dataesr/scientific-paragraphs-categorization huggingface dataset:
These models were run with 1000 iterations and a burn-in of 400. All models included BT, BSAL, SST, SSAL, chlorophyll A and depth, as these variables have been shown to be important for these species. In total, 1,024 possible models were evaluated by training each potential model on 70% of the data (n = 3652 in the fall, n = 4155 in the spring), evaluating insample performance with DIC, and then testing out-of-sample performance on the remaining 30% (n = 1565 in the fall, n = 1780 in the spring). The 10 models with the lowest DIC in-sample were selected, and the final model was selected out of those 10 with the lowest out-of-sample R 2. The selected model for fall CPUE had the following terms: ~ BT + depth + BSAL + SST + SSAL + chla + depth*BT + depth*SEDSIZE + depth*SSAL + depth*chla + SED-SIZE + Benthic position.
Out-of-sample prediction was performed on the remaining 30% (n = 1565 in the fall, n = 1780 in the spring) of the dataset and predicted versus observed values were evaluated (Supplemental Figs. S2  and S3 ) as well as residual versus fitted values (Supplemental Figs. S4  and S5 ). As has been shown in other research  30, 31  , aggregating noisy predictions based on similar environmental preferences can improve performance, especially for larger datasets. Thus, we generated an aggregated data set that uses a k-means clustering of predictors (Supplemental Figs. S8  and S9 ). We performed the same analysis for the spring and the fall as well as with the presence absence data and recorded AUC as well as precision for each species (Supplemental Figs. S6  and S7 ). Precision is defined as the arithmetic mean of precision (proportion of predicted presences actually observed as presences) across all threshold values (at an interval of 0.
01). Final model. We ran the final model on 100% of the data with 20,000 iterations and a burn-in of 8000 iterations for the spring and fall for CPUE as well as presence absence for a total of 4 models. From the final model we obtained coefficients for the species-environment responses, β, covariance between species in how they respond to the environment E, and the residual correlation from the fitted model, R. We subtracted the absolute values from the presence/absence residual correlation matrix from the absolute values of the CPUE residual correlation matrix to observe where these results diverged. For MCMC chains and convergence of the final model as well as example models from both methods of variable selection see Supplemental Figs. S10-S12 ). Comparison to SSDMs. We built single species distribution models for each species in the form of GAMs using the mgcv package in R  32.
The importance of temperature and depth has been documented in other studies  15. The importance of interactions between depth and salinity may be explained by: (1) the Gulf Stream-offshore relatively deep, warm waters, with upwelling that stimulates productivity in the southern region of this study 45 ; (2) the Labrador Current-inshore cool, fresh water from the Labrador shelf in the northern portion of this study  46  ; and/or (3) seasonal offshore migrations that have been documents for several of these species  47. The inclusion of sediment size and benthic position when selecting variables via DIC versus inverse prediction, which removes these variables, is likely the result of these variables being important for several species, but not the community as a whole. Indeed, several benthic species' distributions in this dataset are more likely driven by benthic habitat characteristics  29  , while the community as a whole is more driven by temperature and depth.
The strong influence of bottom salinity in the spring is perhaps due to high precipitation and freshwater runoff in the area  48. The covariance matrix is supported by literature; Atlantic cod, haddock, and pollock covary with the environment and also co-occur in the underlying dataset (Fig. 2b, c ) and are a commonly documented species assemblage  14, 49. The hierarchical structure of this particular JSDM may allow for sharing of information between functionally similar species, which may be especially useful for rare species (i. e. Atlantic croaker in this study)  50. Two functionally similar species, such as Atlantic croaker and scup, which respond similarly to the environment can jointly use information and inform prediction. Atlantic croaker may especially benefit from sharing information from more common species, as this commercially important species may redistribute under continued ocean warming as they are a more southern species.
Administration (NOAA) Northeast Fishery Science Center (NEFSC) U. S. NES bottom trawl survey, which, for almost 50 years, has collected abundance data for over 250 fish species in the spring and fall. The survey employs a stratified random design, with stations allocated proportionally to the stratum area. A 12 mm mesh coded liner is used to retain small-bodied and juvenile fish. All fish caught are weighed and counted 18. We downloaded the data from OceanAdapt. com, which calibrates the CPUE for each species depending on survey ship. We cleaned the data for the years from 1998 to 2020, excluding years prior to 1997 due to many missing values for chlorophyll (Chl a ). We only included strata that were consistently sampled in the spring and fall. To account for the seasonal migrations of many of the studied species, we modeled spring and fall seasons separately.
