dataesr/scientific-paragraphs-categorization huggingface dataset:
The ATLAS FCal calorimeter consists of an absorber matrix (made with copper, in the case of the electromagnetic module) instrumented with cylindrical electrodes parallel to the beamline. Each electrode is composed by a rod (anode) placed inside a tube (cathode). The small gap in between is filled with liquid argon, used as the active material of the sampling calorimeter. The distance of the particle to the active material is an important parameter to determine the characteristics of the originated shower. The frozen library currently used by ATLAS for the FCal fast simulation contains pre-simulated showers parameterized in bins of pseudorapidity η and d, the distance of the low-energy particle to the centre of the closest rod. The energy of the particle is also stored. During the simulation, the matching between the simulated particle and one entry of the library is performed by selecting the closest d and η bin and then finding the closest energy available within the bin.
Similarity search The new proposed strategy  [136]  employs similarity search techniques to select the most suitable particle-shower pair in the library. Such techniques constitute a branch of machine learning and include clustering and indexing methods that enable quick and efficient searches for vectors similar to each other  [137]. These methods are usually employed in applications such as image features search and document retrieval in large scale databases, for example in search engines. A new version of the library is also used. The stored showers are keyed by 6dimensional parameter vectors containing the angular and spatial information of the low-energy particles, as well as their energy. Similarity search techniques can be described as approximate nearest neighbour searches to find similar vectors.
A prediction for a query vector is made from data, by measuring the distance with the instances in the dataset and selecting the most similar ones. The similarity can be defined using different metrics, one of the most common being the Euclidean distance D(p, q) between two vectors across the layers, starting from the region characterized by the longest links. The HNSW is considered state-of-the-art among similarity search algorithms and provides a good trade-off between speed and accuracy both in high-and low-dimensional data.
However, the Faiss implementation is optimized for batch searches. The potential improvement due to batch searches was tested in a standalone mode, using two separate datasets, one as the library, and the other as a collection of query vectors. Fig. 20  shows that the CPU time decreases significantly as the batch size increases. This suggests that the fast simulation implemented in ATLAS would greatly benefit from batch searches. We offer a partial answer to the above question in this section, where we summarize the result of studies aimed at achieving the full end-to-end optimization of physics measurements based on injecting information on the final analysis goal in the dimensionality reduction procedures applied to collider data through supervised classification.
The misalignment problem The typical workflow of the measurement of a physical quantity from data collected by particle collisions at the LHC involves the construction of an informat-ive summary statistic-usually one-dimensional-from multidimensional highlevel features of observed data events, and the extraction of inference on the parameter(s) of interest by a likelihood fit or some other statistical procedure. The high-level features, usually individual four-momenta of identified relevant particles, are themselves the result of a large compression procedure that starts with millions of digital readouts of as many electronic channels; this procedure is generically addressed as event reconstruction. Event reconstruction, of which we have discussed some aspects in the previous Sections (Section 4, Section 7) is a very complex task, and in ATLAS and CMS the development of the relevant software requires an investment of hundreds of person-years; it involves thorough calibrations, tunings of data collection procedures, modelling of detector response, and a number of related studies.
We will not consider these procedures further in this section, other than noting that they necessarily target, as their optimization goal, suitable simplified surrogates of the true goals of the experiment. The complexity of the whole set of procedures that convert a raw dataset of digitized detector readouts into the measurement of a fundamental physical quantity prevent a direct optimization of the ultimate goal of experimentalists, which is usually the minimization of the total uncertainty on the measurement of a set of parameters of interest. For example, the electromagnetic calorimeter of an LHC experiment is naturally built to improve as much as possible the energy resolution and identification capabilities of energetic, isolated photons (see supra, Section 7). Those figures of merit are manageable optimization proxies for the discovery reach of the experiment to high-mass resonances decaying into pairs of photons, which is much harder to study and maximize directly, but in general they are not monotonous functions of the latter quantity, which is dependent on additional parameters and conditions.
