dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene module identification and correlation with physiological data were performed employing the package WGCNA  23  in R. Co-regulated gene modules were identified using a stepwise network construction mode based on coexpression similarity, calculated from adjacency data elevated to soft thresholding power (β) that generates a tree with approximate scale-free topology (fit index = 94%), considering a minimum module size with 30 genes. Modules with highly similar expression profiles (correlation ≥75%) were merged by quantifying the coexpression similarity of entire modules using clustering analyses of the correlations between the Eigengenes in each module. 23 unctional and Relational Analyses of Differentially Expressed Genes (DEGs). Genes exhibiting significant differential expression were classified by gene ontology (GO) analyses and metabolic pathways, according to the Mapman v.
3. 6. 0 RC1 scheme. 24 ene ontology enrichment analyses were performed by a hypergeometric test with 0. 05 significance threshold after BH corrected FDR p-values using agriGO 25 and summarized based on measures of semantic similarity using REVIGO  26  in R. Detailed metabolite pathways and gene expression profiles were generated using the KEGGScape plugin 27 in Cytoscape 3. 4. Authority and hub scores of the nodes (genes) in each network were calculated as the principal eigenvector of t(A)*A and A*t(A), respectively, where A is the adjacency matrix of the graph, using the package igraph 28 in R. Gene Expression Validation by RT-qPCR. Microarray expression data were validated by RT-qPCR for 14 genes chosen based on their expression profile and putative biological function. Primers were designed based on the coding sequences (CDs) of Malus x domestica genome using the software Primer3Plus,  29  to generate amplicons ranging from 80 to 150 base pairs (Table  S3 ).
26 Axis coordinates on x and y represent semantic similarity in the two-dimensional space. Metabolism classification according to the Mapman 24 scheme for modules black, magenta, and red (E). Heatmap represents transcription ration between MCP and CS. ontology analysis tools agriGO 25 and REVIGO  26  (Figure  6 , Table  S2 ). The ontology classification of the genes to Biological Process showed significant enrichment in metabolic pathways associated with ripening, small molecule metabolic processes, lipid, localization and transport, and inositol catabolism (Figure  6A , Table  S2 ). Molecular function also revealed enrichment in catalytic, hydrolase, and oxidoreductase activity (Figure  6B , Table  S2 ). Significant cellular localization ontology was also found for membranes (Table  S2 ).
43 In vegetative Arabidopsis organs, genetic studies have demonstrated that ethylene mediated signaling induces the transcription of redox proteins in response to different abiotic stresses. 44, 45 Thus, taken together our results demonstrate that in apple, where ethylene has a prevalent role in controlling ripening, its suppression by application of the competitive inhibition allows derepression of the transcription of photosynthesis genes and of a subset of redox proteins. Differentially Expressed Genes Exhibit Five Distinct Regulatory Patterns. The expression patterns of the significant DEGs were classified in five distinct transcriptional programs, or co-regulated gene modules, using weighted gene correlation network analysis (WGCNA). 23 The characterization of the functional independence of biological parts and devices or their modularity is essential to the design and modeling steps of plant synthetic biology.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. jafc. 7b01425. Primer information for RT-qPCR expression validation (Table  S3 ), GO classification of the DEGs in biological process, cellular component, and molecular function (Figure  S1 ), REVIGO scatterplot of GO classification of the DEGs (Figure  S2 ), correlation between the modules eigengenes (ME) and the investigated quality traits (Figure  S3 ), gene significance and module membership for the modules with non-significant association to the quality traits (Figure  S4 ), heatmap representation of the gene network in co-regulated modules (Figure  S5 ), and validation of microarray gene expression profiles (Figure  S6 ) (PDF) Table  S1 , Gene Significance (GS) and Module Membership (MM) for the quality traits (XLSX) Gene ontology (GO) classification of the DEGs (XLSX) Table  S4 , hub and authority scores (XLSX).
Funding The work was supported by the  Conselho Nacional de Desenvolvimento Cienti ́fico e Tecnoloǵico (CNPq ,  306771/ 2014-4  and  441856/2014-4 ),  Empresa Brasileira de Pesquisa Agropecuaŕia (Embrapa ,  02. 13. 05. 014. 00. 00 ), Coordenacaõ de Aperfeicoamento de Pessoal de Ni ́vel Superior (CAPES/ Embrapa ,  15/2014 ), and  ComitéFrancais d'E valuation de la Coopeŕation Universitaire et Scientifique avec le Breśil (Capes-Cofecub ,  631/09 ) through scholarship and financial support.
