dataesr/scientific-paragraphs-categorization huggingface dataset:
Tara Paci  c would not exist without the continuous support of the participating institutes. The authors also particularly thank  Serge Planes ,  Denis Allemand  and the  Tara Paci c consortium. Partial support was provided by  NSF   AGS-1639868. This study has been conducted using E. U. Copernicus Marine Service Information and Mercator Ocean products. GD is supported by the  European Research Council (ERC)  project constRaining the  EffeCts of Aerosols on Precipitation (RECAP)  under the European Union's  Horizon 2020 research and innovation programme  with grant agreement No  724602. FL is supported by  Sorbonne Université ,  Institut Universitaire de France  and the  Fondation CA-PCA. The inline and atmospheric optics dataset was collected and analyzed with support from  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX13AE58G  and  NNX15AC08G  to  U.
We report the measurements of single and double differential cross section of muon neutrino charged-current interactions on carbon with a single positively charged pion in the final state at the T2K off-axis near detector using 5. 56 × 10 20 protons on target. The analysis uses data control samples for the background subtraction and the cross section signal, defined as a single negatively charged muon and a single positively charged pion exiting from the target nucleus, is extracted using an unfolding method. The model dependent cross section, integrated over the T2K offaxis neutrino beam spectrum peaking at 0. 6 GeV, is measured to be σ = (11. 76 ± 0. 44(stat) ± 2. 39(syst)) × 10 -40 cm 2 nucleon -1. Various differential cross sections are measured, including the first measurement of the Adler angles for single charged pion production in neutrino interactions with heavy nuclei target.
B. Simulation Detector response, acceptance and efficiency are corrected using simulated Monte Carlo events to model the specific detector and beam configuration of each run with a sample ten times larger than the data statistics. The neutrino flux is predicted using simulations tuned to external measurements. Details of the beam simulation can be found in Ref. [32]. Interactions of protons in the graphite target and the resulting hadron production are simulated using the FLUKA 2011 package  [40, 41] , weighted to match hadron production measurements  [42] [43] [44] [45] [46] [47]. The propagation and decay of those hadrons is performed in a GEANT3  [48]  simulation, which uses the GCALOR package  [49]  to model hadron re-interactions and decays outside the target. Uncertainties on the proton beam properties, horn current, hadron production model and overall neutrino beam alignment are taken into account to assess an energy-dependent systematic uncertainty on the neutrino flux.
C. Event selection The analysis presented here uses data from the three T2K run periods between November 2010 and May 2013, where T2K was operating in neutrino mode. In total 5. 56 × 10 20 protons on target (POT) are used, corresponding to all good quality data, with each sub-detector working optimally. Events with the highest momentum track consistent with a negatively charged particle passing the TPC track quality selection criteria and matched with a track originating in the upstream FGD are selected as muon neutrino interactions candidates. The energy deposition measured in the TPC is required to be compatible with the energy loss of a muon-like, minimum ionizing particle. Further selection criterion are applied to remove events where the interactions occur outside the FGD fiducial volume.
E. Kinematic observables This section discusses the distributions of the reconstructed pion and muon basic kinematic variables for the Figure  3  shows the distributions of muon momentum (left plots) and angle (right plots) for the selected CC1π + events compared with NEUT (upper plots) and GENIE (lower plots) Monte Carlo expectations. The pion momentum distributions, see Figure  4 , are shown for the sub-sample of events where the pion is reconstructed in the TPC (left plots). Data are compared with NEUT 5. 1. 4. 2 (upper plots) and GENIE 2. 6. 4 (lower plots). Similarly, Figure  4  (right plots) shows the distribution of the pion angle with respect to the neutrino beam direction for the sub-sample with the pion direction reconstructed in the TPC. NEUT predictions before background subtraction show better agreement with the data than GENIE that predicts slightly more events.
