dataesr/scientific-paragraphs-categorization huggingface dataset:
Mass spectrometry analysis Data were acquired using the Xcalibur software (v 3. 0. 63) and the resulting spectra were interrogated by the Mascot TM Software through Proteome Discoverer (v 1. 4. 0. 288, Thermo Scientific) with the SwissProt Homo Sapiens database (20131211, 20279 sequences). We set carbamidomethyl cysteine, oxidation of methionine, N-terminal acetylation, heavy 13 C 6 15 N 2 -Lysine (Lys8) and 13 C 6 15 N 4 -Arginine (Arg10) as variable modifications. We set specificity of trypsin digestion and allowed 2 missed cleavage sites and we set the mass tolerances in MS and MS/MS to 2 ppm and 0. 5 Da, respectively. The resulting Mascot files were further processed by using myProMS (v 3. 0,  [38] ) and the estimated false discovery rate (FDR) by automatically filtering the Mascot score of all peptide identifications was set to 1 % (DT count).
Gene expression profiling Total RNA of SILAC-cultured isogenic NCI-H226 cell lines were extracted with NucleoSpin RNA kit (Macherey-Nagel). Total RNA were then hybridized on HG-U133 Plus 2. 0 array (Affymetrix) according to the manufacturer's recommendations (http://www. expressionanalysis. com). Fluorescences were detected with GeneChip® Scanner 3000 software and expression data were extracted with GeneChip Command Console Software (AGCC) v2. 0 software (Affymetrix). After brainarray normalization  [40] , exclusion of non-annotated probes and log 2 (n+1) expression transformation, 2139 genes with standard deviation over 0. 3 and mean value over 6. 5 were considered. ToppGene (toppgene. cchmc. org) was used to estimate enrichment.
Hierarchical clustering of NCI-H226 cell line Public data of gene expression from Cancer Cell Line Encyclopedia (https://portals. broadinstitute. org/ccle/ data/browseData?conversationPropagation=begin) were used to classify NCI-H226 cell line with 1035 other cancer cell lines. Hierarchical clustering and plot were done using 'hclust' function (method = "ward. D2") from R (v3. 3. 2) and expression of the 500 probes with the highest variance.
Images were captured by using upright widefield Apotome microscope (Zeiss) equipped with a Coolsnap HQ2 camera through x63 or x40 NA 1. 4 oil-immersion objective lens and driven by Axiovision software (Zeiss). Images were then analyzed and merged using ImageJ software. For CDH2 immunofluorescence, exposure time was not fixed for NCI-H226 cells: EV: 293. 6ms, N2:363. 2ms and N3: 354. 4ms.
Invasion boyden chamber assay Cellular invasiveness was studied by using Matrigelcoated Transwell-system (BD Biosciences). In all, 1. 10 5 cells were seeded in the Transwell chamber with RPMI with 0. 5% FBS and the lower chamber with 10% FBS. After 24 hours, the invasive cells were stained with crystal violet (Sigma, HT90132) and counted. Data correspond to the number of invading cells calculated with 5 areas (up, down, left, right, and middle) on the bottom part of membrane from three biological replicates. A control well (without scratching) was systematically done to verify the amount of coated cells.
Wound-healing assay and video-microscopy Cellular migration was studied by wound healing assay. Cells were seeded 24h before the experiment in 6 well plates in order to be confluent the day of the experiment. Before the video taking, three wounds were made in each well with a sterile tip. Cells were rinsed two times with sterile PBS and then fresh RPMI. Pictures were acquired with the Leica DMIR2 microscope and the Coolsnap HQ2 (Roper Scientific) camera. Temperature was maintained at 37°C and the C0 2 level was monitored to 5% (Life Imaging Services) under the control of MetaMorph software (Universal Imaging). Pictures were acquired every 30min during 48. 5h. Pictures were analyzed using both MetaMorph and ImageJ software.
