dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation Data Set The Multiple Myeloma Research Foundation (MMRF) CoMMpass (Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile) data set was used to validate findings. Genomic data for patients with newly diagnosed MM in the CoMMpass cohort were downloaded from dbGaP (Accession Number: phs000748. v7. p4). GSS for each newly diagnosed patient was calculated using allelespecific copy number profiles generated from whole-exome sequencing data. Other clinical and genomic variables were integrated with GSS using "MMRF_CoMMpass_IA13_STAND_ALONE_SURVIVAL. csv," "MMRF_CoMMpass_IA12a_LongInsert_Canonical_Ig_Translocations. txt," and "MMRF_CoMMpass_IA12a_CNA_LongInsert_FISH_CN_All_Specimens. txt" files downloaded from MMRF Researcher Gateway (https://research. themmrf. org).
Other Statistical Analyses All other analyses were completed in the R programming language. Survival analysis and Kaplan-Meier plots were generated using survival and survminer packages. Data preparation was done using readr, readxl, bedr, and maftools packages, and color schemas for the figures were selected using RColorBrewer. GSS visualization with clinical annotation was prepared using "ComplexHeatmap"; "ggpubr" and "ggplot2" were used for other figures. Driver mutations were identified using "dndscv. " Mutational signature analysis was completed using signeR, deconstructSigs, and BSgenome. Hsapiens. UCSC. hg38. All these packages were supported with custom scripts. The Kaplan-Meier method was used to estimate time-to-event distributions, and statistical comparisons were done using log-rank tests, with the null hypothesis being all groups have the same outcome.
2017; Frank et al. 2015) , can better describe observed behavioral patterns such as magnitude effects  (Fontanesi et al. 2019) , and even improves identifiability of learning parameters  (Ballard and McClure 2019; Shahar et al. 2019). A current limitation with these models, and cognitive models in general, is that they are time-consuming and complicated to apply to new datasets, as they usually require writing custom scripts that are inflexible to model variations. This issue is particularly salient when the goal is to estimate the impact of neural activation onto behavioral model parameters. Here we present a toolbox developed for the reinforcement learning drift diffusion model (RLDDM;  Pedersen et al. 2017 ). The RLDDM builds on the drift diffusion model (DDM;  Ratcliff 1978) , a sequential sampling model of two-alternative decision making, which, when applied to value-based decisions, assumes that the scaled difference in expected values between options is accumulated during choice.
The RLDDM further estimates the learning rate at which these expected values evolve with probabilistic outcomes across learning, based on RL principles. We describe a novel RLDDM module as an extension to the opensource HDDM python package  (Wiecki et al. 2013 ) that allow users to easily model data with the RLDDM and estimate the correlation of trial-by-trial neural activation and learning and decision parameters. This tutorial begins by first providing a brief introduction to the modeling formalism (see  Pedersen et al. (2017)  for more details). We proceed with a description of the types of tasks applicable to the model, followed by the steps needed to fit a basic model, and various methods for performing model validation, including posterior predictive checks. We then present an RLDDM regression module that can be used to include neural (or other trial-by-trial) regressors to estimate how they impact model parameters.
Extensions to the HDDM Toolbox with the RLDDM Module To allow users to flexibly create RLDDM models (e. g. , to estimate how separable parameters vary with condition or neural activity) we built extensions to the HDDM toolbox  (Wiecki et al. 2013). HDDM is an open-source python software package that uses Markov chain Monte Carlo slice sampling via PyMC  (Patil et al. 2010)  to approximate the posterior distribution of group and individual parameters using a hierarchical Bayesian framework. Advantages of Bayesian hierarchical models include directly describing uncertainty of parameter estimates through the width of the posterior distribution  (Kruschke 2010) , and because group and subject parameters mutually inform each other, they require fewer trials (compared to non-hierarchical Bayesian methods, maximum likelihood estimation, and χ 2 quantile methods) to accurately recover parameter values  (Wiecki et al.
