dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to compare stress responses between P. australiensis and A. digitifera, single-copy HOGs (SC-HOGs) between P. australiensis and A. digitifera were identified. Functional enrichment analysis using DEGs was performed on the web platform DAVID v6. 8  (Huang da et al. 2009 ) with default settings. UniProt IDs from all SC-HOGs of P. australiensis and A. digitifera were used as the background data set of the enrichment analysis, and UniProt IDs assigned to SC-HOGs in both P. australiensis and A. digitifera, or to P. australiensis-or A. digitifera-specific DEGs, were analyzed. Because 99. 7% of UniProtIDs assigned to SC-HOGs had UniProt keywords, we focused on UniProt keywords for the functional enrichment analysis. UniProt keywords (biological process) representing P < 0. 05 were considered significantly enriched terms.
Acknowledgments This study was supported by The  Canon Foundation and Interdisciplinary Collaborative Research Program of the Atmosphere and Ocean Research Institute , the  University of Tokyo , and in part by  KAKENHI  ( 17KT0027 ,  20H03235 , and  20K21860  to CS). We thank  Ms Mariia Khalturina  ( OIST ) for performing genome sequencing of P. australiensis and  Dr Steven D. Aird  for editing the manuscript. We also thank  Prof. Takashi Nakamura  ( University of the Ryukyus ) for sharing coral specimens and  Ms Hiromi Kinjo  for her help and support with the preparation and performance of the tank experiments at Sesoko Station. Computations were partially performed on the NIG supercomputer at  ROIS National Institute of Genetics.
Data Availability Data regarding the Porites australiensis genome assembly have been registered at GenBank under the BioProject accession PRJDB4187. The genome assembly has been deposited in the DNA DataBank of Japan/European Nucleotide Archive/ GenBank under accession numbers BOPM01000001-Conservative Genomic Strategies of a Stress-Tolerant, Long-Lived Scleractinian Coral, Porites australiensis.
Mobility of Internet of Things (IoT) objects is a key characteristic of IoT environments. It brings dynamicity, uncertainty and raises many challenges when it is associated with computation and network resources management for IoT applications. The resources management problem under objects mobility consideration is even more sensitive if we consider that various IoT applications have stringent Quality of Service (QoS) needs. Fog Computing is a distributed computation paradigm that increases data centers computation and storage abilities with nodes between end-users and the Cloud. Fog computing offers a large distributed infrastructure to support IoT applications needs by bringing services closer to end users. However, Fog infrastructures inherit the energy greediness characteristics of both data centers and network infrastructures.
The Round Trip Time measures (RTT) between a data center located in Washington DC and a data center located in Salt Lake City via a fiber link is around 41 ms. This value can goes up to 120 ms with traffic contention. The RRTs from Salt lake City data center to popular public Clouds such as Amazon EC2, Google Cloud Engine and Microsoft Azure Cloud were respectively in average around 55 ms, 13 ms and 25 ms. Those latencies can't ensure IoT applications response time requirements where applications such as vehicles control or augmented reality streams require a response time less than 10 ms. Indoor geolocation and high speed trading applications require a response time around 100 µs. Fog/Edge computing is an emergent computation paradigm aiming to support partial Cloud computation and storage workload at the edge of the network.
3 mM EDTA, and 0. 06% BSA). Cells were then washed, centrifuged and data were acquired on BD FACS Canto flow cytometer. Analysis was performed using FlowJo software (Tree Star). Tissue analysis. Adipose tissues were harvested, shredded with scissors, and then incubated for 30 min with PBS containing 1. 5 mg/ml collagenase A at 37 °C. Digested adipose tissue was homogenized using a 1 mL syringe with a 20G needle and passed through a 100 µm sieve. Spleens were crushed with a piston in PBS through a 100 µm sieve. The resulting suspension was lysed and processed as described in the previous paragraph. Femurs and tibias were harvested and flushed with FACS buffer. Cells were then centrifuged (400 × g, 5 min at 4 °C), lysed, and stained as previously described. BrdU protocol. Mice were injected with BrdU 16 h prior experiments.
