dataesr/scientific-paragraphs-categorization huggingface dataset:
subset of this task is anomaly detection, where the goal is to classify whether an image 156 contains an anomaly or not. We refer to an image without anomalies as a normal image 157 and an image with anomalies as an anomalous image. Since marine animal detection 158 requires predicting the precise location of an animal within an image, we focus on 159 anomaly localization. 160 In the literature, most anomaly detection methods are proposed either in an indus-161 trial or in a medical context. The performance benchmarks are often made on the MVTec 162 Anomaly Detection [21] (MVTec AD) dataset which contains a variety of textures and 163 objects classes. The training set for each of these classes is composed of only normal 164 images. A variety of methods already exists to localize anomalies in images, and some 165 of them are reviewed below.
172 reconstruct the anomalies even though they are not part of the training set, making the 173 anomalies undetectable by standard dissimilarity measures computed from the original 174 and reconstructed images. An anomaly can also lead to a failed reconstruction larger 175 than the original anomaly making the precise anomaly localization impossible. 176 Deep embedding methods use the embedding vectors created by networks trained 177 on other tasks to model the normal data. They can use a model pre-trained on another 178 supervised dataset or on proxy tasks for a self-supervised training mode. To model the 179 training data and detect embedding vectors that are anomalous, several methods have 180 been proposed. Patch-SVDD [25] uses a proxy classifying task to encode the image and a 181 Deep-SVDD [26] one-class classifier to classify the patch as either anomalous or normal.
Proposed Method for Weakly-Supervised Marine Animal Detection 194 Convolutional neural networks (CNN) pre-trained on supervised tasks have proven 195 to be robust image feature extractors [28,29]. Their use in anomaly detection has al-196 ready given interesting results in state-of-the-art benchmarks [11,12,30,31]. Since the 197 benchmark datasets commonly used for anomaly detection from images are different 198 from datasets available for marine mammals detection that can be made of thousands of 199 images and involve a strong texture component, we propose to modify and adapt deep 200 feature embedding methods to tackle the marine animals detection problem.
square root of the negative log-likelihood. If the Gaussian distribution hypothesis is 207 valid, detecting anomalous patches is similar to an out-of-distribution (OOD) samples 208 detection process. With this method, the learnt distributions are depending on the patch 209 location. This gives good performance on the MVTec AD dataset where the normal 210 objects are always located at the same location in the image. This means that the model is 211 not invariant to image transformations such as rotations and translations. However, such 212 geometric transformations are common in aerial imagery, while anomalies should still 213 be located. The Gaussian distribution is also a uni-modal distribution. This method is 214 able to model only one modality of the normal class. This is not a problem in the MVTec 215 AD dataset where all training samples are similar and part of the same modality.
222determinant is known and fast to compute. Usually, p Z is taken to be a centered multi-223 variate Gaussian distribution z ∼ N (0, 1). To perform the transformation T, we use 224 the Masked Autoregressive Flow [33]  (MAF) model which uses a series of masked 225 autoregressive dense layers, as described in [34]. The masked layers and auto-regressive 226 property allow for a fast probability estimation in a single forward pass. Sampling, 227 however, requires computing a series of probabilities p(x i |x 1:i-1 ) because each x i is a 228 regression of the previous i -1 variables. In our case, we only leverage the density 229 estimation and do not make use of the sampling from the learnt distribution p X. 230 The transformation parameters can be trained by maximizing the log-likelihood of 231 the normal only training dataset.
(a) Input image (b) Anomaly map (min-max norm) (c) Binary threshold (x ≥ 0. 8) the patch which contains the center of its bounding box. In both datasets, annotations 270 were validated by specialists in marine mega fauna. 271 The Semmacape dataset comprises a set of 165 annotated aerial images acquired 272 as part the SEMMACAPE (https://semmacape. irisa. fr/ (accessed on 6 January 2022)) 273 project, which partially funded the present research and whose main objective is to 274 automatize the survey of marine animals in offshore wind-farms. The images of this 275 dataset were collected in the Gironde estuary and Pertuis sea Marine Nature Park, 276 France, during the spring of 2020. In total, it contains 165 images of 14,204 × 10,652 277 pixels with 528 ground-truth annotations belonging to one of the following classes: 278 • Dolphin (see some examples in Figures 1 and 2).
