dataesr/scientific-paragraphs-categorization huggingface dataset:
In general, these results show that our model truly learned how to classify the images and is not merely guessing the labels. The promising results presented here indicate that the current method should be extended to an object detection framework. Such a framework would eliminate the need for the image thresholding and DBSCAN steps. This implementation could be done by using a strongly-supervised architecture such as UNet 60 , or by using a weakly-supervised method derived from Grad-CAM, for example. As a final recommendation regarding the machine learning aspect of this paper, we strongly encourage future researchers to add samples to the training dataset that matches their specific research needs instead of blindly applying the provided pre-trained models. We have also presented examples of wave breaking statistics that can be obtained using the proposed method.
Conclusion We described a novel method to detect and classify actively breaking waves in video imagery data. Our method achieved promising results when assessed using several different metrics. Further, by analyzing the deeper layers of our neural network, we showed that the model mimicked how a human classifier would perform a similar classification task. As an application of the method, we presented wave breaking statistics and breaking wave crest length distributions. Our method can thus be useful for the investigation of several ocean-atmosphere interaction processes and should, in the future, lead to advancements in operational wave forecast models, gas-exchange models, and general safety at seas. Finally, we strongly recommend that future research should focus on standardized methods to study wave breaking so that a consistent dataset can be generated and used freely and unambiguously by the community.
All metrics described above were monitored during the training process and were individually assessed to rank model performance. The training curves shown in Figure 2-a were used to assess when overfitting, that is, decreases in the loss function value for the training dataset that were not reflected in the validation dataset, started to occur. Training epochs after overfitting started were discarded. Here we favor the AUC curves as shown in Figure 2-b to indicate better performing models because AUC is a more robust metric than the classification score and at the same time presents a smooth evolution with training epochs. Finally, a confusion matrix (or table of confusion) as shown in Figure 2-c was plotted for each model to assess Type I and Type II errors.
formula. Tr. S. and Ts. S. are the sample size of the train and test datasets, respectively. Black Sea 2011/10/01 14:18 12 07 0. 3 6. 20 3. 10 10. 7* WSW 1000 100 Black Sea 2011/10/04 09:38 12 20 0. 36 6. 10 2. 63 10. 1* WSW 1000 100 Black Sea 2011/10/04 11:07 12 30 0. 45 6. 10 3. 16 12. 2* WSW 1000 100 Black Sea 2011/10/04 13:30 12 30 0. 55 6. 60 3. 71 12. 9* WSW 1000 100 Black Sea 2013/09/22 13:00 10 15 0. 66 4. 30 30 8. 7* E 1000 100 Black Sea 2013/09/25 12:15 12 15 0. 41 4. 10 1. 20 6. 1* N 1000 100 Black Sea 2013/09/30 10:20 12 15 0. 65 5. 70 1. 80 15. 2* N 1000 100 Adriatic Sea 2014/03/27 09:10 12 60 1. 36 5. 02 - 9. 9 ENE 2000 100 Adriatic Sea 2015/03/05 10:35 12 60 1. 33 6. 10 5. 02 9. 0 ENE 2000 100 Yellow Sea 2017/05/13 05:00 10 05 1. 93 5. 20 4. 02 13. 4 NW 2000 100 La Jument 2017/12/15 14:20 10 30 5.
The I183N substitution predicts potential local SdY misfolding To examine more precisely what conformational changes are produced by the I183N substitution, we modelled the SdY-N183 protein 3D structure using the IRF5 domain (PDB code 3dsh) as a template (Figure  3A ). The model revealed that the I183N substitution is localized at the amino terminal end of the β7-strand shaping the hydrophobic β-sandwich core element of the protein (Figure  3B ). The mutation induces a hydrophobic (I) to hydrophilic (N) amino acid pattern change likely modifying at least locally the folding of the β-sandwich. We then tried to find the most suitable model of SdY-N183 in which the hydrophilic side chain may have less negative impact on the folding and protein stability. Being exclusively surrounded by hydrophobic amino acids and with a similar size as I183, it has not been possible to model the N183 in an energetically favorable state.
, 2015; Williamson et al. , 2008). Interestingly, Cavileer et al published a Chinook salmon sdY genomic DNA sequence (GenBank: KC756279) from Tozitna River, Alaska and a sdY cDNA sequence (GenBank: KF006343) from embryonic males from Clearwater River, Idaho  (Cavileer et al. , 2015). The alignment of those sequences with ours revealed the same nucleotide substitution (A/T) in exon 3 but not the (A/G) substitution in exon 2. We also report in the present study RNA-Seq data from a Chinook male from the Umatilla river in Oregon that also has these two mutations. The presence of the deleterious mutation in different populations across the North America coast (Alaska, Oregon and California) support the hypothesis that this mutational event occurred before the establishment of these different populations, and that this mutation could be widespread in many Chinook populations.
