dataesr/scientific-paragraphs-categorization huggingface dataset:
Alignment and threedimensional rendering of the volume EM data (Fig. 3D ) provided an overview of the underlying neurovascular unit despite the coarser axial resolution compared to ATUM-SEM. Based on these reconstructions and inspection of the ultrastructural volume, we selected sections with gold particles potentially crossing the BBB. These tissue sections were detached by acetone treatment and collected onto Formvar-coated slot grids, as needed for subsequent ET. We found that colloidal gold fiducials (20 nm diameter) needed for image reconstruction could be attached to the surface of these sections despite previous SEM imaging. Tomographic reconstructions of semi-thick sections removed from cpcKapton showed surface contamination only at the side that was facing the permanent marker coat penetrating down to a depth of up to 50 nm into the section (Suppl.
Within this volume, clots were selected for high-resolution imaging (4-20 x 4-20 x 200 nm). For ATUM-Tomo, electron micrographs were acquired in a similar hierarchical scheme on an Apreo S2 SEM (Thermo Fisher Scientific) with the T1 detector. Serial-section acquisition was performed using the Maps2 (Thermo Fisher Scientific) software. We acquired images of whole sections at low resolution (100 x 100 x 400 nm) to facilitate re-location. Anatomical landmarks and vasculature patterns of single SEM overview images and a summed (in Fiji) stack of sections were compared to the confocal data sets in Fiji. Within this volume, we selected a region with clots and transition zones and extravasation for high-resolution imaging at 20 x 20 x 400 nm. We acquired subsets of these regions on each or every second section at 10 x 10 x 400 nm 10 x 10 x 800 nm resolution, respectively.
TEM of ultrathin sections Ultrathin sections were removed from cfcKapton or cpcKapton tape using chloroform or acetone. Retrieved sections were collected onto Formvar-coated copper grids (Science Services). Sections were imaged using a JEM-1400+ (JEOL) transmission electron microscope (TEM) equipped with a XF416 (TVIPS) camera operated by the EM-Menu software (TVIPS).
572 nm applying the SerialEM software package  (Mastronarde, 2003 , Mastronarde, 2005). For dual-tilt electron tomography, the grid was rotated for 90° in the XY-plane and the second tilt series was acquired using identical microscope settings  (Mastronarde, 1997). Both tomographic image stacks of the same positions were reconstructed, combined and flattened using IMOD  (Kremer et al. , 1996, Mastronarde and Held, 2017). Serial semithick sections were stitched and combined by using the ZIB Amira (Zuse Institute Berlin, Germany) software package  (Stalling et al. , 2005 , Lindow et al. , 2021). After converting the image data into TIFF format, the image registration was manually refined in Fiji TrakEM2. , followed by a final elastic alignment. Segmentation of cellular features was performed manually with the program VAST  (Berger et al.
The first-level trigger accepts events from the 40 MHz bunch crossings at a rate below 100 kHz, which the high-level trigger reduces in order to record events to disk at about 1 kHz. An extensive software suite  [39]  is used in data simulation, in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the 𝑧-axis along the beam pipe. The 𝑥-axis points from the IP to the centre of the LHC ring, and the 𝑦-axis points upwards. Cylindrical coordinates (𝑟, 𝜙) are used in the transverse plane, 𝜙 being the azimuthal angle around the 𝑧-axis. The pseudorapidity is defined in terms of the polar angle 𝜃 as 𝜂 =ln tan(𝜃/2).
The NNPDF3. 0nnlo set of PDFs were used, along with the dedicated set of tuned PS parameters developed by the Sherpa authors. The samples were normalised to a NLO prediction. A summary of all the features used for the simulation of the signal and background processes is shown in Table  1. In all samples except those produced with Sherpa 2. 2. 1 or Sherpa 2. 2. 2, decays of heavy-flavour hadrons were modelled with EvtGen 1. 2. 0 or EvtGen 1. 6. 0 program  [88] , depending on the process. All samples of simulated events were processed through the ATLAS detector simulation  [89]  based on Geant4  [90]. The effects of multiple interactions in the same and nearby bunch crossings (pile-up) were modelled by overlaying minimum-bias events simulated using the soft QCD processes of Pythia 8. 186  [71]  with the A3 tune  [91]  and the NNPDF2.
