dataesr/scientific-paragraphs-categorization huggingface dataset:
| Clinical data Patient sex, age at symptom onset, National Institutes of Health Stroke Scale (NIHSS) score at presentation and functional outcome 90 days after stroke as well as treatment allocation to alteplase or placebo were collected from the WAKE-UP trial database. The clinical endpoint of excellent outcome  (Saver et al. , 2021)  was defined following the primary efficacy endpoint of the WAKE-UP trial as a score of 0 or 1 point on the modified Rankin Scale (mRS) of neurologic disability (ranging from 0 [no symptoms] to 6 [death]), assessed 90 days after stroke. The effect of alteplase on the evolution of structural connectivity at the highest possible spatial resolution was investigated by a massunivariate linear regression analysis, in which the within-subject change in connectivity strength at each edge was modelled as a linear function of treatment, with both lesion volume and infarct growth included as nuisance regressors.
| Patient characteristics Application of inclusion and exclusion criteria to arrive at the final study population is visualized in Supplementary Figure  1. Of 503 patients randomized in the WAKEUP trial to receive placebo or alteplase, 352 had MR imaging data of acceptable quality both before randomization and 22 to 36 h after stroke. Of those, 278 (79. 0%) had an anterior-circulation stroke (160 lesions in the left hemisphere, 109 lesions in the right hemisphere, nine bilateral) and 269 were included in our analysis. The spatial distribution of stroke lesions at baseline is shown in Figure  1. Lesion maps stratified by treatment allocation and time point can be found as Supplementary Figure  2. Of the 269 patients, 142 were randomized to placebo and 127 to alteplase. Their demographic and clinical characteristics are shown in Table  1.
ACKNOWLEDGMENTS The WAKE-UP trial was supported by a grant (  278276 ) from the  European Union   Seventh Framework Program. Dr Endres received funding from  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germanys Excellence Strategy-EXC-2049-390688087 and from  Bundesministerium für Bildung und Forschung (BMBF; German Ministry for Education and Research) for the Centre for Stroke Research Berlin. Christian Gerloff  also acknowledges funding from  SPP 2041 Computational Connectomics , project  Ge   844/5-1.
DATA AVAILABILITY STATEMENT Fully anonymized patient-level data sufficient to reproduce all analyses, including age, baseline NIHSS score, lesion volumes, treatment allocation, connectivity measurements, and functional outcome, are available on GitHub (https://github. com/csi-hamburg/WU-NeMo-Topology). Imaging data, after de-identification, will be shared with the Virtual International Stroke Trials Archive (VISTA) and be accessible according to the VISTA rules (http://www. virtualtrialsarchives. org/vista).
Interestingly, a hypomethylated CpG island (a feature associated with promoter regions) was found 13 kb upstream of exon 1. In addition, this region harbors binding sites for a transcription factor and a histone marker. These features suggest the existence of an alternative promoter in that region, a hypothesis corroborated by reports of shorter MAPT mRNAs starting on exon 1 in genome databases  [47] [48] [49]. Indeed, Huin and colleagues (2017) reported a functional alternative promoter located in exon 1 of the MAPT gene that originates 5′ truncated transcripts in human brain (Figure  1B ). These 5′ truncated transcripts could be translated into normal or N-terminal-truncated tau proteins, which could lead to changes in protein localization and function. In the brain, up to 60% of the genes are transcribed by APU  [44, 50].
Funding: Our research is funded by  'Centre National de la Recherche Scientifique ', by ' Fondation pour la Recherche Médicale ' and ' Fondation Alzheimer'  (Grant number:  ALZ201912009641 ) and by ' Agence Nationale pour la Recherche'  (Grant number:  ANR-21-CE12-0028 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
7. 3) against mouse genome GRCm38  [23]. Post-alignment processing was done using SAMtools (v1. 9),  [24] , whereupon reads were counted using featureCounts as found in the Subread package (v1. 6. 4)  [25, 26]. Gene features were obtained from Ensembl (v98)  [27]. Resulting counts were imported and analyzed in the R statistical environment (v3. 6. 2) using packages obtained from Bioconductor (v3. 10)  [28]. DESeq2 was used to perform the pairwise differential expression analyses  [29]. Plots were made using pheatmap (v1. 0. 12) and ggplot2 (v3. 3. 1) [30, 31].
