dataesr/scientific-paragraphs-categorization huggingface dataset:
Urban environment characterisation We quantified the landscape context of each plot using data from the Global Human Settlement (GHS) images analytics framework (http://ghsl. jrc. ec. europa. eu/ghs_bu_s1. php) and the Global Forest Change database  58. These data estimate urban extents during 2016 and forest cover during the period 2000 to 2019, thus providing a reasonable estimate of land cover, as the time ranges overlap with that of the selected studies. We included the forest cover to provide an alternative landscape to the built urban land cover, in recognition that vegetation cover can be important in driving species distributions, yet different types of vegetation offer different potential resources and habitat. We recognise that for cities in more arid landscapes, forest may not reflect the natural vegetation communities, but we consider it to still be a useful landscape type given the emphasis of urban forest strategies on increasing tree canopy cover.
We calculated the percent cover and level of aggregation of urban and forest land cover within a radius of 100 m and 500 m centered on each plot for all taxa except birds, for which we use a 1000 m radius centered on each eBird checklist. We calculated the percent urban land cover in a region as the percent cover of 30 m x 30 m cells dominated by urban features (including all built-up features) using GHS. We calculated the percent forest land cover in a region with the same method, using the Global Forest Change database. To account for landscape configuration, we calculated an aggregation index  59  , which is defined as the ratio of "actual shared edges" versus "maximal possible shared edges" of the 30 m x 30 m cells. Because map units do not affect the calculation, the aggregation index can be compared among classes from the same or different landscapes and even the same landscape under different buffer sizes because the map units do not affect the calculation.
We included latitude and climate data in our analyses since the composition of functional traits have been shown to vary with latitude and climate  60, 61. Latitude was based on the geographic coordinate of the sampling plot. The main trends in climatic conditions were characterized using the 19 Bioclim variables of the CHELSA database  62  , which provides information about biologically relevant aspects of climate for a period ranging from 1979 to 2013. We reduced the dimensionality of this dataset to limit the number of climate variables and avoid their correlations. Specifically, we ran a PCA with 100000 randomly sampled cells. We then projected the remaining cells onto the PCA. The first four PCA axes represented the main trends in climate, that is, gradients in mean temperature (PC1), diurnal range (PC2), temperature seasonality (PC3) and precipitation seasonality (PC4).
Altogether, these four axes accounted for ~89 % of the global variation in climate (see also Supplementary Table  S10 ) and were selected for use in the subsequent analyses. To do so, we built various models using the random forests algorithm  66. The random forest algorithm excels at extracting patterns from complex datasets and is becoming more common in ecological studies. This approach being nonparametric, the data need not come from a specific distribution (e. g. , Gaussian) and can contain collinear variables  67. Also, random forests can deal with model selection uncertainty because predictions are based on a consensus of many models and not just a single model selected with some measure of goodness of fit. Specifically, we used the different community functional metrics as response variables, and climate PCA axes, latitude, and the percent and aggregation of urban and forest land cover as explanatory variables.
Because of the observed autocorrelation in model residuals, we added spatial covariates as explanatory variables to the models. As spatial covariates, we used positive Moran's Eigenvector Maps of a distance matrix among sites (dbMEM)  68. Relevant dbMEM were selected using a forward selection procedure based on the residuals of models computed without spatial covariates. The random forest algorithm was trained on 75 % of the data and evaluated on the remaining 25 %. Model training and parameter tuning were done using 2 different cross-validation strategies: 3 time 3-fold stratified CV and 30-fold spatial CV. In stratified CV, partition is stratified according to the response variable in order to balance the class distributions within the splits (function "createDataPartition" in the R package "caret").
In spatial CV, we created 30 spatial folds for cross validation (function "CreateSpacetimeFolds" in the R package "CAST") in order to maximise the spatial transferability of model results and avoid potential overfitting. Parameter tuning used 10 random values of the number of variables to be sampled at each split time. The best model was chosen based on RMSE, MAE, and R 2 measured on the trained dataset. The performances of the selected model were further evaluated on the test dataset using the same metrics. Spatial autocorrelation in model residuals was examined using Mantel correlograms (function "correlog" in the R package "vegan"). Potential overfitting was double-checked by comparing the model evaluation metrics among the train and test sets. We retained the models based on the spatial cross-validation procedure and including spatial covariates because they showed the overall best performances and the lowest potential overfitting and spatial autocorrelation of residuals.
