dataesr/scientific-paragraphs-categorization huggingface dataset:
A stain distribution can be found in the Supplementary Materials, with a sample hue distribution for this dataset shown in Figure  2. DeepFocus 18 : The DeepFocus 18 dataset contains 118800 patches of size 64 x 64, consisting of 16 different slides with 4 types of stains. This dataset, alongside the FocusPath64 43 and BioImage64 16 datasets, were useful for determining the effect that a varying patch size can have on the quality of training and transferability to other datasets. This dataset has less stain diversity than FocusPath  43  , and slightly less hue diversity as well, with a standard deviation 1. 8% smaller than that of FocusPath  43. BioImage  16  : The Broad BioImage Dataset  16  consists of 52224 patches of size 696 x 520. BioImage  16  was useful for investigating the effect that grayscale images have on the quality of training and transferability to other datasets.
It has been observed that color information can positively enhance the FQA performance of a CNN  14. Successful FQA methods should have the ability to distinguish focus levels in an image regardless of the colors in the image. This study therefore also seeks to further investigate the effect of color information on transfer performance. TCGA  64  : The TCGA@Focus dataset contains 14371 image patches in total, with 11328 patches labelled in-focus and 3043 patches labelled out-of-focus. This dataset was chosen due to its wide spectrum of tissue textures and colors.
CNN Analysis This experiment trained ten architecturally diverse CNN models on the five training datasets. The first is FocusLiteNN 10 , a light-weight CNN built for FQA, which was trained using 1, 2, and 10 input channels. Other architectures used include EONSS  11  , DenseNet13 65 , MobileNetv2 66 , variations of ResNet 67 (ResNet18, ResNet50, and ResNet101), and DARTS  58. A summary of each network's parameters and convolutional layers, as well as the floating point operations (FLOPs) cost and GPU latency are shown in  FocusLiteNN (1 kernel)  10  3. 12 0. 58 0. 18 0. 10 FocusLiteNN (2 kernel)  10  3. 90 0. 72 0. 21 0. 12 FocusLiteNN (10 kernel)  10  4. 08 0. 85 0. 30 0. 15 EONSS  11  13. 53 3. 30 1. 15 0. 68 DenseNet-13  65  28. 51  Figure  4  Figure  5  Differentiable Architecture Search This experiment involves a NAS on the FocusPath 43 , BioImage  16  , and DeepFocus 18 datasets.
For the ten CNNs used in the experiment, these metrics were averaged between the input and output channels, then averaged across each layer, then finally over the five trials used in the experiment. Figures  6a, 6b , and 6c plot the Knowledge Gain against the Mapping Condition for each network. A point near the top left of this plot is desired, as this means the network has a strong mapping condition and a high knowledge gain. For BioImage  16  , a change in patch size does not affect the knowledge gain and mapping condition significantly, shown by the proximity of each pair of points. For the FocusPath  43  dataset, the input patch size has a noticeable effect on the knowledge gain and mapping condition. Generally, the larger patch sizes have a worse mapping condition performance. This could be because smaller input patches have fewer features, meaning that input perturbations are less likely to impact the output.
Interestingly, across all three datasets, deeper networks have a worse average knowledge gain and a worse mapping condition performance than shallower networks. For BioImage  16  as an example, FocusLiteNN (10 channel)  10  shows 84. 7% knowledge gain improvement and 87. 8% mapping condition improvement over DenseNet-13  65  and 146% and 97. 4% respective improvements over ResNet-101  67. DARTS-FQA  58  falls outside of this trend with a lower knowledge gain but a more stable mapping condition relative to networks of similar complexity, likely because tailoring the network to a specific dataset makes the network much more stable in response to that dataset. Overall, these deeper networks would benefit from fine-tuning before transferring to other datasets, which is less necessary for shallower networks.
Results The training and testing sets were randomly shuffled and distributed before each trial, and each model was trained five times on each dataset. Each model was then tested on a testing set from the same dataset on which they were trained before being transferred to TCGA@Focus  64. The input patches were normalized by color before being passed into the CNN, for both the training and testing cases. A full description of training hyperparameters, evaluation metrics, and validation results can be found in the Supplementary Materials.
