dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome assembly and gene calling Short-read libraries had adaptor removal  (Martin 2011 ) and quality trimming performed using Trim Galore! (https://github. com/FelixKrueger/ TrimGalore). A minimum phred score cut-off of 25 and a minimum read length of 90 nt was applied to short-read libraries. Long-read libraries were corrected using short reads using Proovread  (Hackl et al. 2014). Corrected long reads were then self-corrected with additional adaptor trimming using Canu  (Koren et al. 2017). Canu was then used for genome assembly with an error rate of 2. 5%. Scaffolding of assembled contigs was performed by scaffolding with corrected long reads using SSPACE long-read hybrid assembler  (Boetzer and Pirovano 2014). This primary assembly was then processed using Purge Haplotigs  (Roach et al.
The H39 assembly wasn't included as it has been shown to have almost complete synteny to H97  (Sonnenberg et al. 2016). For gene calling purposes, RNAseq data from fruitbodies with a read length of 100 nt (phred score. 25) were aligned to the final genome assembly using Bowtie 2  (Langmead and Salzberg 2012). Resulting alignment files were used to train AUGUSTUS using Braker2  (Stanke et al. 2006; Hoff et al. 2016). The completeness of the predicted gene models were assessed using BUSCO with the Basidiomycota BUSCO dataset  (Simão et al. 2015).
Genome functional annotation and characterization Putative open reading frames (ORFs) were assigned protein family (PFam) domains using funannotate (https://github. com/nextgenusfs/ funannotate) using default settings of Interproscan 5  (Jones et al. 2014). Gene ontology (GO) IDs  (Ashburner et al. 2000)  were assigned where available and a corresponding GO term map was obtained using Yeast-Mine  (Balakrishnan et al. 2012). Information on the pathways associated with different genes were analyzed with KEGG (Kyoto Encyclopedia of Genes and Genomes)  (Ogata et al. 1999 ) by assigning KO (KEGG ontology) through BlastKOALA  (Kanehisa et al. 2016). A search for repetitive elements was done by identifying tandem repeats (TR) and transposable elements (TE). TRs were identified over the entire assembly with Tandem Repeats Finder (TRF 4.
Carbohydrate-active enzymes Translated ORFs were used to search dbCAN2  (Zhang et al. 2018)  for presence of Carbohydrate Active Enzymes (CAZys)  (Lombard et al. 2014). For comparative purposes 32 fungal genomes consisting of a variety of Ascomycota and Basidiomycota species (Table  S1 ) were also searched to catalog their CAZy content. As well as including brown rot and white rot fungi, genomes for 7 of the top 10 most highly cultivated mushrooms are also included in this dataset (Table  S1 ).
Agaricus bisporus pangenome dataset assembly Genome assembly data for three Agaricus bisporus strains (H97, JB137 and H39) were obtained from NCBI. Gene sequence and genomic location datasets were generated for each of the three strains through the pangenome analysis pipeline Pangloss; a gene prediction strategy using a combination of HMM-dependent gene prediction with GeneMark-ES and PWM-dependent long ORF prediction was.
Data availability The Bioproject designation for this project is PRJNA544931. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession VCNO00000000. The version described in this paper is version VCNO01000000. Table  S1  shows the genomes, taxonomy and download links for the 32 genomes used in the phylogenomic and CAZy studies. Table  S2  shows the presence of lignocellulolytic genes in the 32 fungal genomes. Figure  S1 : Macrosynteny between A. bisporus H97 chromosomes and all A. bisporus ARP23 scaffolds. Only regions larger than 10,000bp are connected with links. Macrosynteny visualized with Jupiter Plot. Supplemental material available at FigShare: https://doi. org/10. 25387/ g3. 9076991.
A single LTR retrotransposon was found for the endogenous retroviruses (ERV) class I. A single hAT-Charlie family DNA transposon and 7 DNA/TcMar-Tiggers were found out of a total of 55 DNA elements. Overall the ARP23 assembly contains 200 tRNAs, 10 rRNAs and 93 sRNAs (Table  1 ). In total 13,030 putative protein-coding genes were called for ARP23. Of these 7,725 (59. 3%) were annotated with PFam domains. An analysis of the presence of the BUSCO set of orthologous genes for Basidiomycetes in the ARP23 gene set revealed that is missing 40 (3%) of the BUSCO genes. This is comparable to the predicted proteomes of H97 and JB137-S8 which are both missing 39 (2. 9%) of the BUSCO genes. ARP23 gene calling incorporated RNAseq data from fruitbodies, the final gene set showed evidence that 775 (5. 95%) of protein coding genes are alternatively spliced.