ACKNOWLEDGMENTS We thank the  J-PARC  staff for superb accelerator performance. We thank the  CERN NA61/SHINE Collaboration  for providing valuable particle production data. We acknowledge the support of  MEXT, Japan ;  NSERC  (Grant No. SAPPJ-2014-00031 ),  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy ;  National Science Centre (NCN)  and  Ministry of Science and Higher Education , Poland; RSF,  RFBR , and  MES , Russia; MINECO and ERDF funds, Spain; SNSF and SERI, Switzerland; STFC, UK; and  DOE, USA. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system,  NII  for  SINET4 , the  WestGrid and SciNet consortia in Compute Canada , and GridPP in the United Kingdom. In addition, participation of individual researchers and institutions has been further supported by funds from  ERC  ( FP7 ), " la Caixa Foundation  ( ID 100010434 , fellowship code  LCF/BQ/IN17/11620050 ), the  European Unions Horizon 2020 Research and Innovation programme  under the  Marie Sklodowska-Curie  grant agreement no.
Obtained BSE images were processed with the Stream Motion software to distinguish the different phases of the samples using a toll dedicated to contrast the grey levels. Images of NP were taken at random spots, with an analysis over a 10,000 µm 2-square on images with a magnification of 800 times, as illustrated in Figure  3. As shown in a previous study  [34] , the porosity appears red, the anhydrous are blue, and the hydrated cement gel appears green (see Figure  3 ). It took approximately 30 images to cover new paste on three samples of each type of concrete. Materials 2020, 13, x FOR PEER REVIEW 6 of 13 These samples were first introduced into the liquid nitrogen for 5 minutes in order to stop the hydration. They were then lyophilized in an alpha freeze-dryer 1-4 Ld for 48 h to extract the liquid water by sublimation.
Obtained BSE images were processed with the Stream Motion software to distinguish the different phases of the samples using a toll dedicated to contrast the grey levels. Images of NP were taken at random spots, with an analysis over a 10,000 µm 2-square on images with a magnification of 800 times, as illustrated in Figure  3. As shown in a previous study  [34] , the porosity appears red, the anhydrous are blue, and the hydrated cement gel appears green (see Figure  3 ). It took approximately 30 images to cover new paste on three samples of each type of concrete.
Data collection and tools During February-June 2019, at the selected households, field assistants conducted face to face interviews with parents/ guardians using a pretested structured questionnaire (see Annex 1 and Annex 3, Extended data)  11, 12. The field assistant asked the questions verbally to the participants and completed the questionnaires. The structured questionnaires were tested during a pilot survey and were revised according to feedback received during the pilot survey. The questionnaire consisted of four sections. The first section highlighted the socio-demographic information. The second section was comprised of 12 questions that assessed the knowledge of participants about JE disease and prevention (including vaccination). The third section assessed perception. The fourth section included information about vaccination status of the children.
Data management and analysis Data from the survey forms were double-entered and validated using EpiData entry software (version 3. 1, EpiData Association, Odense, Denmark). Data were analyzed using STATA (version 12. 1, STATA Corp. , College Station, TX, USA). There were no missing data in the study. We provided weighted estimates as the analyses were weighted (inverse probability sampling) for the multi-stage sampling design. We used frequency and proportion to summarize the characteristics of the study participants. We assigned a knowledge score to each participant based on the number of correct or appropriate responses. Each appropriate answer was assigned one point and incorrect responses or "do not know" were assigned zero points. The scores were further dichotomized into poor or good (0-6 as poor and 7-12 as good).
So it is possible that the participant can report whether or not their children suffered JE even they did not know the symptoms of JE. Anyway, I removed that variable from table 1 as the author group feels it does not add valuable information. The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000. com.
Immunoblot analysis revealed that JQ1 treatment reduced Src phosphorylation in TAs from mdx mice (Fig. 3e ), suggesting that autophagy rescue may be related to an upstream decrease in oxidative stress. Taken together these data suggest that JQ1 treatment restores autophagy in the mdx skeletal muscles. JQ1 restrains oxidative stress. To further investigate JQ1 impact on ROS metabolism in the mdx muscle, we asked whether ROS levels were affected by JQ1 treatment in C2C12 cells, in which previously published RNA-seq datasets show that BRD2/3/4 are highly expressed, with BRD2 transcript being the most abundant followed by BRD4 and then BRD3  23, [44] [45] [46]. We first performed immunofluorescence experiments with anti-8-OHdG antibody, which confirmed increased oxidative stress in the mdx muscle, when compared to control muscles  47.
