dataesr/scientific-paragraphs-categorization huggingface dataset:
MeerKAT We obtained four DDT observations with MeerKAT (PI: Rhodes, DDT-20210107-LR-01) at 22, 29, 40, and 54 d post burst. Each observation lasted 140 min, made up of a 5 min scan of a primary calibrator (J0408 -6545) preceded by a series of 20 min scans of the target interleaved with 2 min scans of the secondary calibrator (J1808 + 0134). The observations were made at a central frequency of 1. 28 GHz with a bandwidth of 856 MHz, split into 4096 channels. The MeerKAT data were reduced using OXKAT , a set of python scripts used for semi-automatic processing  (Heywood 2020 ). First, the calibrator fields were flagged for RFI as well as the first and last 100 spectral channels. A spectral model from the primary calibrator was applied to the secondary. Delay , bandpass, and complex gain calibration was performed on the primary and secondary calibrators Table  1.
ISS may be the cause of the interobservation variability seen with the VLA. We also search for intraobserv ation v ariability within the VLA data, as shown in Fig. 2. We observe some lo w-le vel v ariability, ∼10-20 per cent (using equation 10 from  Vaughan et al. 2003 ) , in VLA epochs one and three. VLA epochs two and four show no such variability (see Fig. 2  ). The flux densities of the last two observations are too low to search for variability. We split the two e -MERLIN detections, which are 6 h long each, into four-90 min segments. The left-hand panel of Fig. 3  shows that for the first epoch, radio emission was only detected in two of the four segments. In the final epoch, we only detect radio emission for 90 min out of 6 h. Our MeerKAT data set shows no evidence of intra-observation variability on a time-scale of tens of minutes.
We also show the total 10 GHz model from both jet components as the solid blue line. We expect the wide component of the jet to make some contribution of the total X-ray flux observed by XRT at the time of our final observation which would make the model closer to the observed flux density. We are unable to quantify the contribution of the wide jet component to the total X-ray flux as we do not know the location of ν C with respect to the XRT observing band. In comparison to our single shock scenario, the normalized residual values are much lower denoting a better fit (shown in the lower panel of Fig. 8  ). There are still some increased residual values early on in the 10 GHz light curve, although this may be due to weak interstellar scintillation which we cannot model. A much wider outflow makes for a much better fit to the MeerKAT light curve as well as the later VLA data points.
, 2008). When infected, young berries become brown and are covered by white powder resulting from sporulation. As berries ripen, they become less susceptible to the infection, but rachis infections can spread into older berries  (Gessler et al. , 2011). Rotting with subsequent mummification of inflorescences, shoots, clusters, and berries are also present if control measures are ineffective. In this review, bibliographic data were extracted from the SCOPUS database (https://www. scopus. com/) using specific keywords "Plasmopara viticola" or "P. viticola" and "downy mildew" and "grapevine, " from which 1649 documents were obtained. The bibliometric analysis was constructed using different bibliometric indices, including the most specific keywords used in the published literature related to downy mildew, countries, and the top journals.
For the network construction, bibliometric analysis was constructed using the VOSviewer processing software (v1. 6. 9. , Leiden University, Leiden, The Netherlands). The network analysis shows the worldwide distribution of related articles to downy mildew which help to highlight the relationships between countries, authors, and keywords found to get a comprehensive perspective of the current research of this area (Figure  2A ). The analysis illustrated that most research conducted on downy mildew was demonstrated in the United States, China, France, Germany, Switzerland, and Italy (Figure  2B ). It was noted that most articles addressed induced disease resistance, epidemiology, and control (Figure  2A ).