Images of gel surface-conjugated fluorescent beads were acquired for each cell before and after cell removal using a Axiovert 200 M motorized microscope stand (Zeiss) and a ×32 magnification objective. Tractions exerted by MEF were estimated by measuring bead displacement fields, computing corresponding traction fields using Fourier transform traction microscopy, and calculating root-mean-square traction using the PIV (particle Image velocity) and TFM (Traction force microscopy) package on ImageJ (Tseng et al. 2012). To measure baseline noise, the same procedure was performed on a cell-free region. Positron emission tomographic imaging/computed tomography (PET/CT) and post-PET biodistribution of 64 Cu-DOTA-ECL1i. For PET/CT, 45-60 min dynamic scan was performed after the injection of 64 Cu-DOTA-ECL1i (3.
Single-cell RNA-seq data analysis Cell preparation. Adipose tissues were harvested, shredded with scissors, and then incubated for 30 min with PBS containing 1. 5 mg/ml collagenase A at 37 °C. Digested adipose tissues were homogenized using a 1 mL syringe with a 20G needle and passed through a 100 µm sieve. The resulting suspensions were centrifuged (400 × g, 5 min, 4 °C) and stained with anti-CD45-APC-Cy7 antibody. Cells were washed and passed again through a 100 µm sieve. Unstained cells were acquired to measure autofluorescence. Dapi was added to the cell preparation before the acquisition, and CD45 + Dapi -cells were purified using a BD FACS Aria III cell sorter. Sequencing. Cells were loaded on a Chromium Controller (10x Genomics) with a target output of 5000 cells per sample. Reverse transcription, cDNA synthesis/ amplification, and library preparation were performed according to the 10x Genomics protocol (Chromium™ Single Cell 3′ Reagent Kit, v3.
1 Chemistry). scRNA libraries were sequenced on an Illumina NextSeq 500/550 High Output flowcell: the forward read had a length of 28 bases that included the cell barcode and the UMI; the reverse read had a length of 55 bases that contained the cDNA insert. Alignment, barcode assignment, and UMI counting with Cell Ranger v4. 0. 0 were used to perform sample demultiplexing, barcode processing, and single-cell 3ʹ counting. Cell Ranger's mkfastq function was used to demultiplex raw base call files from the HiSeq4000 sequencer into sample-specific FASTQ files. Barcodes in both samples that were considered to represent noise and lowquality cells were filtered out using knee-inflection strategy available in DropletUtils  43  package (version 1. 4. 3). For analysis, Seurat package (version 3. 1. 0)  44 was used, genes which express in less than two cells and cells which have non-zero counts in less than 200 genes were additionally filtered from both barcode expression matrices, and the result matrices were used as analysis inputs.
The fraction of mitochondrial genes was calculated for every cell, and cells with a mitochondrial fraction >2% were filtered out. After all filtering procedures, 2,242 cells were left in the scRNA-seq data of control sample, and 2,139 cells were left in scRNA-seq of Adipo Δ/Δ sample. Both samples were normalized using SCTransform function with mitochondrial percentage as a variable to regress out in a second non-regularized linear regression. For integration purpose, variable features across the samples were selected by SelectIntegrationFeatures function with the number of features equal to 2000. Then the object was prepared for integration (PrepSCTIntegration function), the anchors were found (FindIntegrationAnchors function) and the samples were integrated into the whole object (IntegrateData function).
The dimensionality of the object was reduced by principal component analysis, and the first 20 principal components (PCs) were used further to generate uniform manifold approximation and projection (UMAP) dimensionality reduction by RunUMAP function. Graphbased clustering was run using FindNeighbors and FindClusters with a resolution of 1. 0 and the first 20 PCs as input, and the 17 clusters were identified. In order to exclude the technical bias across the samples, both the counts slot from SCTransform assay and the data slot from integrated assay were used as input for trajectory inference. For visualization purposes, the custom labels were assigned to several clusters by merging multiple clusters for simplification (e. g. , clusters 5, 6, and 14 were merged as T cells, clusters 4, 16 were merged as B cells and clusters 3, 15 were merged as NK cells).
