dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The three extensions of the LS-PLS and PCR-type approaches have been implemented in the R package lsplsGlm. A clinico-genomic model that can predict a binary outcome using dimensionality reduction methods would be a useful computing tool for integrating clinical and gene expression data. In general, the methods using only clinical data or only genomic data perform less well. We show that it is not always advisable to use the PCRtype method, which can lead to suboptimal results that depend on the data type and the number of selected features and therefore the relation between the response variable and the covariate structure. Indeed, in PCR, the principal components that are dropped correspond to the near-collinearities among the genetic data. PCR does not Fig. 4  Mean misclassification rates from the somatic CNA data set using the six methods considering different numbers of selected genes p red : 50, 100, 500 and 750.
In this study, the best rate of misclassification was 0. 2269 on average, while the worst was 0. 2981. In the study in  [15] , regarding methods based on PLS, the best rate of misclassification was 0. 30 on average, while the worst was 0. 43. Hence, the one-step approach using the two data sets simultaneously seems better than the two-step approach using the two data sets separately. A study by  [26]  on an extension of Integrative mixture of experts (ME) models for combining clinical and gene markers to improve cancer prognosis has been published. They illustrate the performance of the methodology on three cancer studies and, particularly, on CNS data sets. Even if the study using integrative ME cannot be considered as a dimensionality reduction approach, the authors first assess the classification performance on each separate data set, as in our study.
Conclusion Despite the great potential of clinico-genomic integration, the topic is still in its elaboration phase. In general, integrating heterogeneous data sets such as clinical and genomic data is an important issue. We have proposed three extensions of LS-PLS approaches for logistic regression models to analyze both clinical and genomic data. The advantage of using those methods for classification and their performances are shown and then used to analyze clinical and genomic data. The corresponding prediction results are encouraging and stable regardless of the data set and/or number of selected features. These extensions have been implemented in the R package lsplsGlm to enhance their use.
Background elements Persée was launched by the French Ministry of Higher Education and Research and Innovation (MESRI) in 2003 in order to develop a complete platform for the digitization, the documentation, the online publishing and the long-term preservation of back files of scientific journals in the field of humanities and social sciences. In 2005, the Persée portal ( www. persee. fr) opened with 7 journals. Currently, 184 partnerships exist with academic publishers and 740. 000 full texts documents and 570. 000 illustrations are available in open access. The original purpose of Persée was expanded according two directions: a disciplinary openness (earth and life sciences in addition to HSS) and the offer of a new range of services. Persée provides software tools, its digital platform and expertise to the researchers and, leads some projects in the field of the Digital Humanities and Open Data.
Technical actions Persée relies on open standards, open source technologies, open access and open data in order to warranty data storage, retrieval and preservation. Considering that "technical sustainability is directly related to the standards and best practices followed when creating the digital files" (Fyffe and Warner, 2005), Persée developed an integrated open source software named jGalith. It manages every step of the process to convert printed documents to electronic format: from digitization, through documentation, quality control, XML schemas compliance check, to online publishing and long term preservation. Printed documents are collected from publishers then digitized in high quality resolution (400 dpi, bitonal and grayscale), post-processed (OCR, image quality improvement), encoded (toc, author, abstract, keywords, illustrations, footnotes, annexes.
• Research grants Persée applies to research grants from national and European sources, with overheads to fund core services. In Search of a Sustainable Model for Digital Heritage Repositories: A Case Study ELPUB 2018 • White-label development With the Perseids, Persée provides a customized service for the research laboratories and the heritage institutions. Each Perseid is powered by Persée (hardware, software and expertise) and, it is tailored and branded for a specific need and entity. The researchers pay one-off fee for subscription and includes maintenance and update costs. 17 In addition, Persée looks at another possibility with the fundraising on a voluntary basis including crowdsourcing.
MM is preceded by a condition referred to as monoclonal gammopathy of undetermined significance (MGUS). Evidence for the familial clustering of MM is consistently replicated  [18] [19] [20] [21] , as is its clustering with MGUS  [22] [23] [24] [25]. Genetic pedigree studies in MM are scarce as it remains a challenge to acquire samples in pedigrees due to rarity and low survival rates. The Utah MM HRPs are one of only a few pedigree the ethical oversight committee for the UPDB. As a result, access to these data requires review by the RGE committee (contact Jahn Barlow, jahn. barlow@utah. edu). Upon RGE approval, we will provide the genotypes and pedigree structure in a format ready to be used by the SGS software. Exome variants within the shared segments identified by the SGS analysis have been provided in a variant call format (VCF) file in the Supporting Information, along with an accompanying ID file describing phenotype and pedigree membership.
