dataesr/scientific-paragraphs-categorization huggingface dataset:
The htSMLM graphical user interface (GUI) is provided as a configuration for µManager's EMU plugin. A pre-compiled version is subsequently installed and its various controls and indicators are associated with relevant hardware properties. 108. Install all manufacturer supplied software and drivers listed under: Software for software/hardware setup. Connect all hardware devices to the host PC, except for the piezo resonant stages and associated controller/distributor boards, as appropriate for the specific hardware device. 109. Test all connected hardware devices in their host software for general functionality. Return defective devices to the manufacturer for replacement. 110. Email firmware@smaract with the model and serial number of the COTS-HCU-3D stage controller to obtain firmware for ASCII communication with the device.
CAUTION! Ensure that the M6x30 mm dowel pin (3D-SMLM-COTS-91585A668) is installed to limit the stage motion. This pin provides a safety feature which, when the system is correctly aligned, prevents the illumination lasers from being inadvertently launched obliquely away from the short optical table edge closest to the microscope body. Nevertheless, follow all laser safety guidelines noted in: Laboratory environment. If the illumination control prism mirror mount (FAB-EMBL-000086) contacts the pin, it may be necessary to rehome the stage (COTS-ELL17-M) using its software (Thorlabs ELLO). 246. Move the inclination control stage (COTS-ELL17-M) to the position that it just contacts the pin and record this position. Rehome the stage and move the stage to: recorded position -0. 75 mm. This defines the nominal position required for non-inclined epi-illumination.
Acknowledgements We thank  Christian Kieser  ( EMBL Electronic Workshop ) for help with construction of and documentation for the microFPGA. We thank  Arthur Milberger  ( EMBL Mechanical Workshop ) for providing all mechanical drawings. We thank  Joran Deschamps  ( Human Technopole, Milan, Italy ) for providing the EMU htSMLM user interface and continued support in various aspects of microscope control. This work was supported by the  European Research Council  ( CoG-724489 ) and the  European Molecular Biology Laboratory.
460. If you have access to Matlab (R2022a or newer) including the toolboxes: Optimization, Image processing, Curve fitting, Statistics and Machine Learning, we recommend installing the Matlab version of SMAP from www. github. com/jries/SMAP, where you also find the installation instructions. 461. Alternatively, you can install the compiled stand-alone version of SMAP following the link on www. github. com/jries/SMAP. 462. Start SMAP. m in Matlab or the SMAP application when using the standalone version. Consult 'Getting started' in the 'Help' menu to familiarize yourself with SMAP. Here you also have access to the User_Guide. First, duplicate the file cameras_Hamamatsu. mat in the SMAP/settings directory and rename it to e. g. , cameras_local. mat. In the SMAP menu / Preferences… select the File tab and in the 'CameraManager file' field select the duplicated file.
We then calculate the connectivity of the coseismic landslides triggered by the 2015 Gorkha (Nepal) earthquake to the river network. We measure channel steepness through long river profile analysis and valley width along the study reaches, and we assess their relation to connectivity. Lastly, in light of the significant impact of the 2021 Melamchi event on the morphology of the Melamchi-Indrawati River, we further use optical satellite data and photographic images to identify the sources of sediment for the Melamchi flash floods. We manually mapped exposed gravel along the Melamchi-Indrawati and Bhote Koshi rivers every year from 2012-2021 to document changes after each monsoon. We used sub-metre resolution imagery from Maxar Technologies and CNES/Airbus, available in Google Earth Pro, for the time period between 2012 and 2019 inclusive, and 3 m resolution Planetscope imagery, 150 obtained through Planet's Education and Research Program, for 2020 onward (see Appendix A for the full breakdown of imagery dates and sources).
Constraining uncertainty in mapping All the mapping was carried out by one researcher. The rigorous uncertainty calculations following  Fan et al. (2018) , who had 175 five independent mappers and assessed the degree of matching between each of the five data sets and a reference mapping data set, could therefore not be applied here. Instead, we selected dates on which imagery was available from both Planetscope https://doi. org/10. 5194/egusphere-2022-1347 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License. and Google Earth Pro, and mapped the full study reach of each image five times, i. e. ten times in total for each river. For the Melamchi-Indrawati rivers, 22 April 2018 was selected, and for the Bhote Koshi, we chose 7/9 December 2017 and 10 December 2017 for Google Earth Pro and Planetscope, respectively.