Discussion Table  5  shows the ROC and PR performance for each network and dataset when transferred to the TCGA  64  dataset, and Figure  7  shows the best and worst two ROC and PR curves for selected datasets. More ROC and PR Curves can be found in the Supplementary Materials. In general, the FocusPath 43 dataset has the highest transferability to the TCGA  64  dataset, with an average ROC of 93. 98% and PR of 87. 10%. DeepFocus 18 has the worst with respective averages of 68. 87% and 43. 31%, which is unacceptable for pathology applications when compared to ROC results from other studies  10, 18. Table  2  shows that FocusPath 43 has a higher organ and stain diversity than any of the other training datasets. This would suggest that the organ and stain diversity does have an impact on the transferability of the model.
The main issue with DeepFocus 18 is the distribution of in-focus and out-of-focus classes, where only 9% of the patches are labelled in-focus. Since there are so few positive cases, it does not take many false positives to heavily impact the precision. This highlights the importance of a dataset with balanced focus levels as well. As suggested by the training quality metrics, deep CNNs do not perform best on every dataset. Rather, it is clear that complex networks such as ResNet  67  exhibit better transferability when training on datasets with large input patch sizes. ResNet-101  67  shows 3. 15% better ROC, and 6. 74% better PR on BioImage 16 compared to BioImage64  16  , and similar performance on FocusPath 43 compared to FocusPath64  43. Conversely, lightweight networks such as FocusLiteNN 10 perform better when working on datasets with small input patch sizes, with 1.
47% better ROC and 3. 58% better PR for FocusPath64 43 when compared to FocusPath  43. Figure  8  reinforces this trend, as datasets with small input patch sizes display negative trends, while datasets with larger input patch sizes have positive trends. While the average transfer performance is worse for the FocusLiteNN 10 networks, the best performance that can be achieved from using a shallower network, using FocusLiteNN (2-channel)  10  on FocusPath64  43  , is only 1. 13% lower than the best performance measured in this study, using DenseNet-13  65  and FocusPath  43. Networks such as FocusLiteNN 10 and EONSS  11  scale so well to high-throughput scanning that using a smaller patch size is acceptable. Using FocusLiteNN (1 channel)  10  with a 64 x 64 input patch size still processes 300 WSIs in just over three hours, while largely outperforming deeper networks on FocusPath64 43 , BioImage64  16  , and DeepFocus 18 as shown in Table  5.
For example, the right side of the image was correctly labelled out-of-focus by DARTS-FQA  58  , while FocusLiteNN (1 channel) 10 misses this region. From the heatmaps analyzed in this paper, there is no pattern concerning how these deeper networks and coarsely sampled datasets will predict the focus quality distributions, which does affect their overall explainability. While Figure  9b  is somewhat aggressive in its evaluation, the Supplementary Materials display numerous examples that are not aggressive enough. In contrast, shallower networks and smaller input patch sizes such as the one shown in Figure  9c  seem to spatially represent the focus quality distribution more closely to expected human perception. From the samples analyzed in this study, models trained on datasets with smaller input patch sizes generally have spatial representations that match human perception more closely, which gives them an explainability advantage.
Conclusions This paper sought to identify the characteristics of datasets and CNN architectures that make them more transferable and more scalable for FQA applications in digital pathology. The FocusPath dataset  43  showed the best transferability performance with an average ROC of 94. 0% and PR of 87. 1%. FocusPath's  43  tissue, stain, and hue diversity were major contributors to this success. Color information in the dataset also had a small transferability advantage of 2. 03% when compared to the grayscale BioImage  16  dataset. DeepFocus  18  showed the worst transfer performance with an average ROC of 68. 9% and PR of 43. 3%, which can be attributed to a heavy imbalance between out-of-focus and in-focus patches. The DARTS-FQA network performed quite well against the other nine CNNs, achieving performance in the intermediate and above-average range for each of the datasets.
This is encouraging performance for a low-cost algorithm, and further optimizations of these networks would allow it to outperform other networks of similar complexity significantly. Shallower networks showed excellent scalability performance, with the FocusLiteNN 10 networks running 300 WSIs in just over 4 hours for the slowest case. They also show acceptable transferability performance, and perform even better than deep networks and searched architectures when trained on datasets with small input patch sizes. Furthermore, shallower networks also have much better training quality metrics results than their deeper counterparts, which suggests that fine-tuning would not be as essential. Shallower networks are more easily explainable when showing focus quality heatmaps as they align better with human perception, which can help build confidence in AI-based solutions for QC in digital pathology.