Likewise, interrogation of RNA-seq datasets, revealed that Nox2, p47-phox, and p67-phox transcript levels increased in skeletal muscles from DMD patients  36  , extending the relevance of our findings to the human pathology (Fig. 5e ). In the mdx muscle, JQ1 administration did not affect mRNA and protein levels of Nrf2, a transcription factor that plays a key role in the antioxidant response pathway (Supplementary Fig. 7A, B ). Likewise, JQ1 treatment did not alter the transcriptional regulation of Nrf2 targets, Hmox1, Gclm and Gclc (Supplementary Fig. 7C ), suggesting that restoration of ROS metabolism was not ascribed to the transcriptional activation of anti-oxidant genes. Next, we mimicked ROS induction in C2C12 myotubes, by treating the cells with 250 μM H 2 O 2 for 24 h. H 2 O 2 is a prooxidant that contributes to the generation of a vicious cycle of ROS production by upregulating NADPH oxidase subunits through the activation of redox-sensitive transcription factors  50, 56.
Satellite cells isolation. Satellite cells were isolated as in Proserpio et al. 31  and growth curve was performed as in Fenizia et al. 81. Analysis of intracellular ROS levels in C2C12 cells. Cells were incubated with 5-(and-6)-chloromethyl-2′,7′-dichlorodihydrofluorescein diacetate acetyl ester (CMH2DCFDA) (Thermofisher) for 30 min at 37 °C and washed with PBS  4. The green fluorescence intensity of the oxidized DCF probe per cell was quantified by Image J software and compared at the different time points. ChIP assay. Chromatin isolated from muscles was subjected to ChIP assay as in Segatto et al. 32. 100 mg of starting tissue was used for each antibody. Chromatin was sonicated to fragments length of approximately 0. 5 Kb and immunoprecipitated with 3. 5 µg of rabbit IgG or antibodies listed in Supplementary Table  1.
The study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance to the criteria set by the Declaration of Helsinki. The participants, or their legal guardians, provided written informed consent. Statistics and reproducibility. Data obtained from functional, morphological, western blot, and mRNA analysis are expressed as means ± SD (standard deviation). Measurements were taken from distinct samples, except for morphological analysis. All the biological replicates were checked for their normal distribution by using Shapiro-Wilk Test. When we compared 2 experimental groups we used unpaired t test and when we compared 3 or more experimental groups we used one-way analysis of variance (ANOVA) followed by the Tukey's post hoc test.
Acknowledgements This work was supported by  Telethon   GGP13165 ,  French Association for Myophaties (AFM-Telethon)  and  Duchenne Parent  project (Italy), Cariplo  2017-0604  and  AIRC IG 21353  grants to G. C. and  MRC  grant#  MR/N010051/1  to  P. F. R. Fittipaldi  was supported by a  FIRC fellowship. We are thankful to  Dr. G. Strimpakos  ( CNR-Cell Biology and Neurobiology Institute, Rome ) and the platform for immortalization of human cells at the  Institut de Myologie (Paris)  and  Dr. C. Bragato  ( IRCSS Besta Milan ) for technical support, to  Dr. S. Vasseur  and the  Myobank-AFM  at the  Institute de Myologie, Paris  ( BB-0033-0012 ) for DMD tissues, to  Dr. M. Mora  and  S. Gibertini  ( Besta Institute, Milan ) for help with muscle sectioning, to  Dr. J. Ervasti  ( Univ. of Minnesota ) for the Tubulin6 antibody, and to  Dr.
The recent explosion of computer vision models leveraging deep learning has led to more accurate, versatile, and efficient algorithms for cellular image analysis  7. Convolutional neural networks (CNNs) in particular have been responsible for massive improvements over classic computer vision in segmentation within electron microscopy  8- 10  or microscopy  11  , automated phenotypic biomarker analysis in live cell microfluidics  12  , subcellular protein localization  13  and fluorescent image segmentation  14  , and identification of cell lineages  15. For example, CNNs have been found to be able to classify hematoxylin-and eosin-stained histopathology slides with super-human accuracy  16  , and these models can sometimes generalize between datasets with minimal or no additional labeled data  17.
