dataesr/scientific-paragraphs-categorization huggingface dataset:
5b  left panel) as well as the top 10 rule-positive patients with the lowest scores (Fig. 5b  right panel). Many of the patients in the former category have numerous observations of prednisone, fluticasone, salbutamol, salmeterol, theophylline, or tiotropium, suggesting that they may indeed be COPD patients (i. e. potential false negatives for the rule). Conversely, many low-scoring patients in the latter category exhibit no evidence of any relevant prescriptions, making them dubious COPD cases (i. e. potential false positives). These results suggest that the predefined disease validation rules may be too rigid since no ICD code or prescription is adequately sensitive or specific. On the other hand, MixEHR-G provides richer and largely complementary information to help re-define these rules in a data-driven manner.
7 million non-zero feature observations over  24, 192  unique features in ∼10 hours on a 2. 2 GHz processor with 10 cores, using a maximum of 60 MB of RAM and 130% CPU (1. 3 cores). As a proof-of-concept analysis, we observe that the MixEHR-G-inferred topics are wellaligned with the phenotypes they represent and complementary to rule-based phenotyping algorithms. We used MixEHR-G trained on the PopHR database to derive meaningful insights about disease similarities, comorbidities, and prevalences in the Quebec population  [46]. We envision two major applications for the trained MixEHR-G model. The first application is phenotype labeling for cohort selection or clinical research. MixEHR-G more closely aligns with expert-curated phenotype rules than other recently published phenotyping methods including MAP and sureLDA across 9 diverse phenotypes (Fig.
MixEHR-G MixEHR-G models EHR data using the generative latent topic model MixEHR as previously described  [27]  but with topic hyperparameters constrained to align with reference diseases using a procedure similar to that detailed in Ahuja et al. [26]. The algorithm consists of 3 key steps: (1) assemble a comprehensive and heterogeneous EHR dataset (i. e. ICD codes, prescriptions, lab data, etc. ); (2) use the Multimodal Automated Phenotyping (MAP) method to obtain initial probabilities for each reference phenotype based on key surrogate features within these data; and (3) train the MixEHR model  [27]  on the entire EHR dataset weighting the K asymmetrical patient-topic Dirichlet hyperparameters by the probabilities obtained in (2). We assume that there are in total K reference phenotypes corresponding to K true binary phenotype states that are unobserved a priori.
Instead MixEHR-G has the capacity to assign probability weights to each EHR code under each phenotype topic, making it robust to a large number of uninformative codes. We included all available EHR codes without any filtering when assembling features. Fig. 1  depicts how multimodal count data are combined into multiple discrete-count matrices -one per data type -for input into the MixEHR-G algorithm. Step 2: Initializing prior probabilities using MAP We obtain prior probabilities for the K reference phenotypes, which we denote as π = (π 1 ,. , π K ) , using a modified MAP algorithm  [22]. Standard MAP estimates π k by fitting Poisson and Lognormal mixture models to the counts of (1) phenotype k's core ICD code and (2) NLP-curated mentions of phenotype k in clinical notes, normalizing using a healthcare utilization feature H, and taking a weighted average of these various mixture models.
Given z (t) dj , we then draw an EHR code x (t) d,j from a multinomial distribution with probability vector φ (t) z (t) d,j. MixEHR-G extends MixEHR by setting the Dirichlet topic hyperparameters for patient d, α d , to a scalar multiplied by the prior probability vector over the K phenotypes: α d = απ d , where π d is obtained from Step 2. Consequently, patient d's expected mixture membership for phenotype k, α d,k is scaled by patient d's prior probability of phenotype k, reflecting the intuition that a higher prior likelihood of phenotype presence should beget a proportionally higher probability of feature attribution to that phenotype. Therefore, patient d will have zero mixture membership for phenotype k if the topic prior is equal to zero (π d,k = 0). We train MixEHR-G using the joint collapsed variational Bayesian inference protocol detailed in Li et al.
