dataesr/scientific-paragraphs-categorization huggingface dataset:
polynesiensis clones were also compared to evaluate the good agreement between these two detection methods. At the exponential growth phase, CBA-N2a data were in good agreement with those of LC-MS/MS. However, as a significant increase in toxin production with the age of the culture was observed only by LC-MS/MS (ANOVA analysis, p = 0. 0003; Table  4 ), there is somewhat more discrepancy between the CBA-N2a results and total concentrations by LC-MS/MS. However, toxicity equivalency factors (TEFs) of all analogs detected by LC-MS/MS are not known to date and thus the differences in total contents (typically in an order of 25% magnitude) should not be over-interpreted. Cell and culture media extracts of strains RIK7, NHA4, RAI-1, and RG92 were also tested for the presence of compounds classically found in Gambierdiscus spp.
For all experiments, absorbance values of OV -and OV + control wells were around 1. 2, corresponding to 100% viability. Absorbance data were fitted to a sigmoidal dose-response curve (variable slope) based on the four-parameter logistic model (4PL) allowing the calculation of EC 50 values (i. e. , the tested cell concentration inducing a loss of 50% of the higher absorbance value) in cells eq. µL -1 of dry extract using Prism v8. 1. 2 software (GraphPad, San Diego, CA, USA). The cell toxin content (Tx) for each tested clone was further estimated using the following formula: Tx (in pg P-CTX3C eq. cell -1 ) = (EC 50 of P-CTX3C/ EC 50 of sample) The limit of detection (LOD = (EC 80 of P-CTX3C/MCE Gambierdiscus)) and the limit of quantification (LOQ = (EC 50 of P-CTX3C/MCE Gambierdiscus)) of CBA-N2a for Gambierdiscus spp.
XRF maps collected from all beamlines were batch fitted using PyMCA. This software fits the fluorescent lines along with a background subtraction pixel by pixel  [45]. The fluorescent maps are shown as RGB images to allow clear visual co-locative representation of the metallic distribution throughout the tissue. Where the Co: Cr ratio map is shown in Fig. 6b , the XRF data was assessed semi-quantitatively. This was achieved by applying a matrix correction (based on a soft tissue composition) and considering excitation efficiencies calculated in PyMCAs inbuilt quantification algorithms  [45, 46]. The XANES map data were stacked, aligned and windowed at the emission energy of the element of interest (±200 eV). Alignment was conducted using MANTiS for the data collected at Diamond Light Source and inhouse scripts, based on Python and PyMCA tools, was utilized for the data collected at the ESRF  [47, 48].
Principle components (PCs) analysis and cluster analysis was undertaken on the XANES stacks. Before analysis each spectrum was normalized by dividing the intensity value by the last energy point. This is important as this eliminates PCs based on intensity rather than spectral differences. Clustered spectra were generated based on the PCs. The grouped spectra were normalized using software package ATHENA  [49]. The spectra were compared against standards and linear combination fitting was attempted to reproduce the experimental data with combinations of standards. Linear regression analysis was used to cross-correlate XANES spectra acquired using different measurement approaches including comparison of point spectra, mapping spectra and spectra acquired at different spatial resolutions.
6. 6 Funded sources O. A. was funded by the National Institute of Health Research Award NIHR/CS/010/001. This article presents independent research funded by NIHR. The views expressed are those of the author(s) and not necessarily NIHR. A. P. M was supported by a Diamond Light Source Studentship. Additional funding is acknowledged: Natural Sciences and Engineering Research Council of Canada (grant No. 2017-05862).
While the literature on the molecular responses of corals to hypo-osmotic stress is very limited, comprehensive datasets are available for some other marine invertebrates  [21, 22]. In mussels, for example, responses to hypo-osmotic stress include increases in levels of oxidative stress proteins and proteolysis, as well as changes in expression of membrane transporter proteins, although closely related species have been shown to respond differently  [23]. In the present study, the transcriptomic response of the common reef-building coral Acropora millepora to hypo-saline conditions was investigated. Through the availability of a whole genome assembly and a comprehensive set of protein predictions for this organism, it is now possible to compare the response of this coral to those of other marine invertebrates, to tease apart specific and general responses to different environmental stressors  [21, 24, 25].