Remarkably, CNNs have shown promise in detecting visual features within images that are undetectable by humans, suggesting that they can be harnessed to drive scientific discovery  18. Nevertheless, a major factor in achieving such strong performance for CNNs is training with large labeled data sets, which can require massive amounts of human curation and/or annotation  19, 20. Thus, it is critical to develop methods that can generate datasets which are sufficiently large to train CNNs, but with minimal human curation. Cell death is a ubiquitous phenomenon within live imaging data requiring substantial attention in image analysis. The physiological properties of live and dead cells are fundamentally different, and it is critical to distinguish live from dead cells within a population. In some cases, cell death is noise that must be filtered from signal coming from live cells, while in others death is a biological phenotype that must be analyzed, for instance in studies of development  21  , cancer  22  , ageing  23  and disease  24.
However human curation based on morphology is subjective, relies on subjective and ill-defined interpretation of features in an image, and thus can be both inaccurate and imprecise  31, 34. Furthermore, in cases  where human curation is incorrect, it remains unknown whether images of cell morphology convey enough information to indicate an irreversible commitment to death, or whether curators incorrectly identify and weigh features within the dying cells. Because the GEDI biosensor gives a readout of a specific physiological marker of a cell's live/dead state, it provides superior fidelity in differentiating between live and dead cells  31. We hypothesized that the GEDI biosensor signal could be leveraged to recognize consistent morphological features of live and dying cells and help improve classification in datasets where GEDI is not present.
Cropped images from a total of 53,638 live and 22,533 dead neurons from the first timepoint of an imaging series, both with and without background subtracted, were sorted to generate a data set for training computer vision models. We selected a widely-used CNN called VGG16, which was initialized with weights from training on the large-scale ImageNet dataset of natural images  36. We refer to this model as GEDI-CNN (Figure  1H , Supplemental Figure  1 ). In validation on held-out data from the first timepoint of the imaging series, the GEDI-CNN classified dead cells from the EGFP channel images with 96. 8% accuracy (0. 96 Area Under the ROC curve (AUC)) in (Figure  1I ). The GEDI-CNN did not have diminished capacity in classifying neurons in the validation set with GEDI ratios that were discarded from the training data because their GEDI signal fell in regions outside the main distribution (Supplemental Figure  1 ), suggesting the removal of discarded neurons during training did not distort the model.
2A ). The GEDI-CNN was slightly better at correctly classifying live neurons than dead neurons, but was still able to successfully classify datasets with imbalanced numbers of live vs. dead cells (Figure  2B, C ). The focal quality of the image 37 did not correlate with the GEDI-CNN accuracy, suggesting the GEDI-CNN is capable of making quality predictions on out-offocus images (Supplemental Figure  2 ). We next tested whether a GEDI-CNN could detect cell death in longitudinallyimaged rat primary cortical neuron models of neurodegenerative disease. Imaging with the GEDI biosensor previously showed that protein overexpression models of Huntington's Disease (HD) (pGW1:HttEx1-Q97) 32 , Parkinson's Disease (PD) (pCAGGs:α-synuclein)  38  , and amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD)(pGW1:TDP43)  26  have high rates of neuronal death  31.
We therefore compared the cumulative rate of death per well (dead neurons/total neurons) as  estimated by the GEDI biosensor versus the GEDI-CNN. We began by quantifying cumulative rate of death per well, which does not require tracking each neuron in the sample, simplifying the comparison between the GEDI biosensor and the GEDI-CNN predictions. From the cumulative rate of death, we derived a hazard ratio comparing each disease model with its control using a linear mixed effects model (Figure  2D, E ). For each disease model, the GEDI biosensor and the GEDI-CNN detected similar increases in neuronal death in the overexpression lines compared to the control lines, confirming the GEDI-CNN can be used to automatically and quickly assess the amount of neuronal death in live imaging data (Figure  2D, E ).