Then, the fit results are used to scale the predicted background contribution in the signal regions. In each signal region, the signal is obtained by counting the number of observed data events after subtracting the background prediction. Figure  10  shows the post-fit distributions of the 𝑆 T variable in the sideband region and the background composition in each channel as a function of the 𝑆 T lower bound threshold used to define the signal regions. Since no significant excess is observed in any of the signal regions, a signal-plus-background fit is performed considering a generic signal in the signal region. As for the LQs search, the parameter of interest of the statistical analysis is the signal strength 𝜇, and the results are translated into upper limits on the number of signal events and, dividing them by the integrated luminosity, they can be expressed in terms of upper limits on the visible cross-section, 𝜎 vis.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF, Austria ;  ANAS ,  Azerbaĳan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias, Colombia ;  MEYS CR, Czech Republic ;  DNRF  and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG, Georgia ;  BMBF ,  HGF  and  MPG, Germany ;  GSRI, Greece ;  RGC  and  Hong Kong SAR, China ;  ISF  and  Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST, Morocco ;  NWO, Netherlands ;  RCN ,  Norway ;  MEiN, Poland ;  FCT, Portugal ;  MNE/IFA, Romania ;  MESTD, Serbia ;  MSSR ,  Slovakia ;  ARRS  and  MIZŠ, Slovenia ;  DSI/NRF, South Africa ;  MICINN, Spain ;  SRC  and  Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  MOST, Taiwan ;  TENMAK ,  Türkiye ;  STFC ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Brie y 500 ng of samples were injected onto a trapping column for desalting followed by a chromatographic separation on a 75 µm x 15 cm, C18 column. Samples were run using a 90 min gradient from 10 to 35 % solvent B (solvent A being a solution of 0. 1 % formic acid and solvent B: 0. 1 % formic acid in acetonitrile) at a ow rate of 300 nl/min. Data were acquired using a positive ESI mode with a voltage of 2. 4 kV in positive mode. Full scans were obtained in the mass range of 400-1250 in 250ms. Data dependent acquisition (DDA) was used for data collection for the high intensity ions in the m/z range 100-1600.
Protein identi cation Data les were submitted for simultaneous searches using Protein Pilot version 4. 0 software (AB Sciex) and Mascot search engine (Matrix Science) with the following criteria: Trypsin digestion, xed modi cation on cysteine of methylthio, variable modi cation iTRAQ (4 plex iTRAQ (K), iTRAQ (N-term), iTRAQ (Y)) and methionine oxidation. Peptide charge was restricted to +2, +3 and +4. Peptide mass tolerance restricted to or set at (+/-0. 1) and fragment mass tolerance was +/-0. 1. Proteins were de ned as differentially modulated if they t the following criteria: at least two peptides with high con dence (95 %) for identi cation (p-value<0. 05) and with a fold change of proteins greater or equal to |1. 5|.
Data analysis Differential gene expression was calculated with the DESEQ2 tool  [20]. Differentially expressed proteins of retinoids treated subject and vehicule subject, were de ned by a fold change | ≥ 1. 5| in 2 or 3 replicates. The biological interpretation of these protein lists was performed by enrichment calculations using the R ClusterPro ler package  [32]  and by using the "Gene Ontology" databases  [9]. A term from Gene Ontology or a Pathway from the KEGG database was signi cantly impacted when it had a corrected p-value <0. 05 with the list of proteins given as input  [16]. Heatmap or volcano plot type visualizations were also generated with the R tool to facilitate visualization and biological interpretation of the results.