Ultrastructure of zoospores was firstly examined by transmission electron microscopy (TEM) in Sclerospora graminicola, where discharge papilla (a significant feature for identification, for example, a Peronosporales order) was found to take a part in zoosporogenesis  (Lange et al. , 1984). Surface ornamentation of conidia was described using scanning electron microscopy FIGURE 2 | (A) Bibliometric analysis of the specific keywords used in the published literature related to downy mildew. Bibliographic data were extracted from the SCOPUS database using specific keywords "Plasmopara viticola" or "P. viticola" and "downy mildew" and "grapevine," and the analysis include different bibliometric indices such as the most popular keywords, countries, and the top journals. For the network construction we used the VOSviewer processing software (https://www.
vosviewer. com/ v1. 6. 9. , Leiden University, Leiden, The Netherlands). (B) Publimetry related to downy mildew across the world. The network analysis, constructed using the VOSviewer software, includes the most popular countries. (SEM) on Peronospora sordida and Peronospora statices, where it was a distinguishing trait in addition to conidial lengths and breadths  (Hall and Humphreys-Jones, 1989). A study using the SEM of three Basidiophora species demonstrated the occurrence of species-specific sporangial ornamentation, sporangiophore detachment scars and sporangiophore pedicels in B. entospora and B. kellermannii  (Barreto and Dick, 1991). In 2004 Spring and Thines discovered additional ultrastructural features by SEM: using a model of downy mildews, they found classical features like conidio-/sporangiophore branching or sporangial germination are not suitable, but structure of the ultimate branchlets and haustorial shape were approved to be phylogenetically informative  (Spring and Thines, 2004).
, 2002; Göker et al. , 2003 Göker et al. , , 2007)). Genera Hyaloperonospora and Perofascia were segregated from the large genus Peronospora; owing to their polyphyly, three species of Bremiella genus were embedded within Plasmopara  (Constantinescu and Fatehi, 2002; Göker et al. , 2007; Thines and Choi, 2016). Based on phylogenetic analyses, genus Rhysotheca, introduced by Wilson in 1907 as closely related to Peronospola viticola, was merged with P. viticola  (Wilson, 1907; Riethmüller et al. , 2002). To obtain insight into the phylogenetic relationships within the different genera of Peronosporaceae family, the large subunit of ribosomal RNA (LSU) gene sequences of the different genera of this family were retrieved from the National Center for Biotechnology Information database (NCBI) (http://www.
ncbi. nlm. nih. gov) and aligned to construct the phylogenetic tree (Figure  4 ). The tree was built using MEGA version X with neighbor-joining method. The dataset was boot-strapped 1,000 times and values at nodes indicate bootstrap values out of 1,000 resampling. Plasmopara was proved be a polyphyletic genus, so several Plasmopara and Bremia pathogens of Poaceae, Cichorieae, Ranunculaceae, and Scorzonera hosts were segregated into new genera Viennotia, Poakatesthia, Graminivora, Protobremia, Plasmoverna, and Novotelnova. Plasmopara euphrasiae was segregated from Plasmopara densa and Plasmopara centaureaemollis was revised and synonymised with Bremia centaureae  (Riethmüller et al. , 2002; Göker et al. , 2003; Voglmayr et al. , 2004; Thines et al. , 2006 Thines et al. , , 2007;; Voglmayr and Constantinescu, FIGURE 4  | Phylogenetic tree represents different genera of Peronosporaceae family.
For phylogenetic tree analysis, a large subunit of ribosomal RNA (LSU) gene sequences of the different genera of Peronosporaceae family were retrieved from the National Center for Biotechnology Information database (NCBI) (http://www. ncbi. nlm. nih. gov). Collected sequences were aligned to construct the phylogenetic tree. The tree was built using MEGA version X with neighbor-joining method. The dataset was boot-strapped 1,000 times. Values at nodes indicate bootstrap values out of 1,000 resampling.
The tree was built using he tree was built using MEGA version X and the dataset was boot-strapped 1,000 times. Values at nodes indicate bootstrap values out of 1,000 resampling. Molecular phylogenetic analyses showed a close phylogenetic relationship of downy mildews to the genus Phytophthora, suggesting an origin as a stem from different Phytophthora groups  (Riethmüller et al. , 1999; Cooke et al. , 2000; Voglmayr, 2003). Later in a multigene (five genes) study  Göker et al. found downy mildews to be monophyletic  (Göker et al. , 2007). Analyzing this five gene dataset by different methods of phylogenetic reconstruction, Göker found indications downy mildews are not monophyletic, and one of Phytophthora group was embedded within the downy mildews clade  (Göker and Stamatakis, 2006). The additional data from other gene regions and taxa seems to be necessary to achieve a correct phylogeny.
