dataesr/scientific-paragraphs-categorization huggingface dataset:
DAP were identified and investigated separately during the time and between treatments. The time associated DAP were identified using paired t-test between each consecutive time-point to cope with carryover effects. At each timepoint, the most important proteins (VIP) involved in the discrimination of the CTRL and EFA + CLA groups was identified using Partial Least Square Discriminant Analysis (PLS-DA) analysis (mixOmics package in R). The VIP contributed to the projection scores (> 1. 5) of the first two components (PC1 and PC2) and to cluster separation between treatment groups were ranked. A permutation test (100 random computations) was applied to disprove the over-fitting of the PLS-DA model. However, the significance of permutation test is more relevant for a prediction purpose, which is not the goal of our study.
The permutation test was not significant at all timepoints and there was a possibility for false positive detection. Therefore, the second filtration step was applied and only VIP with P-value < 0. 05, and log2 (fold change) > 1. 3 (metaboanalyst R package) were maintained and considered as differentially abundant proteins (DAP) for gene ontology functional enrichment analysis. The P-value was assessed by Student's t-test. Statistical analysis was performed in mixOmics and metaboanalyst R-packages in R statistical software (R version 4. 0. 0). DAP were visualized according to their expression (by Volcano plot, EnhancedVolcano R package). The GO categorization, including BP, MF, and CC, and KEGG (www. kegg. jp/ kegg/ kegg1. html)  14  pathway enrichment analysis were conducted using the web-based tool String version 11.
0, summarized in REVIGO web-based tool (http:// revigo. irb. hr/) and visualized in Cytoscape software (https:// cytos cape. org, version 3. 8). The B. taurus interaction map was set as a background list, and pathways (false discovery rate < 0. 05) with at least two protein hits were considered as enriched pathways. Protein-protein interaction networks (PPI) were constructed by inputting the time and treatment DAP to proteINSIDE (V 2. 0) tool. PPI were searched in Homo sapiens to take advantage of the well-characterised physical interactions between proteins, and PPI agreed by curator review were used to construct PPI networks (Supplementary Fig. S1 ). Within the network, proteins that interact to contribute to cellular or metabolic processes have been highlighted using betweeness or closeness centralities as previously described  69.
Proteomics data matched the classical measurements of proteins and metabolites. To validate the proteomics results, we integrated the serum proteome data with plasma metabolites and proteins  6, 70  using sparse partial least squares (sPLS) regression model (mixOmics package in R). With X (proteomics data) and Y (classical plasma proteins and metabolites data) as input matrices (logarithmic transformed values), the sPLS model controls many noisy, collinear (correlated) and missing variables in omics datasets, and relates multiple outcome variables across X and Y. Positive and significant correlations were observed between 1-proteomics and ELISA-measured HP; 2-proteomics and Western blot-measured IGFBP2; 3-NEFA with LBP and IGFBP2; 4-LDL with APOB, APOC4, and APOC3 proteins (Supplementary Fig.
Funding Open Access funding enabled and organized by Projekt DEAL. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme   H2020-MSCA-ITN-2017-EJD: Marie Skłodowska-Curie Innovative Training Networks (European Joint Doctorate)-Grant  agreement nº: 765423. The animal study was supported by  BASF SE (Ludwigshafen, Germany).
IMPLEMENTATION We have designed and developed Intercross, an Android application, that aims to be a simple addition to plant breeding workflows. Intercross is freely available and open source, allowing breeding programs to make code contributions or further customize it for their own needs. At its most basic level, Intercross works to associate a male ID, female ID, and cross ID while simultaneously capturing a timestamp and the name of the person making the cross (Figure  1a ). The cross ID can be supplied by the breeder using pre-printed labels, generated as a unique universal ID, or designed to match a custom pattern (Figure  1b ). Based on the parental combination, Intercross determines if the cross is biparental, selfpollinated, open-pollinated, or a polycross. Intercross can be further extended to capture the entire workflow of generating a new seedlot or family.
After the cross has been created, the cross can optionally be advanced through a workflow that covers the entire lifecycle of that cross. This enables the user to track additional information including the number of flowers used for the cross, the number of harvested items (e. g. fruits, pods, spikes, etc. ), and the ultimate number of seeds created from a specific crossing event (Figure  1c ). The integration of barcodes significantly minimizes error when utilizing complex sample identifiers. Many facets of plant breeding workflows can be optimized by using barcodes. Intercross has built-in barcode scanning support and allows users to search for specific cross IDs via barcode to rapidly navigate to an individual cross page. Breeders making crosses regularly identify specific plants in crossing blocks by their common cultivar or family name and not the barcoded ID.
