dataesr/scientific-paragraphs-categorization huggingface dataset:
webgestalt. org/). RNAseq data have been deposited at ENA with the dataset identifier E-MTAB-12920. For RT-qPCR, 1 µg of total RNA was converted into cDNA using the iScript Reverse Transcription Supermix (Bio-Rad). RT-qPCR was performed using the CFX384 Touch Real-Time PCR Detection System (Bio-Rad) and SYBR® Green Master Mix (Bio-Rad) using the primers listed in Supplemental Dataset 4. Gapdh was amplified as internal standard. Data were analyzed according to the 2-ΔΔCT method  83.
Cells were immediately imaged with the Operetta CLS High-Content imaging system (PerkinElmer) at 37°C and 5% CO 2. Images were taken every 2 hours for 10 hours with a 20x water-immersion objective (1. 0 NA). PI and NucBlue were excited with the 530-560nm and 355-385nm LEDs respectively. Images were analyzed using Harmony 4. 9 software (Perkin Elmer) using the analysis sequence in Supplemental Dataset 4. Quantification of mitochondrial morphology: primary hepatocytes were isolated from mitoYFP+ mice and seeded in Cell Carrier Ultra 96-well plates, treated with 2µM NucBlue for 30 minutes, then, after 2 washes in PBS. Cells were immediately imaged with the Operetta CLS High Content imaging system at 37°C and 5% CO 2. Images were taken 16 hours post-plating with a 63x water-immersion objective (1.
Acknowledgments We thank  David Hardy  and  Maryse Moya-Nilges  for the histology services,  Priscilla Lopes  and for technical assistance. We thank  Nils-Göran Larsson  for providing mitoYFP mice,  Arnaud Mourier  and  Marie Lagouge  for inciteful discussions, and  Marie Lemesle  for excellent administrative assistance. T. W. is supported by the  European Research Council (ERC) Starting  Grant No. 714472  ( Acronym "Mitomorphosis" ) and the  Agence Nationale pour la Recherche  ( ANR-20-CE14-0039-02 ). D. A. S and D. H. H are supported by grants from the  Australian National Health and Medical Research Council  ( GNT2009732 ) and  Medical Research Future Fund  ( MRF2016030 ). We acknowledge the  Bio21 Mass Spectrometry and Proteomics Facility (MMSPF)  for the provision of instrumentation, training, and technical support.
Primers used for genotyping are listed in Supplemental Dataset 4. induce hepatic cell death, Fas antigen, CD95 (BD BioSciences, 554254) was intra-peritoneal Animals were handled according to the European legislation for animal welfare (Directive injected in 18-hour fasted mice at the concentration of 225µg/kg (BW). Mice were sacrificed 24 2010/63/EU). All animal experiments were performed according to French legislation in hours post injection. compliance with the European Communities Council Directives (2010/63/UE, French Law 2013-118, February 6, 2013) and those of lnstitut Pasteur Animal Care Committees (CETEA is Comité d'crossing to Flp-deleter breeding step. Hepatocyte specific FLAG-MTFP1 Knock-In (KI) mice (Alb-Cre tg/+ Mtfp1 +/+ , CAG tg+/ ) were generated by crossing an inducible mouse model for mCherry-P2A-FLAG-MTFP1 69 generated by PolyGene AG (Switzerland) on a C57Bl6/N background expression under the CAG promoter with mice expressing the Cre recombinase under the control of the hepatocyte-specific albumin Cre promoter.
05. Mitoplast respiration assessment. Mitoplasts were supplied with either NADH, malate, and ADP (complex I) or FADH, rotenone, and ADP (complex II) n=5. Data are means ± SD. 2-tailed, unpaired Student's t test. *: p<0. NADH and FADH to complexes I (purple) and II (blue), respectively. ETC creates an electrochemical gradient driving the phosphorylation of ADP by Complex V. Specific inhibitors of complex I (rotenone), complex V (oligomycin). B) Oxygen flux (JO 2 ) of isolated liver mitochondria from 16-week old control and LMKO mice C) Normalized Mtfp1 expression in liver in control and LMKO mice at 24-weeks measured by on NCD measured in the presence of pyruvate, glutamate, and malate (state 2) or succinate RNAseq (Supplemental Dataset 1). n=4. Data are means ± SD, unpaired Student's t and rotenone (state 2) or palmitoyl carnitine and malate (state 2) followed by ADP (state 3).