Sporadic myeloma cases and unaffected controls were obtained with approval from the database of Genotypes and Phenotypes (dbGaP). Exome sequences can be requested through dbGaP (https://www. ncbi. nlm. nih. gov/gap) with the following accessions: phs000348. v2. p1, phs000748. v4. p3, phs000209. v13. p3, phs000276. v2. p1, phs000179. v5. p2, phs000298. v3. p2, phs000424. v6. p1, phs000653. v2. p1, phs000687. v1. p1, phs000814. v1. p1, and phs000806. v1. p1. Funding: Research reported in this publication was supported by funding from the Utah Genome Project, http://healthsciences. utah. edu/utahgenome-project/, (NJC); Utah Hematology Disease Oriented Team, https://healthcare. utah. edu/ huntsmancancerinstitute/research/diseaseoriented-research-teams/hematologicmalignancies-dot. php, (NJC); Leukemia and Lymphoma Society, https://www.
Exome follow-up of shared segments in HRPs Whole-exome sequencing (WES) data was interrogated, targeted to the identified SGS region, to identify potential risk variants in the pedigree sharers in the HRP and in a broader set of 44 pedigrees. WES data was available for: 28 cases from the 11 extended Utah HRPs; and 126 exomes from 44 densely clustered MM/MGUS families from Mayo Clinic Rochester, Weill Cornell, Memorial Sloan Kettering Cancer Center, International Agency for Research on Cancer, and INSERM France (S2 Table ). Prioritization was used to identify variants that were: in the target segment; rare (alternate allele frequency, AAF<0. 001 in the non-Finnish, European, gnomAD individuals), potentially deleterious (variant impact predicted to be high or moderate); and observed recurrently in the appropriate segment sharers (if observed in the segment discovery pedigree).
At 6q16, no rare, potentially deleterious coding risk variants were shared by the 3 UT-571744 MM cases in the 1. 8 Mb genome-wide significant segment, indicating non-coding regulatory variants may be responsible for MM risk in this pedigree. However, two, rare coding and potentially deleterious single nucleotide variants (SNVs) were identified in two MM/ MGUS families (Fig 2C  -2E  and Table  2 ). Both SNVs are in the hydrolase domain of USP45: a stop gain (p. Gln691 Ã ) shared by 3 sibling cases (1 MM and 2 MGUS) in an INSERM family (PET-Nice 0909) and a missense SNV (p. Gln621Glu) shared by 2 siblings (1 MM and 1 MGUS) but not their 2 screened unaffected siblings in Mayo family 485. Coverage of these positions in ExAC sequence data is high (> 99% of the 60,706 ExAC samples had at least 10x read coverage) and neither variant was observed.
Our proposed method is a new analytic tool with the potential to reinvigorate the use of extended HRPs in the identification of risk variants that contribute to common, complex disease. Multiple myeloma is a malignancy of the plasma cells that has been shown to be familial  [43]. Consistent with a role for genetics, case-control studies have been successful in identifying association signals for 17 low-risk variants  [44] [45] [46] [47] [48]. However, despite consistent evidence for familial clustering, our study is the first to explore high-risk MM pedigrees. Using the unique genealogical database available in Utah, we identified and studied extended MM HRPs. We identified a genome-wide significant segment containing USP45, an important regulator of DNA repair (Fig  2  and Table 2 ), and a genome-wide suggestive segment harboring other genes in the GG-NER incision complex (ERCC1 and ERCC2).
SGS analysis in Utah, Myeloma HRPs HRPs and genotyping. All participants were studied with informed consent under protocols approved by the University of Utah IRB. Using the statewide Utah Cancer Registry (UCR), all living individuals with MM in Utah were invited to participate and peripheral blood was collected for DNA extraction. Participants were linked in the Utah Population Database (UPDB), a unique resource that integrates UCR records with a 5M person genealogy. HRPs were defined as pedigrees containing statistical excess of MM (p<0. 05), based on sex and cohort-specific rates in Utah. Eleven of the HRPs identified in the UPDB contained 3 or 4 MM cases with DNA (total MM cases per pedigree ranged from 4 to 37) with 8 to 23 meioses between studied MM cases. DNA from the 28 cases was genotyped on the Illumina Omni Express high-density SNP array.
