dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculations and statistical analysis ADFI was calculated from data collected by the electronic feed dispensers by averaging daily feed intake records for each pig. Average daily feed intake, average daily gain (ADG, g/day) and Feed E ciency (FE, kg of gain per kg of feed) were calculated for each period. Data were analysed using the MIXED procedure of SAS (SAS Inst. , Inc. , Cary, NC, USA) including the xed effects of replicate, breed, sex, period, dietary treatment and their interaction. For BW and ADG, BW at weaning was also included as a xed effect to take into account environmental maternal effect. In all statistical analyses using the MIXED procedure of SAS, the repeated measurements option was used to account for animal effect over time with an unstructured covariance structure, except for thermoregulatory variables for which a compound symmetry covariance structure was used, because of no convergence with unstructured covariance structure.
Acknowledgements The authors gratefully thank all the members of staff and students who contributed to the project. The authors would like to thank  K. Benony ,  B. Bocage ,  M. Bructer ,  M. Giorgi  et  F. Silou  from the experimental unit  INRAE-PTEA  and  J. Hira  for RNA extraction. We are grateful to the  Genotoul bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , https://doi. org/10. 15454/1. 5572369328961167E12 )for providing help computing and storing the resources.
6B  ). Cohesin binding was assessed by calibrated ChIP-qPCR against the Psm3 SMC3 subunit of cohesin tagged with GFP (Psm3-GFP). We observed no strong change in cohesin occupancy between G2 and anaphase in wild-type cells, consistent with the idea that solely 5-10% of the cohesin pool is cleaved by separase at the metaphase to anaphase transition in fission yeast  (Tomonaga et al, 2000 ). In cut3-477 mutant cells, however, we observed a strong increase in Psm3-GFP levels at telomeres (tel0 site) and sub-telomeres (tel1. 2 site), but no change at further distal sites, nor within chromosome arms or at centromeres (Fig. 6B  and S5 ). This specific increase in occupancy at telomeres and sub-telomeres in the condensin mutant was readily visible both during G2 and anaphase. Altogether, our data indicate that cohesin restrain telomere disjunction in anaphase and that condensin prevents the accumulation of cohesin at telomeres.
Cell imaging and fast microfluidic temperature control experiments Live cell analysis was performed in an imaging chamber (CoverWell PCI-2. 5, Grace Bio-Labs, Bend, OR) filled with 1 ml of 1% agarose in minimal medium and sealed with a 22 × 22-mm glass coverslip. Time-lapse images of Z stacks (maximum five stacks of 0. 5 μm steps, to avoid photobleaching) were taken at 30 or 60 sec intervals. Images were acquired with a CCD Retiga R6 camera (QImaging) fitted to a DM6B upright microscope with a x100 1. 44NA objective, using MetaMorph as a software. Intensity adjustments were made using the MetaMorph, Image J, and Adobe Photoshop packages (Adobe Systems France, Paris, France). Fast microfluidic temperature control experiments were performed with a CherryTemp from Cherry Biotech. To determine the percentage of chromatin bridges with unseparated telomeres, cells were fixed in 3.
Antibodies used in this study lyon. fr/LBMC/Bernard/bamcalib v0. 1. 5) takes into inputs a sorted bam file for the IP data and a sorted bam file for the TOTAL data (mapped on the concatenation of the two genomes), and output a calibrated bigwig for the reference genome. For the normalization, we modified a previously described method  (Hu et al, 2015 )  in order to account for biases in coverages in the TOTAL fractions. We introduce the following notation: IP x (t) is the coverage at position t in the IP sample on the reference genome, IP c (t) is the coverage at position t in the IP sample on the calibration genome, WCE x (t) is the coverage at position t in the TOTAL (whole cell extract) sample on the reference genome, and WCE c (t) the coverage at position t in the TOTAL sample on the calibration genome.
Reviewer #2 (Recommendations For The Authors): Further analysis of the telomere segregation foci data could provide additional support for the claim that condensin promotes the uncoupling of telomeres (vs telomere disjunction), in addition to the hiC data presented in Fig 3. The observation that many data points in Figure  2  have less than six foci ( often 2-4) suggests that this data not only shows a defect in disjunction but also in telomere uncoupling. If somehow the two defects could be unpicked in the dataset that would be beneficial to their argument. We agree with the reviewer that our data show not only a defect in disjunction but also in telomere uncoupling (confirmed with HiC). We now provide new microscopy data showing the role of condensin in telomere disjunction (as opposed to uncoupling) by simultaneously visualizing the behavior of the centromere 3 (imr3-tdTomato), nucleolus (Gar1-CFP) , and telomere 1L (Tel1-GFP) during mitotic progression (Figure  S2B ).