In statistical terms, the summary is not sufficient: it does not make optimal use of the input information in a full-blown LHC data analysis is in progress at the time of writing; we fully expect that the solution of the misalignment problem discussed supra, provided by INFERNO, will yield much higher sensitivity to physics analyses. Following INFERNO, a few similar attempts have recently been published, see e. g. [152]. We direct readers interested in more detail on this area of research to a recent review of methods that address the incorporation of nuisance parameters in supervised classification for HEP research  [153].
Concluding Remarks The present document summarizes the most significant studies carried out by The effort that brought these studies to the fore could not have been undertaken without the resources made available by the European Union, as highlighted infra. However, the breadth of results and the significant stride they provide in the direction of more effective data analysis in fundamental physics research would have been impossible without the cohesive motivation of the young participants of the AMVA4NewPhysics training network. For that reason, it is necessary to stress here that an additional untold success of the network's action has been to provide the spark for that collaborative effort.
members of the AMVA4NewPhysics ITN in the years 2015-2019 within the context of searches and measurements of the Higgs boson and of LHC searches for new physics phenomena in datasets collected during Run 1 and Run 2 by the ATLAS and CMS experiments at the CERN LHC. The common denominator of the reported studies is the attempt to go beyond current data analysis practice in HEP experiments, exploiting state-of-the-art techniques from computer science and developing entirely new methods to extract the maximum amount of information from the invaluable datasets of proton-proton collisions.
Similar to the other solutions, ensembling was found to provide a large improvement, however we can see that domain-specific data aug- mentation also provides a significant benefit. Whilst smaller in terms of score improvement, the 1-cycle training schedule allows to halve the required train- ing time, and the dense connections provide some resilience to poor choices of network hyper-parameters. An illustration of the final model is shown in Fig. 1. Code and hardware details of the HiggsML solutions may be found below: • This Work (https://github. com/GilesStrong/HiggsML_Lumin): -GPU: NVidia 1080 Ti, <1GB VRAM, <1GB RAM -CPU (2018 MacBook Pro): Intel i7-6500U 4-core CPU, <1 GB RAM; • 1st place: Melis (https://github. com/melisgl/higgsml) -GPU: NVidia Titan, <24 GB RAM -CPU (AWS m2. 4. xlarge): 8vCPU, <24 GB RAM;.
A potential caveat in current studies is the exclusive reliance on stool samples, which only partly reflect the gastrointestinal (GI) microbiome and are oblivious to interindividual differences in GI probiotic colonization  25, 26. In this study, we performed an analysis of an existing metagenome dataset to characterize the human gut resistome in situ in endoscopy samples with paired stool samples and characterized the effects of antibiotics, probiotics and autologous faecal microbiome transplantation on the ARG reservoir in multiple cohorts  26, 27. We demonstrated that a commercially available 11-strain probiotic mix can reduce the number of ARGs in colonization-permissive, antibiotic-naïve individuals. In contrast, after a course of antibiotics, these probiotic strains exacerbated the antibiotic-mediated resistome expansion in the lower GI tract mucosa but did not contribute to the increase in ARGs from their own repertoire.
5e ). Extracting all the reads assigned by ShortBRED to this ARG and aligning them to the National Center for Biotechnology Information (NCBI) non-redundant database using BLASTX indicated that 54. 1% of the reads were specifically mapped to B. producta. Thus, in two distinct mammalian species, post-antibiotics probiotic supplementation was associated with expansion of the resistome in the GI tract. Interestingly, ARGs associated with vancomycin resistance bloomed in both mice and humans and likely stem from bacterial species that proliferate despite probiotic inhibition of microbiome recovery, rather than from the probiotic supplement itself. Mobile genetic elements (MGEs) may be involved in the spread of ARGs by horizontal transfer between bacterial strains. To assess the potential for horizontal transfer of the ARGs blooming post-antibiotics and probiotics, we quantified the mobilome in all metagenomic samples using ShortBRED in combination with a curated database of transposases, integrases, recombinases and integrons  34.