Colony formation assay after H 2 O 2 treatment Cells were seeded at 2. 10 5 and treated for 8 hours with 200μM or 400μM of H 2 O 2 the day after. Cells were than trypsinized and counted, and then seeded at 7500 cells in a 10cm 2 plates. Well seeding was checked the day after, and culture media was changed 7 days post-seeding. 14 days after seeding, cells were stained with crystal violet. Data corresponds to the number of colonies calculated with two quarters of areas and then doubled for an average of colony numbers per plate. Survival after H 2 O 2 was calculated by doing the ratio between number of colony treated with H 2 O 2 and non-treated number of colonies.
Cells were incubated with RPMI+DMSO as a control of autofluorescence and then subtracted to the MIF. For Mitochondrial Mass measurement, 2. 10 5 cells were seeded 24h before the experiment in 6 well plates in order reach to 60-70% of confluence the day of the experiment. Cells were washed twice with warmed PBS and then incubated with RPMI + 250nM MitoTracker Red CMXRos dissolved in DMSO (M-7512 Invitrogen) for 30min out of the light and at 37°C. Cells were washed twice in PBS then trypsinized for 5min. Cells were then resuspended with PBS and 10% fetal calf serum to inactivate trypsin. Cells were analyzed by FACS CantoTMII (BD Biosciences) through an excitation at 579 nm and emission at 599 nm. The results were analyzed with FlowJo Software (v. 8. 8. 2).
Spare respiratory capacities -seahorse XF-96 metabolic flux analysis Cells were cultured on Seahorse XF-96 plates at a density of 50-70,000 cells per well. Adjustments were done in order to have comparable basal respiration. On the day of metabolic flux analysis, cells were changed to unbuffered DMEM supplemented with 10 mM glucose, incubated at 37°C in a non-CO 2 incubator for 1 h. All medium and injection reagents were adjusted to pH 7. 4 on the day of assay. Three baseline measurements of OCR were taken before and after sequential injection of reagents. The mitochondrial inhibitors used were oligomycin (1μM or 1. 5μM for NCI-H226 and QR cell lines respectively), FCCP (0. 5 μM or 1μM for NCI-H226 and QR cell lines respectively), and antinomycin A/rotenone (1 μM each). OCR rates were automatically calculated and recorded by the Seahorse XF-96 software.
ECAR rates were automatically calculated and recorded by the Seahorse XF-96 software. Glycolytic capacities capacities were calculated by subtracting maximal glycolytic capacity (measurements 7, 8 and 9) to basal measurement (measurements 1, 2 and 3). Glycolytic reserve were calculated by subtracting maximal glycolytic capacity (measurements 7, 8 and 9) to the glycolysis measurement (measurements 4, 5 and 6). The percentage Glycolytic capacities are normalized with EV expressing cells.
ACKNOWLEDGMENTS We thank  Dr. Christopher Mueller  for critically reviewing this manuscript. We are grateful to  Alexandre Houy ,  Guillaume Rieunier ,  Geraldine Gentric ,  Marco Biondini ,  Maria Carla Parrini ,  Georges-Alain Franzetti  and  Karine Laud-Duval  for enriching discussion and/ or their experimental advices,  David Gentien  and the Affymetrix platform for expression array experiments,  Alexandra Ingrid Kasper  and  Alison Gah-Yin Fanton  for proof-reading the grammar and spelling, and  Lenha Mobuchon  for artwork.
The rabbit anti-eRF1 (GTX 108271; 2 µg/ml) was from Gene Tex, the goat anti-TIA-1 (C-20; 2 µg/ml) was from Santa Cruz and the mouse anti-G3BP (2F3; 5 µg/ml) was from Sigma. Secondary antibodies (2 µg/ml) raised against mouse, rabbit or goat IgG (H + L) and conjugated to Alexa Fluor 488, 594 or 647 were from Invitrogen. Plasmids. All the viral sequences were derived from the human RSV strain Long, ATCC VR-26 (GenBank accession AY911262. 1). Expression plasmids of RSV N, P, P-eGFP, L, eGFP-L and M2-1 proteins designated as pN, pP,pP-eGFP, pL, p-eGFP-L and pM2-1 have been previously described  36, 37, 56. The pP-mEos and pL-mEos were derived from pP-eGFP and peGFP-L expression vectors by replacing the eGPF coding sequence by the mEos2 41 coding sequence using respectively the BamHI and the SalI sites.
