dataesr/scientific-paragraphs-categorization huggingface dataset:
S4 ). Only the year 2007 is remarkable in both the data (low reconstructed SMB) and the model. www. the-cryosphere. net/13/1297/2019/ The Cryosphere, 13, 1297-1324, 2019 We thus explored the model more deeply. The highest dexcess value was simulated 7 May (Table  S8 ). When comparing from 6 to 8 May 2007, with daily averages over the period 1979-2014, the model simulates similar near-surface temperature but particularly low precipitation and wind components (zonal and meridional). Despite the small precipitation amount, the daily isotopic anomaly is sufficiently large to drive the annual anomaly (Fig. S5 ). The d-excess values higher than 30 ‰ (threshold chosen as it corresponds to the maximum d-excess mean + standard deviation simulated by ECHAM5-wiso over Antarctica at the monthly scale; see Fig.
SMB The estimated SMB of East Antarctica does not show a clear trend since 1900  (Favier et al. , 2017). Recent studies  (Altnau et al. , 2015; Ekaykin et al. , 2017; Vega et al. , 2016)  report negative SMB trends in coastal areas contrary to positive trends for the plateau. In particular,  Thomas et al. (2017)  report an unprecedented negative trend observed in Victoria Land for the last 50 years. For our study period (17 years for the TA record and the ECHAM5-wiso simulation), we observe no significant trend. In Adélie Land, a quality controlled SMB dataset has been developed  (Favier et al. , 2013) , but the drivers of SMB spatio-temporal variability remain unexplored  (Favier et al. , 2017). This is related to the challenges in monitoring (1) precipitation in windy areas, (2) sublimation of precipitating snowflakes  (Grazioli et al.
The second source of uncertainty lies in the mismatch between inter-annual variations from coastal Adélie Land meteorological observations and the TA records, with ECHAM5wiso outputs. For instance, we only see high correlation for the surface air temperature inter-annual variations for winter and weak correlation for wind speed in spring and summer. These findings suggest limitations in the skills of either atmospheric reanalyses or the ECHAM5-wiso model to correctly capture the processes responsible for local climate variability. We had previously reported the capability of ECHAM5wiso to correctly simulate observed large-scale features of water stable isotopes and SMB across Antarctica, for spatiotemporal patterns identified from datasets spanning the last decades such as mean values, amplitudes and phases of mean seasonal cycles, amplitude of inter-annual variance, strength of isotope-temperature relationships, and d-excess versus δ 18 O relationships)  (Goursaud et al.
This condition allows us to avoid the erosion of seasonal or annual layers, which would lead to removal of the annual cycle of the recorded signal. For this reason, obtaining long-term observations on distributed stake networks around a drilling site or ground-penetrating radar data is crucial to accurately select a drilling site, by retrieving the location of mesoscale accumulation maxima and by rejecting zones with potential erosion. Using an updated database of Antarctic surface snow isotopes, we showed that not only δ 18 O TA but also d-excess TA mean values are in line with the range of coastal values in other locations. Neither in the TA-DDU dataset nor in the ECHAM5-wiso output do we see any significant relationship between interannual variations in δ 18 O and local surface air temperature.
The d-excess versus δ 18 O linear relationship of data from our database provided with d-excess over the whole time series (left side of the table) and over annual averages (inter-annual scale, right side of the table): slope (‰ ‰ -1 ), correlation coefficient (r), p value (p val), and standard error of the slope (SE, ‰ ‰ -1 ). Cells in bold show a significant relationship (p < 0. 05) and the cell in italic is to be taken with caution (see DDU line, p < 0. 10). Inter-annual relationships could not be computed for undated data (D15 and OH ice cores) or for precipitation data monitored for too short of a time and thus appear as empty cells.
Acknowledgements. This study has been supported by the  ASUMA  project supported by the  ANR (Agence Nationale de la Recherche , project no. ANR-14-CE01-0001 ), which funded the PhD grant of  Sentia Goursaud  and the publication costs of this paper. The authors also acknowledge the support from  Institut Paul-Emile Victor  ( IPEV ) for the surface mass balance observatory in Antarctica (GLACIOCLIM-SAMBA). This project also received support from the  Investissements d'Avenir project EquipEX "Equipement d'Excellence "  CLIMCOR  ( ANR-11-EQPX-0009-CLIMCOR ). Review statement. This paper was edited by  Becky Alexander  and reviewed by  Elizabeth Thomas ,  Daniel Emanuelsson , and one anonymous referee.
