dataesr/scientific-paragraphs-categorization huggingface dataset:
HK2 expression negatively correlates with the methylation of AHR promoter in several cancers We next explored the relevance of the HK2/AHR axis in cancer by investigating the correlation between the expression level of HK2 and the expression level of AHR using the data of 33 types of cancer available through The Cancer Genome Atlas (TCGA)  (47, 48, 56). We found that HK2 and AHR mRNA expression levels are positively correlated in cancer, notably in nine cancer types (Figure  6A ). In addition to TCGA datasets, we also analysed two different osteosarcoma (OS) cohorts (NCI/TARGET/OS and GSE21257) publicly available and commonly used by the community  (49). We found no significant correlation between AHR and HK2 mRNA expression in OS tumours in the two cohorts (Supplementary Figure  S6 ). Focusing on the nine cancer types exhibiting a clear correlation between HK2 and AHR expression, we then investigated a possible correlation between HK2 expression and the methylation of AHR promoter.
All these findings highlight that the regulation of HK2 expression by AHR is context-specific and might rely on the binding of additional cofactors of AHR, either at the promoter or nearby exon 3  (1, 50, 51). Using ENCODE ChIP-sequencing datasets we identified binding sites for cMYC and FOXO factors which may cooperate with AHR in the regulation of HK2 expression  (50, 51). The context-specific regulation of HK2 by AHR may alternatively be explained by our findings that HK2 regulates AHR transcriptional activity and thus that AHR or some of its targets might further modulate the kinetic and induction of HK2 expression upon AHR activation. For instance, transglutaminase 2 (TGM2), which is less abundant in U2OS-GFP-HK2 cells compared to controls, is also a known regulator of HK2 expression  (63, 64).
Intriguingly, in yeast, low glucose environment also induces the nuclear localisation of the human HK2 homolog, Hxk2, where it regulates gene expression  (74). Thus, further studies will be needed to clarify whether HK2 might regulate gene expression in high glucose environment as in our cellular models and how it may control DNA methylation levels at the AHR promoter. This new axis HK2/AHR might also be important in the context of cancer. Indeed, using TCGA datasets, in 9 of the 33 cancer types that we analysed, we found that HK2 expression shows a positive correlation with AHR expression and in a subset of these 9 cancer types a negative correlation with AHR promoter methylation levels. Furthermore, tumours with high HK2 expression and low AHR methylation exhibit a poor prognosis in lower grade glioma (LGG) and pancreatic cancer (PAAD).
DATA AVAILABILITY AHR ChIP-sequencing data were previously published and are publicly available on NCBI GEO servers with accession numbers GSE90550, GSE127649 and GSE116638. All software used for data analysis and visualization are freely usable and their references are indicated in the materials and methods. The databases used to collect and generate genechemical relationships and gene expression, chromatin and DNA methylation information in human cell lines and cancer patients (e. g. ENCODE, ChIP-Atlas, CTD, CellMin-erDB, TCGA, TARGET Osteosarcoma) are publicly available and the publication embargoes on the different datasets included in our analyses have expired. The proteomics data produced in this study have been deposited to the Pro-teomeXchange Consortium via the PRIDE partner repository with accession number PXD028992.
The location of CpG island (in green), promoters (in red) as well as enhancers (in grey) from the GeneHancer database are indicated. (F) ChIP-qPCR characterisation of AHR binding sites at the CYP1A1 promoter and the HK2 locus in U2OS cells treated with vehicle (i. e. toluene) or 10 nM TCDD for 1 h and 24 h (n = 3). P-value as determined by paired Student's t-test (vehicle versus TCDD). *P < 0. 05; ***P < 0. 001.
ACKNOWLEDGEMENTS We thank the facilities at  Institut Cochin (Paris, France)  for trainings and help with data acquisition, processing and analysis, notably  Marjorie Leduc ,  Francois Guillonneau  and  Morgane Le Gall  for proteomic data production and analysis. We acknowledge the animal facility of  CDTA (Center for Animal Distribution, Typing and Archiving, CNRS, Orléans, France ) and the animal core facility of  BioMed Tech facilities (Campus Saint Germain des Prés ,  INSERM US36 ,  CNRS UMS2009 ,  Université Paris Cité, Paris, France). We thank members of the 'Epigenetic, DNA replication and  Cancer'  ( Institut Cochin, Paris, France ) and ' Signalling in environmental and drug toxicity ' (Université Paris Cité) laboratories  for helpful comments and criticisms during the course of this work.