A total of 258 annotations subdi-279 vided into four classes: striped dolphin ( Stenella coeruleoalba), common dolphin ( 280 Delphinus delphis), common bottlenose dolphin ( Tursiops truncatus), and a separated 281 class for dolphins whose species could not be determined; 282 • Bird (see some examples in Figure 4). A total of 270 annotations subdivided into 283 flying and landed birds belonging to four species: gannet, seagull, little shearwater 284 Puffinus assimilis), and sterna. 285 Since our focus is on marine animal detection, other classes (seaweed, jellyfish, 286 floating waste,. ) were not included from the testing dataset. The dataset contains a 287 variety of settings from homogeneous sea images to images covered with sun glitters and 288 waves, making it challenging to learn the normal distribution of the data.
After filtering 289 and creating the sub-patches, the dataset is composed of 345 patches containing at least 290 one object (anomalous) and 138,544 patches without objects (normal). The percentage of 291 anomalous images is then about 0. 25%. 292 The Kelonia dataset, provided by the Centre d'Etude et de Découverte des Tortues 293 Marines (CEDTM) and by the Kélonia aquarium (https://museesreunion. fr/kelonia/ 294 (accessed on 6 January 2022)), is composed of aerial images of marine turtles acquired in 295 Réunion island between 2015 and 2018. This dataset contains 1983 images with ground 296 truth bounding boxes belonging to one of these three classes: turtle, unturtle (unsure 297 annotations of turtles), and ray. In our experiments, we will consider only the turtle and 298 unturtles classes, which comprise the majority of the annotations.
Unlike the Semmacape 299 dataset, the images have a larger variety of background and color settings because the 300 sea is shallower, showing the seabed. Furthermore, the training set contains images that 301 may not be representative of the normal class and are not found in the testing set. This 302 makes training on this dataset harder because the learnt distribution may not be optimal 303 for anomaly detection on the testing set. Example samples from the dataset can be seen 304 on Figure 7.
125 the manual workload required to obtain annotated training data. The authors trained 126 a CNN to detect different species of seabirds, including terns and gulls, using only 127 200 training samples per class. To make up for the low number of training samples, 128 prior-knowledge about the spatial distribution of birds was introduced during post-129 processing steps, which led to high precision and recall scores of approximately 90% 130 for the most abundant class, but lower scores for the sparse classes. Though some of 131 the methods reviewed above perform well on their dataset, they require some level 132 of supervised labeling or some prior-knowledge about the distribution of the targeted 133 animals. The literature on unsupervised and weakly-supervised methods for marine 134 animal detection is still scarce, which motivated us to focus on weakly-supervised 135 detection of different kinds of marine animals, such as turtles, birds, and dolphins, 136 as described in the following sections.
155A subset of this task is anomaly detection, where the goal is to classify whether an image 156 contains an anomaly or not. We refer to an image without anomalies as a normal image 157 and an image with anomalies as an anomalous image. Since marine animal detection 158 requires predicting the precise location of an animal within an image, we focus on 159 anomaly localization. 160 In the literature, most anomaly detection methods are proposed either in an indus-161 trial or in a medical context. The performance benchmarks are often made on the MVTec 162 Anomaly Detection [21] (MVTec AD) dataset which contains a variety of textures and 163 objects classes. The training set for each of these classes is composed of only normal 164 images. A variety of methods already exists to localize anomalies in images, and some 165 of them are reviewed below.
193 4. 4 Proposed Method for Weakly-Supervised Marine Animal Detection 194 Convolutional neural networks (CNN) pre-trained on supervised tasks have proven 195 to be robust image feature extractors [28,29]. Their use in anomaly detection has al-196 ready given interesting results in state-of-the-art benchmarks [11,12,30,31]. Since the 197 benchmark datasets commonly used for anomaly detection from images are different 198 from datasets available for marine mammals detection that can be made of thousands of 199 images and involve a strong texture component, we propose to modify and adapt deep 200 feature embedding methods to tackle the marine animals detection problem.
206 square root of the negative log-likelihood. If the Gaussian distribution hypothesis is 207 valid, detecting anomalous patches is similar to an out-of-distribution (OOD) samples 208 detection process. With this method, the learnt distributions are depending on the patch 209 location. This gives good performance on the MVTec AD dataset where the normal 210 objects are always located at the same location in the image. This means that the model is 211 not invariant to image transformations such as rotations and translations. However, such 212 geometric transformations are common in aerial imagery, while anomalies should still 213 be located. The Gaussian distribution is also a uni-modal distribution. This method is 214 able to model only one modality of the normal class. This is not a problem in the MVTec 215 AD dataset where all training samples are similar and part of the same modality.