Quality control. Only bi-allelic SNPs were considered. Genotypes and individual callrates were used to ensure high quality data. PLINK was used to remove SNPs with < 95% call rate across individuals  [55]. The final SNP set contained 678,447 single nucleotide variants. After SNP removal for low call rates, individuals were removed based on < 90% call rate across the genome, or if they failed the PLINK sex check. One MM case was removed. The QC'ed SNP data were transformed to match strand orientation of the 1000Genomes. PLINK relationship estimates were assessed against pedigree structure from the UPDB to identify any potential issues with pedigree structure. None were found. Probability of sharing a segment. SGS analysis identifies contiguous SNPs that are shared identical-by-state (IBS) by cases in a HRP and assigns an empirical probability of chance ancestral sharing  [26].
Targeted sequencing Participants. WES data were interrogated in the regions defined by the shared segments of interest. WES data was available on 964 controls  [30]   Joint calling analysis. To perform joint calling of all of the exome sequences, we utilized the calling pipeline developed at the Icahn School of Medicine at Mt. Sinai, based on GATK Best Practices  [60]. Briefly, FASTQ files were aligned to genome build 37 using bwa version 0. 7. 8, indels were realigned using GATK, duplicates were removed using Picard MarkDuplicates, and base quality scores were recalibrated using GATK. HaplotypeCaller was then used to generate individual GVCF files for each individual, and GenotypeGVCFs was used to generate the final joint calling. The jointly-called VCF was annotated with SNPEff and loaded into a GEMINI (GEnome MINIng) database for ease of querying  [61].
In particular, for our most significant chromosome 6 SGS finding (μ = 0. 042 and G = 9) the overall combined study-wide rate is π = 0. 0026 (11×0. 042×0. 00062×9). Burden testing. Based on the candidate genes generated from the pedigree findings (USP45 and ARID1A), burden testing was performed on jointly called and processed WES from 1,063 MM/MGUS cases and 964 unaffected controls for the 23 genes in the GG-NER incision complex (including USP45) and 15 genes in the SWI/SNF chromatin remodeling complex. The GEMINI software  [61]  was used to perform a c-alpha test  [62]  with 1,000 permutations. Only variants with AAF < 0. 05 and high or moderate predicted impact were included in the analysis. the Utah Population Database and the Utah Cancer Registry. We thank the participants and their families who make this research possible.
SOA formation from polycyclic aromatic hydrocarbons and isoprene oxidation has thus not been studied here. The GECKO-A tool is a computer programme that automatically writes chemical schemes on the basis of a prescribed protocol. Elementary data are taken from laboratory studies if available and structure/property relationships if not. The protocol implemented in GECKO-A is described by  Aumont et al. (2005) , with chemistry updates for gasphase oxidation performed by  Valorso et al. (2011)  and  Aumont et al. (2012 Aumont et al. ( , 2013)). The evolution of alkoxy radicals through decomposition and isomerization is estimated using the structure-property relationship developed by  Vereecken et al. (2009 Vereecken et al. ( , 2010)) , as described by  La et al. (2016). In the current version, GECKO-A generates explicit chemical schemes for non-aromatic species and uses the Master Chemical Mechanism (MCM, version 3.
Development and evaluation of the VBS-GECKO parameterization 4. 1 Structure of the parameterization A VBS-type parameterization (e. g. Donahue et al. , 2006)  was developed on the basis of the explicit GECKO-A simulations. The parameterization, called VBS-GECKO, lumps the secondary organic compounds (SOCs) into bins of saturation vapour pressures at 298 K (P sat 298 K ) and considers the following: V. Lannuque et al. : The VBS-GECKO parameterization for SOA formation -The formation of n volatility bins (VB k,i with i varying from 1 to n) from the gas-phase oxidation for each precursor k (precu k ). The VB k,i values are considered to be formed from OH oxidation for all the precursors and also from O 3 and NO 3 oxidation for alkenes and terpenes. The reaction rate constants for the oxidation of a given precursor k (k precuk+OH , k precuk+O 3 and k precuk+NO 3 ) are taken from the GECKO-A database (see Table  S1 ).
