dataesr/scientific-paragraphs-categorization huggingface dataset:
2004 ). Here, the five worms were placed in an 8-µL drop of artificial sea water between two microscope slides. We used a lateral illumination to make it easier to distinguish the blue-dyed focal, and recorded at 1 frame/s for 3 h, using a digital video camera (DFK 41AF02, The Imaging Source Europe GmbH, Bremen, Germany) and the software BTV Pro 6. 0b7 (http://www. bensoftware. com/). We included five such groups per mating chamber, for a total of four mating chambers per batch. The time-lapse movies were later analyzed blindly with regard to replicate identity using the software KMPlayer (version 4. 1. 2. 2). For each mating group, we counted the total number of copulations (total matings) and the number of copulations in which the focal was involved (focal matings). Immediately after the mating trials, we transferred the focal back into its A group, and isolated all four members of the B group to assess the spermtransfer success of the focal during the mating trial.
Repeatability of focal success We computed the repeatability of male reproductive success and of each of the four fitness components across the three mating groups used in the experiment. For this, we first transformed our fitness measures estimated in each group as follows: Ý(mRS), Ý(F + 0. 5), ÝSTE, and log 10 (SFE + 1). These data transformations were done to provide satisfactory LMM diagnostic plots ("Normal Q-Q" and "Residuals vs. Fitted" plots). Then, we relativized the data so that all fitness components have a mean of 1  (Jones 2009). Finally, we computed repeatability for each fitness component using the rptR R-package  (Stoffel et al. 2017) , where a 95% confidence interval was estimated by bootstrapping (10,000 iterations). For all four fitness components, we assumed Gaussian data distributions because we wanted to assess the repeatability values for individual success across the three groups, whereas using binomial data distributions provides repeatability values at the observation level (i.
We estimated the repeatable opportunity for selection in each fitness component using a bootstrapping protocol with 10,000 iterations. Specifically, for each fitness component, we randomly sampled 150 replicates in our dataset (with replacement), on which we computed the product between the variance in all four fitness components, and the respective repeatability of all four fitness components over the three mating groups. Thus, we obtained the average and the 95% confidence intervals of the repeatable portions of variance for each of the four fitness components. Finally, we statistically compared the repeatable opportunity for selection arising from the four fitness components by using a pairwise signed difference test. In brief, we computed the difference between the repeatable amounts of the variance found in two fitness components in a 10,000 iteration bootstrapping.
Sample sizes Our initial sample size was 160 replicates, but because we lost eight and two replicates due to developmental errors and the GFP penetrance assay, respectively, the final sample size was reduced to 150 replicates. Also, some of these replicates had missing values on specific fitness component for one or more mating groups. Missing data arose when fitness components could not be estimated. For example, sperm fertilizing efficiency could not be estimated when the focal did not successfully transfer sperm to its four partners. In addition, the recording of one mating movie failed. Hence, the final dataset was as follows:  Statistical analyses were performed in R (version 3. 6. 3) (R Core Team 2020).
ACKNOWLEDGMENTS We thank  D. B. Vizoso  and  C. Felber  for assistance during the experiment, and  J. Hottinger ,  D. Lüscher , and  U. Stiefel  for technical support. We are grateful to the  R. Snook , editor,  M. Jennions , and an anonymous reviewer for providing thoughtful comments on an earlier version of the manuscript. The  Swiss National Science Foundation  funded this study (grant to LS,  31003A-143732 ), and supported LMO when analysing the data and writing the manuscript (grant  P2BSP3_158842  and  P300PA_171516 ).
ChIP-seq and analysis The ChIP experiment was carried out as previously described  (Hawkins et al. 2010 ) with H3K9me3 (ab8898, Abcam) and H3K27m3 (9733s, Cell Signaling Technology) antibodies. ChIPed and input DNA were used for library preparation and then sequenced with an Illumina NovaSeq 6000 sequencer. ChIP-seq data were processed with MACS2  (Zhang et al. 2008). First, sequencing data was mapped to hg19 with Bowtie 2 (Langmead and Salzberg 2012) and BAM files were deduplicated by Picard (https://broadinstitute. github. io/picard/). Next, we used "macs2 callpeak -B --SPMR" to generate normalized a pileup signal file of "fragment pileup per million reads". Then, in-house script was used to get normalized pileup enrichment for 10-kbp bins across the whole genome (Supplemental Code).
