dataesr/scientific-paragraphs-categorization huggingface dataset:
Assessment of predictive accuracy Predictive accuracy of dynamic predictions based on Jointlcmm objects can be assessed using the prognostic information criterion EPOCE  (Commenges, Liquet, and Proust-Lima 2012)  implemented in the function epoce. Predictive accuracy is computed from a vector of landmark times on the subjects still at risk at the landmark time and the biomarker history up to the landmark time. On external data, epoce provides the Mean Prognostic Observed Log-likelihood (MPOL) for each landmark time. When applied to the same dataset as used for the estimation, the function provides for each landmark time both the MPOL and the Cross-Validated Observed Log-Likelihood (CVPOL). The latter corrects the MPOL estimate for possible over-optimism by approximated cross-validation. Further details on these estimators can be found in  Commenges et al.
Examples This section details a series of examples of models estimated with hlme, lcmm, multlcmm and Jointlcmm functions described in section 4. All the examples are based on the paquid dataset provided with lcmm R package. Examples also illustrate the initial values specification described in section 4. 5 and the post-fit computations and generic functions described in sections 5. 1 to 5. 5. 3 and listed in table  1. The first step consists in loading lcmm. This automatically loads the datasets. R> library("lcmm").
Paquid data paquid dataset consists of a random subsample of 500 subjects (identified by ID) from the Paquid prospective cohort study  (Letenneur, Commenges, Dartigues, and Barberger-Gateau 1994 ) that aimed at investigating cerebral and functional aging in southwestern France. Repeated measures of three cognitive tests (MMSE, IST, BVRT), physical dependency (HIER, a 4-level factor) and depression symptomatology (CESD) were collected over a maximum period of 20 years along with age at the visit (age), age at dementia diagnosis or last visit (agedem) and dementia diagnosis (dem). Three time-independent socio-demographic variables are provided: education (CEP), gender (male), age at entry in the cohort (age_init). In some cases, owing to the very asymmetric distribution of MMSE, a normalized version of MMSE (normMMSE) obtained with package NormPsy (Philipps, Amieva, Andrieu, Dufouil C.
In this graph, confidence bands are obtained and plotted with function predictlink : R> col <-rainbow(5) R> plot(mlin, which = "linkfunction", bty = l , ylab = "CES-D", lwd = 2, + col = col[1], xlab = "underlying latent process") R> plot(mbeta, which = "linkfunction", add = T, col = col[2], lwd = 2) R> plot(mspl, which = "linkfunction", add = T, col = col[3], lwd = 2) R> plot(mspl5q, which = "linkfunction", add = T, col = col[4], lwd = 2) R> plot(mord, which = "linkfunction", add = T, col = col[5], lwd = 2) R> legend(x = "topleft", legend = c("linear", "beta", + "splines (5equidistant)", "splines (5 at quantiles)", "thresholds"), + lty = 1, col = col, bty = "n", lwd = 2) R> linkspl5q <-predictlink(mspl5q, ndraws = 2000) R> plot(linkspl5q, add = TRUE, col = col[4], lty = 2) R> legend(legend = c("95% confidence bands", "for splines at quantiles"), + x = "left", lty = c(2, NA), col = c(col[4], NA), bty = "n", lwd = 1) As for hlme, plots for the residuals and the predictions versus observations can be provided but they are now in the latent process scale (only the code is provided here): R> plot(mspl5q) R> plot(mspl5q, which = "fit", var.
We considered two landmark ages (80 and 90) and computed the probility of dementia derived from model mj4b for horizons of 1, 3, 5, 8 and 9 years: multivariate longitudinal data based on MCMC estimation; package FlexMix  (Leisch 2004; Grün and Leisch 2008 ) also proposes the estimation of classes of mixture models including the latent class linear mixed model with function FLXMRlmer; and mixtools  (Benaglia, Chauveau, Hunter, and Young 2009)  includes a functionality to fit latent class linear models with random effects. In stata, the program GLLAMM fits some latent class and latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004). Finally, when one is interested in classification of trajectories, exploratory methods such as the latent class growth analysis  (Nagin 1999 ) are usually preferred.