The represented cycle includes asexual stages of the pathogen development, disease cycle and host symptoms connected altogether. The microscopical observations of the pathogen within the plant tissues had been conducted under the epifluorescence microscope (Olympus Bx43, Japan) using a U/B/G filter and images were captured using Infinity Analyze software. Jones, 2008). Some beneficial microorganisms are able to not only directly inhibit pathogenic growth via hyperparasitism or metabolic activities and supply plant by nutrients or hormones, but also they have an ability to stimulate plant defense mechanisms, which lead to ISR  (Pieterse et al. , 1996; Van Loon et al. , 1998). ISR is characterized by rapid molecular and cellular defense responses, priming the plant for more fast and effective reaction to pathogen infections, controlling plant energy costs.
Biological Control Like many other plants, grapevine can be colonized by huge numbers of microbial organisms in exterior (epiphytes) and interior (endophytes) surfaces of aboveground tissues and in the rhizosphere  (Pinto et al. , 2014; Zarraonaindia et al. , 2015; Asghari et al. , 2019). Biological control encompasses the use of beneficial microorganisms, such as specialized fungi and bacteria to assault plant pathogens and control diseases they cause (Table  3 ). Biological control offers an environmentally friendly approach to handle plant diseases as part of an integrated pest management system. In the EU Pesticide Database, 65 strains of microorganisms are currently approved in plant protection: 36 strains of fungi, yeasts, and oomycetes; 21 strains of bacteria; and 7 viruses (ec. europa.
Trial Registration Information EudraCT: 2016-002,441-36; IDRCB: 2016-A01767-44; ClinicalTrials. gov Identifier: NCT02977819. Physical activity is important in preventing pathologic aging and dementia, including Alzheimer disease (AD). 1  Growing evidence indicates that older adults engaging in a higher amount of physical activity have greater brain integrity,  2, 3  including increased gray matter (GM) volume,  4  cerebral glucose metabolism,  [5] [6] [7]  and white matter (WM) microstructural integrity,  8  along with reduced amyloid burden  6, 7, 9  and WM hyperintensities (WMH). 8 However, the relationship between physical activity and brain structure and function is still not fully understood as some inconsistencies remain  10  and the mechanisms by which physical activity exerts its benefits on brain health remain to be determined.
Standard Protocol Approvals, Registrations, and Patient Consents The Age-Well randomized clinical trial was approved by the local ethics committee (Comité de Protection des Personnes Nord-Ouest III, Caen, France; trial registration number: EudraCT: 2016-002,441-36; IDRCB: 2016-A01767-44; Clin-icalTrials. gov Identifier: NCT02977819) and all participants gave written informed consent prior to the examinations.
Image Processing T1-weighted MRIs were segmented using FLAIR images and normalized to the Montreal Neurologic Institute (MNI) space. GM normalized segments were modulated to correct for nonlinear warping effects using the Statistical Parametric Mapping (SPM12) software multiple channels segmentation procedure (fil. ion. ucl. ac. uk/spm/software/spm12). Raw FLAIR images were coregistered onto their corresponding native space T1-weighted MRI and WMH were segmented by the lesion prediction algorithm (LPA)  19  implemented in the Lesion Segmentation Toolbox version 2. 0. 15 (statistical-modelling. de/lst. html) for SPM12. A minimum extend threshold of 0. 01 cm³ was set. Lesion probability maps were binarized by applying a threshold of 0. 5 and lesion masks were thus generated. Lesion masks were then visually inspected and corrected for false-positives in corticospinal tracts if necessary, using a specific corticospinal tract mask for each participant.
20 I images were corrected for susceptibility artifacts, eddy current distortions, and subject motion using Functional Magnetic Resonance Imaging of the Brain diffusion toolbox (FSL 5. 0. 9). DKI data were then processed using MATLAB R2012b (MathWorks) and diffusional kurtosis estimator software (DKE, version 2. 6) to estimate the diffusional kurtosis tensor. 21 Images were smoothed with a 3. 375 × 3. 375 × 3. 375 mm full-width at half-maximum Gaussian filter. Mean kurtosis (MK) and fractional anisotropy (FA) maps, reflecting WM microstructural integrity,  22  were then extracted from the DKE. These maps were coregistered to the T1-weighted MRI and normalized to the MNI template by applying the deformation parameters from the corresponding T1-weighted MRI. PET data were coregistered onto their corresponding MRI, corrected for partial volume effects (PVE) using the Muller-Gartner method, and normalized using the deformation parameters defined from the T1-weighted procedure.
