dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene co-expression and module analysis We used the R library package CEMiTool  [19]  to identify and analyze gene coexpression modules. CEMiTool uses a gene expression file containing genes as rows and samples as columns; we used the matrix create in DESeq2 analysis. This matrix is filtered by an unsupervised method based on the inverse gamma distribution, which will then select the genes used in the analyses. Similarity between pair of genes is evaluated with a modified algorithm created by the authors of the package and then genes are grouped into modules using the Dynamic Tree Cut package  [47]. To know which and how many modules in each condition had, we used the main function cemitool. For the arguments, we used the normalized expression matrix of counts, metadata from every condition, p_value <0.
1 (as suggested by the authors of the study), and ora_pval < 0. 01. In this analysis, we did not subdivide subjects according to the age of death, just by condition, i. e, AD, PSP, and control for human data and Alzheimer and control for mouse data. The final object from the analysis has some information about the identified modules: which genes belong to the modules, Over Representation Analysis (ORA), Normalized Enrichment Score (NES), and Protein Protein Interaction (PPI). All these results were retrieved with the function write_files(). In ORA plots we showed only the GO terms with FDR<0. 01. To identify which modules are up or downregulated between conditions, CEMiTool uses the fgsea  [23]  package. If this enrichment is significant, this information is summarized in the variable (NES), which is the enrichment score for a module in each class normalized by the number of genes in the module.
Plots showing NES are only those modules with FDR<0. 01. The PPI networks in the Fig  7  show networks with AD genes risk and PSP gene risk. The PPI of these figures were retrieved from the final object of CEMiTool analysis and contains interactions between proteins identified in modules. It is important to note that CEMiTool uses an interaction file (PPI) to construct the interaction network. In this work we decided to show only modules with AD or PSP risk genes.
Analysis of gene set signatures enriched in unique neural cell types using Cell-ID To evaluate the enrichment of module-specific gene set signatures in single-cell types, we used the R package Cell-ID  [21]. This package allows a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNASeq. In this work we used the single-cell signature from Leng's dataset  [22] , and performed multiple correspondence analysis (MCA) in scRNA-seq data from the entorhinal cortex of 3 healthy (Braak 0) individuals. Next, with CelI-ID we extracted and calculate the enrichment of per-cell gene signatures using as reference the list of genes from each module (S4 table) identified by CEMiTool. Statistical significance of this enrichment is calculated using a hypergeometric test and shown in function of -log10(pvalue).
PSP To further exploit transcriptomic data obtained from in AD and PSP patients and uncover the latent systems-level functionality of genes, we analyzed modular gene coexpression networks using CEMiTool  [20]. We detected eight different modules in the TCX of AD and PSP individuals (Fig  3A). Modules 1 and 2 in AD (M1_AD and M2_AD) and modules 1 and 3 in PSP (M1_PSP and M3_PSP) were significantly enriched for genes associated with synaptic signaling (Fig3A, Table  S3 ). M3_AD and M2_PSP were significantly enriched for myelination, whereas M5_AD, M8_AD, M4_PSP and M8_PSP were significantly enriched for several terms associated with immune-inflammatory processes. The modules M6_AD, M7_AD and M6_PSP were associated with extracellular matrix and cell differentiation/angiogenesis, whereas M4_AD and M5_PSP were associated with cellular response to growth factors.
Activity of modules is differently altered in AD and PSP brains Using the fgsea (Fast Gene Set Enrichment Analysis) package  [23]  built-in CEMitool, we analyzed the association of module activity to sample phenotypes. In this analysis, genes from co-expression modules are treated as gene sets and the z-score normalized expression of the samples within each phenotype is ranked, providing an assessment of modules across different phenotypes  [20]. We observed that the normalized enrichment score (NES) of M4, M6, M7 and M8 was greatly higher in AD patients, whereas activity of M1 and M3 was higher in PSP patients compared to control subjects (Fig 4A  To directly probe a possible link between pathology progression and gene expression alterations in the AD brain, we analyzed RNAseq data from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB-Mount Sinai NIH Neurobiobank) cohort, which encompassed transcriptome sequencing data from multiple brain regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data  [24].