The wild type (WT; 387 bp) and the conditional (LoxP; 469 bp) alleles are shown for Mtfp WT/WT , Mtfp1 LoxP/WT , and Mtfp1 LoxP/LoxP mice. Respiratory Exchange Ratio (RER) for control and LMKO mice on fed a normal chow diet (NCD). RER analyses for 24 hours are shown. Mean values for day and night periods. N=4. Data are means ± SD. 2-tailed, unpaired Student's t test. ns=not significant. D) Upregulated (orange) and downregulated (cyan) genes in LMKO mice on a normal chow diet corresponding to Supplemental Dataset 1. Figure S2. Impact of Mtfp1 deletion on mitochondrial mass A) Representative images of primary hepatocytes isolated from control and LMKO mice expressing mitoYFP (green). Nuclei in blue. Scale bar=100µm. C) B) Quantification of mitochondrial imaging in primary hepatocytes from control and LMKO expressing mitoYFP mice.
This capability has become available recently through the Community Coordinated Modeling Center (CCMC) SEP Scoreboard 3  , which aims to bring together forecasts from a range of prediction models for current and historical events (although ML models have yet to be onboarded). Studies comparing ML-driven model predictions with the NOAA Space Weather Prediction Center (SWPC) daily operational forecasts and warnings for SEP events may identify weak points in the models and point to potential improvements (e. g. ,  Jeong et al. , 2014; Sadykov et al. , 2021). ML/AI-based models face unique challenges related to the preparation of reliable, uniform observational databases for model development and validation, and the efficient validation and verification of new models. Such considerations have motivated the development of publicly-available frameworks and ML-ready data sets  (Engell et al.
5). Free parameters: Operationally none. The internal (weights and biases) parameters of the NNs as well as their hyperparameters can be considered as free, however these are set after training and not user-defined. Limitations and caveats: The GOES XRS measurements cannot distinguish different solar flares if they occur concurrently and the measured X-rays are aggregated. This could potentially affect the NN performance, however as of yet we have not found evidence of this. Access to model output or forecasts: The model will be made operational at the start of 2023. After a pilot operational period, its outputs will be available through a dedicated section in the SPARC webpage (www. sparc. gr). The model outputs will be also accessible to users by connecting to the SPARC Application Programming Interface (API) through a publicly available Python module (https:// pypi.
Another 47 Xray events which did not produce any associated SEP event were selected for the negative class, producing a balanced dataset. The observations used to train the model are the GOES XRS, Energetic Particle Sensor (EPS) and High Energy Proton and Alpha Detector (HEPAD) channelseight in total. As a distinction from previous work (e. g. , Nun ˜ez, 2011), this model considers not only the correlations between X-ray and energetic proton flux time series, but also correlations across different proton channels. Correlations are expressed using the Vector Autoregression Model (VAR), a stochastic process model used to capture the linear interdependencies among multiple time series. The model considers time windows (called ''spans") before the start of the X-ray event, and each time point within a span is expressed as a linear function of l previous time points (called ''lags").
Access to model output or forecasts: This model is under development and is not available publicly. Model validation: The model was trained and evaluated with a 10-fold cross-validation using stratified sampling. The dataset (N ¼ 47 Â 2 ¼ 94 samples) is randomly divided into 10 subsamples (or folds), and then 10 tests are done where 9 of the folds are used for training and 1fold is used for testing. Evaluation metrics for the testing portion are averaged over the 10 tests. The metrics presented are accuracy, recall, precision, the F1 metric (har-monic mean of recall and precision), and Area Under the Curve (AUC) of the Receiver Operator Characteristic (ROC), which is statistically consistent and more discriminating than accuracy. Models trained using the largest span considered, 30 h, and with intermediate lag, l ¼ 5, performed the best.
SEPForecast is composed of a statistical model  (Dierckxsens et al. , 2015) , which predicts >10 and >60 MeV SEP probability of occurrence and peak fluxes, and a tool to query a run database produced using the physics-based model SPARX  Marsh et al. (2015)  to make time profile predictions for >10 and >60 MeV. An extensive statistical analysis of SEP events observed during SC23 was performed to derive the probability and peak flux relationships implemented in SEPForecast  (Dierckxsens et al. , 2015). The SEPEM 5  reference proton COMESEP predictions are derived from combinations of parameters to allow for the potential (un) availability of data after measuring the flare magnitude. In this vein, the probability of SEP occurrence and >10 MeV and >60 MeV peak proton flux values were derived from both the flare only and flare-CME lists.