They have especially become very popular in psychology  (Bongers, Koot, van der Ende, and Verhulst 2004)  and more recently in public health  (Gill, Gahbauer, Han, and Allore 2010). It should be stressed that the latent class growth analysis as implemented in SAS with proc TRAJ  (Jones, Nagin, and Roeder 2001 ) is a specific case of latent class mixed models in which no random effect is included. As such, it assumes that given a specific latent class, the repeated measures of the same subject are independent. Although of possible interest in an exploratory analysis, the assumption that repeated measures are independent given a restricted number of latent groups is very strict and unlikely so inference based on this approach is usually impossible. Further developments of lcmm will include the development of parallel computations.
~X1 * Time, random =~Time, subject = ID , + survival = Surv(Tevent, Event) ~X1 + X2, hazard = "3-quant-splines", + data = data_lcmm) R> jlcmm2 <-Jointlcmm(Ydep1 ~Time * X1, random =~Time, subject = ID , + mixture =~Time, survival = Surv(Tevent, Event) ~X1 + mixture(X2), + hazard = "3-quant-splines", hazardtype = "PH", ng = 2, data = data_lcmm, + B = jlcmm1)From dataset data_lcmm, the first call (jlcmm1) fits a linear mixed model and a survival model independently (since there is a unique latent class by default). The linear mixed model explains the dependent variable Ydep1 according to a linear trajectory of Time specific to each level of X1 at the population level and accounts for two random effects on the intercept and Time at the individual level. The grouping variable is ID. The survival model for the censored observed time Tevent (with Event the indicator of event) involves a baseline risk function approximated by a basis of cubic M-splines with one internal knot placed at the quantiles of the times of event and an effect of X1 and X2.
Acknowledgements The authors would like to thank  Amadou Diakité  and  Lionelle Nkam  for their contribution to the implementation,  Hélène Jacqmin-Gadda  and  Daniel Commenges  for their contribution to the implemented statistical methods, and  Jean-François Dartigues  and the  Paquid Program  for sharing a subsample of the Paquid cohort. The development of lcmm was funded by two  French agencies: Agence Nationale de la Recherche  [grant  2010 PRSP 006 01 ] and  Institut de Recherche en Santé Publique  [grant  AAP12CanBio16 ].
following lines create normMMSE and age65 variables, and display the first lines of paquid dataset: ID MMSE BVRT IST HIER CESD age agedem dem age_init CEP male normMMSE 1 1 26 10 37 2 11 68. 50630 68. 5063 0 67. 4167 1 1 61. 18 2 2 26 13 25 1 10 66. 99540 85. 6167 1 65. 9167 1 0 61. 18 3 2 28 13 28 1 15 69. 09530 85. 6167 1 65. 9167 1 0 74. 61 4 2 25 12 23 1 18 73. 80720 85. 6167 1 65. 9167 1 0 55. 98 5 2 24 13 16 3 22 84. 14237 85. 6167 1 65. 9167 1 0 51. 44 6 2 22 9 15 3 NA 87. 09103 85. 6167 1 65. 9167 1 0 43. 74 age65 1 0. 350630 2 0. 199540 3 0. 409530 4 0. 880720 5 1. 914237 6 2. 209103 R> library("NormPsy") R> paquid$normMMSE <-normMMSE(paquid$MMSE) R> paquid$age65 <-(paquid$age-65)/10 R> head(paquid).
This tool has now been extended/adapted to other tunicate species  (16, 17). Interference with gene func-tion, initially relying on morpholino oligonucleotides, now includes the use of TALENs  (18)  and CRISPR/cas9 strategies  (19, 20). ANISEED -Ascidian Network for In Situ Expression and Embryological Data--is the major database system in the Tunicate community, hosting and giving access to genomic, genetic and anatomical data on ascidian development. First published in 2010  (21) , the database hosts and gives access to genomic, genetic and anatomical data on tunicates. Its refactoring using the Chado schema philosophy and its extension to 11 ascidian species was completed in 2015  (22). Since then, tunicate laboratories have rapidly embraced genomics, including ChIP-seq  (23, 24) , RNA-seq of whole embryos  (24)  or FACS-sorted single cells  (25) , bisulphite sequencing  (26) , SELEX-seq and TSS-seq  (27).
Taxonomy pages A taxonomy page accessible from the home page was introduced in ANISEED 2017 to place the covered species into their phylogenetic context and facilitate the interpretation of comparative studies. The page shows a cladogram of the twelve species currently described at the anatomical or genetic level in the database. Each node gives access to a page describing the corresponding species. Following the novel taxonomy  (28)  and a community decision at the 2017 international Tunicate Meeting, Ciona intestinalis type A has been renamed Ciona robusta throughout the database; while Ciona intestinalis type B is now referred to as Ciona intestinalis. No molecular data are currently associated to this latter species in ANISEED, as its genome draft is of insufficient quality to build gene models.