Here, the detected intensity is an indicator for the non-ideal polarisation of the neutron beam and labelled as background. (c1) shows the actual measurement of 5400 s measuring time which was extended owing to lower  counting statistics with these settings while (c2) was scaled to the common measuring time of 1800 s. (d) the spectrum where both the resonator and the current-sheet spin-flipper are switched ononly the resonant neutrons have the 'right' polarisation at the analyser and would pass the analyser in the ideal case. As an indicator of the non-ideal polarisation the resonant neutron distribution sits of top of the background (c). Our monitor beam allows us to assess the stability of the neutron source and the experimental setup. For the data shown in Fig. 5  we find an average monitor count rate of 36,284(78) per 1800 s or 20.
The prepared TiO 2 nanotube layers were coated with Al 2 O 3 layers of different thicknesses using an ALD tool (thermal ALD, TFS 200, Beneq). Trimethylaluminum (TMA, Strem, elec. grade, 99. 999+%) and deionized water (18 MΩ) were used as aluminum and oxygen precursors, respectively. Under these deposition conditions, one ALD Al 2 O 3 growth cycle was defined by the following sequence: TMA pulse (100 ms)-N 2 purge (2 s)-H 2 O pulse (100 ms)-N 2 purge (3 s). All processes were carried out at a temperature of 200 °C, using N 2 (99. 9999%) as the carrier gas, at a flow rate of 400 sccm. Al The structure and morphology of the TiO 2 nanotube layers were characterized by a field-emission electron microscope (FE-SEM JEOL JSM 7500F). Dimensions of the nanotubes were measured and statistically evaluated using proprietary Nanomeasure software.
EC/DEC with 1 M LiPF 6 was used as the electrolyte. The cells were kept for 24 h at open circuit potential (OCP) to get stabilized. The AC impedance measurements were carried out using a VMP3 Biologic Potentiostat at OCP in a frequency range of 100 kHz to 10 mHz at amplitude of 10 mV. The obtained spectra were fitted by "Z-fit" in the EC-Lab software (Bio Logic). At the bottom parts, thin layers of Al 2 O 3 could be observed on the inside as well as outside of the nanotube walls. The thin layers were visible due to the different mass contrasts between TiO 2 and Al 2 O 3. In line with our previous work, the ALD Al 2 O 3 coatings on the TiO 2 nanotube layers were very uniform and homogenous. 26 t has to be noted that the double-walled structure that can be seen at the bottom parts of both the uncoated and coated nanotube layers is an intrinsic feature of the TiO 2 nanotube layers prepared in ethylene glycol-based electrolytes and is well known from the literature.
statistical analysis The normality of data distribution was assessed using the Kolmogorov-Smirnov test. When normally distributed, continuous data were compared using Student's t-test; otherwise, the Mann-Whitney U test was applied. Analysis of the discrete data was performed by Fisher's exact test. Statistical analyses were performed using SPSS (SPSS for windows release 17. 0, SPSS Inc. , Chicago, IL, USA). A P-value of ,0. 05 was considered statistically significant for single comparisons. All the reported P-values were two sided.
Among the patients initially improved by NIV during the first 48 h, 13. 8% of them exhibited a second episode of AHRF despite NIV was carried on. This late NIV failure scenario was associated with a dreadful prognosis. This result is in agreement with previously published data in obese  17  and COPD patients. 18 Our population includes frail malnourished individuals with severe chronic respiratory failure and many comorbidities who became even more weakened after the first episode of AHRF. 26 7] [28]  Another explanation lies on the refusal of many frail DNI patients to pursue NIV after several days of hospitalization. Depression and anxiety are common symptoms in hospitalized COPD patients, which are often overlooked in spite of their significant impact on mortality following acute exacerbation of COPD.
Communication The requirement for the scientific data downlink, resulted in the choice of X-band antenna, compatible with the Deep Space Antenna network. To avoid excessive reorientation for communication each of the spacecraft will carry three independent patch antennas to cover provide better coverage. The calculated link margin is 8. 77 dB based on 5 W transmission power. Furthermore, a set of redundant omnidirectional UHF antennas is installed on every satellite to provide communication capabilities during safe mode assuring that contact with the spacecraft can be established at all time.