Violin plots were drawn using the data slot of SCT assay. To generate pathway enrichment plots we took the expression of genes from the pathway from the data slot of SCT counts assay, used standard normalization (zscore) for these vectors, and then calculated the average vector. The gene signature heatmap was drawn using the scaled data slot of the integrated assay. For trajectory analysis, clusters assigned as monocytes and macrophages were used, and infer_trajectory function from the dyno package (version 0. 1. 2) was used with the available slingshot  45  singularity container (version 1. 0. 3). Trajectory visualization was implemented after dimensionality reduction by UMAP using dimred_umap function. Real-time qPCR. All primer sequences used for RT-qPCR are indicated in Supplementary Table  1.
Mouse CXCL12 or CCL2 assays were performed on mice serum or tissue homogenates according to the recommended manufacturer instructions. Statistical analysis. All the data of this paper are expressed in mean ± SEM. Mann-Whitney test was performed with GraphPad Prism 8 software to test the samples significance (*p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001).
Acknowledgements We would like to thank  Dr Pierre Leclère  for the helpful discussions. We would like to thank  Jean-Paul Pais de Barros  from the  Lipidomic Analytic platform (LAP)  of the  University of Burgundy  for technical support. We thank the  C3M Animal facility  for technical support and the  GIS-IBISA multi-sites platform Microscopie Imagerie Côte d'Azur (MICA ), and particularly the imaging site of  C3M  ( INSERM U1065 ) supported by  Conseil Régional ,  Conseil Départemental , and IBISA. We sincerely thank  Maéva Gesson  and  Marie Irondelle  for their help. We acknowledge the flow cytometry facility from the  «Institut de Pharmacologie Moléculaire et Cellulaire» , part of the  MICA GIS IBiSA  labeled platform, and we thank  Julie Cazareth  for her help. We thank  Dr. Jean-François Tanti  for sharing Ucp1 Cre mice and  Dr.
Stephan Clavel  for sharing MEFs. A. G. is supported by the  French government , through the  UCAJedi Investments in the Future  projects managed by the  National Research Agency (ANR)  ( ANR-15-IDEX-01 ). T. B. is supported by  Agence Nationale de la Recherche  ( ANR-18-CE14-0025  and  ANR-20-CE14-0006-02 ). M. F. and K. Z. were supported by the  Government of Russian Federation  (Grant  08-08 ). R. R. G. is supported by  Center National de la Recherche Scientifique (CNRS). D. D. was supported by grants from the  ANR  and the  European Union :  EGID ANR-10-LABX-46. J. W. W. was supported by the  National Institutes of Health (NIH)  grant  HL138163. L. Y. C. is supported by  Institut National de la Sante et de la Recherche Medicale (INSERM) ,  Fondation de France  ( 00066474 ), and the  European Research Council  ( ERC) consolidator program  ( ERC2016COG724838 ).
The multiobjective optimal results were also validated using Sage simulation results. The Sage software produced by Geodon Associates is a one-dimensional thermodynamic modeling tool that is useful to estimate and optimize SE performance  [86]. It has a graphical interface that includes the main components of the SE connected to each other through forces, heat and gas flow interconnections (indicated as boundary connections). In the model  [25] , a displacer, a piston, a rectangular-fin heater, a regenerator, and a cooler were used with expansion, compression and back spaces. The displacer seal, the shuttle losses, the conductive losses of the regenerator and the displacer, and the heat exchangers friction were added to the model. The flow and heat transfer were considered unsteady and periodic, while the working gas was compressible.
Schmidt theory has been adopted to model the mean pressure, while the heat transfer has been implemented with other complementary models. The software Sage developed by Gedeon Associates has been used for thermodynamic and dynamic calculations to take into account the non-linear effects. In some works  [52, 58, [88] [89] [90] [91] , Microgen FPSE and similar prototypes have been set as reference to study micro-CHP performance and to verify model's results. Instead, few works on Qnergy (formerly Infinia) can be found in literature  [20, 92, 93] , mainly focusing on the past Technology Demonstration Convertor (TDC) designed by the Stirling Technology Company (STC), later Infinia Corporation. The TDC is a gamma-type free-piston Stirling convertor, in which both the working piston and the displacer are supported by flexures.
