dataesr/scientific-paragraphs-categorization huggingface dataset:
Scenario 3 Qualitative analysis shows that the Scenario 3 results are very similar to those obtained in the two former configurations. Quantitative analysis confirms our previous observations (Figure  9 ). Scenario 3 achieved an 81% accuracy, in line with the per-class F1-score of Scenario 1. A single class reaches a significantly lower accuracy: Industrial, commercial and transport units (0. 18 instead of 0. 42 and 0. 38 in Scenarios 1 and 2). This may be due to changes between 2012-2018, exacerbated by the limited initial consistency between both data sets and few validation points. Note that the Edge Preservation Index remains stable at 0. 44 in this set-up. This is a relatively low value for a prediction being between 73% identical to CLC 2018. This is simply explained by the fact that most discrepancies between CLC and our prediction are observed on edges (see Section 5.
This problem can be hardly solved without adding extra information and thus involves a modification of the technique proposed here to fuse different types of data. Simply extending the spatial support of our analysis might result in better capturing the context necessary to discriminate land-use classes and it is likely to be detrimental to linear structures and small elements. We believe in the high potential of spatial-semantic translation of land-cover maps to support new applications in inter-operating land-cover data sets. Our method also offers the advantage of generating maps with multiple variations of a nomenclature without requiring remote sensing images and with a quality almost similar to the map specification. Unlike previous approaches, contextual translation shows a high capacity to deal with nomenclatures when difficult semantic relationships exist.
This also enables to correct some of the errors in the source data set. As a current limitation, our method works only when source and target maps spatially overlap, which might prevent some key applications related to map transfer. Therefore, some effort must be devoted to proposing a non-fully supervised method for contextual translation. sea coastline are usually low covered. Thus, this translation is impossible from OSO without taking the information of the geolocation into account. Figure  A2  also shows the difference between the mean agreement kappa computed, respectively, with and without the coordinates sub-module. Green areas show where the network with coordinates sub-module outperformed the version without. Thus, we can state that the network with the coordinates sub-module provides better results almost everywhere in France (except for a small portion of the northeast).
Specific Comments Major Comments  1. The research questions (RQs) are fine for the study. There should be some connection between these 2 RQs and how they represent a "case study of COVID-19. " 2. On page 2, under "Related Work," I would like to see an explanation of word embedding, network embedding, and transformers. I realize these are representation learning techniques to improve topic classification. It would be very helpful to have a basic explanation of what these techniques are doing that would be suitable for someone not in the computer science field. Even providing real-world examples would be helpful here. Since embedding and transformers are key parts of the methodology section, these techniques could use better explanation. 3. In the Methods section, I understand utilizing content analysis of profile words and retweet interactions to classify polarization of Twitter users in the data set.
However, the specific techniques of average word embedding and transformers were hard to follow. I think it would be helpful to have a more layman's definition of sentence embedding, transformers, and how they work in this data set. Perhaps a sample walkthrough of how a set of Twitter users is classified would be really beneficial in my opinion. 4. Under section 5. 1, there is an analysis of bot scores (Figure  2B ). Yet previously it was mentioned that the top 10% of users with a bot score were removed. So, is it still helpful to do this analysis? Can we still state that the presence of bots is being controlled in the Twitter data set? 5. Under section 5. 2, the following is stated: "Figure  3  reveals the proportion of users in each decile of polarity score that are influential. We show that, consistent with all of the influence measures above, partisan users are more likely to be found influential.
" Looking at Figure  3 , only A and E really demonstrate this statement. Figure  3B , C, and D seem much more proportional (mild U shape). 6. In section 5. 1, the classifications discovered are very interesting. These visualizations on partisanship and information dissemination are really nicely done. This finding is certainly a strength of the study. I also appreciate the visualizations for the polarization of influencers in section 5. 2. It is helpful to see how partisanship contributes to information and influence in this Twitter data set. 7. I particularly like the Figure  6  visualization since it is the most intuitive of the visualizations. 8. I would like to see the COVID-19 health implications of these findings on the political polarization of Twitter users in the discussion section.
, 2013). BigBrain, the first dataset of this kind  (Amunts et al. , 2013) , allows computational analysis of cytoarchitecture across the entire human brain. It may be that the dominant pattern of cytoarchitectural differentiation in the DMN is the gradient of laminar elaboration, or an altogether distinct pattern. In the context of the whole cortex, the DMN occupies a distinct topographic position  (Smallwood et al. , 2021a). Parts of the DMN are situated most distally from primary sensory and motor areas  (Leech et al. , 2014; Margulies et al. , 2016). The separation from primary sensory areas reinforces that the DMN is involved in functions that require integration between distinct sources, while also being relatively insulated from the influence of one specific input. Multi-variate decompositions of resting state fMRI support this notion, illustrating antipodal functional dynamics of the DMN and primary sensory areas  (Hong et al.