Overall, using diverse datasets to train shallow networks on small input patches can help optimize scalability without sacrificing significant transferability performance. Table  2 : WSI Dataset Information, listed in alphabetical order. Number of classes refers to the number of focus levels used in the dataset. The subscript for the Hue Mean column represents the standard deviation. Figure Legends Table  3 : CNN architecture and complexity summary. The #FLOPs and latency are listed for an input patch size of 64. Table  4 : Hours taken per GPU to process 300 WSIs. Each WSI has a 25k x 25k pixel size. The shortest time is in green. Table  5 : Performance Metrics for Transfer to TCGA@Focus 64. The best PR and ROC result for each network is in green. The subscript indicates the standard deviation over the 5 trials.
The full set of plots can be found in the Supplementary Materials. Each plot corresponds to a different trained dataset: a) BioImage 16 b) BioImage64 16 c) FocusPath 43 , and d) DeepFocus  18. Figure  9 : Focus Quality heat maps for a WSI from the TCGA 64 dataset. Red corresponds to a lower focus quality meaning out-of-focus, while blue is a higher focus quality. FocusPath  43  and BioImage  16  have a larger input patch size, explaining their coarser heatmap distribution. More heatmaps can be found in the Supplementary Materials.
Data Availability The FocusPath  43  dataset used in this study can be found in an open Zenodo repository (https://zenodo. org/record/3926181#. YWXyJmLMJOQ). The BioImage  16  dataset can be found at the Broad BioImage Benchmark Collection (https://bbbc. broadinstitute. org/BBBC006). The DeepFocus  18  dataset can be found in an open Zenodo repository (https://zenodo. org/record/ 1134848#. YWXyoGLMJOR). TCGA@Focus can also be found in an open Zenodo repository (https://zenodo. org/record/ 3910757#. YWXywmLMJOQ).
Regional climate model and reanalysis datasets MAR is a regional climate model speci cally designed for simulating polar climate  24, 62. MAR atmospheric dynamics are based on the hydrostatic approximation of the primitive equations  63. The exchanges between the atmospheric part of MAR and the surface are handled by the complex energy-balance snow model SISVAT 64 , based on CROCUS 65 that explicitly simulates 30 layers resolving the 20 rst meters of snow or ice. The surface module notably represents percolation of meltwater and its retention into the snowpack. Runoff occurs when the snowpack can no longer absorb additional liquid water (i. e. , snowpack water content exceeding 5% or surface liquid water over bare ice or an ice-lense layer). MARv3. 11 was run at a resolution of 7. 5 km and was forced by 876-hourly outputs of the latest ERA5 reanalysis between 1979 and March 2020.
Even if we present the Larsen C calving event of July 10-12, 2017, we did not consider this event in the nal statistics. This prompted us to consider the iceberg retrieval database from Stuart and Long, 2011  75. Here we extracted the initial location of icebergs around the Larsen ice shelves, but di culty was encountered in tracing with con dence the origin of these icebergs to a given ice-shelf because the rst detection of an iceberg can occur days after calving and its position can be far from its parent ice-shelf. Nevertheless, the information of proximity with an ice shelf provides some hints on calving in the Larsen area. In the latter database, winter occurrences are also largely less frequent. Similar examination was more limited over the Wilkins ice shelf because cloud cover is generally denser, so collapses and calving events are generally observed a few days after their occurrence.
Moreover, contrary to the eastern side, several large events that occurred in the darker autumn and winter periods were not visible in MODIS datasets. This made the attribution of ARs to calving occurrences more di cult on the western side of the AP. Surface melt surface anomalies over the Larsen ice shelves were also retrieved between October 1 and March 31 every year over 1979-2020 76. A series of microwave radiometer observations (namely Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS)) were processed to test our conclusions on AR impact on snow melt (see Supplementary Fig. 6 ). First, a climatology of daily melt surface extent based on mean values for each Julian day mean was computed over the full 41 year-long period.
The daily melt anomalies were computed by comparison with this climatology and compared with AR occurrences. Data obtained before 1993 must be considered with caution, in particular with summer 1987-88  29  Finally, to complement our analysis on the relationship between melt pond observations and AR activity obtained using the MODIS image interpretation by Hubbard et al. 2016  32  , we also studied the Landsat8 image database  77   (Moussavi et al. , 2020), used by Banwell et al. , 2021  29. Information on lake extent on the Larsen ice shelves were retrieved and merged over periods of 16 days (corresponding to the Landsat revisit time). Far larger melt pond areas were usually observed at the end of February and in March, but after a comparison with Sentinel 2 images over the same periods, we concluded that this very likely re ects artefacts resulting from the misclassi cation of scattered cloud shadows as lakes by the algorithm Moussavi et al.