cDNA synthesis and quantitative PCR analysis For quantitative polymerase chain reaction (qPCR), RNA was further cleaned from DSS with the Bioline ISOLATE II RNA mini kit (GC biotech B. V. , Alphen a/d Rijn, the Netherlands). cDNA was synthesized using dNTPs (ThermoFisher Scientific, Landsmeer, the Netherlands), Random primers (Promega, Leiden, the Netherlands), Oligo dT primers (Sigma, Zwijndrecht, the Netherlands), Revertaid and Ribolock (ThermoFisher Scientific) according to the manufacturer's instructions. PCR was performed using SensiFAST SYBR No-ROX (GC biotech B. V. ) on a LightCycler 480 II (Roche Applied Science) to analyze expression levels of genes of interest using Lin-RegPCR software  [32]. For normalization the reference genes hypoxanthine phosphoribosyltransferase (Hprt), cyclophilin, Non-POU Domain Containing Octamer Binding (Nono) and ribosomal protein lateral stalk subunit P0 (Rplp0) were selected, after analysis for stability in geNorm  [33].
Single-cell RNA-sequencing database studies Publicly available single-cell RNA-sequencing (scRNAseq) data sets from two C57BL/6JN mouse spleens were downloaded from the Gene Expression Omnibus (GEO; GSE109774)  [34]. Alignment was done against GRCh38 using Cell Ranger Software (v3. 1. 0; 10 × Genomics, Inc. , Pleasanton, CA, USA) and subsequently data sets were imported in Rstudio (v1. 3. 1093) [35]. Seurat was used to import, integrate, and cluster data, and plots were made with ggplot2 (v3. 3. 3). [31, 36]  Cells were filtered for dead cells that were identified by a low (500-5000) gene count. Clusters were identified by Louvain clustering method, and annotation was performed with use of known markers  [34]. Thereafter, expression of Adrb2 (encoding for β2-AR) was assessed. The top 13 principal components were used to calculate the t-distributed stochastic neighbor embedding (t-SNE).
Results Recently, it was shown that SpNS could decrease disease severity in a mouse model of arthritis and that SpNS causes the release of splenic NE, which in turn can reduce monocyte and macrophage LPS-induced cytokine secretion via β2-AR  [9]. A much debated issue is whether NE targets β2-AR on macrophages or T cells that produce acetylcholine  [8]. Therefore, the expression of Adrb2  in murine splenocytes was examined using a publicly available scRNA-seq data sets. A total of 13,365 features were retrieved in 9568 individual cells. After quality control, the remaining 9407 cells were annotated to main cell populations (i. e. , T, B, natural killer cells (NK), dendritic cells, and macrophages) using previously reported markers (Fig. 1A )  [34]. Subsequent feature analysis showed a marked expression of Adrb2 in a cluster of cells belonging to the B cell population, when compared with other cell populations (log2FoldChange = 0.
Next, to explore the underlying mechanisms through which SpNS affected intestinal inflammation, we performed bulk RNA-seq. Differentially expressed genes in colon were assessed between mice that did not receive stimulation or DSS, sham stimulated mice with DSS-induced colitis, and SpNS-treated mice with DSS-induced colitis (Additional file 4). Comparing the transcriptomes of mice with DSS-induced colitis (without SpNS) and control mice indicated an expected Fig. 2  SpNS reduces systemic TNF-α and survival after LPS injection. A Systemic TNF-α levels 90 min after LPS injection. B Survival after LPS injection. N = 4-10 per group. Data are expressed as mean and individual data points. *indicates a significant difference compared to control. Statistical differences between Sham and Stim mice were assessed using an independent t-test.
