dataesr/scientific-paragraphs-categorization huggingface dataset:
KEGG pathway and GO analyses of DEGs GSA using the KEGG pathway database revealed significant (q<0. 05, GAGE) upregulation of 67 biological pathways in drone antennae and 3 biological pathways in forager antennae (see https:// doi. org/10. 6084/m9. figshare. 12089370. v1, Table  S5 ). Two of the most significant pathways (lowest q-values) in drone antennae were oxidative phosphorylation (ame00190) and protein processing in the endoplasmic reticulum (ame04141) (Fig. S1 ). In contrast, in worker antennae, the two most significant pathways were neuroactive ligand-receptor interaction (ame04080) and tyrosine metabolism (ame00350) (Fig. S2 ). GO enrichment analysis using DEGs (P adj <0. 05, g:Profiler) with more than 2-fold expression differences (539 DEGs in drones and 973 in foragers) showed significant enrichment of 57 and 95 GO terms in drones and foragers, respectively (P<0.
Acknowledgements We are grateful to  W. Roessler  and  J. Spaethe  for constructive discussion regarding the project and experimental procedure during a research stay of R. J. at the  University of Wuerzburg. We thank  S. Unnikrishnan ,  W. Roessler ,  J. Spaethe ,  T. Tanimura  and  F. Marion-Poll  for valuable and instructive comments on an earlier version of the manuscript.
1b ). M/D-driven tumors often displayed: (1) a relatively monotonous population of cells, poorly circumscribed, infiltrating the surround soft and adipose tissues in a loose pattern, (2) cords and nodules of atypical epithelial cells, with some duct or gland formation, and (3) some degree of vascular and lymphatic permeation (Fig. 1c ). Thus, M/D-driven tumors histologically resemble to human HR + BCs. Transcriptomic studies based on The Cancer Genome Atlas (TCGA) database for human BC as a reference revealed that M/D-driven tumors display a striking similarity to highly proliferative HR + HER2 -(luminal B) human BC (Fig. 1d  and Supplementary Fig. 1d ). Immunofluorescence microscopy confirmed that untreated M/D-driven tumors express estrogen receptor 1 (ESR1, best known as ER) but not vimentin (VIM, a marker of basal BC) (Supplementary Fig.
In a preclinical model of benzo(a)pyreneinduced lung carcinogenesis, oral (but not nasal) administration of NAM also demonstrated oncopreventive effects  41. Thus, NAM might exert a relative broad oncosuppressive action, contrasting with other vitamins such as pyridoxine and calcitriol, which failed to enable oncoprevention in observational or interventional clinical studies  [42] [43] [44]. Of note, haploidentical or mismatched related donor NK cells expanded in the presence of NAM are currently being investigated for the treatment of refractory/ relapsed multiple myeloma and non-Hodgkin's lymphoma (NCT03019666), corroborating the elevated potential of NAM as an immunostimulant for clinical applications. As for mode of action, it appears plausible that NAM acts as a therapeutic agent by re-establishing T cell-dependent immunosurveillance via both direct (T cell activation) and indirect (type I IFN and CCL2 secretion by cancer cells) mechanisms.
Alternatively, mouse Gzmb, Ifnb1, Ifng and, Prf1 levels were quantified relative to Rpl13 levels by 1-step RT-PCR based on commercial primer and probe sets (Gzmb, #Mm00442837_m1; Ifnb1, #Mm00439552_s1; Ifng, #Mm01168134; Pfr1, #Mm00812512_m1; Rpl13, #Mm02526700_g1) and the TaqMan™ Fast Virus 1-Step Master Mix (#4444434), all from Thermo Fisher. Amplifications were run on a 7500 RT-PCR system (Applied Biosystems) operated by embedded software v. 2. 3 as per thermal protocols provided by the primer manufacturer. RT-PCR data was normalized according to the ΔC t or ΔΔC t methods. CRISPR/Cas9. TSA cells were transfected with a control commercial CRISPR-cas9 plasmid (#CRISPR06-1EA, from Millipore Sigma) or with a CRISPR-cas9 plasmid specific for Atg7 (custom-made by Millipore Sigma based on #CRISPR06-1EA), using the TransIT-CRISPR® reagent (#T1706, Sigma Aldrich).