Acknowledgements We thank the  National BioResource-Yeast Project ,  J. Cooper ,  JP. Javerzat ,  M. Yanagida  and  J. Kanoh  for strains;  J. Baxter  and  N. Minchell  for teaching the rudiments of Hi-C to  L. C. ,  J. P. Javerzat ,  F. Beckouet ,  V. Vanoosthuyse  and  A. Piazza  for helpful discussions.
Funding L. C. and J. B. are supported by PhD studentships from respectively  la Ligue contre le cancer , and a  University MRT and la Fondation pour la Recherche Médicale. This work was funded by the  CNRS ,  Inserm  (O. C. and S. S. ),  ANR-blanc120601 ,  ANR-16-CE12-0015-TeloMito ,  la Fondation ARC  ( PJA 20191209370  to PB;  20161204921  to ST) and  la ligue régionale contre le cancer -comité Auvergne-Rhône-Alpes et Saône-et-Loire  to PB.
and the nucleotides 4137 to 7223 of pNSU70 were added to the sequence of chromosome 2 at position 4539800 to create a TEL2R-extended version of the genome, which is available under the accession number GSE196149. (B) Principle of the base per base normalisation method applied to ChIPs to assess the occupancy of Cnd2-GFP from S. pombe (S. p. ) at repeated DNA elements using chromatin from S. cerevisiae (S. c. ) cells expressing SMC3-GFP for internal calibration. (C) Results of the base per base normalization method applied to calibrated ChIP-seq data to correct for biases in coverage in the IP and Total (T) fractions. Calibrated read counts obtained in the IP and Total (T) fractions, and their base per base ratios (IP/T) obtained at centromere 2 (cen 2), composed of a central core (cc) flanked by repetitive heterochromatic outer repeats (otr) and at rDNA repeats are shown.
Biomolecules 2023, 13, 728 2 of  18  To determine the correlation between imaging genetics data, the majority of early imaging genetics relied on linear models, such as the Sparse Canonical Correlation Analysis (SCCA) algorithm  [7]. SCCA is a standard high-dimensional data analysis method based on CCA  [8]. By adjusting the weights of different properties of various modal data, it can maximize the correlation of two different modal data. Based on SCCA, Fang et al. incorporated the concept of a joint sparse model and fusion graph lasso. They presented a Joint Sparse Canonical Correlation Analysis (JSCCA) technique to increase the accuracy of the algorithm for the joint analysis of high-dimensional imaging data  [9]. Furthermore, Kim et al. extended JSCCA to include connectivity-based penalties, validating the robustness of the algorithm to select potential biomarkers associated with Parkinson's disease on both simulated and real imaging genetics datasets  [10].
Data Source and Preprocessing The gene expression data and longitudinal brain imaging data of 210 non-Hispanic white participants were downloaded from the Alzheimer's Disease Neuroimaging Disease (ADNI) website (https://adni. loni. usc. edu/ (accessed on 15 March 2022)), including 10 AD, 102 Early Mild Cognitive Impairment (EMCI), 36 Late Mild Cognitive Impairment (LMCI), and 62 Healthy Control (HC) subjects. Specifically, all the data we used in this experiment came from the data of the ADNI1 queue. Because the longitudinal image genetics research in this paper needs to collect sMRI and gene expression data of the same subject at multiple time points, the interval between time points needs to be roughly consistent. Therefore, only 10 AD samples meet our screening requirements. The demographics of the participants are listed in Table  1.
From the ADNI database, we retrieved the sMRI data for the samples with the time distributions of baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4). After selecting, we eliminated brain imaging samples presenting illegible structures and samples having distorted images, leaving us with 210 samples in total. The four brain images of each sample then underwent head motion correction segmentation, registration, and feature extraction using the SPM12 program in MATLAB. After extracting 122 ROIs using the Anatomical Automatic Labeling (AAL) template, 32 cerebellar structures were eliminated and 90 ROIs were kept as the final characteristics of the brain image.
Deep Subspace Reconstruction Algorithm Because of the large amount of noise in biological data, the estimation bias will be introduced when the biological data are constrained by a network or graph. Therefore, considering the multi-subspace structure of the bottom data layer, samples with the same label are gathered in the same subspace, and different samples are distributed in different subspaces. The objective function of the deep subspace reconstruction algorithm  [15, 16]  is defined as: min W x W x p s. t. X = W x X, (diag(W x ) = 0) (1) where W x is the self-expression coefficient matrix, and X is the feature matrix of the original data. Data label information is a crucial piece of prior knowledge, and it is possible to find greater correlation between imaging and genetic data by putting tags into the algorithm.