Valley morphology and channel steepness We use the topographic analysis package LSDTopoTools  (Mudd et al. , 2021)  to extract river profiles, normalised channel steepness, and valley width along the reaches of the Melamchi-Indrawati and Bhote Koshi considered in this study. The aim of this analysis is to further identify potential zones of sediment storage. For valley width, we use the automated method developed by  Clubb et al. (2017)  which first identifies floodplains based on channel relief and local gradient before calculating the width of the extracted floodplain at each point in the channel. We apply the floodplain and valley width extraction to a 30 m resolution Copernicus Digital Elevation Model (DEM). In order to limit the likelihood of overestimating channel width, which typically occurs when channels become narrower than a couple of pixels, all tributary channels below a Strahler stream order of 3 are ignored.
Sediment source derived from satellite data Planet Explorer 3. 7 m optical satellite imagery was used to explore the likely sources of sediment during the Melamchi disaster. The satellite data confirm the break-out of the Pemdang Lake, and the accumulation of fresh sediment observed in the Bhemathan valley. But the data also show widespread sediment excavation in a number of neighbouring valleys in the catchment, suggesting increased discharge over a broad area rather than a point source (Fig. 10 ). For example, many of the channels in the upper Yangri Valley, immediately east of Melamchi, also show evidence of expansion of sediment accumulation, as does a smaller valley to the west. The location at which the headwaters of these channels were reactivated falls between an elevation range of approximately 4500-4800 m elevation, and extends along the range for at least 10 km between the valleys in an WSW-ENE orientation.
A3 Handling incomplete imagery For some dates, imagery either did not cover the entire study area or visibility was not good enough to reliably map the rivers (see Tables  A1  and A2 ). The latter was particularly a problem for Planetscope images in the upper reaches of the study area, and especially in the Bhote Koshi, since the NE-SW orientation and steep flanks of the valley create shadows. Where imagery   was not available or not sufficiently clear, these areas were not included in the data set. Since the figures in the results section show averaged mapped areas along the profile, it was necessary to ensure that the shorter sections of mapped area did not produce erroneous spikes. To that end, we removed the mapped area within one d p (i. e. 500 m) of either end of the mapped reach prior to smoothing the data with a rolling window.
Acknowledgements. ELSG was supported by  NERC  grant  NE/L002558/1. The authors thank  Simon Mudd  and  Jack Blunt  for helpful discussions. The scientific colour maps acton, batlow, batlowW and lajolla (Crameri, 2018) are used in this study to prevent visual distortion of the data and exclusion of readers with colour vision deficiencies (Crameri et al. , 2020). 35 https://doi. org/10. 5194/egusphere-2022-1347 Preprint. Discussion started: 12 January 2023 c Author(s) 2023. CC BY 4. 0 License.
Infant gut vOTUs are largely absent from gut virus databases We checked whether any of our 10,021 curated viral species were found within three gut virus databases built mainly on adult faecal metagenomic data. The Gut Virome Database (GVD)  29  contained only 819 of our vOTUs, while the larger and more recent Gut Phage Database (GPD)  28  and Metagenomic Gut Virus catalogue (MGV)  27  covered 2,307 and 2,171 vOTUs, respectively. Combined, 7,046 (70%) of the infant gut vOTUs identified here were not found in any of the three gut virus databases. At the family level, however, most of the 248 VFCs had some representatives in either database, with Crassvirales VFCs being particularly well represented in both GPD and MGV. Importantly, the majority of our most species-rich VFCs (for example, candidate family 'Amandaviridae') were poorly represented in all three databases, while the VFCs best covered by the databases were often minor in our data (Fig.
1 ). In other words, most large gut phage clades in databases are only occasionally found in the infant gut viromes, and vice versa. This pattern suggests that the infant gut is a unique niche harbouring specialized viruses distinct from the adult gut. Alternative explanations for this lack of overlap could be library selection differences (bonafide viromics in our case versus Phages, like bacteria, appear in the gut during the first months of life following a host-specific pattern  [11] [12] [13] [14]. Virulent phages undergo the lytic cycle in which they readily multiply and kill their host cell through lysis and release new virions into the ecosystem. Temperate phages can integrate into the bacterial chromosome, thereby becoming prophages. This prophage status postpones the killing of the host until certain environmental conditions induce the prophage to enter the lytic cycle.