6 MPa of mean pressure and with temperatures equal to 923 K (hot-side) and 353 K (cold-side). Demko et al. [93]  studied the TDC engine via the Sage software, starting from a simple model and adding at each step (totaling six case) some components to reach a more realistic model. Cold and hot-end temperatures, mean pressure and frequency were set as operating conditions. Electrical power, heat input and net efficiency were evaluated and compared to experimental data. Each part of the Stirling engine was identified by components connected by different type of connectors (e. g. , heat flow, mass flow, etc. ). The first case was the simplest structure, containing constrained working piston and displacer, heat exchangers, expansion and compression spaces. No heat conduction and other losses were included for energy and heat flows evaluation.
The predicted efficiency was equal to 40. 7%. The second case introduced the Sage bar conductor to simulate heat conduction losses through the displacer shell and cylinder liner. The new efficiency (37. 6%) did not change too much, except by the different way in which Sage software made the calculation; thus, the conduction losses did not affect the thermodynamic cycle. The bounce space and the working piston seal were considered in the third case. Piston seal loss, piston shuttle loss, and heat loss from the bounce space were implemented in the energy balance. The efficiency decreased up to 37. 2%. Adding the displacer-body seal and the appendix gap in the fourth case, a higher reduction of the total power (4. 3 W) and a little decreasing in the efficiency (0. 4%) were found. In the fifth case, the variable volume inside of the displacer and the displacer rod seal were introduced.
Implementation of a ShAREdT-Based Collaborative Application In this section, we expose some implementation details about the improved version of our application, which has been developed using Unity 3D  [38] , a game engine commonly employed to develop many kinds of applications besides games. Unity offers a wide range of useful utilities to create multiplatform 2D and 3D applications for desktop computers running Windows, MacOS, or GNU/Linux, and also on mobile devices running iOS or Android. We have also used Vuforia [39], a development library that facilitates the incorporation of AR functionalities in Unity-based applications, Android, and iOS. Vuforia relies on computer vision algorithms to identify a prior learned marker and to augment virtual objects placed parallel to it. Our prototype is built inside the Unity engine.
With the aim of tracking each time a collaborator taps on these buttons, the tap gesture behavior from TouchScript (TouchScript is a multitouch framework for Unity, designed to receive touch events from different sources and to facilitate the handling of gesture interactions. It can be found in the Unity Asset Store) has been added to them. A sphere shape is used to implement the rotation widget, and the pan gesture behavior has been attached to it, in order to track the dragging action on the sphere. This action causes the terrain mesh rotation. The sprite is a GameObject instance, whose Mesh instance is simply a plane associated with a pair of transparent textures to show the hard and smooth brushes. The double tap gesture and pinch gesture behaviors have been added to this plane to invoke the TerrainUIManager methods that modify the brush radius and smoothness.
Terrain Mesh Widgets According to Figure  9 , the terrain representation in the 3D scene is a GameObject instance, which is associated with the MeshCollider class that handles the collision of touch events with the Mesh instance of the 3D terrain (implemented as a GameObject instance). The PaintScript class has been added to handle the reactions of the terrain mesh to touches; it is also responsible for calculating the right index in the terrain array from the touch coordinates. PaintScript obtains touch events from MeshCollider and performs operations via the TerrainManager class. This one contains the terrain array and applies editing operations on it, using any collaborator state included in an array of PlayerState instances. In addition, TerrainManager has a reference to the Mesh instance of the terrain and maintains the Z coordinate values of its vertices up-to-date, thanks to the data stored in the terrain array; therefore, the terrain shape is modified in real-time.
, 2018; Christin et al. , 2019; Eikelboom et al. , 2019; Gray et al. , 2019). The strength of deep-learning models lies in their ability to automatically detect and extract features from images based on a large number of labeled examples  (LeCun et al. , 2015). However, obtaining sufficiently large data sets to train deeplearning models remains a major challenge. Indeed, such algorithms require hundreds of images per species in various contexts to achieve high accuracy  (Villon et al. , 2018; Christin et al. , 2019; Ferreira et al. , 2020). Building training databases is even more challenging for rare and threatened marine megafauna because most wild individuals remain in particular and often remote locations  (Letessier et al. , 2019). As a consequence, successful deep-learning applications for marine megafauna detec-tion have been restricted to seasonally predictable aggregations, such as those of sea turtles in nesting grounds  (Gray et al.
