3. Previous assessments of coupled models have found that the CMIP5 and CMIP3 models represent NAO very similarly as in reanalyses, with no general improvements of CMIP5 compared to CMIP3 models  (Davini and Cagnazzo, 2014). In EC-Earth3, the ENSO spatial patterns and amplitude are well represented (not shown). In order to assess the variability of ENSO in the EC-Earth3 ensemble we calculated the Niño3. 4 SST power spectra (Fig. 18 ) for the model and for the HadISST observational dataset  (Rayner et al. , 2003). HadISST shows the highest peak at 5. 5 years and a range of periods between 3. 3 and 3. 7 forming the second highest peak. The mean of the EC-Earth3 ensemble spectra shows the highest peak at around 3.
7 years. Some individual members show their main peak at about 5. 5 years, similar to the HadISST dataset. Most of the ensemble members (two with the highest peaks) show their maximum in the 3-4-year period. EC-Earth3 shows larger ENSO spectral power than EC-Earth2. 3, with an ensemble mean closer to the HadISST observations, especially in frequencies with high energy. Thus, we see a clear improvement in the representation of ENSO in EC-Earth3. Similar improvements were seen earlier for CMIP5 models  (Jha et al. , 2014). While the frequency distribution in EC-Earth3 is distinctly improved compared to EC-Earth2. 3, a challenge is still seen in representing distinct peaks in the power spectrum. The range among the ENSO spectra of different members is considerable. Climatologically, most ensemble members show only small differences over the tropics when compared with the whole ensemble mean.
23a ) and terrestrial CO 2 sink (SLAND; Fig. 23b ) compared to the estimates from the DGVMs in GCP 2019. This interannual variability originates from both climate differences between EC-Earth3-Veg and the observation-based forcing, which includes the right timing of weather events like El Niño in the offline simulations used for the GCP  (Harris et al. , 2014)  and the mismatch between the dynamic vegetation in LPJ-GUESS compared to the land use dataset LUH2  (Hurtt et al. , 2020)  that dictates where and when land use change occurs.
Acknowledgements. This paper and the development of EC-Earth3 would not have been possible without the member institutions of the  EC-Earth consortium  and their sustained support of the development and application to CMIP6. Those members are the following:  Agencia Estatal de Meteorología (AEMET, Spain) ;  Institute of Atmospheric Sciences and Climate of the Consiglio Nazionale delle Ricerche, ISAC-CNR (Italy) ;  Danmarks Meteorologiske Institut, DMI (Denmark) ;  Finnish Meteorological Institute, FMI (Finland) ; the  Portuguese Institute for Sea and Atmosphere, IPMA  (Por-.
, 2021) Solar 3. 2 Matthes et al. (2017) Aerosol optical properties MACv2-SP Stevens et al. (2017) and relative change in cloud droplet number concentration Land use v2. 1 (h)istoric and (f)uture Hurtt et al. (2019a, b, 2020) Used only in combina- tion with dynamic veg- etation model Nitrogen deposition v2. 0 http://blogs. reading. ac. uk/ccmi/ Used only in combina- forcing-databases-in-support-of-cmip6/ (last tion with dynamic veg- access: 18 March 2022; Hegglin et al. , 2021) etation model.