dataesr/scientific-paragraphs-categorization huggingface dataset:
We focus here on a question that asked respondents to identify whom they considered close friends ('pengyou') with whom one would hang out or 'chat' ('liaotian') after dinner, a common activity among friends. Our goal was to obtain responses to interview questions for one adult man and one adult woman in each household. In cases where the opposite-gender respondent was not available at the time of interview, the main respondent answered in their stead. We did not note the names of any additional people present during the interview. Because data collection included a complete household census, we were able to infer the identity of the opposite gender respondent in most cases (with certainty if they were the only adult member of opposite gender residing in the household and with high confidence if the friendship network consisted mainly of similar-age peers).
In some cases, additional cues were available, such as the respondent indicating that friends were 'my son's'. For cases where multiple opposite-gender individuals of similar age resided in the same household, we did not assign an identity for the second household respondent. A census of households allowed us to identify and verify individual-level attributes for egos, inferred egos, and alters who were present in the census; we also collected some individual-level data on egos and alters at the time of social network interview. Data Analysis: The populations of interest in this study were all adults in two geographically distinct communities: 312 adults in the matrilineal area and 219 in the patrilineal area. In order to estimate the patterns of friendship ties in each location, we constructed social networks as follows.
Acknowledgments: Thanks first and foremost to study participants who generously gave their time and insights to help us with our work. Three reviewers, the editors, and  Jeremy Koster  provided valuable criticisms of the work. The work was funded by the  National Science Foundation  ( BCS 1461514 & 1461520 ). This material is based upon work supported by (while serving at) the  National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation.
Considering the approximate ventricular wall thickness at ED, the synthetic images have between ca. 2 (for 7 mm tagging) and 5 (for 3 mm tagging) tag lines within the thickness of the myocardium. Three stacks are generated, with i = 1, 2, 3 for each tagging direction. Thereafter, images are resampled to 1 mm pixel size using zero-filling after cropping in k-space and convolution in time (Fig  1b-2 ) using a convolution window size of 40 ms  [5]. Then, the images are resampled in time to yield 100 time steps throughout the cardiac cycle. These three series of images are then combined using: I 0 X ð Þ ¼ ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi In addition to the reference images, datasets with varying image properties are generated (Fig  1c ).
First, different levels of Gaussian noise with zero mean are added to generate sets of images with varying SNR, which is defined as the ratio of the magnitude of the signal to standard deviation of the noise: sin pX 1 s � � � � � � � � � � � sin pX 2 s � � � � � � � � � � � sin pX 3 s � � � � � � � � � � SNR ¼ Max signal Std noise : ð3Þ Then, image spatial resolution is changed by filtering the reference dataset in k-space using a box filter with different bandwidth (.
FE-based image registration is then performed on the synthetic datasets to investigate the effect of image properties on deformation quantification (Fig 1d). A continuum formulation of the image registration problem is introduced and later discretized using the FE method. Considering the reference and current configurations of the object represented by the images, O 0 and O t , related image intensity fields are denoted by I 0 and I t. A deformation map F(X) is defined between these two configurations to map the reference points X 2 O 0 onto their spatial counterparts x = F(X) = X + U(X) 2 O t , where U is the displacement field. In the presence of image noise, this problem is ill-posed and requires to be formulated as a minimization problem. Hence, the problem of finding the displacement field can be formulated as find U ¼ argmin fUg ¼ fhðUÞ ¼ ð1 À bÞC im ðUÞ þ bC reg ðUÞg; ð4Þ which aims to minimize the functional h expressed in terms of regularization strength β.
It is then linearized, and discretized using standard continuous Lagrange elements. More details on the formulation and solution procedure can be found in  [26]. The method implementation is freely available as a python library (https://gitlab. inria. fr/ mgenet/dolfin_warp) implemented based on FEniCS  [42]  and VTK (http://www. vtk. org) libraries. The impact of the regularization parameter and the mechanical model utilized are further discussed in  [26, 43]. In this study, for each image set, we run the registration algorithm for a range of regularization strength β and use the best performing value (i. e. estimated motion closest to ground truth) in the analysis. The reader is referred to  [26]  for a more detailed discussion on the choice of β in case the ground truth is unknown. By designing this "optimal" registration technique, we focus the study on the image content itself, not the image processing approach.