Statistical significance on growth curves was assessed by the TumGrowth software v. 1  58  or two-way ANOVA corrected for row (time) and column (treatment) factors. Statistical significance on Kaplan Meyer curves (TFS, OS and TTD) was assessed by two-sided log-rank (Cox proportional hazards model) or one way-ANOVA plus Fisher LSD test. Statistical analysis-in vitro, imaging and flow cytometry assays. Statistical significance on RT-PCR, immunofluorescence microscopy, and flow cytometry (including cell death) data were assessed by one way-ANOVA plus Fisher LSD test for comparisons involving more than two groups of samples or unpaired, twosided Student's t test for comparisons involving only two groups of samples. Statistical analysis-bulk RNAseq. For data presented in Fig. 1 , we used the Affymetrix © Mouse Gene v.
1. 0 ST Array. Data were normalized using Affymetrix © TAC v. 4. 0. 1. Analyses was performed within the R computational environment v. 3. 4. Specifically, genes were selected according to their differential expression by using limma v. 3. 32. 10 (ref. 59 ), using as thresholds |logFC| > 2 and p value < 0. 05. GO enrichment was analyzed using the DAVID Functional Annotation Tool v. 6. 8 (ref. 60 ) on differentially expressed genes. For comparing mouse RNAseq data with gene expression in human BC (data presented in Supplementary Fig. 1 ), we harnessed microarray data from the TCGA database. In the R computational environment v. 3. 6. 0, we renormalized mouse data (centered and reduced) in order to obtain similar distribution as human data. We then applied the function subtype. cluster of genefu v.
2. 16. 0 (ref. 61 ) based on previously published BC classification algorithms  62, 63. Other classification methods of the package genefu produced similar results. Heatmap with hierarchical clustering analysis was done using ComplexHeatmap v. 2. 0. 0 based on the Euclidean distance and ward. D2 clustering method. ENSEMBL Genes 100 was used to identified orthologous genes between human and mouse. For data presented in Fig. 6  and Supplementary Fig. 3 , analysis was performed by GenoSplice technology (www. genosplice. com). Reads were mapped using STAR v. 2. 4. 0 (ref. 64 ) on the mm10 mouse genome assembly, followed by gene expression analysis  65. In brief, for each gene present in Mouse FAST DB v. 2016_1 annotations, reads aligning on constitutive regions (that are not prone to alternative splicing) were counted.
Based on these counts, normalization and differential gene expression were performed using DESeq2 v. 1. 28. 1 (ref. 66 ) on the R computational environment (v. 3. 2. 5). Only genes expressed in at least one of experimental conditions were analyzed further. To this aim, genes were considered as expressed if their rpkm value was greater than 93% of the background rpkm value based on intergenic regions. Results were considered statistically significant for p values ≤ 0. 05 and fold changes ≥ 1. 5. Gene modules were defined based on coexpression of genes within a given module. All genes within a given module had to be correlated to each other. A minimum Pearson correlation coefficient of 0. 7 (i. e. , only positive correlation) was required and a minimum of 50 genes was used to define a module.
The 10X Genomics Cell Ranger Single Cell Software Suite v2. 1. 0 was used to perform sample de-multiplexing, alignment (based on the mm10 mouse genome assembly), filtering, UMI counting, single-cell 3′end gene counting and performing quality control, using the manufacturer parameters. Unsupervised cell clustering was carried out using Seurat v. 2. 3. 4 (ref. 67 ) in the R computational environment v. 3. 5. 3. Cells with <1000 genes or <3000 UMIs or >10% mitochondrial genes and genes detected in <3 cells were excluded from the analysis. Gene expression raw counts were normalized following a global-scaling normalization method with a scale factor of 10,000 and a log transformation, using the Seurat NormalizeData function. The top 2775 highly variable genes were selected, followed by principal component analysis (PCA).
