dataesr/scientific-paragraphs-categorization huggingface dataset:
Technical Validation Two approaches were used to establish the technical validity of the datasets described. First, the biological samples used to generate the transcriptomic and proteomic assay results reported here have also been assessed using various criteria to ensure technical validity. For example, productive viral infections following inoculation of animals used for in vivo studies was confirmed by examining viral titers, viral mRNA levels, and body weight loss to quantify virus replication and host response to infection as detailed below. For in vitro studies, viral genomic RNA, viral mRNA, and viral titers were quantified to ensure that the virus was actively replicating and transcribing its genome in cell culture. Second, the processed data produced from in vivo and in vitro studies were compared, using Boolean logic, to quantify the number of overlapping significant genes identified in multiple studies performed under similar experimental conditions as an assessment of general reproducibility of the derived results between experiments.
In vivo validation To validate the in vivo studies, mean viral mRNA levels, mean % body weight, and mean viral titers were examined as surrogates for viral infection in the animal subjects. The CA04M001 and IM001 experiments, in which mice were infected with influenza A virus strains A/California/04/2009 (pandemic H1N1) and A/Viet Nam/1203/2004 (H5N1) respectively, were used as representative datasets for this validation process. For both experiments, the viral mRNA levels indicate that the virus was actively transcribing mRNA within the host organism with mean viral mRNA levels peaking at either 2 or 4 days post infection (dpi), when either a high or low initial inoculum was used, respectively  8. Similarly, the mice that were exposed to virus in either experiment showed a dose-dependent decrease in mean % body weight.
In the evaluation reported here, we used datasets reporting results from wild-type A/California/04/2009 (H1N1), A/Viet Nam/1203/04 (H5N1), and SARS-CoV as being representatives of all experiments. In order to avoid complications related to slight differences in infection kinetics, we took the union of all biosets across the entire time course from each experiment (i. e. the non-redundant set of differentially expressed genes) for comparison. This approach can lead to a discrepancy between the total number of genes in each experiment due to differences in number of time points sampled. As an alternative strategy, the data could also be more strictly paired using specific time points and dosages. Seven situations in which the same treatment conditions (viral strain and viral amount) were reproduced in separate experiments were identified.
Thus, the studies compared here demonstrated considerable reproducibility in results in spite of the number of sources of variability inherent in their designs. Furthermore, reduction in the number of false positive results in any individual experiment (high specificity) could be achieved by focusing on the host factors that were reproducibly identified in similar experiments. Reduction in the number of false negative results (high sensitivity) could be achieved by using the union of host factors identified among similar experiments. Overall, the results from the independent in vitro and in vivo validation experiments and the high degree of reproducibility of the derived host factor lists show that the datasets described here are of high quality and can be useful for in-depth investigation and hypothesis generation in follow-up analyses.
Usage Notes The systems biology studies described herein were designed to characterize how the host responds to viral infection. Separate experiments were performed to measure the response to multiple independent variables that include sampling at multiple time points after infection, at different viral dosages, in various model systems, and in systems with various genetic backgrounds (both viral and host). A number of publications have reported the use of these datasets to address questions concerning pathogenesis. For instance, comparisons between the similarities and dissimilarities of the host response at early and late stages of infection with genetically modified viral strains have been performed to identify possible correlates of disease severity  9. Likewise, the host response was measured in different model systems to identify host factors that have a role in disease progression  45.
Previous analyses of the pathways and functions that were over-represented in the list of differentially expressed genes revealed that inflammatory response, cell-to-cell signaling, immune cell trafficking, lipid metabolism, amino acid metabolism, cardiovascular disease, and cancer gene responses were associated with virulence differences  6. To illustrate one potential way to compare datasets in ViPR/IRD, we quantified the overlap between differentially expressed genes identified from samples infected with this collection of 'pre-pandemic' H1N1 influenza A virus. As before, we compared the non-redundant union of significant genes derived from the transcriptomic results reported in the IM002 experiment to determine differences detected in mice infected with these viruses. Although the pairwise comparisons of the host response to virus infection were somewhat variable, the differences appeared to correlate with the virulence of the infecting virus.