In this study, we investigate both the individual and combined effects of two image characteristics, Tag distance to Pixel size Ratio (TPR) and SNR on images with isotropic ( The second part is focused on the analysis of anisotropic image resolutions. Similarly, individual effects of resolution and SNR are analysed first. For this purpose, we generated noiseless images with 7 mm tag distance and pixel size ranging from 1. 5 mm to 3. 5 mm in the tagging direction, while the transverse directions are assigned to a larger pixel size ranging from 3. 5 mm to 7 mm. For the SNR analysis, three datasets are chosen to represent different levels of pixel size (2. 0x4. 0x4. 0 mm 3, 3. 0x6. 0x6. 0 mm 3, and 3. 5x7. 0x7. 0 mm 3) with varying noise levels in the same interval as used for the isotropic case.
French National Research Agency   Research Grant Nr. ANR-10-EQPX-37  received by  Martin Genet. •  Swiss National Science Foundation (SNF)   Research Grants Nr. CR23I3_166485  received by  Sebastian Kozerke. Ezgi Berberoğlu 's salary was paid by this grant. •  Swiss National Science Foundation (SNF)   Research Grants Nr. PZ00P2_174144  received by  Christian T. Stoeck. His salary was also paid by the mentioned grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Although this approach does not imply an HCC monophyletic evolution from healthy hepatocytes to P2 HCC cells, it allowed to infer unbiased cell differentiation. The analysis revealed a lineage tree consisting of a single differentiation branch connecting clusters 1 (healthy hepatocytes), 2  and 3 (HCC P1),  and 4 (HCC P2),  and then bifurcating into HCC P2 clusters  5  and 6 (Fig 2C). On the inter-tumor level, our lineage analysis confirms unbiasedly the histological grading of both HCCs on the single-cell level (Fig 2A  -C ). Clusters 7 and 8 were not included in the lineage tree, lacking strong links with any other cluster. We then evaluated pseudotemporal expression profiles along the differentiation branch to identify differentially expressed between the different clusters. Selforganizing maps (SOM) of the pseudo-temporal expression profiles were calculated, grouping genes of similar expression profiles into joint modules  (Fig 2E).
Expression t-SNE maps clearly show that, despite comparable viral loads in both patients' serum, higher levels of HBV reads were detected in the HCC P1 compared to the HCC P2, and higher levels of HBV reads were observed in the most differentiated cluster (cluster 2) within the HCC P1 (Fig 2B  -D ). HBV read levels along the differentiation trajectory confirm a strong positive association between cell differentiation and HBV reads (module 9 in Fig  2E  and F ). Confirming this hypothesis, gene set enrichment analysis (GSEA) from module 9 using the GSEA online "Investigate Gene Sets" tool revealed significant enrichments of 16 genes (False discovery rate [FDR] = 1. 43 × 10 -12 ) associated with liver specific genes  (Hsiao et al, 2001) , and of 13 (FDR = 3. 28 × 10 -9 ) genes associated with a more differentiated tumor phenotype according the Hoshida "subtype S3" signature  (Hoshida et al, 2009)  (see Table  S3 ).
22 × 10 -6 ) associated with liver specific genes  (Hsiao et al, 2001) , and of 18 genes (FDR = 1. 54 × 10 -7 ) associated with a more differentiated liver cancer phenotype according the Hoshida "subtype S3" signature  (Hoshida et al, 2009)  (see Table  S6 ). Both sets include Glucose transporter 2-encoding SLC2A2, highly expressed in hepatocytes and favorable prognostic marker in HCC (Uhl én et al, 2015;  Uhlen et al, 2017) , confirming intratumorally the association between HBV RNA levels and cell differentiation as we previously described at inter-tumor level  (Fig 2). Analysis of HBV reads in single cell expression along the differentiation trajectory confirmed the hypothesis that HBV reads are higher in more differentiated cells also  To identify functionally relevant virus-host interactions for hepatocarcinogenesis, we analyzed the correlation between HBV reads and expression of oncogenes and TSGs from Cancer Gene Census database (https://cancer.
One limitation of our study is the small number of patient samples. However, the reproducibility of key findings within inversely correlated with tumor suppressor genes RB1, TET1, and RFWD3 at single-cell level; P-values for Spearman correlations. (F) Pathway analysis comparing enriched pathways in HBV-stimulated PHHs (GEO ID GSE69590), and HCC P1 single cells using, as gene set, genes significantly correlating with HBV RNA level. (G) Top three HBV integrations sites per single cell showing clonal origin of HCC P1. and among the tumors and by the two complementary RNA-Seq protocols SMART-seq2 (providing full mRNA coverage for integration site detection) and mCEL-seq2 (providing deep coverage also of lowly expressed host factors) suggest that our findings are relevant for viral hepatocarcinogenesis in general.
