dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and future work This paper presents the ATLAS framework for design space exploration and tradeoff analysis of MRS architectures and CI algorithms. We described the framework's architecture, the core concepts of the ATLAS domain-specific language, and the ATLAS middleware. To this end, ATLAS is underpinned by: (i) a domain-specific modelling language, which enables the specification of MRS architectures, capabilities, and missions (including functional and non-functional requirements); and (ii) a code generation engine, which consumes models specified in the ATLAS language and produces the necessary infrastructure for experimenting on robotic simulators and platforms. The evaluation of the framework is based on an MRS healthcare case study built with ROS/Gazebo  [11]  and two MRS case studies built with MOOS-IvP  [12].
Cell viability assay in vitro The cytotoxic activity of ZBH-01 was evaluated against the above cell lines using MTT or Cell Counting Kit-8 (CCK8) assay as previously described  [9, 11]. HEK293 and HcoEpic cells were used as control cells. The different cell lines, including the colon cancer cell lines, were treated with different concentrations of ZBH-01, CPT-11, and SN38. After 72 h of incubation, the absorbance of cells in each group was measured at 450 nm using a spectrophotometer. The IC 50 values (50% inhibition of cell growth) were calculated by SPSS (Statistical Product and Service Solutions) 23. 0.
Paired-end sequencing was performed using Illumina HiSeq X Ten (Illumina, San Diego, CA). Raw data (raw reads) in the fastq format were processed by the Fastp software. All downstream analyses were based on clean high-quality data. Paired-end clean reads were aligned to the reference genome using STAR V20201. Cu inks v2. 2. 1 was used to count the number of reads mapped to each gene. The FPKM of each gene was calculated based on the length of the gene and the count of reads mapped to this gene. The resulting p-values were adjusted using Benjamini and Hochberg's approach for controlling the false discovery rate (FDR).
Differential expression analysis of mRNAs To compare mRNA expression differences among ZBH-01, CPT-11, and SN38 groups, a p-value < 0. 05 and |log 2 fold change| > 1 were set as the threshold for signi cantly differential expressions. First, the gene expression pro les of the three groups were compared to the control group. Then the three datasets obtained were analyzed together to obtain commonly expressed genes and a ZBH-01 group-speci c differentially expressed gene list.
Bioinformatics analyses Gene Ontology (GO) classi cations (biological process, cellular component, and molecular function) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were hierarchically investigated using 'clusterPro ler' in R 3. 4. 0 (R Foundation, Vienna, Austria)  [13]  and KEGG Pathway Database (http://www. genome. jp/kegg/pathway. html). The proteins encoded by upregulated and downregulated differentially expressed mRNAs (DEmRNAs) in the ZBH-01 group were used to construct a PPI network using STRING (https://string-db. org/) and Cytoscape 3. 5. 1 (http://www. cytoscape. org/, The Cytoscape Consortium, San Diego, CA, USA)  [14, 15]. We used Cytotype MCODE to identify key network modules and select hub genes.
The 20µL reaction volumes contained 1 µg RNA prepared by combining 4 µL transcript all-in-one supermix for qPCR, 1 µL gDNA remover, and variable RNase-free water. The reverse transcription reactions were performed for 15 min at 42°C and 5 s at 85°C. Each reverse transcription product was diluted 10 times by adding 180 µL H 2 O to 20 µL cDNA. The qPCR with TransStart Tip Green qPCR SuperMix kit (Transgen) was also performed according to the manufacturer's protocol. Brie y, 20 µL reaction volume were prepared by combining 10 µL transstart tip green qPCR supermix, 0. 4 µL passive reference dye, 0. 5 µL forward primer and 0. 5 µL reverse primer, 3 µL cDNA and 5. 6 µL ddH 2 O. The cycling conditions were: 94°C for 30 s, 40 cycles at 94°C for 5 s, and 60°C for 30 s. The relative gene expression data were analyzed by the 2 -△△CT method.
Flow cytometry To analyze the changes in the cell cycle, cell apoptosis, and mitochondrial membrane potential (MMP), SW1116 and LS174T colon cancer cells were harvested after exposure to 50 nmol/L ZBH-01, CPT-11, and SN38 for 12-72 h. Then, cells were stained by FxCycle™ PI/RNase Staining Solution (Invitrogen), FITC Annexin V Apoptosis Detection kit I (BD Biosciences, San Diego, CA, USA), and Mitochondrial Membrane Potential Detection Kit (BD Biosciences, San Diego, CA, USA) according to the manufacturer's protocol. Flow cytometry was performed using a FACS Calibur system (BD Dickinson). A minimum of 10,000 events was recorded for each sample. Data were acquired and analyzed by CellQuest (BD Biosciences), ModFit 4. 0 (BD Biosciences), and FlowJo (TreeStar, Inc. , Ashland, OR) software  [11].