NATuRE MICRobIoloGy they were also present in a high percentage of strains of the same species (Extended Data Fig. 6c ) and in high prevalence in many other species of the same genera (Extended Data Fig. 6d ) in the NCBI database. Thus, the presence of ARGs is not uncommon in probiotic strains, although their presence does not necessarily imply phenotypic resistance to antibiotics; other mechanisms (Figs. 4  and 5 ), rather than horizontal transfer, can mediate probiotic-associated resistome expansion. We next sought to determine whether other probiotics studies reflect our findings. To our knowledge, there are no additional publicly available datasets displaying shotgun metagenomics data from in situ GI tract samples of probiotic-supplemented individuals. Therefore, we analysed data from published studies that utilized stool samples as a proxy of gut-related microbiomes.
5 ± 8. 24 years, mean body mass index 24. 9 ± 4. 14 kg m -2 ); (3) same protocol but no post-antibiotic intervention (n = 7, 14. 3% female, 36 ± 6. 83 years, mean body mass index 23. 77 ± 1. 95 kg m -2 ). Additional information regarding the experimental protocols and cohort data can be found in the published works  26, 27. The human trials were approved by the Tel Aviv Sourasky Medical Center institutional review board (approval nos. TLV-0553-12, TLV-0658-12 and TLV-0196-13) and Weizmann Institute of Science Bioethics and Embryonic Stem Cell Research oversight committee (approval nos. 421-1, 430-1 and 444-1) and were reported to https://clinicaltrials. gov/ (identifiers: NCT03218579 and NCT01922830). Written informed consent was obtained from all participants. Probiotic supplements. All probiotics groups received the same supplement (Bio-25, SupHerb), which contained 11 common probiotic strains: Lactobacillus acidophilus; Lactobacillus casei; Lactobacillus paracasei; Lacticaseibacillus rhamnosus; Lactiplantibacillus plantarum; Bifidobacterium bifidum; Bifidobacterium breve; Bifidobacterium longum subsp.
longum; Bifidobacterium longum subsp. infantis; Lactococcus lactis; and Streptococcus thermophilus. The quantity and viability of the strains was performed in our published work  26. We performed shotgun metagenomic sequencing to 18 supplement tablets from different batches. We further performed single-end metagenomic sequencing of three tablets of three additional oral probiotic supplements: Culturelle (L. rhamnosus GG); VSL#3 Since samples were sequenced in a single-end configuration, the resistome profile was analysed using the ShortBRED and CARD databases. Mice. This work includes newly performed shotgun metagenomic sequencing of intestinal lumen microbiome samples collected from mice in a published study  27. In this experiment, 8-week-old male C57BL/6J mice (average initial weight 20 g) were purchased from Envigo and allowed to acclimatize to the animal facility environment for 2 weeks before the experiments.
Microbiome composition analysis. Reads were preprocessed with fastp  47  for adaptor removal and base quality sliding window trimming. Host reads were removed by Bowtie2 v. 2. 4. 1 (ref. 48 ) using the human (hg37dec_v0. 1) or mouse genome reference (C57BL_6NJ). The cleared FASTQ files were subsampled using Seqtk v. 1. 3-r114 (https://github. com/lh3/seqtk). We carried out the taxonomic assignment of bacterial DNA relying on exact alignment of k-mers with Kraken2 v. 2. 0. 9 (ref. 49 ) against the Genome Taxonomy Database release 89 (https://gtdb. ecogenomic. org/). To improve the accuracy of species level classification, we applied Bayesian re-estimation of bacterial abundance with Bracken v. 2. 5. 3 (ref. 50 ). Analysis of antibiotic resistance gene content. For ARG quantification, four different pipelines were used.
Subsampled quality-controlled reads were processed with ShortBRED v. 0. 9. 5 (ref. 30 ) using CARD v. 1. 05 (ref. 29 ) as a reference database to define the composition and abundance of ARGs of each sample. This database includes, by expert human curation, the known molecular sequences and mutations conferring resistance to antibiotics with clinical relevance. ARGs are classified into ARG families (genes with similar function) and drug classes (types of antibiotics targeted by ARGs). Subsampled FASTQ files were also processed with ARG-OAP v. 2. 0 to obtain the annotation of ARG profiles. ARG-OAP v. 2. 0 provides model-based identification of assembled sequences using SARGfam, a high-quality profile Hidden Markov Model containing profiles of ARG subtypes and including cell number quantification by using the average coverage of essential single-copy marker genes  28.