No overlap was observed between all three virulent viruses and the A/Brisbane/59/2007 strain. In contrast, 262 host factors (66%) were shared in mice infected with the three virulent viruses (Fig. 3a ). Performing a functional annotation analysis for this list of 262 genes identified the same list of GO terms as reported in the original study  6  except for the exclusion of cardiovascular disease and cancer. Similar analyses designed to compare the host response to other related viruses could be performed using the datasets that are currently available in IRD/ViPR. For example, the ICL010-R experiment examines the host transcriptional response to 2 different 2009 influenza A (H1N1) pandemic strains in Calu-3 cells at 12-, 24, and 48 hpi. The ECL001-R and SCL005-R experiments could be analysed to better understand the similarities and differences in the host response during infection with either MERS or SARS-CoV in Calu-3 cells across multiple time points.
Host responses to mutant viruses In order to investigate how specific viral proteins might function to regulate host intracellular processes, experiments were performed to identify shared and unique host genes that are differentially expressed in hosts infected with either wild-type or mutant viruses  5, 11, 13. For example, analysis of experimental results involving variants of SARS-CoV with a deletion in the ORF6 coding region performed in human cell lines (SCL005) or mice (SM012) determined that antagonism of karyopherin-based transport was an important function of the ORF6 gene product in SARS-CoV. As nuclear signaling disruption is a strategy used by many viral pathogens, identifying trends in these data presents a possible area for general therapeutic intervention. Using Host Factor Bioset data in ViPR/IRD comparing human cell lines infected with either wild-type or ORF6 mutant viruses, 74% of significant host factors were shared between the two infection conditions (Fig.
3b ). When an enrichment analysis was performed on the non-overlapping host factors (i. e. unique to infection with the ORF6 mutant strain), we found enrichment for terms relating to cell proliferation and immune response (P o0. 05), which matches what had been reported previously  12. Similar analyses can be performed with data from other studies in IRD/ViPR to compare infection with wild-type versus genetically-engineered mutant strains. For instance, there are multiple studies that characterize the host responses to SARS-CoV variant strains in a human cell line (SCL006) and a mouse model (SM003, and SM012), and host responses to four influenza mutant variants of VN1203 in mice (IM004, IM005, IM006A, IM006B, and IM007) and a human cell line (ICL011 and ICL012).
In these cases, proteomic data was integrated with the corresponding transcriptomic data to improve the biological networks produced and to support the associated statistical analysis of SARS-CoV host responses in general, and the karyopherin transport hubs in particular. We performed a similar comparison of RNA and protein Host Factor Biosets in ViPR/IRD from the same tissue/cell samples using either microarray or mass spectrometry technologies (ICL004). In this case, transcriptomic data from the 12 h time point of human Calu-3 cells infected with A/Viet Nam/ 1203/2004 influenza virus was chosen because this time point contained the highest number of significant probes. These transcriptomic data were compared with a pooled list from the time-matched and all subsequent time points from the related proteomics data in order to control for the 'lag' that occurs between when the changes in mRNA expression are translated into corresponding changes in the translated protein product.
Meta-analysis While many of the usage examples provided involve comparative analysis of results obtained in the same experiment, in some cases similar comparisons can be performed between experiments. Indeed, the Validation section focused on the reproducibility of derived results and involved the comparison of similar experiment conditions performed in separate experiments. Combining and contrasting results from different studies is often referred to as meta-analysis. One of the goals behind making these datasets freely available through public database resources like GEO, ViPR and IRD is to support their incorporation into meta-analysis approaches that include new experiments being performed by the research community. To facilitate this kind of meta-analysis, considerable effort has been dedicated to providing complete descriptions of all experimental and data analysis methodologies, combined with structured, standardized metadata describing selected key aspects of the experiment design, especially regarding the experiment factors that serve as the independent (manipulated) variables in the study in ViPR and IRD.
Re-analysis Although we have emphasized the use of the derived Host Factor Biosets, in every case a close linkage is maintained between the processed data and the primary assay results from which they were derived-even though they may be stored in distinct resources. Thus, the final usage example is related to the re-processing and re-analysis of primary assay results. Different processing algorithm(s) chosen to perform a specific processing or analysis step in a multistep workflow can yield different results. Many bioinformatics data processing workflows currently implement a 'one size fits all' approach with a single algorithm being applied to all datasets without regard for the specific nuances of each individual dataset. The problem with this approach is that the processing algorithms and parameters that are ideal for one dataset may not be optimal for other datasets-especially when dealing with different experimental systems.
