dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic Map Construction The le with the ltered SNPs including the two parents alongside the progenies was used for the linkage analysis using MAPpoly package [48] in R environment  [42]. Chi-square test was applied for all the markers, considering the expected segregation patterns under Mendelian inheritance, random chromosome pairing and no double reduction using the lter. non. conforming function in MAPpoly library in order to remove non-informative SNP markers from further analysis. Linkage grouping was performed using an initial LOD value of > 4 in MAPpoly. The LOD value of 4. 0 that established known linkage groups was then chosen as the signi cance criterion for multipoint linkage testing. Marker order and diagnostics were performed using the thresh. LOD. ph and thresh. LOD. rf function in MAPpoly R package.
Qtl Analysis And Annotation The QTL analysis was performed using the Composite Interval Mapping (CIM) method in R/QTL2 package  [49]. A forward and backward simple stepwise regression was run to select background markers having a signi cant level of P < 0. 10. The threshold levels to declare signi cant QTLs were empirically determined through 1000 permutations of the data, which maintained chromosome-wise Type I error rate of 0. 05  [51]. The location of a QTL was described according to its LOD peak location and the surrounding region with 95% con dence interval using the Bayesian model. The proportion of phenotypic variance accounted for by each detected QTL was estimated by a single-factor analysis of variance using General Linear Model.
Acknowledgments We acknowledge funding from  IITA  for the  AfricaYam  project through a  research grant  ( OPP1052998 ) received from the  Bill and Melinda Gates Foundation (BMGF). Special thanks to  Ranjana Bhattacharjee  for contributing to the population development. The  African Union Commission  is highly appreciated for funding  Kwabena Darkwa  at the  Pan African University, Institute of Life and Earth Sciences (PAULESI). The yam research at IITA is supported by the  CGIAR Research Program on Roots, Tubers and Bananas (RTB) , supported by  CGIAR Fund Donors.
Introduction Semi-arid ecosystems -which cover ∼ 40 % of the Earth's terrestrial surface and which include rangelands, shrublands, grasslands, savannas, and seasonally dry forests -are in zones of transition between humid and arid climates and are characterized by sparse, patchy vegetation cover and limited water availability. Moisture availability in these ecosystems is therefore a major control on the complex interactions between vegetation dynamics and surface energy, water, and carbon exchange  (Biederman et al. , 2017; Haverd et al. , 2016). Given the sensitivity to water availability, semiarid ecosystem functioning may be particularly vulnerable to projected changes in climate  (Tietjen et al. , 2010; Maestre et al. , 2012; Gremer et al. , 2015). IPCC Earth system model (ESM) projections and observation-based datasets indicate these regions will likely experience more intense warming and droughts, increases in extreme rainfall events, and a greater contrast between wet and dry seasons in the future  (IPCC, 2013; Donat et al.
Likewise,  Grippa et al. (2011)  and  Scanlon et al. (2019)  demonstrated that LSMs underestimate seasonal amplitude of total water storage in semi-arid (and tropical) regions. However, compared to more mesic ecosystems, semi-arid ecosystem LSM water flux and storage simulations have rarely been tested extensively against in situ observations, apart from a few exceptions  (Hogue et al. , 2005; Abramowitz et al. , 2008; Whitley et al. , 2016; Grippa et al. , 2017). Whitley et al. (2016)  compared carbon and water flux simulations from six LSMs at five OzFlux savanna sites. Their study highlighted two key deficiencies in modelling water fluxes: (i) modelled C 4 grass T is too low; and (ii) models with shallow rooting depths typically underestimate woody plant dry season ET. As part of a model inter-comparison for western Africa (the AMMA LSM Intercomparison Project -ALMIP), LSM water storage, fluxes, runoff, and land surface temperature were evaluated against in situ and remote sensing data in the Malian Gourma region Hydrol.
General model description The ORCHIDEE LSM forms the terrestrial component of the French IPSL ESM  (Dufresne et al. , 2013) , which contributes climate projections to IPCC Assessment Reports. ORCHIDEE has undergone significant modification since the "AR5" version  (Krinner et al. , 2005) , which was used to run the CMIP5 (Coupled Model Inter-comparison Project) simulations included in the IPCC 5th Assessment Report  (IPCC, 2013). The model code is written in Fortran 90. Here, we use ORCHIDEE v2. 0 that is used in the ongoing CMIP6 simulations. ORCHIDEE simulates fluxes of carbon, water, and energy between the atmosphere and land surface (and within the sub-surface) on a half-hourly time step. In uncoupled mode, the model is forced with climatological fields derived either from climate reanalyses or site-based meteorological forcing data.