Data are expressed as mean or median and individual data points. Statistical differences between sham and stim mice were assessed using an independent t-test or a Mann-Whitney U test. P < 0. 05 was considered significant. DSS dextran sulfate sodium, SpNS splenic nerve plexus stimulation, MCP monocyte chemoattractant protein, IL interleukin, TNF tumor necrosis factor, IFN interferon predominantly found in the domains of leukocyte chemotaxis and migration. Noteworthy is the SpNS-induced downregulation of S100A8 and S100A9, which encode subunits of calprotectin, the clinical biomarker for IBD. Genes of interest for colonic inflammation were validated in a second independent experiment using qPCR. Indeed, TNF-α was significantly reduced in mice that received SpNS compared with sham stimulated animals (0.
Conclusions We demonstrated the effects of SpNS in a murine model of colitis and showed that SpNS ameliorated clinical features of colitis and caused differential expression of genes in the spleen and colon involved in inflammation. Future experimental studies should focus on the connection between the spleen and colon to gain more insight in the therapeutic mechanisms of SpNS. Meanwhile, a clinical trial in patients undergoing esophagectomy will investigate safety and feasibility of SpNS for application in humans (www. clini caltr ials. gov; NCT04171011).
Acknowledgements The authors thank  Prof. Anna Akhmanova  ( Utrecht University ) for a generous gift of the MDA-MB-231 mCherry:LifeAct cell line, and  Dr. Peter Bellstedt  ( NMR platform Jena ) for excellent support. We thank the  Studienstiftung des deutschen Volkes  for a PhD fellowship (to N. A. V. ) and the  Deutsche Akademischen Austauschdienst  for a PhD fellowship (to V. N. ). We thank the  Deutsche Forschungsgemeinschaft  ( SFB 1032  project  B09  number  201269156  and Emmy Noether number  400324123  to O. T. S. ; Cluster of Excellence  EXC2051 Balance  of the  Microverse-Project -ID 390713860 , equipment grant  INST 275/442-1 FUGG  to H. D. A. ), and the  National Institutes of Health  (Grant  R01GM126228 , to D. T. ) for financial support.
Acknowledgements This research was supported by the doctoral school ED3C and by the  SFRMS (Société Française de Recherche et Médecine du Sommeil ; grants to CL), as well as by  Inserm  ( Institut National de la Santé et de la Recherche Médicale ; research funds to DO). We thank  C. Bastoul  and  A. Le Coz  for helping acquire some data,  S. Leu-Semenescu  for sleep stage coding, and J. Frain for helping pilot the experimental task.
Aesthetic quality assessment for photographs is an important research topic since it can be used for a number of applications, such as image database management or image browsing. In 2012, the aesthetic visual analysis (AVA) dataset has been proposed. It has since then been used to train the majority of computational models of aesthetics assessment. We observe that AVA is mainly composed of competitive photographs which notion of aesthetics differs from other kinds of photographs, such as professional photographs. In this paper, we evaluate whether or not recent aesthetics assessment models generalize well and perform well over professional photographs. We noticed that the different models we tested behave differently on both categories, and therefore do not generalize well. Besides, we fine-tuned one of the tested model using professional photographs and the results show that this fine-tuning is effectively improving the coverage of the methods.
INTRODUCTION Assessing the aesthetic quality of images using computational models has been a problem in the computer vision field for many years. Aesthetic quality assessment has some applications in image sorting for databases management, or in aesthetics-driven image processing for example. Yet, automatically scoring the beauty of an image is still a difficult problem. Compared to the problem of image quality assessment,  1, 2  aesthetics assessment has to be measured by using high level features, which are hardly described by common low level features. Beyond this point, the problem is even trickier since the aesthetic quality of an image is a highly subjective quantity. For predicting the aesthetic quality of an image, many computational models have been proposed. The first models are based on specific photographic rules, such as the rule of thirds related to image composition, or narrow depth of field.