If the location or CME parameters are available after the flare intensity, the alert is subsequently updated. Via this methodology, the likelihood of SEP occurrence and peak flux are quickly provided from statistical relationships. The SEP time profile is forecast from a previously generated database of runs by the physics-based SPARX model (see Section 3. 31), which is queried to produce profiles at Earth for ten-minute averaged >10 MeV and >60 MeV  (Marsh et al. , 2015). SPARX assumes a broad acceleration region located at a radius of 2R Sun with a standard width 48 Â 48. The injection region can be increased or decreased by combining a set of 6 Â 6 tiles. With this approach, SPARX does not need any information about the associated CME and can be triggered solely by flare observations. SPARX is triggered by the automated detection of a flare by the Flaremail tool and the measurement of flare location from Solar DEMON.
Introducing CME information improves predictions, but these measurements are not always available on a useful timescale. Additionally, it should be noted that the CME relationships developed for SEPForecast were calculated using values from the CDAW LASCO CME Catalog, while the model uses CME measurements from the automated CME database CACTus. The parameters in these two catalogs show large differences in derived CME width and speed. Access to model output or forecasts: Forecasts are available through the ESA Space Weather Service Network at https://swe. ssa. esa. int/bira-comesep-federated. Model validation: Model validation for COMESEP has been performed for SC23 events and reported in an internal report that was generated upon delivery of the model as part of the project, however this work has not yet been published.
, 2009; Laurenza et al. , 2018)  in the NOAA Solar Radiation Storms Scale (https://www. swpc. noaa. gov/noaa-scales-explanation). A main focus of the ESPERTA model is to quickly produce an SEP intensity forecast. For this reason, ESPERTA relies primarily on flare characteristics: location, 1-8 A SXR fluence, and $1 MHZ radio fluence corresponding to Type III radio bursts. Additionally, the model requires that a flare reaches M2 class or higher to produce a forecast. The PS1 ESPERTA forecast is produced within 10 min following the SXR of a flare  (Laurenza et al. , 2009). ESPERTA was derived from a database of GOES measurements between 1995-2005, selecting time periods for which >10 MeV proton fluxes exceeded 10 pfu for three consecutive 5-min intervals. Each proton flux increase was associated with its solar flare source, resulting in a list of 93 SEP events.
55 (44/80), and Percent Correct (PC) = 87% (240/276)  (Alberti et al. , 2017). The validation over the whole dataset from 1995 to 2014 led to the results: POD = 63% (69/69 + 20 + 21), and FAR = 38% (42/69 + 42)  (Laurenza et al. , 2018; Alberti et al. , 2019). More recently,  Stumpo et al. (2021)  reinterpreted the ESPERTA model for PS1 events in the framework of machine learning. These authors performed a cross validation, leading to a comparable performance. They also demonstrated that a quite high FAR is a natural consequence of the small size of the SPE sample with respect to the SPE non-associated flares, which is a critical issue of any statistical approach. In  Laurenza et al. (2009) , the Advance Warning Time (AWT) was calculated by comparing the time that the ESPERTA forecast was issued with the onset in the SOHO/Electron Proton and Helium Instrument (EPHIN) 31-50 MeV energy channel published by  Posner (2007).
In nowcasting mode, the model will use SF and CME information to predict SEP event characteristics  (Anastasiadis et al. , 2017). To develop the flare and CME forecasting module  (Anastasiadis et al. , 2017) , a database of active region (AR) s was compiled from SDO/HMI full-disk magne-tograms. The ARs were selected between longitudes of 70 E/W on the solar disk and the effective magnetic field (B eff ) was calculated for each. Due to limb effects, a proxy was used to find B eff between 50 to 70 E/W. The probability of flare occurrence from C, M, and X-class flares was derived for each B eff. The likelihood of a flare of a particular peak SXR flux to erupt with a CME was inferred from  Yashiro et al. (2005). Folding the two probability functions gives the likelihood an AR will produce a CME.
Additionally, the CME speed is predicted using a relationship with B eff derived in  Georgoulis (2008) : V CME ¼ 87:3B 0:38 eff [km/s]. A separate SEP forecasting module was derived from a database of SEPs (>10, >30, >60, and >100 MeV), flares, CMEs and radio fluxes compiled by  Papaioannou et al. (2016)  spanning three decades from 1984 to 2013. The solar disk was divided into five longitudinal bins and 4000 flares in each bin were used to derive the probability of SEP occurrence per SF magnitude at each longitude: P ðSEP flare Þ i ¼ ðN SEP Þ i =ðN all;i Þ. For each AR location, the probability that an SEP will occur per SF magnitude is multiplied by the probability distribution output by the flare forecasting module that a flare of a given magnitude will be produced by the AR. This produces the final output from the forecasting module: the maximum probability of SEP occurrence for each AR  (Anastasiadis et al.