Transcriptome analysis. Reads were trimmed using fastp v0. 20. 0, and the quality of the reads was assessed using FastQC v0. 11. 8 and MultiQC v1. 8. The quantification was performed with Kallisto v0. 46. 2 against the Macaca mulatta transcriptome (Mmul_10 downloaded from Ensembl release 103). The PCA was completed with the FactoMineR v2. 4 R package. The PCA and volcano graphical representations were produced with the ggplot2 v3. 3. 3 package. Differential expression analysis was performed using the DESeq2 v1. 30. 1 package. This package overcomes the lack of power, due to the high uncertainty of the variance estimation, by pooling information across genes  (99). Differentially expressed genes were defined as genes with an adjusted P value smaller or equal to 0. 05, a fold-change greater than 2.
5 or smaller than 0. 4, and a mean transcripts per million count across all samples greater than or equal to 2. All R analysis was done with R v4. 0. 3. Gene Ontology Biological Process terms (GO_BP) were analyzed using the ToppGene (ToppFun) tool (100). Gene classifications in Supplemental Table  2 , C and D, were performed by combining 3 web interfaces: g:Profiler (101), GeneCodis 4. 0 (102), and ToppGene (100). Genes related to metabolism pathways were classified by using the Reactome and KEGG Knowledgebase interface (103, 104). Cell sorting. Liver and lung myeloid and CD4 + T cells were sorted using a BD Influx cell sorter upon staining with specific antibodies: anti-CD3, -CD4, -CD20, -HLA-DR, -CD11b, -CD14, and -CD206 mAbs (Supplemental Table  4A ). Myeloid cells were identified as cells with a CD3 -CD20 -CD11b + HLA-DR + phenotype and were sorted based on their differential expression of CD14 and CD206 into 3 different subsets, as follows: CD14 + CD206 -, CD14 + CD206 + , and CD14 -CD206 +.
Acknowledgments JE was supported by grants from the  Canadian Institutes of Health Research (CIHR)  ( HBF-123682 ,  HBF-126786 , and  MOP-133476 ), the  ANRS (France) , and the  Canadian HIV Cure Enterprise  Grant  HIG-133050  from the  CIHR  partnership with  CANFAR  and IAS. JAC was supported by a fellowship from  Laval University (Fonds de recherche sur le Sida) ,  CHU de Québec  ( Formation Desjardins pour la Recherche et l'Innovation ), and  FRQNT. HR was supported by a fellowship from  Laval University (Fonds de recherche sur le Sida) ,  CHU de Québec (Formation Desjardins) , and  FRQS. MAJ holds the  CIHR Canada  Research Chair Tier 2 in  Immuno-Virology. JE also thanks the  Canada Research Chair program  for financial assistance (Tier 1). We would like to thank  Merck ,  ViiV , and  Gilead  for providing antiretroviral drugs to JE.
Deep multi-source domain adaptation is based on deep neural networks and exploits knowledge from multiple source domains to improve predictions on a target domain. In this paper, we are especially interested in investigating domain adaptation in a supervised context, with limited data and class imbalance. We propose a new multi-source supervised domain adaptation approach that is able to transfer both shared knowledge across all source domains and source domain specific knowledge toward a target domain. Transfer contribution weights are computed during training based on domain divergence. They are used to balance each source domain impact on learning during the model training phase, limiting negative transfer as much as possible. We conduct extensive experiments to show that our approach competes and even outperforms other state-of-the-art domain adaptation approaches on both image benchmark datasets and real-world tabular medical data.
Introduction Learning from imbalanced data requires a specific learning approach in order to pay specific attention to the class distribution during the training phase. Deep Learning is notoriously hard when dealing with limited data. Indeed, if the data is too limited, it is impossible to properly train a deep model, and simpler Machine Learning approaches should be preferred. When independent limited but similar datasets are available, it becomes adequate and beneficial to use deep multi-source domain adaptation to improve predictions on the target domain  [1] [2] [3] [4]. Exploiting knowledge from several source domains can help to minimize the negative impact of both limited data and class imbalance. In this paper, we propose a new interesting multi-source supervised domain adaptation approach, which we ultimately aim to apply on real-world limited and imbalanced medical tabular data.