Benchmarking method All analyses were performed in R 3. 5. 0 R  [11]. We designed a benchmarking procedure to compare the performance of MGMM against imputation followed by standard GMM (also implemented by MGMM) and another package that allows for missing values (MixAll). The imputation methods included in the benchmark were: naive mean and median imputation; k-nearest neighbors imputation, as implemented by the VIM package  [12] ; multiple imputation by chained equations, as implemented by the MICE package  [9] ; and random forest imputation, as implemented by the missforest package  [13]. We defined clustering performance as the capacity of the algorithm to recover the true cluster assignments when applied to example data sets. We assessed the quality of the clustering by calculating the adjusted rand index (ARI) between the recovered and true class assignments.
Evaluation metric The quality of clustering was evaluated using the ARI  [15, 16]. Briefly, the Rand Index (RI) is a measure of similarity that assesses the agreement between two partitions of a collection of n objects. All possible pairs of objects are examined, and the proportion of pairs that are either 1. in the same cluster or 2. in different clusters according to both partitions is calculated. ARI is a variation of the RI that is adjusted for chance, and is permutation invariant. A value near zero suggests the agreement between the two partitions is no better than expected by chance, while a value of one occurs when the two partitions are identical. We define the quality of clustering as the ARI between the reference or true clustering, established in the data set description, and the clustering performed in the presence of missingness.
Benchmarking procedure We designed the benchmarking procedure outlined in Fig. 1  and described in Algorithm 1 to compare the performance of MGMM with imputation followed by standard GMM. Fig. 1  Benchmark procedure schematic. The input data are continuous vectors with known class assignments. Missing values are introduced completely at random. GMMs were then fit to the incomplete data in several ways: 1. by using MGMM, which allows for missing values and arbitrary covariance structures; 2. by using MixAll, which allows for missing values but assumes a diagonal covariance structure; 3. by imputing the missing values, then fitting a standard GMM. The GMMs were evaluated based on the adjusted Rand index between the predicted and true cluster assignments. This procedure was repeated N = times.
Conclusion We have presented MGMM, a powerful, general purpose R package for maximum likelihood-based estimation of GMMs in the presence of missing data, and demonstrated that MGMM often outperforms both MixAll and imputation followed by standard GMM on various real and simulated data sets. In contrast to estimation after imputation, MGMM uses the ECM algorithm to efficiently and unbiasedly obtain the maximum likelihood estimates of all model parameters while properly accounting for the uncertainty introduced by the presence of missing values; and in contrast to Mix-All, which also employs maximum likelihood estimation, MGMM does not assume the data are uncorrelated. To our knowledge, MGMM is the only publicly available method for fitting GMMs that properly accounts for missing data without imposing simplifying assumptions, and our benchmark is the first extensive study of how estimating GMMs while properly accounting for missing data compares with the ad hoc procedure of estimation after imputation.
In addition, the supporting information (Additional file 1) provides a clear step-by-step derivation of our ECM algorithm, providing a foundation for extending this work to missingness-aware mixtures of other distributions. The functionalities of the MGMM package  [7]  are carefully documented and comprise: the generation of random data under a specified GMM, the fitting of GMMs to data sets containing missing values, the drawing of multiple imputations for a fitted model, and the computation of a panel of clustering criteria to identify the optimal number of clusters.
Measures The volunteers downloaded the WellBeNet app at the Play Store and informed the researcher about age and sex. They filled in height and weight in the app. They were then asked to use the eMouve and Nutri-Quantic parts of the WellBeNet application for three to five consecutive days both from 11 April to 7 May, and after the lockdown from 12 May to 24 May). They were told to wear the smartphone in a pant pocket to collect accelerometry data for the waking period (8:00 AM to 10:00 PM) by using eMouve. In previous studies, we developed and published eMouve algorithms to quantify PA and sedentary behaviors. From accelerometry data collected during different types of activity in controlled and free-living conditions, we defined three activity thresholds to discriminate immobility/light intensity activity, light-/moderate-intensity activity, and moderate/vigorous intensity activity in normal weight and overweight subjects  (Guidoux et al.