Our framework for detecting rare megafauna on images has 6 key implementation steps (Figure  1 ): data collection, image preprocessing, CNN training, CNN application, CNN accuracy assessment, and CNN deployment. In step 1, social media videos of the species of interest are collected by searching social media websites with appropriate keywords. In parallel and independently, field video surveys are conducted in the study region. In step 2, images from social media videos are extracted and annotated (i. e. , bounding boxes are manually drawn around the species of interest). Annotated images are then partitioned into independent training and testing sets and the training set is artificially augmented. Images from field video surveys are also extracted and annotated. In step 3, a publicly available, pretrained, object-detection CNN is downloaded and retrained for species detection on the social media data set.
Data collection We searched for aerial videos of dugongs on social media websites (Facebook, Instagram, and YouTube) with the keywords dugong plus either drone or aerial. Following ethical standards, we requested the consent of owners (amateurs, nongovernmental organizations, universities, or consultancy companies) to reuse their publicly-shared videos to ensure they were fully aware of our intended use  (Ghermandi & Sinclair, 2019). In total, 22 videos were gathered from social media (hereafter WEB videos). Total footage focused on dugongs was of 25 min (Table  1 ). These videos were acquired with drones in 6 regions throughout the dugong's Indo-Pacific range and were characterized by various resolutions and contexts (details in Appendices S1-S3). In parallel, we acquired aerial videos from an amphibious ultralight motor plane (ULM) (AirMax SeaMax) operating tourist flights over the Poé Lagoon (Figure  2 ).
We also annotated ULM images following the same procedure. To facilitate annotation, ULM videos were visualized and the times at which dugongs were spotted were recorded. Only ULM videos that contained dugong occurrences were imported for annotation (57 ULM videos for a total duration of 11 h [Table 1]). The annotation step led to the collection of 161 and 1303 images with at least 1 dugong for the ULM and WEB data sets, respectively (Table  1 ). To maximize the detection of small dugongs, we split each ULM image into 4, 1352 × 760-pixel images, which yielded 172 ULM images (original ULM images may contain more than 1 dugong). Image-splitting approaches efficiently boost detection accuracy by increasing the relative size of small objects with respect to the entire images, thereby limiting detail losses when images are processed throughout the CNN  (Unel et al.
, 2014). The training was stopped after 12,000 iterations to prevent overfitting, as would be indicated by an increasing loss for the validation set  (Sarle, 1995). We used the open-source Tensorflow object detection API version 1  (Abadi et al. , 2016)  in Python 3 to train our CNN. The training process lasted on average 4 h on a NVIDIA Quadro P6000 GPU with 64 GB of RAM and 24 GB of GPU memory.
The addition of 136 ULM images from New Caledonia to the training data set led to a nearly 50% reduction in the number of FNs, which is critical when surveying rare species. The CNNs still yielded imperfect results when tested on independent WEB image sets, highlighting the need for intrinsic CNN improvements to maximize detection accuracy across image sources. These results show that social media offer a solid basis to train CNNs for the automated detection of rare species and that the addition of few field images into the training data set can further boost the detection capacity. At a time where digital technologies shape contemporary society, social media have become an unprecedented source of information for environmental research  (Ghermandi & Sinclair, 2019). Images and videos accumulated on social media are increasingly exploited to address biodiversity conservation challenges  (Toivonen et al.
, 2019)  and generate ecological knowledge within the broader iEcology approach  (Jarić, Correia, et al. , 2020; Jarić, Roll, et al. , 2020). This process is referred to as passive or opportunistic crowdsourcing because materials spontaneously generated by humans are posted online and shared independent of formal citizen science programs  (Ghermandi & Sinclair, 2019). To our knowledge, we are the first to exploit the potential of iEcology for training algorithms, as most deep-learning applications have relied on active crowdsourcing through structured citizen science programs  (Van Horn et al. , 2015 , 2018; Terry et al. , 2020). Megafauna species are particularly suited to this purpose because they are extremely popular among the public, eliciting abundant social media activity. Leveraging social media resources allowed the collection of an unprecedented training database for dugong, owing to its sample size (1303 images) and broad geographical reach (6 regions spanning the dugong's range) (map in Appendix S3).