In transfer learning, we aim to exploit knowledge from one or several source dataset(s) to improve learning performance on another target dataset. For transfer learning to be beneficial, the dataset(s) used as source(s) should be similar enough to the target dataset. A source that is not similar enough to the target will negatively impact learning performance, and should not be used in this context. We talk about domain adaptation when we aim to learn a single and common task by transferring knowledge from one or several source domain(s) to a target domain. A very well-researched area of domain adaptation is single-source domain adaptation  [5] [6] [7] [8] , that is, when we use only one source domain to transfer towards the target domain. A more complex and less researched area is multi-source domain adaptation  [9] [10] [11] [12] , where we use several source domains to transfer as much knowledge as possible to the target.
Domain adaptation can help largely improve prediction performance on the target domain by exploiting more knowledge from source domain(s) than available on the sole target domain. Domain adaptation is often used to make prediction possible on an entirely unlabeled target domain, that is, unsupervised domain adaptation. In our work, we are interested in a case where the target domain is labeled as any standard dataset, in this case we talk about supervised domain adaptation, which is a less researched domain adaptation area, despite being a common real-world occurrence. In this paper, we propose a new original approach for multi-source domain adaptation in a supervised context, and demonstrate its performance on limited and imbalanced data. Namely, Weighted Multi-Source Supervised Domain Adaptation (WMSSDA).
We show that WMSSDA outperforms other state-of-the-art domain adaptation approaches on both image benchmark datasets and real-world medical tabular data. We perform an ablation study to validate the pertinence and positive impact of each component in our method. The source code used to conduct our complete experiments is available at the following GitHub repository 1. In the rest of the paper, we first formally describe our domain adaptation learning scenario in section 2, we then present related works from transfer learning and learning on imbalanced data literature in section 3. Section 4 describes our proposed approach, we show our experimental results in section 5 and conclude with a summary of our contributions, results and future perspectives.
Dealing With Class Imbalance Class imbalance occurs when the labels we aim to predict are not uniformly distributed over the dataset, resulting in certain classes having a much higher, or lower, number of instances compared to others. When trained on an imbalanced dataset, standard Machine Learning approaches will lead to poorer results than when trained with a similar balanced dataset  [21]. When dealing with class imbalance it is primordial to use approaches that help improve learning performance. Nowadays, two main ways are used to deal with imbalanced data in the literature, sampling approaches, and cost-sensitive approaches  [22]. Sampling approaches aim to artificially adjust the class distribution, by either removing instances the majority class(es), and/or adding more instances from the minority class(es).
Under-sampling aims to reduce the number of majority class instances to achieve a more balanced class distribution. The simplest under-sampling approach that can be used to artificially re-balance a dataset is random under-sampling  [22] [23] [24]. With random under-sampling instances from the majority class(es) are randomly removed until the desired class distribution is achieved. This simple approach can lead to improved inference results but can also result in the loss of important information from the majority class(es), and so, reduce the generalization ability of the model as it misses crucial information. To assess this drawback more researched and advanced under-sampling approaches have been proposed, such as: Condensed Nearest Neighbor  [25] , or Tomek Links  [26]. Overall, under-sampling leads to improved inference results but might also lead to losing important information for inference if the amount of instances is too low to afford removing instance from the majority class(es).
On the other hand, over-sampling involves increasing the representation of minority classes by duplicating or generating synthetic examples. The simplest over-sampling approach that can be used to artificially re-balance a dataset is random over-sampling  [22] [23] [24]. Random over-sampling duplicates randomly selected instances from the minority class(es) until the desired class distribution is achieved. As with random under-sampling, over-sampling leads to improved inference results but highly increases the risk of overfitting, leading to biased inference models that lack in generalization capacity. The Synthetic Minority Oversampling Technique (SMOTE) method is the most popular and most widely used advanced oversampling method, it has been proposed in  [27]  and it is known to largely improve inference results on imbalanced data  [22, 23].