The pmGFP-N and pmEos-N were constructed by adding a BamHI site upstream of the N coding sequence by site-directed mutagenesis (Stratagene) in pN and inserting either the mGFP coding sequence (Clontech) or the mEos2 coding sequence in frame with N open reading frame (ORF) in the BamHI site. The stop codon of the M2-1 ORF in pM2-1 was deleted by site-directed mutagenesis (Stratagene) and either the mGFP coding sequence (Clontech) or the mEos2 coding sequence was inserted in XhoI restriction site in frame with M2-1 ORF to obtain the p-M2-1mGFP and pM2-1mEos. The pM/Luc subgenomic replicon, which contains the firefly luciferase (Luc) gene under the control of the M/SH gene start sequence, has been described previously  36. The full-length cDNA clone of RSV pACNR-rHRSV (GenBank accession KF713490) was previously described  43.
The M2-1mGFP coding sequence was amplified from the pM2-1mGFP vector by PCR using specific primers (Supplementary Table  1 ) containing RSV gene start and gene end sequences from the RSV SH gene and was cloned into a EcoRV restriction site between the RSV SH and G genes in the pACNR-rHRSV. All constructs were verified by sequencing. The nucleotide sequence of RSV-M2-1mGFP was deposited in the Genbank nucleotide database with accession code KX348546. Cell infection, transfection and minigenome assay. Infections were performed at 37 °C on exponentially growing HEp-2 cells at a multiplicity of infection of 1. BSR/T7-5 at 90% confluence in 24-wells plates were transfected with Lipofectamine 2000 (Invitrogen) according to the manufacturer recommendations with a plasmid mixture containing 125 ng of pN, 125 ng of pP, 62.
Sequences of probes for in situ hybridization are given in Table  1. Imaging of IBs and internal structures. Confocal microscopy was used to study characteristics of individual IBs. Z-stack image acquisitions of multi-labeled (Hoechst, N, M2-1mGFP, FISH or 5EU) cells were performed under a ×63 apochromatic lens and a numerical zoom comprised between ×1 and ×5 (LSAF acquisition software). For quantifications, eight independent stacks at zoom ×2. 5 of each experimental condition were taken, allowing a detailed analysis of approximately 40 cells, corresponding to 85 IBs. Image analyses were realized with the Image J software. The number and size of IBs (based on N staining) were determined by using the ImageJ's "analyze particles" function. Specific labeling of FISH Poly(A) inside IBs was used to determine the number of IBAGs per IBs.
The same parameters were applied for all experimental conditions and quality of image segmentation was checked by comparing object staining with their calculated outlines. For time lapse microscopy, HEp-2 cells were seeded on Ibidi μ-slide eight-well polymer coverslips and infected with RSV-M2-1mGFP as described above. At 24 h p. i. , cells were placed under a Leica SP8 confocal microscope equipped with a chamber heated at 37 °C. Z-stacks about 30 μm thick with a 0. 3 μm Z-step were acquired every 5 min during 4 h. Videos were finally visualized in 3D using the Imaris software. Dual COLOR PALM/STORM microscopy. The super-resolution microscopy experiments were done on a custom-built microscope as described in more detail before  57. This microscope features a Nikon Ti-E Eclipse body equipped with a perfect focus system, which ensures that the same z-section remains in focus throughout PALM/STORM imaging, a ×100, NA = 1.
