dataesr/scientific-paragraphs-categorization huggingface dataset:
representativeness of training datasets and the efficiency of classification methods are crucial for ice type classification. Most SIT products are based on a priori "training" datasets. Some use fixed threshold classification algorithm, while others employ dynamic threshold to account for the variability of training dataset, seasonality and shift in sensor types. For the former, the algorithm works relatively well under conditions similar as the training dataset, however it gives anomalous SIT distribution results in other conditions. For instance, KNMI-SIT uses fixed threshold extracted from mid-winter. Extensive anomalous SIT misclassification is found in the beginning winter, when backscatter characteristics of MYI and FYI vary significantly in different stages of winter, especially based on ASCAT.
The Pacific region is experiencing a non-communicable disease epidemic largely driven by an ongoing nutrition transition from nutrient rich whole foods to imported staples and highly processed foods. Food trade is a major driver of this transition. We characterize regional and sub-regional trade from 1995 to 2018 for 18 Pacific Island Countries and Territories using the Pacific Food Trade Database. The analysis shows the growing dependence on imports of rice from South-East Asia and wheat from Australia, and recent growth in imports of meat from Australia, New Zealand and USA, and highly processed foods from South-East Asia. Findings are discussed in terms of policy and trade agreements, and global shocks including COVID-19 and the war in Ukraine.
g. Gewertz and Errington, 2010; Ravuvu et al. , 2017). Given the lack of regionally integrated knowledge of food trade, combined with the reported sub-regional and commodity-specific trends in import dependence, an integrated regional and sub-regional characterisation of food trade is required to move towards improving food security and nutrition for the region and an evidence base, through which regional policy discussions can develop. This characterisation is made possible through application of the recently developed Pacific Food Trade Database (PFTD). The PFTD resolved significant error in original data that, if left unresolved, would prevent accurate regional analysis. The PFTD contains trade records for 18 PICTs (Fig. 1 ) and their trade partners, and data on quantity (t) and value (USD) for the years 1995-2018 at the subheading level following the HS92 harmonised coding system.
Data analysis All data and results are derived from the PFTD. Except for analysis presented in Fig. 2  (objective 1), we exclude Papua New Guinea, as a PICT, because its larger population, economy and domestic food production would mask trends for other PICTs when aggregated. Trade records of imports and exports between Papua New Guinea and other PICTs are retained. Additionally, finfish trade data, including tuna, are excluded from all analyses because international trade data does not accurately depict the flow of fish between countries, due to obscuring factors such as catch by foreign vessels and transhipment. Alcohol and tobacco are also excluded from the analysis. Specific inclusions and exclusions are outlined in Table  S1. Where countries are grouped to analyse trade networks the UN statistics division categories (https://u nstats.
Change in import dependence across PICTs and commodity clusters Dependence on food imports has increased dramatically since the mid-1990s (Fig. 2 ). For the commodities and PICTs included in Fig. 2 , regional per capita trade grew from 25 kg net export in 1995 to 50 kg net import in 2018. The dominant food exporters in the Pacific are Papua New Guinea and Fiji. Papua New Guinea exports large quantities of palm oil, coffee, copra and cocoa. The smaller, dominantly atoll, PICTs export very limited food, and have become increasingly import dependent since the mid to late 1990s. However, a portion of atoll country trade, not captured in the database due to small quantities and/or the informal nature of the trade, is dominated by locally produced fresh perishables traded between PICTs. Overall, the change in import dependence across all PICTs, combined with variable production of export cash crops across limited commodities highlights the rapid increase reliance on imports.
Future research directions This article provides an overview of trade-related food security and nutrition in the Pacific region. Priority areas of future research include a thorough assessment of trade, framed through a nutrition lens, including but not limited to caloric intake, fats, salt, sugars, and micronutrients. This work would enable PICTs to better understand how trade influences incidence of non-communicable diseases. Similarly, calculation of import dependency ratios across key food groups, through combining PFTD data with FAOSTAT production data would highlight vulnerabilities in the Pacific food system to global shocks such as COVID-19. The PFTD will be periodically updated as more data-years are available, and the data will be publicly available on the SPC Pacific Data Hub (https ://pacificdata.