( (a) Input image (b) Anomaly map (min-max norm) (c) Binary threshold (x ≥ 0. 8) the patch which contains the center of its bounding box. In both datasets, annotations 270 were validated by specialists in marine mega fauna. 271 The Semmacape dataset comprises a set of 165 annotated aerial images acquired 272 as part the SEMMACAPE (https://semmacape. irisa. fr/ (accessed on 6 January 2022)) 273 project, which partially funded the present research and whose main objective is to 274 automatize the survey of marine animals in offshore wind-farms. The images of this 275 dataset were collected in the Gironde estuary and Pertuis sea Marine Nature Park, 276 France, during the spring of 2020. In total, it contains 165 images of 14,204 × 10,652 277 pixels with 528 ground-truth annotations belonging to one of the following classes: 278 • Dolphin (see some examples in Figures 1 and 2).
The model is then able to reconstruct the turtles accurately and fails to detect 345 them during testing. The classification performance on the Kelonia dataset can also be 346 explained by the fact that some normal images are very dissimilar from the rest, which leads the model to classify them as anomalous. Method F1 Score Recall Precision AUROC PaDiM [11] 0. 383 0. 434 0. 343 0. 606 OrthoAD [12] 0. 458 0. 373 0. 594 0. 795 AnoVAEGAN [13] 0. 469 0. 531 0. 420 0. 697 Ours, 1× MAF [33] 0. 530 0. 757 0. 408 0. 919 Ours, 2× MAF [33] 0. 486 0. 523 0. 455 0. 869 347.
In this study, we used RT-qPCR and imaging to characterize the GFM content in the mouse vagina and CMP. We found that Muc5b and Muc5ac were the two major GFMs and that mucus cells were present in both the vaginal and cervical compartment. Using in vivo experiments, we found that Muc5b-deficient CMPs were more porous and led to preterm birth after an experimental vaginal infection. Together, our data support the idea of the crucial protective function of the CMP and its mucin content.
Histology and IHC were performed on a Leica DM4000B microscope. High-quality bright field and fluorescence captures were digitized on a Carl Zeiss Axio Scan Z1 scanner and processed with ZEN software. Image analysis was performed using ImageJ/Fiji freeware. The total number of epithelial cells and PCNA + epithelial cells in the vagina and cervix were counted for 3-6 distinct sections per mouse (n=3/genotype) in a blinded manner by two independent observers (GL and VG). The number of PCNA + cells was expressed as the total number of PCNA + cells per 100 epithelial cells.
Thickness of the mucosal epithelium and nonmucosal epithelium The thickness of ME and nonmucosal epithelium (NME, defined as the epithelium with no visible mucus-laden vacuoles) was measured as follows: 20-40 measurements of the ME and NME were performed for each field using ImageJ. Data were obtained using at least three AB-PAS-stained sections of vagina per mouse. Four mice per genotype were analyzed to ensure accurate results. independent observers (GL and VG). The total area (m 2 ) of the vaginal and cervical epithelium was measured using ImageJ. The results are expressed as the number of TUNEL + cells per m 2 of the vaginal or cervical epithelium (n=5/genotype). iDISCO iDISCO was performed as described by Belle et al. (Belle et al. , 2017)  with minor modifications. Bleaching and clearing were performed as described.
For immunostaining, primary and secondary antibodies were diluted in 1 PBS containing 0. 2% gelatin, 0. 5% Triton X-100, and 0. 1% saponin. The GFP tag in Muc5b-GFP was immunodetected using anti-GFP antibody. 3D imaging was performed using an ultramicroscope I with ImspectorPro software  (Belle et al. , 2014). The step size between each image was fixed at 2 m. Stack images were first converted to an Imaris file (. ims) using ImarisFileConverter. Images, 3D volume, and movies were generated using Imaris x64 software.