Acknowledgements We are grateful to  F. Lenfant  ( INSERM U1048, Toulouse, France ) and  J. Penninger  ( Institute of Molecular Biotechnology, Vienna, Austria ) for kindly providing us with mice bearing genetic alterations in Esr1 and Atg5, respectively. We are indebted to  B. Levine  ( UT Southwestern, Dallas, TX, USA ) and  Dr. M. F. Krummel  ( UCSF, San Francisco, CA, USA ) for kindly providing us with Becn1 +/-and MMTV-PyMT mice, respectively. We thank  Genom'IC  ( Cochin Institute, Paris, France ) and  GenoSplice (Paris, France)  for help with RNA-Seq analysis. We thank  S. Hoda  ( Weill Cornell Medicine, New York, NY, US ) for providing human breast cancer microphotographs,  G. Inghirami  ( Weill Cornell Medicine, New York, NY, US ) for pathology assessments,  G. Petroni  ( Weill Cornell Medicine, New York, NY, US ) for critical reading and help with the finalization of the paper, and the  Proteomics and Metabolomics Core Facility of Weill Cornell Medicine (New York, NY, US)  for NAM quantification.
M. P. L. is supported by  Fondation pour la Recherche Médicale  ( FRM FDT201904008383 ). J. H. is supported by the Foundation Philanthropia. B. S. is supported from grants by the  German Cancer Aid  (# 70113311  and # 34102524 ). G. K. is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR) -Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases; Association pour la recherche sur le cancer (ARC) ;  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINO-TAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085  and  GDW20181100051 ),  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology ; the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
Data availability Transcriptomic data generated in the context of this study have deposited at Gene Expression Omnibus (GEO) and are publicly available under accession numbers GSE150921, GSE150966, GSE150967 and GSE151197. The Cancer Genome Atlas (TCGA) breast cancer (BRCA) dataset is publicly available at https://xenabrowser. net/ datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA) &removeHub=https%3 A%2 F%2Fxena. treehouse. gi. ucsc. edu%3A443. The mm10 mouse genome assembly and ENSEMBL Genes 100 can be accessed at https://useast. ensembl. org/Mus_musculus/Info/Index and https://www. ensembl. org/biomart/ martview/eb26fab860902330900c3b118e3cd906, respectively. GO terms, KEGG pathways, REACTOME pathways and Hallmarks terms are freely available to academic users at http://geneontology.
T h eI n F u s ec o n s o r t i u m ,ap a r t n e r s h i po fs i xa c a d e m i c and industrial actors in the European space sector, has developed a modular software architecture for the design, implementation, evaluation, and onboard deployment of multisensor data fusion algorithms. This software architecture is called the Common Data Fusion Framework (CDFF): common because it supports a wide range of sensory modalities and is not specific to any robotic middleware, and framework because it is "a collection of software tools, libraries, and conventions, aiming at simplifying the task of developing software for a complex robotic device. "  3  This framework is designed for the needs of space robotics but can be used in any robotic application domain, but it does have a particular focus on space robotics.
Relevance to space has been ensured through the guidance of European Union (EU)-mandated representatives of the European Space Agency (ESA) and several national space agencies. We have released it as free and open-source software (https://gitlab. com/h2020src/og3), with optional proprietary components provided by the french and german space agencies (CNES and DLR) for the usage of follow-up EU-funded consortia. At the conceptual level, the CDFF implements multisensor data fusion tasks by breaking them down into small atomic data processing tasks organized in a data flow graph, with sensor data flowing in and data fusion products flowing out. At its core, it features a collection of reusable software modules that implement a large number of atomic data processing tasks, each with a software library written in Cþþ that exposes a common interface and uses Abstract Syntax Notation One (ASN.
Related work Multisensor data fusion. Decades of research in robotics have brought about a large variety of multisensor data fusion algorithms, many of which have been released as programs or libraries of varying quality, reusability, and adoption rate. These algorithms and their implementations offer dynamic state estimation, environmental modeling, or both. State estimation, for instance, can be achieved at low computational cost using a nonlinear complementary filter  4  or a gradient descent algorithm  5  that fuses together angular velocities, linear accelerations, and optionally magnetic field readings into an orientation estimate: Reference implementations of these methods in C, C#, and MATLAB are available. 6 If using video cameras as the main sensory modality instead, images or image pairs can be fused together across time into an ego-motion estimate by visual odometry: Recent libraries available for that task, each with their own particular focus and strong points, include LIB-VISO,  7  fovis, 8 DVO,  9  direct sparse odometry,  10  and semidirect visual odometry,  11  the latter a proprietary solution contrary to the previous ones.