Each species page briefly describes the species, its distribution and its ecology, lists the number of ANISEED entries and provides a link to the corresponding ANISEED genome browser. This page also links to other tunicate molecular databases (DBTGR  (29) , CiPro  (30) , Ghost  (31)  and FABA  (32)  for Ciona robusta), to the CRISPOR CRISPR/Cas9 guide RNA design tool for ascidian species  (33) , as well as to major ecological databases, including the Ascidiacea World Database from the World Registry of Marine species (WoRMS)  (34) , the Global Biodiversity Information Facility (GBIF)  (35) , or the Global Invasive Species Database (http://www. invasivespecies. net/). Finally, for practical purposes, the page lists for each species the animal providers used in the scientific community. Interactive gene phylogenies.
The diversity of ascidian species with an assembled genome provides a framework to study the evolution of developmental mechanisms. To identify orthologous genes within tunicates and with chordates, ANISEED 2015 used an orthology pipeline based on the OrthoMCL software  (36)  and listed for each gene the cluster of homologous genes it was associated to. In many cases, Species are color-coded and the tree can be compressed or extended lengthwise and zoomed in and out. Note the difference between the two groups of genes. In group I, the gene phylogeny follows the species phylogeny, and tunicate genes are considered orthologous to the vertebrate genes of the group. In group II by contrast, the gene phylogeny does not follow the species phylogeny, and we consider that the tunicate genes have no unequivocal vertebrate orthologs.
New genome browsers ANISEED 2015 featured Gbrowse 2. 0 (39) genome browsers, which showed limitations as the size of the dataset increased. The WashU Epigenome browser (40) was selected as a replacement and adapted with the help of its developer. It offers increased speed and the ability to represent genome-wide chromatin interaction data  (41). Within or between genome comparisons are facilitated by the display in a unique window of concatenations of sets of gene loci within a species, or between species (e. g. orthologous loci to explore synteny relationships). Tracks presented are grouped into three categories. 'Annotation tracks' provide general background information (gene models, repeated elements, inter-species genome sequence comparison, genome alignment, operons, cisregulatory sequences, Ns and GC content).
Functional gene annotations Functional genome annotation (transcript and gene models, orthology to other tunicates, to vertebrates and to other deuterostomes, best blastp hit to human proteins, Inter-pro and GO annotations) was extended to all species with a genome assembly of sufficient quality to support gene model building, including three species previously only represented on the genomic browser: Phallusia fumigata, Halocynthia aurantium and Molgula occidentalis. Gene models are annotated on each gene page and also provided as annotation tracks on the browsers. A link to alternative NCBI and ENSEMBL gene models is provided on each gene card. Manually-curated gene names respecting the tunicate nomenclature guidelines  (43)  are currently being generated by a dedicated committee and will be gradually added until the next official release.
Genes can currently be searched for in ANISEED and in the WashU browser by unique gene or gene model Ids, by name of their most related human gene, or through the ANISEED blast server, which can now blast protein or nucleotide queries against both whole genomes or transcript model sets. The functional annotation of ascidian gene models is based on Gene Ontology (44) terms. Because of the complexity and depth of the GO ontology, containing more than 40 000 terms, genes with similar function can be annotated with distinct terms, which can confound GO term enrichment analyses  (45). To overcome this issue, we used an objective framework Information Content algorithm (46, stringency parameter 0. 2) to define a smaller Tunicate GO Slim of 633 terms (MF:129; BP: 374; CC: 130), which provide higher level and more consistent annotations.
Manually curated data ANISEED hosts both large-scale data, automated annotations, and manually curated data from the literature or communicated by partner laboratories. This latter category, which now forms 30% of total gene expression patterns in Ciona robusta, is crucial to contextualize largescale datasets, and is the primary source of expression profiles in altered developmental conditions (gene knock-down or overexpression; 6% of Ciona robusta dataset). Table  1  presents the growth of major sections of ANISEED between 2010 and 2017. The majority of the new data originates from the manual curation of 33 articles describing the development of Ciona robusta, published between 2012 and 2016, including 19 describing neural development, a very active field of Ciona research. The newly curated dataset set also includes 3 Halocynthia roretzi articles.