Chinook samples genotyping Family panels and genetic samples were the same as the ones described in Williamson and May  (Williamson and May 2005) , and were produced from a fall-run Californian Chinook population harvested at the Merced Hatchery. PCR sequencing analysis of exon 2, intron 2 and exon 3 was performed using a long-range PCR protocol and primers designed upstream and downstream of the sdY Chinook gene sequence (GenBank ID = KC756279. 2), followed by targeted Sanger resequencing of these PCR fragments with internal primers for exon 2, intron 2 and exon 3. Long-range PCR were carried out in a final volume of 50 µl containing 0. 4 µM of each primers (sdYChinook-F2: TTGGCTCCCAGGAAAACATTTCT; sdYChinook-R1: CAGAACAAACAGCATGAAGTAAGCA), 80 ng gDNA, 1X of 10X AccuPrime™ buffer II (including dNTPs), and 1.
Chinook testis RNA-seq Chinook testis was sampled from an adult male from the Umatilla river (OR), and the testis library was prepared using the TruSeq RNA sample preparation kit, according to manufacturer instructions (Illumina, San Diego, CA) as previously described  (Pasquier et al. 2016 ). These testicular transcriptome reads were mapped on a female Chinook genome assembly (Otsh_v1. 0, GCA_002872995. 1) plus the sdY Chinook gene sequence (GenBank ID = KC756279. 2) using BWA (Li and Durbin 2009) with stringent mapping parameters (maximum number of mismatches allowed -aln 2). High quality reads (MAPQ > 40) remapping on the sdY gene were visualized and analyzed with the IGV software  (Robinson et al. 2011).
Protein structure prediction The 3D model of SdY-I183N was predicted using the X-ray structure of the dimeric interferon regulatory factor 5 transactivation domain at 2 Å resolution (PDB ID 3DSH) as template  (Chen et al. , 2008). The three-dimensional views of SdY-N183 were obtained with PyMOL software (Molecular Graphics System, Version 1. 7. 4 Schrödinger, LLC).
Following the washes, membranes were incubated with SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific) for 1 min. The signal from the membranes was detected using the Photo Image Station 4000MM (Kodak). At least two independent experiments were performed and representative protein blot images are shown. Quantitative analysis was performed with ImageJ 1. 48v software (www. imagej. nih. gov).
Clinical Trials Data from adolescent patients receiving dupilumab or placebo in three clinical trials were included in this analysis, including two trials for adolescents with moderate-to-severe AD and one trial for adults and adolescents with moderateto-severe asthma. The design, methods, and primary findings from each trial have been reported previously  [16, 19, 23] , and are briefly summarized here. LIBERTY AD ADOL (ADOL; R668-AD-1526, Clinical-Trials. gov Identifier: NCT03054428)  [16]  was a randomized, double-blinded, placebo-controlled, parallel-group, phase III trial. Patients aged ≥ 12 to < 18 years with moderate-tosevere AD and inadequate response to topical medications were randomized 1:1:1 to 16 weeks of treatment with placebo (n = 85), subcutaneous (SC) dupilumab 300 mg every 4 weeks (q4w; n = 84), or SC dupilumab 200/300 mg every 2 weeks (q2w; n = 82; weight-based: 200 mg in patients weighing < 60 kg at baseline, or 300 mg in patients weighing ≥ 60 kg at baseline).
After a 16-week treatment period, patients could enter an open-label extension (OLE). Those who did not enroll in the extension could be followed for up to 12 additional weeks. LIBERTY AD PED-OLE (PED-OLE; R668-AD-1434, ClinicalTrials. gov Identifier: NCT02612454)  [19]  was a phase III OLE available to pediatric patients who participated in a previous dupilumab AD trial. This analysis includes only patients aged ≥ 12 to < 18 years when they entered the OLE. After a 4-week screening period following completion of the previous dupilumab trial,  275  patients received open-label SC dupilumab 2 mg/kg or 4 mg/kg weekly (qw; up to a maximum of 300 mg qw) or a fixeddose regimen of 300 mg q4w until withdrawal or regulatory approval of dupilumab in their geographic region. LIBERTY ASTHMA QUEST (QUEST; EFC13579, ClinicalTrials.
gov Identifier: NCT02414854)  [23]  was a randomized, double-blinded, placebo-controlled, parallelgroup, phase III trial. Patients aged ≥ 12 years with uncontrolled moderate-to-severe asthma were randomized 2:2:1:1 to 52 weeks of add-on therapy with SC dupilumab 200 mg or 300 mg q2w or matched-volume placebo (1. 14 mL or 2 mL, respectively). The present analysis includes the subset of QUEST patients aged ≥ 12 to < 18 years at baseline (placebo 1. 14 mL, n = 21; dupilumab 200 mg q2w, n = 34; placebo 2 mL, n = 18; dupilumab 300 mg q2w, n = 34).