RNAseq libraries (18 for the adults and 23 for the juveniles) were constructed using the NEB Next Ultra Directional RNA Library Prep Kit for Illumina (NEB, E7420S) following the manufacturers recommended protocol, and 100 bp paired-end sequence data obtained using a HiSeq 2000 at the Biomolecular Resource Facility (Australian National University). After trimming (70-80% of reads retained), reads were mapped onto the Acropora millepora genome (Ying et al. , in prep) using TopHat2  [31]  and counts data generated using htseq-count  [32]  for subsequent analysis. The mapping efficiency varied significantly, in general being much higher for larvae (> 80%) than for adults (45-60%) due to the presence of Symbiodiniaceae in the latter. Approximately 70% of mapped reads were counted as genes. Genes IDs in this paper refer to the A.
millepora protein predictions (e. g. 1. 2. 1. m1) that have been deposited in the Gene Expression Omnibus (GEO) under the reference number GSE96916. The read count matrix was analysed in R (R Core Team 2014) using the package arrayQualityMetrics  [33]  to check for outliers, and was transformed (variance stabilizing transformation (VST)) to be visualized using a principal component analysis (PCA). Although potential effects of pseudoreplication caused by using only one tank per condition could not be tested, note that this has also frequently also been the case in similar published gene expression studies  [34, 35]. The DESeq2 package  [36]  was used to test for differential gene expression due to the effects of salinity, while controlling for the effect of the genotype in the case of the adult dataset (design = ~genotype + treatment).
For the juvenile dataset, as each sample was a mixture of genotypes, genotype effects could not be considered (design = ~treatment). Default functions for estimating size factors, dispersion and negative binomial Wald Test were used in DESeq2. Log2 fold changes (log2FC) in gene expression levels were obtained in DESeq2 by comparing control (35 PSU) vs. salinity treatment of four different comparisons: (i) control vs. treatment at 1 h in the adults, (ii) control vs. treatment at 24 h in the adults, (iii) control vs. treatment at 24 h in the juveniles and, (iv) control vs. treatment at 48 h in the juveniles. False discovery rate (FDR) adjusted p values were controlled at 5% for each gene according to the methods of Benjamini and Hochberg  [37]. Statistically over-represented gene ontology (GO) categories were determined in BiNGO  [38]  in Cytoscape 3.
1. 1 [39]  using the hypergeometric test and a FDR significance level of < 0. 01 on the set of genes that were differentially up-or down-regulated in each dataset. These GO categories were used to search specific pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) by downloading pathway sequences (using Homo sapiens and Nematostella vectensis as references for the pathways: protein processing in endoplasmic reticulum, nve0414, hsa04141; proteasome, nve03050, hsa03050; peroxisome, nve04146, hsa04146; lysosome, nve04142, hsa04142; glycine, serine and threonine metabolism, nve00260, hsa00260; glutathione metabolism, nve00480, hsa00480; alanine, aspartate and glutamate metabolism, nve00250, hsa00250; ABC transporters, nve02010, hsa02010) and blasting these sequences against the A. millepora protein predictions.
The systems involved in ER protein processing and ER stress are indicated: glycosylation, ER associated degradation (ERAD), ubiquitin-proteasome system (UPS), and the unfolded protein response (UPR). A complete list of the genes involved in this pathway and log 2 FC values is provided as Additional file 4: Table  S3. Figure adapted from KEGG pathway database homologues of both of which were up-regulated after 1 and 24 h of hypo-saline stress. Hypo-saline stress also caused changes in the expression of genes involved in ammonia assimilation. The coral NADH-dependant glutamate dehydrogenase (GDH1), which catalyses the release of ammonia from glutamate, was up-regulated after 1 and 24 h of stress (log 2 FC of 0. 47 and 2. 54 respectively; FDR 1. 21E-02 and 4. 28E-55). Conversely, genes involved in ammonia assimilation -the NADPH-dependant GDH (GDH2), glutamine synthase (GS), and glutamate synthase (GOGAT) -were down-regulated (Fig.
Discussion Gene expression data revealed a strong response of the coral A. millepora to hypo-saline stress, with clear differences between acute salinity shock (1 h) and more prolonged (24 h) exposure in adult corals. Here we describe a group of genes that are part of a general response to stress in corals, and a second group that are known to respond to osmotic stress in other organisms but were not previously described in corals. The first group includes genes involved in antioxidant production, and in protein homeostasis (comprising molecular chaperones, components of the ER associated protein degradation (ERAD) and unfolded protein response (UPR) systems). The second group comprises genes involved in osmoregulation, including molecular transporters and amino acid metabolism, particularly glycine betaine.