3 Performances of such models are however rather limited. We are currently witnessing a new breakthrough in this field thanks to the emergence of huge datasets (e. g. , AVA  4  ) and new machine learning methods relying on deep learning algorithms. Thanks to deep networks, trained over millions of images,  5  it is now possible to get a large number of features able to describe complex and abstract patterns. A more thorough study on recent models can be found in Reference 6. In 2012, Murray et al. 4  proposed AVA, a dataset specifically designed for aesthetics assessment methods. This dataset, composed of more than 250,000 aesthetically annotated and scored images from the photography website http://www. dpchallenge. com, greatly helped research in this domain. However, this dataset is mainly composed of competitive photographs aimed to be shown in juried exhibitions, to be published in specialized photography magazines or web sites, and to compete for recognition and prizes, as described in Reference 7.
For the specific case of AVA, the scores range from 1 (i. e. , ugly) to 10 (i. e. , beautiful). The mean score is 5. 10; the maximum and minimum scores are 8. 52 and 1. 81, respectively. As current models are trained over the AVA dataset, we could say that current aesthetic models are mainly dedicated to scoring competitive photography. Using such a dataset suggests that some assumptions are implicitly made, in particular the definition of aesthetic quality used. If we consider an image with a high score in AVA dataset as an image with high aesthetic quality, this means that we recognize the general agreement as an accurate measure of the aesthetic quality: if enough people find an image beautiful, then it must be objectively beautiful. While this assumption is quite restrictive, it certainly fits the competitive nature of the photographs of AVA.
This test allowed us to quantify the coverage of the models-kinds of photographs accurately rated by the model-to eventually improve it. As models are mainly trained using competitive photographs, we have collected several datasets of professional photographs to test the recent models NIMA  8  as well as the Ranking Network. 9 his first study shows that there exists a significant difference in the behavior of the models on competitive and professional photographs, and between different categories of professional photographs. This observation led us to our second contribution: we propose a solution to reduce the impact of the training dataset on the coverage of the networks by fine-tuning the already trained models using other kinds of photographs (in our case, using professional photographs).
RELATED WORK 1] [12] [13]  However, as our work do not consist in devising a new aesthetic quality assessment model, we do not focus this section on these papers. Very few studies on the relevance of training datasets for aesthetics assessment have already been conducted. Wang et al. 14  propose a new architecture for aesthetics models based on neurosciences. In parallel, the authors conduct some user experiments to assess how the image transformations used in data augmentation affect aesthetics. In their study, participants were presented with rotated, color-changed or cropped images and they were asked to rate the images according to their preference. They conclude that only three transformations do not severely impact the aesthetic quality of an image: horizontal flip, scaling with a small random factor, and adding some small Gaussian noise.
This conclusion gives some insights when using data augmentation for training aesthetic assessment models, but does not question the validity of currently available datasets. Carballal et al. 15  recently expose the limits of existing training datasets, and create their own dataset composed of images from http://www. dpchallenge. com. Those images are rated in three ways: the mean score given to the image by users of http://www. dpchallenge. com; an aesthetic score given by observers in an environment with controlled viewing conditions; and a preference score given by observers in the same controlled environment. This is the first and only dataset dedicated to aesthetics prediction with scores from several populations. However, this dataset has not been compared to the dataset most used today (AVA) and contains only 1000 images.
Some models were proposed to counter the bias caused by the imbalance of AVA. 4  In that optic, Jin et al. 16  propose the weighted CNN architecture. This is an architecture based on the VGG-16 network, but differs from the VGG-16 network in the cost function used. Instead of using a classic mean square error function, they add weights corresponding to the frequency of apparition of the score of the image. To compute those weights, the authors use the histogram of scores of the training dataset (in this case, AVA). The weight w(I) of an image I is proportional to the inverse of the number of images having the same average score as I. The cost function is then defined as C(ŷ) = ∑ I∈ w(I)||y 0 (I) -ŷ(I)|| 2 2 (1) with  the set of training images, ŷ the vector composed of the computed scores of each image in the dataset and y 0 the vector composed of the ground truth score of the images.
This method allows to assign greater weights to images which scores are less represented in the training dataset. This leads to a network having a better coverage and being able to rate accurately images with very high (s > 6) or very low (s < 4) scores. Rather than assessing the aesthetics of images, a number of authors addressed the problem of Blind Image Quality Assessment (BIQA). These methods use new techniques based on machine learning, such as continual learning, 17 meta-learning  18  or datasets fusion. 19, 20 Differently from aesthetic quality assessment methods, BIQA methods process images purely as signals. This implies that the quality of an image as measured by BIQA methods is an objective value, contrary to the aesthetic quality which may differ depending on the observer. Therefore, we think that these methods are not suited for general purpose aesthetics assessment models, but could be adapted to personalized aesthetics prediction.
