Buchnera, are members of a species interaction network. Ecological interactions across such networks can result in phenotypic change in network members mediated by molecular signals, like microRNAs. Here, we interrogated small RNA data from the aphid, Myzus persicae, to determine the source of reads that did not map to the aphid or Buchnera genomes. Our analysis revealed that the pattern was largely explained by reads that mapped to the host plant, Brassica oleracea, and a facultative symbiont, Regiella. To start elucidating the function of plant small RNA in aphid gut, we annotated 213 unique B. oleracea miRNAs; 32/213 were present in aphid gut as mature and star miRNAs. Next, we predicted targets in the B. oleracea and M. persicae genomes for these 32 plant miRNAs. We found that plant targets were enriched for genes associated with transcription, while the distribution of targets in the aphid genome was similar to the functional distribution of all genes in the aphid genome. We discuss the potential of plant miRNAs to regulate aphid gene expression and the mechanisms involved in processing, export and uptake of plant miRNAs by aphids.Like all organisms, aphids, plant sap-sucking insects that house a bacterial endosymbiont called  Typically, insects that feed on plant sap critically depend on bacteriome housed endosymbionts to supplement dietary shortfalls in essential amino acids and vitamins4. Many insects also host facultative symbionts that confer benefits such as resistance to attack by parasitoids and increased tolerance to heat stress5. For example, the pea aphid, Acyrthosiphon pisum, that obligately hosts the endosymbiont Buchnera aphidicola, has also been reported to host a facultative symbiont, Candidatus Hamiltonella defensa encoding a bacteriophage that provides strong protection against parasitic wasps7. While aphid guts contain a relatively limited microbiome9, aphids such as M. persicae, vector hundreds of different plant viruses11. Thus, the species interaction network of an aphid includes its obligate and facultative symbionts, its host plant, its gut microbiome, the microbiome of its host plant, and the viruses it vectors  data repository as BioProject PRJNA395356 identified a striking difference between gut and bacteriome tissue samples .For gut samples, we mapped reads of unknown origin against two microbial libraries. The first was a bacterial library that contained the genomes of aphid facultative symbionts, aphid gut associated bacteria, and plant associated bacteria, while the second was a viral library that contained the genomes of plant viruses that aphids are known to vector Fig. . We founB. oleracea small RNAs found in M. persicae gut contain plant miRNAs, we annotated miRNAs in the B. oleracea genome using publicly available small RNA-seq data. Out of a total of 147 miRNA precursors in the B. oleracea genome 138 were homologous to known plant miRNA precursors (miRBase v22) and 9 were novel miRNA precursors , including bol-miR395d, bol-miR_novel_104, and bol-miR168b-3p, as well as three members of the bol-miR166 family , three members of the bol-miR159 family , two members of the bol-miR162 family , and two members of the bol-miR168 family . In contrast, we found perfect matches for only four B. oleracea miRNAs in the aphid bacteriome tissue datasets with each miRNA represented between one and 16 times. Two of the four B. oleracea miRNAs identified in bacteriome were also represented in the gut datasets , we predicted 576 B. oleracea target sites for the 32 B. oleracea miRNAs detected in M. persicae gut , we predicted 926 gut Fig. . The 926gut Fig. .Figure 4B. oleracea miRNAs to clusters of orthologous groups (COGs)19. We used Monte Carlo simulations to test the significance of over and under-representation of each COG in our predicted target sets relative to the representation of COGs in the official gene set of the corresponding genome. In the M. persicae genome, we assigned a functional COG to 664 of the 965 predicted targets, and in the B. oleracea genome, we assigned functional COGs to 330 of the 576 predicted targets.We performed a broad functional analysis by assigning the transcripts targeted by the 32 p = 3.5 × 10−3), “RNA processing and modification” (p = 2.2 × 10−2), and “Nuclear structure” (p = 6.2 × 10−3) were significantly overrepresented while four COGs “Amino acid transport and metabolism” (p = 2.0 × 10−4), “Cytoskeleton” (p = 1.6 × 10−2), “Replication, recombination, and repair” (p = 1.5 × 10−3), and “Translation, ribosomal structure and biogenesis” (p = 2.6 × 10−2) were significantly underrepresented , “Amino acid transport and metabolism” (p = 6.0 × 10−3), and “Chromatin structure and dynamics” (p = 1.8 × 10−2) were significantly overrepresented with “Transcription” being the most significantly overrepresented as well as the COG that included the largest fraction of the B. oleracea genome targets , “Intracellular trafficking, secretion, and vesicular transport” (p = 1.5 × 10−2), “Translation, ribosomal structure and biogenesis” (p = 1.6 × 10−3), “Secondary metabolites biosynthesis and catabolism” (p = 1.9 × 10−2), “Energy production and conservation” (p = 1.1 × 10−2), “Replication, recombination, and repair” (p = 4.2 × 10−2), and “Defense mechanisms” (p = 2.1 × 10−2). In both genomes, “Replication, recombination, and repair” was significantly underrepresented.In the set of targets in the M. persicae gut small RNA data against full-length miRNA precursors of the 32 B. oleracea miRNAs present in our M. persicae gut datasets , 15926 (from mature leaves), and 156 (from young leaves)27 miRNAs in B. oleracea, the intersection of these three sets only indicated 15 miRNAs . Of the 213 miRNAs, 86 were previously reported in at least one of the earlier B. oleracea annotations27 44 into exosomes within multivesicular bodies43. Co-packing of the miRNAs with Ago1 facilitates stabilization and function of the secreted miRNAs when the exosome reaches its destination45. As the multivesicular bodies fuse with the plant cell membrane, the miRNA-containing exosomes are exocytosed into the circulating apoplastic fluid of B. oleracea47. The apoplast then carries the exosomes to the phloem where they can achieve long-distance transport and contact with the feeding aphids. Since aphids feed passively on moving phloem, miRNA-containing exosomes are brought into the aphid digestive tract through the aphid stylet when it is inserted into the plant sieve elements. Once in the aphid digestive tract, the host plant miRNAs are protected by their exosome membrane and move into aphid gut cells by endocytosis44. Once inside aphid cells, the B. oleracea miRNAs and associated proteins are released to function alongside the aphid’s own long-distance miRNAs48.If plant circulating miRNAs do indeed function to target aphid gene expression, what processing, export, transport and uptake mechanisms may be involved? By integrating our observations with current knowledge about the production, processing, and export of miRNAs, we propose that trafficking of plant miRNA into aphid tissues is mediated by exosomes. First, B. oleracea circulating miRNAs in gut and bacteriome tissues of the aphid M. persicae. COG analyses did not reveal strong targeting of any one functional group of aphid genes by host plant miRNAs. However, we argue that other work with transgenic plants that have been engineered to intracellularly express miRNAs and dsRNA that target aphid genes demonstrate the presence of a physiological mechanism for cross-kingdom miRNA targeting from plants to aphids. Future in vitro and in vivo studies are required to test the cross-kingdom miRNA::mRNA interactions predicted here.Here we document the presence of Myzus persicae lines G006, G002 and USDA from NCBI BioProject PRJNA395356. Sample preparation and initial data processing is described in Feng et al.12. Briefly, we processed the raw small RNA sequencing reads by first filtering out low quality reads, and then trimming sequencing adapters and primers. Mature miRNAs are typically 22 nucleotides in length, and thus following initial filtering and trimming, we retained reads that were 17–35 nucleotides long. For each sample, the set of 17–35 nucleotide long processed reads were used for genome mapping as described below. Aphid lines were raised at the University of Miami on Brassica oleracea var. Wisconsin Golden Acre.We used the small RNA (sRNA) data for M. persicae G006 v2.0 genome23 from AphidBase (http://www.aphidbase.com) and their respective Buchnera aphidicola genomes: G006, NCBI RefSeq GCF_001939165.123; G002, NCBI RefSeq GCF_000521565.124; and USDA, NCBI RefSeq GCF_000521525.124. Following alignment, we identified reads that aligned with less than two mismatches and those that failed to align to the aphid and/or Buchnera genome; these reads were interrogated for alignment against three custom libraries. The first library contained the genome of the host plant, Brassica oleracea. The B. oleracea var. oleracea genome (NCBI RefSeq: GCF_000695525.113) was used because the genome of var. Wisconsin Golden Acre had not been sequenced. The second library contained the genomes of aphid secondary bacterial symbionts, aphid gut associated bacteria, and plant associated bacteria14 (Supplementary Table 16 (Supplementary Table Using Bowtie2 v2.2.6. (parameters: -L 10–very-sensitive -a) gut and bacteriome sRNA data were each aligned against the B. oleracea small reads from our three aphid gut libraries included plant miRNAs, we reannotated B. oleracea miRNA precursors  using the recently revisited set of plant miRNA annotation criteria18. Prior to annotation we used FastQC v.0.11.4 to assess the quality of these datasets trimming sequences with a phred 33 score less than 28 and retaining sequences 17–25 nucleotides in length. For each of the four B. oleracea BioProjects, we used ShortStack v3.8.549 to identify miRNA precursors against the B. oleracea genome13. In ShortStack, hairpins were limited to 300 nucleotides, and miRNA size was limited to 20–24 nucleotides18. We set minimum coverage at 0.25 reads per million mapped and determined the placement of reads with multiple mapping locations by considering the density of all mapped reads. ShortStack predicts miRNA precursors from a user-supplied reference genome and small RNA-seq data. The small RNAs are aligned to the genome, and areas of high coverage are identified. High coverage areas with appropriate mapping are analyzed for hairpin structures and low mismatch interactions between the mature and star miRNAs. In ShortStack, mature miRNAs are those that have the higher number of mapped reads while star miRNAs have the lower number of mapped reads. For those miRNAs annotated here, mature or star miRNA designations are assigned as such. This does not reflect the 5′/3′ naming convention.To test our hypothesis that −10 in local BLAST searches against miRBase v2250. Precursors with e-values ≥10−3 were identified as novel. Precursors with e-values <10−3 and ≥10−10 were further scrutinized such that precursors with query coverage ≥60% were identified as known, while precursors with query coverage <60% were subject to additional BLAST searches of their mature and star sequences using local short-BLAST against miRBase v22. Short-BLAST matches with e-values <10−10 were identified as known, and matches with e-values ≥10−10 were identified as novel. Ultimate determination of novel precursors was performed following the updated plant miRNA precursor criteria found in Axtell and Myers18.Following annotation, we collapsed miRNA precursors from each of the four BioProjects into a single data set. Duplicates across the BioProjects were removed by retaining the duplicate with the most reads mapped to the mature miRNA. Known precursors were identified as those having e-values <10B. oleracea miRNAs in M. persicae 3′ UTRs (AphidBase GFF) and in B. oleracea cDNA transcripts using psRNAtarget51. Potential miRNA targets in the aphid genome were predicted using three programs: miRanda v.3.3a52 , PITA v653 , and RNAhybrid v2.1.254 . Targets in the plant genome were predicted using psRNAtarget51 and TargetFinder55. The two plant genome prediction programs were selected based on the analysis of Srivastava et al.56 using their optimized scoring parameters. All five miRNA target prediction programs use sequence complementarity in the seed region of the miRNA as well as the energetics of miRNA::mRNA binding to assess the ability of a miRNA to successfully bind to an mRNA. Predicted targets were manually filtered to include only those in which all respective target prediction programs overlapped in the miRNA seed region  built from the insect (inNOG) database in EggNOG. As a comparative dataset, the entire M. persicae G006 proteome v2.0 (AphidBase) was run through a parallel COG annotation pipeline. In comparing these COG datasets, Monte Carlo simulations were performed to analyze the over/under-representation of target COGs relative to the reference proteome distribution. To generate null distributions, 926 3′ UTRs  using eggnog-mapper v1.0.3B. oleracea cDNA library (psRNAtarget51) was run through a parallel COG annotation pipeline. To produce Monte Carlo simulations that complement the aphid proteome analysis, 576 cDNA transcripts  database in EggNOG. As a comparative dataset, the entire 59 and visualized using VARNA v3-9360 (Supplementary Document M. persicae gut small RNA data against miRNA precursors of the 32 B. oleracea miRNAs present in our gut datasets, filtering alignments to exclude any with mismatches or gaps (Supplementary Document The secondary structure of each precursor was produced using RNAfold v2.4.4Not required. The work did not involve vertebrates.Supplementary informationSupplementary informationSupplementary informationSupplementary informationSupplementary informationSupplementary informationSupplementary information