[27]. The key variational topic inference step is: γ (t) d,j,k ∝ α * k + ñ-(d,j) d,. ,k    β (t) x (t) d,j + [ñ (t). ,x (t) d,j ,k ] -(d,j) w β (t) w + [ñ (t). ,w,k ] -(d,j)    (1) where γ (t) d,j,k denotes the variational probabilities of the k th topic assigned to EHR code j of patient d: q(z (t) d,j = k) ≡ γ (t) d,j,k and the notation n -(d,j) indicates the exclusion of the contribution from patient d and EHR code j. The sufficient statistics are simply the summation of the inferred topic probabilities over all patients and all EHR codes except for patient d and EHR code j: ñ-(d,j) d,. ,k = T t=1 M (t) d j =j γ (t) d,j ,k , [ñ (t). ,x (t) d,j ,k ] -(d,j) = D d =d M (t) d j=1 [x (t) d ,j = x (t) d,j ]γ (t) d ,j,k (2) Unlike MixEHR, instead of iteratively optimizing the asymmetrical hyperparameters α, we now just optimize the scalar α.
Implementation of existing methods We implemented sureLDA  [26] , MAP  [22] , and PheNorm  [21]  using their respective R packages: sureLDA: https://cran. r-project. org/web/packages/sureLDA/index. html; MAP: https://search. r-project. org/CRAN/refmans/MAP/html/MAP. html; PheNorm: https://cran. r-project. org/web/packages/PheNorm/index. html. We implemented MixEHR  [27]  using the source code from the github page: https://github. com/li-lab-mcgill/mixehr.
Application to the MIMIC-III dataset Medical Information Mart for Intensive Care (MIMIC-III) is a large, single-center database comprising multimodal EHR data associated with 53,423 distinct hospital admissions for 38,597 adult patients and 7,870 neonates admitted to critical care units at the Beth Israel Deaconess Medical Center in Boston, MA between 2001 and 2012  [31]. The dataset was downloaded from the PhysioNet database (mimic. physionet. org) and used in accordance with the Phys-ioNet user agreement. For all data types, we removed nonspecific ICD, DRG, and CPT codes that were frequently observed among patients based on their inflection points. Clinical notes from NOTEEVENTS. csv were pre-processed and converted to bag-of-words format using the R library tm. We filtered out common stop words, punctuations, numbers, and whitespace, and we converted all remaining words to lowercase.
csv. Our pre-processed MIMIC-III dataset consisted of 33,388 unique tokens from clinical notes, 6,215 ICD-9 codes, 1,770 CPT codes, 564 lab tests, 8,409 prescriptions, and 3,086 DRG codes, altogether totaling 53,432 unique features. The MIMIC-III data are not longitudinal. Most patients in the MIMIC-III dataset only have one ICU admission, wherein each ICD-9 code is observed at most once upon discharge. As a result, each PheCode is observed at most once for each patient. In lieu of running MAP on PheCode counts to obtain prior phenotype probabilities as detailed in Step 2: Initializing prior probabilities using MAP, we set the hyperparameters α d,k for phenotype k to one or zero depending on whether the associated PheCode was observed for patient d or not, respectively. We ran MixEHR-G on parent phenotypes and subphenotypes separately.
Application to the PopHR dataset The Population Health Record (PopHR) is a multimodal database that integrates massive amounts of longitudinal heterogeneous data from multiple distributed sources (e. g. inpatient and outpatient physician claims, hospital discharge abstracts, outpatient drug claims) for 1. 3 million patients in the Quebec province of Canada between 1998 and 2014  [30]. We used three data modalities from this dataset: ICD-9 diagnostic codes, administrative procedure codes (ACT), and prescriptions all from outpatient physician encounters. For prescriptions we used drug identification numbers (DINs) as unique IDs; for the other two we used ICD-9 and ACT codes respectively. We removed all features with document frequencies above 25% to mitigate the influence of common, imprecise features.
This left 6,120 unique ICD-9 codes, 6,920 ACT codes, and 11,152 DIN codes for a total of 24,192 unique count features. Some DIN codes indicate different dosages or formulations of the same active drug ingredient. We found this to be useful information for modeling phenotypes as the same drug is often prescribed differently for different conditions. However, for qualitative topic analyses such as the heatmap in Fig. 2a , we aggregated topic probabilities for DIN codes by active drug ingredient to simplify interpretation. Unlike MIMIC-III, PopHR is a longitudinal dataset in which a subject can encounter multiple occurrences of any given ICD-9 code (and thus PheCode) over time. Thus, we computed prior phenotype probabilities using the MAP procedure described in Step 2: Initializing prior probabilities using MAP.
Phenotyping accuracy evaluation For the PopHR data  [30] , we have rule-based phenotyping algorithms for 12 diseases that were derived solely from ICD codes  [50]. Some of these rules rely on consecutive observations of the same ICD code(s) for a patient within a small (i. e. 1 month) time interval. Since we aggregated ICD code frequencies over time for a given patient, our model does not use the same temporal information as the rules, making them a good reference for MixEHR-G. Therefore, we used the phenotype labels derived from the rules as gold-standards to evaluate our unsupervised model performance.