Obtaining Hi-C interaction matrices Sequencing reads were aligned, filtered, and iteratively corrected as described previously using HiC-Pro  (Servant et al. 2015). Briefly, paired-end reads were mapped to the human reference genome hg19 with default parameters. We are confident that realigning reads to hg38/GRCh38 would not alter our conclusions, as the analysis in this manuscript does not particularly focus on a specific region of the human genome or genome annotations that were updated between hg19 and hg38. After all alignment and filtering steps, the data were binned at different resolutions followed by matrix balancing. HiCRep was used to examine the reproducibility  (Yang et al. 2017). Biological replicates showed high reproducibility. Therefore, for the downstream analysis, reads obtained from biological replicates were pooled except for special note.
Interaction matrices and other genomic data were uploaded to the WashU Epigenome Browser for visualization  (Zhou et al. 2013). DNA replication timing (wgEncodeUwRepliSeqBjWaveSignal) and CTCF binding sites (wgEncodeUwTfbsBjInputStdRaw) of the BJ cell line and TF binding regions data (wgEncodeRegTfbsClusteredV3) from the ENCODE Project  (Rosenbloom et al. 2013)  were downloaded from the UCSC Table Browser  (Karolchik et al. 2004 ). Histone modifications of the BJ cell line were obtained from the Roadmap Epigenomics Project (Roadmap Epigenomics  Consortium et al. 2015).
Acknowledgments We thank all members of the  Xiaowo Wang laboratory  for providing helpful advice and support for this project. The work was supported by the  National Natural Science Foundation of China  grants (nos. 62050152 ,  61773230 , and  61721003  to X. W. ,  31830047  and  31725018  to W. X. ,  81890991  to J. G. ), the  State Key Research Development Program of China  ( 2020YFA0906900  to X. W. ,  2019YFA0508900  to W. X. ,  2017YFA0505503  to J. G. ),  CAS Interdisciplinary Innovation Team  ( JCTD-2020-04  to J. G. ),  Beijing Municipal Natural Science Foundation  ( Z200021  to J. G. ), the  THU-PKU Center for Life Sciences  (W. X. ) and the  THU-PKU Center for Life Sciences  postdoctoral fellowships (Z. D. ). W. X. is an  HHMI  international research scholar.
Main Groups of Poisoning Syndromes and Associated Toxins Recently, Bresnan et al. , 2021 used the IOC-ICES-PICES Harmful Algal Event Database (HAEDAT) to describe the diversity of harmful algal events along the Atlantic margin of Europe from 1987 to 2018 (Figure  2 ). Closures of shellfish production areas are mainly due to the regular annual occurrence of Diarrhetic Shellfish Toxins (DSTs) with protracted closure periods typical over the summer months. Paralytic Shellfish Toxins (PSTs) and Amnesic Shellfish Toxins (ASTs) occur less frequently and usually result in shorter closure periods. The occurrence of Azaspiracid toxins (AZA's), in the arc area, although observed from the Southwest along the West and through to the Northwest coast of Ireland, is not a regular occurrence in the region.
, 2017) , from March to November. These blooms appear linked to the seasonal and interannual frequency  (Husson et al. , 2016)  of physical and hydrological processes (i. e. , river discharge, upwelling, vertical mixing). In Galicia, blooms of Pseudo-nitzschia have been reported for decades  (Míguez , 1996; Velo-Suarez et al. , 2008) , but with only one report of human intoxification, involving two people, in 2004 (HAEDAT database). In the Basque Country, the detection of toxins in April 2017 coincided with a bloom of Pseudo-nitzschia spp. , but the causative species could not be elucidated. During the upwelling season in Portugal, from spring to late summer, blooms of Pseudo-nitzschia spp. are recurrent  (Palma et al. , 2010)  although harvesting closures are very few per year and of short term duration (1 month maximum).