Replication analyses We repeated the imaging analyses in a separate dataset of 100 healthy young adults (HCP). These analyses replicated our conclusions that (i) the DMN balances navigation efficiency across cortical types, (ii) navigation efficiency to the most granular types decreases along E1, (iii) input from non-DMN cortex to the DMN is focused on the skewed aspects of E1 and (iv) output of the DMN to non-DMN cortex is not clearly constrained by our cytoarchitectural definitions. Additionally, the DMN was unique in these features, relative to other functional networks. Key statistics are presented for both datasets in Supplementary Tables  3 4 5 , and replication findings are presented in Supplementary Figures  7 8 9.
Histological data An ultra-high resolution Merker stained 3D volumetric histological reconstruction of a post mortem human brain from a 65-year-old male was obtained from the open-access BigBrain repository on September 1, 2020 [https://bigbrain. loris. ca/main. php;  (Amunts et al. , 2013) ]. The post mortem brain was paraffinembedded, coronally sliced into 7,400 20μm sections, silver-stained for cell bodies  (Merker, 1983)  and digitised. Manual inspection for artefacts (i. e. , rips, tears, shears, and stain crystallisation) was followed by automatic repair procedures, involving non-linear alignment to a post mortem MRI, intensity normalisation, and block averaging  (Lepage et al. , 2010). The 3D reconstruction was implemented with a successive coarse-to-fine hierarchical procedure  (Mohlberg et al.
MRI acquisition -Replication analyses Replication MRI analyses were conducted in 100 unrelated healthy adults (66 females, mean±SD age=28. 8±3. 8 years) from the minimally preprocessed S900 release of the Human Connectome Project (HCP)  (Glasser et al. , 2013). MRI data were acquired on the HCP's custom 3T Siemens Skyra equipped with a 32-channel head coil. Two T1w images with identical parameters were acquired using a 3D-MPRAGE sequence (0. 7mm isotropic voxels, matrix=320×320, 256 sagittal slices; TR=2400ms, TE=2. 14ms, TI=1000ms, flip angle=8°; iPAT=2). Two T2w images were acquired using a 3D T2-SPACE sequence with identical geometry (TR=3200ms, TE=565ms, variable flip angle, iPAT=2). A spin-echo EPI sequence was used to obtain diffusion weighted images, consisting of three shells with b-values 1000, 2000, and 3000s/mm 2 and up to 90 diffusion weighting directions per shell (TR=5520ms, TE=89.
Compared to other nonlinear manifold learning techniques, the algorithm is relatively robust to noise and computationally inexpensive  (Tenenbaum et al. , 2000; Von Luxburg, 2007). A single parameter α controls the influence of the sampling density on the manifold (α = 0, maximal influence; α = 1, no influence). As in previous studies  (Margulies et al. , 2016; Vos de Wael et al. , 2018) , we set α = 0. 5, a choice retaining the global relations between data points in the embedded space. Notably, different alpha parameters had little to no impact on the first two eigenvectors (spatial correlation of eigenvectors, r>0. 99). The DMN comprises 71,576 vertices on the BigBrain surface, each associated with approximately 1mm 2 of surface area. Pair-wise correlation and manifold learning on 71,576 data points was computationally infeasible, however.
Effective connectivity of the DMN The position of the DMN in large-scale cortical dynamics was explored with regression dynamic causal modelling [rDCM;  (Frässle et al. , 2017) ], freely available as part of the TAPAS software package  (Frässle et al. , 2021a) , a highly scalable generative model of effective connectivity that allows inferences on the directionality of signal flow. Input to and output of the DMN were evaluated separately in the following analyses. First, we estimated the variance explained in DMN effective connectivity by cortical types and the data-driven cytoarchitectural axis. To do so, we calculated the type-specific effective connectivity for each parcel of the DMN, that is the average effective connectivity to non-DMN parcels of a certain cortical type. Then, we fit a multiple linear regression model with type-specific effective connectivity as the response variable, cortical type as a categorical predictor and E1 as a continuous predictor.