Funding: We acknowledge support from the  Agence Nationale de la Recherche  projects  ANR-20-CE01-0013  ( ARCA ),  ANR-14-CE01-0001  ( ASUMA ),  ANR-16-CE01-0011  ( EAIIST ),  ANR-15-CE01-0005-01  ( TROIS AS ) and  ANR-15-CE01-0015  ( AC-AHC2 ). IVG thanks  FCT/MCTES  for the nancial support to CESAM ( UIDP/50017/2020  and  UIDB/50017/2020 ) through national funds and support to project  ATLACE  ( CIRCNA/CAC/0273/2019 ) funded by  FEDER , through  COMPETE2020 -Programa Operacional Competitividade e Internacionalização (POCI) , and by national funds (OE), through  FCT/MCTES. CA acknowledges support from the  Fondation Albert 2 de Monaco  under project  Antarctic-Snow  ( 2018-2020 ). Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  F.
Data and materials availability: The Modèle Atmosphérique Régionale (MAR) data is available from C. K (ckittel@uliege. be). MODIS-Terra Aqua images were acquired at the (https://worldview. earthdata. nasa. gov). AVHRR imagery was acquired from the NSIDC(86). The code for the AR detection algorithm discussed in this paper is available at https://doi. org/10. 5281/zenodo. 4009663 and the output can be downloaded from the ARTMIP database at https://www. earthsystemgrid. org/dataset/ucar. cgd. ccsm4. artmip. tier1. html.
PDS induces DNA damage in primary neurons, astrocytes and microglial cells We found that pharmacologically stabilizing G4-DNA promotes the formation DNA DSBs in primary cortical neurons  [44]. Cancerous cells exhibit DNA damage upon exposure to G4-DNA ligands  [45, 46]. Nevertheless, glial cells may respond differently to small-molecule G4-DNA stabilizers  [47]. Primary astrocytes and microglia were cultured and treated with a vehicle or PDS overnight, fixed and stained for γH2A. X. Neurons, astrocytes and microglial cells exposed to PDS had γH2A. X punctate staining in the nuclei (Figure  3A-3F ). Our data indicate that stabilizing G4s with PDS promotes DNA AGING  stained for γH2A. X (green), Iba-1 (red), and with the nuclear DAPI dye (blue). Samples were imaged with a confocal microscope.
PDS downregulates Brca1 in neurons, but not in astrocytes and microglial cells We then wondered if G4-DNA-dependent Brca1 downregulation is specific for neurons or if it also occurs in astrocytes and microglia. Primary microglia and astrocytes were treated with a vehicle or PDS, and mRNAs were extracted and analyzed. Since the Tbp gene does not contain PQFS and Tbp transcription is not affected by PDS  [44] , TBP mRNA was used as loading control. PDS did not downregulate Brca1 in astrocytes and microglia, but it did reduce levels of Brca1 mRNA in primary cortical neurons (Figure  4A ). Our data indicate that Brca1 in neurons is specifically sensitive to PDS, suggesting a cell-type-specific G4-DNA landscape in these cells. PDS may alter DNA topology by trapping topoisomerase II on DNA  [46].
CD and TDS experiments CD and UV-Vis spectra were recorded on the JASCO J-815 spectropolarimeter in a 10-mm path-length quartz semi-micro cuvette (Starna). CD and UV-Vis spectra of 3 µM of DNA sample (Eurogentec) were recorded over 220-350 nm at 25 and 90° C (bandwidth = 1 nm, 1 nm data pitch, 1 s response, scan speed = 200 nm. mn -  , averaged over 4 scans) in 100 µL (final volume). Experiments in K + -conditions were performed in Caco. K10 buffer (10 mM lithium cacodylate buffer (pH 7. 2) plus 10 mM KCl and 90 mM LiCl), and experiments in Na + conditions contained Caco. Na100 buffer (10 mM lithium cacodylate buffer (pH 7. 2) plus 100 mM NaCl). Final data were treated with OriginPro ® 9. 1 (OriginLab Corp. ). TDS signatures were AGING calculated by subtracting the spectra collected at 25° C from those collected at 90° C and then zeroed at 350 nm.