FUNDING Laboratory of B. M. is partner of  Labex  ' Who am I?'  [ ANR-11-LABX-0071 ,  ANR-11-IDEX-005-02 ] and is supported by  Fondation pour la Recherche Medicale  [AJE20151234749]; INSERM; CNRS; University Paris Cité;  INCa-Plan Cancer  [ASC15018KSA];  Groupement des Entreprises Ile-de-France  contre le cancer -GEFLUC [RAK19154KKA];  Ligue Régionale Contre le Cancer Ilede-France  [RAB20191KKA]. Funding for open access charge: INSERM.
We therefore performed comparative phylogenetics of the VIPs in primates and bats. Specifically, we studied the 332 host proteins identified by  Gordon et al. in mass-spectrometry assays of SARS-CoV-2 proteins in human cells  (Gordon et al. , 2020) , in addition to the angiotensin converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2), both necessary for virus entry into the cells. To perform the phylogenetic and positive selection analyses, we used the Detection of genetic innovation DGINN bioinformatic pipeline  (Picard et al. , 2020)  that entirely automates the analyses and combines several methods to test for selection across large datasets (Figure  1A ). Briefly, from each of the 334 human reference gene sequences, DGINN automatically retrieved bat and primate homologs (from NCBI nr database), curated the coding sequences, and performed a codon-alignment followed by a gene phylogenetic reconstruction (Figure  1A , Table  S1 ).
Several SARS-CoV-2 VIPs under positive selection are VIPs of other coronaviruses and may also be interconnected with other viral families To investigate whether the SARS-CoV-2 VIPs under positive selection are also known to interact with other coronaviruses, we interrogated the VirHostNet database  (Guirimand et al. , 2015)  for interconnection with SARS-1 and MERS (beta coronaviruses), and CoV-NL63 and CoV-229 (alpha coronaviruses). We found 58 genes (i. e. 54% of 108 genes under positive selection in bats or primates) that are adaptive SARS-CoV-2 VIPs and also known interacting proteins of at least another coronavirus (Figure  2A ). The positive selection marks in these VIPs therefore likely represent adaptations on host proteins that have regulated or interacted with coronaviruses over million years of coevolution with mammals.
RIPK1 has been under adaptive evolution in bats at residues that are crucial for human RIPK1 regulation Human RIPK1 is an adaptor protein involved in inflammation through the tumor necrosis factor alpha receptor 1 (TNFR1) and the Toll-like receptors 3 and 4 (TLR3/4), leading to pro-survival, apoptotic or necroptotic signals (Figure  5A )  (Delanghe et al. , 2020; Liu et al. , 2018). A curated analysis of RIPK1 interactors showed that it is a central hub for 79 cellular partners involved in key inflammatory and cell survival/death processes (Reactome database; Figure  S8A ). RIPK1 interacts with SARS-CoV-2 NSP12 (RdRp)  (Gordon et al. , 2020) , and is further involved in several bacterial and viral infections, being usurped by pathogens or involved in anti-microbial immunity (Figure  S8B ).
MAIC Scores. MAIC scores were obtained from the database for COVID-19 (https://baillielab. net/maic/covid19/, 2020-11-25 release)  (Parkinson et al. , 2020). The 334 VIP genes were cross-referenced against the 10,000 best hits of the MAIC database. Detailed phylogenetic analyses on genes of interest. Alignments from the DGINN screens were retrieved and sequences that appeared erroneous were taken out. To obtain a maximum number of species along primate and bat phylogenies, further sequences were retrieved from NCBI databases using BLASTn. Final codon alignments were then made using PRANK  (Löytynoja and Goldman, 2008)  or Muscle Translate  (Edgar, 2004) , and phylogenetic trees were built using PhyML with HKY+I+G model and aLRT for branch support  (Guindon et al. , 2010). Each curated gene alignment and tree were then submitted to positive selection analyses using the DGINN pipeline: HYPHY BUSTED and MEME, PAML Codeml (M0, M1, M2, M7, M8, M8a) and Bio++ (M0, M1 NS , M2 NS , M7 NS , M8 NS , M8a NS ) (references in "DGINN screen").