SHORT-TERM FORECAST OF HAB OCCURRENCE AND TOXIN PRESENCE IN SHELLFISH A development within modern alert systems is the use of a range of approaches to provide a forecast rather than a "now cast" of HAB and biotoxin conditions. These can be summarized into several major types  (Davidson et al. , 2016; Maguire et al. , 2016; Mateus et al. , 2019). 1. Type 1: Industry alert "bulletin" reports, which are compiled from datasets including data generated from biotoxin and/or phytoplankton monitoring programs, and include, where available, additional environmental variables. These parameters are often synthesized into graphical and map images for easy interpretation by stakeholders. The simplest bulletins are only warning systems reporting current findings, and do not necessarily function as early warning systems.
However, most of these models work at aggregated trophic levels and hence struggle to differentiate between harmful and benign phytoplankton. The degree of complexity of these systems varies, dependent on the complexity of the models they rely on. Forecasting systems are rarely comprised of just one approach, but instead are a combination of shared information from different multiple approaches. For example, the boundary between type 4 and type 5 models may not be clear, since statistical modeling always uses some of the mechanistic knowledge available (e. g. , variables to include), whereas mechanistic models use statistical inference of equations and parameters. The above approaches are currently incorporated in "bulletin" reports from each region utilizing the various datasets and data products available (Supplementary Table  1 ).
Most of this information in Atlantic arc countries, especially HAB in situ data, had not been previously available publicly. However, this information is now becoming available through public bulletins, datasets, websites and media platforms providing access to monitoring data and data products in combination with forecasting models (Table  5 ). The current status of forecasting systems in Atlantic Arc countries is summarized below. In Scotland, the alert system includes a "traffic light" based risk index and a number of other data products that summarize current and historical HAB and shellfish biotoxin risk for the country as a whole. In addition, a more detailed "expert interpretation" based risk assessment is provided for the Shetland Islands. Bulletins have been produced weekly since 2014 (type 1 system) and are available via www.
6 s. Boundary conditions: Water movement within hydrodynamic models is predominantly driven by tidal forcing applied at the "open" (water) boundaries. The tidal forcing was derived from the 1/30 • degree implementation of the Oregon State University Tidal Prediction Software (OTIS) for the European shelf  (Egbert and Erofeeva, 2002). At each of the domain's open boundary nodes, the 11 primary tidal constituents were used to derive sea surface elevation time series. Open boundaries also included a 6-km-wide "sponge layer" to filter high-frequency numerical wave reflection noise using a Blumberg-Kantha implicit gravity wave radiation condition  (Wu et al. , 2011). The use of this model to operationally simulate HABs is explained more fully by  Aleynik et al. (2016)  and  Davidson et al. (2021).
The model is forced by an operational WRF for surface forcing and the lateral boundary data are obtained from the CMEMS AMM15 model. The river input is modeled from the WRF temperature and precipitation. The Lagrangian particle tracking model PyLAG  (Uncles et al. , 2020)  is run offline on saved hourly outputs from FVCOM. This Lagrangian model uses a Milstein numerical scheme for advection and diffusion, with the diffusivities provided directly from the hydrodynamic model. There are no biological behaviors, and the virtual particles are modeled as buoyant, however, it is possible to set up with 3D advection and some simple behaviors (e. g. , temperature dependence). Particles are seeded based on the HAB-risk product  (Kurekin et al. , 2014)  in a 200-m radius around identified high risk areas.
These are advected until the end of the forecast period and the results of the model are served as both a gridded particle density and probability field: this takes into consideration both the uncertainty from the identification algorithm and the drifting particles. ShellEye also investigated single site statistical models of the environmental conditions that promote the release of algal toxins (Type 3 models). It was found that sea surface temperature (SST), solar radiation, wind speed, time-lagged rainfall and wind direction, were useful in predicting the onset of DSP toxins  (Schmidt et al. , 2018). In Ireland, since 2013, the Marine Institute has produced a weekly HAB bulletin which is published and publicly available in PDF format through the in-house developed HABs database and website platform.