Events were coded according to Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs). Unless otherwise specified, the term "conjunctivitis" refers to a compiled group of MedDRA PTs that included the word "conjunctivitis," (i. e. , conjunctivitis, allergic conjunctivitis, bacterial conjunctivitis, viral conjunctivitis, adenoviral conjunctivitis, and atopic keratoconjunctivitis). All assessments for the compiled conjunctivitis term included data for the compiled term and for the component PTs, unless indicated otherwise. The study protocols did not require any specific query about ocular symptoms, and most cases were not diagnosed by an ophthalmologist. In ADOL, association of dupilumab dose/ concentration and risk of conjunctivitis was assessed. Descriptive statistics were used to summarize all assessments.
Because of the small number of events, only the data from the combined dupilumab group and the combined placebo group are interpretable. Additional analyses of conjunctivitis included severity of conjunctivitis as assessed by the investigators (mild, moderate, severe; number and proportion of patients, and number of events); resolution of conjunctivitis (e. g. , recovered/resolved) during the treatment period (number and proportion of patients, and number of events); whether events were considered related to study treatment, as assessed by the clinical investigators, who were primarily dermatologists and allergists (none were ophthalmologists); and number and proportion of reported conjunctivitis treatments used. In all three trials, the choice of treatment for conjunctivitis was at the discretion of the investigator.
PED-OLE A total of 275 adolescents with AD who had participated in dupilumab clinical trials enrolled in PED-OLE and received at least one dose of dupilumab during that study. The baseline prevalence of allergic conjunctivitis was 21. 5% among adolescent patients entering PED-OLE. A total of 142 patients completed 16 weeks, 69 completed 26 weeks, and 34 completed at least 52 weeks of treatment at the time of data cut-off for this analysis, for a total exposure of 141. 7 PYs. There were 22 conjunctivitis events (15. 52 per 100 PYs) during PED-OLE; 12 patients (4. 4%) had one or more conjunctivitis event (9. 15 per 100 PYs; Tables  5  and 6 ). Among patients with one or more conjunctivitis event, median time to onset of the first event was 65. 5 days for the dupilumab combined group, and 145.
Discussion Adolescent patients treated with dupilumab in clinical trials for moderate-to-severe AD had a greater incidence of conjunctivitis than placebo-treated patients, consistent with data reported in clinical trials of adults with AD  [26]. By contrast, the incidence of conjunctivitis among adolescent patients in the asthma trial was lower than that observed in For patients with events, the number of PYs was calculated up to the date of the first event; for patients without events, it corresponds to the length of the study observation period. For the number of events, total PYs were calculated as the sum of the study observation period over all patients the AD trials and was similar in dupilumab-and placebotreated patients. In both the AD and asthma trials, most cases of conjunctivitis were mild or moderate in severity and resolved during the study period with conventional treatments; no cases led to permanent discontinuation of the study drug.
Acknowledgements The authors thank the patients and their families for their participation in these studies; their colleagues for their support; and  Erika Culotta ,  Marcella Ruddy , and  Linda Williams  ( Regeneron Pharmaceuticals, Inc. ) and  Ana B. Rossi ,  Adriana Mello , and  El-Bdaoui Haddad  ( Sanofi Genzyme ) for their contributions. Medical writing and editorial support were provided by  Vicki Schwartz, PhD , of  Excerpta Medica , funded by  Sanofi Genzyme and Regeneron Pharmaceuticals, Inc.
In the following, we assume that any remaining change in atmospheric pressure is attributable to weather events. A total of 36 major typhoons are identified in the data. Rainfall peak intensity ranges roughly between 0 and 57. 6 mm. h -1 , and atmospheric pressure drop reaches down to -4. 5 kPa. Among the 36 major typhons, some have led to a strong pressure drop and/or to intense rainfall (Fig. 5a ). For this study, 3 contrasted typhoons are used to compare atmospheric and rainfall effects: Matsa, Krosa and Morakot. Typhoon Masta in July 2005 is the event showing the highest peak of rainfall intensity among the dataset. Typhon Masta has led to several mudslides and floods in Taiwan, but no major landslide. Typhoon Krosa in October 2007 is associated with the highest atmospheric depression in the dataset.