Reactome analyses. Gene pathway enrichment analyses were carried out on the Reactome biological pathways tools (https://reactome. org/). Interactors of RIPK1 were retrieved using the Reactome FIV plugin in Cytoscape  (Gillespie et al. , 2022). Protein structure predictions. Protein structure prediction of human and Rhinolophus ferrumequinum RIPK1 were modeled using RaptorX  (Wang et al. , 2017)  and structures were visualized using the Chimera software  (Pettersen et al. , 2004). Since no crystal structure is available for TMPRSS2 protease, the 3D structure of TMPRSS2 was predicted using the Iterative-Threading ASSEmbly Refinement (I-TASSER) server  (Yang and Zhang, 2015). A total of 492 amino acid sequence of human TMPRSS2 obtained from NCBI Genbank (accession number AF329454) was used as query.
The best model inferred by I-TASSER was selected using the C-Score -a measure assessing the quality of the models. The final estimates are: model C-score, -0. 41; estimated TM-score, 0. 66±0. 13; RMSD, 8. 2±4. 5Å. The corresponding TMPRSS2 structure was generated using Swiss PDB viewer software  (Johansson et al. , 2012). Sequence logo generation. The amino acid sequence logos of TMPRSS2 were generated using WebLogo (V. 2. 8. 2,  (Crooks et al. , 2004) ), based on an alignment of the positively sites from mammalian species reported as naturally susceptible and/or experimentally permissive to SARS-COV2, SARS-COV and MERS-COV. Model M0. In black, the bisector. In red, the linear regression. The names correspond to genes that we comprehensively analyzed (Table  1 ). C, Overview of the number of VIPs under significant positive selection (i.
S1. A, Role of TMPRSS2 in SARS-CoV-2 entry. B, Diagram of TMPRSS2 predicted domains, with sites under positive selection in primates represented by triangles (Table  1 ). Codon numbering based on Homo sapiens TMPRSS2. C, 3D-structure modeling of human TMPRSS2 (amino acids 1-492) with the positively selected sites (red), the SARS-CoV-2 predicted interface (light blue), the catalytic site (dark blue). D, The positively selected sites identified in primate TMPRSS2 are highly variable in primates (top), but more conserved in bats (bottom) where they are not identified as under adaptive evolution. Left, cladograms of primate and bat TMPRSS2 with species abbreviation and accession number of sequences. Amino-acid color-coding, RasMol properties (Geneious, Biomatters). Icon legend is embedded in the figure, with multicolored pictograms/triangles showing cases fulfilling multiple conditions.
Primase consists of the small catalytic subunit PRIM1 (p49) and the large regulatory subunit PRIM2 (p58), while polymerase consists of the catalytic subunit POLA1 (p180) and the accessory subunit POLA2 (p70). During primer synthesis, the primase subunits generate short RNA oligos, which are then subsequently extended with DNA by Pol α to be further elongated by replicative DNA polymerases. While many viruses encode their own Prim-Pol  (Guilliam et al. , 2015) , including a putative CoV primase consisting of NSP7 and NSP8, SARS-CoV-2 NSP1 interacted with all four subunits of Prim-Pol α  (Gordon et al. , 2020). From our initial DGINN analysis, we identified positive selection on three out of the four Prim-Pol α subunits: POLA1 (four tests for primates and bats), PRIM1 (three tests for primates and two tests for bats), and PRIM2 (five tests for primates only).
Our codon-specific positive selection analysis identified multiple residues that have rapidly evolved in primates and bats. We specifically focused on sites that were found with more than one test of selection (Table  1 ). To determine if these sites of positive selection were found at the interface between subunits of the Prim-Pol α complex or at surface exposed sites, we mapped PS sites onto the human Prim-Pol α crystal structure (PDB: 5EXR)  (Baranovskiy et al. , 2016). None of the sites were found at Prim-Pol α complex interfaces, and while many were clustered close together, all were surface exposed or found in putative unstructured regions that were not present in the crystal structures (Figure  S10 ). Together, this indicates that the positive selection identified for this complex is not being driven by complex formation and intra-complex coevolution, or DNA replication.
Acknowledgements We sincerely thank  Janet Young  for sharing her raw data on the positive selection of primate genes for comparative analyses, as well as for her precious feedback on the project. We also thank  Michael Emerman ,  Pierre-Olivier Vidalain ,  Dominique Guyot , and the members of LP2L for helpful discussions. We thank  Sara Clohisey  for generously providing us with the raw data for MAIC scores. We thank all the contributors of publicly-available genomic and genetic sequences, and of phylogenetic programs. Some molecular graphics and analyses were performed with  UCSF Chimera , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from  NIH   P41-GM103311. This work was performed using the computing facilities of the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon , the  IFB (Institut Francais de Bioinformatique) , and the  CC LBBE/PRABI , and with the support from the  BIBS (Bioinformatic and biostatistics service)  of the  CIRI, Lyon.