3. 5 The proteins encoded by DEmRNAs speci c to the ZBH-01 group were prominently involved in cell cycle regulation Further, we constructed a PPI network (Fig. 5A ) based on the proteins encoded by ZBH-01 group-speci c DEmRNAs using the STRING database (http://string-db. org) [22]. We ltered out one prominent module (score = 61. 833) from the PPI network using Cytotype MCODE (Fig. 5B )  [23]. This module consists of 73 downregulated genes. Then, we conducted enrichment analysis again with these 73 genes demonstrating that they were principally associated with cell cycle, progesterone-mediated oocyte maturation, and oocyte meiosis. Fourteen genes (CDC45, CDC20, BUB1, CCNA2, BUB1B, TTK, CHEK1, CDC25C, MCM3, MCM7, ORC1, CDK2, CDC7, and PKMYT1) were involved in the most enriched cell cycle pathway (Fig.
An optimized Random Forest algorithm is proposed, and outputs are compared with an evaluation dataset. At building scale for all cities, at least 50% of the buildings have their height estimated with an error being less than 4 m (the city median building height ranges from 4. 5 m to 18 m). Two communes (Paris and Meudon) demonstrate building height results out of the main trend due to their specific urban fabric. Putting aside these two communes and when building height is averaged at regular grid scale (100m × 100m), the median absolute error is 1. 6 m and at least 75% of the cells of any city have an error lower than 3. 2 m. This level of magnitude is quite reasonable when compared to the accuracy of the reference data (at least 50% of the buildings have an height uncertainty equal to 5 m).
The objective of this study is to develop a method to estimate the height of a building from its topographical context using only data available in OSM. Modelling building footprints and their height value have been largely covered by remote sensing. It can cover large areas at once quite efficiently and the resulting data sets can be updated quite easily with a repetitive coverage. Different techniques for building height extraction have been developed, based on photogrammetric processing  (Fradkin et al. , 1999; Zeng et al. , 2014) , analysis of point clouds from airborne light detection and ranging  (Sohn et al. , 2008; Shan and Toth, 2018) , shadow detection  (Song et al. , 2013; Shao et al. , 2011)  and more recently deep learning approach  (Cao and Huang, 2021). In the mean time, the recent and global movement on open data, specifically for vector topographic databases such as OSM, offers new opportunities to estimate the building height.
The overall methodology is illustrated in Figure  1  and consists in the following steps: 1. Building characterization: each building and its environment are characterized by spatial indicators (building area, number of buildings neighbors, vegetation fraction, etc. ). These indicators are the independent variables of the statistical model. 2. Building height attribution: the reference building heights (BDT building heights) are attributed to each OSM building according to their footprints. The resulting height in the OSM dataset is the dependent variable of the statistical model.
Study area Building organization and height may differ a lot throughout the world, limiting the ability to model the height of a building based on the characteristics of its environment. Thus, although the method can be used to estimate the height of any building in the world, the application area of this preliminary work is limited to the French territory. The training and evaluation areas are selected to cover all types of commune (from small villages to large conurbations). Thus the dataset contains communes belonging to each of the four French commune types defined by the French National Institute of  Statistics and Economic Studies (INSEE, 2020) : main urban area, secondary urban area, peripheral urban area, rural area. According to the French 2020 census data, the types are defined based on the following communes characteristics: number of inhabitants, density of population, number of employees, population flow between households and workplaces.
70% of the training data is randomly drawn to construct the RF. The accuracy is calculated using the remaining 30% of the data. This process is performed ten times for each combination C i and uniqueness value U V i. The scikit-learn Python algorithm is used for this investigation. The optimized combination C opt and uniqueness value U V opt leading to the lowest Mean Absolute Error (MAE) are used to construct the final RF model used in Geoclimate. For this purpose, the entire training dataset is used as input of the Smile library algorithm (since Geoclimate is Java-based).