2013). For similar reasons they also facilitate the robust identification of links between noisy neural signals and variability in model parameters  (Wiecki et al. 2013). HDDM uses the approximation described by  Navarro and Fuss (2009)  to calculate the wfpt probability density. The RLDDM module in HDDM can be represented with the graphical model in Fig. 1  and uses the following prior distributions for parameters: μ a ∼G 1:5; 0:75 ð Þ σ a ∼HN 0:1 ð Þ a j ∼G μ a ; σ 2 a À Á μ v ∼N 2; 3 ð Þ σ v ∼HN 2 ð Þ v j ∼N μ v ; σ 2 v À Á μ z ∼N 0:5; 0:5 ð Þ σ z ∼HN 0:05 ð Þ z j ∼invlogit N μ z ; σ 2 z À Á À Á μ α ∼N 0; 3 ð Þ σ α ∼HN 2 ð Þ α j ∼invlogit N μ α ; σ 2 α À Á À Á μ t ∼G 0:4; 0:2 ð Þ σ t ∼HN 1 ð Þ t j ∼N μ t ; σ 2 t À Á s v ∼HN 2 ð Þ s t ∼HN 0:3 ð Þ s z ∼B 1; 3 ð Þ ð5Þ where N represents a normal distribution parameterized by mean and standard deviation; HN represents a positiveonly, half-normal distribution parameterized by standarddeviation; G represents a Gamma distribution parameterized by mean and rate; and B represents a Beta distribution parameterized by alpha and beta.
Tutorial Here we describe some of the functionality of the RLDDM modules in HDDM. This tutorial shows how to use the RLDDM modules to simultaneously estimate reinforcement learning parameters and decision parameters within a fully hierarchical Bayesian estimation framework, including steps for sampling, assessing convergence, model fit, parameter recovery, and posterior predictive checks (model validation). More details, including a more comprehensive tutorial with instruction on installing the package, can be found online at http://ski. clps. brown. edu/hddm_docs/.
Overview of Models The HDDM software has two RLDDM modules, and an additional option to include only classical RL without RTs using the softmax function to model choices (but still in a hierarchical Bayesian framework). The HDDMrl module is built to estimate group and subject parameters with hierarchical Bayesian estimation. Models can be set up to assume that subjects utilize the same learning and decision parameter values across conditions. Alternatively, it can be set up as a between-subject model assuming one or more parameters vary as a function of discrete conditions. The HDDMrl cannot be used to estimate the impact of continuous regressors within a condition or to estimate within-subject effects. For this purpose, users can utilize the HDDMrlRegressor module. Lastly, the Hrl module has the same functions as the HDDMrl module but captures choosing using the softmax choice function instead of the DDM and therefore does not require that the data include reaction times.
Running Basic Model To illustrate how to run the model we will use example data from the learning phase of the probabilistic selection task (PST) (e. g. ,  Frank et al. 2004 ). During the learning phase of the PST subjects choose between two stimuli presented as Hiragana-letters (here represented as letters from the Latin alphabet). There are three conditions with different probabilities of receiving reward (feedback = 1) and non-reward (feedback = 0). In the AB condition A is rewarded with 80% probability and B with 20%. In the CD condition C is rewarded with 70% probability and D with 30%, while in the EF condition E is rewarded with a 60% probability and F with 40%. The dataset is included in the installation of HDDM. Once the data are uploaded a model can be created and run with the following commands: m = hddm.
Values close to 1 indicate convergence and that there is small variation between chains, i. e. , that the asymptotic distribution is similar for each chain. A common heuristic is to assume convergence if all values are below 1. 1. As shown in the code below, to get a Gelman-Rubin statistic, users need to run the model multiple times, combine the models, and then apply the gelman_rubin function. Increasing number of samples can help if there are problems with convergence, as the resulting samples should be a closer approximation to the true posterior distribution. {'a': 1. 000, 'a_std': 1. 000, 'a_subj. 1': 1. 000, 'a_subj. 2': 1. 000, 'v': 1. 001, 'alpha': 1. 003, 'alpha_std': 1. 001}.