Statistical Analysis Subjects' age statistics were presented in the form of mean ± standard deviation (SD) and statistically analyzed using the t-test of SPSS 20. 0 software. Figure  1  was drawn by using online software called ProcessOn (https://www. processon. com (accessed on 30 November 2022)). Figure  2  was created by using Microsoft Excel. Matlab 2018a was used to draw Figure  3. Figure  4  was displayed by using BrainNet Viewer of Matlab. Figure  5  was displayed by Matlab 2018a. Figures  6  and 7  were obtained by using R package "ggplot2". Adobe illustrator was applied to splice multiple pictures. models. The formula for calculating R2_score is: 𝑅 = 1 - 𝑆𝑆𝐸 𝑆𝑆𝑇 It can be simplified to: 𝑅 = 1 - ∑ (𝑦 -𝑦 ) 𝑛 ⁄ ∑ (𝑦 -𝑦 ) 𝑛 ⁄ = 1 - 𝑀𝑆𝐸 𝑉𝑎𝑟 where MSE stands for mean squared error and Var stands for variance.
Statistical Analysis Subjects' age statistics were presented in the form of mean ± standard de (SD) and statistically analyzed using the t-test of SPSS 20. 0 software. Figure  1  was by using online software called ProcessOn (https://www. processon. com (accessed November 2022)). Figure  2  was created by using Microsoft Excel. Matlab 201 used to draw Figure  3. Figure  4  was displayed by using BrainNet Viewer of Matla ure 5 was displayed by Matlab 2018a. Figures  6  and 7  were obtained by using R p "ggplot2". Adobe illustrator was applied to splice multiple pictures.
On Simulation Data To evaluate the performance of the proposed DS-HBTGSCCA, we simulated neuroimaging datasets T (i = 1,2,3,4) and genetic dataset X with n samples, where T has p features, and X includes q features. We set n = 200, p = 90, and q = 450. Assuming that the two data are correlated, α represents the sMRI feature weight vector with p elements, and β represents the gene feature weight vector with q elements, where each element independently obeys a uniform distribution U(-1, -0. 5) ∪ U(0. 5,1). The correlation between sMRI and genes can be simulated by using a latent variable ε with a normal distribution N(0, σ ). For sMRI data, T = βϵ + e represents correlated voxels, T = e represents uncorrelated voxels and e represents noise with the normal distribution N(0, σ ). T = T + ∆v (∆v ~ N (0, 0.
On Simulation Data To evaluate the performance of the proposed DS-HBTGSCCA, we simulated neuroimaging datasets T i (i = 1, 2, 3, 4) and genetic dataset X with n samples, where T i has p features, and X includes q features. We set n = 200, p = 90, and q = 450. Assuming that the two data are correlated, α represents the sMRI feature weight vector with p elements, and β represents the gene feature weight vector with q elements, where each element independently obeys a uniform distribution U(-1, -0. 5) ∪ U(0. 5, 1). The correlation between sMRI and genes can be simulated by using a latent variable ε with a normal distribution N 0, σ 2. For sMRI data, T i = β + e represents correlated voxels, T i = e represents uncorrelated voxels and e represents noise with the normal distribution N 0, σ 2 e. T i+1 = T i + ∆v (∆v ∼ N (0, 0.
VEGF signaling stimulates YAP/TAZ through cytoskeletal remodeling and inactivation of the Hippo effectors LATS1/ 2  25, 53. While these studies have focused on the analysis of YAP/TAZ activating pathways, our data identify a major YAP/TAZ inactivating mechanism involving FAT1/MIB2-mediated YAP/TAZ protein degradation. Whether FAT1/MIB2-mediated YAP/TAZ regulation is itself controlled is currently not clear. However, our analysis of FAT1 levels during endothelial cell culture suggests that FAT1 protein levels can in turn be regulated, resulting in changes in YAP/TAZ protein turnover and activity. Endothelium-specific loss of YAP/TAZ has been shown to result in impaired tumor and developmental angiogenesis  [24] [25] [26] 29, 51  , whereas endothelium-specific overexpression of YAP or TAZ increased angiogenesis  28, 29, 54.