For mEos, the 488 nm laser was used for initial widefield imaging, then both the 405 and 561 nm lasers were used for activation and excitation, respectively. The 405 nm laser power was continuously increased in order to maintain the number of activated (and not yet photobleached) molecules roughly constant. A modified version of the Matlab based software MTT  59  was used to compute molecular localizations in each channel and an in-house Matlab software (PALMvis) to correct for drifts and chromatic shifts using images of Tetraspec beads, and for super-resolution image visualizations. Based on the standard deviation of coordinates in small localization clusters, the resolution of the PALM/ STORM images was estimated to ~40-50 nm or better.
Acknowledgements We thank  Dr Qin Yu  from  AstraZeneca R&D Boston-USA , for providing the AZ27 drug. We are grateful to  Felix Rey ,  Jean-Louis Gaillard ,  Charles-Adrien Richard  and  Monica Bajorek  for helpful discussions. Benoit Maury  is gratefully acknowledged for technical assistance in microscopy experiments. We are grateful to the  Cymages  platform for access to SP8 Leica, which was supported by grants from the  region Ile-de-France. We acknowledge  Ignacio Izeddin  for the gift of a mEos2 encoding plasmid. We acknowledge support from the  INSERM  and the  Versailles Saint-Quentin University. This study was supported by grants from the  ANR (French national research agency) ,  RESPISYNCY-CELL  project, from the  Region Ile-de-France  and by the  "Assistance Publique des Hôpitaux de Paris" (AP-HP ).
After that, Babenko et al. [4]  show that a pre-trained CNN can be fine tuned with domain specific data (objects, scenes, etc. ) to improve retrieval performance on relevant datasets. In  [14] , the authors propose extracting activations of fully connected layer from multiple regions sampled in an image, followed by aggregating VLAD descriptors on these local CNN activations. While this results in highly performant descriptors, the starting representations are orders of magnitude larger than descriptors proposed in this work. [15, 18]  show that spatial max pooling of intermediate maps is an effective representation and higher performance can be achieved compared to using the fully connected layers. Babenko et al  [16]  in their very recent work, show that sum-pooling of intermediate feature maps performs better than max-pooling, when the image representation is whitened.
Note that the approach in  [16]  provide limited invariance to translation, but not to scale or rotation. Another very recent work  [17]  proposes pooling across regional bounding boxes in the image, similar to the popular R-CNN approach  [19]  used for object detection. Unlike FVs based on interest point detectors like the Difference-of-Gaussian (DoG) detector, CNN does not have a built-in mechanism to ensure resilience to geometric transformations like scale and rotation. In particular, the perfor- mance of CNN descriptors quickly degrade when the objects in the query and the database image are rotated or scaled differently. To illustrate this, in Figure  1 , we show retrieval results when query images are rotated with respect to database images for descriptors: (a) Fisher Vectors based on Difference of Gaussian interest points and SIFT descriptors (FVDoG), (b) Fisher Vectors based on dense interest points and SIFT descriptors, at just one scale (FVDS), (c) Fisher Vectors based on dense interest points and SIFT descriptors, at multiple scales in the image (FVDM), and (d) CNN descriptors based on the first fully connected layer of OxfordNet  [5].
-Then, the NIP descriptors are hashed to the target code size (32-256 bits) with a Restricted Boltzmann Machine (RBM). We propose a novel batchlevel regularization scheme specifically designed for the purpose of hashing, a scheme we refer to as RBMH from hereon. -A thorough empirical evaluation with state-of-the-art shows that the results obtained both with the NIP descriptors and the NIP+RBMH hashes are consistently outstanding across a wide range of datasets. To the best of our knowledge, the results reported at 128 bits hashes are the highest reported results in state-of-the-art literature. 4 Method.
Evaluation Framework We evaluate the performances on 4 popular datasets for image instance retrieval: (1) Holidays. The INRIA Holidays dataset  [34]  consists of outdoor holiday pic- by Nair&Hinton  [24]. We compute the statistics with 32 bits binary hashes on Holidays dataset (1491 images in total). (b) Comparison of our RBMH with RBM  [24]   [37] , which was used in the MPEG standard titled Compact Descriptors for Visual Search (CDVS)  [38]. This dataset contains objects like CDs, DVDs, books, prints, business cards. There are 500 unique objects, 1500 queries, and 1000 database images. Note that Graphics is different from the other datasets as it contains images of rigid objects captured under widely varying scale and rotation changes. For large-scale experiments, we present results on the 4 datasets combined with the 1 million MIR-FLICKR distractor images  [39].