Acknowledgements This work has been developed during the Alpbach Summer School 2015 and we would like to thank the  European Space Agency, Austrian Research Promotion Program (FFG)  and  International Space Science Institute (ISSI)  for their support. The work has been made possible through funds provided by the  FFG. We would also like to thank all the summer school tutors, especially  Christian Killow ,  Vitali Müller ,  Oliver Jennrich ,  Jose Sanjuán , and of course our tutor  Martin Gehler  for his support and help. Lastly, we would like to thank the anonymous reviewer for a great number of recommendations. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Patients The source population for the biomarker analyses included all patients enrolled in the two phase-3 trials evaluating canakinumab for SJIA (ClinicalTrials. gov, NCT00886769 (trial 1) and NCT00889863 (trial 2)) conducted by the members of the Pediatric Rheumatology International Trials Organization (PRINTO) and the Pediatric Rheumatology Collaborative Study Group (PRCSG)  [13]. All patients with available samples who received treatment with either canakinumab or placebo in trial 1 or who were treated with canakinumab in the open-label phase of trial 2 were selected for inclusion in the biomarker study. For selected biomarker analyses, donor samples from agematched, race-matched, and gender-matched healthy subjects were used as a comparator. Eligibility criteria and study designs for the original phase-3 trials have been previously described  [13].
Integrity and purity of RNA and cDNA was confirmed using the Agilent 2100 Bioanalyzer system (Agilent Technologies Inc. , Wilmington, DE, USA). Gene expression values were derived using Affymetrix MAS 5. 0 software and scaled to a trimmed mean value of 150. Expression values were stored in CEL files and were analyzed using Bioconductor packages or customized R-scripts  [21] [22] [23]. For group-wise comparison of transcript levels, robustly expressed transcripts were identified by filtering for probe sets with a median expression value of at least 64 in at least one of the groups (typically around 16,000 probe sets). The Wilcoxon test was applied with a threshold P value of ≤0. 05 to identify genes that were significantly differentially expressed at baseline in patients with SJIA relative to healthy controls.
To further examine the functional pathways affected by genes that were differentially expressed in baseline SJIA samples, we mapped SJIA-induced genes (≥1. 5-fold differential expression) to protein interaction networks as defined in the METACORE™ database/software. Network maps indicated a strong innate immune response, with widespread activation of inter-related pro-inflammatory signaling pathways. Figure  2  illustrates the interactions between key signaling pathways mediated by selected proinflammatory proteins, including IL-1β, IL-6, and the tolllike receptors TLR2 and TLR4.
Second, baseline gene transcription was evaluated using the single pre-treatment blood sample that was available for each patient. Given the dynamic nature of the disease with frequent spikes in fever, it is possible that the differences between patients with SJIA and healthy controls were attenuated or obscured and that the contrast in gene transcription profiles might be enhanced by integrating data from multiple pre-treatment measurements. This might also reduce the apparent heterogeneity observed in subgroups of patients who achieved a comparable clinical response to canakinumab. Finally, patients without fever and with a more prominent polyarticular disease course were excluded from enrollment in the original phase-3 clinical trials from which blood samples for the current study were obtained.