Statistical analysis The sample size was estimated to validate the Labin electronic dynamometer for measuring maximal grip strength compared to the gold standard Jamar. More precisely, it was decided to include 30 healthy subjects to highlight a correlation coefficient greater than 0. 5 (expected value) for a two-sided type I error at 5% and a statistical power equal to 80% using Fisher's z test comparing a correlation value to a reference value (0 as the null hypothesis). Statistical analyses were performed using Stata software (version 13, StataCorp, College Station, US). Continuous data were expressed as mean and standard-deviation (SD) or median [interquartile range] depending on whether they were normally distributed. The Shapiro-Wilk test was used to confirm the Gaussian normality assumption.
2C  and Table  S2 , P < 0. 05). This known phenomenon  50  circumstantially suggests that Hsa21 trisomy might affect molecular pathways regulating global gene expression, such as canonical Wnt signalling. Given our findings in human hippocampi, as well as the known roles of Wnt signalling in several biological mechanisms likely to be important in DS, such as brain development and AD, we employed QIAGEN Ingenuity ® Pathway Analysis (IPA) 51 to search the DEX gene dataset for Wnt signalling abnormalities. Hsa21 genes were excluded from the analysis, in order to isolate secondary transcriptional effects mediated by trisomy, but not directly associated with overexpression of triplicated genes. In the Tc1 hippocampus, pathway analysis (Fig. 3B ) revealed a significant association with canonical Wnt signalling (P = 0.
We first probed the entire DYRK family of genes (DYRK1A/B, DYRK2-4) for association with the Wnt pathway (Fig. S3 ), employing the STRING protein interaction database, v10. 0  58. For all DYRK family members, a significant association was detected with the gene ontology (GO) and KEGG terms 'Wnt signalling pathway'. We then consulted a database of protein-protein interaction networks in DS, generated via Yeast Two-hybrid (YTH) screening. We searched the database for DYRK1A interactors with known functional relevance to Wnt signalling. In yeast, DYRK1A interacted with the Wnt inhibitor DKK3 at amino acids 589-763 (Fig. S4 ), a sequence reportedly important for its nuclear localisation  59. DYRK1A was also found to interact with Lipoprotein receptor-related protein (LRP) 1/1B/4, Wnt inhibitory factor 1 (WIF1), Wnt3 and α-catenin (Summarised in Fig.
Importantly, we consistently observed DYRK1A interactions with DVL1 and DKK3 via overexpression (Fig. 4A-C ) as well as endogenously (Fig. 4D ). The latter finding suggests the physiological occurrence of these protein interactions. To contextualise them further, we employed the Cytοscape software to build a network of potentially relevant DYRK1A-Wnt interactions and pathways. We combined the most reliable DYRK1A partners from YTH, coIP and literature data (Fig. 4E , red circles) into a single network enriched via STRING (Fig. 4F ). Interestingly, we found that some of the network nodes were altered in humans DS and Tc1 mouse hippocampi, as shown in our biochemical and transcriptomic studies (Fig. 4F , blue circles). Overall these data suggest DYRK1A might be involved in Wnt signalling protein interaction networks, and may thus be able to influence Wnt function.
Methods Study design. Sample size. Selected based on sample/tissue availability following standard procedures for each technique, in order to generate statistically significant results, see relevant sections of materials and methods. Data inclusion/exclusion criteria. All data collected for each experiment presented were included. Outliers. No outliers were excluded, all data are presented as box plots including minimum and maximum values. Replicates. For all mouse and human DS immunoblotting experiments, samples were quantified in duplicate and averaged, reported n for these experiments represent averaged duplicates. For example, n = 8 Tc1 hippocampal samples indicates analysis of 16 total samples (including 8 WT controls) measured twice (total of 32 values quantified) in separate gels and averaged.
For all cell-based experiments, a minimum of three independent repeats was performed, with variable sample sizes reported in the main text and legends. In this case, n represents all individual replicates added together. For further information on the above and all other analyses conducted see Supplementary Tables  S4  and 5  and relevant sections of materials and methods. Pre-specified hypotheses: 1. Wnt signalling dysregulation may be associated with gene expression and/or protein profiles of DS mouse models and humans. Statistical analysis and graphs. RNAseq data was analysed by Deseq. All P values reported were adjusted for false discovery rate (FDR). All IPA-generated P values reported for pathway, upstream regulator, and disease and function analyses were produced by Fisher's exact test and thresholded at P < 0.