Acknowledgements The authors thank  Victor Beltran  and the staff of the  National Sea Simulator (SeaSim)  at the  Australian Institute of Marine Science  for their support, and  Simon Davy  ( Victoria University, Wellington ) for permission to cite results from an unpublished thesis. The authors also gratefully acknowledge  Andrew Negri  and  Jan Tebben , for providing the coral larvae and extracts of crustose coralline algae, respectively, and assistance during the experiments. Finally,  Sylvain Fôret  was a major contributor to work reported here, and his untimely passing is not only a source of great sadness to many, but also represents a major loss to the marine genomics community.
Statistical Analyses Data were analyzed using an AMMI (additive main effects and multiplicative interaction) model, following Gauch  [14] , with the R package PPBstats. In the first step, an ANOVA model (called model 1) was run with population, farm, and year as main effects, block within farm and year as a nested effect, and all three second order interactions effects among population, farm, and year: Y ijkl = µ + α i +θ j +β l + (αθ) ij + (αβ) il + (θβ) jl +rep k θβ jl +ε ijkl ; ε ijkl ∼ N 0, σ 2 where Y ijkl is the phenotypic value for replication k, population i, farm j, and year l, µ is the general mean, α i is the effect of variety i, θ j is the effect of farm j, β l is the year l effect, (αθ) ij is the interaction effect of variety × farm, (αβ) il is the variety × year interaction effect, (θβ) jl is the farm × year interaction effect, rep k θβ jl is the effect of the replication k nested in farm j in year l, and ε ijk is the residuals.
It is generally described as a dynamic stability indicator  [16]. The PCA was also applied symmetrically on the variety × year interaction and the "temporal" ecovalence was calculated for each variety. The ANOVA was also run on the set of populations restricted to the PPB populations, i. e. , excluding the two commercial varieties Renan and Hendrix, in order to evaluate whether their particular variability did influence the relative importance of the different effects and interactions. This will be referred to as dataset b while the complete dataset will be referred to as a. Finally, the results of the statistical analyses were discussed among all the actors involved, i. e. , the farmers, facilitators, technicians, and researchers, in order to obtain a common understanding based on the academic knowledge and on the more experiential knowledge shared within the group.
Effects and Interactions in the ANOVA Results from the analysis of variance with model 1 on dataset a showed that for all traits measured on individual plants (i. e. , all traits except TKW, GY, and PC), all effects and interaction effects, except the block effect, were highly significant. This was also the case for TKW, GY, and PC, except for the interaction variety × year for GY (Table  S1 ). However, variance components strongly varied in magnitude according to the trait considered (Figure  1 ). First, it should be noted that for all traits except plant height (PH), thousand kernel weight (TKW), grain yield (GY), and protein content (PC), although most effects in the model were significant, the residual part of the variation remained quite high. This was due to the fact that the within population among plant variation was large for most populations due to both genetic and environmental heterogeneity.
In general, the variety × year contribution to variation was very low, while variety × farm was larger, in particular for TKW, GY, and PC, indicating that genetic variability among populations, although limited, was rather more specific to the farm than to the year, which makes it available for local selection. When removing the two commercial varieties (model 1 on dataset b, Figure  1 ), the relative contributions of the different effects and interactions to the overall variation changed drastically for PH and LLSD, with the variety effect much reduced due to the fact that the commercial varieties were much shorter than all PPB populations. For the other traits, there was, at most, a marginal decrease in the variety contribution. In addition, it can be noted that for PC, which was quite sensitive to the year effect, removing the two commercial varieties slightly reduced the year and year × variety contribution, indicating that the two commercial varieties might be even more sensitive to these effects.
local selection. When removing the two commercial varieties (model 1 on dataset b, Figure  1 ), the relative contributions of the different effects and interactions to the overall variation changed drastically for PH and LLSD, with the variety effect much reduced due to the fact that the commercial varieties were much shorter than all PPB populations. For the other traits, there was, at most, a marginal decrease in the variety contribution. In addition, it can be noted that for PC, which was quite sensitive to the year effect, removing the two commercial varieties slightly reduced the year and year × variety contribution, indicating that the two commercial varieties might be even more sensitive to these effects.
Sustainability 2019 , 15 Figure 4. 2019154 Figure 4. Matrix of variety × farm interaction terms estimated from model 1 on dataset a for (a) protein content, (b) grain yield, and matrix of variety × year interaction terms estimated from model 1 on dataset a for (c) protein content, (d) grain yield. Wricke's ecovalences are indicated in brackets nearby the variety name and the farm name.
