dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We assessed device performance, usability of interaction, user satisfaction and immersion with VM exhibition. The results of this study contributed to design the content for interactive on-site installation VM application. This paper achieved contributions to understand the development of VM applications to better immerse museum visitors into VM. A Storytelling platform to design interactive content with various devices was proposed. This feature allowed developers to study user behavior with applications through interaction systems using different interaction techniques. We added functionality for reporting user interaction to the platform, allowing us to capture user data during interaction with the system. The platform report elapsed times of each user the navigation time, the manipulation time and the selection time.
Recently, es-fMRI has been directly compared between monkeys and humans. Rocchi et al. (2021)  assessed fMRI results from stimulation of monkey and human auditory cortex ( Fig. 5  ). They assessed the pattern of es-fMRI effects in frontal and medial temporal lobe regions evoked by stimulating different sites in the auditory cortex on the supratemporal plane. They observed remarkably similar patterns of activation in inferior frontal and parahippocampal regions in both species. Some of the monkey results also recapitulated auditory cortex to frontal cortex effective connectivity expected from neuronal tracing studies  ( Romanski et al. , 1999 ). There is now also a human es-fMRI resource that has been established  ( Thompson et al. , 2020 ) , which is expected to grow as the neurosurgery community contributes additional human es-fMRI data from brain sites stimulated during fMRI for clinical purposes.
Imaging artifacts Even with careful consideration of the MRI-compatibility of used materials, there may still be distortions in the MRI signal induced by headposts, head caps, chambers or intracranial implants. Scanning sequences with short TE ( < 30 ms) have less signal dropout. Spin-Echo EPI's are less affected by artifacts than Gradient-Echo EPI's but come with higher heating risk and lower BOLD sensitivity. RF artifacts and signal noise introduced by the presence of stimulation connections or active current delivery can be assessed with special quality assessment sequences. Alternatively, an adjusted standard functional EPI sequence with the transmit RF coil power set to zero can be used for artifact assessment. The latter option affords a convenient possibility to visualize any RF noise artifacts against the background level of noise in real-time, while connecting and adjusting the parts of the stimulation circuit and associated devices ideally one by one, with the exact same EPI sequence that will be used for actual functional data acquisition.
g. , Electronics & Innovation A075, 75 W, 300 kHz -35 MHz). The input signal to the amplifier is produced by an arbitrary waveform generator (e. g. , Keysight 32210A), which can be programmed via USB, or by a digital function generator (e. g. , Handyscope HS5, TiePie engineering, Sneek, the Netherlands) controlled via Matlab routines. To fill the air gap between the face of the transducer and the subject's scalp, the transducer can have a bladder-type coupling system that is filled with degassed water (e. g. , Coupling Cone, Model C-101, Sonic Concepts). Degassed water may be replaced by or combined with polyvinyl alcohol (PVA) hydrogel  ( Lee et al. , 2014 ). What matters is the use of an interface that will favor the undistorted propagation of the soundwave from the transducer to the skull.
, 2019 ) , for instance, when the FUS focal point is close to a large vessel like the sagittal vein. Despite the fact that FUS uses sound frequencies well outside the range of normal hearing, there is evidence that FUS pulse sequences can cause auditory activation  ( Braun et al. , 2020 ; Guo et al. , 2018 ; Sato et al. , 2018 ). However, due to the long delay between stimulation and data collection, offline effects cannot be explained by an auditory artifact  ( Verhagen et al. , 2019 ). For online protocols, an auditory mask can be used to avoid auditory confounds  ( Braun et al. , 2020 ). The tissue-effects of low-intensity FUS can be monitored online by thermometry and acoustic radiation force (ARF) imaging. ARF causes displacement of objects, including biological tissue, in the path of the ultrasound wave.
Nonetheless, even for these or other approaches, work with NHPs may be required to understand the underlying neural mechanisms and the combination with neuroimaging could be an important component  ( Balan et al. , 2017 ). This paper offers a snapshot of where the field stands in terms of NHP brain perturbation and neuroimaging advances. However, because many developments are ongoing, we envision a dynamic resource on brain perturbation and neuroimaging that evolves along with advances in the field as a complementary resource to this paper. The PRIME-RE platform  ( Messinger et al. , 2021 ) , that was recently launched by members of the PRIME-DE community as an online research exchange platform for all things related to NHP neuroimaging. It offers an excellent outlet for such dynamically evolving content and will encourage data sharing of legacy and new data, as well as offering primers to support laboratories to use and develop the techniques further.