, 2017). After a SF has occurred, the FORSPEF flare nowcasting module uses information from Solar DEMON to make SEP predictions. Probability distribution functions for the observed flare longitude are calculated from the FORSPEF database and the SEP probability of occurrence is identified following a similar approach to the forecasting mode. The module also reports the expected SEP values derived from the SXR peak flux, e. g. , proton peak flux, time of peak, duration, and fluence for each energy channel  (Papaioannou et al. , 2016). FORSPEF also forecasts SEP characteristics from CMEs observed by the automated real time CACTus CME catalog. CACTus is the only near real-time solution that provides reliable and continuous outputs that can be used for SEP nowcasting  (Anastasiadis et al. , 2017; Papaioannou et al.
, 2018). To develop the CME to SEP nowcasting tool, however, SEP event information from the FORSPEF database  (Papaioannou et al. , 2016)  was paired with CME information from the CDAW CME Catalog 8. Data from 1997-2013 were used, representing the overlapping time period between the two catalogs, comprising a total of 1905 CMEs and 158 SEP events (>10 MeV). The CMEs were sorted into a matrix of nine subgroups according to their speed and width. The speed ranges were divided into slow (400 km/s 6V < 1000 km/s), moderate (1000 km/s 6V < 1500 km/s) and fast (V 61500 km/s) and the Angular Width (AW) ranges divided into full halo (AW = 360°), partial halo (120°6AW 6359°) and non halo (AW < 120°). The probability that an SEP event is produced by a CME in each subgroup was calculated along with the correspond 68% confidence level.
Radio data at 0. 944 MHz from the Wind/WAVES instrument were compiled and calculations of the radio and SXR data were refined. Revised versions of the SEP occurrence equations in ESPERTA were produced. However, the Wind/WAVES data or an appropriate substitute is not available in near-real time, so this module cannot presently be used in the operational version of FORSPEF. Inputs: Forecasting: 1) SDO/HMI full-disk magnetograms, 2) AR number from NOAA/SWPC, 3) FORSPEF database for SEPs, SFs, CMEs  (Papaioannou et al. , 2016). Nowcasting: 1) near real-time SF data from Solar DEMON 9 , 2) near real-time CME identifications obtained from the automated CACTus CME catalog 10 , 3) integrated radio flux at 1 MHz for the ESPERTA Model (not available in real-time). Both forecasting and nowcasting modes use the FOR-SPEF database for SEPs, SFs, CMEs  (Papaioannou et al.
There is a higher level of uncertainty when forecasting flares from ARs near the limbs between 50 to 70 E/W. No forecasts can be made from ARs beyond 70 E/W. SEP and CME relationships applied in FORSPEF were derived using values from the CDAW CME catalog, which are determined by hand, however the model runs using CME parameters reported in the CACTus catalog and are derived through an automated detection algorithm. Access to model output or forecasts: The FORSPEF system has been online since April 2015 and may be accessed at http://tromos. space. noa. gr/forspef/main/ where it provides continuous forecasts of SFs and SEPs. The system will inform registered users when an SEP event is expected to take place. All forecasts are stored in a fully accessible archived database. Model validation: Anastasiadis et al.
As a next step,  Papaioannou et al. (2018)  extended the FORSPEF database until the end of 2015, aiming at utilizing the whole time span from the beginning of SC23, i. e. , 1997 until 2015. Similar to  Anastasiadis et al. (2017) , out of the whole sample 85% of all SFs, CMEs, SEPs was chosen as the control sample and the rest 15% as the test sample. This procedure was repeated ten times, and ten different completely independent control and test samples were obtained. For all paired samples (control and test) categorical scores were obtained. The mean POD -out of the 10 different pairs of samples -was 70% and the mean FAR was 44%, which is consistent with the results presented in  Anastasiadis et al. (2017). Papaioannou et al. , 2018  assessed the performance of the peak flux prediction using the same set of 43 SEP events.
Limitations and caveats: The All-Clear forecasting heuristic makes two major assumptions to provide conservative All-Clear forecasts: first, it assumes that all predicted eruptions (CMEs) are directed towards Earth face-on (i. e. , they are halo CMEs) and, second, it assumes that all of them can potentially lead to an SEP event. Therefore, if either of these events is expected, an All-Clear forecast will not be issued. Access to model output or forecasts: Model forecasts can be accessed at https://dmlab. cs. gsu. edu/sep-prediction/. Model validation: The various sub-models of the system were trained and tested with a set time segmented partitions to enable cross-validation. For instance, the SOHO-FP flare prediction sub-model utilized a SOHO dataset much like the HARP dataset, divided into three partitions allowing a 2 3 training vs.