Hence, processing of the data using a single universally-applied algorithm may not sufficiently address the individual requirements of each dataset. To truly solve this underlying problem, an analytical workflow that explores the relevant parameter space for a diverse set of algorithms would need to be implemented. This approach would provide an objective and automated process to intelligently 'choose' the optimal combination of parameters and algorithms that produce the best set of results for each individual dataset. Such an approach could be implemented in analytical workflow suites like Galaxy or GenePattern  57, 58  to objectively and confidently reprocess the raw data from each '-omics' dataset in a data-driven fashion to further improve the quality of the derived data. Until such a system is developed, we advocate for using the processed data that was provided by the original submitters and subjected to the peer-review process when performing meta-analyses given its overall high quality.
, GSE33266 (Data Citation 21), GSE50000 (Data Citation 22), GSE51386 (Data Citation 23), GSE50878 (Data Citation 24), GSE51387 (Data Citation 25), GSE49262 (Data Citation 26), GSE49263 (Data Citation 27), GSE40824 (Data Citation 28), GSE40827 (Data Citation 29). Data Record 2 Raw data files and associated parameter files (. raw,. mzXML) for all proteomics experiments including those used for AMT-TAG database generation have been uploaded to PeptideAtlas (http://www. peptideatlas. org). Experiment accession numbers are as follows: PASS00416 (Data Citation 30), PASS00417 (Data Citation 31), PASS00418 (Data Citation 32), PASS00419 (Data Citation 33), PASS00420 (Data Citation 34), PASS00421 (Data Citation 35), PASS00422 (Data Citation 36), PASS00423 (Data Citation 37), PASS00424 (Data Citation 38), PASS00425 (Data Citation 39), PASS00426 (Data Citation 40), PASS00427 (Data Citation 41), PASS00428 (Data Citation 42), PASS00429 (Data Citation 43), PASS00430 (Data Citation 44), PASS00431 (Data Citation 45), PASS00432 (Data Citation 46), PASS00433 (Data Citation 47).
Data Record 3 Structured experiment metadata and derived data (. xls,. txt) including fold change values and p-values are available in the Host Factor Component of the Influenza Research Database (IRD, www. fludb. org) and the Virus Pathogen Resource (ViPR, www. viprbrc. org) under accession numbers: IRD_SV_CA04M001-P (Data Citation 48), IRD_SV_ICL004-P (Data Citation 49), IRD_SV_ICL006-P (Data Citation 50), IRD_SV_ICL010-P (Data Citation 51), IRD_SV_ICL011-P (Data Citation 52), IRD_SV_ICL012-P (Data Citation 53), IRD_SV_IM001-P (Data Citation 54), IRD_SV_IM004-P (Data Citation 55), IRD_SV_IM005-P (Data Citation 56), IRD_SV_IM006A-P (Data Citation 57), IRD_SV_IM006B-P (Data Citation 58), IRD_SV_IM007-P (Data Citation 59), IRD_SV_SCL005-P (Data Citation 60), IRD_SV_SCL006-P (Data Citation 61), IRD_SV_SM001-P (Data Citation 62), IRD_SV_CA04M001-R (Data Citation 63), IRD_SV_ECL001-R (Data Citation 64), IRD_SV_ICL004-R (Data Citation 65), IRD_SV_ICL006-R (Data Citation 66), IRD_SV_ICL010-R (Data Citation 67), IRD_SV_ICL011-R (Data Citation 68), IRD_SV_ICL012-R (Data Citation 69), IRD_SV_IM001-R (Data Citation 70), IRD_SV_IM002-R (Data Citation 71), IRD_SV_IM004-R (Data Citation 72), IRD_SV_IM005-R (Data Citation 73), IRD_SV_IM006A-R (Data Citation 74), IRD_SV_IM006B-R (Data Citation 75), IRD_SV_IM007-R (Data Citation 76), IRD_SV_IM009-R (Data Citation 77), IRD_SV_IM010-R (Data Citation 78), IRD_SI_SBRI_AA_E1 (Data Citation 79), IRD_SV_SCL005-R (Data Citation 80), IRD_SV_SCL006-R (Data Citation 81), IRD_SV_SHAE002-R (Data Citation 82), IRD_SV_SHAE003-R (Data Citation 83), IRD_SV_SHAE004-R (Data Citation 84), IRD_SV_SM001-R (Data Citation 85), IRD_SV_SM003-R (Data Citation 86), IRD_SV_SM004-R (Data Citation 87), IRD_SV_SM007-R (Data Citation 88), IRD_SV_SM009-R (Data Citation 89), IRD_SV_SM012-R (Data Citation 90), IRD_SV_SM014-R (Data Citation 91), IRD_SV_SM015-R (Data Citation 92), IRD_SV_SM019-R (Data Citation 93), IRD_SV_SM020-R (Data Citation 94).