Maintained by the community, researchers can upload technique primers, tips, tricks, risks, solutions and warnings for all relevant methodologies. This ongoing effort will help the scientific community to keep up to date with recent developments and to support researchers aiming to implement the techniques in their own labs. PRIME-RE also maintains collections of links to open NHP neuroimaging datasets and could facilitate the distribution and accessibility of valuable perturbation datasets, that might for instance be hosted on PRIME-DE  ( Milham et al. , 2018 ).
An improved understanding of a functioning system can thus provide the bedrock for designing or improving treatments for dysfunctional neural systems. This approach is also a core component of the Research Domain Criteria (RDoC) framework suggested for the study of mental disorders  ( Insel et al. , 2010 ). The sharing and combining of datasets and methods is another way to increase sample sizes and facilitate meta-analyses. The PRIME-DE  ( Milham et al. , 2018 ;  The PRIMatE Data Exchange (PRIME-DE) Global Collaboration Workshop and  Consortium et al. , 2020 )  and PRIME-RE  ( Messinger et al. , 2021 )  initiatives are specifically designed to facilitate these efforts for NHP neuroimaging research. Finally, rather than assuming that any primate brain will be a good model for the human brain, basic science studies, including cross-species comparative neuroimaging, will continue to be needed to assess this assumption by explicitly testing for correspondence or divergence across species  ( Orban et al.
Acknowledgements This work was supported by  NIH   -MH112142  (V. F. and E. K. );  California National Primate Research Center Base Grant  (  P51-OD011107  ) (A. F. ); Fondation Bettencourt Schueller, France and Human Brain Project  (B. J. , L. U. , J. T. ); the  Intramural Research Program  of the  NIMH  ( ZIA MH002918 ) (A. M. );  Wellcome Trust  (  WT 110157/Z/15/Z  ) (A. S. M. );  European Research Council (ERC StG , Op-toVision, 637638 ) (M. C. S and M. O. R. );  National Key R&D Program of China   2018YFA0701400  (A. W. R. ); Chinese  NSF  (  81430010  and  31627802  ) (A. W. R. ); Zhejiang province 2020C03004 (A. W. R. );  Chinese National R&D Program  Grant  2015AA020515  (A. W. R. );  IDEXLYON "IMPULSION 2020 grant  ( IDEX/IMP/2020/14 ) (J. S. ); Howard Hughes Medical Institute  (D.
T. );  KU Leuven  grant  C14/17/109  (W. V. ); the  National Eye Institute Intramural Research Program  ( ZIA EY000511  ) at the  National Institutes of Health, U. S. A. (W. V. );  Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO)   G0D5817N  ,  G0B8617N  ,  G0C1920N  ,  G0E0520N  ,  VS02219N  (W. V. ); the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No  945539  ( Human Brain  Project  SGA3 ) (W. V. ); Hermann and  Lilly Schilling Foundation ;  German Research Foundation (DFG)  grants  WI 4046/1-1  and Research Unit  GA1475-B4 ,  KA 3726/2-1 ,  Primate Platform of DFG Center for Nanoscale Microscopy & Molecular Physiology of the Brain (CNMPB)  (M. W. and I. K. );  Wellcome Trust  (  WT092606AIA  ) (C. I. P. );  European Research Council Horizon 2020  ( ERC CoG ,  MECHI-DENT 724198 ) (C.
I. P. ); the  Bettencourt Schueller Foundation  ( Ultrabrain  project) (J. -F. A. ); the  French National Agency for Research  (  ANR-10-EQPX-15  ) (J. -F. A. ):and the  National Institutes of Health  (with  Matthew Howard III :  R01-DC04290  ) (C. I. P. ); All research discussed in this work was performed in accordance with institutional and nationally approved oversight, such as the  NIH Guide for Care and Use of Laboratory Animals , the  U. K. Animals (Scientific Procedures) Act ,  1986 , and European Directive  2010/63/EU.