Robustness to atlas definition The robustness of cytoarchitectural heterogeneity to the DMN definition was assessed with two alternative atlases. Given the origins of the DMN as a "task-negative" set of regions  (Raichle et al. , 2001; Shulman et al. , 1997) , the first alternative atlas involved identifying regions that are consistently deactivated during externally-oriented tasks. In line with a recent review  (Smallwood et al. , 2021b) , we used pre-defined contrast maps from 787 healthy young adults of the Human Connectome Project ("HCP_S900_GroupAvg_v1 Dataset"). Each map represents the contrast between BOLD response during a task and at baseline. Fifteen tasks were selected [Working Memory (WM) -2 Back, WM-0 Back, WM-BODY, WM-FACE, WM-PLACE, WM-TOOL, GAMBLING-PUNISH, GAMBLING-REWARD, MOTOR-AVG, SOCIAL-RANDOM, SOCIAL -Theory of Mind (TOM), RELATIONAL-MATCH, RELATIONAL-REL, EMOTION-FACES, EMOTION-SHAPES] to correspond to initial studies of the DMN  (Raichle et al.
We reasoned that it should be possible to design a set of probes with a broader specificity, recognizing, if not all HCV isolates, then at least all viral RNAs within a given genotype. In this work, we used a consensus sequence derived from 20,000 genotype 1b sequences deposited in the euHCVdb database (http: //euhcvdb. lyon. inserm. fr/euHCVdb/; accessed on 26 March 2016) to design 61 probes, the locations of which are shown in Figure  1A  (and Supplementary Materials Table  S1 ). We predict that every viral genome should be recognized by a minimum of 40 probes. The sensitivity and specificity of the pan-genotype 1b HCV probes were first tested on Huh7 Nneo/c-5B cells that harbor the full-length genotype 1b replicon  [23]. As shown in Figure  1B , a strong signal was detected in the replicon cells, but not in the parental Huh7 line.
Gene Expression Profiling of HCV-Positive and HCV-Negative Cell Clusters We used smiFISH-labelled tissue sections from patient #8 to delineate HCV-positive and negative regions. For this, we examined confocal tile scan images combining the smiFISH data for the tissue sections representing average areas of 2. 5 cm 2 , corresponding to circa 10,000 cells. Clusters of infected and HCV-free hepatocytes were identified by visual examination (Figure  3A ). A total of 11 HCV-positive and 7 HCV-negative regions, each composed of 100-300 cells, were used as the dissection guide on serial tissue sections surrounding these tile scan images for laser microdissection, RNA extraction, and RNAseq analyses. Indeed, since the smiFISH procedure is not compatible with good quality RNA preparation, the microdissection was performed on the adjacent cuts of serial sections used for the smiFISH imaging.
In total, 124 liver biopsy samples were retrieved (GEO accession number: GSE84044) and used for the bioanalysis (S0 = 43 patients, S1 = 20 patients, S2 = 33 patients, (D) Kaplan-Meier analysis of TCGA data indicates that a high GOLT1B expression is correlated with poor overall survival probability in patients with HCC associated with viral hepatitis. Analysis conducted using Kaplan-Meier Plotter  [40]  (E) GOLT1B mRNA expression is significantly higher in HBV patients with severe fibrosis than in patients with mild fibrosis, according to the histological staging of fibrosis (Scheuer score "S"). In total, 124 liver biopsy samples were retrieved (GEO accession number: GSE84044) and used for the bioanalysis (S0 = 43 patients, S1 = 20 patients, S2 = 33 patients, S3 = 18 patients, S4 = 10 patients).
For statistical analysis, the Kruskal-Wallis test (non-parametric ANOVA) was performed and GP p-values calculated: 0. 0021 (**), GOLT1B expression in whole liver tissue was analyzed from 39 samples from HBV-associated HCC patients "Non tumor area (HBV)" and 81 samples from HBV-associated HCC patients "Tumor area (HBV)". The samples derived from 11 HBV-associated HCC patients who underwent liver transplantation for tumor (GEO accession number: GSE107170). GOLT1B expression in whole liver tissue was analyzed from 31 samples from HCV-associated HCC patients "Non tumor region (HCV)" and 44 samples from HCV-associated HCC patients "Tumor region (HCV)". The samples derived from 11 HCV-associated HCC patients who underwent liver transplantation for tumor (GEO accession number: GSE107170). For statistical analysis, the Mann-Whitney test was performed and GP p-values were calculated: >0.