Posterior Predictive Checks It is not sufficient to fit a model and to only check for convergence before assessing its parameters. An important model validation step is to assess whether the model with fit parameters is sufficient to reproduce key features of the observed data. The Bayesian approach to this step is called a posterior predictive check, because it simulates data by sampling from the full posterior distribution of parameter estimates. One can then check if the observed patterns of data are within the predicted range taking into account the uncertainty of the parameters. The toolbox includes functions to generate data. For the sake of brevity, we refer the reader to the online tutorial (http://ski. clps. brown. edu/hddm_docs/) to see the code for generating data with estimated parameters.
HDDMrlRegressor The module described above cannot estimate the impact of continuous variables onto decision and learning parameters. The HDDMRegressor module was designed for this purpose for the DDM. We build on it here to introduce HDDMrlRegressor to estimate the impact of trial-by-trial regressors, such as time-varying data from brain imaging, onto learning and decision parameters from the RLDDM. As an illustration of its usage we next create and run a model estimating the impact of a trial-by-trial regressor onto decision threshold a (any parameter or combination of parameters is also possible). For this example, we assume a dataframe with a column "neural," where the generated data on which the trial-by-trial regressor was sampled from a normal distribution with mean 0 and standard deviation of 1.
For simulating choice and response data with the RLDDM a coefficient, with a fixed value of 0. 2, was multiplied with the trial-by-trial regressor and added to the baseline decision threshold parameter, which was set to 1. The code below illustrates how to create a model with continuous regressors and also shows that the model is capable of recovering the neural coefficient used for generating the data: The columns in the table represent the mean, standard deviation, and quantiles of the approximated posterior distribution of each parameter. HDDM uses Patsy to create a linear model that estimates intercept and regressor parameters according to the model specification. More details about model specification can be found in the online tutorials for HDDM and the RLDDM modules in HDDM (http://ski.
clps. brown. edu/hddm_docs/) and on the documentation for Patsy (patsy. readthedocs. org). Importantly, in contrast to HDDMrl, the HDDMrlRegressor module allows the user to estimate effects within participants while still ensuring proper updating of expected values. The model can further be used to estimate the effect of multiple regressors on multiple parameters (e. g. , ['a ~brain','t ~eeg']). Note that for the HDDMrlRegressor the learning rate parameter has to be specifically included (include='alpha'), and that, as is the case also for the HDDMrl and Hrl modules, the estimated learning rate is not bound between 0 and 1, because it was transformed to improve sampling. To transform alpha back to the range 0-1 one can apply the inverse logit: e alpha /(1 + e alpha ).
Parameter Recovery An important validation step for a computational model is to determine how well its parameters are identified, particularly if the parameter estimates are interpreted or compared across groups. We generated 81 datasets with all combinations of three plausible values for each parameter (a: 1. 5, 2. 0, 2,5; t: 0,3, 0. 4, 0. 5; α: 0. 15, 0. 3, 0. 45; v: 1. 5, 2. 25, 3. 0). Within datasets, the parameter values assigned to subjects were drawn from normal distributions with the aforementioned values as the mean and the following standard deviations (a: 0. 1, t: 0. 02, α: 0. 1, v: 0. 25). Each dataset consisted of 40 synthetic agents performing 60 trials in each of the three conditions in the learning phase of probabilistic selection task. Example code for generating data can be found at (github.
8 and 0. 2 (AB), 0. 7 and 0. 3 (CD), and 0. 6 and 0. 4 (EF), respectively. Trials were binned to create smoother curves, where each bin represents 4 trials; i. e. , participants performed 40 trials in each condition. In general, the model generates data that closely follows the observed behavior, with the exception of overpredicting performance early in the intermediately difficult condition (condition CD). Uncertainty in the generated data is captured by the 90% highest density interval of the means across simulated datasets. Bottom, density plots of observed (blue) and predicted (green) response time across conditions. RTs for lower boundary choices (i. e. , worst option choices) are plotted as negative (0-RT) to facilitate visualization of proportions and latency distributions for upper and lower bound responses.