To bridge these two different naming systems, Intercross allows users to import a list of parents that includes the barcode ID, the sex of the parent, and the common name of the parent into the app (Figure  2a , Table  S1 ). When these barcode IDs are used to make crosses, they are visually replaced with the parent names (Figure  2b ). Users can actively manage this parent list in the app by adding females, males, or pollen groups (e. g. for polycrosses) with UUIDs or custom barcode IDs. To facilitate tracking crosses in programs creating polycrosses, Intercross allows users to group individuals, generate a unique identifier, and print a barcoded label. When exporting polycrosses, the male parent is replaced with a list of all males that were used to make the pollen group. Intercross can be paired with Zebra Bluetooth printers (Zebra Technologies Corporation, Lincolnshire, Illinois, USA) to print both individual parent and cross labels.
Labels can be further formatted to a users' specification by importing a custom ZPL template file. Crossing blocks are generally designed each season with an idea of which and how many specific crosses should be made. Creating additional crosses beyond what is needed for any given parental combination is often seen as an inefficient use of resources. Intercross allows breeders to import a wishlist that is used to track progress toward a desired production threshold (Table  S2 ). Breeders can track their progress toward the number of targeted crosses via two different interfaces: a simple list with the different combinations and a grid with specific parental combinations highlighted. Collected data can be exported to a CSV (comma separated value) file in the internal device memory with each row including the cross ID, both parent IDs, a timestamp, the person who made the cross, the type of cross, and any additional metadata that was collected about the crossing event.
ACKNOWLEDGEMENTS The authors wish to express gratitude to the breeders and technicians at  The International Potato Center  and the  International Institute of Crop Agriculture  who contributed ideas and willingly tested early versions of Intercross, including  Elisa Salas ,  Jolien Swanckaert ,  Prasad Peteti ,  Ismail Rabbi , and  Afolabi Agbona. We also appreciate the valuable suggestions and critiques provided by  Lukas Mueller ,  Bryan Ellerbrock , and  Mary Guttieri.
Statistical Analysis The effects of the thyme and oregano oils on the growth performance, feed conversion ratio, and the incidence of muscle abnormalities were assessed using an ANOVA (GLM procedure in SAS Statistical Analysis Software, version 9. 1, 2002). Duncan's test was employed to separate means in the case of the presence of statistical differences (p < 0. 05). Pearson's correlation was used to test the relationships between pairs of continuous variables (i. e. , the feed conversion ratio, carcass, and visceral organ variables).
Dead wood was sampled to assess wood-rotting fungi relative abundance and OTU richness (using DNA metabarcoding and sequencing of the ITS2 fungal marker) and wood physicochemistry (final wood density and L/D ratio). Fungal operational taxonomic units (OTUs) were classified as BR, SR, or WR decay types using databases and literature resources. Decay rates (wood density at sampling), wood decay type (L/D ratio), and wood-rotting fungal guilds were linked with climate parameters (mean annual precipitation [MAP] and mean annual temperature [MAT]) using structural equation modeling. Based on the physiologies and ecologies of the different wood-rotting fungal guilds, we tested the following predictions: (P1) WR fungi will dominate aspen and birch dead wood, and pine dead wood will be rich in BR fungi as compared to aspen and birch; (P2) BR fungi will dominate northern, colder, sites while WR fungi will dominate southern, warmer, forests; (P3) Pine will have a lower-thanaverage L/D ratio due to more abundant BR fungi; and (P4) Vertically standing pine wood logs will be rich in BR fungi as compared to horizontal pine logs and will also have a lower L/D ratio.
The DNA was subsequently extracted from the samples following the protocol of  Lindner and Banik (2011). The first step of the extraction involves a 2-h incubation at 65C, following this incubation, the three samples from each log were pooled for the remainder of the extraction. Negative controls (CLS with no sample) were included during every batch of DNA extraction to control for possible contamination at the DNA extraction step. After extraction we PCR amplified the fungal DNA for HTAS libraries using individually barcoded Ion fungal-specific primers fITS7 and ITS4  (Ihrmark et al. , 2012)  that were modified to be compatible for Ion Torrent sequencing  (Ihrmark et al. , 2012)  according to manufacturer's recommendations. The forward primer consisted of the Ion A adapter sequence, the Ion key signal sequence, a unique Ion Xpress Barcode sequence (10-12 bp), a single base-pair linker (A), and finally the fITS7 primer  (Ihrmark et al.