, 2015) , both from changes in consumer demand for livestock products and expanding use of antimicrobials-both of which can alter AMR profiles of these cities. The International Metagenomics and Metadesign of Subways and Urban Biomes (MetaSUB) Consortium was launched in 2015 to address this gap in knowledge on the density, types, and dynamics of urban metagenomes and AMR profiles. Since then, we have developed standardized collection and sequencing protocols to process 4,728 samples across 60 cities worldwide (Table  S1 ). Sampling took place at three major time points: a pilot study in 2015-2016 and two global city sampling days (June 21st) in 2016 and 2017. Each sample was sequenced with 5-7 million 125bp paired-end reads using Illumina NGS sequencers (see STAR Methods). To deal with the challenging analysis of our large dataset, we generated an open-source analysis pipeline (MetaSUB Core Analysis Pipeline, CAP), which includes a comprehensive set of state-of-the-art, peer-reviewed, metagenomic tools for taxonomic identification, k-mer analysis, AMR gene prediction, functional profiling, de novo assembly, taxon annotation, and geospatial mapping.
Here, we present the results of our global analyses and a set of tools developed to access and analyze this extensive atlas, including two interactive mapbased visualizations for samples (metasub. org/map) and AMRs (resistanceopen. org), an indexed search tool over raw sequence data (https://metagraph. ethz. ch/search), a Git repository for all analytical pipelines and figures, and application programming interfaces (APIs) for computationally accessing results (https:// github. com/metasub/metasub_utils).
A core urban microbiome centers global diversity We first investigated the distribution of microbial species across the global urban environment. Specifically, we asked whether the urban environment represents a singular type of microbial ecosystem or a set of related but distinct communities, especially in terms of biodiversity. We observed a bimodal distribution of taxa prevalence across our dataset, which we used to define two separate sets of taxa based on the inflection points of the distribution: the putative ''sub-core'' set of urban microbial species that are consistently observed (>70% of samples) and the less common ''peripheral'' (<25% of samples) species. We also defined a set of true ''core'' taxa, which occur in essentially all samples (>97% of samples) (Figure  1A ). Applying these thresholds, we identified 1,145 microbial species (Figure  1B ), as defined by the NCBI annotation in KrakenUniq, that make up the sub-core urban microbiome with 31 species in the true core microbiome (Figure  1A ).
Core and sub-core taxa classifications were further evaluated for sequence complexity and genome coverage on a subset of samples. Of the sub-core species, 69 were flagged as being low-quality classifications (see STAR Methods). The sub-core microbiome was principally bacterial, with just one high-confidence eukaryote identified: Saccharomyces cerevisiae. Notably, no archaea or viruses were identified in the group of sub-core microorganisms. For viruses in particular, this may be affected by the DNA extraction methods used, limitations in sequencing depth, or missing annotations in reference databases used for classification. The three most common bacterial phyla across the world's cities ordered by the number of species observed were Proteobacteria, Actinobacteria, and Firmicutes. Despite their global prevalence, the core taxa were not uniformly abundant across all cities.
Our rarefaction curve did not reach a plateau, and even after including all samples, it still showed a marginal discovery rate of roughly one new species for every 10 samples added to the study. For clarity, we note that this analysis only considers taxa already present in reference databases, not newly discovered taxa (below). Despite the remaining unidentified taxa, we estimate that most (80%) of the classifiable taxa in the urban microbiome could be identified with roughly 1,000 samples. Since humans are a major part of the urban environment, the DNA in our samples could be expected to resemble commensal human microbiomes. To investigate this, we compared non-human DNA fragments from our samples to 50 randomly selected The number of samples collected from each region. samples from five commensal microbiome sites (stool, skin, airway, gastrointestinal tract, urogenital tract; 10 samples of each type) in the Human Microbiome Project (HMP)  (Consortium et al.