Estimation of relative population phenotype prevalence stratified by age and sex For a given phenotype k, we set the overall phenotype prevalence ρ k to the estimate from the Global Health Data Exchange, a comprehensive catalog of health-related data compiled by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington  [42]. We sought to estimate the relative prevalence stratified by age and sex using the PopHR dataset, which is representative of the general population (Fig. 7 ). First, we set a threshold τ ≥ 0 to the patient-topic mixtures Θk to derive predicted phenotype outcomes [ Ŷdk ] D with mean value equal to ρ k : τ * ← min τ 1 D d Ŷdk -ρ k Ŷdk =    1 if θdk ≥ τ * 0 otherwise.
Acknowledgements Y. L. is supported by  Natural Sciences and Engineering Research Council (NSERC)   Discovery Grant  ( RGPIN-2019-0621 ),  Fonds de recherche Nature et technologies (FRQNT) New Career  ( NC-268592 ), and  Canada First Research Excellence   Fund Healthy Brains for Healthy Life (HBHL) initiative New Investigator start-up award  ( G249591 ). D. B. is supported by a  Canada Research Chair. The authors declare no conflicts of interest.
For a given age t age , we identified the set of patients d ∈ S tage such that T min,d ≤ t age ≤ T max,d , where T min,d and T max,d denote the ages at which patient d respectively enters and exits the PopHR dataset. We then estimated the population phenotype prevalence at age t age as the mean predicted phenotype outcome over S tage : ρk,tage = 1 |S tage | d∈St age.
Datasets description We consider the daily close-to-close returns of US equities, adjusted for dividends, splits, and other corporate events. More precisely, the dataset consists of large-capitalization stocks, from 1992-02-03 to 2018-06-29. The number of stocks with data varies over time: it ranges from 399 in 1992-02-06 to 723 in 2018-06-29 and is roughly constant from 2008 onwards. The list of tickers is reported in S. I. DNA microarray data  [20]  can be downloaded from  [23]. It consists of gene expression intensity of 327 tissues of patients affected by pediatric acute lymphoblastic leukemia and a subset of 271 genes.
Numerical simulations with financial data All the simulations are carried out in the same way: each point of each plot is an average over 10, 000 simulations, each of which includes an in-sample window of length t in and an out-of-sample window of length t out = 42 days (about two trading months) unless otherwise specified; it starts from a random day uniformly chosen in the available dataset. To have meaningful inand out-of-sample windows given the maximum t in considered, the first day of the out-of-sample must be after 01-01-2000; each simulation selects n = 100 assets at random among the assets with no missing value in both inand out-of-sample windows.
Frobenius norms We use rescaled Frobenius norms to account for the fact that the number of assets in our dataset depends on time: X Σ F = n×n i,j x 2 ij n 2. (4) In addition, because CV, LW and QuEST methods do not guarantee the identity on the diagonal of filtered correlation matrices; therefore, contrarily to BAHC, we do not include the diagonal elements in the metric and thus define X C F = n×n i>j 2 x 2 ij n(n -1). (5) We found that the performance of CV, LW, QuESTbased correlation estimators is slightly improved by replacing c ij with cij √ cii cjj , which also ensures that the diagonal elements equal one, and thus have used this modification in our analysis.
Source code We have written a BAHC package for both R and Python, available from CRAN and PyPI, respectively. by replacing all its elements with their average: mathematically one builds a matrix C < with elements c < ij = c < ji = 1 -ρ pq where (p, q) ∈ G, (i, j) ∈ F pq , (A5) ρ pq is the average distance between clusters p and q (see (A4)) and the diagonal of C < is set to 1. An equivalent description of this approach is in terms of the factor loading matrix, as in the original paper  [5]. It is important to stress that the matrix C < will be positively defined by construction  [5]. The main feature of this model is to obtain the simplest matrix C < that shares the same dendrogram as C; this means that by applying the HCAL to both C and C < , the resulting dendrograms will be identical. However, we believe that this is also one of the main limitations of this approach; in fact, it does not account for the presence of overlap among clusters.