Following clustering, any padded Ns were removed, and the processed sequences were mapped to the OTUs. We used SynMo  (Palmer et al. , 2018)  to account for observed rates of barcode crossover using the filter module in AMPtk. Following filtering, the LULU script (version 0. 1. 0; Frøslev et al. , 2017)  was used for further post-clustering curation. Taxonomic assignment of the OTUs was achieved using the hybrid taxonomy algorithm in AMPtk. All non-fungal OTUs were removed following taxonomy assignment.
Data analyses Data analyses and visualizations were performed using R software version 4. 0. 5 (R Core Team, 2021). To account for variations in total sequence read counts across samples, all fungal community analyses were based on counts rarefied to 10,072/sample (see Fig. S1  for the rarefaction curves) in the vegan package  (Oksanen et al. , 2013). The 196 samples had a total of 1,974,112 sequences and 3334 fungal OTUs present in the final quality-controlled and rarefied dataset. Based on the assigned taxonomy, fungal OTUs were classified as BR, SR, or WR fungi using a combination of different databases and literature resources  (Levy 1969; Nilsson 1973; Bugos et al. , 1988; Zabel et al. , 1991; Worrall et al. , 1997; KarunasekeraDaniel, 2015; Nguyen et al. , 2016; Pedersen et al. , 2020; Põlme et al.
Thus, we calculated the WR/BR, WR/SR, and WR/(BR + SR) ratios. We tested for relationships between L/D and wood rot fungi ratios using Pearson's correlation (P ≤ 0. 05) for the different combinations of three species and log orientations (horizontal aspen, birch, and pine, and vertical pine). We applied structural equation modeling (SEM) based on an a priori model (Fig. S2 ) to elucidate the direct and indirect effects of climate (MAP and MAT), decay stage (final wood density), and woodrotting fungi ratios on L/D. Final wood density (referred to as density in the graphs) was used as a proxy for the decay stage  (Zalamea et al. , 2007; Mäkipää and Linkosalo 2011; Chambers et al. , 2000). SEM was performed using the lavaan package in R software  (Rosseel, 2012). Wood density, MAP, and MAT variables were rescaled to limit variance inflation before SEM.
Again, in temperate forests,  Cline et al. (2018) ,  Fukasawa and Matsuoka (2015a)  and  Viotti et al. (2021) each reported minor proportions of BR fungi in comparison to WR fungi in birch, pine, and oak dead wood. While relative abundance is often used as a metric of wood-rotting dominance in the recent literature, our results also showed that OTU richness depicted the same patterns. Taken together, these pieces of evidence parallel our results and point to a wood-rotting fungal balance being dominated by WR fungi in temperate forests. This generalization does not undermine the importance of BR or SR in shaping global carbon emissions from wood. First, BR fungi in ITS datasets in Japan were recently shown not to align well with visual qualifications of BR in wood  (Fukasawa 2015a; 2015b; Fukasawa and Matsukura 2021).
Acknowledgements We would like to thank  L. Boddy  and two anonymous reviewers for their helpful comments and suggestions on previous versions of this manuscript. We thank the  Caspar Creek ,  San Dimas ,  Sierra Ancha, Fraser ,  Tenderfoot Creek ,  Marcel, Coweeta ,  Santee Experimental Forests (USDA Forest Service)  and the  Duke Forest  for hosting the FWDE. We appreciate the field crews led by  Julie Arnold  ( Southern Research Station ) and  Joanne Tirocke  ( Rocky Mountain Research Station ) for the sample collections and maintenance of the FWDE sites.
Sequences and alignments We collected complete spike gene sequences from Alphacoronavirus 1 type II CoVs available in GenBank (accession numbers appear in Supplementary Table  S1 ). Partial spike sequences of Alphacoronavirus 1 were excluded from our analysis, as were strains determined from associated publications, to have been be serially passaged (including experimental inoculations of any kind). CCoV type II viruses are currently split into two groups: CCov2a and CCoV2b. Our choice of CCoV2b as our representative CoV from the dog host is based on the fact the most N-terminus subdomain of the spike protein of CCoV2b and TGEV share sequence similarity to CCoV-HuPn-2018, whereas this is not the case for either CCoV2a or FCoV2. Spike domain site positional mapping is based on serotype I feline infectious peritonitis virus (FIPV1;  Yang et al.