ELISA Frozen uterus samples were thawed on ice and solubilized in 400 L ice-cold protein lysis buffer (100 mM DTT, 0. 01% NP40) containing protease inhibitors using a Bead Mill 24 homogenizer (4 m/s for 10 s, 10 s dwell time between runs, three times). Total protein concentration was determined using a Pierce BCA Protein Assay kit as per the manufacturer's guidelines and then diluted 1:4 in PBS. All samples were measured in the same assay using ELISA kits for mouse Il-1, CXCL1/KC, Il-6, Il-10, and Tnf- according to the manufacturer's guidelines. The cytokine content of tissue homogenates was normalized to protein content and the data are expressed as pg/g tissue. Cytokine concentration was determined in 100 L of pure serum and is expressed as ng/mL.
Acknowledgments We thank  J. Devassine  ( Animal Core Facility ,  UMS 2014 -US 41 -PLBS/ Plateformes Lilloise en Biologie et Santé ), and  M-H. Gevaert  ( UMS 2014 -US 41 -PLBS ) for the histological sections,  M. Tardivel  and  A. Bongiovanni  for cell imaging (BICeL,  UMS 2014 -US 41 -PLBS ),  N. Barois  for electron microscopy (BICeL,  UMS 2014 -US 41 -PLBS ) and  S. Plet  ( Inserm U1286 ) for technical support. The  45M1 , anti-TFF3 and Eu1/Eu2 antibodies were kindly given by  J. Bara ,  C. Tomasetto  ( IGBMC, Université de Strasbourg, France ) and  D. Swallow , respectively. This work was supported by the «  1000 days for health » program  ( FHU from Univ. Lille, France ) and the  University of Lille, France  (GL PhD fellowship).
Application to SPIDER Data To illustrate the application of the transfer matrix, we present results from the SPIDER balloon-borne telescope. During its first Antarctic long-duration balloon (LDB) flight in January 2015, SPIDER mapped 4. 8% of the sky with polarimeters operating at 95 and 150 GHz to constrain the B-mode power spectrum from primordial gravitational waves (SPIDER Collaboration 2021). An upcoming flight with additional 280 GHz receivers will provide improved characterization of the polarized Galactic dust foregrounds  (Shaw et al. 2020 ). SPIDER's processing pipeline is described more fully in SPIDER Collaboration (2021) and SPIDER Collaboration (2022, in preparation); here we briefly highlight the most relevant steps and note that they are sufficiently linear to allow usage of Equation (8).
Once features such as cosmic-ray hits, payload transmitter signals, and thermal transients have been removed from the raw detector timestreams, they are filtered to reduce quasi-stationary noise. Null test performance was used to identify the weakest filter that sufficiently removed quasistationary noise, resulting in a fifth-order polynomial fit to each detector's data as a function of azimuth angle between scan turnarounds. The impact of scanning, filtering, and flagging are determined by applying the entire processing pipeline to an ensemble of time-domain signal simulations in a procedure known as reobservation. The reobserved timestreams are produced at the full data sample rate without any downsampling or binning. Unlike the BICEP/Keck experiment, SPIDER's observing strategy does not allow for the creation of an observing matrix, so obtaining the transfer matrix J ℓℓ¢ requires producing an appropriate set of reobserved CMB maps.
SPIDER's measured and simulated timestreams are converted into two-dimensional maps of the sky with a binned mapmaker. This approach assembles the detector data into spatial pixels based on the telescope pointing and polarization sensitivity as described further in SPIDER Collaboration (2021). The computational simplicity of this method is crucial for enabling the generation of large simulation ensembles, including those used in this work. The main SPIDER cosmological results presented in SPIDER Collaboration (2021) use two complementary pipelines for power spectrum estimation. Here we describe results only from the Noise Simulation Independent (NSI) pipeline, while the other pipeline is presented in  Gambrel et al. (2021). The NSI pipeline is similar to Xspect  (Tristram et al. 2005)  and Xpol  (Tristram 2006) , as well as to those used by the South Pole Telescope  (Lueker et al.
2010) , CLASS  (Padilla et al. 2020) , and SPIDER's circular polarization analysis  (Nagy et al. 2017). It decomposes the 2015 flight data into 14 maps by splitting the timestreams into interleaved 3 minute segments for each of the six receivers. This timescale was chosen to maximize the number of complete and well-conditioned maps of the sky region, taking into account SPIDER's scan rate and observing strategy. These maps are then co-added by frequency to produce 14 maps in each of the two SPIDER bands, 95 and 150 GHz. The cross-power spectra for all pairs of maps (neglecting the auto-spectra) are produced with PolSpice  (Chon et al. 2004) , and the distribution of the cross-spectra allows for an empirical measurement of the uncertainty without reliance on an instrumental noise model. In SPIDER's NSI pipeline, these cross-spectra are used to estimate cosmological parameters by comparing them to theoretical models that depend on the parameters of interest.