This absolute error magnitude is within the accuracy of the reference building data set. Indeed, according to the data supplier (IGN) information (based on a 7,299,422 buildings sample), 8. 2% of the buildings have an accuracy of 1 m, 13. 5% an accuracy of 2. 5 m, 68. 8% an accuracy of 5 m while 9. 5% have no accuracy information. There is a need for a world-wide database of morphological indicators useful for many physical process interests (eg. parametric urban climate models, noise modelling, urban planning). The GeoClimate tool aims to tackle this issue using the Open-StreetMap data. However, most of the OSM buildings do not have any information concerning their height while it is a crucial parameter for urban climate studies. A Random Forest model has been integrated within Geoclimate to estimate the height of a building based on spatial indicators describing its shape, its relations to other buildings and the 2D characteristics of its close environment.
Acknowledgements. The method presented in this paper has been integrated in the GeoClimate tool and developed within the following research projects: -URCLIM (2017-2021), part of  ERA4CS , a project initiated by  JPI Climate  and co-funded by the  European Union  under grant agreement No  690462 -CENSE  ( 2017-2021 ), funded by the  French National Research Agency (ANR)  under grant agreement No  Projet-ANR-16-CE22-0012.
Code and data availability. The major part of this work can be reproduced directly using the Software GeoClimate version 0. 0. 1 (the source code and executable file of this software version are permanently available on Zenodo at https://zenodo. org/record/6372337) and the scripts and data available on Zenodo at https://zenodo. org/record/6396361. GeoClimate downloads OpenStreetMap data using the overpass API from the end point https://overpass-api. de/api, estimates building height when missing and calculates geographical indicators. The resulting data set presented in this paper have been obtained using the OpenStreetMap data between June and September 2021. It can be freely accessed at https://zenodo. org/record/6396361. The French BDTopo (version 2. 2) is used only for training and evaluation purpose.
110buildings. Most of the OSM buildings do not have any information concerning their height. The few buildings with height information could have been considered as training and evaluation data set, however these buildings are sparse and not representative of common buildings (since most of them are filled by OSM users because they are well-known buildings with specificities). Therefore, the training and evaluation data set are created only with OSM buildings having no height. The reference height is set using the BDT data. However, a single building in OSM may match with several buildings in the BDT 115 (Figure 4).
1H ) analysis. Across both virus and host, our proteomics data captured 58,000-62,000 unique peptides (Fig. S2A ) mapping to 4800-5100 proteins (Fig. S2B ) per sample for abundance, and 23,000-35,000 unique phosphorylated peptides (Fig. S2C-D ) mapping to 3700-4300 phosphorylated proteins (Fig. S2E ), with strong reproducibility across biological replicates and experiments (Fig. S2F-G ). We found viral replication kinetics to be reproduced at the RNA and protein level in the RNA-seq (Fig. 1I ) and proteomics (Fig. S1B ) datasets, as well as at single-cell resolution using flow cytometry (Fig. S1C ). At the protein level, we noticed a slight decrease in protein production and/or viral spread for Beta in experiment 1. Of all detected molecules per dataset collected in Calu-3 cells, most changes during infection, relative to mock, were observed at the level of RNA (38%) and phosphorylation (57%) with fewer changes in protein abundance (7%; Fig.
(D) Pearson's R correlation between viral genomic (leader + Orf1a) RNA counts (R=0. 59) or sum of non-structural protein (Nsps) intensities (R=0. 77) versus VIC plotted against the AHR defined in (C). Each dot represents one virus condition at 24 hpi. (E) Pearson's R correlation between the average log2 fold change of each module, per dataset, and the RNA-derived AHR. Red colored dots indicate the 10 modules most correlated with the AHR based on a geometric mean across RNA, abundance proteomics, and phosphoproteomics datasets which we term the "composite R". Similarly, blue dots indicate the 10 modules least correlated with the AHR. (F) The 10 most (red) and 10 least (blue) correlated modules with the AHR, annotated by the most prevalent GO Biological Process term, module number, and number of genes within the module that connect to the top GO term.
Systems analysis reveals conservation and divergence of host response to variants We next sought to understand how the VOCs impact host cellular biology during infection by developing an integrative computational analysis of our systems level datasets and ranking the modulation of host cellular processes across the VOCs to (i) reveal therapeutic targets for panvariant antivirals by identifying similarly-hijacked cellular processes and (ii) uncover how viral evolution has led to divergent host responses. Compiling differentially regulated host genes and proteins (as compared to mock) from our mRNA sequencing, abundance proteomics, and phosphoproteomics datasets, resulted in 5284 genes regulated during infection (Fig. 5A , see Methods). We overlaid regulated genes onto the STRING network  (41)  and used measures of network proximity to cluster genes into 85 separate pathway modules (Fig.
