dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This review is based upon work from  COST Action  COMBAR  CA16230 , supported by  COST (European Cooperation in Science and Technology)  and from the  Livestock Helminth Research Alliance (LiHRA). HRV and ERM are supported by  UK Research and Innovation (UKRI)  through  BBSRC  grant  BB/ M003949/1 ; HRV is also supported by the  University of Liverpool's Institute of Infection and Global Health , and ERM by  UKRI BBSRC  project  BB/R010250/1  and the  UK Department of Food, Environment and Rural Affairs, Veterinary Medicines Directorate  grant  VM0543. DJB is supported by the  Scottish Government's Rural and Environmental Science and Analytical Services (RESAS) division. MMV was funded by the  Spanish "Ramón y Cajal" Programme of the Ministry of Economy and Competitiveness (MMV ,  RYC-2015-18368 ).
MK and JV were supported by the  Czech Republic Ministry of Education, Youth and Sports INTER-COST  project ( LTC19018 ). The authors would like to thank all COMBAR consortium members who contributed to the development of the database but who did not meet the ICMJE criteria for co-authorship. Thanks are also due to  Carine Paraud  ( ANSES Niort ) and  Philippe Jacquiet  ( National Veterinary School, Toulouse ) for providing unpublished data from sheep and goat surveys for France and to  Vasile Cozma  ( University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Romania ) for providing data from surveys for Romania.
Increasingly, many young people are choosing to marry later or not at all. Data from the OECD Family database show that, between 1990 and 2017, the OECD average mean age at first marriage increased by almost six years  (OECD, 2020a). Women are now, on average, 30 years old when they marry for the first time; men are almost 33 years old. Cohabitation is becoming increasingly popular, especially among young people. As of the last round of major population and housing censuses (for most countries around 2011), on average across the OECD 17% of 20-to 34-year-olds reported living as part of an unmarried cohabiting couple. This is not far off the 23% living either as part of a married couple or in a civil or register partnership (OECD, 2020a). Cohabitation is most common among young people in several of the Nordic countries (Denmark, Norway and Sweden), and Estonia, France, the Netherlands, and New Zealand (OECD, 2020a).
The OECD-31 average excludes Japan. Source OECD Family Database, http://www. oecd. org/els/family/database. htm) In several OECD countries, many mothers use part-time work as a means of combining work and family responsibilities (Fig. 9. 2). Part-time maternal employment is most common in Austria, Germany, Switzerland, the United Kingdom, and especially the Netherlands, where over two-thirds of working mothers work part-time (OECD, 2019b). Short working hours are less common for mothers in other countries, especially the Nordic countries, where comprehensive childcare supports help mothers work full-time, as well as many of the Baltic and Eastern European OECD countries like the Czech Republic, Hungary, Latvia, and the Slovak Republic, where part-time opportunities in general are rare. Increasing maternal employment has contributed to the growth of dualearning.
paternity leave and paid fatherspecific parental and home care leave, in weeks, 2016 (Note Information refers to entitlements to paternity leave, "father quotas" or periods of parental leave that can be used only by the father and cannot be transferred to the mother, and any weeks of sharable leave that must be taken by the father in order for the family to qualify for "bonus" weeks of parental leave. Data refer to entitlements in place as of April 2016. Data reflect entitlements at the national or federal level only, and do not reflect regional variations or additional/alternative entitlements provided by states/provinces or local governments in some countries [e. g. Québec in Canada, or California in the United States]. Source OECD Family Database, http://www. oecd. org/ els/family/database.
Isolated specimens of M. luci in Europe were previously misidentified as M. ethiopica due to their high similarity  (Gerič Stare et al. , 2017). We used long-read Pacific Biosciences Sequel and short-read Illumina HiSeqX sequencing data to produce a high-quality Meloidogyne luci genome assembly. The M. luci population SI-Smartno was isolated from tomato plants grown in a commercial production greenhouse in Šmartno, Slovenia  (Gerič Stare et al. , 2018). A line (V13) was reared from the progeny of a single female and multiplied on tomato (Solanum lycopersicum "Val"). Nematode eggs were obtained by hypochlorite extraction  (Hussey and Barker, 1973)  and cleaned by sucrose flotation  (McClure et al. , 1973). Genomic DNA (gDNA) was obtained by phenol-chloroform extraction from the nematode eggs ground in liquid nitrogen.