We provide a dataset for our research article “Profitability, Efficiency and Inequality in Double Auction Markets with Snipers” [1]. This dataset [2] includes configuration files, raw output data, and replications of calculated metrics for our robot-populated market simulations. The raw data is subdivided into a hierarchy of folders corresponding to simulation treatment variables, in a 2 × 2 × 21 design for 84 treatments in total. Treatments variables include: (i) robot population ordering, either “primary” or “reverse”; (ii) two market schedules of agent's values and costs: equal-expected-profit “market 1” and unequal-expected-profit “market 2”; (iii) 21 robot populations identified by the number of Sniper Bots (0–20) on each side of the market. Each treatment directory contains a simulator input file and outputs for 10,000 periods of market data. The outputs include all acceptable buy and sell orders, all trades, profits for each agent, and market metrics such as efficiency-of-allocation, Gini coefficient, and price statistics. An additional public copy in Google Cloud is available for database query by users of Google BigQuery.Econ1.Net. We provide instructions for repeating individual simulations using the free open source simulator and the free container tool Docker.The market simulator software is a private product created by Paul Brewer at Economic and Financial Technology Consulting LLC. Free open source modules are available for tech-savvy users at GitHub, NPM, and Docker Hub repositories and are sufficient to repeat the simulations. An easier-to-use paid market simulation product will eventually be available online from  The proportion of SB robots is always 5% multiplied by the folder number or case id. The “sim.json” file authoritatively defines the various agent and market parameters fed to the software for generating the accompanying output data. It can be used to repeat a treatment (subject to randomness).Output example:/primary/market1/data/12/buyorder.csvAgain, this raw data output file belongs to the specific simulation involving the three treatment settings “primary”, “market 1”, and “case id 12”. As described in the “Archive-File-Guide”, files named “buyorder.csv” contain one buy order per row, for the 10,000 periods of data generated for that specific treatment.2./primary/metrics/data and /reverse/metrics/data. All metrics involve a replicated analysis generated through Google BigQuery SQL and/or the gini-csv tool [Analyzed data  similar to the data in tables and figures in Ref.  can be fcsv tool  from thecsv tool  either iExample:/primary/metrics/data/Table2.csv contains a table reporting an aggregate stats calculation like that reported in Ref. [/primary/metrics/software/Table2.sql. in Ref.  at Table3.Econ1.Net orchestration tools is in the associated data folder. The configuration files associated with individual treatments are produced from this file and are in the numbered treatment folder.The configuration file associated with a group of simulations executed via the proprietary Example:/primary/market1/data/config.json contains the proprietary Econ1.Net configuration file used to generate the various single-treatment configurations in /primary/market1/data/NN/sim.json  for the open source portion of the simulator software.4.market1 or /*/ market2 folder in a file called original-file-name. These timestamps are also the original zip filenames created by the Econ1.Net software running on Google Cloud.Final Timestamps in Universal Time Zone (UTC) for the multi-simulation outputs are recorded in the relevant /*/Data files are organized as follows:1.1‘project:dataset’, ‘project:dataset.table’ or ‘project.dataset.table’, depending on context. These identifiers are as follows:A copy of archive data is stored in Google BigQuery database for ease of future analysis or replication. Therein, datasets and tables have global identifiers Project is always ‘eaftc-free’.Dataset is one of:‘BrewerRatan2019Market1’ or ‘BrewerRatan2019Market2’ for primary raw market data‘BrewerRatan2019Market1R’ or ‘BrewerRatan2019Market2R’ for reverse raw market data‘BrewerRatan2019Metrics’ for analyzed metrics based on primary data‘BrewerRatan2019MetricsR’ for analyzed metrics based on reverse dataTable is one of:‘buyorder’, ‘sellorder’, ‘trade’, ‘ohlc’, or ‘effalloc’ for primary or reverse raw market data;or, generally, the associated archive file name without “.csv” or dashes for the analyzed dataThe cases 00 to 20 contained in multiple files spread across the numbered data folders in the data archive are consolidated into single tables in Google BigQuery and are distinguished using the ‘caseid' column.22.1•(2 choices) A sort order for replacing ZI robots with SB robots, either “primary” (buyer 1 and seller 1 (id 21) are first to change from ZI to SB) or “reverse” (buyer 20 and seller 20 (id 40) are the first to change from ZI to SB).