The RNAseq analysis of samples microdissected from clinical specimens remains technically challenging, and the quantity and quality of the recovered RNA did not allow high-depth analysis. It is therefore very likely that our dataset represents only a subset of alterations in gene expression triggered by the viral presence. Indeed, the number of genes dysregulated in the course of in vitro viral infection is 5 to10-fold higher than that detected in our work  [10]. Noteworthy, however, our methodology is expected to strongly enrich the identification of cell-autonomously regulated genes and it has been performed on clinical samples from a chronic hepatitis C patient. Further analyses of additional patient samples will be required to better define the sets of genes commonly deregulated by HCV in a clinically relevant setting.
HCV Pan-Genotype 1b smiFISH Probe Design More than 20,000 HCV genotype 1b sequences >9. 5 Kb in length from the NCBI database were aligned using Galaxy  [56]. A consensus HCV genotype 1b sequence was generated in Aliview (the Open Source Software License 'GNU General Public License, version 3. 0 (GPLv3)). In the event of missing sequence information, the non-identified nucleotides were arbitrarily designated as C. Probes were designed by R-script Oligostan  [16] , which automatically eliminates C-stacks and thus excludes any artificially created C-blocks. Sixty-one primary probes corresponding to the HCV genotype 1b consensus sequence were designed. The same software was used to design primary probe sets for negative controls, the Firefly luciferase and Hygromycin resistance genes, as well as the positive controls, i.
RNA Extraction and RT-qPCR RNA was extracted from frozen samples of either tumoral or matched non-tumoral parts of HCC patient biopsies using the RNeasy mini kit (Qiagen, Hilden, Germany); 0. 25 µg of total RNA was reverse-transcribed with the QuantiTect Reverse Transcription kit (Qiagen), and cDNA was quantified using LC Fast start DNA Master SYBR Green I Mix (Roche, Basel, Switzerland). Primers for RT-qPCR were designed using LightCycler probe design 2 software (Roche), targeting the pan-genotype small (i. e. , 300 bp) conserved region in the 5 UTR of HCV viral sequence: forward: 5 -CAGGAGATGGGCG-GAAAC -3 , reverse: 5 -GCCGCAATGGATATTTCATTCTCA-3. Results were normalized to SRSF4 housekeeping gene expression; forward: 5 -CGGAGTCCTAGCAGGCATA-3 , reverse: 5 -TTCCTGCCCTTCCTCTTGT-3. GOLT1B expression was quantified using qPCR primers GOLT1B-fw (5 -CGGCTTCATTTCTCCCGACT-3 ) and GOLT1B-rv (5 -TCCAATTTTCTGCGTGTCCG-3 ) using the SYBR green method using a CFX96 Touch Thermal Cycler (Bio-Rad, Hercules, CA, USA).
Microscopy and Imaging In vitro smiFISH microscopy was performed using a widefield Zeiss Axioimager Z1 to capture Z-stacks using an X63 1. 4 NA oil objective equipped with a CCD camera (Axiocam) and controlled with Metamorph (Molecular Devices). Tissue smiFISH imaging was performed using an inverse SP8-UV confocal microscope (Leica) for Z-stack capturing with a X63 1. 4 NA oil objective and HyD detectors. Z-stacks and confocal tile scans were compiled using LAS software (Leica Application Suite). Signals were detected after adjustment of the negative control probe to zero (considered background noise). The remaining non-specific signal, likely due to spectral bleed-through, was calculated by image overlying upon excitement of the Cy3 and GFP channels. For the HCV signal the non-specific component was estimated to be 1.
(D) Kaplan-Meier analysis of TCGA data indicates that a high GOLT1B expression is correlated with poor overall survival probability in patients with HCC associated with viral hepatitis. Analysis conducted using Kaplan-Meier Plotter [40]  (E) GOLT1B mRNA expression is significantly higher in HBV patients with severe fibrosis than in patients with mild fibrosis, according to the histological staging of fibrosis (Scheuer score "S"). In total, 124 liver biopsy samples were retrieved (GEO accession number: GSE84044) and used for the bioanalysis (S0 = 43 patients, S1 = 20 patients, S2 = 33 patients,.