Summary statistics of the Meloidogyne luci genome assembly compared to the genome assemblies of other Meloidogyne spp. currently available in DDBJ/ENA/GenBank. The assembled M. luci SI-Smartno genome consists of 327 contigs with a minimum contig length of 10,147 bp and N50 contig length of 1,711,905 bp. The total length of assembly is 209. 16 Mb.
2 31. 4 30. 6 29. 8 30. 2 29. 9 30. 0 30. 2 30. 3 29. 8 30. 0 30. 2 23. 1 27. 4 N50 1,711,905 12,786 16,520 38,588 14,128 10,388 3,698 13,261 10,504 16,462 204,551 10,552 20,482 37,608 Number of contigs/ scaffolds 327 9,538 33,351 12,091 34,316 31,341 58,696 8,887 46,436 26,196 2,224 42,008 4,304 3,450 Genome coverage 200 5 100 100 300 100 200 100 100 100 60 200 180 10 Assembly size (Mb) 209. 16 82. 10 121. 96 183. 53 150. 35 235. 80 96. 67 74. 85 163. 75 258. 07 284. 05 162. 97 38. 19 53. 01 Accession (DDBJ/ENA/ GenBank) ERS3574357 GCA_000180415. 1 GCA_003693645. 1 GCA_900182535. 1 GCA_003693625. 1 GCA_900003945. 1 GCA_000751915. 1 GCA_003693605. 1 GCA_003693565. 1 GCA_900003985. 1 GCA_003133805. 1 GCA_003693675. 1 GCA_002778205. 1 GCA_000172435. 1 Strain/ Species isolate desig- nation M.
The circadian clock is a transcriptional module induced by M1 activation To assess transcriptional regulators that modulate the energetics and inflammatory function of macrophages, we performed RNA sequencing (RNA-seq) comparing interferon-g (Ifn-g) primed bone-marrow-derived macrophages (BMDM) without or with LPS stimulation (10 ng/mL for 8 hr, referred to as M1 activation). Gene ontology analysis using the DAVID platform was performed to identify clusters of transcription factors that were up-or downregulated in inflammatory macrophages, which were used to generate a protein-protein interaction map using STRING (Table  1  and Figure  1 -figure supplement 1A). Several activators of mitochondrial function or biogenesis were repressed, including Myc  (Li et al. , 2005) , Pparg, Pparg co-activator 1 beta (Ppargc1b), and the mitochondrial transcription factor B1 (Tfb1m) and Tfb2m.
C. | >1. 5) in WT bone-marrow-derived macrophages (BMDM) were identified by gene ontology analysis using the DAVID platform. F. C. , fold change. Differentially regulated genes that matched the Transcription GO term in the Biological Processes GO database (accession GO:0006350) were used to generate a protein-protein interaction map using String (Figure  1 -figure supplement 1). Uncharacterized zinc-finger proteins (ZFPs) were omitted from analyses by String. stimulation, compared to control cells (Figure  2 -figure supplement 1F-G). To examine aerobic glycolysis directly, glucose was injected during the extracellular flux assay with or without coinjection of LPS. There was no difference in the basal glycolytic rate between WT and M-BKO macrophages (Figure  2E ). LPS increased ECAR in both genotypes and to a greater extent in M-BKO macrophages.
RNA sequencing RNA-seq was performed on RNA from three biological replicates per treatment. Sequencing and raw data processing were conducted at the Institute of Molecular Biology (IMB) Genomics Core and IMB Bioinformatics Service Core, respectively, at Academia Sinica (Taipei, Taiwan, ROC). In brief, RNA was quantified using the Quant-iT ribogreen RNA reagent (ThermoFisher, R11491), and RNA quality was determined using a Bioanalyzer 2100 (Agilent; RIN > 8, OD 260/280 and OD 260/ 230 > 1. 8). RNA libraries were prepared using the TruSeq Stranded mRNA Library Preparation Kit (Illumina, RS-122-2101). Sequencing was analyzed with an Illumina NextSeq 500 instrument. Raw data were analyzed using the CLC Genomics Workbench. Raw sequencing reads were trimmed by removing adapter sequences, low-quality sequences (Phred quality score of <20) and sequences >25 bp in length.