Two sets of data of immediate interest to the community were quantified. The first dataset is composed of unpublished stranded RNA-seq data for whole embryos of seven developmental stages ranging from egg to hatching larva in three species (Ciona robusta, Phallusia mammillata and Halocynthia roretzi). This dataset allows comparing the expression dynamics of orthologous genes in these three divergent ascidian species. Note that the Ciona data are from Ciona intestinalis embryos, but in the absence of a high quality C. intestinalis genome assembly, were mapped onto the Ciona robusta genome and considered to reflect the expression of the Ciona robusta orthologs. The second dataset (NCBI bioproject PRJDB3843) compares wild-type, Bmptreated and Bmp-inhibited (Dorsomorphin) Ciona robusta late gastrula embryos.
Additional datasets will be progressively integrated as they become public. Each of these datasets is also integrated as a public hub in the WashU browser. RNA-seq usually poorly represents immature unspliced transcripts. This technique is therefore inadequate to identify Transcription Start Sites (TSS), in particular in ascidians, which make frequent use of trans-splicing  (10). Re- cently, high-throughput RNA-sequencing techniques were developed to produce precise and quantitative genomewide mapping of TSS, thereby allowing a better analysis of promoters  (48). The Ciona robusta WashU browser of ANISEED 2017 includes a public hub with TSS-seq mapping for a set of adult tissues, and whole larvae  (27).
WEBSERVICES AND DOWNLOADS To facilitate the programmatic extraction of datasets and the development of external modules relying on ANISEED data, we developed an Application Programming Interface (API), accessible from the main menu. Currently, the main functions allow retrieving genes according to their expression patterns, articles and authors, cis-regulatory regions and cis-regulatory constructs and cDNA clones. The download section was enriched with standardized GAF files with functional gene annotations for all species. Two functional annotations are proposed for each species, one based on the full Gene Ontology, the other on the tunicate GOslim we developed, which can also be downloaded. The gene trees are also available (. tre format).
FUTURE PESPECTIVES ANISEED 2017 marks a substantial improvement in the content and organization of the main tunicate model organism database. The diversity and depth of the added data attests of the dynamism of this small scientific community of less than a hundred labs worlwide. One major evolution over the past two years was the discovery that the species initially called Ciona intestinalis comprises at least two independent species, Ciona robusta (formerly called Ciona intestinalis type A and found in all major oceans) and Ciona intestinalis (formerly called Ciona intestinalis type B, and mostly restricted to the North Atlantic)  (28). In this version of ANISEED, we therefore reallocated all Ciona intestinalis data to Ciona robusta, which is now the most studied Ciona species. To facilitate interoperability and comparison between databases and to avoid confusion between species, it will be important for the Ciona community to also press for a reassignment of data to their correct species in generalists databases including NCBI, ENSEMBL, Uniprot or UCSC, which have so far not adapted to the new taxonomy.
We would also like to extend the database, beyond ascidians, to appendicularians and thaliaceans. While genomic information remains very partial in thaliaceans  (52) , highquality genomic  (53) , transcriptomic  (54)  and functional (55) data exist for the appendicularian Oikopleura dioica, and we have established contact with the scientific community working on these animals. Computing accurate orthology relationships for most genes of these fast evolving taxa may, however, prove particularly challenging, and may require further optimization of our procedures.
ACKNOWLEDGEMENTS We thank the  Si2C2 IT service of CRBM/IGMM/IRIM  for support throughout the project, in particular  Danielle Avinens  and  Patrice Langlois. We are grateful for the advice provided by the  WashU Epigenome Browser team , in particular  Daofeng Li , during the adaptation of this browser. This is contribution ISEM 2017-259 of the  Institut des Sciences de l'Evolution de Montpellier (ISEM).
FUNDING Agence Nationale de la Recherche (ANR)  [ Equipex Mor-phoscope2   ANR-11-EQPX-0029  to P. L. ; Chor-Reg-Net  NT05-2 42083  to P. L. , C. C. ;  Chor-Evo-Net   ANR-08-BLAN-0067-01  to P. L. ;  Institut de Biologie Computationnelle   ANR-11-BINF-0002  to P. L. ;  Renabi-IFB ,  ANR-11-INBS-0013  to P. L. ;  TED ,  ANR-13-BSV2-0011-01  to P. L. , E. D. ;  ANR-10-BINF-01-03 ,  ANR-10-LABX-54   MEMO-LIFE  and  ANR-10-IDEX-0001-02 PSL* Research University  to A. L. ;  ANR-16-CE92-0019  evobooster to J. N. V. ]; Dopaminet European project (to P. L. );  National Institutes of Health (NIGMS)  [ R01 GM096032  to L. C. ];  Japan Society for the Promotion of Science (JSPS) KAKENHI  [ 16H04724  to K. N. ];  ANR  and  CNRS  (to K. R. N. );  ANR   TED  project (to P. S. );  Marie Curie IIF  cis-reg-logic (to E.