Genome phylogeny The availability of whole genomes permits the reconstruction of phylogenomic trees. From our dataset of 32 fungal genomes we located 71 ubiquitously distributed gene families. These were individually aligned and concatenated to give a supermatrix of 27,861 amino acids. Using this supermatrix, phylogenomic reconstruction analyses were performed using both Maximum likelihood and Bayesian inference (Figure  2 ). The resultant phylogeny successfully resolved strongly supported monophyletic clades for the Ascomycota and Basidiomycota phyla. It also resolved monophyletic clades for the Agaricales, Boletales and Polyporales orders within the Basidiomycota clade (Figure  2 ). Within in the Agaricales order a strongly supported monophyletic Marasmioid clade containing Schizophyllum commune, Moniliophthora roreri, Gymnopus luxurians and Lentinula species is present.
Levels of synteny with respect to other flanking genes are also very high (Figure  3 ). The JB137-S8 assembly contains six ORFs between the homeodomain proteins, sequence analysis leads us to believe that this is a misassembly artifact, furthermore all ORFs have homologs in H97 and ARP23 but are located on different scaffolds. Relative to the other mating loci of the other two assemblies, H97 has an additional putative ORF (Figure  3 ). A homolog for this gene is absent for the assemblies of both ARP23 and JB137-SB, furthermore it does not contain any known Pfam domains and a BLASTP search against GenBank retrieves a single significant hit to another Agaricales species (Leucoagaricus sp) therefore it may be dubious.
2005; Fouts et al. 2012). SOCs with syntenic orthologs from all four A. bisporus strain genomes in our dataset were classified as "core" SOCs, and clusters missing an ortholog from $1 strain genome were classified as "accessory" SOCs. After initial construction with PanOCT, the A. bisporus pangenome was refined by merging accessory SOCs based on reciprocal strain best hits between all members of a given pair of accessory SOCs  (McCarthy and Fitzpatrick 2019a, 2019b). In total, we identified 7,732 core SOCs and 8,478 accessory SOCs within our A. bisporus dataset (16,120 in total) (Figure  4 ). The proportion of core SOCs relative to the total number of protein coding genes per genome ranged from a low of 60% in ARP23 to a high of 71% in H97. This proportion of core to accessory genes is lower than we have previously observed in a number of model fungal species  (McCarthy and Fitzpatrick 2019a).
Analysis of the distribution of SOCs within the A. bisporus accessory genome was performed within Pangloss using UpSetR, which is an R implementation of the UpSet method for visualization of set intersections and occurrences within a dataset using matrix representation. The UpSetR plot in Figure  4  shows that singleton SOCs (i. e. , singleton genes) are the most common within the accessory genome, with 2,161 singleton SOCs from ARP23 alone and 6,574 in total (78% of all syntenic SOCs in the accessory genome). The distribution of the remaining 1,904 non-singleton SOCs within the A. bisporus accessory genome appears to follow evolutionary history (Figure  2 ), for example H97 and ARP23 share 727 accessory SOCs either exclusively or along with one other strain, while JB137 and ARP23 only share 615 accessory SOCs exclusively or with another strain (Figure  4 ).
ACKNOWLEDGMENTS EOC is funded by a  Teagasc Walsh Fellowship Scheme  (grant reference number  10564231 ). JM and CGPM are funded by postgraduate scholarships from the  Irish Research Council, Government of Ireland  (grant numbers  GOIPG/2016/1112  and  GOIPG/2015/2242 ). We acknowledge the  DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC)  for the provision of computational facilities and support.
Materials and methods Meta-analyses evaluating interventions to improve medication adherence were searched in PubMed in January 2019 and reviewed for primary studies. Experimental studies with a comparison group assessing an intervention to enhance medication adherence in adult patients with reported adherence outcomes were included. Two authors extracted data for study characteristics, interventions and adherence outcomes. Interventions were categorized into four groups or combinations: educational, attitudinal, technical and rewards. Four network meta-analyses were performed to compare interventions based on patient followup time. Medication adherence effect sizes were reported as odds ratios (OR) with a 95% credibility interval (CrI) and surface under the cumulative ranking curve (SUCRA) to allow ranking probabilities.