Our data identify a novel mechanism of YAP/TAZ activity regulation in endothelial cells, in which the protocadherin FAT1 controls YAP/TAZ protein degradation by interacting with the E3 ligase MIB2. FAT1 thereby promotes YAP/TAZ degradation and negatively controls endothelial proliferation, a mechanism which could serve as a target for therapies aiming at inhibiting angiogenesis.
Scale bar (g): 50 µm. i LLC1 tumorbearing mice received EdU through their drinking water for 1 week before the end of the experiment and EdU-positive endothelial cells (Edu + /CD31 + ; CD45 -) were analyzed by flow cytometry (n = 8 mice per group). j Mice subcutaneously injected with B16 tumor cells were sacrificed after 11 days, and the percentage of CD31positive cells in tumors was analyzed by flow cytometry (n = 7 mice (WT); n = 8 mice (EC-Fat1-KO)). Data are presented as mean ± SEM. n. s. non-significant. Comparisons were performed using two-tailed unpaired t-test (b, c, f, h-j) or two-way ANOVA and Bonferroni's post hoc test (e). cultured at 37 °C and 5% CO 2 and were tested negative for mycoplasma contamination before experiments.
Final desalting, concentration and storage utilized "stop and go extraction" (STAGE) tips  58. Eluted peptides where subjected to electrospray ionization (ESI)mediated liquid chromatography/tandem mass spectrometry (LC/ MS2) using in house-packed column emitters (15 cm length, 70 µm ID, 1. 9 µm ReprsoSil-Pur 120 C18-AQ, Dr. Maisch) and a buffer system comprising solvent A (0. 1% formic acid) and solvent B (80% acetonitrile, 0. 1% formic acid). Instrumentation details and parameters were extracted and summarized using MARMoSET  59  and are repository deposited (see below). Peptide/spectrum matching and label free quantitation were performed using the MaxQuant suite of algorithms  [60] [61] [62]  against the human UniProt database (canonical & isoforms; downloaded on 2019/01/23). Parameters are included in the repository deposition (ProteomeXchange Consortium (http:// proteomecentral.
Acknowledgements The authors thank  Svea Hümmer  for secretarial help and  Martina Winkels ,  Ulrike Krüger  and  Claudia Ullmann  for technical support. We are grateful to  Pascal Meier  ( The Institute of Cancer Research, London, UK ) for providing different mutants of Mib2. This work was supported by the  Max Planck Society  and by the  Collaborative Research Center 1531 of the German Research Foundation  (S. O. ).
2017)  as biogeography classically represents species in space  (Von Humboldt & Bonpland 1805 , Lomolino et al. 2017). Empirical evidence supports plasticity and stochasticity of interactions and would encourage sampling of trophic interactions through space and time  (Poisot et al. 2015 , CaraDonna et al. 2017 , de Aguiar et al. 2019). However, sampling interactions in multiple sites is challenging since it requires joint observations of species. It is especially problematic when it involves organisms from different kingdoms and various body sizes  (Jordano 2016). Sampling taxa is far easier than sampling interactions, using naturalist knowledge  (Moser et al. 2005) , camera traps  (Steenweg et al. 2017)  or environmental DNA  (Bohmann et al. 2014). A convenient case to study networks in space is then to build a potential network at the regional scale, the metaweb, using expert knowledge or machine learning methods to complete interaction databases  (Strydom et al.
2021). Once the metaweb is built, local networks are deduced using sampled abundances. Such an approach have been used for various organisms, from terrestrial vertebrates  (Galiana et al. 2014 , Braga et al. 2019)  to marine or freshwater communities  (Kéfi et al. 2015 , Kortsch et al. 2019 , Blackman et al. 2022)  or soil communities  (Bauer et al. 2022). While losing interaction plasticity and stochasticity, local networks nevertheless have distinct structures due to sampling effect. Hereafter, a collection of networks in space or time is called a metanetwork, as a collection of communities is called metacommunity. For simplicity, we refer to the potential interaction network as the 'metaweb'. While trophic network databases are becoming increasingly available  (Poelen et al. 2014) , tools to handle and represent them remain scarce.
The present paper describes and implements a new layout algorithm built for trophic networks, using trophic levels and a diffusion based algorithm. This contribution also describes several additional methods to handle, represent and analyse trophic metanetworks at different resolutions as suggested in the literature  (Thompson & Townsend 2000 , Guimarães Jr 2020). All the described methods are implemented in the R package, metanetwork, that eases manipulation and representation of trophic metanetworks. metanetwork is available on CRAN while several vignettes on several open data sets are accessible online at https://marcohlmann. github. io/metanetwork/. We first describe inputs and methods to build and handle metanetwork objects. We then focus on the proposed 'TL-tse' and 'group-TL-tsne' layout algorithms and the visualisation methods wrapped in metanetwork.