Acknowledgements This research was conducted as part of the  Urban Biodiversity Research Coordination Network (UrBioNet ) funded by the  National Science Foundation  ( NSF RCN :  DEB 1354676/1355151 ). We initiated this project as part of the  Workshop Group "Patterns, Drivers and Traits" of the Urban Biodiversity Research Coordination Network (UrBioNet , https://sites. rutgers. edu/urbionet/dsg/). We would like to thank  Madhusan Katti ,  Christopher Trisos , and  Julie Goodness  for helping to conceptualise the study at the New Jersey workshop;  Eliana Geretz  and  Carmela M. Buono  for assistance with initial data compilation; and  Béla Tóthmérész ,  Cecilia Tobar-Suárez ,  Etienne Normandin ,  Gary Luck ,  Lisa Smallbone ,  Maryna Kyrychenko-Babko ,  Rebecca Acosta ,  Yocoyani Meza-Parral  for providing data included in this study.
Overtraining is characterized by latent improvements in piriform coding After mice have learned the task, they exhibit consistent performance. Their accuracy as assessed by the choice index does not change across days (Fig 5A;  Table 1, row 12). Additionally, the average lick latency does not change (Fig 5B;  Table 1, row 13). We next wanted to see whether there was any covert learning that was not reflected in the mice's behavior. We turned to see whether the responses of piriform neurons were changing during this same period. modulated neurons (bottom). Red lines depict odor onset and offset. All error bars and shading represent mean +/-SEM. *** P < 1 × 10 -11. The underlying data for this figure are available for download from https://datadryad. org/stash/share/bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU.
There is a significant negative correlation (Pearson's correlation R = -0. 28, N = 1,402, P = 2. 2 × 10 -27 ). Monte-Carlo significance test p-value = 0. 001. (g) The immediately preceding T-NT similarity of each correct (blue) and incorrect (red) probe trial. Correct trials have significantly less T-NT similarity than incorrect trials (one-sided signed-rank test, P = 0. 022; Monte-Carlo significance test p-value = 0. 013). (h) The lick latency of each correct (blue) and incorrect (red) probe trial. Correct trials have significantly higher latencies than incorrect trials (one-sided signed-rank test, P = 1. 09 × 10 -18 ). (i) The same analysis computed in g but with a p-value computed across different look-back variables. Red dotted line depicts p = 0. 05. Error bars represent mean +/-SEM. The underlying data for this figure are available for download from https://datadryad.
(f) Overtraining. Session 1 is the first session with random trial structure. (g) Sessions with probe trials. Session 1 is when probes were first introduced. The underlying data for this figure are available for download from https://datadryad. org/stash/share/ bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU. https://doi. org/10. 1371/journal. pbio. 3002086. g001.
Gray lines represent the mean and SEM for each mouse separately. ANOVA details in Table 1, row 11. All error bars and shading represent mean +/-SEM. *** p < 3 × 10 -4. The underlying data for this figure are available for download from https://datadryad. org/stash/share/bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU.
Gray lines represent the mean and SEM for each mouse separately. ANOVA details in Table 1, row 11. All error bars and shading represent mean +/-SEM. *** p < 3 × 10 -4. The underlying data for this figure are available for download from https://datadryad. org/stash/share/bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU. https://doi. org/10. 1371/journal. pbio. 3002086. g004.
nontarget trials across days. Black is a best-fit line. Each color is a different mouse. Significant change across sessions (ANOVA inTable 1, row 14). Note that breaks the data represent days that the mouse was trained but neural data were not available. (e) Proportion of neurons significantly selective between target/nontarget odors across days. Black is a best-fit line. Each color is a different mouse. Significant change across sessions (ANOVA in Table1, row 15). All error bars and shading represent mean +/-SEM. The underlying data for this figure are available for download from https://datadryad. org/stash/share/bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU.