Positive selection and recombination Both alignment sets were screened for recombination with breakpoints identified using GARD (Kosakovsky  Pond et al. 2006) ; set II was also evaluated with RDP5  (Martin et al. 2020)  for an additional level of granularity with regard to determining the polarity of sequence exchanges. Each of the resulting GARD fragments served as input to the selection analyses, concomitant with their respective phylogeny, which was inferred using RaxML  (Stamatakis 2014)  under the GTR+Γ nucleotide substitution model. We performed site-, branch-, and alignment-level selection tests based on the dN/dS (nonsynonymous / synonymous) ratio estimation as implemented in the HyPhy software package v. 2. 5. 31  (Kosakovsky Pond et al. 2020). We used the MEME method  (Murrell et al.
1, temporal signal was confirmed using TDR. The R package TipDatingBeast  (Rieux et al. 2017 ) was used to generate ten random permutations of sample dates for each GARD alignment. BEAST2  (Bouckaert et al. 2014 ) was then used to estimate the evolutionary rate for both alignments with the true sample dates and alignments for each randomized replicate. If the mean clock rate estimate of the alignment with real sample dates fell outside the 95% highest posterior density (HPD) for the randomized date set, temporal signal was deemed sufficient for subsequent analyses.
Model selection For each alignment that had sufficient evidence of a temporal signal, the fit of combinations of two molecular clock models (strict and uncorrelated relaxed exponential  Drummond et al. 2006 )) and two demographic models (constant coalescent and Bayesian skyline plot  (Drummond et al. 2005 )) were assessed using marginal likelihood estimation. For each model tested, marginal likelihood was calculated using PathSampling  (Lartillot & Philippe 2006)  within the Model-Selection package in BEAST 2 with 12 steps, 1,000,000 MCMC steps with 25% burn-in, and an alpha of 0. 3. The average marginal likelihood estimates from two path sampling runs were compared to other model combinations using Bayes Factors  (Kass & Raftery 1995).
Discrete trait analysis The ancestral state of host species (cat, dog, pig, human) was inferred using discrete ancestral trait mapping in BEAST2  (Bouckaert et al. 2014)  for each GARD alignment. Bayesian phylogenies were created using 100 million MCMC steps in BEAST2, sampling every 10,000 steps. Trees were summarized using the BEAST2 package TreeAnnotator v2. 6. 0, discarding 20% of trees as burn-in. Convergence of the MCMC chains was assessed using Tracer v1. 7. 1  (Rambaut et al. 2018 ) and the effective sample size for each estimated parameter was confirmed to be greater than 200. Phylogenetic trees were annotated using FigTree v1. 4. 4 (available from http://tree. bio. ed. ac. uk/software/figtree/). GARD partition 1 (Supplementary Fig. S1 ) includes CCoV-HuPn-2018 as a separate branch intermediately placed between the CCoV2b and TGEV sequences.
S2 ). Two of the recombination events identified by RDP5, at the 5' end of the A-domain (events 5 and 13 in Fig. S2 ), have similar predicted breakpoint locations which overlap almost exactly with GARD partition 2. For one of these events (event 5), CCoV-HuPn-2018 is identified as a possible sequence donor and FCoV2 as a recombinant, and in the other (event 13), CCoV-HuPn-2018 is the proposed recombinant with FCoV2 as the proposed donor sequence. The detection of both events involved the same FCoV2 sequence (strain WSU 79-1683; accession number JN634064), isolated at Washington State  University in 1979 (McKeirnan et al. 1981). A 2009 survey of cats in two Malaysian catteries, using PCR primers designed from strain WSU-79-1683 and FIPV79-1146, found a high prevalence of FCoV test positives  (Sharif et al.
Acknowledgements This study received funding (FOA PAR-18-604) from the  U. S. Food and Drug Administration's Veterinary Laboratory Investigation and Response Network (FDA Vet-LIRN)  under grant  1U18FD006993-01 , awarded to LBG and MJS. SLKP and JDZ were supported in part by grants  R01 AI134384  ( NIH/NIAID)  and  U01 GM110749  ( NIH/NIGMS ). We gratefully acknowledge  Jean Millet  for helpful advice on Alphacoronavirus biology and spike structural domains.