The albedo is calculated based on the average of the defined albedo coefficients for vegetation (one coefficient per PFT), soil (one value for each grid cell, referred to as background albedo) and snow weighted by their fractional cover. Snow albedo is also parameterized according to its age, which varies according to the underlying PFT. The albedo coefficients for each PFT and background albedo have recently been optimized within a Bayesian inversion system using the visible and near-infrared MODIS white-sky albedo product at 0. 5 • × 0. 5 • resolution for the years 2000-2010. The prior background (bare soil) albedo values were retrieved from MODIS data using the EU Joint Research Center Two Stream Inversion Package (JRC-TIP). As in most LSMs, all vegetation is grouped into broad PFTs based on physiology, phenology, and, for trees, the biome in which they are located.
However, since ORCHIDEE v2. 0  (Ducharne et al. , 2020) , E can also be reduced by including an optional bare soil evaporation resistance term, r soil , which depends on the relative water content and is based on a parameterization fitted at the FIFE grassland experimental site at Konza Prairie Field Station in Kansas  (Sellers et al. , 1992) : r soil = exp(8. 206 -4. 255W 1 ), (1) where W 1 is the relative soil water content of the first four layers (2. 2 cm -Table  S1  in the Supplement). W 1 is calculated by dividing the mean soil moisture across these layers by the saturated water content. The calculation for E then becomes E = min(E pot /(1 + r soil /r a ), Q), (2) where E pot is the potential evaporation, r a the aerodynamic resistance, Q the upward water supply from capillary diffusion through the soil, and r soil the soil resistance to this upward exfiltration.
The model also accounts for sublimation, snow settling, water percolation, and refreezing. Snow mass cannot exceed a threshold of 3000 kg m -2. Snow age is also calculated and is used to modify the snow albedo. Default snow albedo coefficients have been optimized using MODIS white-sky albedo data as per the method described in Sect. 2. 2. 1. Snow fraction is calculated at each time step according to snow mass and density following the parametrization proposed by  Niu and Yang (2007).
Site-level meteorological and eddy covariance data and processing Meteorological forcing and eddy covariance flux data for each site were downloaded from the AmeriFlux data portal (http://ameriflux. lbl. gov, last access: 5 November 2020). Meteorological forcing data included 2 m air temperature and surface pressure, precipitation, incoming longwave and shortwave radiation, wind speed, and specific humidity. To run the ORCHIDEE model, we partitioned the in situ precipitation into rainfall and snowfall using a temperature threshold of 0 ET with site-level water balance measurements at a few of these sites show an average agreement within 3 % of each other, but could differ by -10 % to +17 % in any given year  (Scott and Biederman, 2019). Estimates of T/ET ratios were derived from  Zhou et al.
This may explain why the model T/ET ratios do not increase as rapidly as estimated values at the start of the monsoon (Fig. 6 ). However, discrepancies in the timing of T/ET ratio peak and troughs between the model and data-derived estimates at the forested sites could also be due to the fact that evergreen PFTs have no associated phenology modules in ORCHIDEE; instead, changes in LAI are only subject to leaf turnover as a result of leaf longevity, which may be an oversimplification. At US-SRM, the modelled spring T/ET ratio overestimates the Z16 estimate and underestimates the SB17 estimate (Fig. 6 ). The current state of the art is that different methods for estimating T/ET typically compare well in terms of seasonality but differ in absolute magnitude; therefore, the uncertainty in data-derived estimates of T/ET magnitude during the spring at US-SRM makes it difficult to glean any information on whether T or E (or both) are responsi-ble for the 11LAY overestimate of modelled springtime ET (Fig.
As mentioned in the results, it is also possible that LSMs contain an inaccurate representation of different semi-arid vegetation phenology, including drought-deciduous shrubs and annual vs. perennial C 4 grasses. The model does yet discern between perennial grasses and annual C 4 grasses that only grow during the warmest, wettest periods  (Smith et al. , 1997). It is possible that LSMs need new phenology models that account for annual C 4 grass strategies in order to obtain accurate simulations of semi-arid water and carbon fluxes. Finally, it is possible that incorrect seasonal LAI trajectories are also causing the issues in the T/ET seasonality seen at the higher-elevation forested sites due to the lack of an evergreen phenology module in ORCHIDEE. Recently, a new evergreen phenology module has been implemented in OR-CHIDEE  (Chen et al.