05. STRING protein interaction networks for the DYRK family were analysed by STRING, correcting for FDR. All other statistical analyses were performed in GraphPad Prism 07a. For human and mouse biochemistry and qPCR reported P values were calculated by two-tailed Mann-Whitney U test, due to the non-Gaussian nature of the datasets. DYRK1A expression experiments and immunocytochemistry were also analysed with the same test. For all Luciferase experiments, the data were analysed by Kruskal-Wallis test with post-hoc correction for multiple comparisons by two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli, due to the non-Gaussian nature of the datasets. All P values there reported represent FDR-adjusted q values. For dose-response curve experiments (Fig. 5C ), treatment groups/doses were matched by individual experimental repeat and analysed by Friedman test with the same correction as above.
Primers and probes were designed employing Primer Express ® Software (Life Technology) set on TAQman qPCR design. Primers were specifically engineered to bridge exon-exon boundaries. Furthermore, each primer pair and probe was tested for specificity through the NCBI reverse ePCR web service (http://www. ncbi. nlm. nih. gov/projects/e-pcr/forward. cgi). FAM ™ and TAMRA ™ were employed as fluorophore and quencher, respectively. Upon delivery, primers were solubilised in ddH20 while probes were dissolved in qPCR Probe Dilution Buffer (10 mM Tris-HCl, pH 8, 1 mM EDTA) to a 100 μM stock concentration and stored at -20 °C according to manufacturer's instructions. qPCR and quantification. For experimental use, primer and probe stocks were diluted to 40 μΜ and 5 μM in ddH20 respectively. Furthermore, each 50 ng/μl cDNA preparation was diluted 1:10 following routine laboratory practice for moderately-to-highly expressed genes.
β-actin and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were employed as control reference genes. Primers and probes for both were labeled with VIC ® (Life Technology) and quenched by MGB. All reactions were incubated in TaqMan ® Gene Expression Master Mix (AmpliTaq Gold ® 55 DNA Polymerase (Ultra Pure), Uracil-DNA glycosylase, dNTPs (with dUTP), ROXTM Passive Reference, and optimised buffer components). Each experiment was performed in a standard 96-well plate format. For all experiments performed in this study, sample WT A was selected as standard. The original 50 ng/μl cDNA preparation was diluted 1:5 and then 1:2 serially up to 1:80. All samples and controls were loaded in technical triplicate. All experiments were performed employing a 7500 Fast Real-Time PCR System (Life Technologies) controlled by homonymous software.
pB27 and pB29 derive from the original pBTM116 vector  98  , and pP6 is based on the pGADGH plasmid  99. For the N-LexA-DYRK1A-C and the N-DYRK1A-LexA-C bait constructs, 65 million (6. 5-fold the complexity of the library) and 115 million (11. 5-fold the complexity of the library) clones were screened using a mating approach with YHGX13 (Y187 ade2-101::loxP-kanMX-loxP, matα) and L40ΔGal4 (mat a) yeast strains as previously described  100. 269 and 253 His+ colonies, respectively, were selected on a medium lacking tryptophan, leucine and histidine and 2 mM 3-AT for N-LexA-DYRK1A-C and 5 mM 3-AT for N-DYRK1A-LexA-C to maintain a strong selectivity and manage the slight autoactivation. The prey fragments of the positive clones were amplified by PCR and sequenced at their 5′ and 3′ junctions. The resulting sequences were used to identify the corresponding interacting proteins in the GenBank database (NCBI) using a fully automated procedure.
1 mg/ml poly-D lysine and grown into 6-well plates. Transfection was performed as above but employing 3:1 fuGene HD (Promega) as transfection reagent. Treatment was also performed as above. After 18 hours cells were fixed with 4% (w/v) paraformaldehyde (PFA) for 20 min at RT. Samples were then permeabilised for 30 min in 0. 5% (w/v) Triton-X 100, and blocked in 5% (w/v) FBS with 0. 05% (w/v) Triton-X 100. The DYRK1A (Abcam) rabbit polyclonal primary antibody was incubated as above at 1:500 dilution. Secondary anti-rabbit Alexa 488 was incubated as above. A 1:1000 DAPI (ThermoFisher Scientific) dilution in block buffer was incubated for 30 mins at RT to label the nucleus. Airyscan and confocal imaging. Airyscan imaging was performed using a Zeiss 880 outfitted with an Airyscan module. Data was collected using a 63 × 1.