The feed was delivered by ARVOTEC feeders at precise schedules using the computer-controlled Imetronic software (version 2008). Rearing tanks were all equipped with a video-surveillance camera (Full HD: 1920 x 1080 px, 105°, VIZEO -Adrien Alarme) installed above each tank allowing continuous recordings. Conditions of sh raised with different possibility to predict feeding After an acclimation period of 18 four feeding predictability treatments were applied (three tanks treatment containing 156 sh each) during twelve days (Fig. 7A ). In each treatment, sh received each day ve diffusions of bubbles (during 15 seconds each) and ve feedings (total of 60 sequences of bubbles and feedings) (Fig. 7B ). (i) Treatment BUBBLE: a signal of 15 seconds of bubble diffusion was systematically followed 5 seconds later by feeding (20-40 seconds).
Group behaviours during the 12 days of conditioning Previous studies showed that sh activity was a relevant behaviour as an indicator of anticipation of feed delivery  10. Thus, group activity in the rearing tank was automatically analysed by EthovisionXT® software (v. 15. 0. 1418) (Noldus, Wageningen, The Netherlands). Activity was given as a proportion of pixels that changed between two successive images (25 images per second). Data are given as percent of activity (%). Behavioural recordings were conducted at three different feedings or bubble sequences (1 st , 3 rd , and 5 th sequences of the day) on the 1 st and the 12 th days of the conditioning to examine its progression over the experiment. Depending on the period targeted, analyses were performed into the bubble area or into the whole tank (Fig.
Bubble diffusions were also stopped the day before testing. Water was changed every four tested sh. Behavioural responses were video recorded for 28 min. We analysed the rst 20 min, by 5 min time-bins with the Ethovision ® XT software. At 23 min, individuals automatically received 80 feed pellets (approximately 100 mg) and the remaining pellets were counted after 5 min to measure a possible inhibition of feed intake (anorexia) following a stressful situation (novel tank exposure). Indeed, feed consumption recovery after an acute stressor is a classical stress indicator in sh  16. The following behavioural parameters were scored for each individual: total distance moved (in cm), maximum swimming velocity (in cm/s), angular velocity (in °/s) (i. e. , erratic swim), and time spent (in %) in the border zone (i.
Concentration of suspended material Sediment transport in the Capesterre river occurs only during floods. We therefore extract four flood events from our dataset and observe the evolution of the water level and of the concentration of suspended sediment in more details (Fig. 2  and 3 ). We find that the concentration of suspended sediment follows the fluctuations of the water stage: it is equal to zero before the flood, rises when the water stage exceeds a threshold of about 20 centimeters, increases with the water stage until the flood peak, and finally decreases back to zero during the recession limb of the flood (Fig. 2 , left panels). A closer look at the data, however, reveals a time lag between the flood peak and the concentration peak. For three of the floods we selected, the concentration peak occurs about 30 minutes after the flood peak (Fig.
2 , left panels). A plot of the concentration as a function of the water stage better highlights this time lag between concentration and water-level: the concentration vs water-level relation forms an counterclockwise hysteretic loop (Fig. 2 , right panels). The flood peak, however, is not systematically ahead of the concentration peak. Sometimes, it is the concentration peak that precedes the flood peak, and the concentration vs water-level relation then forms a clockwise hysteretic loop (Fig. 3 ). To determine the proportion of clockwise versus counterclockwise loops in the Capesterre river, we first extract individual flood events from our dataset. To do so, we define a flood as a period during which the water stage is higher than a threshold set to 20 cm. This value roughly corresponds to the threshold above which the flow is strong enough to carry suspended sediment.
For lack of additional constraint, we choose the simplest possible form, and propose that: E = ϵ h h t -1 n H h h t -1 , ( 4 ) where ϵ is a characteristic erosion rate (mass per unit time and bed area), n is a dimensionless exponent, and H is the Heaviside function. Combining equations (  3 ) and (  4 ) with the mass balance (2) yields: dϕ dt = ϵ h h t -1 n H h h t -1 -V s ϕ h , (5) where we introduce the mass of suspended sediment per unit of bed area, ϕ = hC. Equation (  5 ) is an ordinary differential equation. It describes how the concentration of suspended sediment evolves in response to the water level h(t), which acts as a forcing function. To compute the sediment concentration, we use the ode package of the scipy. integrate python library to solve equation (5) numerically over a given time period.