Acknowledgment The authors thank  Marine Faure ,  Jeanne Lepetit ,  Marie Saville , and the  Cyceron MRI-PET  staff members for help with recruitment and neuroimaging data acquisition; INSERM administrative, financial, and legal departments;  INSERM Transfert  ( Delphine Smagghe ) and  Aurélia Cognet  for administrative support; the sponsor ( Pole de Recherche Clinique at INSERM ,  Dr. Hélène Espérou ); the  Euclid Team  ( Dr Eric Frison ) for data management; and the participants of the study and their families.
Study Funding The Age-Well randomized clinical trial is part of the  Medit-Ageing  project and is supported by the  European Union 's  Horizon 2020 Research and Innovation Program  (grant  667696 ),  Region Normandie (Label d'Excellence) , and  Fondation d'Entreprise MMA des Entrepreneurs du Futur. Institut National de la Santé et de la Recherche Médicale (INSERM)  is the sponsor. F. F. was funded by the  European Union 's  Horizon 2020 Research and Innovation Program  (grant  667 , 696 ) and the  MMA IARD SA. Disclosure G. Chételat reports grants, personal fees, and nonfinancial support from  INSERM , grants from  European Union 's  Horizon 2020 research and innovation programme  (grant  667 , 696 ), and  Fondation d'Entreprise MMA des Entrepreneurs du Future  during conduction of the study; personal fees from  Fondation Entrepreneurs MMA ; grants and personal fees from Fondation Alzheimer; and grants from  Région Normandie (Label d'Excellence) ,  Fondation Recherche Alzheimer , and  Association France Alzheimer  outside the submitted work.
Another Drosophila spice variant contains a large insertion in the linker domain which abolishes binding to Lsd1  [29]. Our data point to a strong functional connection between NvLsd1 and NvCoREST. Not only do they interact but both proteins are also expressed in precisely the same manner and the phenotypes of loss of NvCoREST and NvLsd1 are highly similar regarding the size of the animals as well as the loss of cnidocytes  [51]. We consider this as strong support for the hypothesis that most of the roles of NvLsd1 in Nematostella are mediated through the NvCoREST complex. A similar picture has also emerged in bilaterians where Lsd1 and CoREST interact, co-localize, and indeed phenocopy each other in multiple systems  [15, 29-31, 41, 43]. In the future, it will be useful to interrogate H3K4 methylation in different cell types in Nematostella in the presence or absence of NvLsd1 and NvCoREST in order to refine the precise molecular basis of these phenotypes and directly compare the phenotypes by, for example, comparing all differentially expressed genes.
Orthology search The presence/absence of orthologs of Lsd1 and HDAC1/2 in different groups was extracted from existing literature  [10, [52] [53] [54] [55]. The presence of choanoflagellate Lsd1 orthologs was assessed by reciprocal blast searches in the genomes of Salpingoeca rosetta and Monosiga brevicollis. Homologs of HMG20a/b and PHF21a were identified by reciprocal blast search in the Nematostella genome using human HMG20a (UniProt: Q9NP66) and PHF21a (UniProt: Q96BD5), respectively as a query. CoREST homologs were searched by BlastP and TBlastN through the NCBI BLAST interface on available proteins, genomes, and transcriptomes as well as on dedicated databases (see Additional file 1: Table  S1 )  [75] [76] [77] , using the human CoREST1, Nematostella vectensis CoREST, and Chromosphaera perkinsii CoRESTlike sequences as queries.