The RMSE was calculated on the entire duration of a set of reference simulations as RMSE = n i=1      n time j =1 m RED i,j -m i,j 2 n time      n , (3) where m REF i,j and m RED i,j are the mass of the volatility bin i at the time step number j simulated with the GECKO-A and the VBS-GECKO, respectively, n time the number of time steps over the reference simulations and n the total number of bins. The optimization was run following a bound optimization by quadratic approximation (BObyQA) method (Powell, M. , 2009) under the R software (R Core Team, 2017). The fitted stoichiometric coefficients were bounded between 0 and 1, and the photolysis factors between 0 and 100. The iterative process to minimize the RMSE stops when none of the parameters vary more than 0. 2 % between two successive iterations.
Evaluation scenario Results of the GECKO-A simulations performed in the previous sections were used as a training dataset to fit the coefficients of the parameterization. Here, new simulations are conducted with GECKO-A to produce a validation dataset to assess VBS-GECKO. New box model scenarios were therefore designed for that purpose. These scenarios use a mixture of precursors and environmental conditions not encountered during the training phase, including variations of environmental parameters with time (C oa , temperature, photolysis and NO x levels). Scenarios were run for two constant C OA values (2 and 20 µg m -3 ), two types of meteorological conditions for temperature, relative humidity and zenith angle (a "summer" and a "winter" case, called hereafter SUM and WIN, respectively) and two NO x conditions (a "high NO x " and a "low NO x " case, called hereafter HNOx and LNOx, respectively).
, 2016). For the first time in this study, a reduction protocol has been developed and evaluated to optimize SOA parameterizations from explicit simulations for a large number of precursor species and under various environmental conditions. The optimization procedure has been automated to facilitate the update of the VBS-GECKO on the basis of the future GECKO-A versions, its extension to other precursors and/or its modification to carry additional information (e. g. optical properties, hygroscopicity, N/C ratio). The implementation and the evaluation of VBS-GECKO in a 3-D model are the purpose of a companion paper. Data availability. The explicit GECKO-A dataset used to optimize the VBS-GECKO parameterization has been made available on Zenodo: https://zenodo. org/record/1402601/ (last access: 5 September 2018).
Acknowledgements. This work was financially supported by the  French Environment and Energy Management Agency (ADEME)  and  INERIS. We also thank  French Ministry of Ecology  for its financial support. This publication was developed under assistance agreement no. 83587701-0  awarded by the  U. S. Environmental Protection Agency  to  A. Hodzic. It has not been formally reviewed by  EPA. The views expressed in this document are solely those of authors and do not necessarily reflect those of the Agency. Edited by: Markus Petters Reviewed by: two anonymous referees.
The same steps as previously described were followed to define the CTV, ITV and PTV. A total dose of 66 Gy (2. 75 Gy per day) was prescribed for PTV_1 alone (high-risk level). No low-risk level was used. The prescribed dose was delivered in 24 fractions, 5 days a week, over 4 weeks. This schedule was introduced in February 2014, in patients unfit for chemotherapy (concomitant or sequential). Data on the dose constraints associated with the two schedules are shown Table  2. For each patient, Pinnacle v9 treatment planning system (Philips Medical Systems, Best, Netherlands) was used to create a treatment plan comprising five modulated 6 MV photon beams from an Elekta Synergy linear accelerator (Elekta, Crawley, the UK). Treatment delivery was performed using a step-and-shoot IMRT technique. Direct Machine Planning Optimization (DMPO) inverse planning algorithm (Philips Medical Systems, Best, Netherlands) was used to optimize the beams' fluence.
Data collection and statistics During treatment, a weekly physical examination was performed. Chest radiography was performed 6 weeks after treatment completion. Tumour response was evaluated using a 18F-FDG-PET/CT scan at 3 months after the end of RT. In case of partial response, a second 18-FDG-PET/ CT was performed 3 months later. In the case of complete response, the follow-up comprised a CT scan of the chest and abdomen every 3 months. Toxicities were assessed in terms of duration and severity. Acute oesophageal toxicity was recorded as the maximum severity of acute oesophagitis and acute lung toxicity as the first recorded treatment-related pneumonitis experienced. Toxicity was scored according to the Common Terminology Criteria for Adverse Events (CTCAE) 4. 0. Loco-regional recurrence was defined as the progression of the target lesion or the appearance of a new lesion in the previous irradiated volume.