4 NA objective and immersion oil optimised for 30 °C (Carl Zeiss). Colors were collected sequentially by frame to minimise crosstalk, and Airyscan processing was performed using the Airyscan module in the commercial ZEN software package (Carl Zeiss). Confocal imaging was performed using a Zeiss 710, also using a 63 × 1. 4 NA objective and immersion oil. Confocal imaging quantification. Confocal data collected from the Zeiss 710 was used to quantify the nuclear to cytoplasmic ratio of HA-DYRK1A under Wnt stimulation and control conditions. Bitplane (IMARIS) was used to create masks to the nucleus and entire cellular volume. Alexa 488 conjugated HA-DYRK1A fluorescence intensity was integrated across both masks. The nuclear signal was subsequently subtracted from the signal of the entire cellular volume, providing cytoplasmic HA-DYRK1A fluorescence.
Data is displayed as a ratio of nuclear to cytoplasmic fluorescence intensity. Western blot and luciferase quantification. Protein bands were quantified from non-processed, raw image files only, directly at the acquisition stage (Syngene GeneGnome system). Automatic background correction was applied to all values before normalising each protein of interest to its relevant β-actin loading control. For phospho/de-phosphoproteins analysed (active β catenin, pS9 GSK3β) values were normalised to total amounts of the relative protein, which were also normalised to β-actin. Values were further normalised to the mean of the control condition (WT for mice, euploid for humans, and empty vector control for cells) for each individual experimental repeat. For all luciferase experiments, TOP/FOPFLASH values quantified alone (Fig.
5A-D ) or as a ratio to renilla luciferase activity (Fig. 5E ) were normalised to the average control condition in each repeat. Data were then log 2 -transformed prior to analysis. This step was undertaken to more appropriately visualise lucid erase data, due to large fold changes in some cases. In Fig. 5C , the mean normalised signal of treated cells with LiCl +0. 1% DMSO was assumed as maximum response, and all other values expressed as percentages of the former. For IVIS experiments (Fig. 5D ), preset circular regions of interest (ROIs) were applied to each well, and background was automatically corrected for by subtracting from an average background ROI. These data were then analysed directly without log 2 -transformation, to more appropriately reflect depicted light emission quantified by IVIS.
Ingenuity pathway analysis. The Qiagen Ingenuity ® Pathway Analysis (IPA ® ) tool was employed to probe mouse and human RNAseq datasets for alterations in primary Wnt components and upstream regulators. For Fig. 3B -D, Tc1 RNAseq analysis was performed on a dataset of significantly altered genes at adjusted P < 0. 05. In all cases, IPA analysis parameters were left at default, searching only for direct relationships and for human, mouse and rat orthologs. For each dataset, pathway, upstream regulator and diseases/functions analysis were performed. Curated gene lists were produced by manually selecting all affected genes in the dataset indicated as primary Wnt components, or under regulation of β-catenin/Wnt3a. Curated lists of upstream regulators were generated by searching for Wnt-related proteins contained in the analysis output.
STRING and cytoscape network analysis. The protein interaction database and analysis tool STRING v10. 0 58 was employed to build the networks in Fig. 4E ,F. To achieve this goal, DYRK1A was placed as the hub of a basic network (Fig. 4E , red circles) comprising: (1) The Hybrigenics-reported DKK3 interaction; (2) Previously published interactions (solid black lines) between DYRK1A, p-120 catenin (CTNND1)  31  , and GSK3β 35 ; (C) Experimentally detected DKK3 and DVL1 interactions (dotted black lines). Components of the basic network in Fig. 4E  were used as starting nodes, selecting human as default organism. This initial network, or first shell, was enriched with the 10 best-scoring interactors. A further shell was then added, comprising the 10 best-scoring secondary interactors of the network.
(D) IPA Upstream analysis of the Tc1 dataset, revealing several canonical Wnt components as regulators of DEX genes (left panel). Predictive activation z-scores calculated by IPA for upstream regulators with sufficient overlap (right panel) plotting scores as a function of significance (-log 10 P). (E) Immunoblot analysis of male Tc1 mouse hippocampi (n = 8, 160 ± 28 days) compared to agematched littermates demonstrated enhanced levels of DYRK1A, as expected (red), with reduced canonical Wnt signalling activity (blue). Signal represents the ratio of Ser37/Thr41 dephosphorylated β-catenin to total amounts of β-catenin. (F) Quantitative real-time PCR (qPCR) of DKK3 and AXIN2, two DEX Wnt components in the Tc1 hippocampus, demonstrating significantly elevated DKK3 expression (red), and a decreased AXIN2 levels (blue).