Serum panels and serum standards Participants were provided with a panel of 30 samples by the University of Ghent. Each panel consisted of 3 pre and 20 postvaccination human sera (FLUCOP_QIV clinical trial, Fluarix Tetra vaccine containing the following influenza strains: A/Michigan/45/ 2015 (H1N1)pdm09, A/Hong Kong/4801/2014 (H3N2), B/ Brisbane/60/2008 and B/Phuket/3073/2013 samples), 4 duplicated samples (for assessing within-run variability), 2 serum standards and a negative control. The two serum standards included in the study were pools of equal volumes of 4 post-vaccination human serum samples (FLUCOP_QIV clinical trial, Fluarix Tetra vaccine) all of which have med-high titres for all tested influenza A subtype and B lineage viruses. These serum standards were used as calibrators to normalise data from each laboratory (i.
Serological testing Participants were asked to test the serum panel with four seasonal influenza viruses. They were asked where possible to use reassortant viruses IVR-180 (H1N1), NYMC-X263B (H3N2), NYMC-BX35 (B Victoria lineage) and NYMC-BX59A (B Yamagata lineage). Antigenically identical wild type (WT) viruses were also considered acceptable for testing. Participants were asked to carry out three independent replicates using any or all of the following: In-house or FLUCOP protocols for HAI testing (2); Inhouse or the WHO protocol for 2-day ELISA-based MN testing (4); In-house or FLUCOP protocols for 3-day+ MN testing (available upon request). Serum panels were shipped in August/September 2021. Each laboratory returning data was assigned a number (and a colour for all graphical representations of data); each laboratory was only given their own number and colour, thus anonymizing the results from participating laboratories.
Statistical analysis As HAI and some MN methods return discrete data within a specific dynamic range, results are often reported as <10 or >(upper assay limit) e. g. >1280. Data returned indicating <t were assigned the value 1/2t, data returned indicating >t were assigned the value 2*t. Intra-assay (within-run) variability was assessed using maximumminimum ratios of four coded duplicate samples included in the serum panel. Intra-laboratory (between-run) variation was assessed using maximum-minimum ratios of the 3 independent replicates returned for HAI (with the exception of Lab no. 13 where only 2 replicate runs were returned) or 2-3 independent replicates for MN (Labs no. 7/10/11 returned two replicate runs). Any sample with a ratio greater than 3. 5 was excluded from inter-laboratory (betweenlaboratories) comparisons.
Any sample with a Geometric Mean Titre (GMT) <10 was excluded from statistical analysis (shown on graphs for information only). Sample titres were log 10 transformed and % Geometric Coefficient of Variation (GCV) calculated using the following equation: (10 s -1)x100% where s is the standard deviation of the log 10 titres. %GCVs were statistically compared using the Wilcoxon matched paired t test (comparison of raw and normalized titres) or Mann-Whitney U test (comparison of native and ether split B antigen titres). Geometric Mean Ratios (GMRs) were calculated as the titre of a sample/a reference titre. For overall inter-laboratory variability the reference titre is the GMT of that sample across all testing laboratories (after data exclusion as described above). For comparisons of in-house and FLUCOP protocols, the reference titre is the GMT of that sample tested with the appropriate protocol (i.
e. the GMT of a sample across all laboratories testing with the FLUCOP protocol, or the GMT of a sample across all laboratories testing with in-house protocols). For normalization of titres, from the two serum standards (pools of post-vaccination serum as previously described) included in the panel, we selected the serum standard for which the greatest number of valid data were returned -sample no. 17. For each run a calibration factor was calculated as the ratio of titre of sample no. 17 in a run/the global GMT of sample no. 17 (GMT across all testing runs/laboratories after data exclusion as described above). The calibration factor was then applied to all other titres within that run to calculate normalized titres. Each participant received a report giving an overview of all returned data, with laboratory specific information for the following: intra-assay variability (maximum-minimum duplicate sample ratios %>3.