The business motive for commercial members is making a profit and building economic resilience from sustainability. TSC is continuously building a global indicator database based on an online platform where TSC members can compare key performance indicators, metrics and targets to construct Life Cycle Assessments and Category Sustainability Profiles that identify the focus areas and performance measures for a particular product. The latter contain up to 15 Key Performance Indicators (KPIs), each with several response options, though many of the KPIs are generated by binary scores of best practice which are secondarily aggregated at a whole product level from many upstream and downstream strands of the supply chain  [75]. Sustainability "hotspots" and "opportunities for improvement" are automatically identified and a summary of results is available instantly.
The Role of Knowledge and Values The Sustainability Consortium is focused on measures of impacts across the entire lifecycle of the products based on scientific knowledge. Quantification is a key element, such as the generation of indicators from binary scores of best practices, which allows for subsequent calculation of metrics that compare performance between competitors. TSC's global database entails a harmonization for comparability that allows for performance hotspot analysis and benchmarking. However, it also channelizes what is measured and how, which carries risks of unintended effects of losing local tuning and diversity and dumbing down other measurements and things that are hard to measure. There is no explicit, system-wide value-rational process that can guide the overall sustainability of decisions and initiatives by the direct agents of change.
Acknowledgments: This work has been supported by the  New Zealand Sustainability Dashboard project , which is primarily funded by  New Zealand's Ministry of Business, Innovation & Employment  (contract  AGRB1201 ), with additional co-funding from  BioGro New Zealand , Zespri and Kiwifruit packhouse and orchard management companies, New Zealand Wine, and  Te R ūnanga o Ngāi Tahu. Marion Sautier  was supported by an  AgreenSkills+ fellowship , funded by the  EU's Seventh Framework Programme  (grant agreement  FP7-267196 ). We would like to thank all the participants in the  New Zealand Sustainability Dashboard  project for their interest and contributions to the ideas elaborated in this paper, and all the partners in the HealthyGrowth and MultiTrust projects for discussions and collaborations that have contributed to the ideas in the paper.
Applying the same partition to Θ, the normal equations matrix can be rewritten as (2. 6) AΘA ⊤ = A B Θ B A ⊤ B + A N Θ N A ⊤ N. The splitting preconditioner is defined as Θ -1/2 B A -1 B. The symmetric application of this preconditioner to AΘA ⊤ gives: (2. 7) (Θ -1/2 B A -1 B )(AΘA ⊤ )(Θ -1/2 B A -1 B ) ⊤ = Θ -1/2 B A -1 B (A B Θ B A ⊤ B + A N Θ N A ⊤ N )A -T B Θ -1/2 B = I + (Θ -1/2 B A -1 B A N Θ 1/2 N )(Θ -1/2 B A -1 B A N Θ 1/2 N ) ⊤. Sufficiently close to an optimal solution, with a suitable choice of the columns of A B , the diagonal entries of Θ -1 B and Θ N are very small and (Θ -1/2 B A -1 B A N Θ 1/2 N ) approaches the zero matrix. Some strategies for identifying a suitable matrix A B are discussed in  [25, 7]. This specialized IPM has been recently implemented in a software package called BlockIP  [12] , that will be used for the computational results of this paper.
It is worth to remark that α are not the principal angles between linking constraints matrices L i and block constraints matrices N i , but it is expected that small α values (that is, small rotations) will result in small principal angles. The BlockIP package  [12]  implementing the specialized interior-point method has been extended with the Θ -1 0 preconditioner introduced in this work. The main features of BlockIP are: (1) it implements a long-step infeasible IPM, including both Newton and predictor-corrector directions  [29] ; (2) it uses the approach described in Section 2-which relies on Cholesky factorizations and PCG-for the solution of the normal equations  [10, 11, 13] ; (3) Cholesky systems are exactly solved; (4) PCG systems are stopped when the error (computed as 1cos(Qx, d), for some system Qx = d) is below some tolerance;  (5)  in the computational results of this paper the initial PCG tolerance was set to ǫ 0 = 10 -4 , and it was updated at each interior-point iteration t as ǫ t+1 = min{0.