In combination with a suite of statistical tools commonly used for clinical trials called survival analysis  39  and Cox proportional hazard (CPH) analysis  40  , the analysis of time of death in single cells can provide 100-1000 times more sensitivity than the analysis of single snapshots in time  3. Additionally, singlecell approaches can facilitate co-variate analyses of the factors that predict neuronal death  28, 29, [41] [42] [43]. Yet, each of these analyses is limited by an inability to acutely differentiate live and dead cells  27, 31. We performed automated single-neuron tracking on HD, PD, and ALS/FTD model datasets while quantifying the timing and amount of death detected with GEDI biosensor or GEDI-CNN (Figure  2F ). With accurate single-cell tracking and live/dead classification in place, we applied CPH analysis to calculate the relative risk of death for each model, which closely recapitulated previously reported hazard ratios (Figures 2G-  J )  31  , and mirrored the cumulative rate of death analysis.
GEDI-CNN outperforms human curation The high accuracy with which the GEDI-CNN classifies dead cells in imaging time series suggested that it could also be an improvement over standard human curation. To test this hypothesis, we manually curated 3,000 images containing GEDI biosensor that were not included in model training. Four trained human curators classified the same set of neurons as live or dead using the EGFP morphology signature by interacting with a custom-written ImageJ script that displayed each image and kept track of each input and the time each curator took to accomplish the task. The GEDI-CNN  was 90. 3% correct in detecting death across six balanced batches of 500 images each, significantly higher than each human curator individually (curator range 79-86%) Figure  3A ). In addition to accurately detecting cell death, the GEDI-CNN was two orders of magnitude faster than human curators at rendering its decisions on our hardware; for the first time, the rate of data analysis was faster than the rate of data acquisition (imaging) (Figure  3B ).
These data suggest that GEDI-CNN can exceed accuracy of human curation, but it is also limited by the information present in the EGFP morphology image. CNNs often fail to translate across different imaging contexts, so we next examined whether GEDI-CNN accurately classifies within datasets collected with different imaging parameters and biology. To probe how well GEDI-CNN translates to neurons from other species, we prepared plates of mouse cortical primary neurons expressing the GEDI biosensor and imaged them with parameters similar though not identical to those used on the rat primary cortical neurons used for initial training and testing. The GEDI-CNN showed human-level accuracy across each mouse primary neuron dataset (Figure  3K , Supplemental Figure  3 ). While the GEDI-CNN significantly outperformed some human curators in some datasets, it did not have a consistent edge in accuracy over all human curators, suggesting some superhuman classification accuracy is lost when translating predictions to new biology or imaging conditions.
Whether reduced survival in culture is a common characteristic of all iMNs with SOD1 mutation remains unknown. To test if the GEDI-CNN can detect reduced survival in human iPSC-derived neurons, we differentiated iPSCs from SOD1 I113T and SOD1 H44R ALS patients and healthy volunteers into iMNs, transfected neurons with RGEDI-P2a-EGFP, and imaged them longitudinally (Figure  6A-C ). Overall classification accuracy based on morphology alone of i-neurons was 86. 1% (0. 68 AUC), with a markedly higher accuracy for live neurons (91. 2%) than dead ones (41. 4%) (Figure  6D ). Across balanced randomized batches of data, GEDI-CNN averaged only 70% accuracy, and was not significantly different from human curation accuracy, indicating that death is difficult to discern from the EGFP morphology alone in this dataset (Figure  6E ).
A potential explanation for this difficulty is that iMNs often underwent a prolonged period of degeneration characterized by neurite retraction before a positive GEDI signal was observed (Figure  6C ), a phenomenon not observed in mouse primary cortical neurons. This problem would make death difficult to discern from EGFP morphology alone in this dataset. GradCAM signal from correctly classified live and dead neurons defined the contours of neurons of iMNs (Figure  6F ) and showed similar signal changes in response to neuronal death as observed in mouse cortical neurons (compare Figure  6  G, H to Figure  4 ). Single-cell KM and CPH analysis of SOD1 I113T and SOD1 H44R neurons yielded similar results with the GEDI-CNN classification and the GEDI biosensor classification. In both cases, SOD1 I113T neurons showed reduced survival compared to control neurons while SOD1 H44R neurons showed significantly increased survival (Figure  6 I, J ).