Data sources A systematic search of the medical literature was conducted for relevant meta-analyses comparing patient-targeted interventions to improve medication adherence in adult populations reporting adherence outcomes. The search was conducted on PubMed in January 2019 without any restriction based on publication date or language. The complete search strategy is available in S1 Table. Two investigators (EW, ATR) independently reviewed all abstracts and full-text articles and discrepancies were solved by a third reviewer. The primary studies included in the meta-analyses were then fully reviewed.
Data extraction and quality assessment The following data from primary articles was extracted by two investigators (EW, ATR) using a standard data sheet piloted with 28 studies: study baseline characteristics (authors, year, title, sample size, clinical condition, demographics, duration of study, evaluated interventions), study design, measure of adherence used, variable type (continuous versus categorical) and corresponding adherence rates before and after the intervention. To standardize the results obtained from different measures of adherence, an overall composite adherence outcome was used for categorical variables that represented the rate of adherent patients obtained from any of the measures in each study. The overall composite score was validated by Tonin et al, 2018  [27]. If a study included more than one measure, a mean rate from the different measures of adherence was calculated.
Results Database searching identified 1081 records. Sixty-nine meta-analyses were included for primary study extraction. From these, 1234 primary studies were identified and 468 studies were included for qualitative synthesis (see Risk of bias assessment revealed most studies having an unclear risk of bias. The domains with higher risk of bias were attrition bias (around 25% of studies) and performance bias (30% of studies) as studies lacked complete outcome data or were unable to blind participants due to the nature of the interventions. More than 90% of studies were free of selective reporting. Fewer than 10% of trials were sponsored by industries or presented conflict of interest (S1 Fig and  S3  Table ). For the quantitative network analyses, 219 studies were excluded due to the absence of categorical data on patient's adherence.
We present a method for deriving stellar fundamental parameters. It is based on a regularized sliced inverse regression (RSIR). We first tested it on noisy synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously their atmospheric fundamental parameters: T eff , log g, [M/H] and v sin i. Different learning databases were calculated using a range of sampling in T eff , log g, v sin i, and [M/H]. Combined with a principal component analysis (PCA) nearest neighbors (NN) search, the size of the learning database is reduced. A Tikhonov regularization is applied, given the ill-conditioning of SIR. For all spectral types, decreasing the size of the learning database allowed us to reach internal accuracies better than PCA-based NN-search using larger learning databases. For each analyzed parameter, we have reached internal errors that are smaller than the sampling step of the parameter.
Many new techniques are being developed. In  Ness et al. (2015)  and  Casey et al. (2016) , a data-driven approach is introduced (CANNON) for determining stellar labels (fundamental parameters and detailed stellar abundances) from spectroscopic data. Their learning databases (LDB) are based on a subset of reference objects for which the stellar labels are known with high accuracy. Dimension reduction techniques are also developed and used, such as applying the Principal Component Analysis (PCA) for data reduction (see e. g. Jolliffe 1986 ). PCA has shown its effectiveness in inverting the fundamental stellar atmospheric parameters in several studies  (Bailer-Jones et al. , 1998 , Gebran et al. , 2016 , Paletou et al. , 2015a ,b, Re Fiorentin et al. , 2007). Xiang et al. (2017)  estimated the stellar atmospheric parameters as well as the absolute magnitudes and α-elements abundances from the LAMOST spectra with a multivariate regression method based on kernelbased PCA.
The LAMOST spectroscopic survey data has also been recently analyzed by  Boeche et al. (2018)  to invert stellar parameters and chemical abundances in which several combined approaches and techniques were compared. The authors developed a code called SP_Ace which utilizes nearest neighbor comparison and non-linear model fitting techniques. In  Wilkinson et al. (2017) , a spectral fitting code (FIREFLY) was developed to derive the stellar population properties of stellar systems. FIREFLY uses a χ-squared minimization fitting procedure that fits stellar population models to spectroscopic data, following an iterative best-fitting process controlled by a Bayesian information criterion. Their approach is efficient to overcome the so-called "ambiguities" in the spectra. More recently,  Gill et al.