When training the approach, we iterate through one batch from each source domain for each target domain batch. Batches are processed as a pair between Fig. 2 : Architecture of our approach WMSSDA, common modules appear in purple, i-th source domain specific modules appear in the same color as the i-th associated source domain. Lines of color symbolize the data flow, each color corresponding to a batch of the corresponding domain. Dashed red lines symbolize the computation and application of transfer contribution weights, computed from the MD measure and applied as scaling in the classification loss terms. the b-th target batch and the b-th batch of the i-th source domain, which we will refer to as a pair of batches in the following. Each pair of batches is fed through a common feature extractor used to extract low-level features on all domains alike.
Datasets With our experiments we aim to show that our method performs well compared to other state-of-the-art approaches, on both popular benchmark domain adaptation datasets and a real-world medical domain adaptation dataset composed of mixed-type tabular data. We are interested in comparing state-of-theart domain adaptation approaches in the context of limited data and class imbalance. The 5-Digits multi-domain dataset is widely used in domain adaptation studies  [6, 8, 9, 11, 12] , it is composed of five digits recognition datasets, with grayscale or color images of various sizes: 1. MNIST 2  , the widely known handwritten digit recognition dataset. 2. MNIST-M 3  , a more complex version of MNIST, created by combining MNIST images with randomly extracted patches of photos of the BSDS500 dataset as their background.
3. Street View House Numbers (SVHN)  4  , a Real-World image dataset of house numbers extracted from Google Street View images. 4. Synthetic Digits (SYN)  5  , synthetically generated images of digits with random backgrounds. 5. USPS 6  , a handritten digit recognition dataset similar to MNIST. The DomainNet dataset is a multi-domain dataset recently released with paper  [11] , and accessible at the following webpage  7. It aims to provide a new difficult and more advanced benchmark dataset for domain adaptation approaches. It is composed of six domains of color images of various sizes, with a total of 345 common categories across all domains: 1. Clipart, a collection of clipart images. 2. Infograph, infographic images of specific objects. 3. Painting, artistic depictions of objects in the form of paintings.
4. Quickdraw, drawings of the worldwide players of the game "Quick Draw!". 5. Real, photos and real-world images. 6. Sketch, sketches of specific objects. The 5-Digits and DomainNet datasets are domain adaptation benchmark image datasets that are known for their covariate shift between domains. We perform experiments on those two datasets to demonstrate the capacity of our approach to perform well on known experimental domain adaptation datasets. As we are interested in an experimental scenario with limited data in each domain and class imbalance, we preprocessed the datasets to follow our setting of interest. We randomly selected subsets of each dataset domain to create both a limited amount of data and a class imbalance. After this selection step, the class representation in the 5-Digits dataset spans from 4.
22% (10 samples) for the least represented class to 21. 09% (500 samples) for the most represented class in each domain. For the DomainNet dataset, the class representation spans from 1. 1% (10 samples) for the least represented class to 13. 33% (120 samples) for the most represented class in each domain, out of 17 classes, leading to a total of 900 instances in each domain. The Covid dataset was provided by the Mexican government and is composed of health data about patients that suffer from, or have symptoms that could be related to, Covid19. It is downloadable from the following Kaggle repository 8 , it originally contains 1, 048, 576 samples from patients suffering from Covid19, with 20 tabular mixed-type (continuous and categorical) features. The goal of this dataset is to predict the survival outcome of patients.
The dataset is naturally imbalanced, odds of survival to Covid19 being, fortunately, higher than the odds of death. We used the categorical feature "Medical Unit" to split the original data into 5 domains depending on the type of medical institution that provided the care to the patient. We could not find more information about those kinds of medical institutions, apart from their ID in the dataset, we consider in the following that they correspond to different hospitals as we observe a covariate shift between the institutions. During our preprocessing, we selected 800 unique patients per domain to simulate a limited amount of training data, and we dropped two features containing too many missing values. With the subtraction of the feature used to split the data into separate domains, there remain 17 features after preprocessing.
̸ = P (X D5 ). ) ̸ = P (Y D2 ) ̸ =. ̸ = P (Y D5 ). With the preprocessing described above for each dataset, we observe both covariate and prior shifts in all datasets, with limited and imbalanced data in each domain. All datasets might suffer from concept shift, in precaution, the β version of our approach is designed to better handle concept shift.