Background Natural Saccharomyces cerevisiae yeast strains exhibit very large genotypic and phenotypic diversity. Breeding programs taking advantage of this characteristic, are widely used for yeast selection in the wine industry, especially in the recent years when winemakers need to adapt their production to climate change. The aim of this work was to evaluate a Marker Assisted Selection (MAS) program to improve malic acid consumption capacity of Saccharomyces cerevisiae in grape juice. Methods Optimal individuals of two unrelated F1-hybrids were crossed to get a new genetic background carrying many "malic consumer" loci. Then, eleven QTLs already identified were used for implementing the MAS breeding program. Results By this way, extreme individuals able to consume more than 70% of malic acid in grape juice were selected.
A part of L-malic acid can be also consumed by the Malo-Ethanolic Fermentation (MEF) as well as by the fumarase activity  [42, 43]. Recently, the genetic determinism of malic acid consumption by Saccharomyces cerevisiae has been partially unraveled  [46]. Numerous Quantitative Trait Loci (QTL) related to the malic acid consumption were precisely mapped in the SBxGN background. Additional loci controlling the consumption of malic acid in S cerevisiae have been also reported  [47]. The functional exploration of these QTLs reveals the role of seven genes (MAE1, PMA1, PYC2, PNC1, YBL036c, PTC5, and SDH2) by reciprocal hemizygosity assay  [46, 47]. In this present work, a large-scale breeding program was implemented for addressing how much, the rational use of MAS can be implemented for boosting malic acid consumption during the wine alcoholic fermentation.
Statistical analyses All the statistical and graphical analyses were carried out using R software  [54]. Plots were computed using the ggplot2 package and analyses of variance were carried out using the car package. Four linear models were applied: LM1 investigated the effect of QTL numbers on MAC according to the formula : 𝑦 𝑖 = 𝑚 + 𝐸𝐴 𝑖 + 𝜖 𝑖 , ( 1 ) where y is the MAC value for a strain carrying i Enhancers Alleles (i=1…11) and   the residual, m is the overall mean of the 154 FMGSs progenies phenotyped. The LM2 model estimated the effects of the following factors must, strain, temperature, nitrogen, and malic acid as well as the first order interaction of all the factors on MAC value according to the formula: where   is the MAC value for a strain i (i=1…6) fermented in a must j (j=1,2) at the temperature k (k=1,2) and containing the nitrogen level l (l=1,2) and the malic acid level m (m=1,2,3).
The effect of each factor is represented in the Figure  6a  and summarized in Table  S6. All conditions combined; MAC varies mainly with the nature of the must (55. 2% of total variance explained). Indeed, yeast strains consumed in average 32. 5 % more malic acid in white must compared to red must (Figure  6a ). In contrast, temperature had a low impact on MAC and can be considered as poorly significant in the present study (Figure  6b ). The important must-effect observed is likely due to the highest malic acid concentrations found in white must (SB16) compared to those of the red one (M15). This difference strongly impacts the pH of the matrix and may affect the diffusion of malic acid through cell membrane since it is protonated below pH=3. 4  [61]. In order to dissociate this matrix effect, a second set of ANOVA was carried out by splitting the dataset according to the must.
Assesment of a wide MAS program for improving a complex trait Marker Assisted Selection (MAS) constitutes the applied side of quantitative genetics and has been widely used for improving plant varieties and animal races  [32, 65, 66]. Since two decades, Saccharomyces cerevisiae has been raised as a perfect model for addressing fundamental questions in quantitative genetics  [67] [68] [69] [70]. Furthermore, the budding yeast is also the prime industrial microorganism involved in bioethanol and fermented foods production. Although methods for identifying QTLs are perfectly controlled  [31] , their use for improving yeast strains for industrial applications is poorly documented. In the present study, we aimed to increase the malic acid consumption of wine starters in order to propose new strains with outlier features.
To achieve this program, we get a new F1hybrid (FMGS-1) resulting from the cross of two optimal unrelated strains (GS-28b and FM-8d). Then, we implemented for the first time a wide MAS program using 11 QTLs controlling the same trait. First, a robust protocol was developed for isolating and genotyping a wide number of meiotic progenies derived from prototrophic and homothallic yeast. Although these features complexify breeding operations  [14] , the purification of FMGSs hybrids yielded more than 80% of spore clones (doubled haploids) or sibling-pair hybrids (F2) (Figure  S1 ). The use of robotic-assisted DNA extraction and Mass Array genotyping allow a SNP call average of 94% and most of the strains were unambiguously genotyped. This technique allows the parallel genotyping of up to 40 markers which would strongly decrease the genotyping cost per SNP.