The trimmed reads were then mapped to the mouse genome assembly (mm10) from University of California, Santa Cruz, using the following parameters: mismatches = 2, minimum fraction length = 0. 9, minimum fraction similarity = 0. 9, and maximum hits per read = 5. Gene expression was determined by the number of transcripts per kilobase million. Functional annotation clustering of differentially regulated genes was done using DAVID (https://david-d. ncifcrf. gov/), and the interaction maps of transcriptional regulators that were induced or repressed by M1 activation that are shown in Figure  1 -figure supplement 1A were generated using STRING (https://stringdb. org/). Significantly changed genes were determined by p<0. 05 and FDR <0. 05. Data have been deposited in GEO under the accession number GSE148510.
Acknowledgements We thank  Drs D Cohen ,  D Sinclair , and  P Weller  for critical comments,  A Yesian ,  L Dai ,  P Basak ,  R Chen ,  P Hu , and  F Onal  for technical help and the  Academia Sinica  ( Taipei, Taiwan, ROC ) for RNA sequencing/data analysis ( IMB Genomics Core and Bioinformatics Service Core)  and grant support ( AS-106-TP-L08  [to NSL],  AS-106-TP-L08-1  and  AS-106-TP-L08-3 ). YHL was supported by funds from the  Ministry of Science and Technology, Taiwan , ROC. This work was supported by grants from  NIH  ( F31GM117854  to RKA;  F31DK107256  to NHK;  R01DK113791  and  R21AI131659  to CHL) and the  American Heart Association  ( 16GRNT31460005  to CHL).
Reducing postharvest losses (PHLs) of food crops is a critical component of sustainably increasing agricultural productivity. Many PHL reduction interventions have been tested, but synthesized information to support evidence-based investments and policy is scarce. In this study, PHL reduction interventions for 22 crops across 57 countries in sub-Saharan Africa and South Asia from the 1970s to 2019 were systematically reviewed. Screening of the 12,907 studies identified resulted in a collection of 334 studies, which were used to synthesize the evidence and construct an online open-access database, searchable by crop, country, postharvest activity and intervention type. Storage technology interventions mainly targeting farmers dominated (83% of the studies). Maize was the most studied crop (25%).
Measurement of PHL. PHLs are multidimensional and can be measured in different ways, both quantitatively (physical loss) and qualitatively (for example, increased damage, decay, breakage, contamination with toxins, reduced seed viability and deterioration in the nutrient content or economic value of a product)  5, 16. These losses can be assessed using a range of metrics depending on the focus of the research or intended use of the crop. For each intervention studied, data for one quantitative and one qualitative loss metric were included depending on the evidence presented in the respective study. To support the comparative efficacy analyses, the different loss measurements were aggregated into groups (Supplementary Tables  4  and 5 ).
Searches. A search strategy was developed in May 2019 and used to sequentially search CAB Abstracts, Web of Science, Scopus and 47 additional electronic database and grey literature sources. The 14,576 records identified were deduplicated, resulting in 12,786 documents for title and abstract screening. A second search was done on 30 October 2019 to ensure that the evidence-base was as current as possible, yielding 121 additional studies.
Study exclusion criteria. Studies were excluded if they: • Did not include a PHL reduction intervention for one of the 22 focal food crops • Did not take place in SSA or South Asia • Were not relevant to PHL reduction by small-scale producers or their associated value-chain actors Title and abstract screening. The titles and abstracts were auto-coded by semantic machine-learning models and prescreened using filters and a Python script to expedite the exclusion of studies not related to the focal crops or geographies. The title and abstract of each of the 12,907 studies were then screened independently by two of the postharvest researchers. Full-text article screening. The 1,906 studies included during the title and abstract screening stage were read to determine whether to include them in the evidence-base.