This is intended to complement the tSNE plot presented in Figure  5G. (C) Network propagation analysis of multiomics datasets as done for integrative computational analysis to confirm findings using an orthogonal approach. MR, NM, SA, AH, HF, KO, JMF, MS, RH, IJ, MK, IE, KV, PB, RS, JAD, LMS, AP, MP, LM, KW, DLS, AG-S, CJ, LZA, GJT, NJK Visualization: MB, AKR, BJP, MU, LGT, AG-S, CJ, LZA, GJT, NJK Funding acquisition: AG-S, CJ, LZA, GJT, NJK Supervision: KV, PB, RS, JAD, LMS, AP, MP, LM, KW, DLS, AG-S, CJ, LZA, GJT, NJK Writing: MB, AKR, BJP, MU, LGT, AG-S, CJ, LZA, GJT, NJK Resources: MS DATA AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by NJK (nevan. krogan@ucsf. edu). The mass spectrometry proteomics infection proteomics and affinity purification mass spectrometry (APMS) data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.
proteomexchange. org) via the PRIDE partner repository  (74)  with the dataset identifiers PXD036968 (reviewer_pxd036968@ebi. ac. uk and password OoQuRtk5) and PXD036798 (reviewer username reviewer_pxd036798@ebi. ac. uk and password feVx3D2k), respectively. All mRNA sequencing data have been deposited in NCBI's Gene Expression Omnibus  (75)  and are accessible through GEO Series accession number GSE213759 (reviewer token cxshmmmaxpqtxuj). All other data are available in the main text or the supplementary materials.
Western blotting For western blotting, whole-cell lysates were separated by SDS-PAGE, transferred onto nitrocellulose and blocked in PBS with 0. 05% Tween 20 (v/v) and 5% skimmed milk (w/v). For detection of Spike, N, Orf6, Orf9b, spike, MX1, IFIT1 and β-actin expression membranes were probed with rabbit-anti-IFIT1 (CST, #14769, clone D2X9Z), rabbit-anti-MX1 (CST, #37849, clone D3W7I) rabbit-anti-SARS spike (Invitrogen, PA1-411-1165), rabbit-anti-Orf6 (Abnova, PAB31757), rabbit-anti-Orf9b (ProSci, 9191), Cr3009 SARS-CoV cross-reactive human-anti-N antibody (a gift from Laura McCoy, UCL) and rabbit-anti-beta-actin (SIGMA), followed by IRDye 800CW or 680RD secondary antibodies (Abcam, goat anti-rabbit, goat anti-mouse or goat antihuman). Blots were imaged using an Odyssey Infrared Imager (LI-COR Biosciences) and analyzed with Image Studio Lite software.
Flow cytometry of infected cells Adherent cells were recovered by trypsinization and washed in PBS with 2 mM EDTA (PBS/EDTA). Cells were stained with fixable Zombie UV Live/Dead dye (BioLegend) for 6 min at room temperature. Excess stain was quenched with FBS-complemented DMEM. Unbound antibody was washed off thoroughly and cells were fixed in 4% PFA before intracellular staining. For intracellular detection of SARS-CoV-2 nucleoprotein, cells were permeabilized for 15 min with intracellular staining perm wash buffer (BioLegend). Cells were then incubated with 1 μg ml-1 CR3009 SARS-CoV-2 cross-reactive antibody (a gift from L. McCoy) in permeabilization buffer for 30 min at room temperature, washed once and incubated with secondary Alexa Fluor 488donkey-anti-human IgG (Jackson Labs). All samples were acquired on a BD Fortessa X20 using BD FACSDiva software.
4NA. Four channel automated acquisition was carried out sequentially. Images were acquired across a well area density resulting in 31 FOV/well and ~20,000 cells. Images were pre-processed by applying a batch rolling ball background correction in FIJI ImageJ software package (79) prior to quantification. IRF3 translocation analysis was carried out using the Athena Image analysis software (IDEA Bio-Medical, Rehovot, Israel) and data post-processed in Python. Infected cell populations were determined by thresholding of populations with greater than 2 segmented dsRNA punctae.
mRNA quantitative analysis All reads were mapped to the human host genome (ensembl 101) using HISAT2 aligner  (83). Host transcript abundances were estimated using human annotations (ensembl 101) using StringTie  (84). Differential gene expression was calculated on the basis of read counts extracted for each protein-coding gene using featureCount and significance was determined by the DESeq2 R package  (85). On average, we quantified ~16,000-17,000 mRNA transcripts above 1 count per million reads.