Analytical plan and statistical analyses The analytical plan for data analysis was determined before the data collection. We chose to compare behavioral components between the two periods (during and post lockdown in female and male students). Statistical analysis software, SAS version 9. 4, was used to test the normal distribution of the data (Shapiro-Wilks). We used a log transformation when the variables were not normally distributed. A mixed model approach (MIXED procedure) was then carried out to assess the effect of sex, subject nested in the sex, period and sex*period interaction on PA and diet components. Subject was considered as a random factor in this model. A mean comparison test (LSMeans) was carried out when p < 0. 05 and the effect size (ES) was calculated with the Hedges'g equation: ES = mean(F1) -mean(F2) SDpooled Where F1 and F2 are the two levels of the studied factors (lockdown and post-lockdown for the period factor, or men and women for the sex factor), SD was the standard deviation.
Irradiation with X-rays was done at room temperature at a distance of 57. 6 cm from the window of a 6 MeV linear accelerator (Radiotherapy Department, University of Debrecen). AM580 was applied to the cells at 100 nM final concentration for 30 min at 37 ºC before imaging. Point FRAP: When it was made possible by the kinetics of recovery, point FRAP was used to better detect possible spatial heterogeneities. For measurement of GFP-HMGB1 mobility, point FRAP measurements were performed on an Olympus FluoView 1000 confocal microscope based on an inverted IX-81 stand with an UPlanAPo 60× NA 1. 2 oil immersion objective. EGFP was excited by the 488 nm line of an Ar-ion laser, and emission was detected through a 500-520 nm band-pass filter by a PMT. The measurement for point FRAP data acquisition started with a confocal image of a cell (512×512-pixel, pixel size: 0.
103 µm), followed by the selection of a laser spot at which the laser beam was focused. Before bleaching, 5120 pre-bleach pixels were collected with a pixel dwell time of 10 μs (51. 2 ms) followed by bleach period for 51. 2 ms with 100% laser power 55. 6 µW and then collecting 40,000 post-bleach pixels from the same spot for a total time of 297. 59 ms. In order to change the laser power of the Ar-ion laser shorter than the pixel dwell time, a dedicated LabVIEW program was developed. The analogue output of a NI 7833 field programmable gate array (FPGA) card (National Instruments, Austin, TX) was fed into the laser power controller input pin of the acousto-optic tunable filter (AOTF, AA Opto electronic MOD. NC) of the laser combiner unit, allowing fast (~1 μs) voltage driven laser power switching by an FPGA card.
The operating of the FPGA card was initiated by a TTL output of the trigger port of the FV1000 system, thus controlling the laser power and collecting the fluorescence data were synchronized. The model function fitting of the recovery post-bleach data was performed by a custom-written Matlab (The Math Works, Natick, MA) program. Data were fitted assuming a one-component exponential recovery with the following equation: 𝐼(𝑡) = 𝐼 0 (1 -𝑒 -𝑡 𝜏 ⁄ ) + 𝐼 𝑏𝑔 (eq. 1) Where I(t) is intensity at a given time point; t, time; I0, amplitude of the intensity; τ, recovery time; Ibg, background intensity. Strip FRAP: When point FRAP recoveries were too fast to provide accurate mobility data, a strip FRAP protocol was followed. These FRAP measurements for histone H1-GFP were carried out on an Olympus FluoView 1000 confocal microscope as described previously  18  with a minor modification; 10 images (512×512 pixels, 4 µs/pixel, ) were collected before bleaching, followed by 15 bleaching images in a ROI (25×12 pixels, 20 µs/pixel, 56 µW) and 80 post-bleach images.