Nevertheless large-scale studies establishing the early life virome composition and structure are sparse, and human virome studies in general have been challenged by the large proportion of uncharted viral diversity, which is sometimes referred to as the viral 'dark matter' problem  23. The latter means that only a small fraction of nucleic acid sequences in a virome can be linked to any known virus. Attempts at de novo virus identification have been limited by the lack of universal viral marker genes, while de novo classification of novel viruses into taxa was hampered by the lack of standardized methods. However, progress has been made in recent years  [24] [25] [26]  , leading to several human gut virus databases  [27] [28] [29]  , although these are still developing and currently lack viral taxonomies for all the novel viruses they contain.
2 ). Family-and order-level MRA and prevalence estimates were determined by first mapping sample reads to vOTUs, then aggregating their counts on the basis of taxonomic affiliation. All three measures were highly correlated (Fig. 2  and Extended Data Fig. 6 ), meaning that the most diverse VFCs and VOCs were also the most widespread and abundant. The correlation between these measures is predicted by the neutral community model, which also applies to bacterial community structures  42, 43. CRESS ROW AVI RAL ES PE TI TV IR A LE S T U B U L A V IR A L E S C R A S S V IR A L E S V O C 1 VOC2 V O C 3 VO C4 VOC 5 V O C 6 V O C 7 V O C 8 V O C 9 V O C In our data, vertebrate-infecting ssDNA anelloviruses (Anelloviridae) and bacterial ssDNA microviruses (Petitvirales) were amongst the most abundant viral clades (Fig.
Phage-host abundances are linked in spite of virus lifestyle Bacterial hosts for the vOTUs were predicted using 317,968 CRISPR spacers from our metagenome assembled genomes (MAGs)  38  , 11 million spacers from the CRISPR spacer database  57  and using WIsH  58. These predictions were merged by their last common ancestor. Bacterial host genera were predicted for 63% of the vOTUs, with 77% being covered at the order level (Fig. 4a ), and 79% at the host phylum level. Bacteroides was by far the most commonly predicted host genus followed by Faecalibacterium and Bifidobacterium. At the order level, however, approximately half of the annotated vOTUs had Clostridiales as hosts, with Bacteroidales covering just one-quarter (Fig. 4a ). This mirrors the corresponding pattern for the bacterial taxa in the metagenomes, where Bacteroides was the most abundant genus, while Clostridiales were more diverse (Fig.
Discussion The recent publication of several large and curated gut virus databases illustrates the massive diversity of the human gut viral community  [27] [28] [29]. Yet, significant parts of this ecological niche remain uncharacterized. A thorough description of the gut viruses is essential to understand their roles, especially if one aims at modulating the GM for prevention and treatment of chronic disease. We deeply sequenced 647 infant gut viromes and mapped the uncharted viral diversity by de novo assembly and classification. The approach led to the uncovering of 248 VFCs, 232 of which were previously unknown, and most of which belonged to the Caudoviricetes viral class. Temperate phages dominate the 1-year-old gut virome, and crAssphage is overshadowed by several previously undescribed viral clades.
Moreover, we made a thorough comparison linking plaque forming units to virome abundances (Extended Data Fig. 9 ). We conclude that our results on viral abundances are relevant in quantitative terms despite using sMDA, at least for dsDNA viruses. Skunaviridae, our most abundant caudoviral family, comprised only eight complete vOTUs in the dataset. This is atypical considering the hundreds of vOTUs in most of the other abundant viral families. All reference phages belonging to the family infect Lactococcus while our vOTUs were predicted to infect Streptococcus, but this could be an artefact caused by the lack of CRISPR-Cas systems in Lactococcus  63. Streptococcus, although very prevalent in the children, may not have been abundant enough to support the high counts of virulent Skunaviridae.
The COPSAC2010 cohort The study was embedded in the Danish population-based COPSAC2010 prospective mother-child cohort of 736 women and their children followed from week 24 of pregnancy, with the aim of studying the mechanisms underlying chronic inflammatory diseases  37  (Supplementary Table  1 ). The study was conducted in accordance with the guiding principles of the Declaration of Helsinki and was approved by The National Committee on Health Research Ethics (H-B-2008-093) and the Danish Data Protection Agency (2015-41-3696). Both parents gave written informed consent before enrollment. Faecal samples were collected for 660 participants at age 1 year.