Acknowledgements Data described here was funded in whole or in part with federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services. Data generation was performed under the Systems Virology Contract  HHSN272200800060C  and Systems Influenza contract  HHSN272200800058C. The Influenza Research Database (IRD, www. fludb. org)and the  Virus Pathogen Resource  (ViPR, www. viprbrc. org)also received generous support from the  NIAID  under  N01AI40041. We also wish to thank  Valentina Di Francesco  and  Alison Yao  for their programmatic support of the  Systems Biology  and the  Bioinformatics Resource Centers Programs at the NIAID. Additional support for methodology development was provided by  NIH/NCATS  ( 5UL1RR024140 ).
DNA and protein sequence analysis The complete sequence of the AmyHha cDNA was initially analyzed in BLASTx and BLASTn software  (Altschul et al. , 1997)  to obtain homologous sequences in the NCBI database (National Center for Biotechnology Information). The search for the ORF (open reading frame) was performed using the program ORF Finder (http://www. ncbi. nlm. nih. gov/gorf/gorf. html). The nucleotide and amino acid sequences were aligned with the BioEdit software using the ClustalW algorithm  (Hall, 1999). The molecular weight and isoelectric point (pI) of the predicted protein were determined by the tool Compute pI/MW available on the ExPASy platform (http://us. expasy. org). The sequence of the signal peptide was predicted by the program SignalP 4. 1 (http://www. cbs. dtu. dk/ services/SignalP/).
Prediction of putative O-glycosylation sites was obtained with the program YinOYang 1. 2 (http://www. cbs. dtu. dk/services/ YinOYang/). Putative N-glycosylation sites were predicted by NetNGlyc 1. 0 (http://www. cbs. dtu. dk/services/NetNGlyc/) software. Phylogenetic analysis was performed using the MEGA version 5, applying the bootstrap method with 10,000 replications to evaluate the reliability of the topology of the phylogenetic tree obtained  (Tamura et al. , 2011).
Rox plus Sybr Green Master Mix 0. 5× (LGC Biotecnologia), 200 nM of each primer (sense and antisense) and 2 μL of 40-fold dilution of cDNA for each reaction, were used with a final volume of 10 μL. All experiments were performed in technical triplicate and biological duplicate. The sequences of the primers used for RT-qPCR analysis were designed using Primer 3 software. AmyHha primers used were: forward 5′GGAGTGCGAATTTATG CTG3′ and reverse 5′CACGTAATCTTGGCTCTGATCC3′. β-Actin reference gene primers were designed based on A. grandis (GABY01014421) and T. castaneum actin mRNA sequences (XM_970777. 1), which were forward 5′CCTTCAACACCCCCGCCATG3′ and reverse 5′TGAGGTAGTCGG TCAAGTCG3′. The PCR cycling conditions were: 95 °C for 15 min to activate the hot-start Taq DNA polymerase, 40 cycles at 95 °C for 20 s, 55 °C for 30 s and 72 °C for 30 s.
Computational modeling After performing the AmyHha amino acid sequence prediction, alignment against the PDB structure database was carried out to obtain an optimal structural template, with TMA the best mold found (sequence ID: PDB|1JAE). Thus, a structural model of the enzyme AmyHha was developed based on the crystal structure of TMA enzyme. The protein sequences were aligned using the ClustalW software. The program, Modeller 9v11, was used to construct 100 three-dimensional models  (Eswar et al. , 2007). The final step of the modeling process was done to evaluate the different levels of structural organization. At this stage the various levels of overall packing and possible structural errors were analyzed. For validation of the structural model, the program PROCHECK was used in the evaluation of stereochemical parameters  (Laskowski et al.
, 1996). The program 3DSS (http://cluster. physics. iisc. ernet. in/3dss/) was used for the calculation of RMSD by superimposing the structure of the built model and that of the mold structure  (Sumathi et al. , 2006). The three-dimensional structures were viewed and analyzed using PyMOL (http://pymol. sourceforge. net/) (DeLano, 2002).