Dataset were filtered, analyzed, and normalized using RaceID3  (Aizarani et al, 2019). As quality control, we removed all the cells with more than 80% of mitochondrial genes. Transcripts correlating to KCNQ1OT1 with a Pearson's correlation coefficient of >0. 4 were removed. RaceID3 was run with the following parameters: mintotal = 1,500, minexpr = 2, minnumber = 5, knn = 10. This step filtered out the cells with <1,500 transcripts found at least in number of two in a minimum of five cells. A total of 486 cells for HCC P1 and 452 from HCC P2 were then analyzed. For normalization, the total transcript counts in each cell were normalized to 1 and multiplied by the minimum total transcript count across all cells that passed the quality control threshold. Integration of 200 random hepatocytes from the human cell atlas  (Aizarani et al, 2019)  were performed using RaceID3 with the same parameters as mentioned before, applying RaceID batch effect and filtering out HBV-RNA from minimum transcript count and clustering.
Reads were aligned to the human hG19 UCSC reference as well as the HBV (strain ayw) reference genome (GenBank: V01460. 1, included as an additional chromosome) using hisat2 and suppressing discordant alignments for paired reads. Reads were counted using htseq-count  (Anders et al, 2015). Following steps were performed in R using RaceID3  (Masuda et al, 2019; Grün, 2020; R-Core-Team, 2020). Cells expressing >2% of KCNQ1OT1 transcripts, a marker of low-quality cells  (Grün et al, 2016) , were removed. Transcripts correlating to KCNQ1OT1 with a Pearson's correlation coefficient of >0. 4 were also removed. RaceID3 was run with the following parameters: mintotal = 1,000, minexpr = 2, minnumber = 5, knn = 10. This step filtered out the cells with <1,000 transcripts found at least in number of two in a minimum of five cells.
Single-cell clustering and lineage tree analysis Single cell clustering, marker gene analysis, lineage tree calculations, and the calculation of SOMs of modified CEL-seq2 and Smart-seq2 data were performed using the RaceID/StemID pipeline as described before  (Aizarani et al, 2019; Grün, 2020)  using unfiltered normalized dataset and with the following parameters: corthr = 0. 75, minsom = 8 for mCEL-Seq2 data and corthr = 0. 85, minsom = 8 for Smart-seq2 data.
GSEA of single cells The filtered and normalized read count matrix served as input for performing single sample gene set enrichments for each cell applying the R package gene set variation analysis (Hänzelmann et al, 2013; R-Core-Team, 2020). Enrichments were calculated for MSigDB v7. 0  (Subramanian et al, 2005)  pathways applying the "ssgsea" method with options "min. sz = 5," "max. sz = 12,000," and "ssgsea. norm = T. " Enrichment scores were plotted using the ggplot2 package in R  (Wickham, 2016b).
GSEA of clusters in association with HBV loads Correlations between individual gene expressions and HBV loads for all annotated human genes were determined through the Spearman's rank correlation significance (P-value), and genes were then ranked according to the Spearman's P-value. Enrichment analysis for MSigDB v7. 0 pathways was assessed using the pre-ranked GSEA  (Subramanian et al, 2005)  according to the obtained ranking. FDR below 0. 05 was considered as statistically significant. Another experimental dataset (GSE69590) was used in this study, where gene expression of HBV-stimulated PHHs was compared to naïve PHHs. Pathway analysis was also assessed through the pre-ranked GSEA based on P-values of DESeq2 differential expression analysis. The normalized enrichment scores of pathways significantly differentially expressed between HBV-stimulated PHHs and naïve PHHs were then compared to the normalized enrichment score of pathways significantly modulated in association with HBV load in single cells.
Survival analysis Prognostic genes information for liver cancer was imported from The Human Protein Atlas  (Uhl én et al, 2015). The survival data for specific gene was retrieved from the TCGA website  (Cancer Genome Atlas Research Network et al, 2013) , and survival curves were calculated and drawn using corresponding R packages (survminer, survival, and ggplot2)  (Therneau & Grambsch, 2000; Wickham, 2016a Wickham, , 2016b)). N Pochet: software and methodology. C Schuster: validation, visualization, and methodology. ER Verrier: conceptualization, supervision, funding acquisition, investigation, methodology, project administration, and writingoriginal draft, review, and editing. TF Baumert: conceptualization, supervision, funding acquisition, investigation, methodology, project administration, and writingoriginal draft, review, and editing.