The Wilcoxon rank-sum test was used for ordinal data and the Chi square or Fisher's exact test for categorical variables. For all tests, a two-tailed P value lower than or equal to 0. 05 was considered statistically significant. We further assessed how patients' characteristics were associated with cognitive impairment detected with at least one of the two tests both at baseline and 6-9 months after treatments. Analyses were performed with STATA, version 12 software (Stata Corp, College Station, TX) and Prism (GraphPad Software, La Jolla, CA).
DISCUSSION This study compared the MMSE and MoCA tests to identify cognitive impairment in older patients receiving cancer treatment. Before any oncological treatment, more cognitive impairment was detected with the MoCA than with the MMSE. Furthermore, 32 patients (48%) had impairment on the MoCA but a normal MMSE score whereas only one patient had impairment on the MMSE but a normal MoCA score. Similar findings were obtained after chemotherapy, with nine patients (33%) having impairment on the MoCA and a normal MMSE score. The results of this study are consistent with data from the literature. The mean MoCA score at baseline was 22. 3, which is lower than that observed in the normal aging population  (24, 37)  and in young adults with cancer  (26) (27) (28). However, the mean MoCA score in our population was higher than in subjects with mild cognitive impairment (MCI)  (24, 37).
ACKNOWLEDGEMENTS We wish to thank the patients who participated in this study and the teams in the participating centers. The Northwest Data Center ( CTD-CNO ) is acknowledged for managing the data. It is supported by grants from the  French National League Against Cancer (LNC)  and the  French National Cancer Institute (INCa). This study was supported by a grant from the  Cancéropole Nord-Ouest. We are also grateful to  Audrey Dugué  for her involvement in the study design.
Microstructural analyses For electron backscatter diffraction (EBSD) analyses, the uncoated thin sections were mounted with conductive carbon tape to reduce charging. Analyses were carried out at high vacuum (in the range of 10 -5 mbar) using a Zeiss EVO MA 15 SEM equipped with Oxford Instruments Nordlys Nano EBSD detector and AZtec v. 3. 1 data acquisition software (Oxford/HKL) at the Instituto Andaluz de Ciencias de la Tierra (IACT, Armilla, Granada, Spain). Instrumental settings were 17 kV acceleration voltage, 24 mm working distance, probe current of 0. 5-1. 0 nA, using Agar Scientific tungsten filament as a source of electrons. Acquisition conditions in the EBSD software module were 4 × 4 binning and   Brey and Köhler (1990). Dash symbols at SOU-003 wehrlite indicate that thermometers could not be applied in this sample; blank cells are xenoliths without EMPA data.
c BA, BA index of olivine; M Ol , M index of olivine; M Opx , M index of orthopyroxene; M Cpx , M index of clinopyroxene  (Skemer et al. , 2005). d Oriented thin section. low (0) gain with grid steps varied between 17 and 40 µm (depending on the grain size), covering most of the sample surface. All major constituent minerals of the rocks were included in the phase list, and the percentage of indexed points in the raw maps typically exceeded 75 %. In some xenoliths (samples DZ-005, GU-003, HAM-004, HAM-006, HAM-016), the interstitial adhesive used for impregnation has resulted in local charging effect of the thin section surface, reducing the EBSD indexing rate to 40 %-60 %. The obtained data, however, are representative of the entire thin section, and EBSD data from these samples have been included in the database.
Post-acquisition data treatment to clean raw maps was carried out following the procedure presented in  Soustelle et al. (2010) , using the built-in functions of the Oxford/HKL Channel 5 software package. T solv : clinopyroxene-orthopyroxene geothermometer (±16 • C), For further data processing and calculations -carried out on cleaned EBSD datasets -we used the builtin functions of the free MTEX 4. 5. 2 Matlab toolbox (https://mtex-toolbox. github. io, last access: February 2018;  Hielscher and Schaeben, 2008; Bachmann et al. , 2010). For the complete database of calculated parameters, see Table  S1. Grain boundaries are defined at 12 • minimum misorientation between neighboring pixels (segmentation angle); the correctness of grain definition has been checked in an optical microscope. Low misorientation boundaries in the range of 2-12 • are considered as subgrain boundaries.