Acknowledgements: We wish to thank  Adél Vezendiné Nagy  and  Edina Nagy  for excellent technical assistance. Grant support:  GINOP-2. 3. 2-15-2016-00026 , NN129371 ,  ANN135107  from the  National Research, Development and Innovation Office, Hungary  (to GV),  GINOP-2. 3. 2-15-2016-00044 , OTKA K128770  and  COST CA 15214  (to GS) and  Stipendium Hungaricum  scholarship by the  Tempus Public Foundation  (to RB, LF, PS, AKS, MUN). GK is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)  -Projets blancs; AMMICa  US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Ruban Rose" ;  Cancéropôle Ile-de-France ;  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  Equipex Onco-Pheno-Screen ;  European Joint Programme on Rare Diseases (EJPRD) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Projects Oncobiome and Crimson ;  Fondation Carrefour ;  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  Leducq Foundation ; a  Cancer Research ASPIRE Award  from the  Mark Foundation ; the  RHU Torino Lumière; Seerave Foundation ;  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and  SIRIC Cancer Research and Personalized Medicine (CARPEM).
We simulate the CMEs that were ejected during our period of interest and the SEPs accelerated by their shocks using the WSA-Enlil-SEPMOD modeling chain and four sets of input parameters, forming a "mini-ensemble. " We validate our results using in situ observations at six locations, including Earth and Mars. We find that, despite some limitations arising from the models' architecture and assumptions, CMEs and shockaccelerated SEPs can be reasonably studied and forecast in real time at least out to several tens of degrees away from the eruption site using the prediction tools employed here. Plain Language Summary Coronal mass ejections (CMEs) and solar energetic particles (SEPs) are phenomena from the Sun that are able to cause significant disturbances at Earth and other planets. Reliable predictions of these events and their effects are among the major goals of space weather forecasts, which aim to tackle all processes related to solar activity that can endanger human technology and society.
Spacecraft Data The observations used in this study include remote-sensing observations from two viewpoints (Earth and STEREO-A, representing views from L1 and L5, respectively) and in situ measurements from six locations (PSP, STEREO-A, Mars, Earth, Bepi, and SolO) as shown in Figure  1. The corresponding heliocentric distances throughout the analyzed period are approximately: 0. 79 AU for PSP, 0. 96 AU for STEREO-A, 1. 47 AU for Mars, 0. 99 AU for Earth, 0. 83 AU for Bepi, and 0. 86 AU for SolO. Remote-sensing observations include extreme ultra-violet (EUV) solar disc and white-light (WL) coronagraph data, while in situ measurements include, where available, magnetic field, plasma, and energetic particle data. In the following, we refer to SEPs of energy in the range ∼1-10 MeV as "lower-energy particles" and those of energy ≳10 MeV as "higher-energy particles.
Multiple studies have performed ensemble modeling of CME propagation using WSA-Enlil (e. g. ,  Cash et al. , 2015; Emmons et al. , 2013; Lee et al. , 2015 Lee et al. , , 2013;; Mays, Taktakishvili, 2015; Mays, Thompson, et al. , 2015; Pizzo et al. , 2015)  as well as other models (e. g. ,  Amerstorfer et al. , 2021 Amerstorfer et al. , , 2018;; Dumbović et al. , 2018; Kay et al. , 2020). In this work, we explore how outcomes such as CME arrival time, interplanetary magnetic field connectivity, and SEPs spatial spread and peak fluxes vary with respect to CME input parameters. Our WSA-Enlil-SEPMOD mini-ensemble consists of four members, with the CME input parameters for each shown in Table  1  and derived in the following way. 1. Run1:For this run, we use CME input parameters derived in real time and stored at the Space Weather Database Of Notifications, Knowledge, Information (DONKI; https://kauai.
ccmc. gsfc. nasa. gov/DONKI/), maintained by the CCMC at the NASA Goddard Space Flight Center. The CMEs are analyzed using the Space Weather Prediction Center CME Analysis Tool (SWPC CAT;  Millward et al. , 2013) , in which a teardrop-shaped 3D structure is visually fitted to coronagraph data so that it best matches observations. Since DONKI runs are performed in real time, SOHO and STEREO-A data are used simultaneously only if available at the time of the analysis, otherwise single-spacecraft imagery is employed. We remark that STEREO-A data, if available, are in the so-called "beacon"  (Biesecker et al. , 2008)  format, which is characterized by a lower processing level than science-level data. Links to the DONKI CME analysis results for each of the eruptions under study are provided in the Data Availability Statement section.