Acknowledgements. We would like to thank the ORCHIDEE team for developing and maintaining the ORCHIDEE code and for providing the ORCHIDEE version used in this study. Finally, we thank the two anonymous referees for their comprehensive and useful reviews. Financial support. Funding for AmeriFlux data resources and data collection at  US-SRM, US-SRG, US-Wkg , and  US-Whs  was provided by the  U. S. Department of Energy's Office of Science  and the  USDA. Data collection at  US-Fuf  was supported by grants from the  North American Carbon Program/USDA CREES NRI  (grant no. 2004-3511115057 ), the  U. S. National Science Foundation MRI Program, Science Foundation Arizona  (grant no. CAA 0-203-08 ), the  Arizona Water Institute , and the  Mission Research Program, School of Forestry ,  Northern Arizona University (McIntire-Stennis/Arizona Bureau of Forestry).
Code availability. The ORCHIDEE v2. 0 model code and documentation are publicly available via the ORCHIDEE wiki page (http://forge. ipsl. jussieu. fr/orchidee/browser/tags/ORCHIDEE_ 2_0/ORCHIDEE/,  Peylin et al. , 2020)  under the CeCILL license (http://www. cecill. info/index. en. html, CeCILL, 2020). The OR-CHIDEE model code is written in Fortran 90 and is maintained and developed under an SVN version control system at the Institute Pierre Simon Laplace (IPSL) in France. Simulation post-processing and plotting scripts were performed in Python and are provided on NM's GitHub repository: https://github. com/nmacbean/ SW-US-Hydro-Model-Eval-HESS (last access: 2 November 2020; https://doi. org/10. 5281/zenodo. 4198088, MacBean, 2020). Data availability. Meteorological forcing and evapotranspiration data for each site can be downloaded via the Ameriflux site: https: //ameriflux.
lbl. gov/data/download-data/ (US-SRM: Scott, 2004a-Present; US-SRG: Scott, 2008-Present; US-Whs: Scott, 2007-Present; US-Wkg: Scott, 2004b-Present; US-Fuf, Dore and Kolb,  2006-2010; US-Vcp: Litvak, 2007-Present). Soil moisture was obtained directly from site PIs. Vegetation and soil texture characteristics were derived from the published literature, as specified in Table  1 , and from site PIs. Model simulations are provided on NM's GitHub repository: https://github. com/nmacbean/ SW-US-Hydro-Model-Eval-HESS. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-5203-2020-supplement. Author contributions. NM, RLS, JAB and DJPM designed the overall study. NM carried out the model simulations, postsimulation analysis and figure plotting.
CO, NV and AD provided detailed inputs on model description/code and recommendations for further tests to diagnose model-data deficiencies. NV provided scripts to gap-fill the meteorological data. JAB gap-filled the ET data. RLS, JAB, TK and ML provided gap-filled soil moisture data and information on site characteristics and typical behaviour of seasonal vegetation cover, LAI, and snowfall. NM wrote the manuscript. All the co-authors provided detailed comments, suggestions, and edits on the first and second drafts of the manuscript.
frequencies between eight males and eight females by χ 2 analysis (Dataset S1); 765 SNPs spanning ∼160 kb on chromosome 8 showed significant allele frequency differences (P ≤ 1. 0 × 10 -10 ) (SI Appendix, Fig. S1A ). Analysis of SNP genotypes across the sex-associated region for the 16 fish revealed that males were heterozygous (XY), whereas females were homozygous (XX) for most sex-associated SNPs (SI Appendix, Fig. S1B ). Thus, males are the heterogametic sex. An additional 17 Atlantic herring of unknown sex followed this pattern perfectly with individuals either being heterozygous or homozygous across the sex-associated region. The sequence of this region in the current assembly  (17)  represents the female haplotype (SI Appendix, Fig. S1B ). In conclusion, herring chromosome 8 can now be split into chromosome (Chr) X and Y.
CatSper3Y and CatSperG Are Essential Components of the Herring Sperm Proteome. The location of two CatSper genes on the herring Y chromosome is intriguing because CatSper has been thought to be lost in several metazoan lineages including fish  (32). We revisited this issue and identify here CatSper genes in many but not all fish genomes (Fig. 4  A and B and Dataset S2). CatSper is a uniquely complex Ca 2+ channel that consists of at least nine different subunits CatSper1-CatSper4, CatSperB, CatSperG, CatSperD, CatSperE, and CatSperZ. The Atlantic herring genome contains all CatSper genes except CatSperZ (33) (Dataset S2). Using mass spectrometry (MS), seven CatSper subunits including CatSper3 and CatSperG were identified in mature herring sperm (Datasets S3 and S4). Signaling pathways control CatSper and, thereby, sperm motility, navigation, and fertilization from marine invertebrates to mammals  (34) (35) (36).