Funding: LE and LG are supported by grants from the  ANR   LABEX ECOFECT  ( ANR-11-LABX-0048  of the  Université de Lyon , within the  program Investissements d'Avenir  [ ANR-11-IDEX-0007 ]) and the  French Agence Nationale de la Recherche , under grant  ANR-20-CE15-0020-01  (Project " BATantiVIR "). LE is further supported by the  CNRS  and by grants from the  French Research Agency on HIV and Emerging Infectious Diseases ANRS/MIE  (no. ECTZ19143  and  ECTZ118944 ), and the  CNRS France-U. Arizona Institute for Global Grand Challenges. AM is supported by the  Fondation pour la Recherche Médicale , FRM: AJE201912009932.
Code and Data availability. All codes are available in: https://gitbio. ens-lyon. fr/ciri/ps_sars-cov-2/2021_dginn_covid19, and the DGINN pipeline is available at: http://bioweb. me/DGINN-github. All results from the DGINN screens ("DGINN full dataset") and from the detailed evolutionary analyses ("DGINN genes of interest") are available through the VirHostEvol web service https://virhostnet. prabi. fr/virhostevol/. The Shiny web application open-source code is available from the IN2P3 gitlab https://gitlab. in2p3. fr/vincent. navratil/shinyapps-virhostevol.
Combining the power of low-cost depth sensors and deep learning techniques, the proposed framework is integrated into a control platform that streams real-time mapping of input-output signals using the animal's current behavior and position. First, we analyze the performance of advanced action recognition deep learning networks on the rodent behavior dataset. Acknowledging the importance of integrating temporal information in behavioral feature learning, we hypothesized whether abstract spatiotemporal features obtained from simple deep networks are suitable for recognizing multiple behaviors. In particular, the behavior of networks for increasing temporal extents and with multiple timescales' branches (partially inspired in Feichtenhofer, et al. 28  ) was compared regarding their performance in detecting behavioral events.
High performances achieved with a reduced training dataset In order to determine the approximate amount of annotated training data required for good network performance, the size of the training set was systematically varied (Fig. 3c ). As expected, overall performance increases for increasing number of training images. Even 10k labeled frames (approximately 21 min of labeled data) were enough to achieve a good generalization, above 70%, with performance degradation in walking and grooming events. In fact, the effect of changing training size is most significant in these classes, where increasing 20 min of annotated data leads to a gain of almost 45% in per-class performance. Peak performance was reached with 30k training examples (corresponding to approximately 1hour of labeled data).
User-interface allows end-to-end control of behavioral experiments Acknowledging the importance of embedding all algorithms in a user-friendly application suited for reasearch environments, we developed a full-featured, easy-to-use and freely available software interface (Fig. 6a ), requiring no programming by the end-user. Behavior classification and/or tracking are performed using different available methods, chosen by the user, and detected using uploaded trained models. The GUI provides online information regarding hardware modules states, animal's behavior and position, allowing full control of the entire system. In particular, the state of 4 sensors and 4 actuators are updated in real-time, in which a LED-type icon is turned on upon the first image in which a behavioral pattern was detected, and subsequently turned off upon the first image in which the pattern is no longer detected (Fig.
Recent works have made some progress toward the goal of supervised classification of rodents' behavior using deep learning techniques to improve conventional feature-engineering-dependent methods. Marks, et al. 22  developed SIPEC:BehaveNet for behavior recognition, which was tested in a dataset acquired with a conventional camera and containing freely behaving mice whose behavior was labeled with only 3 classes  38. Although claiming superior performance to Sturman, et al. 38  proposal, SIPEC:BehavNet achieved lower overall performances for supported rearing and grooming events (mean ± standard error of the mean: 0. 84 ± 0. 04 and 0. 49 ± 0. 21, respectively), when compared to what we were able to report here. DeepEthogram is another recent tool for frame-based classification of animal behavior in RGB videos  39.