Impact of a study standard on HAI interlaboratory variation We included two pools of human sera as study standards within the serum panel tested by participating laboratories. The impact of normalisation using these study standards was explored by comparison of %GCV of log 10 titres before and after normalisation with the study standard and a comparison of GMRs before and after normalisation. Pool 1 was selected as more laboratories returned a valid titre for Pool 1 than Pool 2. Supplementary Figure  S1B  shows the overall returned HAI data from all laboratories after normalization with Pool 1 (sample 17 in the serum panel). Figure  3A  shows the GMR of each sample by laboratory before and after normalization, and Figures  3B, C  shows the %GCV of each sample before and after normalization.
Impact of using FLUCOP vs. in-house protocols for HAI testing Six Laboratories carried out testing using both in-house and FLUCOP protocols. Intra-assay agreement (measured by max-min ratios of four coded duplicate samples in the serum panel) was slightly better when testing with the FLUCOP protocol: only 3/256 ratios were >2 with FLUCOP testing compared to 9/256 for inhouse testing. Intra-laboratory performance was generally better for FLUCOP testing than in-house testing: 11. 5% of data failed to pass the >3. 5 max-min ratio requirement when using in-house testing compared to only 6. 8% when FLUCOP testing was used (data compared for the 6 laboratories testing both protocols only). These results indicate an overall better performance within a laboratory when using the FLUCOP protocol. A comparison of %GCV and GMR for in-house and FLUCOP testing is shown in Figure  4.
Here the agreement between the 6 sets of in-house data is compared with the agreement between the 6 sets of FLUCOP testing data. GMRs in general do not show substantially closer agreement when using FLUCOP testing compared to inhouse testing (Figure  4A ). There is a marginal trend for GMRs to be closer to 1 using the FLUCOP protocol. %GCVs show a similar result (Figure  4B ), with slightly lower (but not statistically significant) %GCVs for FLUCOP testing with the B strains, but little or no difference for H1N1 and H3N2 testing.
MN data analysis 3. 3. 1 Intra-assay variability Intra-assay (or within-run) variability was assessed by calculating the maximum to minimum ratio of four pairs of coded duplicates in the serum panel. 92% (334/364) of coded duplicates had maximum to minimum ratios of ≤3. 5, with 41% of duplicates being equal. A small number of coded duplicates (5/ 364) have higher ratios due to conversion of any titres stating >t to 2*t. Labs no. 4 and 7 had slightly higher incidence of duplicate ratios >3. 5 (33% for Lab no. 4 and 25% for Lab no. 7 compared to 8. 2% across all testing laboratories). Overall, the MN assay is intrinsically more variable within a run than the HAI. Considering the more complex nature of the assay and the use of live cells, this is perhaps not surprising.
Intra-laboratory variability We conducted an assessment of intra-laboratory (or betweenrun) variability by calculating maximum-minimum ratios across the independent replicates performed by the testing laboratories. Several laboratories returned only 2 independent replicates, so we included data where 2 or more independent replicates were carried out. Table  2  gives the % of samples giving equal, ~2-fold and~4-fold or greater difference in titres across the replicates for each individual laboratory broken down by influenza A subtype and B lineage. The majority (95%) of samples have max-min ratios of ≤3. 5, demonstrating good intra-laboratory reproducibility. Unlike the HAI, variability is fairly uniform across laboratories and subtype/ lineages with some small laboratory specific differences: Labs no.
Inter-laboratory variability Supplementary Figure  S3A  shows the overall returned MN data (after data exclusion as described in statistical analysis) from all laboratories for each influenza A subtype and B lineage. Data from in-house testing are shown in black, data from FLUCOP testing are shown in red. A between laboratory analysis was carried out using two different comparison methods: GMRs of each sample and % GCV of log 10 titres. GMRs show a large difference in overall titres between different laboratories and protocols (see Figure  6A ). Unlike HAI data, high levels of variation are seen for each subtype and lineage tested in MN. Ratios in general cluster for each laboratory, suggesting a systematic bias in testing rather than random error. % GCVs across the serum panel were much higher for MN testing than for HAI (see Figures  6B, C ).