4C  and Dataset S4). Two functionally important variations are striking. First, herring sperm carry an HCN-like (HCNL) channel that is distinct from classic HCN channels and falls into a new gene subfamily  (42). HCNL1 in zebrafish sperm, unlike classic HCN channels, is proton-selective and not cAMPsensitive  (42). Although herring sperm carry the HCNL2 isoform that has not yet been characterized functionally, structural features underlying proton permeation are conserved between HCNL1 and HCNL2  (34). Second, the zebrafish CNGK channel is also gated not by cyclic nucleotides but by alkaline pH i , indicating that the cellular messengers in fish sperm are protons rather than cAMP or cGMP  (42, 43). These results suggest that herring sperm employ a signaling pathway for fertilization that combines signaling proteins and mechanisms from marine invertebrates, zebrafish, and mammals.
Annotation of the Male-Specific Region. We identified male-unique regions on unassigned scaffolds from the current PacBio-based herring assembly which was not annotated by the Ensembl annotation pipeline (https://www. ensembl. org) because the male-unique region was not included in this assembly  (17). Thus, we annotated these regions by the annotation pipeline implemented by National Bioinformatics Infrastructure Sweden based on the Maker package (version 3. 01. 02) (49) (https://github. com/NBISweden/ GAAS, version 2019-07-11). We first generated an evidence-built annotation based on protein similarity using Uniprot (50) (downloaded  August 11, 2016). We also generated an ab initio built annotation by first training Augustus (version 2. 7) (51) using 1,213 genes annotated by Ensembl  (17).
PCR products were sequenced using the LSK309 1d 2 ligation sequencing protocol (ONT). Genomic Alignments. Similarity between CATSPER3Y and BMPR1BBY and their presumed homologous autosomal loci was assessed by performing local alignments of the genomic nucleotide sequence, including flanking regions, of the two Y-specific gene copies against genomic sequence fragments, extracted from assembly version Ch_v2. 0. 2 (GCA_900700415. 1) that covered the potentially orthologous autosomal genes. The targets were identified from the Ensembl-provided annotation of the herring genome (Database version 99. 202). The alignments were performed using Clustal Omega version 1. 2. 3 (53) using default parameters and analyzed in R (54) using the package "ape" version 5. 3  (55). We compared the gene content in the sex-associated region on X and Y using Clustal Omega version 1.
2. 3  (53)  implemented in "ape" R package version 5. 3 (55) as described above. Phylogenetic Analysis by Maximum Likelihood (PAML). First, we collected a reference set of CATSPER3 and BMPR1BB genes from the ENA database. After manual inspection of the possible transcripts, we retained the option for each species that most closely resembled the herring Y copy. Second, inframe nucleotide alignments spanning the extent of the Y-specific copies were generated for both genes. For BMPR1BB, which is highly conserved, the nucleotide alignments were already in frame, as verified by the "trans" function from "ape" and subsequent comparison with the reference sequences. For CATSPER3, which is more variable, PAL2NAL  (56)  was used to translate the amino acid alignment to a nucleotide equivalent using the reference cDNA sequences.
In both cases, ambiguous positions were discarded during the rate analysis. Next, we extracted the taxa represented in each alignment from the Fish Tree of Life  (57)  to generate unrooted guide trees for the PAML analysis. These trees were edited to split the herring entry into a Y-specific and autosomal copy, and for CATSPER3, the coelacanth was added as an additional unresolved branch. Finally, the alignments and guide trees were fed into the "codeml" module of PAML version 4. 9  (58)  and run under three different models: 1) All branches evolve at the same rate (NULL model). 2) Only the "herring Y-" (for BMPR1BB) or the "herring A-" (for CATSPER3) branch has a unique rate (two-rates model). 3) Both the "herring Y" and "herring A" branches have a unique rate (three-rates model). The models were then compared using a likelihood ratio test statistic, which was evaluated against a χ 2 -distribution with degrees of freedom equal to the difference in estimated parameters between the compared models.