As foreground component separation becomes an increasingly important part of CMB analyses, future experiments can benefit from the development of new signalindependent analysis techniques to improve the power spectrum reconstruction. SPIDER is supported in the U. S. by the National Aeronautics and Space Administration under grant Nos. NNX07AL64G, NNX12AE95G, NNX17AC55G, and 80NSSC21K1986 issued through the Science Mission Directorate and by the National Science Foundation through PLR-1043515. Logistical support for the Antarctic deployment and operations is provided by the NSF through the U. S. Antarctic Program. Support in Canada is provided by the Natural Sciences and Engineering Research Council and the Canadian Space Agency. Support in Norway is provided by the Research Council of Norway.
Most genetic loci associated with stroke have been identified in populations of European ancestry. The largest published GWAS meta-analysis to date (67,162 cases and 454,450 control individuals, MEGASTROKE) reported 32 stroke risk loci  1. To identify new genetic associations and provide insights into stroke pathogenesis and putative drug targets, we first performed a cross-ancestry GWAS of 1,614,080 participants, including 110,182 patients who had a stroke, and followed up genome-wide significant signals in an independent dataset of 89,084 patients who had a stroke and 1,013,843 control individuals. We then characterized the identified stroke risk loci by leveraging expression and protein quantitative trait loci, cross-ancestry fine-mapping and shared genetic variation with other traits. Finally, we used a series of approaches for genomics-driven drug discovery for stroke prevention and treatment, and examined the prediction of stroke with polygenic scores (PGSs) across ancestries in the setting of both population-based studies and clinical trials.
Independent follow-up of GWAS signals We followed up genome-wide significant stroke-risk loci both internally and externally. First, we sought to replicate the 42 stroke-risk loci that reached genome-wide significance in individual ancestries in at least one other ancestry group among the discovery samples. We successfully replicated, with consistent directionality, 10 of these loci at P < 1. 19 × 10 -3 (accounting for the number of loci tested), of which 7 were genome-wide significant in Europeans, 1 in East Asians, and 2 in both Europeans and East Asians. An additional 15 loci showed nominal association (P < 0. 05) in at least one other ancestry (Supplementary Table  15 ). Second, we gathered an independent dataset of 89,084 individuals who had a stroke (AS; of which 85,546 AIS; 70. 0% European, 15.
Third, we used protein quantitative trait loci (pQTL) for 218 drug-target proteins as instruments for MR and found evidence for causal associations of 9 plasma proteins with stroke risk (4 cis-pQTL and 6 trans-pQTL), of which 7 were supported by colocalization analyses, with no evidence for reverse causation using the Steiger test (PROC, VCAM1, F11, KLKB1, MMP12, GP1BA and LAMC2; Supplementary Table  33 ). All of these replicated (at FDR < 0. 05) with consistent directionality using at least one independent plasma pQTL resource and cerebrospinal fluid pQTL for PROC and KLKB1, with evidence for colocalization for PROC, F11, KLKB1 and MMP12, but not for GP1BA (for which both concordant and discordant directionality was observed) and LAMC2 (pQTL available in one replication dataset only; FDR = 0.
08). Using public drug databases, we curated drugs targeting those proteins in a direction compatible with a beneficial therapeutic effect against stroke based on MR estimates and identified such drugs for VCAM1, F11, KLKB1, GP1BA, LAMC2 (inhibitors) and PROC (activators; Supplementary Table  34 ). Drugs targeting F11 (NCT04755283, NCT04304508, NCT03766581) and PROC (NCT02222714) are currently under investigation for stroke, and our results provide genetic support for this. Notably, F11 and KLKB1 are adjacent genes with a long-range linkage-disequilibrium pattern and complex co-regulation  28  , as illustrated here by the presence of a shared trans-pQTL in KNG1 (Supplementary Table  33 ). Additional studies are needed to disentangle causal associations and the most appropriate drug target in this region  29, 30.