The mean sequencing depth was 4,305,780. 54 (s. d. = 4,644,960). Several subsampling sizes (1 M, 1. 5 M, 2 M, 3 M, 4 M) were tested in all analyses. Comparative analysis using the aforementioned methods highlighted a trade-off between specificity and sensitivity: the ShortBRED algorithm uses protein markers generated against a background protein reference database (for example, UniRef) that could lead to higher specificity compared to other algorithms. By contrast, the ARG-OAP pipeline includes an ARG database with curated and complete ARG sequences, improving the coverage of ARG detection. Due to the unique nature of this study, including stool samples paired with endoscopy samples at relatively low sequencing depth, we selected 1. 5 M and 2 M based on the saturation of resistome alpha diversity (Shannon index) and to maintain sufficient sample size and sensitivity for ARG detection and quantification in all comparisons.
We employed cross-validation between ARG-OAP and either ShortBRED and CARD or the NCBI non-redundant database. Analysis of MGE content. Subsampled quality-controlled FASTQ files were processed with ShortBRED v. 0. 9. 5 using a reference database of MGEs (transposases, integrases, recombinases and integrons) curated by NanoARG  34. Correlation analysis. ARG and MGE abundances were systematically correlated with species abundances using linear models. Benjamini-Hochberg correction was used for multiple hypothesis testing. Detection of ARG types in Bio-25 tablets. Reads from three paired-end sequenced Bio-25 tablets were coassembled using SPAdes v. 3. 14. 1 (metagenomic mode)  51. The repertoire of ARGs present in the assembled contigs was detected using sraX v. 1. 5 (ref. 52 ) with the ARGminer database v1.
1. 1 (ref. 53 ). Analysis of NCBI genomes. Protein FASTA files from 18,831 strains of the Bio-25 species and other species from the same genera were downloaded from the NCBI. Sequences were aligned to the ARGminer database v. 1. 1. 1 (ref. 53 ) using BLASTP (identity > 85 and length > 60). Only those ARGs previously detected in the assembled contigs were quantified. For each species, the percentage of strains containing an ARG was computed. Analysis of published probiotics studies. Samples were downloaded from their respective NCBI BioProjects (PRJNA643353, PRJNA554501 and PRJNA324129).
acknowledgements We thank the members of the  MiHAR laboratory , members of the  Elinav laboratory , the  Weizmann Institute of Science and members of the German Cancer Research Center (Cancer-Microbiome Division ) for discussions. We thank the  Genomics and Bioinformatics Core Facility of Nantes (GenoBiRD, Biogenouest)  for its technical support. R. V. M. is supported by a  La Caixa Foundation  Postdoctoral Fellowship. E. E. is supported by the  Leona M. and Harry B. Helmsley Charitable Trust, Adelis Foundation ,  Pearl Welinsky Merlo Scientific Progress Research Fund, Park Avenue Charitable Fund, Hanna and Dr. Ludwik Wallach Cancer Research Fund ,  Daniel Morris Trust ,  Wolfson Family Charitable Trust  and  Wolfson Foundation, Ben B. and Joyce E. Eisenberg Foundation ,  White Rose International Foundation ,  Estate of Malka Moskowitz, Estate of Myron H.
code availability No custom code was used in the analysis. The following databases were used: H. sapiens, GRCh37 (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/bigZips/); Mus musculus, C57BL/6J (https://www. ncbi. nlm. nih. gov/genome/52?genome_ assembly_id=422183); Genome Taxonomy Database release 89 (https://data. gtdb. ecogenomic. org/releases/); CARD v. 1. 05 (https://card. mcmaster. ca/download); Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Data and code availability.