Most schemes, including our approach, require an unsupervised training step. We train on a randomly sampled set of 150K images from the 1. 2 million ImageNet dataset  [40]. No class labels are used in this work. For the starting global descriptor representation, we use the pool5 layer from the 16-layer OxfordNet  [5] , which is widely adopted in instance retrieval literature  [15] [16] [17]. The input image size for OxfordNet is fixed at 224 × 224. The dimensionality of the pool5 descriptor is 25088, organized as 512 feature maps of size 7 × 7. For rotation invariance, rotated input images are padded with the mean pixel value computed from the ImageNet dataset. The step size for rotations is 10 degrees yielding 36 rotated images per orbit. For scale changes, 10 different center crops are considered varying from 50% to 100% of the total image.
Method Dims Dataset Oxford5K Holidays UKB T-embedding  [9]  1024 0. 560 0. 720 3. 51 T-embedding  [9]  512 0. 528 0. 700 3. 49 FV+Proj  [41]  512 -0. 789 3. 36 FC+PCAWhitening  [13]  500 0. 322 0. 642 -FC+VLAD+PCA  [14]  512 -0. 784 -FC+Finetune+PCAWhitening  [4]  512 0. 557 0. 789 3. 30 Conv+MaxPooling  [15]  256 0. 533 0. 716 -FV+FC+PCAWhitening  [42]  512 -0. 827 3. 37 Conv+SPoC+PCAWhitening  [16]  256 0. 589 0. 802 3. 65 R-MAC+PCAWhitening  [17]  512 0. 668 --R-MAC+PCAWhitening  [17]  256 Overall, A G S -S G T -M G R improves results over starting representation pool5 by 39% (Oxford5K) to 87% (Graphics) depending on the dataset. Better improvements with Graphics can be explained with the presence of many rotations in the dataset (smaller objects taken under different angles) while Oxford5K consisting mainly of upright buildings is less significantly helped by incorporating rotation invariance.
Comparing NIP with state-of-the-art including variants of VLAD/FV  [9, 41] , deep descriptors  [4, [15] [16] [17]  and descriptors combining deep CNN and VLAD/ FV  [14, 42]. As shown in Table  2 , we observe that 512-D NIP descriptors largely outperform most state-of-the-art methods with 512 or higher dimensions, on all datasets. Following  [15] [16] [17] , we also perform PCA whitening to reduce the dimensionality of NIP to 256. One can see that the 256-D NIP descriptors yield superior performance to  [15] [16] [17]  on all datasets. First, we compare NIP to the most related papers  [15] [16] [17]  which propose 256-D deep descriptors by aggregating convolutional features with various pooling operations 1. [15, 16, 18]  can be considered a special case of our work, with just one layer of pooling, which only provided limited levels of translation invariance.
The very recently proposed Regional Maximum Activation of Convolutions (R-MAC)  [17]  reports outstanding results on building dataset Oxford5K with very small dimensionality (e. g. 0. 668 mAP for 512-D R-MAC and 0. 561 mAP for 256-D R-MAC). The authors propose a fast R-CNN type pooling  [43] , which is effective when the object of interest is in a small portion of the image. Such an approach will be less effective when the object of interest is affected by groups of distortions like rotation and perspective, and located at the centre of the image. Here, we observe that nested pooling over many types of distortions with progressively increasing moments is essential to achieving geometric invariance and high retrieval performance with low dimensional descriptors. Besides, we argue that the technique proposed in  [17]  can be incorporated with NIP to further improve performance.