Data are shown as kernel density estimates. The continuous gray lines indicate the mean values, and the black dots indicate medians. Top: target, Middle: nontarget, and Bottom: nontarget repeat. The relationship between session and accuracy assessed with a Pearson's correlation (also significant in an ANOVA, details in Table 1, rows 17-19). (d) Pearson's correlation calculated in c compared to a distribution of correlations calculated with shuffled (1,000 times) session numbers. P values from Monte-Carlo significance test. The underlying data for this figure are available for download from https://datadryad. org/stash/share/ bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU. https://doi. org/10. 1371/journal. pbio. 3002086. g006.
row 14). Note that breaks the data represent days that the mouse was trained but neural data were not available. (e)Proportion of neurons significantly selective between target/nontarget odors across days. Black is a best-fit line. Each color is a different mouse. Significant change across sessions (ANOVA in Table1, row 15). All error bars and shading represent mean +/-SEM. The underlying data for this figure are available for download from https://datadryad. org/stash/share/bC3NdXWDllJZYrRtq60q0WDQYhjZZuLulv91dm9WcYU.
Sensory evaluation Compusense 5. 6 software was adopted to gather the scores from a 12-member trained sensory panel based on an amended version of AMSA (2015) guidelines  [12]. Specifically, the steaks were allowed to come to room temperature for 60 min without any packaging before being cooked on a Velox CG-3 grill at 210 • C until reaching an internal temperature of 71 • C. Then, steaks were rested in foil for 4 min, cut into approximately 2. 5 cm × 4 cm pieces, wrapped individually in foil, and served. Members of a 12-member trained sensory panel conducted assessments on each sample. All panellists would cleanse their palates with water and plain crackers between each sample. Each panellist was instructed to bisect the steak piece along the cooked surface and assess the sample according to the same methodology (replicated) using the provided assessment sheet  [35].
Shotgun proteomics and LC-MS/MS data processing 12% polyacrylamide Mini-PROTEAN® TGX™ precast gels (8. 6 × 6. 7 × 0. 1 cm, Bio-Rad Laboratories, Deeside, UK) were used to concentrate the protein extracts in stacking gel. The shotgun proteomics was performed according to the protocol of Zhu et al. [12] , the details about the process of protein bands, as well as the operating parameters of LC-MS/MS has also been given in that paper. The raw data from the LC-MS/MS were then aligned against the Bos taurus database (i. e. ref_bos_taurus, 23,970 sequences) with Mascot V. 2. 5. 1 (http://www. matrixscience. com). The precursor and fragment mass tolerance were set up at 10 ppm and 0. 02 Da, respectively. Carbamidomethylation (C), oxidation (M) and deamidation (NQ) were included in the variable modifications.
Statistical analyses Statistical analyses were carried out with XLSTAT 2018. 2 (Addin-Soft, Paris, France). Data were examined for entry errors, missing data and outliers, and the keratin proteins were excluded from the raw database to avoid interference. Log2 transformation and mean normalization was performed on individual protein abundance. Pearson correlations were computed between the individual tenderness, chewiness, stringiness and flavour values and the quantified protein abundances. Correlation analysis values were considered significant at P < 0. 05. The proteins were further categorised manually into different biological pathways based on their annotation provided by UniProtKB (http://www. uniprot. org/). To get the best performance from the regression model built using the protein abundances for each quality traits, the limitation of four proteins, to respect the parsimony principle, was set up for the settings in each regression equation for each quality trait  [39].
Bioinformatics analyses Proteins that significantly correlated with those four eating quality traits had been selected by overlap analysis and then by different biological pathways based on the information provided by UniProtKB (https://www. uniprot. org/). ProteQTL tool included in ProteINSIDE (http://www. proteinside. org/) was performed on the above proteins to identify their potential role as quantitative trait loci (QTL) of beef qualities. A digital library of published Animal QTL Database (https ://www. animalgenome. org/QTLdb) was included in the ProteQTL engine which includes cattle QTL and association data curated from published scientific papers. Gene Ontology analysis was performed to investigate GO terms for potential functions and molecular mechanisms by using two web-based tools, ProteINSIDE (http://www.
[18] , HSPB6 is reported as a robust biomarker of beef tenderness which is strongly enumerated as the top 5 biomarkers in that database. In the present study, HSPB6 was negatively correlated with beef tenderness, which may be due to its protective function against proteolysis in skeletal muscle. The relationship between tenderness and HSPB6 is not always straightforward though as a similar frequency of positive and negative associations have been discovered in previous studies. This may be explained by the influence of factors such as animal breed, gender, muscle type and preslaughter conditions  [46, 62, 107]. Superoxide dismutase [Cu -Zn], also known as SOD1, is an antioxidant enzyme that is mainly expressed in the sarcoplasm and the inter membrane of mitochondria. SOD is engaged in the removal of surplus reactive oxygen species (ROS) to protect the muscle proteins from damage, and further maintaining cell homeostasis  [108].