One or more Hsa21-encoded protein may functionally regulate Wnt/β-catenin signalling, particularly: A. Interact with Wnt components B. Affect luciferase-reported Wnt signalling activity Hypotheses suggested after initiation of the data analyses: 1. The Wnt phenotypes observed in DS models and humans are likely to be mediated by Mmu16 Hsa21 orthologs. 2. Lack of APP expression in the Tc1 mouse indicates DYRK1A as a more likely Wnt modulator in our current system, and this gene was thus tested for pre-specified hypotheses 2A,B 3. Wnt signalling activation states may affect subcellular distribution of DYRK1A, Research subjects or units of investigation. Tc1 mouse hippocampal RNA; Tc1, Dp(17)1Yey and Dp(10)1Yey hippocampal samples, post-mortem hippocampal samples of DS patients; Hybrigenics-generated Y2H interaction database from the Inter-PP project (Jerome LeJeune Foundation); SH-SY5Y, HEK-293 and HeLa cell cultures.
Acknowledgements We are grateful to the  Jerome LeJeune foundation (Paris, France ) and Hybrigenics services for access to the  InterPP  project database (https://www. fondationlejeune. org/en/our-missions-and-actions/research/researchprogramme/). We thank  Dr. Jean-Maurice Delabar  ( French National Centre for Scientific Research, Paris, France ) for mediating the collaboration with the Jerome LeJeune foundation. We thank  Nneka Anyanwu  ( Francis Crick Institute, London, UK ) for the kind help with IPA analysis. We also thank  Oke Avwenagha  ( UCL Institute of Neurology, London, UK ) for providing access to postmortem human samples. This work was supported by  SG 's UCL Impact PhD studentship to K. H. and E. M. C. F. , the  MRC  grant  MR/M00676X/1  to K. H. , the  Wellcome Trust  grant  WT095010MA  to K.
2. 2 One or more Hsa21-encoded protein may functionally regulate Wnt/β-catenin signalling, particularly: A. Interact with Wnt components B. Affect luciferase-reported Wnt signalling activity Hypotheses suggested after initiation of the data analyses: 1. The Wnt phenotypes observed in DS models and humans are likely to be mediated by Mmu16 Hsa21 orthologs. 2. Lack of APP expression in the Tc1 mouse indicates DYRK1A as a more likely Wnt modulator in our current system, and this gene was thus tested for pre-specified hypotheses 2A,B 3. Wnt signalling activation states may affect subcellular distribution of DYRK1A, Research subjects or units of investigation. Tc1 mouse hippocampal RNA; Tc1, Dp(17)1Yey and Dp(10)1Yey hippocampal samples, post-mortem hippocampal samples of DS patients; Hybrigenics-generated Y2H interaction database from the Inter-PP project (Jerome LeJeune Foundation); SH-SY5Y, HEK-293 and HeLa cell cultures.
Data Collection and Analysis Data for each patient were sent to the study manager at Gustave Roussy (Villejuif, France) using questionnaires that summarized the main clinical, radiologic, surgical, and pathologic data; the chemotherapy administered; and information on the evolution of the tumor and survival. The histologic material received was to be sent for central review to Casablanca with an additional revision in Paris, France. Data were reviewed at the study data management unit, then recorded and analyzed using Epi-info software. We estimated survival using the Kaplan-Meier method. For study patients, the date of origin was the date of nephrectomy, as currently used when reporting on WT. For OS, death was considered. Living patients were censored at the time of last follow up. In a first analysis, events considered for event-free survival (EFS) were relapse or death.
Quality of Data Questionnaires on the initial clinical description and treatment were correctly completed and returned in all but two cases. Sixty-two percent of pathology and 90. 5% of surgical questionnaires were returned for entry into the database. Furthermore, despite repeated requests, written reports were often missing or noncontributive, which did not permit validation of the staging. Pathology slides were sent for review for only 40 of 209 children who underwent surgery and concerned 32 of the 169 patients included in the analyses.
1°C step every 5 seconds. All assays were performed in duplicates. For each run, a negative control (water) and two distinct positive controls (plasmids pPVP130 and pPVP131) were added, each in duplicates. To test the specificity of the HRM primers, sturgeon samples producing no signal by taqman PCR were assessed. Additionally, a European sample (14/117) positive for a variant of NV was tested  (Bigarré et al. , 2017). Similarities with the published partial sequences of other NCLDV were searched by aligning DNA sequences: WSIV (Genbank DQ897645), NV from Canada (JX155659), NV from Europe (KU301309) and Acipenser herpesvirus type 2 from USA (FJ815289). In a first step to evaluate the analytical sensitivity, serial 10-fold dilutions (10 8 to 1) of each pure solutions of plasmids were assayed in duplicates using PCR-HRM in one session.