In this study, we had 16 laboratories returning dataa number that was likely impacted by the ongoing Covid-19 pandemic. This represents a limitation of the study, however, the dataset collected is still valuable and serves to highlight where improvements in intraassay, intra-laboratory and inter-laboratory agreement can be made. We have not sought to implement limits to which we consider inter-laboratory performance to be acceptable or not. Instead, we have set arbitrary ranges that laboratories may consider (for example the indicative range of 0. 8-1. 25 for geometric mean ratios) and have put other measures of inter-laboratory variation into the context of existing literature for comparison (for example the %GCVs of samples across the testing laboratories). Intra-assay performance and intra-laboratory performance have been analyzed B A FIGURE 4 Impact of using the FLUCOP protocol on inter-laboratory variation in HAI testing.
3A ; Supplemental Data S3;  Burgess et al. 2021; Li et al. 2021; Liu et al. 2021; Zhang et al. 2021b). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we then determined the presence of a relative number of m 6 A residues in the SARS-CoV-2 RNA genome. In these experiments, viral genomic RNA was isolated from infected Vero cell supernatant, and the ratio of A/m 6 A was measured using a synthetic m 6 A-containing RNA oligo as a standard. Using the parallel reaction-monitoring (PRM) mode, we monitored LC-MS/MS profiles for m/z 268. 0-136. 0 and m/z 282. 0-150. 1, which correspond to A and m 6 A, respectively  (Sun et al. 2021). We observed that each SARS-CoV-2 genome contains, on average, 10 m 6 A modifications (Supplemental Fig. S3A ), consistent with a recent report  (Li et al.
2021; Li et al. 2021; Liu et al. 2021; Zhang et al. 2021b ). Inspection of publicly available viral-RNA protein interaction data shows that m 6 A reader protein partners are enriched in viral RNA interacting proteins  (Schmidt et al. 2021). The m 6 A readers previously shown to interact with SARS-CoV-2 RNA were searched for known interacting protein partners in the STRING database. We found that these interacting proteins are enriched in RNA metabolism and viral process pathways (Supplemental Fig. S3C , top) and that these proteins show overlap with the SARS-CoV-2 RNA-protein interactome (Supplemental Fig. S3C , bottom). These observations suggest that SARS-CoV-2 may use m 6 A residues in its genome to recruit other RNA-binding proteins to the viral RNA using m 6 A readers as intermediates.
Processing of RNA-seq data Single-end sequencing reads from SMARTer stranded total RNAseq kit v2 were analyzed with FastQC for quality control (https ://www. bioinformatics. babraham. ac. uk/projects/fastqc/), and adapters were removed using Trim Galore! v0. 6. 6 (https://github. com/FelixKrueger/TrimGalore) with a minimal length threshold of 20 bp. Trimmed reads from Vero infected and noninfected cells were aligned to the following reference genomes: concatenated chlSab2 (Chlorosebus sabeus), wuhCor1 (SARS-CoV-2), plus Escherichia coli str. K-12 substr. MG1655 (spike-in), or the SARS-CoV-2 + E. coli genomes, obtained from the UCSC Genome Browser (http://hgdownload. soe. ucsc. edu/goldenPath/). Alignments were made using HISAT2 v2. 2. 1  (Kim et al. 2019)  with the parameters (-U --rna-strandness R).
Sequencing data from human bronchial and nasal epithelial noninfected and infected cells were mapped to the concatenated human (hg38) + SARS-CoV-2 (wuhCor1) + E. coli (spike-in) reference genomes. Duplicate alignments were labeled using MarkDuplicates from Picard v2. 23. 4 (http://broadinstitute. github. io/picard), and marked alignment files were further processed using Sambamba v0. 7. 1  (Tarasov et al. 2015; Kim et al. 2019) , keeping only mapping reads separated by strand after duplicate removal.
Analysis of RNA-seq data for differential gene expression Differential gene expression following SARS-CoV-2 infection and Gene Ontology (GO) analysis is described in detail in the Supplemental Material. m 6 A RIP-seq data analysis m 6 A peak calling on SARS-CoV-2 viral genome and infected cell samples was performed with callpeak from MACS2 v2. 2. 6  (Zhang et al. 2008 ) on IP and input processed alignments mapped to SARS-CoV-2 wuhCor1 or the concatenated genomes (chlSab2 + wuhCor1) or (hg38 + wuCor1), using the parameters "--no-model --keep-dup auto --call-summits" and effective genome sizes according to each genome with a P-value cutoff of 0. 05 for SARS-CoV-2 genome and a P-value cutoff of 0. 01 for the Vero and human data sets. Samples processed using MACS2 were scaled by default to the library size to account for differences during peak calling.