6. 3) and aligning proteins all-against-all using FASTA  75 (v36. 3. 6f)  for building an APS tree  76  using custom code (https://github. com/ shiraz-shah/VFCs). The tree was cut close to the root to obtain the decontamination clusters. Bacterial MAGs from the same samples  38  were mined for CRISPR spacers using CRISPRDetect 77 (v2. 2), and the virome decontamination clusters were ranked by their extent of CRISPR targeting multiplied by sample prevalence. The protein alignment results were passed through an orthology filter  78  (https://github. com/ shiraz-shah/VFCs) and clustered using Markov clustering  79  (v14-137) to obtain VOGs de novo. VOGs were used to visualize the gene contents of contigs within each decontamination cluster. The top 400 ranked clusters were inspected visually for two viral signatures, namely conservation of contig sizes and of gene content.
OTU delineation and protein annotation Species-level (95% ANI) de-duplication of contigs into OTUs was done using BLAT  80  and custom code for clustering (https://github. Fig. 4  | Phages and their bacterial hosts in the 1-year-old infant gut. Prediction of bacterial hosts for the 10,021 vOTUs found in the infant gut virome shows that Bacteroides, Faecalibacterium and Bifidobacterium are the three most prominent host genera. a, Distribution of virus host predictions collapsed to bacterial order and genus levels, respectively. Numbers in parentheses denote the number of vOTUs with a given host genus or order, respectively. b, The top 100 gut bacterial genera found in gut metagenomes from the same infant faecal samples, as represented by a taxonomic tree. The MRA of each bacterial genus is shown in the blue heat map, while the fraction of the 647 infants harbouring the host genus (that is its prevalence) is shown with the brown bar plot.
Resource https://doi. org/10. 1038/s41564-023-01345-7 com/shiraz-shah/VFCs). Reference phages were de-duplicated to the species-level using the same strategy. Comparisons of the vOTUs to the GVD, GPD and MGV were also performed similarly. Decontaminated vOTUs and reference phage species  41  were pooled and the APS tree and VOGs were recomputed. Multiple sequence alignments (MSAs) of VOGs were constructed with MUSCLE 81 v3. 8. 425. VOG MSAs were aligned against MSAs from Pfam 82 , the Conserved Domains Database  83  , the Clusters of Orthologous Groups of proteins database  84  and TIGRFAMs 85 using HH-suite3 (ref. 86) v3. 0-beta. 3 to gain functional annotations.
Resolution of viral taxonomy We first used FigTree (v1. 4. 4) to root the APS tree by selecting an outgroup that branched out directly from the stem of the tree. Next we used phylotreelib and treetool (https://github. com/agormp/phylotreelib) to generate viral genera, subfamilies, VFCs and VOCs as follows. First, treetool's cladeinfo option was used to retrieve the distances from the root to the branch points corresponding to existing phage genera, subfamilies, families and orders  32, 35. Next, treetool. py's-clustcut option was used to cut the rooted APS tree at the above distances in order to obtain clades of both vOTUs and reference phages corresponding to viral genera, subfamilies, families and orders. The distances we used to cut the tree were 0. 250, 0. 125, 0. 04 and 0. 025, respectively, corresponding to average amino-acid identity (AAI) and coverage thresholds of 70%, 50%, 28% and 22% for each respective taxonomic level.
Abundance estimation Bacterial contamination was estimated for each virome sample using ViromeQC 40 (v1. 0) along with a custom approach where we leveraged the metagenomes cognate to each virome: Reads were mapped from both fractions against the 16S rRNA gene  90  and cpn60 (ref. 91) and the degree of contamination was calculated as the ratio between the two fractions. Abundances of vOTUs in each sample were determined by mapping sample reads to sample contigs using the Burrows-Wheeler Aligner 92 (v0. 7. 17-r1188) with the option mem -a, then using the msamtools (v0. 9. 6) profile to determine depth and length-normalized relative abundances with iterative redistribution of ambiguously mapped reads proportionally to uniquely mapped reads (https://github. com/arumugamlab/msamtools). The obtained contig abundances were then aggregated at the OTU level using custom code (https://github.