Proteins were considered as putative CoREST-like if they included successive ELM2 and SANT domains. The analyzed genomes are listed in Additional file 1: Table  S1  and the accession numbers are given in Additional files 2, 3 and 4: Fig. S1-3. ELM2 and SANT domains were then extracted manually from the retrieved sequences and aligned with a selection of ELM2 and SANT domain sequences obtained from the PFAM database. ELM2, SANT and CoREST metazoan proteins were each aligned using the MAFFT v7. 271 L-INS-I algorithm  [78]. Neighbor-joining analyses including bootstrap analyses (500 replicates) were performed using MEGA v7  [79].
The MS1 resolution and the scan range were set as above, AGC target was set to standard, maximum injection time was automatic and RF lens at 30%. The intensity threshold was also at 5. 0e 4 and dynamic exclusion lasted for 30 s. The MS/MS scans consisted of HCD with collision energy at 30%, quadrupole isolation window at 4 m/z, and Orbitrap resolution at 30,000. FAIMS was set up with the standard resolution mode and a total gas flow of 4. 6 L/min. The CVs were set to -45 and -65 V. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [80]  partner repository with the dataset identifier PXD033068.
Statistical and bioinformatic analyses The LC-Q Exactive raw files were searched in MaxQuant (version 1. 6. 14. 0, Max Planck Institute for Biochemistry, Martinsread, Germany)  [81]  and the spectra were searched against the nveGenes. vienna database version 2008_02 (https:// figsh are. com/ artic les/ datas et/ Nemat ostel la_ vecte nsis_ trans cript ome_ and_ gene_ models_ v2_0/ 807696). The LC-Eclipse raw files were searched in Proteome Discoverer Software (version 2. 5, Thermo Fisher Scientific, Bremen, Germany) using the SEQUEST HT database search engine with Percolator validation (FDR < 0. 01), and against the uniprot-proteome UP000001593 database version 2021_02. In order to test for GFP within the samples, we added GFP to this database (UniProt ID: P42212). Perseus (version 1. 6. 15.
We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys.
109 On their dataset, whose number of images was not provided, the best precision and 110 recall scores were 27% and 80%, respectively. Similarly to  [7] , the authors of  [9]  targeted 111 marine bird detection through a combination of an unsupervised region proposal with a 112 classification CNN. Even though high accuracy scores (>95%) for their pre-trained CNN 113 were reported, visual results presented in the paper show the difficulty of obtaining a 114 model which is robust to sun glitters similar to the ones illustrated in Figure  4. In  [6] , 115 the authors performed an end-to-end supervised detection of dolphins and stingrays 116 in aerial images. Due to the high density and occlusion of animals in some areas, low 117 average precision scores were obtained for both species: 30% and 35% for the detection 118 of dolphins and of stingrays, respectively.
In  [8] , both marine and terrestrial birds are 119 targeted. As a novelty, the authors were able to boost the number of birds in their 120 dataset by introducing samples of bird decoys. Using some of the state-of-the-art object 121 detection models, including Faster R-CNN  [19]  or YOLOv4  [20] , an average precision 122 (AP) score of over 95% was reported on a set of positive samples, i. e. , samples which 123 contain at least one ground-truth bounding box.
201 As first proposed in  [28] , to model the normal training set, the images are first encoded using a ResNet  [32]  model pre-trained on the ImageNet  [16]  dataset. To use different semantic levels, activations from the three intermediate layers are concatenated to create a feature map as used in  [11, 12, 28]. Since this feature map is deep, the number of channels is often reduced using either random-dimensions selection  [11]  or a semiorthogonal embedding matrix  [12]. In practice, we found that using a semi-orthogonal embedding yields more consistent results because the random dimension selection requires to test multiple dimensions in order to find a good combination. The method  [11]  then models these normal feature maps using a Gaussian distribution for each patch location. During training, only a single forward pass is necessary to encode the training set and to compute the mean vectors and covariance matrices estimating the Gaussian distribution.
328 art AnoVAEGAN  [13]. On this dataset, the classification of patches into anomalous 329 and normal images is also significantly improved by our method, as attested by an 330 augmentation of 12. 4% of AUROC in comparison to OrthoAD  [12]. On the other hand, 331 more modest improvements were observed on the Kelonia dataset: 1. 3% and 5. 2% in 332 terms of F1 and recall scores, respectively, when compared to OrthoAD  [12].
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.