, 2018). The operational model also produces an estimate of the ocean state as a 3-7 days forecast of the dominant regional physical processes that result in water exchange events between the bay and its adjacent shelf for these three areas. Upwelling and downwelling signals indicate potential for future toxic contamination of shellfish in certain bays in the south-west of Ireland. The skill of the model in simulating toxic HAB episodes up to 10 days in advance is demonstrated by  Cusack et al. (2016). Similar to the preparation of the weekly Scottish bulletin the data summary graphics are produced using automated routines, which are then assessed by an expert evaluator. This evaluator provides expert opinion and analysis based on the datasets and all the parameters described above, and provides text to the weekly bulletin to provide a national forecast for the current week (3-5 days forecast), describing the cautionary predictive risk of a potential new HAB event occurring, or an existing event continuing/dissipating per toxin group or for bloom forming species  (Dabrowski et al.
The bathymetry was obtained from the European Marine Observation and Data network  (Vasquez et al. , 2015) , smoothed to ensure stable and accurate simulations  (Haidvogel et al. , 2000). The atmospheric forcing used in ROMS is obtained from the Weather Research and Forecasting model (WRF) run by Euskalmet (Basque Meteorological Agency). The boundary conditions are obtained from the 3-D hourly data provided by the NEMO model for the Iberian-Biscay-Ireland region. The online 3-D Lagrangian particle tracking module existing in ROMS is activated and is generating forecasts of the 4-day evolution of several virtual particles released at fixed points and at 00:00 UTC in the ROMS domain at high temporal resolution (1 min). These virtual particles are initially located at the sea surface to forecast the drift of HABs from and to the pilot aquaculture farm (located in the Mendexa region).
, 2015; Uusitalo et al. , 2016; Hernández-González et al. , 2019; Taconet et al. , 2019; Cruz et al. , 2021). Data consolidation is a major challenge in developing a machine learning based system. As noted above, data sources may include in situ regulatory monitoring, sensors, meteorology, models and satellite: often provided in different formats, undocumented or with missing information. EWS developments will accelerate and reach their full potential only with FAIR data: findability, accessibility, interoperability, and reusability  (Wilkinson et al. , 2016; Bax et al. , 2019; Stall et al. , 2019). These curated datasets incorporate training information and so provide an opportunity to improve the efficiency and strength of statistical models. This method is already employed for essential biodiversity variables (EBVs), to study global biodiversity  (Kissling et al.
| Isolation of human TIF For proteomic analyses, TIF was taken from three men diagnosed as azoospermic due to distal reproductive tract obstruction (obstructive azoospermia, OA); however, all data suggested that their testicular function was normal (Supplemental Dataset 9). Normal histology of their testis was determined by morphological evaluation. Specimens from each testicular incision site (Supplemental Dataset 9) were immediately fixed in Bouin's solution and processed according to routine protocols. The semi-quantitative score count evaluation of spermatogenesis was performed according to Bergmann and Kliesch. 28 For each individual retrieval site, the number of tubules containing elongated spermatids is divided by the total number of tubules examined × 10; hence, score values range from 0 to 10.
For Western blotting, TIF was taken from n = 8 men with OA defined as above and from eight men with presumed Sertoli cell-only phenotype. 28 Clinical data from the OA and SCO groups, including hormones and testis volumes, are shown in Supplemental Dataset 10A. SCO men had significantly higher levels of FSH than OA men. Spermatogenesis score  28  for the biopsies taken from each patient is shown in Supplemental Dataset 10B; all SCO patients had biopsy scores of zero and were unable to have sperm retrieved from their testes during surgery. Protein concentrations were determined by the BCA method (Pierce, Rockford). For mouse TIF, equal amounts of mouse TIF lysate (60 μg) from DTX (n = 11) and control (vehicletreated) mice (n = 12) were prepared for mass spectrometry analysis using the FASP protein digestion method  30  with the following modifications.
| Offline HPLC fractionation Tryptic peptides from each of the three human TIF samples were subjected to high pH reverse-phase analysis on an Agilent 1100 Series HPLC system equipped with a variable wavelength detector (280 nm). Fractionation was performed on XBridge Shield C18 column (10 × 100 mm, 3. 5 μm bead size, Waters). Peptides were separated by their hydrophobicity at a high pH at a flow rate of 0. 1 mL/min using a gradient of mobile phase A (5 mM ammonium formate, pH 10) and a mobile phase B (100% ACN), from 3% to 35% over 60 mins. Fractions were collected every minute across the gradient length and concatenated into 24 fractions. 5. 3 | Mass spectrometry and data analysis Peptides were separated by reverse-phase chromatography on a 1. 6 μm C18 fused silica column (ID 75 μm, OD 360 μm × 25 cm length) packed into an emitter tip (IonOpticks, Australia), using a nano-flow HPLC (M-class, Waters).