Acknowledgments We thank all remaining PRINTO and PRCSG participating centers that participated in the trial. Argentina:  Ruben Cuttica , MD; Austria: Wolfgang Emminger, MD; Belgium:  Bernard Lauwerys, MD ,  Carine Wouters, MD ,  Laurence Goffin, MD; Brazil: Flavio Sztajnbok, MD ,  Sebastiao Radominski, MD ,  Sheila Oliveira, MD; Canada :  Elie Haddad , MD; France:  Isabelle Kone-Paut, MD ,  Marine Desjonqueres, MD ,  Michel Fischbach , MD; Germany:  Angelika Thon, MD ,  Dirk Foell, MD ,  Elisabeth Weibarth-Riedel, MD ,  Gerd Horneff, MD ,  Ralph Trauzeddel, MD ,  Reinhard Berner, MD ,  Tilmann Kallinich , MD; Greece:  Maria Trachana , MD; Hungary: Tamás Constantin, MD; Israel:  Judith Barash, MD ,  Yackov Berkun, MD ,  Yosef Uziel , MD; Italy:  Fabrizia Corona, MD ,  Maria Alessio, MD ,  Rolando Cimaz, MD ,  Stefania Viola , MD; Norway:  Berit Flato, MD; Peru :  Manuel Ferrandiz , MD; Spain:  Inmaculada Calvo, MD ,  Jordi Anton, MD ,  Juan Carlos Robledillos, MD ,  Maria Luz Gamir, MD; Sweden: Bo Magnusson , MD; Switzerland:  Michael Hofer , MD; Turkey:  Erbil Unsal, MD ,  Muferet Erguven, MD ,  Seza Ozen , MD; United Kingdom:  Nick Wilkinson, MD ,  Alice Chieng, MD ,  Athimalaipet Ramanan, MD ,  Helen Foster, MD ,  Kiran Nistala , MD; USA:  Gloria Higgins, MD ,  Katherine Marzan, MD ,  Kenneth Schikler, MD ,  Paula Morris, MD.
A fundamental challenge for both sustainable fisheries and biodiversity protection in the Neotropics is the accurate determination of species identity. The biodiversity of the coastal sharks of Guyana is poorly understood, but these species are subject to both artisanal fishing as well as harvesting by industrialized offshore fleets. To determine what species of sharks are frequently caught and consumed along the coastline of Guyana, we used DNA barcoding to identify market specimens. We sequenced the mitochondrial co1 gene for 132 samples collected from six markets, and compared our sequences to those available in the Barcode of Life Database (BOLD) and GenBank. Nearly 30% of the total sample diversity was represented by two species of Hammerhead Sharks (Sphyrna mokarran and S. lewini), both listed as Endangered by the International Union for Conservation of Nature (IUCN).
DNA barcoding using universal primers for the cytochrome oxidase I mitochondrial gene (co1), allows for rapid and reproducible assessment of species identification, both in the field and in consumer markets. The success and accessibility of barcoding technology has led to a wealth of publicly-available sequences in the Barcode of Life Data System (BOLD;  Ratnasingham, Hebert, 2007). Several studies have used the BOLD database to identify seafood products, including shark fins  (Wong, Hammer, 2008; Holmes et al. , 2009; Fields et al. , 2015). In Guyana, sharks caught in offshore driftnet fisheries are landed in markets without their heads (Fig. 1 ), leaving the relative placement and size of the fins as the key features for species identification. In order to manage coastal resources more efficiently and sustainably, a catalogue of coastal fish biodiversity is needed as a foundation.
1) were used to sanger-sequence the amplified co1 fragment at the Centre for Applied Genomics (TCAG, Toronto, Canada). Alignment and analysis. Sequence chromatograms were edited in Geneious v6  (Kearse et al. , 2012)  for the 132 samples that were successfully sequenced. Two approaches were used to determine species identity from this project's pool of sequences. First, the Barcode of Life Data System (BOLD) Batch ID engine was used to determine the species identity for each sample, individually  (Ward et al. , 2008)  as BOLD is the largest repository of co1 barcode sequences. Second, GenBank's Basic Local Alignment Search Tool (BLAST) was used to examine similarities between each of the samples and the sequences databased in GenBank. The use of both GenBank and BOLD improved species identification metrics by maximizing the amount of co1 sequences for comparison.
Both BOLD and GenBank BLAST search results display sequences which most closely match the input query, in order of highest similarity. In cases where samples matched to more than one species, rather than consider the top sequence hits to the samples, the three species with highest similarity matches out of the first 100 matches were recorded (S1 -Available only as online supplementary file accessed with the online version of the article at http://www. scielo. br/ni). BOLD includes both published sequence data and data from 'private' sources, for which there are no publicly available information collection location or collection method. Private matches were avoided in tabulated results unless no public matches were found, as the lack of publicly-available data on these specimens makes them difficult to validate.