Using this dataset to compare transcriptional signatures of different brain regions of AD patients, we have previously shown that gene expression alterations are more prominent in the Broadmann area 36 (perirhinal cortex -PRh) than in the Broadmann area 10 (rostrolateral prefrontal cortex -RLPFC)  [14] , thus providing an interesting source to observe transcriptional changes associated with pathological progression  [25]. Using CEMItool, we identified 4 gene co-expression modules both in RLPFC and PRh (Fig S3A, Table  S3 ). Interestingly, activity of M2 in the RLPFC (affected at late pathological stages) was higher in AD patients and genes in this module significantly enriched for GOs associated with immune-inflammatory responses  (Fig S3B). In this same brain region, no modules associated with synapses could be identified (Table  S3 ).
Conversely, M1 and M2 in PRh (affected at early pathological stages) significantly enriched for several synapse related GOs and showed a weaker activity in AD patients compared to controls (Fig S3C, Table  S3 ). Collectively, these observations in two independent datasets suggest that gene expression alterations related to immune-inflammatory processes are an early event in the pathogenesis of AD, whereas gene expression changes related to synapses are mainly a late outcome. AD = Alzheimer's Disease. Values are mean ± SD.
AD and PSP susceptibility genes are linked to specific gene co-expression networks Genome-wide association studies (GWAS) have identified polymorphisms in or near several genes that are associated with AD or PSP risk  [26, 27, 28]. We used CEMitool to visualize the interactions between these risk genes and the co-expression modules identified in each pathology (  Fig 7). In AD modules, we found WNT3 and PLCG2 in M1  shown in blue and red, respectively. Node's size is proportional to its degree (i. e. , number of genes that interacts with a node). B) Same for PSP.
Our findings suggest that, at early ages/stages of disease, tauopathy would be primarily associated with alterations in synaptic signaling processes, whereas amyloidopathy would be mainly associated with immune-inflammatory responses in the brain of PSP and AD patients, respectively. Accordingly, in mouse models of tauopathy and amyloidopathy, gene expression alterations associated with synaptic or immuneinflammatory processes, respectively, predominate at early stages of pathology progression. Last, but not least, we also identify AD risk genes in co-expression modules associated with those biological processes, thus shedding light on their possible contribution to disease onset/progression. In this study, we analyzed RNAseq data generated from PSP patients showing neuropathological signs of tauopathy in the temporal lobe (Braak stages 1-3, when NFTs are already distinguished the trans-entorhinal and entorhinal cortices) and AD patients with widespread NFTs in the mesocortex, allocortex and neocortex (Braak stages 5-6).
Acknowledgments The results published here are in whole or in part based on data obtained from the  AMP-AD Knowledge Portal  (https://adknowledgeportal. synapse. org/). Study data were provided by the following sources: The  Mayo Clinic Alzheimer's Disease Genetic Studies , led by  Dr. Nilufer Taner  and  Dr. Steven G. Younkin ,  Mayo Clinic, Jacksonville , FL using samples from the  Mayo Clinic Study of Aging , the  Mayo Clinic Alzheimer's Disease Research Center , and the  Mayo Clinic Brain Bank. Data collection was supported through funding by  NIA  grants  P50 AG016574 ,  R01 AG032990 ,  U01 AG046139 ,  R01 AG018023 ,  U01 AG006576 ,  U01 AG006786 ,  R01 AG025711 ,  R01 AG017216 ,  R01 AG003949 ,  NINDS  grant  R01 NS080820 ,  CurePSP Foundation , and support from  Mayo Foundation. Study data includes samples collected through the  Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona.
2b ). To determine whether mRNA stability was masking an effect on transcription per se, we performed 4-thiouridine sequencing (4SU-seq) to assay nascent transcription. Transcription was surprisingly similar between WT and Y430C mESCs (Pearson correlation coefficient = 0. 98) (Fig. 2c , d and data from an independent Y430C in Supplementary Fig. 2a ). In particular, decreased BRD4 binding at SEs did not lead to transcriptional changes at stem cell identity genes (Fig. 2c-e , replicate in Supplementary Fig. 2b ), or of eRNAs at the SEs themselves (Fig. 2f , Supplementary Fig. 2c ). Due to normalisation, these experiments could not rule out that transcription is not globally decreased in the mutant ESCs. We therefore performed a spike-in RNA-seq experiment, using RNA from Drosophila cells for normalisation.