Simulation Results Simulation results for each of the six in situ locations, including model-data comparisons, are shown in Appendix A (Figures  A1-A6 ). Additionally, successive snapshots of the ecliptic plane for each of the four mini-ensemble runs are reported in Appendix B (Figures  B1-B4 ). According to in situ measurements, CME1 impacted PSP, CME2 impacted PSP and STEREO-A, CME3 impacted STEREO-A, CME4 did not impact any observer, and CME5 impacted Earth and possibly Mars (more discussion of this aspect can be found in Section 4. 1). Note that by "impact," we refer exclusively to the arrival of CME-driven shocks. None of the CMEs considered in this work impacted Bepi or SolO. SEPs from CME2/Flare1 were observed at all in situ locations except for Bepi, while SEPs from CME5/Flare2 were detected at PSP, STEREO-A, Mars, and Earth, with possible effects at Bepi and SolO (more discussion of this aspect can be found in Section 4.
CME-Driven Shock Arrival Time and Speed As described in Section 3, the five CMEs modeled in this study are inserted in the Enlil heliospheric domain as hydrodynamic structures that are able to drive shocks in the heliosphere. Hence, the first step of our analysis is centered on evaluating whether a CME-driven shock is correctly predicted to impact each observing location by comparing the results of the different WSA-Enlil runs with the corresponding in situ observations (shown in Appendix A). As briefly mentioned above, we identify six total shock arrivals, which could be attributed to CME1 (at PSP), CME2 (at PSP and STEREO-A), CME3 (at STEREO-A), and CME5 (at Earth and Mars). While the first five impacts present clear signatures of CME-driven shocks in in situ measurements, the impact of a shock driven by CME5 at Mars is more ambiguous (see Figure  A3 ), in particular because of the lack of upstream magnetic field data and the low cadence of plasma data.
by a large data gap centered on the CME2 shock arrival, where the ∼4-MeV particle intensity tends to peak. If we consider only the best-connected observers-that is, PSP (excluding the low energies) and STEREO-A for CME2, as well as STEREO-A, Earth, and Mars for CME5-the percentages of underestimated-overestimatedacceptable fluxes become 33%-13%-54% for a factor 5 and 44%-23%-33% for a factor 3. This suggests that SEP peak fluxes tend to be better modeled for well-connected observers. Again, there is no mini-ensemble member that performed consistently better than the others, the only significant outlier being Run3 that predicted exceptionally low SEP fluxes at STEREO-A for CME5 compared to the other runs (which translated into a "miss" in Table  3 ). In particular, we note that Run4 often does not provide the best results, again showing that improving CME arrival predictions may not lead to improved SEP predictions.
, 2017). Parker Solar Probe (PSP), STEREO, Wind, and Advanced Composition Explorer (ACE) in situ data are publicly available at NASA's Coordinated Data Analysis Web database (https://cdaweb. sci. gsfc. nasa. gov/index. html/). Mars Atmosphere and Volatile Evolution (MAVEN) data can be accessed at the Planetary Plasma Interactions Node of NASA's Planetary Data System (PDS; https://pds-ppi. igpp. ucla. edu). Mars Express (MEX) data are stored at ESA's Planetary Science Archive (PSA; https://archives. esac. esa. int/psa). Mars Odyssey (MOdy) data are available at the Geosciences Node of the PDS (https://pds-geosciences. wustl. edu/). SOHO particle data can be downloaded from the SOHO/Energetic and Relativistic Nuclei and Electron (ERNE) webpage (https://srl. utu. fi/projects/erne/). The BepiColombo data used in this study are stored at https://doi.
Photonic Ring Immunoassay The presence of serum antibodies specific for the viral antigen was determined using the Maverick SARS-CoV-2 Multi-Antigen Serology Panel (Genalyte Inc. USA). This technology uses an antigen-bound chip to detect the following antibodies for SARS-CoV-2; nucleocapsid, spike S1 RBD, full length spike S1S2, spike S2, and spike S1, as well as the those specific for the common coronavirus HCoV-NL-63; nucleocapsid, HCoV-OC-43, HCoV-229-E and HCoV-HK-U1 spike proteins  (27, 28). It detects and measures changes in resonance when antibodies bind to their respective antigens. All threshold values for positivity were set by the manufacturer. The raw data are shown in Supplementary Tables  1 2 3 4.