Acknowledgements We thank  Mauro Muraro  ( Single Cell Discoveries, Utrecht, Netherlands ) for mCEL-seq2 sequencing. This work was supported by  Inserm , the  University of Strasbourg , the  European Union  ( ERC-2014-AdG-671231-HEPCIR and Horizon 2020 research and innovation programme  under grant agreement  667273-HEPCAR ), the  Agence Nationale de Recherches sur le Sida et les Hépatites Virales  ( ANRS ECTZ104527 ), the  French Cancer Agency  ( TheraHCC 2. 0 IHU201901299 ), and the  US National Institutes of Health/National Institute of Allergy and Infectious Diseases/ National Cancer Institute (NIH/NIAID   U19 AI123862-01 ,  NIH/NIAID   R03 AI131066 ,  NIH/NCI   R21 CA209940 ,  R01CA233794 ). This work has been published under the framework of the LabEx  ANR-10-LAB-28  and  INSERM-Plan Cancer  "single cell" 2018 (HCCMICTAR) and benefits from a funding from the state managed by the  French National Research Agency  as part of the Investments for the  Future (Investissements d'Avenir) program.
Across the different datasets, the average correlation between the data and predicted covariances was  = 0. 28 ± 0. 03 for the linear coupling model and reached 0. 40 ± 0. 02 for the nonlinear coupling model (see also Fig. S2 ). Furthermore, scan-classification based on parameters   yielded 86% and 45% correct classifications between awake and anesthetized conditions and among the six experimental conditions, respectively (Fig. S3A, B ). Using parameters   the classifier performance decreased to 75% and 28%, respectively (Fig. S3B ). Thus, the learned linear coupling-MEM showed consistent variations in parameters   (associated to   ) across the different arousal states.
Statistical analysis We used one-way ANOVA followed by Tukey's post hoc analysis to compare the means of three or more distributions and Wilcoxon rank sum test to compare the medians of two distributions. We measured the dissimilarity between two distributions (i. e. , data vs. model distribution) through the Jensen-Shannon divergence. Correlation matrices were analyzed using standard PCA. Statistical models (i. e. , maximum entropy models) were estimated using likelihood and pseudo-likelihood maximization. MNI space  (51) , and band-pass filtered in the frequency range of interest (0. 0025-0. 05 Hz). Timeseries were obtained for  = 82 previously defined cortical regions of interest (ROIs) (CoCoMac Regional Map parcellation). Scans that presented signs of artifacts in time-series or power spectral density were discarded.
Anatomical connectivity We used a fully weighted whole-cortex macaque structural connectivity matrix (connectome) derived by combining the information from fiber-tracing and tractography  (52). The connectome is publicly available here: https://zenodo. org/record/1471588#. X44C6dAzY2x. Briefly, the tractography algorithm was optimized to best reproduce the weighted but partial-cortex tracer connectome from Markov et al. (53) , before estimating whole-cortex connectome weights. The directed connectome weights between ROIs of the CoCoMac parcellation were given as the number of streamlines detected between them, divided by the total number of streamlines that were sent from the seed. Tractographyderived structural connectivity matrices were averaged across nine macaque monkeys. For details see Shen et al.
This method is feasible because, for coupling-MEM, the partition function can be analytically obtained from the model parameters and thus providing  and ; see details in  (29). The algorithm was stopped when the estimation errors for all constraint observables became lower than 10 -6. We found that this algorithm correctly converged for the sparse binarization. The code for learning the coupling-MEMs is available at: https://github. com/ChrisGll/MaxEnt_Model_Population_Coupling. The pairwise-MEM was learned using pseudo-likelihood maximization  (32). This method approximates the likelihood function: 𝐿(𝛀) ≈ ∏ ∏ 𝑃 ̃(𝜎 𝑖 |𝛀, 𝜎 ≠𝑖 ), 𝑁 𝑖=1 𝑛 𝑡=1 (S11) where  is the number of time points of the data and  ̃(  |,  ≠ ) is the conditional Boltzmann distribution for a unit. This was done by updating the biases and couplings as: ℎ 𝑖 (𝑘+1) = ℎ 𝑖 (𝑘) + 𝑎[〈𝜎 𝑖 〉 data -〈𝜎 𝑖 〉 mf ], (S12) 𝐽 𝑖𝑗 (𝑘+1) = 𝐽 𝑖𝑗 (𝑘) + 𝑎[〈𝜎 𝑖 𝜎 𝑗 〉 data -〈𝜎 𝑖 𝜎 𝑗 〉 mf ], (S13) Where  denotes the updating iteration (up to 5.