The PAML control files, guide trees, and alignments used are found at https://github. com/nimarafati/Atlantic_herring_SDR. We used dS values from the PAML analysis to estimate divergence times by extracting fossil-calibrated LCA estimates from the Fish Tree of Life  (57)  and dividing the time interval proportionally to the lengths of the terminal and internal branches. Structure Modeling. Structural prediction of the intracellular domain of BMPR1BB, full-length L-BMPR1BBY, and full-length S-BMPR1BBY was performed using the RaptorX server  (59) , which gave the best models using Protein Data Bank 3MDY (human BMPR1B) (60) as the template. Structure alignments and protein representations were generated using the PyMOL Molecular Graphics System (Version 1. 3, Schrödinger, LLC). Preparation and Mass Spectrometric Analysis of Flagella from Herring Sperm.
The preparation of flagella from C. harengus sperm was as described  (39)  using Herring Ringer's (HR) solution instead of artificial seawater. Briefly, sperm were washed two times with HR (in mM: 206 NaCl, 7. 2 KCl, 2. 1 CaCl 2 , 3. 1 MgCl 2 , 10 Hepes/NaOH, pH 7. 6)  (61). Hepes was used for buffering instead of NaHCO 3. Sperm suspension was sheared 20 times by centrifugation for 30 s at 75 × g and 4 °C through a 40-μm mesh of a cell strainer (BD Biosciences). Flagella were washed in HR and stored as pellet. Flagella protein (20 μg) was subjected to GeLCMS as described  (62)  with the following modifications: excision of 10 gel slices, use of Proteome Discoverer Rev. 2. 2, C. harengus protein sequences from National Center for Biotechnology (NCBI) release GCF_900700415. 1 (date April 16, 2019) supplemented with additional sequences (Dataset S4), recalibration of precursor MS spectra, precursor mass error tolerance 8 ppm, MS/MS mass error tolerance 0.
6 Da, oxidation of Met, phosphorylation at Ser, Thr, Tyr, and N-terminal pyro-Glu as variable, Cys-Propionamide as static modification. Bioinformatic Analysis of CatSper Genes. All fish genomes available from NCBI (https://www. ncbi. nlm. nih. gov/genome/) as of March 11, 2020, were analyzed for the presence of highly conserved β actin as positive control. The whole-genome shotgun databases (WGS) for these genomes were searched with TBlastN (https://blast. ncbi. nlm. nih. gov/Blast. cgi?PROGRAM=tblastn) for β actin, using the Danio rerio (zebrafish) sequence as query. Genomes which were unsearchable or yielded poor hits were excluded. The research resulted in 271 genomes, which were evaluated for the presence of CatSper1 to CatSper4 and CatSperB, CatSperG, CatSperD, and CatSperE. We then performed TBlastN searches in the WGS databases of fish genomes using previously annotated protein sequences for CatSper1 to CatSper4 and CatSperB, CatSperG, CatSperD, and CatSperE of Lepisosteus occulatus (spotted gar), Latimeria chalumnae (coelacanth), Callorhinchus milii (elephant shark), Salmo salar (Atlantic salmon), Esox lucius (northern pike), and Carassius auratus (goldfish).
The search was recursive, whenever more closely related sequences were identified. Candidates were evaluated according to e value until outgroup genes were found; a rigid cutoff by e value was ineffective. Candidate genes were predicted by genewise (https://www. ebi. ac. uk/Tools/ psa/genewise/) (63) using closely related validated sequences as template. To validate candidates, we employed phylogenetic tree analysis. Trees were constructed from the candidate genes, the query sequences and other validated CatSper genes (e. g. , Mus musculus [mouse], for full list, see Dataset S2). For CatSper1 to CatSper4, representative members from related ion channel families were used as outgroup (CACNA and SCN channels of mouse and zebrafish, TPCN and TRP channels of zebrafish). CatSperB, CatSperG, CatSperD, and CatSperE served as each other's respective outgroup.
After 24 h, cells were washed with 2× PBS solution and lysed with 75 μL of passive lysis buffer (Dual Luciferase Reporter Kit Assay; Promega) and then subjected to luciferase assay. Firefly luciferase activity (UAS-luc reporter constructs) was quantified using the Dual Luciferase Reporter Assay System (Promega) and normalized against cotransfected Gaussia luciferase expressing plasmid. Datasets are the results of at least four independent cell transfections and luciferase measurements. Data Availability. The reference genome used in this study is deposited in European Nucleotide Archive (ENA) (PRJEB31270) and the sequence of the SDR together with its annotation as well as other sequences and codes used in this study are available on Github: https://github. com/nimarafati/Atlantic_ herring_SDR.