The called peaks were annotated according to the nearest genomic feature using annotatePeaks. pl from HOMER v4. 11  (Heinz et al. 2010). Retained, gained, and lost peaks between noninfected and infected cells were identified using BEDTools intersect from BEDTools v2. 29. 2  (Quinlan and Hall 2010). Briefly, peaks were classified as "retained" depending on whether a peak was intersected between noninfected and infected samples; "lost" if a peak was present in noninfected cells but not intersecting with peaks of infected samples; and "gained" if a peak was present in infected cells but not in noninfected cells. Motif analysis on m 6 A called peaks was performed with findMotifsGenome. pl from HOMER and visualized with the universalmotif R package (https://bioconductor. org/packages/universalmotif/) using R computing language (R core team 2022).
Changes in overall m 6 A enrichment across transcripts after infection were calculated by extracting the spike-in normalized m 6 A/input signals from bigWig files within the annotated gene coordinates in the chlsab2 genomes using the rtracklayer package  (Lawrence et al. 2009 ). Further correlation between m 6 A enrichment and gene expression changes (log 2 foldchanges infected vs. noninfected) were then generated. Potential N6,2 ′ -O-dimethyladenosine (m6Am) signals were removed from m 6 A RIP-seq data using the method as described in the Supplemental Material.
Metagene analysis To analyze the genome-wide distribution of m 6 A, a metagene analysis of m 6 A peak density distribution was performed by overlapping the peak coordinates with the following genomic features: 5 ′ UTR, CDS, and 3 ′ UTR obtained from the GTF genome annotation files from UCSC (for chlsab2) or GENCODE v36 (for hg38), considering the longest isoform for each gene. Each transcript was scaled to fixed-size metagene bins according to each reference genome. m 6 A peak density distribution profiles were generated after mapping the m 6 A peaks to the metagene coordinates using the plyranges R package  (Lee et al. 2019). To compare multiple conditions, the relative m 6 A density distributions were calculated using the relative density function from the ggmulti package (https://cran. r-project.
Magnetic beads were washed with low salt buffer (1X PBS, 0. 1% SDS and 0. 5% IGEPAL-CA630) and high salt buffer (5X PBS, 0. 1% SDS and 0. 5% IGEPAL-CA630) before eluting in elution buffer (10mM Tris pH7. 4, 100mM NaCl, 1mM EDTA, 0. 5% SDS) with Proteinase K. RNA was extracted using TRIzol reagent and cDNA synthesis and RT-qPCR were performed as described above. The data is represented as percentage input values.
Excluding potential N6,2′-O-dimethyladenosine (m 6 Am) signals To evaluate the presence of potential m 6 Am modification located at the first-transcribed nucleotide  (Tan et al. 2018; Liu et al. 2020b) , we bioinformatically excluded m 6 A RIP-seq signals located near the transcription start sites (TSS) that could correspond to m 6 Am modifications captured by the m 6 A antibody. For this, the summit coordinates of predicted m 6 A RIP-seq peaks were overlapped with the first 20 nt (close to transcription start sites) of all transcripts using the plyranges package  (Lee et al. 2019)  according to each reference genome (chlsab2, hg38 or wuhCor1). The m 6 A peaks located within the 20 bp regions from transcription start sites were excluded from the analysis.
Data were obtained from the Enrichr database. (H) Scatter plots of differentially expressed genes depicting the correlation between the log2 fold changes in gene expression after B. 1, B. 1. 1. 7, and B. 1. 351 infection and the log2 fold changes reported in publicly available data of SARS-CoV-2 infected Vero cells 24-hour post infection (Riva et al. , 2020). The blue line depicts the linear regression line with 95% confidence interval. Statistics: Pearson's correlation test. (I) Top GO biological process terms associated with up-regulated and down-regulated genes after infection of Vero cells with the B. 1, B. 1. 1. 7, and B. 1. 351 strains. The size of the dots represents the enrichment of genes for a given GO term; the dots are colored according to their significance in log10 p-value. (J) Node connectivity degree distribution of 1000 randomly generated networks of the same size.