To visualize where Guyana shark samples fell within an all-carcharhiniform gene tree, all 1691 carcharhiniform co1 sequences were downloaded from GenBank using PhyLoTA  (Sanderson et al. , 2008) , aligned to the sample sequences using the MUSCLE algorithm  (Edgar, 2004)  in Geneious, and then clustered using maximum likelihood in RaxML v8  (Stamatakis, 2006)  under the GTR+I+G model of evolution, with 100 thorough bootstrap replicates. The suitability of this GTR+I+G substitution model was selected over 56 other models using PartitionFinder  (Lanfear et al. , 2012). The phylogeny was rooted to a clade of Hemigaleidae + Triakidae species, following the molecular phylogenetic relationships reported by Vélez-Zuazo, Agnarsson (2011, S2 -Available only as online supplementary file accessed with the online version of the article at http://www.
Results Despite the poor quality of the market samples, co1 fragments from 132 out of 144 samples (92% of the total sample size) were successfully amplified and analyzed. Samples which were unable to be amplified likely experienced DNA degradation because of intense sunlight and prolonged heat exposure. Attempts were made to amplify these samples with a variety of primers, but consistent failure implicates poor DNA quality, rather than poor primer complementarity. Identification with BOLD and BLAST. All thirteen species of sharks found in Guyanese fish markets were carcharhiniforms, belonging to the families Sphyrnidae (Hammerhead Sharks) and Carcharhinidae (Requiem Sharks) (Tab. In general, high percentage similarity (98-100%) was found between the sample sequences and the matches from BOLD and Genbank (S1-Available only as online supplementary file accessed with the online version of the article at http://www.
scielo. br/ni). Results from GenBank tended to have lower similarity of queried samples to database sequences (92-99% similarity) compared to BOLD results (S1). The BOLD system uses BLAST's search algorithm, so it is likely that the difference in percent similarity is either due to the difference in number of specific catalogued co1 sequences between the databases, or because most sequences in BOLD are at the 5' end of the co1 gene, and therefore might have different sequence overlap compared to GenBank  (Wong, Hanner, 2008). While many of the samples were >99% similar to database sequences in either BOLD or Genbank, sample #11804 was an interesting exception. This sample showed 94. 7% similarity to S. tudes according to BOLD, and 95% similar to sequences of S. tiburo according to GenBank.
Photographs of this specimen from the markets (Fig. 2 ), one of the few animals sampled which was not decapitated, suggests that this specimen may be S. media (IUCN status: Data Deficient,  Casper, Burgess, 2006) , a species not represented in BOLD or GenBank. This specimen is putatively identified as S. media because it has a short and broad, wedge-shaped cephalofoil, with a convex medial notch. In addition, this specimen has the origin of its first dorsal fin over the inner margins of the pectoral fins, and the trailing edge of the second dorsal fin not reaching the caudal fin, all characters which suggest S. media. Tab. 2 Shark species in Guyanese fish markets as identified through DNA barcoding. In several cases, specimens showed very high similarity (99-100%) to database sequences from more than one species, whether BOLD or GenBank results were considered.
The most abundant carcharhinids in our dataset were Sharpnose Sharks, comprising over a quarter of the sampled specimens, which are classified as either 'Data Deficient' or of 'Least Concern' by the IUCN  (Rosa et al. , 2004; Lessa et al. , 2006; Cortés, 2009). However, despite generally high fecundity, these small sharks are under some pressure from overfishing in Brazil, where R. lalandii is listed as locally 'Vulnerable' due to intense harvest of all size and age classes in artisanal gillnet fisheries  (Rosa et al. , 2004; Motta et al. , 2005 Motta et al. , , 2007)). Rhizoprionodon lalandii made up 83% of the samples from Guyanese fish markets. Given the 'Vulnerable' status of R. lalandii in Brazil, this might suggest that this species needs to be monitored more carefully in Guyana as well, given its prevalence in our sample.
Alternatively, if R. lalandii populations in the Guianas are both stable and contiguous with Brazilian Sharpnose Shark populations, over harvested Brazilian populations could theoretically be replenished through immigration by coastal Guyana individuals  (Mendonça et al. , 2011). Several samples were equally similar to both R. porosus and R. terraenovae reference sequences according to both BOLD and GenBank. However, another explanation for this situation is that one or more specimens used for these databased reference sequences were misidentified, or represent hybrids (hybrids have been recognized in Requiem Sharks;  Morgan et al. , 2012). Unfortunately, in the case of BOLD, some of the relevant reference sequences are "private," with no corresponding voucher or metadata are publicly available at this time.