Acknowledgements This research was funded by  Teagasc  project number  NFFQ0017  and a  Walsh Scholarship  to  Yao Zhu. We are grateful to  Carol Griffin  and  Carmel Farrell  for conducting the trained sensory panel analysis. The authors wish to thank the  Irish Cattle Breeding Federation  for access to samples and  David Sheehan ,  Dilip K. Rai  for their guidance to  Yao Zhu. The authors further acknowledge  Didier Viala  from  Metabolomic and Proteomic Exploration Facility (PFEM) ,  INRAE, France  for the proteomics data.
Generation of Tg(uas:starmaker-GFP) nw22Tg line The open reading frame (ORF) of starmaker (stm: ENSDARG00000035694) was amplified by PCR from zebrafish cDNA. The amplified PCR products were fused to a C-terminal eGFP-Tag separated by a 5'-TCTAGAACTATA-3' linker sequence containing an XbaI restriction enzyme site. The starmaker-GFP DNA was inserted into the pUAS-Self vector (gift from Sebastian Gerety) downstream of the zebrafish-optimized 5'-GCAAAC-3' Kozak sequence through Gibson cloning. The starmaker sequence in this plasmid construct lacks the 45 base pairs corresponding to Exon 15 of the Ensembl transcript stm-202 (ENSDART00000105174. 6), resulting in a 15 amino acid in-frame deletion. To generate the transgenic line, 2 nL of a mixture of the uas:starmaker-GFP plasmid DNA (60 pg) and tol2 mRNA (10 pg) was microinjected into one-cell stage embryos.
Transcriptomic analysis RNA sequencing data for human, rat, and mouse were obtained from the GEO repository  (Barrett et al, 2013; Edgar et al, 2002). Rat choroid plexus transcriptome data was obtained with GEO accession number GSE194236 (sample: SRR17713531)  (Andreassen et al. , 2022). The human choroid plexus transcriptome data was obtained with GEO accession number GSE137619 (samples: SRR10134643-SRR10134648)  (Rodriguez-Lorenzo et al. , 2020). The mouse choroid plexus transcriptome data was obtained from GEO accession number GSE157386 (samples; SRR12576642-SRR12576645, SRR12576650-SRR12576653)  (Planques et al. , 2021). The zebrafish choroid plexus transcriptome data was obtained from this study. All program parameter settings for library building and mapping, together with all scripts for the gene annotation and analysis are available at https://github.
com/Sorennorge/Choroid-plexus-orthology-study. The sequencing data for human and mouse were quality controlled with fastqc  (Andrews, 2010) , and the human data were trimmed with Trimmomatic  (Bolger et al, 2014)  (Sliding window 4:20, minimum length of 35 bp). The obtained sequencing data were mapped to reference genome for human (Homo sapiens GRCh38 v. 104), rat (Rattus norvegicus Rnor_6. 0 v. 104), mouse (Mus musculus GRCm39 v. 104), and zebrafish (Danio rerio GRCz11 v. 104) using Spliced Transcripts Alignment to a Reference (STAR) RNAseq aligner (v. 2. 7. 2a)  (Dobin et al, 2013). The reference genomes were trimmed prior to mapping to only contain gene/transcripts of biotype 'protein coding'. The mapped alignment by STAR was normalized to TPM with RSEM (RNA-Seq by Expectation Maximization v.
1. 3. 3)  (Li & Dewey, 2011). The means of calculated TPM were used for the further analysis of human, mouse, and zebrafish. Especially, the TPMs of zebrafish presented in SuppleTable 2 are averaged counts from the 2 sequencing datasets. Gene information for 'gene symbol' (gene names) and orthologs were gathered from Ensembl biomart  (Cunningham et al, 2022; Kinsella et al, 2011). The density analysis was generated utilizing the seaborn distribution python library  (Hunter, 2007; Waskom, 2021). The Venn diagrams were generated utilizing nVennR package for R  (Perez-Silva et al, 2018). Transporters were collected based on the protein class which was obtained by utilizing the Panther database  (Mi et al, 2021)  with gene symbols. Transporters were ranked according to their TPM expression within each species.
2% DMSO in AFW or 0. 2% DMSO in AFW in a 28°C incubator, with regular light/dark cycles (14 hours light / 10 hours dark). Following treatment, the fish were washed with fresh AFW three times and transferred to the nursery tank until the start of the experiments. MTZ treatment experiments were performed on three different batch of fish on different dates. Data were collected and analyzed from the three experiments.