1 3 testing split for each iteration. As discussed in  Ji et al. (2020) , training and testing on the DSDO-FP sub-model utilizes 5 partitions from a machine learning ready dataset for the flare prediction task described in  Angryk et al. (2020). Similarly, the NSDO-FP sub-module utilizes the same partitioning strategy on the NRT HARP data to construct the partitions, instead of the definitive data used in  Angryk et al. (2020).
, 2021). Inputs: Depend on the dataset used Characteristics of solar flares and CMEs features were extracted from the source database  (Papaioannou et al. , 2016) , in order to construct the input variable space to all models. These features include: i Solar-flare heliographic longitude in degrees ii The log10 value of flare peak intensity (W/m2) in SXR (1 -8 A) iii The log10 value of SXR flare time-integrated flux in J/m 2 iv Duration of SXR flare in minutes v Rise time of SXR flare in minutes vi CME apparent angular width in the sky-plane projection in degrees vii CME linear speed in km/s viii Cycle index was created as a simple numerical feature, indicative to the magnitude of solar activity at the time when an event occurred Then datasets were created as follows: 1. Flare dataset (includes i-v & viii features) 2.
CME dataset (includes vi-viii features) 3. Flare & CME dataset (includes all i-viii features) In addition, these datasets were treated as Imbalanced and Balanced in two parallel experimental settings, depending on whether all (i. e. , negative and positive) class instances were included, or a subset of negative class instances was kept via undersampling, so that the dataset contained an equal number of positive and negative instances. Finally, another condition was imposed based on the treatment applied to Missing Value (MV) s. Thereby, Noiseless datasets are the ones where all entries having at least one MV were eliminated and Noisy datasets are those for which MVs were filled with the feature's median value. Outputs: Binary classification includes SEP-associated eruptive events that are assigned a label of + 1, hence the positive class, while the negative class includes eruptive events without SEP association that are assigned a label of À1.
All of the above models (see SubSection 3. 12) provided outputs for all of the created datasets (see SubSection 3. 12). Free parameters: The free parameters (hyper-parameters) for each of the examined ML models, are tuned in the inner loop of the adopted nested cross-validation scheme. In this process, each model is built for 1000 randomly chosen hyperparameter configurations within an extended hyperparameter grid. The optimal configuration is selected based on mean validation scores. For the best-performing and more robust amongst the examined models, RF, optimized with respect to Precision (=1-FAR), the optimal hyperparameter set recovered in all five folds is: We note however that different hyper-parameter configurations may be recovered in each fold, but still provide consistent predictions, as is the case when we optimize RF by means of the F1-score.
The reported results in Lavasa et al. , 2021 are calculated on the unseen, test partitions of a nested crossvalidation scheme, averaged across folds. Out of the datasets configured and employed, the examined models achieve higher SEP predictability effectiveness, in the real-world Imbalanced setting, when applied on the Flare_CME_Noiseless one. As it can be seen in Fig. 3 , RF, a tree-based ensemble model is the best performing and more robust solution in our evaluation setting, running on both flare and CME data, achieving POD = 0. 76 (AE0. 06), FAR = 0. 34(AE0. 10), TSS = 0. 75(AE0. 05) and HSS = 0. 69(AE0. 04). As a next step in the evaluation of the ML models the most important features employed in our study that, in turn, would signify the highest prediction capability, were searched for.
MagPy -Magnetogram Forecast in Python Model developers: David Falconer (University of Alabama at Huntsville) and Tilaye Tadesse, Yash Kadadi, and Ian Fernandes (developed at NASA/Johnson Space Center (JSC)/ SRAG). Model description: MagPy is the Python version of MAG4. It is a near real-time large-database program for forecasting an AR's next-day production rate of major flares, CMEs, and SEP events from its free-energy proxy and short-term previous flare productivity. MAG4 is written in Interactive Data Language (IDL) and despite its success, the IDL platform upon which it was built may not serve robust development requirements that are necessary for the model's growth, suggesting a more modern and widespread programming language. With the explosion in popularity of Python, the number of packages and abundant online documentation has grown dramatically, resulting in functions that duplicate and greatly exceed much of IDL's capabilities.