O'DONNELL Et aL Raw files consisting of high-resolution MS/MS spectra were processed with MaxQuant (version 1. 5. 8. 3) for feature detection and protein identification using the Andromeda search engine. 32 Extracted peak lists were searched against the UniProtKB/Swiss-Prot Mus musculus or Homo sapiens databases (October 2016) and a separate reverse decoy database to empirically assess the false discovery rate (FDR) using strict trypsin specificity allowing up to two missed cleavages. The minimum required peptide length was set to seven amino acids. In the main search, precursor mass tolerance was 0. 006 Da and fragment mass tolerance was 40 ppm. The search included variable modifications of oxidation (methionine), amino-terminal acetylation, the addition of pyroglutamate (at N-termini of glutamate and glutamine), and a fixed modification of carbamidomethyl (cysteine).
The "match between runs" option in MaxQuant was used to transfer identifications made between runs on the basis of matching precursors with high mass accuracy. 33 Protein abundance was calculated using the intensity-based absolute quantification (iBAQ) metric. 34 Peptide-spectrum matches (PSMs) and protein identifications were filtered using a target-decoy approach at an FDR of 1%. Protein identification was based on a minimum of one unique peptide. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  35  with the dataset identifier PXD014333 and the following Username: reviewer23562@ebi. ac. uk and Password: A7eE5hnQ.
| Label-free quantitative proteomics pipeline Statistically relevant protein expression changes between the DTX and control mouse TIF samples were identified using the default workflow in the R package Proteus (version 0. 2. 10) where quantitation was performed at the peptide level, with some minor differences. 36 Only unique and razor peptides were considered for quantification with intensity values present in at least two out of three replicates per group. Missing values were replaced by values drawn from a normal distribution of 1. 8 standard deviations and a width of 0. 3 for each sample (Perseus-type). Peptides were assigned to their leading razor protein and peptide intensities were aggregated to protein intensities using the aggregateHifly function based on the high-flyer method. Peptides were assigned to their leading razor protein and peptide intensities were aggregated to protein intensities using the aggregateHifly function based on the high-flyer method.
| Analysis of mouse TIF protein localization The gene symbol of each TIF protein was interrogated in an RNAseq dataset from normal mice and from those with adult germ cell ablation using the germ cell-specific toxicant busulfan. 39 Proteins were deemed to be predominantly expressed in germ cells when their mRNA levels were decreased in whole testes by >70% after busulfan treatment. The genes corresponding to TIF proteins were also interrogated in a microarray dataset of isolated seminiferous tubule cells (Sertoli cells, spermatogonia, pachytene spermatocytes, or round spermatids), whole testes, and 18 other normal mouse tissues. 40 Proteins were deemed likely to be contributed to TIF by adluminal germ cells when: (a) the protein was significantly reduced (P <. 05) in TIF by DTX; (b) the mRNA was reduced >70% in whole testes by germ cell ablation,  39  (c) their mRNA was >5-fold enriched in isolated adluminal germ cells (PSC and/or rST) compared to Sertoli cells  40  and (d) their mRNA was either undetectable (<6.
| RESULTS 3. 1 | Defining the contribution of the seminiferous epithelium to the mouse TIF proteome Testicular interstitial fluid was collected from mice using established methods,  24, 27  and proteins and peptides were separated by reverse-phase chromatography and identified by mass spectrometry (see Methods). A total of 3902 proteins were identified in TIF from normal (control) mice (Figure  1A , Supplemental Dataset 1A). To define the contribution of the seminiferous tubules to the mouse TIF proteome, we utilized a model of diphtheria toxin-induced acute Sertoli cell ablation (DTX mice). 25, 26 One week after DTX treatment, testes do not contain Sertoli cells, some advanced germ cells remain in the tubules but many are apoptotic, there is a marked decrease in spermatid marker gene expression (likely due to the induction of apoptosis), and the initial testicular inflammatory response to Sertoli cell ablation, as measured by inflammatory marker expression, has largely resolved (Supplemental Figures  S1  and S2 ).