(D) Kaplan-Meier analysis of TCGA data indicates that a high GOLT1B expression is correlated with poor overall survival probability in patients with HCC associated with viral hepatitis. Analysis conducted using Kaplan-Meier Plotter [40]  (E) GOLT1B mRNA expression is significantly higher in HBV patients with severe fibrosis than in patients with mild fibrosis, according to the histological staging of fibrosis (Scheuer score "S"). In total, 124 liver biopsy samples were retrieved (GEO accession number: GSE84044) and used for the bioanalysis (S0 = 43 patients, S1 = 20 patients, S2 = 33 patients, S3 = 18 patients, S4 = 10 patients). For statistical analysis, the Kruskal-Wallis test (non-parametric ANOVA) was performed and GP p-values calculated: 0. 0021 (**), GOLT1B expression in whole liver tissue was analyzed from 39 samples from HBV-associated HCC patients "Non tumor area (HBV)" and 81 samples from HBV-associated HCC patients "Tumor area (HBV)".
The samples derived from 11 HBV-associated HCC patients who underwent liver transplantation for tumor (GEO accession number: GSE107170). GOLT1B expression in whole liver tissue was analyzed from 31 samples from HCV-associated HCC patients "Non tumor region (HCV)" and 44 samples from HCV-associated HCC patients "Tumor region (HCV)". The samples derived from 11 HCV-associated HCC patients who underwent liver transplantation for tumor (GEO accession number: GSE107170). For statistical analysis, the Mann-Whitney test was performed and GP p-values were calculated: >0. 05 (not significant, n. s. ), <0. 05 (*), <0. 01 (**), <0. 001 (***), <0. 0001 (****).
Acknowledgments: We gratefully acknowledge help from the Montpellier Biocampus imaging platform in the analysis of the smiFISH results (MRI, a member of the national infrastructure  France-BioImaging , supported by the  French National Research Agency  ( ANR-10-INBS-04 , Investissements d'avenir)), and the  Laser Microdissection platform at Bordeaux Neurocampus  for performing the laser capture. We thank  Thierry Gostan  ( Seranad platform, IGMM ) for help in designing probes and  Raphaël Gaudin  ( CRBM, Montpellier ) for sharing reagents and advice on endoplasmic reticulum dynamics. We thank  Frank Jühling  ( U1110, Strasbourg ) for bioinformatic advice. We thank  Yasamine El-Ahmadi  and  Ella Dawson  for their contribution in conducting smiFISH analysis of frozen tissue samples during their student rotation in the laboratory and all the members of the  Hibner laboratory  for discussion and support.
cs. ut. ee/gpro ler/gost). The Gene Ontology (GO)biological processes associated with the selected genes were listed, nodes (GO-biological process) with adjusted p-value less than 5% were reported as important. To avoid overly speci c and general processes, only GO-biological processes with a size between 30 and 300 were considered. When the large number of GO-terms are affected, redundancy between them is high making it di cult to read graphs. To visualize and interpret those results, we used the EnrichmentMap and AutoAnnotate apps in Cytoscape to visualize the GO-terms network  (35). Contrastingly, when too few biological processes were detected, larger GObiological processes (up to 500 genes) were also considered. In these cases, to get further functional insights, Kyoto Encyclopedia Gene and Genomes (KEGG (36)) and Reactome  (37)  databases were also used to perform pathway enrichment analyses.
Tonga comprises 36 inhabited islands across five major groups; over 70% of the population lives on the largest island, Tongatapu. Tonga's health services are provided through a national hospital (Vaiola), three outer island hospitals, 14 community health centres, and 34 reproductive, maternal and child health clinics  [16]. The routine death reporting systems that operate in Tonga are: (1) the mortality database based on medical certificates of cause of death (MCCD) managed by the Ministry of Health (MoH) Health Planning and Information Division; (2) the national hospital (Vaiola) information system records of deceased at hospital discharge (separation); (3) Reproductive Health Services (RHS) data from community nursing reports;  (4)  The Civil Registry of Births, Deaths and Marriages managed by the Ministry of Justice; and (5) Town and District Officer reporting to the Ministry of Internal Affairs (not accessed in this analysis) (Fig.
1 ). MCCD are issued for deaths occurring in health facilities. For deaths that occur at home or in the community, in the absence of an authorised certifier, a notice of death form is completed by public health nurses at community health centres. Notice of death forms are also recorded in monthly community nursing reports, which are collated at the Reproductive Health Section of the hospital (Vaiola). Locally elected Town Officers are required to verify notice of death forms before a MCCD is completed  [17]. In practice, a MCCD is only completed for a notice of death form at the request of the family of the deceased. Data on all MCCD issued in Tonga are entered into the MoH MCCD database. The registration of births and deaths is required by law  [18]. A MCCD is generally requested to complete the death registration, but it is not legally required and does not always occur.