A flow chart of the number of studies and exclusion reasons is shown in Extended Data Fig. 1 , and the included studies are listed in Supplementary Table  1. Data extraction and synthesis. Data from the 334 included studies were extracted into an SQL database version of the coding framework (Supplementary The systematic method that we followed aims to capture and rigorously screen all the relevant literature to fully explore, map and compare the existing evidence and to identify gaps and reduce authorial bias. The full details of the methodology are provided in the Methods, and the preregistered protocol is available at https://osf. io/6zc92/.
Just one study investigated the effect of farmer training, and that was nearly 50 years ago  17. Maize. For the analysis of quantity loss for different maize storage interventions (tier 3), percentage weight loss data were used. For quality loss, the percentage of damaged or discoloured grains was used, although many other quality loss measurements were recorded in the maize studies (Supplementary Table  5 ). As the studies presented loss data from different storage durations (ranging from 1 to 12 months), the data for a standardized storage period of six months were used to facilitate comparison. Of the 78 studies on maize storage methods, 74 were from SSA and 4 were from South Asia (India and Nepal). The storage method included details of both the facility in which the crop was stored and the protectant used.
4a, b ). The storage methods investigated included the effect of admixing pesticides (botanicals, synthetic chemicals, DEs or ashes) with grain legumes stored in bags (with and without air-tight liners), clay pots, plastic or metal containers, or traditional granaries. Most of the legume loss data were for non-synthetic chemical interventions, such as cowpea storage in hermetic bags (Extended Data Fig. 4b ). Hermetic bags were clearly more effective in reducing quantity and quality losses in cowpeas, groundnuts and beans, when compared with traditionally used practices or untreated controls (Supplementary Fig. 3a, b ). Other interventions that kept the grain damage levels at least 20 percentage points lower than the untreated control included mixing synthetic chemicals, botanicals or DEs with cowpeas or beans before storing them in sacks (Supplementary Fig.
Additionally, more data on multiseason and multisite testing of interventions are required to provide a critical understanding of their replicability and degree of variation. Loosening the inclusion criteria would increase the number of studies, but it would compromise the value and quality of evidence on which the synthesis was based. Despite the systematic approach used and the recognition of the four principles (inclusive, rigorous, transparent and accessible) identified by Donnelly et al. 48 for synthesizing evidence for policymakers, the present evidence-base is subject to non-publication bias, as studies of less effective interventions would not have been widely shared. Furthermore, where there is no requirement for PhD or MSc theses or project reports to be registered in public databases, digital search strategies do not always identify these important sources of evidence.
Cost, access, ease of use and reuse, cultural acceptability, one-time subsidies, willingness to pay, scale, awareness and demonstrations, and training are just some of the factors influencing the uptake of PHL reduction interventions along with technical efficacy  11, 46, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73]. Notwithstanding the limited size of the evidence-base, the efficacy of a number of interventions in reducing PHLs was recognized. A summary of these notable interventions and critical gaps in the evidence-base is presented in Table  1 , followed by a set of policy recommendations (Box 2). A deeper analysis of the dataset is available from the authors, and the interactive database at https:// PHCeres2030. net/, which will be updated biannually, provides users with an opportunity to identify relevant studies and better tailor the data outputs to their specific needs.
Search strategy. A comprehensive search strategy was developed to identify the relevant published and grey literature. The search terms included variations of the key concepts in the research question: PHLs in quantity or quality, postharvest activity stages, PHL causing factors, focal food crops and focal countries. The search strings used are shown in Supplementary Table  10a-c. The following online databases of peer-reviewed publications were sequentially searched on 27 May 2019: CAB Abstracts (date coverage, 1973-2019), Web of Science Core Collection (date coverage, 1900-2019) and Scopus. These searches returned 8,880 records, 3,570 records, and 315 records, respectively, after screening for duplicates using Zotero bibliographic software. The searches were not limited by date or language.