SARS-CoV-2 genome reconstruction and evaluation from mRNA sequencing data Forward and reverse reads were merged into one file and were not treated as paired-end for denovo assembly. Reads were normalized such that the maximum genome coverage would be no more than 200x using bbnorm from the bbmap package (https://jgi. doe. gov/data-andtools/software-tools/bbtools/bb-tools-user-guide/bbmap-guide/). This aided assembly of genomes with high variability in coverage. Normalized reads were then assembled with SPAdes (86) (v3. 15. 3) with various k-mer sets ranging from 27 to 97. Assemblies were compared to the reference SARS-CoV-2 genome and complete assemblies of the entire genome were selected for further analysis.
Proteomics data search Mass spectra from each DDA dataset were used to build experiment-specific libraries for DIA searches using the Pulsar search engine integrated into Spectronaut v. 16. 0. 220606. 53000 (Hawking) by searching against a database of Uniprot Homo sapiens sequences (downloaded 22 March 2022) and a SARS-CoV-2 proteome of 149 protein sequences spanning five variants of concern (Alpha 20I V1, Beta 20H V2, Gamma 20J V3, Delta 21J, and Omicron 21K) and 32 viral proteins, including N-star (N*). For Experiment 3, directDIA search settings were used. For protein abundance samples, data were searched using the default Biognosys (BGS) settings, variable modification of methionine oxidation, static modification of carbamidomethyl cysteine, and filtering to a final 1% false discovery rate (FDR) at the peptide, peptide spectrum match (PSM) and protein level.
Proteomics quantitative analysis Quantitative analysis was performed in the R statistical programming language (v. 4. 1. 3). Initial quality control analyses, including inter-run clusterings, correlations, principal component analysis (PCA), peptide and protein counts and intensities were completed with the R package artMS (v. 1. 12. 1). On the basis of obvious outliers in intensities, correlations and clusterings in PCA analysis, one run was discarded from the phosphoproteomics dataset: Experiment 1, IC10_10h. 1. Statistical analysis of phosphorylation and protein abundance changes between mock and infected runs, as well as between infected runs from different variants were computed using peptide ion fragment data output from Spectronaut and processed using artMS. Quantification of phosphorylation differences was performed using artMS as a wrapper around MSstats, via functions artMS::doSiteConversion and artMS::artmsQuantification with default settings.
Quantifying viral RNA and protein Quantifying viral RNA Coronavirus RNA transcripts were characterized by the junction of the leader with the downstream subgenomic sequence. Reads containing possible junctions were extracted by filtering for exact matches to the 3' end of the leader sequence "CTTTCGATCTCTTGTAGATCTGTTCTC" or the mutated form "CTTTTGATCTCTTGTAGATCTGTTCTC" (mutation underlined) as appropriate for BA. 2, BA. 4 and BA. 5 using the tool bbduk from the BBTools package (version 38. 96; Bushnell B. -sourceforge. net/projects/bbmap/). This subset of leader-containing reads were then left-trimmed to remove the leader, also using bbduk. The filtered and trimmed reads were then matched against a full length genomic sequence with the bbmap tool from BBtools using settings (maxindel=100, strictmaxindel=t, local=t).
Targeted proteomics for Orf9b phosphorylation A spectral library was constructed from the DIA data to obtain Orf9b-specific transitions for abundance and phosphorylation measurements. Four Orf9b peptides were included to determine Orf9b abundance. To determine site-specific Orf9b phosphorylation, we included peptides representing Ser50 (LGS(+80)PLSLNMAR) and Ser53 (LGSPLS(+80)LNMAR). Unrelated heat shock peptides were included and used for normalization when appropriate. All samples were loaded on a 15 cm PepSep column (Bruker) packed with 1. 5 μm, 150 Å ReproSil C18 particles connected to a nanoLC easy 1200 (Thermo Fisher Scientific). For abundance measurements, peptides were separated with the following 50 minute gradient at 0. 6 μl min-1: 2% to 6% B (0. 1% FA in 80% ACN) in 1 minute to 25% B in 35 minutes, 34% B over 4 minutes, 90% B over 1 minute, followed by a 10 minute wash at 90% B.