High overall performances (overall accuracy) were obtained for datasets containing mice behavior with more than 4 classes. However, performance per-class (F1score) is substantially impaired for some behaviors, in particular, the rarest and most challenging behaviors in the dataset (average F1-score above 70%). This shows evidence that attention must be paid to metrics performance when dealing with highly unbalanced datasets. Overall, both methods fall behind some strengths that our method shows, needing more than 70 min of labeled data to achieve a comparable performance (overall accuracy above 70%) and not being suitable for natural environmental conditions in the analysis of rodents' behavior. In order to improve the potential of the proposed system and create an integrated tool that would boost future development in understanding behavioral patterns and neuronal activity relationship, deep learning-based detection of behavior was used to provide event-triggered feedback in realtime.
Also, many sensors and actuators can be easily adapted to the Arduino microcontroller to finer control of animal's maze, and the automation control code is prepared to be further extended. Even so, recent advances in multiple animal behavior analysis and tracking 9,16,40 could be included to further enhance this versatility. System adaptation is, in theory, straightforward, however, the triggers for feedback control need to be carefully designed when dealing with complex social behavior. Furthermore, the list of behavioral events/classes can be further extended. Here, the potential of deep neural networks can be explored, since they are able to extract relevant features without the need for feature re-engineering, unlike conventional machine learning methods. Taking all the contributions together, we believe that the flexibility and yet easy-to-use characteristics of this real-time feedback framework may open the door to further studies and broader applications, allowing more high-throughput and rigorous behavioral experiments while less invasive for laboratory animals.
Dataset An open-access RGB-D behavioral dataset, available at https://doi. org/10. 5281/zenodo. 3636135  10 , was used for all experiments. Details on the experimental procedures, video acquisition and manual annotation of rodent's behavior can be found in  10. In brief, the dataset is composed of 10 to 15 min RGB-D video sequences of individual Wistar rat behavior, recorded with a Microsoft Kinect v2 camera (512x424 depth pixel resolution). The maximum frame rate is 30 frames per second (fps), but this value typically drops to 10 to 15 fps in low light conditions. A subset list of classes was considered here with the four most commonly used state behavior states: standstill, walking, rearing and grooming. A randomly selected subset of these fully annotated recordings was considered for the experiments and denoted as dataset-100k (~2.
Training The models were trained from scratch using the Adam optimizer, with a batch size of 16 video sequences with a time-window of T ms , and a learning rate of 1 × 10 -4 , for 100 epochs. A dropout layer was used before the recurrent layer, with a dropout ratio of 0. 5. Initially, the dataset was split into training (70%), validation (10%) and testing (20%) sets that are maintained throughout the experiments. The validation set was used to compare the performance of different models when performing ablation studies. To address the problem of having a highly imbalanced dataset (standstill 40. 3%, walking 28. 7%, rearing 11. 7%, and grooming 19. 3%), the video sequences of each class were oversampled until their frequencies were uniform.
Experiments For a systematic study of networks' performance, the effect of increased temporal information was evaluated, by changing different parameters in each experiment. First, the impact of changing the time-window  of the input sequence was tested, with  ∈ {0, 1, 4, 10, 19} , corresponding to a network input with 1 (single-frame), 2, 5, 11 and 20 frames in total, respectively, sampled with a fixed temporal stride  of 133 ms. Also, the temporal stride  between adjacent frames ( ∈ {67, 133} ), was also evaluated, which corresponds to approximately 15 or 8 frames sampled per second, with a fixed time-window. Finally, the frame rate ratio  between short-and long-time scale pathways for the multi-branch architecture ( ∈ {5, 10}) was varied. These temporal parameters were chosen in order to make the network responsive to the different behavior timescales present in the original dataset.
In this sense, and taking into consideration the camera's frame rate, the capability of the network of capturing both fast behavioral events (in the order of a few hundred milliseconds) and slower events (in the order of a few seconds) was explored. Also, different spatial resolutions of {64, 128, 256} pixels and input encoding modalities were tested. Besides raw 8-bit depth frames, depth jet-encoding  41  was applied to depth frames, in which the depth information is distributed according to the jet colormap, transforming the one-channel depth map to a three-channel color image. Also, surface normals were used to encode the depth frames into a three-channel image representing form and surface structure (implementation details in  42  ). Unless otherwise noted, the full dataset-100k was considered for analysis, and the default parameters for the systematic study were:  = 10,  = 133 ms, spatial resolution of 128 pixels in raw depth frames.