Environmental modeling can be performed by structurefrom-motion and multiview stereo methods that respectively produce sparse and dense 3D point clouds of the environment, which can then be postprocessed into possibly textured 3D models such as object meshes and elevation maps. These methods fuse together images or more usually image pairs across time. Well-known free and open-source software for that task include Bundler 12 (sparse only), CMVS/PMVS2 13 (dense only), VisualSFM  14  (sparse) (proprietary graphical front-end but open-source underlying libraries), OpenMVG  15  (sparse), OpenMVS  16  (dense), MVE  17  (both), and the more recent Theia  18  (sparse) and COLMAP  19 (both). Additionally, other methods of environmental modeling are available for working with depth cameras instead of, or in addition to, video cameras.
Other noteworthy libraries include, among others, the recent RGB-D SLAM system ElasticFusion  25  and the 2D and 3D lidar SLAM software Cartographer. 26 hese data fusion libraries are always specific to the problem they address: They implement a particular algorithm, or a few related ones, to deal with a particular robotics or computer vision problem. In contrast, the CDFF strives to be wider in scope. It is a modular software architecture where any multisensor data fusion algorithm can be implemented, whether it already exists or is being designed: a common software framework for any kind of data fusion work. In this respect, a close analog is the recently released Sensor Fusion and Tracking Toolbox for MATLAB,  27  which like the CDFF "includes algorithms and tools for the design, simulation, and analysis of systems that fuse data from multiple sensors to maintain position, orientation, and situational awareness.
ROS is a standard in many application domains, where its simplicity, practicality, and large community are appreciated. In space robotics, every agency develops and uses mission-specific software. NASA developed the CLARAty 32 framework with 44 CLARAty modules which provides software components for a higher level decisional layer and a lower level functional layer. However, publication and development of CLARAty has ended, and the only recent public reports of middleware usage concern several prototype systems using ROS and/or Real-Time Innovations' commercial middleware for mission critical systems. 33, 34 owever, those are full data fusion pipelines, whereas the focus of the CDFF is on assembling such pipelines. For this task to be possible with ROS, the components of the pipelines would have to be implemented as ROS nodes, with a data exchange interface based on the publish-subscribe model of the message-passing middleware.
As we used ROS during our experimental validation of the framework, we have written such an interface to ROS, but it is still experimental, so we have not released it yet. In Europe, the ESA has been investigating robotics software for mission critical systems too, with the successive projects The Assert Set of Tools for Engineering (TASTE),  35  Space Automation and Robotics General Controller (SARGON), and European Space Robotics Control and Operating System (ESROCOS). 36, 37 TASTE is an open-source toolchain for the development of "correctby-construction" software for embedded, real-time, heterogeneous, mission critical systems. It has a strong focus on formal modeling and on generating safe, optimized Ada and C code from Architecture Analysis and Design Language and ASN. 1, two formal description languages.
It is the basis of SARGON and ESROCOS, two successive steps toward a space-grade robotic framework for future European missions, including software and hardware modeling tools and communication and formal verification tools. Thanks to its middleware agnosticity, the CDFF c o u l db ei n t e g r a t e di n t oE S R O C O S ,w h o s ec o n s o r t i u m worked in parallel and in cooperation to ours. As a matter of fact, to make it easier to integrate both projects, we have used the same TASTE data types as ESROCOS, transcompiled to the same C structures by the same TASTEprovided ASN. 1-to-C compiler.
The three main components of CDFF are the DFPCs, the Orchestrator, and the DPM. These components are depicted in Figure  1. The framework consists of three major components, named CDFF-Core, CDFF-Support, and CDFF-Dev. CDFF-Core and CDFF-Support are designed to be deployed in the target robotic system, while CDFF-Dev is intended for software engineering of data fusion software and performance evaluation. The source code of the CDFF that is designed to be deployed on a robotic system is developed in C/Cþþ, while CDFF-Dev is implemented in Python.