Inputs of the 'metanetwork' object To build a potential metanetwork (hereafter metanetwork), we need a metaweb, G ⋆ , that is a directed and connected network including focal species and known potential trophic interactions in the study region. We can also include a community matrix P, indicating species relative abundances, and a trophic table T , indicating species belonging to broader taxonomic or functional groups. Local networks are then induced subnetworks of G ⋆ by local communities (with abundances). Our package encodes a metanetwork through a R S3 object of class 'metanetwork'. The function build_metanet builds a 'metanetwork' object from the triplet (G ⋆ , P, T ) and computes local networks. The metaweb G ⋆ must be of class 'igraph', 'matrix' or 'data. frame'. The matrix P and the table T can be NULL contrary to G ⋆.
Append aggregated networks In order to investigate trophic networks at different aggregation levels (e. g. broader taxonomic groups, functional groups or output of node clustering algorithms) as suggested in  (Thompson & Townsend 2000 , Ohlmann et al. 2019 , Guimarães Jr 2020) , our package can compute aggregated networks using the trophic table T that describes aggregation levels (Fig. 1 ). The nodes of the aggregated network are broader taxonomic groups given by the trophic table whereas edges are aggregated based on the structure of the original network. Considering aggregated networks is particularly welcome since nodes and edges composition can vary at the original resolution but be   What are the differences between the local networks? Table  1 : Main functions of the metanetwork package and the corresponding addressed ecological questions.
Visualisation Besides proposing a new layout method, metanetwork package allows incorporating these layouts in the two recent R packages dedicated to network visualisation: 'ggnet' and 'visNetwork'. The 'ggnet' package represents networks as 'ggplot' objects  (Wickham & Wickham 2007 , Schloerke et al. 2018). Our function ggmetanet provides a static representation of the network using 'TLtsne' layout combined with 'ggnet' visualisation and additional features (legend, node abundances and edge weights). The 'visNetwork' package represents the network in an interactive way using vis. js javascript library  (Almende et al. 2019). Our function vismetaNetwork provides 'TL-tsne' layout and wraps 'visNetwork' dynamic visualisation with additional features (javascript events linked to the nodes, legend, node abundances and edge weights).
Representing the difference between networks In order to ease local network comparisons, metanetwork implements a function diff_plot that highlights differences and similarities between two network. More precisely, let G 1 and G 2 be two local networks (with vertex sets V 1 and V 2 ), we note G diff the difference network between G 1 and G 2 , whose vertex set is V diff = V 1 ∪ V 2. It is the induced subgraph of the metaweb, G ⋆ , by V diff. We assign then node abundances and edge weights to G diff. Node abundance of the difference network consists in the difference between node abundances of G 1 and G 2 , as edge weights. We use a color code to distinguish nodes that are present in both networks with different abundances from nodes that are absent in one of the networks. A color code in the visualisations indicates the sign of the node abundance difference and the edge weight difference between networks (see Fig.
Computing network metrics, diversities and dissimilarities In order quantitatively assess network structure and compare local networks at the different resolutions, our package implements functions to compute network metrics (compute_metrics), network diversity (compute_div) and pairwise dissimilarity (compute_dis) indices (see Fig. 1 ). compute_metrics computes mean and max trophic levels of the metaweb and local networks (using the output of compute_TL(). This function also computes mean shortest path length (using 'igraph' mean_distance() function). The package also implements the function compute_div that computes network diversity indices based on Hill numbers developed in  Ohlmann et al. 2019. This indices can be computed both on nodes (relying then on node abundances) and edges (relying then on edge weight and node abundances), at the different network resolutions.
Angola coastal network We first look at a dataset from Angola, which has been extracted from Web of Life (http: //www. web-of-life. es). It consists in a coastal trophic metaweb of 28 nodes (species or groups) and 127 interactions sampled along Angola's coastline  (Angelini & Vaz-Velho 2011). The study aimed at estimating impact of Angola's fishery on the coastal trophic network by quantifying biomass using times series from multiple sources (see Angelini & Vaz-Velho 2011 for more details). Available abundance data consists of two time steps: 1986 and 2003. Interactions are weighted according to the relative frequency of prey species in the diet of each predator species. We represented the metaweb using ggmetanet with 'TL-tsne' layout (β = 0. 02) in Fig. 3. The metaweb has two basal nodes, 'Phytoplankton' and 'Detritus', leading to a primary producer and detritus channel that mix up higher in the network.