LRC was calculated from the first day of RT to the date of the first loco-regional recurrence. Overall survival (OS) was calculated from the first day of RT to the date of death from any cause. Patients alive at the time of analysis were censored at the date of the last follow-up. Follow-up was calculated using reverse Kaplan-Meier estimation  [13]. LRC and OS estimations were computed using the Kaplan-Meier method, and a two-sided log-rank test was used to compare groups. The association of the pre-treatment parameters with LRC and OS was assessed using Cox analyses. A Chi-square test was used to compare the toxicity rates of the schedules. All analyses were performed using R software 3. 2. 4 (R Development CoreTeam; http://www. r-project. org). The database was locked on October 1, 2017.
Results We recently showed that in the absence of external constraints (no task, no reward), CA1 activity self-organizes into sequences of neuronal activation ('run sequences') representing distance or duration during spontaneous run episodes in head-fixed mice running on a nonmotorized, uncued treadmill  [5]. After expanding this dataset with additional calcium imaging experiments, we further analyzed the spatio-temporal representation in run sequences from 34 imaging sessions (n = 7 mice). In these experiments, mice were head-fixed under the objective of a microscope. Expression of calcium indicator GCaMP5, 6m or 6f was virally induced and a surgery was performed to provide a chronic optical access to the pyramidal cell layer of dorsal CA1  [5] ,  [21]. During 20 to 30 minutes imaging sessions, 140+/-47 neurons were detected as being active within the 400x400µm² field-of-view, 39+/-11% of them were activated in run sequences (Fig 1A&B, 28+/-14 run sequences per imaging session).
Acknowledgements This project has received funding from the  ERC  under the  FP7  and  H2020  programs (grants # 242842  and  646925 ), the  DFG  (#  RE 3657/1-1 ), the  WHRI Cofund  ( PCOFUND-GA-2013-608765 ) (S. R. ) and the A*MIDEX grant (No. ANR-11-IDEX-0001-02 ) funded by the " Programme Investissements d'Avenir " (D. A. -G. and A. T. ).
10186 G > A mutation in exon 26 of the APOB gene, which led to the substitution p. Ala3396Thr as previously described  19. This mutation was then confirmed by Sanger sequencing. It segregated with the disease in the family with a penetrance of 100% and with no phenocopy. It was not reported in the different databases: Exome Variant Server (evs. gs. washington. edu/EVS/), dbSNP (ncbi. nlm. nih. gov/SNP/), and gnomAD browser (gnomad. broadinstitute. org/). The in-silico bioinformatics tools predicted that it is disease causing, and showed a high conservation of the alanine at position 3396 among different species (Fig. 1B ). Furthermore, exome sequencing analysis showed in patient I. 1 a variation in exon 2 of PCSK9: c. 286 C > T leading to the p. Arg96Cys substitution. This variant was confirmed by Sanger sequencing.
Only one case of incomplete penetrance was detected over the eleven studied members of the family for whom DNA were available: individual III. 8 who carries the variation but does not present high lipid levels as depicted in Fig. 3. This variant is not reported in the databases. The arginine residue at position 50 is a conserved amino acid in different species (Fig. 3B ). The p. Arg50Gln variant is predicted to be damaging by bioinformatics tools such as Sift, mutation taster and Polyphen-2 as shown in Fig. 3C. Subsequently, we analyzed by Sanger sequencing the DNA of 127 French probands with hypercholesterolemia in whom we excluded the known mutations in LDLR, APOE and PCSK9 genes and fragments of exons 26 and 29 of APOB, to look for the p. Arg50Gln variation in the APOB gene. None of those probands carried this variation.
Sanger Sequencing and MLPA. In all subjects, genes were studied sequentially: first, the p. Arg3527Gln mutation of the APOB (NM_000384. 2) was looked for as previously described  42  and then regions 3475-3635 and 4363-4460 of APOB were analyzed by Sanger sequencing. The promoters and the 18 exons of LDLR (NM_000527. 4), as well as close flanking intronic sequences were amplified and sequenced. If no mutation was found, the search for a deletion/duplication of one or several LDLR' exon(s) was performed with SALSA MLPA kit (P062) and data were analyzed with Coffalyser software (MRC-Holland). Finally, if no deletion/duplication was discovered, the 12 exons of PCSK9 (NM_174936. 3) and the 4 exons of APOE (NM_000041. 3) as well as the flanking intronic regions were sequenced. Primer sequences and annealing temperatures are available upon request.