Here we tested whether RLDDM would be able to recover similar effects and also estimate individual differences in learning parameters. We reasoned that in principle, the effects could get stronger, if the subject-specific estimates of learning rates improve the estimate of the conflict time series. However, it was also possible that some of the effects would no longer hold or would be better explained by other factors. We thus used the same dataset and the same regressors, but instead of taking expected values from an ideal observer we use the RLDDM to simultaneously estimate individual learning rates, decision parameters, and the influence of neural regressors onto parameters. Specifically, trial-by-trial values of decision threshold a were modeled as which assumes an intercept for a, main-effects, and hypothesis-driven two-way interactions and one three-way interaction.
2011) , which could be accumulated  (Ratcliff and Frank 2012; Ding and Gold 2010). We modified the HDDMrl model to estimate the effects and interactions of the neural regressors used in  Frank et al. (2015). Although HDDMrlRegressor could be used to estimate neural regressor effects onto parameters, this module is (currently) not designed to estimate the effect of regressors onto inferred latent states, such as the conflict term used here, given that conflict itself depends on the learned values and hence learning rate. Code for allowing this function in the modified model can be found online (https://github. com/madslupe/hddm/tree/conflict_rlddm). The model fit, as measured by the deviance information criterion (DIC;  Spiegelhalter et al. 2002) , indicated that the RLDDM provided a better fit to the data compared to the previously reported model (DIC rlddm = -357, DIC ddm = 71).
Discussion We have introduced modules that allow users to easily analyze instrumental learning data with the reinforcement learning drift diffusion model (RLDDM), which describes choosing in reinforcement learning using the drift diffusion model. The modules are built in the Python-based HDDM toolbox and have functions to estimate, assess, and validate models, in addition to estimating the impact of neural regressors onto learning and decision parameters. The validity of the model was illustrated by reanalyzing data showing that simultaneous estimation of learning and decision parameters increased sensitivity to how a mediofrontal-subthalamic network is related to decision threshold adjustments under conflict. Bridging the fields of cognitive neuroscience and mathematical psychology in understanding how neural activation relate to cognitive processes has become increasingly popular in recent years  (Turner et al.
2019). The approach described here allows users to directly test the association of neural data to latent variables of behavior. The models can further be used to relate dysfunction in cognitive or motivational processes to alterations in neural activation  (Maia and Frank 2011). During recent years several other hierarchical Bayesian toolboxes have been developed with the specific aim of simplifying the process of analyzing data with cognitive computational models. Popular toolboxes in R include the hBayes package  (Ahn et al. 2017 ) that allows users to analyze data with the DDM and several RL models and the ggdmc package that includes sequential sampling models  (Lin and Strickland 2020). The VBA toolbox in matlab also includes several RL models  (Daunizeau et al. 2014 ). To our knowledge, the HDDM toolbox is the only that has functions to analyze data with the RLDDM and that facilitates estimating the impact of continuous regressors onto learning and decision parameters.
To capture the influence of the mediofrontalsubthalamic network on conflict described above, we modified the code of the HDDMrl model to incorporate the neural regressors. Future versions of the toolbox will hopefully include this function in a more general way, but until then, users can adapt the code to their needs using our modified code as an example (https://github. com/madslupe/hddm/tree/conflict_ rlddm). Fig. 6  Posterior predictive checks. a Observed (blue) and predicted response time distributions across conditions from the RLDDM described here (orange) and the DDM model reported in  Frank et al. (2015, green). RTs for lower boundary choices (i. e. , worst option choices) are set to be negative (0-RT) to be able to separate upper and lower bound responses. Here, the models generally capture the response time distribution, except for predicting somewhat longer tails for choices in favor of the best option (i.
to assess the status and changes in benthic fauna with genomic-based methods, notably DNA metabarcoding, in combination with image-based identifications. This article presents the results of a 30-month pilot phase in which we established an operational and geographically expansive ARMS-MBON. The network currently consists of 20 observatories distributed across European coastal waters and the polar regions, in which 134 ARMS have been deployed to date. Sampling takes place annually, either as short-term deployments during the summer or as long-term deployments starting in spring. The pilot phase was used to establish a common set of standards for field sampling, genetic analysis, data management, and legal compliance, which are presented here. We also tested the potential of ARMS for combining genetic and image-based identification methods in comparative studies of benthic diversity, as well as for detecting non-indigenous species.