Timing of the failure during an extreme weather event Most datasets on landslides occurring during a triggering event are based on comparison between pre-and post-event satellite images  (Cheng et al. , 2004; Nichol and Wong, 2005; Martha et al. , 2015)  or even Lidar data  (Bernard et al. , 2020) , often days or weeks apart. The timing of landslide occurrence during the event itself remains poorly constrained. This is problematic when trying to attribute landslides to their triggering factor, whether rainfall or atmospheric pressure drop. It results that most landslides are by default attributed to rainfall. At first order, this is a reasonable assumption given that it is the effect leading to the greatest disturbances compared to atmospheric effects. However, this prevents a better understanding of landslide triggering during storms, as   and ′  behave differently, with potential implication for landslide hazard.
undergoing several typhoons each year and subjected to landslides, Taiwan is a relevant study area. Weather data were obtained from the Data Bank for Atmospheric Research at the Taiwan Typhoon and Floods Research Institute. The data is an hourly report of rainfall and atmospheric pressure, from 01/01/2003 to 30/06/2017. The weather station is located in the Taroko national park, east of Taiwan (C0U650, 24. 6753 lat, 121. 5871 long).
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 803721 ). The authors acknowledge the  Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories , for providing the  Data Bank for Atmospheric & Hydrologic Research service  (https://www. narlabs. org. tw/). We also acknowledge support by the  France-Taiwan International Associate Laboratory  " From Deep Earth to Extreme Events ".
Informed Consent Statement: Not applicable. Data Availability Statement: Data can be found in the references cited along the article. Acknowledgments: This article has been supported and funded by the following H2020 projects:  H2020 NAIAD (Grant Agreement 730497 ),  H2020 Naturvation (Grant Agreement 730243 ),  H2020   Connecting Nature  (Grant Agreement  730222 ),  H2020 EdiCiNet (Grant Agreement 776665 ),  H2020 Urban Greenup  (Grant Agreement  730426 ),  H2020 Nature4Cities (Grant Agreement 730468 ),  H2020 REGREEN (Grant Agreement 821016 ),  H2020 Clever Cities  (Grant Agreement  776604 ),  H2020 Grow-Green  (Grant Agreement 730283).
We present a new Arctic sea level anomaly dataset based on the combination of three altimeter missions using an optimal interpolation scheme. Measurements from SARAL/AltiKa, CryoSat-2 and Sentinel-3A are blended together, providing an unprecedented resolution for this type of product. Such high-resolution products are necessary to tackle some contemporaneous science questions in the basin. We use the adaptive retracker to process both open ocean and lead echoes on SARAL/AltiKa, thus removing the need to estimate a bias between open ocean and ice-covered areas. The usual processing approach, involving an empirical retracking algorithm on specular echoes, is applied on CryoSat-2 and Sentinel-3A synthetic aperture radar (SAR) mode echoes. SARAL/AltiKa also provides the baseline for the cross-calibration of CryoSat-2 and Sentinel-3A data.
The final gridded fields cover all latitudes north of 50 • N, on a 25 km EASE2 grid, with one grid every 3 d over 3 years from July 2016 to April 2019. When compared to tide gauge measurements available in the Arctic Ocean, the combined product exhibits a much better performance than mono-mission datasets with a mean correlation of 0. 78 and a mean root-mean-square deviation (RMSd) of 5 cm. The effective temporal resolution of the combined product is 3 times better than a single mission analysis.
, 2016)  in some parts of the basin. Despite those pressing matters and due to harsh conditions the Arctic Ocean remains poorly observed  (Smith et al. , 2019). In this context remote sensing, and satellite altimetry in particular, is of great interest. While satellite altimetry was designed to measure the global ocean circulation  (Stammer and Cazenave, 2018) , it is also used in the Arctic Ocean to retrieve sea level (SL) and sea ice freeboard  (Quartly et al. , 2019). First estimates of Arctic SL variability from satellite altimetry were made by  Peacock and Laxon (2004). The same methodology was successfully used by  Giles et al. (2012)  to estimate freshwater variations in the Beaufort Gyre. More recently a state-of-the-art Arctic SL dataset  (Armitage  Published by Copernicus Publications.