Benchmarking The versions of virus discovery tools used for benchmarking (Supplementary Table  2 ) were DeepVirFinder (v1. 0), VIBRANT (v1. 2. 1), VIRSorter (1. 0. 6), VIRSorter2 (v2. 0 commit 22f6a7d), Seeker (commit 9ae1488), PPR-Meta (v1. 1) and CheckV (v. 0. 7. 0). The random prediction was created by randomly sampling the 362,668 OTUs 12,500 times without replacement. The number 12,500 was chosen because it was reasonably close to our own positive set and the number of positives generated by most tools. 1  was drawn by first collating data at the family level using phyloseq  89  then using Circos v0. 69-8 (ref. 93) for rendering. Figures  2 3 4 , Extended Data Figs. 4-8 and corresponding statistical analyses were generated using the statistical software R and the tidyverse suite, including ggplot2 (ref.
94) and related add-on packages ggraph  95  , ggforce  96  , ggpubr  97  , ggrepel  98  , ggstance  99  and patchwork  100. For deriving unique branch lengths (Fig. 4 ), we used the function pd. calc from the caper package  101. The neutral. fit function from the MicEco R library (https://github. com/Russel88/MicEco) was used for fitting the family-level abundances to the neutral community model. colored by VOG affiliation. VOG definitions against the PhROGs database 87 can be looked up by clicking on each ORF. ORF gene product (GP) numbers are displayed by mouse-over hovering. GenBank files for each vOTU can be viewed along with virus and host taxonomy by clicking on the OTU name. Caudoviral maps were inverted and zeroed according to TerL gene coordinates, while the GenBank files were not.
Nature Microbiology Resource https://doi. org/10. 1038/s41564-023-01345-7 Extended Data Fig. 4  | Features of vOTUs versus non-viral sequence clusters within data. Distribution of size, MRA and sample prevalence for contaminant non-viral sequence clusters and curated vOTUs respectively. The vOTU size distribution shows peaks corresponding to genome lengths for the three major classes of viruses in the dataset, namely anelloviruses, microviruses and caudoviruses (3 kb, 5. 5 kb, and 40 kb). The contaminant size distribution peaks at the contig inclusion cutoff (1 kb) continuing with a long uniform tail, consistent with the unspecific origin expected for contaminating DNA. Curated vOTUs were more abundant and prevalent than contaminating species. The majority of the contaminating sequences were sample-specific, in contrast to most curated vOTUs which were found in more than one sample.
Figure1was drawn by first collating data at the family level using phyloseq 89  then using Circos v0. 69-8 (ref. 93) for rendering. Figures2-4, Extended Data Figs. 4-8 and corresponding statistical analyses were generated using the statistical software R and the tidyverse suite, including ggplot2 (ref. 94) and related add-on packages ggraph 95  , ggforce 96  , ggpubr 97  , ggrepel 98  , ggstance 99  and patchwork 100. For deriving unique branch lengths (Fig. 4), we used the function pd. calc from the caper package 101. The neutral. fit function from the MicEco R library (https://github. com/Russel88/MicEco) was used for fitting the family-level abundances to the neutral community model.
Acknowledgements We express our deepest gratitude to the children and families of the COPSAC2010 cohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team. This work is supported by the  Joint Programming Initiative  ' Healthy Diet for a Healthy Life ', specifically here, the  Danish Agency for Science and Higher Education ,  Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) , and the  Canadian Institutes of Health Research  ( Team grant on Intestinal Microbiomics, Institute of Nutrition, Metabolism , and  Diabetes , grant number  143924 ). M. B. D. is recipient of graduate scholarships from the  Fonds de Recherche du Québec-Nature et Technologies  ( 259257 ) as well as Sentinel North and is a recipient of the  Goran-Enhorning Graduate Student Research Award  from the  Canadian Allergy, Asthma and Immunology Foundation.
J. T. is supported by the  BRIDGE Translational Excellence Program  (bridge. ku. dk) at the  Faculty of Health and Medical Sciences, University of Copenhagen , funded by the  Novo Nordisk Foundation  (grant no. NNF18SA0034956 ). S. M. holds the Tier 1  Canada Research Chair in Bacteriophages  ( 950-232136 ). S. A. S. is a recipient of a  Novo Nordisk Foundation  project grant in basic bioscience ( NNF18OC0052965 ), along with M. A. R. , J. S. and D. S. N. are recipients of  Novo Nordisk Foundation  grant  NNF20OC0061029.