Acknowledgments: "Mass array assay was performed at the  Genome Transcriptome Facility of Bordeaux  (grants from the  Conseil Régional d'Aquitaine n°20030304002FA  and  20040305003FA , from the  European Union   FEDER n°2003227  and from  Investissements d'Avenir   ANR-10-EQPX-16-01 ), with the help of Erwan Guichoux". Enzymatic assays were performed at the metabolomic Facility of Bordeaux (grants from  PHENOME-ANR-INBS-0012 ) with the help of  Yves Gibon.
For this, we use the 2-D dispersal model HAZMAP (version 2. 4. 2), together with several eruptive scenarios based on the stratigraphical record (and including the maximum expected scenario for Martinique) and the ERA5 dataset that accounts for the daily variability of winds. We chose to use the HAZMAP model for its low computational cost (a single simulation can be run in less than 5 seconds) and its ease of use. First, we describe the current hazard map for tephra fallout. We then simulate the maximum known eruptive scenario at Mount Pelée and generate probability maps based on this scenario. Then, we describe the other Plinian eruptions identified and reconstructed at Mount Pelée, which allow us to build a matrix of 16 eruptive scenarios. The latter are used, together with an extensive wind database, to produce robust probability hazard maps for tephra fallout.
The model requires several ESPs to run the simulations. The deposit mass, the maximum column height and the total grain-size distribution (TGSD), the three key parameters for tephra dispersal, were retrieved from field data and published in a previous study  (Carazzo et al. 2020). We summarize below these volcanological parameters, together with the other inputs required by HAZMAP and used in our simulation. More detail about the methods used to combine our field observations and physical models of a volcanic plume in order to estimate the ESPs and their uncertainties can be found in  Carazzo et al. (2020). The maximum column height estimated from the distribution of lithic fragments by  Carazzo et al. (   2020 ) is 30 km. The volume of the P3A unit inferred from the thinning behavior of the deposits together with best fit functions  (Daggitt et al.
Meteorological dataset In order to produce probability hazard maps for Mount Pelée volcano, we use wind azimuth and velocity profiles extracted from the European Centre for Medium-Range Weather Forecasts ERA-5 reanalysis for the years 1979-2019  (Hersbach et al. 2019). The initial content of ERA5 files consists of hourly global fields of zonal and meridional winds at a horizontal resolution of 0. 25°×0. 25°( ≈ 31 km) and vertically distributed on 37 pressure levels from 110 m (1000 hPa) to ≈ 48 km (1 hPa). These wind fields have been interpolated to match HAZMAP format by converting each of the 37 pressure levels into an altitude level using the altitude model (Figure  S1 ). We select the wind components over Martinique at each time step and each pressure level in an area ranging from 14. 7°N to 14.
8°N and from 61. 1°W to 61. 2°W, and we calculate a mean daily wind profile for every day from January 1, 1979 to December 31, 2019. Our final dataset is thus composed of 14,975 vertical wind profiles (365 or 366 days times 41 years) shown in Fig. 4. The lower troposphere (from the surface to ≈ 7 km in altitude) is characterized by E-NE trade winds during the winter season, with a maximum speed that is generally comprised between 0 and 15 m s -1 but that can reach 27 m s -1 in some rare cases (Fig. 4a ). The upper troposphere (from 7 to 17 km) is characterized by strong westerlies (i. e. , originating from the west) counter-trade winds with a speed reaching 40 m s -1 , thus of higher mean velocity compared to the trade winds (Fig. 4c ). In the stratosphere (>17 km, Fig. 4e ), the wind often comes from the east or the west, with a strong variability in speed (up to 80 m s -1 in some rare cases).
Probability map with daily winds We now use the model HAZMAP in its probability mode to compute ash loading probability maps for the maximum expected eruption scenario (i. e. , the P3 event). We use the same volcanological parameters as for the P3 simulation, together with the complete 41-year wind data database (i. e. , containing 14,975 daily wind profiles, Fig. 4 ). HAZMAP used in its probabilistic mode also requires static load threshold values to compute output hazard maps. We chose to use five thresholds based on the literature, each corresponding to a tephra thickness and a degree of damage on vegetation or infrastructures (Table  1 ). According to  Wilson et al. (2017) , a 1 mm-thick ash deposit (thus corresponding to a minimum loading of 1. 07 kg m -2 ) leads to the airport closure, and to a required maintenance on all kinds of supply networks (e.