Intra-slices covariance matrix In SIR, all spectra are organized based on an increasing order of the considered parameter for inversion. For example, if we are to invert T eff of each star, the spectra database should be organized in increasing order of T eff while having the other parameters ordered randomly. We then build-up subsets of spectra, also called "slices", having the same value of the parameter one wishes to determine first. These slices should not overlap each other  (Li, 1991). Then we calculate the means x h of the slice of the spectra found in each slice S h that contains n h synthetic spectra (h being the index of each slice). For the inversion of each parameter, x h and x are used to calculate the "intra-slices" covariance matrix, Γ: Γ = H h=1 n h N (x h -x). (x h -x) T , (6) where x h = 1 n h x S h x i.
LDB reduction via PCA PCA is a numerical technique that allows for the reduction of dimension of each spectrum by projecting it on a set of orthogonal basis vectors called principal components (PC's). These components are the eigenvectors of the global covariance matrix Σ. Paletou et al. (2015a)  and  Gebran et al. (2016)  showed that for databases similar to the ones used in this study, only 12 PC's associated to the largest eigenvalues are enough to reduce the LDB, while the reconstruction error remains less than 1%. Therefore after this first pass, the new LDB has dimension of N spectra × 12. The original LDB may reach to a dimension of N spectra ×N λ 10 6 ×10 4. This is due to the fine sampling in the parameters, the high dimension of the spectra, and the large wavelength range which makes the process of SIR computationally heavy in terms of memory and time.
The learning databases As done in  Paletou et al. (2015b)  and  Gebran et al. (2016) , model atmospheres were calculated using ATLAS9 with the new opacity distribution function  (Castelli & Kurucz, 2003 , Kurucz, 1992). These models assume local thermodynamic equilibrium (LTE), hydrostatic equilibrium, and a 1D plane-parallel atmosphere. Convection Fig. 3. log 10 (χ 2 N ) versus log(δ) for T eff of a synthetic spectra having T eff =7 600 K, log g=2. 50 dex, v sin i=197 km s -1 , and with a S/N of 196. Each curve displays the minimization using different sets of nearest neigbor generated iteratively. was treated using a mixing length parameter of 0. 5 for 7 000 K ≤ T eff ≤ 8 500 K, and 1. 25 for T eff ≤ 7 000K, following the prescriptions of  Smalley (2004). Synthetic spectra were calculated using SYNSPEC48 (Hubeny & Lanz, 1992).
Parms A stars F/G/K T eff (K) [6 800,11 000] [4 000,8 000] log g (dex) [2. 0, 5. 0] [3. 0, 5. 0] [M/H](dex) [-2. 0, 2. 0] [-1. 0, 1. 0] v sin i (km s -1 ) [0, 300] [0,100] λ/∆λ 76 000 50 000 NIST 3  databases (for more details see  Gebran et al. 2016 ). The calculation time for one spectrum depends mainly of the selected wavelength range. For instance, calculating one spectrum in the range of 4450-4990 Å at a resolution of 76 000 requires ∼20 seconds on a personal computer 4. Around 500 days were necessary for the calculation of the A stars LDB used in Sec. 4. 2. For the wavelength range between 5000-5400 Å, one spectrum requires ∼22 seconds.
Inversion of simulated A stars We used the LDB of  Gebran et al. (2016)  in which the effective temperature of the data varies from 6 800 up to 11 000 K. The wavelength region was chosen between 4 450-4 990 Å. This wavelength region harbors lines that are sensitive to all stellar parameters, and insensitive to microturbulent velocity which was adopted to be ξ t =2 km/s based on the work of  Gebran et al. (2014 Gebran et al. ( , 2016)). The adopted resolution is 76 000 as it corresponds to most of the analyzed stars in Sec. 5. The ranges of all parameters in the A-star LDB are summarized in Tab. 1. Noise added synthetic spectra were calculated to be used as simulated observations. Around 1 500 spectra were calculated for A stars with parameters randomly selected within the range of the LDB but not necessarily at the grid points.
To analyze the effect of the sampling on the RSIR technique, we have inverted these spectra using 3 different LDB's. For the same range in all the parameter only the step was modified in each database. As an example, in the LDB 1, T eff has a step of 100 K, whereas in LDB's 2 and 3, the steps are 200 K and 400 K, respectively. The same was done for all parameters and the details about the steps are found in Tab. 2. The sampling of the v sin i in the LDB's is not constant and depends on the value of v sin i  (Gebran et al. , 2016). To compare the results of the inversion of PCA NNsearch and RSIR for 1 500 spectra, we estimate the root mean square error for both techniques, Λ, defined as: Λ = N i=0 ( y (inv) i -y (true) i ) 2 N , (13) where y (true) i is the known parameter of the i th synthetic spectrum and y (inv) i its corresponding inverted one.