10 3 ) and 〈. 〉 mf are the expected values using the mean-field approximation: 〈𝜎 𝑖 〉 mf = 1 𝑛 ∑ tanh (ℎ 𝑖 + ∑ 𝐽 𝑖𝑗 𝜎 𝑗 (𝑡) 𝑗≠𝑖 ) , 𝑛 𝑡=1 (S14) 〈𝜎 𝑖 𝜎 𝑗 〉 mf = 1 𝑛 ∑ 𝜎 𝑗 (𝑡)tanh (ℎ 𝑖 + ∑ 𝐽 𝑖𝑘 𝜎 𝑘 (𝑡) 𝑘≠𝑖 ) , 𝑛 𝑡=1 (S15) where   () is the activity of ROI  (taking values 1 or -1). The estimator of the maximum pseudolikelihood approaches the maximum-likelihood estimator for  → ∞ (56). For this reason, pairwise-MEMs were fitted to concatenated data for each of the six experimental conditions. Also, since this method uses a mean-field approximation, it is accurate when the nodes of the network receive many inputs, thus we used the dense binarization scheme (see Methods). The code for learning the pairwise-MEMs is available at: https://royalsocietypublishing. org/doi/suppl/10. 1098/rsta. 2016. 0287.
Acknowledgements: APA, BJ, and GD received funding from the  FLAG-ERA JTC  ( PCI2018-092891 ). GD acknowledges funding from the  European Union 's  Horizon 2020 FET Flagship Human Brain Project  under Grant Agreement  785907 HBP SGA1 ,  SGA2  and  SGA3 , the  Spanish Ministry Research  Project  PSI2016-75688-P (AEI/FEDER ), the Catalan Research Group Support 2017 SGR 1545, and  AWAKENING  ( PID2019-105772GB-I00 ,  AEI FEDER EU)  funded by the  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI)  and  European Regional Development Funds (FEDER). MLK is supported by the  ERC  Consolidator Grant: CAREGIVING (n. 615539),  Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ), and Centre for Eudaimonia and  Human Flourishing  funded by the  Pettit and Carlsberg Foundations.
Bulk RNAseq data from human and animal models All RNAseq datasets used in this work were obtained from AMP-AD Knowledge Portal (https://www. synapse. org) following all terms and conditions to the use of the data. From Mayo Clinic Alzheimer's Disease Genetics Studies (Mayo), we analyzed RNAseq data generated from two areas (Temporal Cortex and Cerebellum) and 3 types of subjects: individuals with AD; PSP: and elderly individuals with no neurodegenerative disease. To classify the subjects in control (elderly individuals with no neurodegenerative disease) or with one of the two conditions mentioned above we used the 'Group' column from metadata obtained from the AMP-AD Knowledge portal (Table  1 , Table  2 ). The "Age_Group" column was used to divide individuals into three groups: We used two animal models in this study: 5XFAD and TauD35.
Differential gene expression analysis To discover differentially expressed genes (DEGs) we used the R library DESeq2  [40]  with the gene expression at transcription-level strategy, as described in our last work  [14]. Kallisto output was import in R (version 4. 0. 5) programming environment and a DESeq2 object was created and filtered based on sum of rows with counts bigger than 10. Next, DESeq function was used to filter results based on a model using the negative binomial distribution and correction method FDR (False Discovery Rate). Genes were considered differentially expressed only with FDR < 0. 01 and absolute fold change (|FC|) > 1. 3. Isoform switch/differential transcript usage (DTU) analysis was performed using the R library IsoformSwitchAnalyzeR  [41]. The analysis was made following the pipeline instructions of the package.
First, we imported kallisto abundance tables (same used in DESeq2 pipeline) using importIsoformExpression and importRData functions to create a switchAnalyzeRlist object. Equal to kallisto realignment we used the same cDNA release and its correspondent annotation (http://ftp. ensembl. org/pub/release-94/gtf/homo_sapiens/Homo_sapiens. GRCh38. 94. chr_patch_hapl_scaff. gtf. gz) as input. Data was filtered using gene expression cutoff = 3, differential isoform fraction (dIF) = 0. 01 and removed single isoform genes. Results of this first part were used in the second part of the analysis with addition of external analysis (CPC2, Pfam, SignalIP and Netsurfp2), as indicated in the pipeline of IsoformSwitchAnalyzer. To confirm our results, we used the R package stageR  [42]  to generate isoforms overall false discovery rate (OFDR).