Acknowledgements. We acknowledge the help of  Zoltán Konc  in the EMPA and EBSD data acquisition. We are grateful to  David Mainprice  ( Géosciences Montpellier, France ) for providing scripts and help in MTEX, and to  László Előd Aradi  ( Lithosphere Fluid Research Lab, Eötvös Loránd University of Budapest, Hungary ) for contributing to an earlier version of the EBSD database. We appreciate the help of  Yassamina Meddi  ( Université des Sciences et de la Technologie Houari Boumédiène, Algeria ) in providing documentation and illustration for the overview of geological context and sampling. We thank  Theodoros Ntaflos ,  Rachel E. Bernard  and  Vasileios Chatzaras  for the constructive ideas in their reviews, and we appreciate the editorial work of  Florian Fusseis. Károly Hidas acknowledges funding by  Ministerio de Economía y Competitividad (MINECO)  ( FPDI-2013-16253 ) and the  Agencia Estatal de Investigación  ( CGL2016-81085-R ).
Claudio Marchesi  acknowledges funding by  Ramón y Cajal Fellowship  ( RYC-2012-11314 ) by the  MINECO. Financial support. This research has been supported by the  Agencia Estatal de Investigación  (grant nos. CGL2016-75224-R ,  CGL2016-81085-R  and  CGL2015-67130-C2-1-R ), the  Junta de Andalucía  research groups  RNM-131  and  RNM-148 , and the  International Lithosphere Program  (grant no. CC4-MEDYNA ). Fellowships, research and infrastructure grants leading to this research.
| Patients A French series of 3,412 patients suffering from IPN has been analysed thanks to medical records and a clinical questionnaire, so as to identify patients presenting IPN and hearing loss. The 3,412 patients had been selected on clinical and inheritance criterion, and all have been genetically screened. Phenotypes were screened on the basis of clinical data and electroneuromyograms (ENMG) for IPN, and audiograms, OtoAcoustic Emissions (OAE), and Auditory Brainstem Responses (ABR) for hearing loss.
| Pathogenic variant detection Genomic DNA was extracted by standard methods (Illustra DNA Extraction kit BACC3, GEHC). For neuropathy screening, a Next Generation Sequencing (NGS) strategy was implemented using a 92-gene custom panel designed for CMT and associated neuropathies diagnosis (Table  S1 ). It included the 44 known CMT genes, 27 genes involved in HSN (Hereditary Sensitive Neuropathy) and HMN (Hereditary Motor Neuropathy) and 21 other genes of interest involved in neuropathies of differential diagnosis. The amplified library was prepared with Ion P1 HiQ Template OT2 200 kit (Ampliseq Custom [Life technologies]), sequenced on Proton sequencer (Life technologies), and mapped to the human reference sequence GHCh37. Variants were assessed with Alamut Mutation Interpretation Software (Interactive Biosoftware, Rouen, France).
Databases such as ExAC Genome browser (http://exac. broad insti tute. org), dbSPN135 (National Center for Biotechnology Information [NCBI], Bethesda, Maryland, USA, http://www. ncbi. nlm. nih. gov/ proje cts/SPN/), ClinVar (www. ncbi. nlm. nih. gov/clinvar), and HGMD (www. hgmd. cf. ac. uk) were also screened. In silico studies were performed thanks to Polyphen-2 (http://genet ics. bwh. harva rd. edu/pph2/), SIFT (https ://sift. bii. a-star. edu. sg), UMD-Predictor (http://umd-predi ctor. eu/), and Mutation Taster (http://www. mutat ionta ster. org/). Pathogenic variants of interest were verified by Sanger sequencing using forward and reverse primer pairs. Data have been submitted into a freely accessible public database, namely LOVD at https :// datab ases. lovd. nl/share d/genes/ DMD. For hearing loss screening, MLPA and Sanger sequencing for GJB2 and GJB6 were performed for all the deaf patients.
| Clinical description Among the series of 3,412 patients, we had the information in medical records and clinical questionnaire of hearing loss associated with IPN in only 44 patients (1. 30%). The clinical data of 27 patients, 15 women and 12 men, were usable for this study. The clinical description is presented in Table  2. The mean age was 60. 77 years (from 10 to 90). Demyelinating neuropathy was diagnosed in 15 cases and axonal neuropathy in 12 cases. Age at onset varied from 2 to 73 years. In case of early onset, both demyelinating and axonal forms were observed (Patients I, IV, VIII, XIII, XV, XVI, XVII, XXI, XXIII and XXVI). Hearing loss varied from mild to profound, could be progressive, and five cases of auditory neuropathy (AN) were related (Patients II, XII, XIV, XVIII and XXVI).