Library concentration and correct size distribution was confirmed on the Agilent 2100 Bioanalyser with the DNA HS Kit. Libraries were sequenced at BGI (Hong Kong; 100-base paired-end reads) using the HiSeq 4000 system (Illumina). Fastq files were quality controlled using FastQC and mapped to the mm9 genome using tophat (parameters:-library-type fr-firststrand -r 200). Homer was used to make tagdirectories (makeTagDirectory, parameters: -unique -sspe -flip -fragLength 150), and to make bedgraphs for visualisation on UCSC (makeUCSCfile, parameters: -strand separate -style rnaseq). Cufflinks was used for peak calling; transcripts were assembled for individual experiments (cufflinks, parameters: -m 200 -library-type fr) and both replicates of WT and Y430C were combined to form one assembly (cuffmerge, parameters: default).
Acknowledgements We thank the individuals with CdLS and their families for generously donating their samples and genetic information and for consenting to their use in research studies. We thank  Tom Strachan  (Newcastle) and  Matt Deardorff  and  Ian Krantz  ( Children's Hospital of Philadelphia ) for their gifts of CdLS LCLs. We thank the  Institute of Genetics and Cancer (IGC) Advanced Imaging Resource  for assistance with imaging and Ilya.
Software and algorithms Bowtie2 Langmead and Salzberg, 2012 http://bowtie-bio. sourceforge. net/bowtie2/index. shtml MACS2 https://github. com/taoliu/MACS FACSDiva software BD Bisoscience TopHat Trapnell et al. , 2012 https://ccb. jhu. edu/software/tophat/index. shtml Cufflinks Trapnell et al. , 2012 http://cole-trapnell-lab. github. io/cufflinks/ Deeptools2 Ramirez et al. , 2016 http://deeptools. readthedocs. io/en/latest/index. html SAMtools Li et al. , 2009 http://samtools. sourceforge. net/ Deposited Data BRD4-WT ChIP-seq This paper GSE130659 BRD4-Y430C ChIP-seq This paper GSE130659 WT 4sU-seq This paper GSE130659 Y430C 4sU-seq This paper GSE130659 WT Spike-in RNAseq This paper GSE130659 Y430C Spike-in RNAseq This paper GSE130659 This paper GSE130659.
Other evidence indicates a predominantly righthemisphere activity in stressful situations. We also analyzed the effects of lockdown on coping strategies, which typically show an opposite pattern of hemispheric asymmetry, favoring the left hemisphere. If so, then pseudoneglect should increase during the lockdown and be negatively correlated with the efficacy of coping strategies. Methods: One week before the start of the lockdown due to COVID-19 in Italy (T1), we had collected data from a battery of behavioral tests including tasks of peri-personal spatial cognition. During the quarantine period, from late April to early May 2020 (T2), we repeated the testing sessions with a subgroup of the same participants (47 righthanded students, mean age = 20, SD = 1. 33). At both testing sessions, participants performed digitized neuropsychological tests, including a Cancellation task, Radial Arm Maze task, and Raven's Advanced Progressive Matrices.
Participants Throughout the month of February 2020, before the Italian lockdown, we conducted data collection sessions on peripersonal spatial cognition tasks. The data collection ended on March 2, exactly 1 week before the start of the lockdown due to COVID-19 in Italy. We therefore decided to perform a second data collection with the same participants during the quarantine period. Specifically, the session lasted 2 weeks from late April to early May. Before the beginning of the lockdown, 102 Psychology and Philosophy students (81 females) of the University of Naples Federico II aged between 18 and 26 years (mean = 19. 5, SD = 1. 5) voluntarily enrolled in the first experimental session. Selection criteria for participants' recruitment included normal or corrected-to-normal vision. Students were contacted later on during quarantine, and 55 out of 102 students agreed to participate in the second session.
Raven's Advanced Progressive Matrices The Raven's Advanced Progressive Matrices (APM;  Raven et al. , 1962)  are used to assess non-verbal and "fluid" intelligence and require the direct analysis, construction, and integration of a series of visual items. Raven's matrices questions consist of visual geometric designs with a missing piece. Participants are asked to choose the missing piece between eight alternatives, arranged along four vertical columns disposed from the left to the right of the page below the test image. A digital version of the Raven's APM was administered in the present study, for which the matrices were transposed on Google Modules. Only set I (12 items with 8 possible responses) was administered. Costa et al. (1969)  administered the Raven's APM to patients suffering from left neglect after right-hemisphere damage and assessed the spatial side (left or right) of the error responses.