However, the search terms were done only in English. The search strategy was pretested and refined, and it used eight benchmark articles to maximize its comprehensiveness (Supplementary Table  11 ). However, three of the eight benchmark articles (a 1991 Acta Horticulturae study and two grey literature reports) were not indexed and were not accessible in any of the databases searched. In addition, 47 electronic database and grey literature sources identified by the postharvest team members were searched by librarians on 24 May 2019 (Supplementary Table  12 ). These grey literature searches involved various combinations of the following terms: 'post-harvest' , 'post-harvest loss' , 'post harvest losses' , 'post harvest' , 'postharvest' , 'value chain' , 'crops' and 'food'. After searching, the results were screened to ensure that 'postharvest' and 'loss' were found in each report.
These searches returned 1,811 records, which were combined with those from the databases to give 14,576 records, which were deduplicated using a Python (v. 3. 8. 0) script. Duplicates were detected using the title, abstract and year of publication, where the year of publication was a match, the title cosine similarity was greater than 85% and the abstract cosine similarity was greater than 80% (or one or both of the abstracts was missing). When this occurred, the duplicate entry was removed. CAB Abstracts was the priority source of record. In contrast to the PHL review in six SSA countries by Affognon et al. 11  , where grey literature physically acquired through national teams made up 57. 3% of the documents, the current study's digital search strategy captured relatively few PhD/MSc theses, working papers or project reports.
More study of how national policies, financial access and infrastructure investments affect PHL reduction 5. Implementation of policies that support quality-sensitive markets to provide incentives for PHL reduction 6. Multistakeholder postharvest platforms or institutions to promote co-learning and co-innovation, support access to information, and support multilocation and multiseason studies with active participation of stakeholders along the commodity value chains • Targeting of the aforementioned recommendations may be needed depending on limitations of financial resources and information, and whether the main objective for reducing PHLs is improved food security and nutrition or lower environmental impacts. processing before title and abstract screening by the team. A second search phase of the three online literature databases for the year 2019 was done on 30 October 2019 to ensure that the evidence-base was as current as possible.
The fumigation aspect was then added to the intervention's description. In the studies of the perishable crops, those with less than 20 kg per treatment for roots and tubers and with less than 10 kg per treatment for fruits and vegetables were excluded. For studies where the number of fruits was stated but the weight was not, we used a typical weight for that produce type to determine inclusion or exclusion. Studies that failed to state the size used in the treatments and where the size could not be inferred from the data were excluded. For some studies, in which the interventions were evaluated on a range of different grades or varieties, the results were averaged to achieve the weight expectations required for inclusion. • Language: studies written in a language other than English or French were excluded.
• Date: no date restrictions were applied, but the searches were limited due to the coverage of the individual databases searched. Title and abstract screening. The titles and abstracts of the search outputs were auto-coded by semantic machine-learning models and then prescreened by six team members in MS Excel using filters and a Python script to expedite the identification and exclusion of studies not related to the focal crops or geographies. The auto-coded fields-topics, countries, plant and animal products, populations, outcomes, interventions (technology, socio-economic, ecosystem, storage and mechanization) and measurements for interventions and for crops-were intended to help derive metadata from the individual citations for later sensitivity analyses and expedite the process of synthesizing the evidence.
However, these fields were not found to be sufficiently accurate for this study. For example, the machine could not distinguish between countries mentioned in the title, abstract, bibliographic information or organism names (for example, Rhyzopertha dominica) and the country where the study happened, as it could not understand the context. Further training of the machine in close collaboration with expert researchers would improve the utility of the auto-coded outputs, but the rapid start-up and tight time frame of this study meant that building a more contextual base to train the machine was out of our scope. The auto-coded search outputs were then imported into the web-based software platform Covidence for screening. Those studies identified for exclusion during the prescreening filtering (that is, wrong crop or wrong country) were then manually excluded on Covidence.
There were many irrelevant studies in the initial library (for example, studies on cocoa or coffee beans, silage or soil; reviews; and studies from other countries and languages), and filters were used to search for and exclude them. The titles and abstracts of the additional 121 studies from 2019 were double-screened using MS Excel, and the reasons for exclusion were recorded. The use of Covidence for the title and abstract screening enabled the records to be double-blind screened and the decision on whether to include them to be captured. However, it did not enable the reasons for exclusion to be recorded. Full-text article screening. A total of 1,887 studies from the initial search and 19 from the 2019 updated search were selected for full-text article screening. The full texts were sourced by the librarian team members from July to December 2019.