CD spectra were recorded on the JASCO J-815 spectropolarimeter in a 10-mm path-length quartz semimicro cuvette (Starna). Spectra of 3 µM of DNA sample were recorded from 25-90° C at 264 and 320 nm (sampling = 2° C, ramp rate = 2° C. mn -1 ). Samples were prepared in 100 µL (final volume) of Caco. K10 buffer (10 mM lithium cacodylate buffer (pH 7. 2) plus 10 mM KCl and 90 mM LiCl) with or without PDS (15 µM, 5 mol. equiv. ). Final data from triplicates were treated with OriginPro ® 9. 1 (OriginLab Corp. ), subtracting the spectra collected at 320 nm from the spectra collected at 264 nm and averaging the triplicates.
FUNDING This work was supported by the  National Institute on Aging  [ 1RF1AG068292 01] (A. S. T. ) , the  National Institute of Neurological Disorders and Stroke  [ R01NS094543 ] (L. D. M. ), [ R01 NS093042 ] and [ R01 NS108779 ] (F. L. ), and  Agence Nationale de la Recherche  [ ANR-17-CE17-0010-01 ], the  Université de Bourgogne ,  Conseil Régional de Bourgogne  and the  European Union  [ PO FEDER-FSE Bourgogne 2014/2020  programs] (D. M. ).
Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. Some examples are Open Academic Graph 3  , Scholarlydata. org  [10] , Microsoft Academic Graph 4   [11]  (MAG), Scopus  5  , Semantic Scholar  6  , Aminer  [12] , Core  [13] , OpenCitations  [14] , and Dimensions 7. These resources provide substantial benefits to researchers, companies, and policy makers by powering data-driven services for navigating, analyzing, and making sense of research dynamics. However, the current generation of knowledge graphs lacks of an explicit representation of research knowledge discussed in the scientific papers. This is usually only described by not machine-readable metadata, such as natural language text in the title and abstract, and in some cases a list of topics or keywords from a domain vocabulary or taxonomy (e.
g. , SWRC 12 , BIBO 13 , BiDO 14 , FABIO 15 , SPAR 16 , CSO 17 , and SKGO 18  [23]. Some recent solutions, such as RASH 19   [24] , and the Open Research Knowledge Graph 20  [25]  7 https://www. dimensions. ai/  1 SKGOhttps://github. com/saidfathalla/Science-knowledge-graph-ontologies 19 RASHhttps://github. com/essepuntato/rash 20 ORKGhttps://www. orkg. org/orkg/ highlighted the advantages of describing research papers in a structured manner. However, the resulting knowledge bases still need to be manually populated by domain experts, which is a time consuming and expensive process. We still lack systems able to extract knowledge from large collection of research publications and automatically generate a comprehensive representation of research concepts. It follows that a significant open challenge in this domain regards the automatic generation of scientific knowledge graphs that contain an explicit representation of the knowledge presented in scientific publications  [25] , and describe entities such as approaches, claims, applications, data, results reported in each paper.
Problem Statement Given a large collection of research papers, we want to generate a large-scale knowledge base, that will include all relevant entities in a certain domain and their relationships. More in detail, given a set of scientific documents D = {d 1 ,. , d n }, we build a model γ : D → T , where T is a set of triples (also referred as relationships) (s, p, o) where s and o belong to a set of entities E and p belongs to a set of relations labels L. Each triple needs also to be associated with the set of papers it was extracted from, allowing to assess how the claim is supported in the original collection of documents. The resulting knowledge graph can be employed for different problems of new research fields (e. g. , detection of research communities, their dynamics and trends, forecasting of research dynamics using sentiment analysis, measuring fairness of open access datasets, etc.
g. , Machine Learning and Data Mining. Therefore, we split entities that contain the conjunction and. Referring to our example, we obtained the two entities Machine Learning and Data Mining. Handling Acronyms. Acronyms are usually defined, appearing the first time near their extended form (e. g. , Web Ontology Language (OWL)) and then by themselves in the rest of the abstract (e. g. , CSO). In order to map acronyms with their extended form in a specific abstract we use a regular expression. We then substituted every acronym (e. g. , OWL) in the abstract with their extended form (e. g. , Web Ontology Language). Since acronyms can be ambiguous, we perform this operation only on entities from the same abstract. Detection of Generic Entities. Entities might be too generic for the purpose to describe the knowledge of a domain (e.