, 2016; Bean et al. , 2017). Recent studies have also tested the applicability of this method for hardbottom monitoring and non-indigenous species (NIS) detection, comparing genetic with conventional methods  (Pearman et al. , 2016; Kelly et al. , 2017; Cahill et al. , 2018; Couton et al. , 2019). Most of these studies concluded that metabarcoding adds substantial value for monitoring marine biological communities, especially when combined with other methods in biological monitoring programs. The use of metabarcoding for rapid identification of species in marine communities, however, is not without limitations (e. g. ,  Cahill et al. , 2018). One obvious drawback is the still incomplete taxonomic reference databases, which limit positive identifications and can also lead to misidentifications  (Meiklejohn et al.
Resulting amplicons are further sequenced using Illumina MiSeq Reagent Kit v3 (2 × 300 bp). Currently, samples are processed and sequenced as batches twice a year. In the future, samples may be processed more frequently and potentially involving additional partners, if these follow the established protocols. All raw sequence files produced by the network are submitted to the European Nucleotide Archive, ENA  (Amid et al. , 2020) , and processed with the Pipeline for Environmental DNA Metabarcoding Analysis, PEMA  (Zafeiropoulos et al. , 2020). Repeatable workflow procedures for integrated processing of image and sequence data are currently under development as part of the LifeWatch-ERIC Internal Joint Initiative on nonindigenous species. The final cleaned sequence files are stored in the PlutoF data management system  (Abarenkov et al.
Detailed protocols for sampling and sample processing are documented in the Handbook and in the molecular standard operating procedures (MSOP) available at the ARMS-MBON website (www. arms-mbon. eu). In addition, we also developed a specific partner registration sheet to allow new partners to register new observatories and receive consultation as well as training. To date, 74% of all concluded sampling events have been sequenced (120 out of 162 material samples). The status of sequence processing and data publication is shown in Supplementary Table  2  and will be updated regularly on the ARMS-MBON website (www. arms-mbon. eu). Data are published as packages-one for each ARMS (i. e. , sampling event) and allow for continuous enrichment, as well as integration of sequence and image data during analysis  (Exter et al.
, 2020). Detailed explanations of the ARMS-MBON data formats are given in the Supplementary Material. All data are published under the Open Access license CC BY 4. 0, with a moratorium period of 1 year starting from the point when raw sequence data become available to the partner network. Two examples with data from the ARMS used in the case study are available under the Data Availability Statement. Data from all remaining and future sampling events will become automatically available under the same license after the embargo period, starting in May 2021.
Case Study Comparison of sequence reads derived from DMSO and ethanol preserved samples showed a higher yield for DMSO in the sessile fraction and a lower yield for DMSO in the motile fraction from Greece (500 µm), while DMSO and EtOH yield were almost equal in the sessile factions from Sweden (Figure  2 ). Despite this substantial variation of sequence reads in relation to the fixative, the representation of taxa, as well as the resulting species composition, remained very similar across both preservation methods (Figures  2, 3 ). Combined analysis of genetic and image data resulted in 72 identified species from the Swedish ARMS, with 8% overlap between genetic and image-based species observations (Figure  3 , Supplementary Table  3 ). In comparison, the analysis of the Greek ARMS resulted in 69 identified species with only 4% overlap between genetic and image-based communities, highlighting the high degree of complementarity of image and genetic data collected by the ARMS.
Comparison of the taxonomic composition between the three genetic fractions (40 µm sessile, 100-500 µm, and 500 µm -2 mm motile) shows a shift in taxonomic dominance between fractions and hence also indicates a high degree of complementarity (Figure  2 ). While the sessile fractions are dominated by sequences from chordates (tunicates), the motile fractions are co-dominated by arthropods, nematodes, mollusks, and single cellular eukaryotes. The complementarity between the fractions is also reflected by the distance between motile and sessile fractions in the NMDS plot (Figure  3 ). We matched species observations obtained in the case study against the EASIN Catalog of Alien Species (https://easin. jrc. ec. europa. eu/easin/Catalogue) as well as the AquaNIS database  (Olenin et al. , 2014)  and identified 16 non-indigenous species (NIS) that are either alien or cryptogenic in the region of detection (Table  1 ).