Application to observed spectra The performance of the RSIR has been tested on two samples of stellar spectra. The first sample is the one of the Spectroscopic Survey of Stars in the Solar Neighborhood (S 4 N, Allende  Prieto et al. 2004 ). These are spectra of bright FGK stars that are at distance less than 15 pc. We have estimated the S/N of these spectra in the wavelength range used for the inversion of the parameters  [5 000-5 400Å]. This ratio ranges between 40 and 450. These spectra are at a resolution of λ/∆λ ∼50 000. All the details about the acquisition and the reduction procedure of the S 4 N data can be found in Allende  Prieto et al. (2004). These spectra were inverted using the database of  Paletou et al. (2015a) , made of 905 spectra retrieved from the ELODIE stellar library  (Prugniel & Soubiran, 2001 , Prugniel et al.
If we compare our inverted values to the median of Vizier,   HD 28355, and HD 222603) and have more than 120 references each. The source of these high resolution spectra is explained in detail in  Gebran et al. (2016). They were observed using ELODIE, NARVAL, ESPaDOnS and SO-PHIE spectrographs. ELODIE has a resolution of 42 000 whereas NARVAL, ESPaDOnS and SOPHIE are at a resolution of ∼76 000. We have applied the RSIR on these data using the database of Test 1 in Sec. 4. 2 at both resolutions. The S/N of these spectra is between 180 and 360. The inverted parameters of each star were compared to the ones retrieved from Vizier and added to the plots of Fig. 5  as filled triangles. We found an average signed difference and a standard deviation of -0. 14±245 K, -0. 20±0. 30 dex, -0. 11±0. 09 dex and -2.
15 to 0. 3 dex. This parameter is very important for chemical analysis as some line profiles could be very sensitive to log g values. Spectroscopic determinations of surface gravity have always been assigned moderately large error bars, especially for A stars  (Smalley, 2005). The same applies to FGK stars but with smaller error bars. Asteroseismic log g determinations remain the best tools for achieving accuracies less than 0. 05 dex  (Chaplin et al. , 2014 , Creevey et al. , 2013 , Hekker et al. , 2013). RSIR is mainly based on finding the best set of spectra in the database that correspond to the observed one. As it is a spectroscopic method, we should not expect an accurate recovery for log g. Using our values for ∆logg we can, a posteriori figure out what it means in terms of discernibility between two spectra whose respective log g differ from this quantity.
Discussion and conclusion RSIR tests for nearly 4 000 synthetic stars of different spectral type and different noise levels showed an improvement over the PCA-based method of  Paletou et al. (2015a,b)  and  Gebran et al. (2016)  for the inversion of stellar parameters. Results of Tabs. 2 and 3 and Fig. 4 , for FGK and A stars, show that for most of the tests, the Λ values of RSIR are lower than nearest neighbor PCA approach. Having a prior information about the star using PCA as a pre-process allows us to narrow down the selection of the optimized reduced databases. This decreases drastically the size of the LDB's. Achieving lower Λ with bigger steps helps in decreasing the prohibitive computation time for the construction of databases and the calculations of the PC's. Simulated tests revealed that computation time of RSIR is nearly 1% of that of the process of PCA nearest neighbor approach.
Such a "sieve algorithm" was first proposed by López Ariste &  Casini (2002)  in the context of the characterization of magnetic fields from spectropolarimetric data (see also  Casini et al. 2013). Another line of development relates to the "structure" of our LDB's. Smarter, or optimal LDB's, using different methods of samplings, should be considered. Such a general issue was already evoked by  Bijaoui et al. (2012)  for instance. Available online databases are usually calculated with large steps in T eff and log g. Our RSIR technique, as it does not require small steps in the LDB, is a good tool to be used with online available synthetic spectra such as the POLLUX 6 database  (Palacios et al. , 2010)  that contains models with temperature ranging between 3 000 and 50 000 K or TLUSTY Non-LTE Line-blanketed Model Atmospheres of O-Type Stars  (Lanz & Hubeny, 2003)  with T eff ranging between 27 500 and 55 000 K with 2 500 K steps, and log g between 3.