A digital version of the Raven's APM was first administered, for which the matrices were transposed on Google Modules. Participants used a 14-inch PC and a mouse to perform the APM. Then, the Cancellation task was administered on the tablet. The instructions were as follows: "As soon as you select the green button with the pen you will see little circles, which you will have to select all in the shortest possible time. If you happen to touch the white screen it will turn black for a moment, then you go on. " Finally, the digitized RAM task was performed by participants. The instructions were as follows: "The objective of this task is to explore all the arms of the mazes, dragging the ladybug there, and find the ladybugs hidden under the jars within 60 s. Remember that you must always go to the center before moving from one arm to another.
It also provides a basis to address the linkages between ice sheets and climate change in collaboration with the Ice Sheet Model Intercomparison Project for CMIP6 (IS-MIP6)  (Nowicki et al. , 2016). In this paper, we start with a brief overview of the experimental design of the lig127k  (Otto-Bliesner et al. , 2017). We briefly summarize the simulation of temperature, precipitation, and sea ice, in the subset of CMIP6 piControl simulations that have a corresponding lig127k simulation, as compared to observational datasets. We then provide an initial analysis of the multi-model ensemble mean and model spread in the lig127k surface temperature, precipitation, and Arctic sea ice responses as compared to the CMIP6 DECK piControl simulations. A new syntheses of surface temperature and precipitation proxies, targeted for 127 ka, is used for comparison to the model simulations.
Model evaluation The 17 modeling groups that have completed CMIP6 lig127k simulations are presented in this paper (Table  2 ). All used the CMIP6 version of their model also used for their DECK experiments. The equilibrium climate sensitivity (ECS) varies from 1. 8 to 5. 6 • C. The years analyzed for each model and DOIs for each of the simulations are given in Table  S1  in the Supplement. The analysis uses data available on the CMIP6 ESGF (Earth System Grid Federation) for surface air temperature (tas), precipitation (pr), and sea ice concentration (siconc).
(2020). Brierley et al. (2020)  provide an extensive evaluation of the CMIP6 preindustrial simulations as compared to observational datasets: reanalyzed climatological temperatures (between  1871 -1900 CE; CE; Compo et al. , 2011)  for the spatial patterns, zonal averages of observed temperature for the period 1850-1900 CE from the HadCRUT4 dataset  (Morice et al. , 2012; Ilyas et al. , 2017) , and climatological precipitation data for the period between 1970 and the present day  (Adler et al. , 2003). In summary, they find that the PMIP4-CMIP6 models are in general cooler than the observations, most noticeably at the poles, over land, and over the NH oceans. The poleward extent of the North African monsoon, in particular, is underestimated in the CMIP6 preindustrial simulations.
S2 ). Further, evaluation of the piControl simulations can be found in  Kageyama et al. (2021). In particular, they find that in comparison to sea ice reconstruction sites, the models generally overestimated sea ice cover at sites close to the sea ice edge. Figure  4  shows the seasonal cycle of Arctic sea ice area in the piControl simulations for each model and the MMM. These are compared to the NOAA OI_v2 observational dataset, with higher temporal and spatial coverage than the HadISST dataset. The NOAA_OI_v2 dataset  (Reynolds et Clim. Past, 17, 63-94, 2021  https://doi. org/10. 5194/cp-17-63-2021 observations. The area-averaged, annual mean surface air temperature for 30 • latitude bands in the CMIP6 models and a spatially complete compilation of instrumental observations over  1850 -1900 (black; Ilyas et al.
, 2017; Morice et al. , 2012). (b) Changes in zonal average, mean annual surface air temperatures (lig127k minus piControl). al. , 2002), also used in  Kageyama et al. (2021) , only extends back to 1981. It should be noted that atmospheric CO 2 concentrations had already risen to 340 ppm by 1981, as compared to 284. 7 ppm specified in the piControl simulations. We find a large spread across the piControl simulations. The range in March is 12. 27 to 19. 16 × 10 6 km 2 and the MMM is 15. 30 ± 1. 89 × 10 6 km 2. The range in September is 3. 56 to 9. 73 × 10 6 km 2 , and the MMM is 6. 13 ± 1. 66 × 10 6 km 2. Generally, those models with less sea ice in March than the MMM also have less sea ice in September than the MMM. Observed estimates of sea ice area from the NOAA-OI_v2 dataset for 1982-2001 are 14.