Quantification of the brain parenchyma and fish size on confocal images To check the size of brain parenchyma in the control and ChP ablated groups, the width of telencephalon and rhombencephalon was measured by ImageJ/Fiji. The tel-/rhombencephalic width was the widest area observed at the tel-/rhombencephalon. The rhombencephalon, parenchyma width was measured where the RV was widest. The values were imported and plotted in MATLAB. The fish size was measured using a ruler before intravenous injection and these datapoints were also imported and plotted to MATLAB.
Acknowledgements We thank the  Fluorescent Reporter Zebrafish Cooperation Center (FRZCC), Republic of Korea , for providing the zebrafish line (FRZCC1104),  Valérie Wittamer  ( Université Libre de Bruxelles, Belgium ) for sharing the 4C4 antibody,  Sebastian Gerety  ( Wellcome Sanger Institute ) for sharing the the pUAS-Self vector, the fish facility support staff in our respective institutes for zebrafish maintenance, and all members of the  Jurisch-Yaksi and Yaksi laboratories  for their feedback on this work. BioRender was used to generate some illustrations. This work was supported by a  FRIPRO research  grant from  The Research Council of Norway  ( NJY 314189 ) to NJY, the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( 2022R1A2C1091336 ) to HCP, and the  Lundbeck Foundation  (to NM;  R276-2018-403 ) the  Fondation ARC pour la Recherche sur le Cancer  ( PJA2018208167  to MF,  A2011   Postdoctoral fellowship  to MP) and the  Human Frontiers Science Program  ( CDA00036/2010  to MF).
Software and algorithms ImageJ/Fiji (Schindelin et  al, 2012) https://imagej. net/Fiji. ht ml#Downloads MALTLAB 2023a MathWorks https://se. mathworks. co m/ Huygen software Scientific Volume Imaging https://svi. nl/Huygens-Software Radius software version 2. 0 EMSIS GmbH https://www. emsis. eu/p roducts/radius CHOPCHOP (Labun et al. , 2019) https://chopchop. cbu. ui.
Proteome analysis 2. 4. 1. Protein extraction, SDS-PAGE analysis and immunoblotting analyses The myofibrillar proteins fraction was extracted according to  Marino et al. (2013). The protein concentration of each muscle extract was determined in duplicate employing the 2-D Quant kit (GE Healthcare) using bovine serum albumin (BSA) as standard. Denatured samples were separated with an 8-18% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gradient gel using a Protean II xi system (Bio-Rad Laboratories, Hercules, CA) in presence of a known molecular weight standard (Precision Plus protein standard-broad range, Bio-Rad Laboratories). Gels were analyzed with the Image Lab software (version 5. 2. 1, Bio-Rad Laboratory) to determine the percentage of the signal intensity of the defined bands in a lane.
1% FA (from 3% to 40% in 19 min), at a flow rate of 250 nL min -1. Protein identification was performed using Proteome Discoverer 1. 4 (Thermo Fisher Scientific) aligned against the Ovis aries database (version Nov 2021, 23,110 entries) with Mascot Search Engine server (version 2. 2. 4, Matrix Science). Cys residue carbamidomethylation was defined as a static change, whereas Met oxidation was defined as a variable modification. The tolerances for the precursor and fragment were established at 10 ppm and 0. 6 Da, respectively. Finally, only proteins identified with at least three distinct peptides with high confidence (False Discovery Rate 0. 01) and a coverage rate ≥20% were considered (high confidence criteria).
Bioinformatics analysis A total of 44 proteoforms corresponding to 26 unique proteins (gene names) were gathered from the 13 different abundant protein spots and analyzed using different bioinformatics. To take benefit of the most comprehensive annotation available, since the Gene Ontology (GO) annotation in ovine databases is limited, the ovine gene Uniprot IDs were converted into the human orthologs EntrezGene ID using the Uniprot Retrieve/ID Mapping as reported by  Gagaoua et al. (2021). The web-based search STRING database (https://string-db. org) was applied to study the Protein-Protein Interactions (PPI) between the 26 unique proteins based on their molecular function. Confidence intervals was set to 0. 400 and false discovery rate (FRD) stringency of 1. 0 to obtain as many significant features as possible while incurring a relatively low proportion of false positives.