Isolation and characterization of AmyHha cDNA To clone the AmyHha cDNA, total RNA was extracted from 2nd-instar larvae and cDNA was synthesized. Initially, a fragment of 413 bp was obtained by RT-PCR using degenerate primers, whose design was based on conserved regions found in other coleopteran insect α-amylases. Over 40 colonies were sequenced and only one cDNA sequence was found. Subsequently, two segments of the 5′ and 3′ ends were amplified by 5′-3′ RACE strategies using specific primers. The full-length cDNA sequence of AmyHha (GenBank ID: KF963103) was obtained by the overlapping of the three PCR fragments (Fig. 1A ). The 1879-bp AmyHha cDNA contains a 1458-bp full-length open reading frame encoding 485 amino acid residues (Fig. 1B ). The deduced amino acid sequence of AmyHha shows high identity with other insect α-amylases, presenting 79% identity and 55% identity with isoforms 1 and 2 from A.
grandis, respectively, 74% identity and 73% identity with isoforms A and B from I. typographus, respectively, 69% from S. oryzae, 58% from D. virgifera virgifera and T. castaneum, 57% from C. chinensis and Z. subfasciatus and 56% from T. molitor. The alignment of AmyHha with other insect α-amylases shows a high degree of conservation, particularly in amino acid residues involved in substrate binding and catalytic site (Fig. 2 ). The AmyHha sequence has a signal peptide composed of N-terminal 18 amino acid residues, predicted by the SignalP program. The molecular mass of the mature AmyHha protein was estimated to be 51. 2 kDa with an isoelectric point estimated to be 4. 88. The catalytic triad is found in AmyHha as Asp184, Glu220, and Asp285 (Fig. 1B ). In silico analysis predicted thirteen potential O-glycosylation sites in AmyHha protein sequence using the program YinOYang, while only one single potential N-glycosylation site was predicted by the program NetNGly (Fig.
AmyHha expression profile during the H. hampei life cycle The characterization of the AmyHha transcription pattern during the H. hampei phenological phases was obtained by quantitative real-time PCR experiment. The level of AmyHha transcripts varied during H. hampei development (Fig. 5 ). The low levels of AmyHha transcripts were detected in eggs and first instar stage, whereas a 200-fold increase could be observed at the 2nd-instar stage. We noticed a significant reduction of AmyHha transcripts during the pupal stage, a period in which insects do not feed. After ecdysis, AmyHha transcript accumulation increased again to 200 times during the adult stage which resumes feeding. Fig. 2. Alignment of mature predicted protein sequences of coleopteran α-amylases using ClustalW. Residues of the catalytic site are boxed; residues involved in calcium ion binding site are marked in gray and residues of the chloride ion binding site are marked in black.
High-density SNP arrays for genome-wide assessment of allelic variation have made high resolution genetic characterization of crop germplasm feasible. A medium density array for apple, the IRSC 8 K SNP array, has been successfully developed and used for screens of bi-parental populations. However, the number of robust and well-distributed markers contained on this array was not sufficient to perform genome-wide association analyses in wider germplasm sets, or Pedigree-Based Analysis at high precision, because of rapid decay of linkage disequilibrium. We describe the development of an Illumina Infinium array targeting 20 K SNPs. The SNPs were predicted from re-sequencing data derived from the genomes of 13 Malus 6 domestica apple cultivars and one accession belonging to a crab apple species (M.
Calls with a support value of at least 10 reads were retained in the. vcf file, while the others were considered unreliable and therefore set to empty. To avoid the inclusion of potential paralogy-related SNPs, variants with a heterozygous genotype in the DHs were filtered out. Finally, known ambiguous bases in the primary reference were annotated. A quality filter was then applied to remove potentially unreliable variants. Custom scripts written in Python (www. python. org) were developed to remove variants with low phred-scaled quality scores (i. e. below 20); a high combined read depth (i. e. higher than 2,000); and a low single-cultivar read depth (i. e. lower than 10) in more than 50% of the cultivars. A minimum phred-scaled quality score of 20 was chosen to ensure that only SNPs with a probability of less than 1% for the alternative allele being called wrongly were selected, and the maximum read depth value was used to ensure the removal of SNPs derived from paralogous regions rather than true heterozygous regions as done previously in the development of the IRSC apple array  [3]  and the 9 K peach array  [22].