Finally, temperature anomalies relative to the preindustrial period are calculated in both cases for marine sites using the HadISST dataset  (Rayner et al. , 2003) , over the intervals 1870-1899 and 1870-1889 CE, in the compilations by  Capron et al. (2017)  and  Hoffman et al. (2017) , respectively. For both compilations, the provided 2σ uncertainties integrate errors linked to relative dating and surface temperature reconstruction methods. Nevertheless, because of (1) the different reference chronologies used, (2) the different tie points and associated relative age uncertainties defined to derive the chronology of each site, and (3) the different calculation methods (Bayesian statistics versus linear interpolation between tie points) used in the Monte Carlo age model analysis of each site (despite apparently relatively similar approaches), the two compilations by  Capron et al.
Ice core temperatures Surface air temperature records for one site (NEEM) on the Greenland ice sheet and four sites on the Antarctic ice sheet are deduced from ice core water isotopic profiles  (Capron et al. , 2014 (Capron et al. , , 2017) )  (Tables  S2  and S4 ). For ice cores, preindustrial conditions are estimated using borehole temperature measurements for Greenland and 1870-1899 CE water isotopic profiles for Antarctica  (Capron et al. , 2017). Temperatures are again the median for the 126-128 ka period and are considered to represent annual averages. Uncertainty is estimated using the same Monte Carlo procedure as was used for the marine cores in the compilation of  Capron et al. (2017). Clim. Past, 17, 63-94, 2021 https://doi. org/10. 5194/cp-17-63-2021 Because it uses the same reference timescale (AICC2012), the ice core dataset can be considered coherent with the marine SST dataset of  Capron et al.
Terrestrial temperatures Calibrated, well-dated reconstructions of Last Interglacial temperatures over the continents are quite limited. We have assembled two distinct compilations of continental air temperature reconstructions: a dataset of air temperatures over Europe at 127 ka based on  Brewer et al. (2008)  and a compilation of peak Last Interglacial summer temperatures reconstructed at Arctic sites from pollen and insect assemblages (Table  S6 ). For both we report anomalies comparing reconstructed temperatures with preindustrial climate estimated from 1871-1900. In Europe, favorable geological conditions have led to the accumulation of numerous LIG sediment sequences from a variety of depositional environments  (Tzedakis, 2007). These include former kettle lakes overlying late Saalian (MIS 6) till, depressions left by the penultimate alpine glaciation or local ice caps, and volcanic crater lakes or tectonic grabens mainly in the unglaciated south.
, 2008). Here we present the mean value and standard deviation for mean annual temperature, mean temperature of the coldest month, and mean temperature of the warmest month across all sites for 127 ka. Of the 15 terrestrial sites used by  Brewer et al. (2008) , 8 were excluded due to uncertainties over their chronostratigraphical or chronological assignments or because they did not extend to 127 ka. The Arctic dataset compiles the most stratigraphically complete, best time-constrained, and calibrated summer temperature reconstructions published from above 65 • N latitude. We report the mean of the two warmest consecutive reconstructions at each site, utilizing the original published models and reconstructions. For sites where both insectand pollen-based temperature reconstructions have been published or where multiple models have been applied to the same proxy, we report here the average of those reconstructions.
We report the original published model uncertainties (e. g. , root mean square error of prediction for weighted averaging models), including the most conservative (largest) model uncertainties for sites where multiple proxies/models are applied. This differs from error reporting for the European dataset above. Importantly, the Arctic compilation also differs from the other paleotemperature datasets used here, in that it reports the warmest LIG conditions registered at each site rather than temperatures at 127 ka. This approach was necessitated by the coarse temporal resolutions and chronologies of the North American Arctic reconstructions, which come from stratigraphically discontinuous deposits dated by 14 C (non-finite 14 C ages) and in some cases luminescence or tephrochronology. In contrast to the North that by definition this reconstruction reports the warmest period from each Arctic site rather than the 127 ka time slice.