This suggests that unclassified DNA in our samples may represent uncharacterized taxa that are not known commensal or soil species. We next estimated the fraction of sequences in our data that did not resemble sequences in known reference databases. We took a subset of 10,000 reads from each sample and aligned these reads to four large-scale sequence databases using BLASTn  (Altschul et al. , 1990). We identified reads that mapped at 80%, 90%, and 95% average nucleotide identity (ANI) (Figure  1G ) to sequences in the RefSeq reference database, NCBI's NT Environmental database, a large set of Metagenome Assembled Genomes (MAGs) from  Pasolli et al. (2019) , and MAGs from MetaSUB itself (see widespread observation of biology not in reference databases). At 80% ANI, the most permissive threshold, we observed that 34.
6% of reads did not map to any database, while 47. 3% of reads did not map to any database except MAGs from MetaSUB itself. This mirrors results seen by previous urban microbiome works  (Afshinnekoo et al. , 2015; Hsu et al. , 2016). When we broke alignment rates down by region, we found that samples from Europe had the highest fraction of unaligned reads, followed by the Middle East, while samples from Sub-Saharan Africa had the smallest fraction of unaligned reads (Figure  S1B ). Previous ecological studies have observed a decrease in taxonomic diversity as the distance from the equator increases  (O'Hara et al. , 2017). Our data recapitulated this result and identify a significant decrease in taxonomic diversity (though with significant noise, p < 2e16, R 2 = 0. 06915) as a function of absolute latitude; samples are estimated to lose 6.
info). To gauge the impact of time, we also compared variation in matched sites from cities with two consecutive years of sampling on the summer solstice (June 21). While taxonomic change within a city between 2016 and 2017 was usually less than the difference between cities (Figure  S3D ), this may become a more important factor over longer time periods. We next quantified the degree to which metadata covariates influence the taxonomic composition of our samples using MAV-RIC, a statistical tool to estimate the sources of variation in a count-based dataset  (Moskowitz and Greenleaf, 2018)  according to each samples' metadata of city, population density, average temperature in June, region, elevation above sea level, surface type, surface material, elevation above or below ground, and proximity to the coast.
Microbial signatures reveal urban characteristics To facilitate more straightforward mapping and comparison of sequences, we created GeoDNA and MetaGraph (https:// metagraph. ethz. ch/search), a high-level web interface (Figure  3A ) to search raw sequences against the MetaSUB dataset. Users can submit sequences to be processed against a k-mer graph-based representation of the MetaSUB data and other sequence databases (e. g. , SRA). Query sequences are mapped to samples and collection metadata, and then a set of likely sample hits from around the world is returned to the user. This inter-face allows researchers to probe the diversity in this dataset and rapidly identify related genetic sequences, as well as the discovery of city-defining k-mers and sequences that might have forensic implications.
To test this idea of a sample's predictive capacity for mapping to its city of origin, we trained a Random Forest classifier (RFC) from the taxonomic profile of each metagenome. Specifically, we trained an RFC with 100 estimators on 90% of the samples in our dataset and evaluated its classification accuracy on the remaining 10%. We repeated this procedure with multiple subsamples of our data at various sizes (with five replicates per size) to show how performance varied with the amount of input data (Figure  3B ). The RFC achieved 88% on held-out data, which compares favorably to the 7. 01% that would be achieved by a randomized classifier. Of note, we obtained similar results even with lower numbers of estimators (e. g. , 10 estimators showed an accuracy of 78. 9%). These results from our RFC demonstrate that city-specific taxonomic signatures and kmers can be predictive for a sample's origin.
Antimicrobial resistance genes form distinct clusters Quantification of antimicrobial diversity and AMRs are key components of global antibiotic stewardship. Yet, predicting antibiotic resistance from genetic sequences alone is challenging, and detection accuracy depends on the class of antibiotics (i. e. , some AMR genes are associated with main metabolic pathways, while others are uniquely used to metabolize antibiotics). As a first step toward a global survey of antibiotic resistance in urban environments, we mapped reads to known antibiotic resistance genes, using the MegaRES ontology and alignment software. We quantified their relative abundance using reads/ kilobase/million mapped reads (RPKM) for 20 classes of antibiotic resistance genes detected in our samples (Figures  4A  and 4B ).