All shotgun metagenomics sequencing data analyzed in this work can be found in the European Nucleotide Archive (https:// www. ebi. ac. uk/ena/browser/home) under accession numbers ENA: PRJEB28097 (human and Bio-25 pills) and PRJEB42567 (mouse and all probiotic pills). No custom code was used in the analysis. The following databases were used in the analyses: H. sapiens, GRCh37 (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/bigZips/), Mus Musculus, C57BL_6nJ (https://www. ncbi. nlm. nih. gov/genome/52?genome_assembly_id=422183), Genome Taxonomy Database r89 (https:// data. gtdb. ecogenomic. org/releases/), Comprehensive Antibiotic Resistance Database (CARD) (1. 05) (https://card. mcmaster. ca/download), Database of mobile genetic elements (transposases, integrases, recombinases and integrons), curated by NanoARG (1.
Methods Between June 2017 to December 2018, 56 patients had PPVI with Sapien3 (Edwards, USA) valves in Marie Lannelongue hospital. We retrospectively selected twelves patients with the largest RVOT and/or with the most challenging procedures (RVOT> 26 mm or tortuous shape). Additionally, we included 3 consecutive patients with very large RVOT (>27 mm on the CT scan) that were referred to surgery after IBD. CT scan data of these patients were transferred for external 3D printing of their RVOT. In these 15 included patients was performed a retrospective multi-observer analysis of RVOT morphology and diameters using CT scan 3D post-processing and using 3D printed models compared to IBD and per-procedural results. Indications for PPVI met current guidelines  [3, 10]. Procedures were performed as previously described  [1, 15].
RVOT Morphology, presence of other abnormalities, and the proximity of the LZ with the coronary arteries were noticed. (Fig. 2 ). 3D printed models were built automatically from cardiac cavity segmentation on CT-derived DICOM data and then adjusted manually. Each structure was segmented separately: PA/RVOT paying attention to high resolution of trabecula's, aortic root and coronary arteries based on a grey scale, using alternatively Maximal or minimal intensity signal rendering to enhance cavity or the cardiac tissue (Minip) and add one to another to obtain a final STL object (IntelliSpace Portal Philips V10) and then exported to computer-aided design software (Geomagic,Inc. , Version 10, 32-bit) for post-processing. A high-resolution 50-model was then 3D printed on a laser-sintering printer using thermoplastic polyurethane with a distensibility shore A 50 precision of 50 μm, thickness of 1.
Statistics Statistical analyses were performed on stata software version 11. 2 (StataCorp, College Station, TX, USA). Non-normally distributed quantitative variables are shown as median and interquartile range. Clinical and procedural data correlation was studied by Rho of Spearman and linear regression test. Band Altman method was used to calculation of mean bias AE 2SD. Inter-operators agreement was evaluated by Kappa  coefficient. Alpha risk was set at 5%.
We also experimented with confocal resonant scanning and subsequent denoising with Denoise. ai (NIS-Elements AR software) to reduce image acquisition time at the cost of obtaining high resolution (Figure  S1 ). A whole depot was imaged in only 2h 35min (Figure  S1A ). Unfortunately, we found that by reducing resolution to image faster, we lost most of the small neurites when compared to the slower point scanning approach (Figure  S1B ). Because of this we have determined that higher resolutions and longer imaging times are required to capture an accurate representation of parenchymal innervation. However, depending on an investigator's needs resonant scanning can provide very fast information of gross morphological changes within the tissue. Although the use of light sheet microscopy can decrease image acquisition time from hours to minutes, in our experience (using multiple platforms) imaging an intact whole adipose depot from various physiological states was extremely challenging with commercially available models.
Uniform staining of large whole mount tissues has always been problematic due to the increased incubation times required for antibodies to completely diffuse throughout the tissue  (Chung et al. , 2013; Kim et al. , 2015). Incubation times were reduced by 1-2 days by reducing tissue thickness with ZDR. Depth coding of immunostained scWAT following ZDR indicated homogeneous penetration of antibodies throughout the whole tissue (140mm total depth) (Figure  3C ). This was confirmed for various fluorescent staining approaches, including directly labeled b3-tubulin, indirectly labeled TH, and IB4 staining. Depth information can even be somewhat retained following Z-maximum projecting by creating a height color coded projection, albeit with no accompanying color-coded depth scale provided at this time due to LASX software limitations (Figure  S3F ).