P. Prandi et al. :  Arctic sea surface height maps from  multi-altimeter combination et al. , 2016)  has been used to estimate the shape and extent of the Beaufort Gyre  (Regan et al. , 2019). Current state-of-the-art Arctic SL datasets  (Armitage et al. , 2016; Rose et al. , 2019)  rely on the measurements of one altimeter at a time to produce SL maps with monthly temporal resolution and spatial resolutions of several hundred kilometers, which is not enough to characterize mesoscale activity  (Regan et al. , 2020). Armitage et al. (2016)  processed Envisat and CryoSat-2 data to produce monthly dynamic ocean topography estimates from 2003 to 2014 on a 0. 5 • latitude by 0. 75 • longitude grid. Rose et al. (2019)  processed the full altimeter record from 1991 (ERS-1) to 2019 (CryoSat-2). Monthly sea level anomaly (SLA) fields are available on a 0.
25 • latitude by 0. 5 • longitude grid. The field is interpolated onto the grid using a least squares collocation technique and specifying correlation scales of 500 km. Both those datasets are undefined above 81. 5 • N. The combination of several altimeters can improve the resolution of global SL maps  (Ducet et al. , 2000; Pascual et al. , 2006). This multi-mission combination is operational in the Data Unification and Altimeter Combination System (DU-ACS)  (Pujol et al. , 2016; Taburet et al. , 2019). In this paper we present a new Arctic SL dataset based on the combination of three satellite altimetry missions: SARAL/AltiKa (SRL), Sentinel-3A (S3A) and CryoSat-2(C2). Section 2. 2 details the along-track data processing scheme, as well as the standards used. The multi-mission cross-calibration and combination methods are described in Sect.
SARAL/AltiKa SARAL/AltiKa (herein abbreviated SRL) is a joint French (CNES) and Indian (ISRO) satellite radar altimetry mission  (Verron et al. , 2015). Its main instrument is a Ka-band pulselimited radar altimeter. The higher frequency compared to Ku-band missions, combined with a higher pulse repetition frequency permits a higher along-track sampling. A better range resolution is also achieved thanks to a larger bandwidth  (Steunou et al. , 2015). SRL was launched in February 2013 and is still operational today. Initially launched on the Envisat orbit it provides measurements up to 81. 5 • latitude. SRL data used in this study are taken from the CNES PEACHI project dataset  (Valladeau et al. , 2015).
CryoSat-2 CryoSat-2 (herein abbreviated C2) was launched in April 2010 and is an ESA satellite radar altimetry mission designed to monitor the Earth's cryosphere  (Wingham et al. , 2006). Polar regions are observed up to 88 • thanks to its 92 • orbit inclination. The SAR Interferometric Radar Altimeter (SIRAL) radar on board CryoSat-2 can operate in low-resolution mode (LRM), synthetic aperture radar (SAR) mode or synthetic aperture interferometric mode (SARIn). The switch from one mode to another is based on a geographical mode mask. SAR mode is generally used over sea ice areas and provides an unprecedented along-track resolution of 300 m. In this study, only SAR mode data from C2 are used, and the area covered therefore varies over time with the geographical mode mask. C2 data used in this study are L1b data taken from ESA payload data ground segment Ice Baseline C processor.
Retracking Retracking designates the process of extracting geophysical parameters from the radar waveforms. There is a variety of retracking algorithms, from purely empirical algorithms to physical ones which require a waveform model. In this study several retracking algorithms are used depending on the measurement mode (LRM or SAR) and echo type (Brownian or peaky). For SRL, all echoes are retracked by the adaptive algorithm  (Poisson et al. , 2018). This physical retracker provides a processing continuity between open ocean and icecovered areas, thus removing the need to estimate a bias between the two surfaces. This represents an important difference with respect to other Arctic SL datasets  (Armitage et al. , 2016)    retracked by the threshold first maximum retracker algorithm (TFMRA) (50 % threshold;  Helm et al.
Cross-calibration Cross-calibration is designed to remove long-wavelength errors (LWEs) in along-track altimetry prior to the optimal interpolation (OI). In a typical global ocean processing intermission cross-calibration would be performed through empirical orbit error estimation  (Le Traon and Ogor, 1998). Orbit error estimation requires a global crossover dataset which is not available in this case as we processed only areas north of 50 • N. Moreover empirical orbit errors are not well constrained over the Arctic, which is surrounded by large continental areas where no crossovers are available. As a result a much simpler regional bias removal technique is used here, which is based on the comparison of regional SL estimates from the three altimeters. Time series of regional SL for SRL, C2 and S3A are shown in Fig.