To examine these phenomena, we identified clusters of AMR genes that cooccurred in the same samples (Figure  4D ). We measured the Jaccard distance between all pairs of AMR genes found in at least 1% of samples and performed agglomerative clustering on the resulting distance matrix. We identified three large clusters of genes and numerous smaller clusters. Of note, these clusters often consist of genes from multiple classes of resistance, and the large clusters contain far more genes than are typically found on plasmids. Next, we performed a rarefaction analysis on the set of all resistance genes in the dataset, which we call the ''panresistome'' (Figure  S5D ). Similar to the rate of detected species, the panresistome also shows an open slope with an expected rate of discovery of 1 new AMR gene per 10 samples.
Given that AMR gene databases are rapidly expanding, and that no AMR genes were found in some samples, it is likely that future analyses will identify many more resistance genes in these data. Additionally, AMR genes showed a ''neighborhood'' effect within samples that are geographically proximal, analogous to the effect was seen for taxonomic composition (Figure  S5C ). Excluding samples where no AMR genes were detected, the Jaccard distance between sets of AMR genes increases with distance for pairs of samples in the same city. As with taxonomic composition, the overall effect is weak and noisy but nonetheless significant.
Widespread observation of biology not in reference databases To examine these samples for large genetic elements, we created metagenome-assembled genomes (MAGs) with meta-SPAdes to look for viral, bacterial, and archaeal genomes and for CRISPR arrays (see assembly methods). These MAGs comprised 1,304 total high-quality genomes, of which 748 did not match any known reference genome within 95% ANI. 1,302 of the genomes were classified as bacteria and 2 as archaea. Bacterial genomes came predominantly from four phyla: the Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidota. Bacterial genomes that did not match any reference were evenly spread across these phyla (Figure  5A ), and assembled bacterial genomes were often identified in multiple samples. Several of the most prevalent bacterial genomes were species with no known reference genome with >95% average nucleotide identity (Figure  5B ).
, 2019). This analysis revealed a set of 16,584 total UViGs. Taxonomic analysis of the predicted UViGS yielded 2,009 viral clusters, containing a total of 6,979 UViGs and 9,605 singleton UViGs for a total of 11,614 predicted viral species. Predicted viral species from samples collected within 10, 100, and 1,000 km of one another were agglomerated to examine their planetary distribution at different scales (Figure  5B ). At any scale, most viral clusters appear to be weakly cosmopolitan; the majority of their members are found at or near one location, with a few exceptions. We compared the MAG-derived viruses to known viral sequences in the Integrated Microbial Genome and Viral database (IMG/VR) at JGI, which contains viral genomes from isolates, a curated set of prophages, and 730,000 viral MAGs from other studies.
Finally, we used a database of 20 million metagenome-derived CRISPR spacers to provide further rough taxonomic assignments. Our predicted viral hosts aligned with our taxonomic profiles; 41% of species in the core microbiome (see a core urban microbiome centers global diversity) had predicted viral-host interactions. Many of our viral MAGs were found in multiple locations (Figure  5D ). Many viruses were found in South America, North America, and Africa, and viral MAGs in Japan often corresponded to those in Europe and North America. From these MAGs, we identified 838,532 CRISPR arrays, of which 3,245 could be annotated for specific CRISPR systems. The annotated CRISPR arrays were mostly type 1-E and 1-F, but a number of type II and III systems were identified as well (Figures  5E  and 5F ), and some arrays had unclear (ambiguous) type assignment.
, 2015; Hsu et al. , 2016) , airborne microbiomes  (Yooseph et al. , 2013) , work done by the Earth Microbiome Project  (Thompson et al. , 2017) , and others. As noted in Figure  1 , more sensitive alignment methodology only marginally increases the proportion of classified DNA. We consider the DNA that would not be classified by a sensitive technique to be truly unclassified DNA and postulate that it may derive from genes or species not in reference databases. Given that our samples did not closely resemble human commensal microbiomes or soil samples, it is possible this represents DNA sequences specific to the urban environment. The fraction of predicted viral sequences that belonged to previously unobserved taxa was particularly high in our study (94. 1%); however, taxonomic associations of these viruses to observed microbial hosts and associations with novel CRISPR sequences suggest these results are not spurious.