CDFF-Core The CDFF-Core consists of a set of DFNs, each of which performs a specific data processing task commonly part of robotic perception tasks, for instance, feature extraction in an image or Kalman filtering of a system state. They have a minimalist interface consisting of inputs, outputs, a configuration, and a process operation. The implementation of a DFNs can be as simple as a stand-alone Cþþ class or involve the usage of complex libraries. All DFNs are Cþþ instances of classes that inherit from a Common Data Fusion Node Interface. An example of a DFN interface is shown in the Code Sample 1. Although in our experiments all DFNs have been deployed as single processes in the target middleware (e. g. ROS), the CDFF does not impose any particular deployment restriction: We consider the deployment view a feature to be covered by the middleware and very dependent of the particular robotic mission's hardware.
Each DFPC is characterized by its function or functions, the data streams that it receives and produces, including the corresponding metadata (for instance, timestamps and geometric models), the operations that it can execute on demand, the DFNs it uses, and how these are configured and set up. We use description files such as the one in Code Sample 2 to have a clear definition of the functionality of each DFPC. Currently, the DFPCs that are available are presented in Table  1 ; these DFPCs have been tested on real systems in analog to planetary and orbital missions. The Orchestrator has the main task of receiving queries from the onboard planners (deliberative and executive), which consists of symbolic, task, path, and motion planning for planetary rovers and on-orbit servicing satellites.
This last function does not interfere with internal DFPC decision-making processes. The role of the DPM is to handle the selection, structuring, and storage of all the data processed or produced by the CDFF that may be reused, either internally by the CDFF processes or to satisfy requests from the autonomy modules. Additionally, it is foreseen to become the interface through which robots expose and retrieve the CDFF data Code Sample 2. Description of the Reconstruction 3D DFPC. Descriptions are available for each DFPC. The interfaces can be created directly from them. products in multirobot scenarios and also the interface through which ground operators can access the CDFF data products. The DPM should become a roboticsdedicated geographic information system. With respect to the activated DFNs and DFPCs in the CDFF, the DPM processes the data insertion requests.
CDFF-Dev: Development tools CDFF-Dev provides software development, performance analysis, and data management tools for implementing and evaluating data fusion algorithms. Contrary to CDFF-Core and CDFF-Support, which are deployed on the robotic system, CDFF-Dev tools are meant to be used in a development environment during implementation and exploitation activities. The first step to evaluate or implement a DFN or DFPC using CDFF-Dev is to write a DFN or DFPC description file. From these, code generators will produce a scaffold for the actual implementation. This ensures that the code will conform to interface conventions without having to rely on a heavy framework. Furthermore, it allows to analyze DFPCs before they are actually implemented and to check connections of DFNs and DFPCs without running the code.
Testing DFNs or DFPCs off-line with log data is possible with the provided Python bindings. Two essential elements are needed for a user to be able to replay data logs from a desired robotic middleware: (1) a conversion from the data log format used by the middleware to an intermediate format that is used by CDFF and (2) a data type conversion from the middleware to CDFF data types. Mes-sagePack is the intermediate log file format that can be handled by CDFF-Dev. Although it is also easily possible to extend CDFF-Dev to support new log formats, log iterators will read log files and stream them to the data flow control. Log iterators can be joined or replayed sequentially. They can handle multiple files and extract only specific data streams from log files. Logs are replayed with a data flow control module that emulates the communication layer of a robotic middleware and a log player that replays logged data chronologically.
An example of a replay script is shown in Code Sample 3. During off-line execution of DFNs and DFPCs, we can store log data and fused data in an EnviRe graph  38  ; hence, we can use the visualizer of EnviRe for 3D visualizations of the data. We can visualize poses, models of robots (in Universal Robot Description Format [URDF]), trajectories, maps, (colored) point clouds, laser scans, and depth maps. An example of a visualized point cloud is shown in Figure  5. CDFF-Dev also offers various tools that can be used to analyze log data and configurations. It provides command line tools for log inspection, a visualization of DFPC configurations from DFPC description files, and a simple exporter to comma-separated values files. The latter can be used for easy integration with existing tools from the Python scientific ecosystem, for example, pandas.