Method Dims (size in bits) Dist. Dataset Oxford5K Holidays UKB Binarized FV  [1]  520(520) Cosine -0. 460 2. 79 FV+SSH  [44]  256(256) ADC -0. 544 3. 08 FV+SSH  [44]  128(128) ADC -0. 499 2. 91 FV+SSH  [44]  32(32) ADC -0. 334 2. 18 FV+PQ  [2]  128(128) ADC -0. 506 3. 10 VLAD+PQ  [25]  128(128) L2 -0. 586 2. 88 VLAD+CQ  [25]  128(128) L2 -0. 644 3. 19 VLAD+SQ  [26]  128 Evaluation of NIP+RBMH binary hashes. NIP+RBMH binary hashes are produced by feeding invariant NIP descriptors into the proposed RBM hashing layer. Small scale retrieval results with NIP+RBMH are shown in Figure  6. We compare NIP+RBMH to other popular unsupervised hashing methods at code sizes from 32 to 256 bits, including ITQ  [21] , Bilinear Projection Binary Codes (BPBC)  [45] , PCAHash  [21] , LSH  [20] , SKLSH  [46] , SH  [22]  and RBM  [23, 24].
We used the software provided by the authors in  [21, 23]  to generate results for the baseline hashing methods. In addition, we also include the results of 512-D NIP descriptors, as the baseline uncompressed scheme. We observe that NIP+RBMH outperforms other methods at most code sizes on all data sets. First, there is a significant improvement at smaller code sizes like 32 bits, due to the proposed batch-level regularization: 0. 457 vs. 0. 369 in terms of mAP, compared to the second performing method RBM on Holidays at Large scale experiments. In Figure  7 , we present large scale retrieval results, combining the 1 million MIR FLICKR distractor images with each data set respectively. Trends consistent with small scale retrieval results in Figure  6  are observed.
Comparison of CNN and FV descriptors retrieval performance with rotated queries on Holidays and Graphics datasets (see experimental section for details on datasets). FVDoG, FVDS, and FVDM are Fisher Vectors based on DoG interest points, Dense interest points at Single scale, and Dense interest points at Multiple scales respectively: all use the SIFT descriptor. FVDoG is robust to rotation, while CNN, FVDS and FVDM suffer a sharp drop in performance as query image is rotated.
Comparison of CNN and FV descriptors retrieval performance with rotated queries on Holidays and Graphics datasets (see experimental section for details on datasets). FVDoG, FVDS, and FVDM are Fisher Vectors based on DoG interest points, Dense interest points at Single scale, and Dense interest points at Multiple scales respectively: all use the SIFT descriptor. FVDoG is robust to rotation, while CNN, FVDS and FVDM suffer a sharp drop in performance as query image is rotated. Graphics 0. 9 0. 8 Mean Average Precision 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 OxfordNet FVDoG FVDS FVDM 0. 1 -200 0 -150 -100 -50 0 50 100 150 200 Query Rotation Angle Fig. 1:.
in terms of mAP on Holidays and UKB. Both schemes are built upon the best NIP descriptors. RBMH outperforms RBM at very low code sizes. [36]s. There are 500 queries and 991 database images. (2)UKB. The University of Kentucky Benchmark dataset[35]consists of 2550 groups of common objects, 4 images per object. All 10200 images are used as queries. (3)Oxford5K. The Oxford buildings dataset[36]consists of 5063 images representing landmark buildings in Oxford. The query set contains 11 different landmarks, each represented by 5 queries. (4) Graphics. The Graphics dataset is part of the Stanford Mobile Visual Search dataset.
Locomotor activity was recorded just after scoring refuge use, following  [36]. Real-time automated recording of swimming activity was performed in a Petri dish filled with CW without substrate, under moderate light intensity (400 lux). After an acclimatization time of 3 min, time spent below and above a speed threshold of 15 mm s -1 was recorded for 3 min using the Lighting infrared system and an infrared camera connected to a laptop (ZEBRALAB software, View Point ® , Lyon, France). Preliminary tests showed that below 15 mm s -1 , gammarids locomotion is achieved by crawling rather than swimming.