NIMA model In the following, we present the NIMA architecture we use and the training process. More details can be found in the original paper. 8 The main feature of NIMA model is its ability to rely on existing pre-trained deep networks. It consists in replacing the last layers of an image classification network with a fully-connected layer, and then train only the final layer. The model is first completely trained on ImageNet, and then fine-tuned using a dataset specific to the final use of the network. Two different examples of uses are proposed: (1) prediction of the aesthetics score of an image and (2) prediction of the quality of an image. The former uses the AVA dataset whereas the latter uses the TID2013 dataset. In this study, we focus only on the aesthetics prediction models. NIMA model relies on existing architectures, two of which are Inception  22  and MobileNet.
Datasets of professional photography In this section, we present the photograph datasets we used to test the above presented models on professional photography. In order to cover a wide range of professional photograph, we used six datasets corresponding to different photography genres. Among the six datasets, we have created five of them by collecting images by hand. As the process of collecting images one by one is very time consuming, we have collected around 100 images per category, as it is sufficient to test the different models. Figure  2  illustrates a sample of images of these photography genres. Fashion category contains photographs coming from various editorial fashion photo-shoots and published in fashion magazines during the year 2018. These photos are captured by professional photographers in collaboration with magazine art director.
The photographs are of different aesthetic styles (black and white, color, high key, low key, etc. ) These images are not only of high aesthetic quality but also have an artistic dimension. These images have been collected for a long time and thus, the dataset collected is larger than the other categories. We have collected 1373 images. Architecture category contains real estate, indoor design, and architecture photographs published in Architectural Digest Magazine. These photographs have been captured by professional photographs and promote the beauty of architecture. They are of high aesthetics quality but in a more classic manner than fashion ones. We have collected 117 images. Cars category contains photographs done by various car manufacturers to advertise their new cars. Due to marketing strategy, the aim of these photographs is to promote different values such as power, robustness and sometimes beauty.
We have collected 109 images. Sport category contains photographs of various sports from the French journal L'équipe dedicated to sport news. These images have been captured by professional photographers and try to capture crucial moments. We have collected 155 images. War category contains war photographs from the photography agency called Agence VU. Obviously the first aim of these photos is not to make aesthetically pleasant photos, but to tell the truth about war. We have collected 138 images. National Geographic category contains wildlife and landscape photographs from the National Geographic website. Similarly to the previous datasets, the photographs have been captured by professional photographers. The usual objective of these photographs is to show the beauty of Earth and wild life.
Overview of models Figure  3  shows histograms of scores for images from the professional datasets we have built presented in section 3. 3 computed by the different models we tested. We can draw several observations from these distributions. First, we notice that the histograms for NIMA (green/blue on Figure  3 ) and for the Ranking Network (named "Kong", in orange on Figure  3 ) are extremely different from each other. Two histograms are noteworthy: War and Sport from Kong model. The War histogram (Figure  3e  in orange) is well below the average score while the Sport histogram (Figure  3f  in orange) is well above the average. The scores from War were expected. While they come from a professional dataset, their main goal is not to have a high aesthetic value, but to show the truth of war.
Fine-tuning of Nasnet-based model We want to address the over-fitting problem. As we already have a trained model to start with, we do not use classic regularization techniques. It is easier and faster to train again the network using the professional photographs. Indeed, using the already trained network as a basis requires less epochs and less training image for the second training. That is what we call fine-tuning. We fine-tune the Nasnet-based model using one of the professional datasets. We consider Fashion for two reasons: first, this category contains the most images among all professional datasets we have, second, based on the content and the aim of those photographs, they must have a high aesthetic score according to competitive photography aesthetic. We construct a training dataset using the 1373 images from Fashion and 1373 random images of AVA.