•(2 choices) A set of market value and cost parameters for each agent, either “market1”  or “market2” . See Ref. [•21 choices) A population treatment of ZI and SB robots. Treatment N, 0≤N ≤ 20, has N SB Buyer robots, (20-N) ZI Buyer robots, N SB Seller robots, and (20-N) ZI Seller robots. See Ref. [ choices The 2 × 2 × 21 design involves every combination of the following three treatment variables:2.22.2.1Both authors participated in the process of data generation for exploration and topic identification with earlier prototypes of the simulator software.2.2.1.11.config.json file provided. The robot-replacement direction (primary or reverse) and the market value/cost parameters (market1 or market2) are held constant in this configuration and the variation from one simulation to another occurs in the robot populations from all ZI (0% SB) to all SB (100% SB). The robot replacement direction requirement (primary or reverse) determines the fields ‘morph.buyerAgentType’ and ‘morph.sellerAgentType’ . These fields control transforming the first configuration (where fields ‘buyerAgentType’ and ‘sellerAgentType’ each contain an array of 20 “ZIAgent”) provided in config.json into the second configuration (where fields ‘buyerAgentType’ and ‘sellerAgentType’ each contain an array of 20 ‘KaplanSniperAgent’). The market1 or market2 value and cost parameters appear in the fields ‘buyerValues’ and ‘sellerCosts’ within the ‘common’ subsection as they are constant.Design a 21-simulation configuration in Econ 1.Net's web-based editor, creating the 2.config.json file in Econ1.Net for 10,000 periods on Google Cloud. Econ1.Net rents on demand a pre-emptible Google Compute Engine instance of type n1-highcpu-16 , and transfers the config.json file. The instance boots Linux, runs proprietary software, and the open source simulator from npm: single-market-robot-simulator . The proprietary software creates a primary folder and 21 simulation folders numbered 00 to 20. It creates the 21 sim.json files for the individual simulations, and orchestrates running independent copies of single-market-robot-simulator in 21 parallel processes, reporting progress and eventually delivering all raw market data output files within a single timestamped.zip file to a Google Drive folder.Run the For this dataset, Paul Brewer performed the data generation procedure four times, once for /primary/market1, /primary/market2, /reverse/market1, and /reverse/market2. The files reported here are the original files as generated and organized by the simulator, except that the files of buy and sell orders not meeting the bid/ask improvement rule (rejecbuyorder.csv and rejectsellorder.csv) have been omitted for reasons of space and relevance. The data generation procedure involves 2 steps:1.Paul Brewer shares the relevant data folders in Google Drive as produced by the simulator with Anmol Ratan in Australia. Both researchers download the zip files, decompress the contents, and modify the data folder organization to suit tasks at hand.2.Ad hoc analysis occurs with various tools. Procedures and numerical outputs are discussed and compared between the researchers during Google Hangout videoconferences.3.The Gini coefficients - based on total profit aggregated over 10,000 periods - were calculated in Google Sheets in a custom function .4.The final tables and figures were polished and finalized by Anmol Ratan in Stata.Original Analysis Procedure.In the research article , we focu2.2.2data and software subfolders for /primary/metrics/*. Each file in a data folder has been generated either by Google BigQuery or the gini-csv tool [software folder provides relevant sql queries and scripts for producing the data files.1.○“Primary” raw data is loaded into datasets “BrewerRatan2019Market1” and “BrewerRatan2019Market2″○“Reverse” raw data is loaded into datasets “BrewerRatan2019Market1R” and “BrewerRatan2019Market2R”.All raw data were first uploaded to Google's BigQuery database by consolidating files with the same name (e.g. buyorder.csv) and creating publicly accessible datasets corresponding to the first two treatment variables within the “eaftc-free” namespace:2.Table names within each dataset are determined automatically by keeping the first part of each.csv file name: buyorder, effalloc, ohlc, sellorder, and trade.3.software were provided to generate SQL views  and organized in these additional Google BigQuery datasets:○Analyzed “Primary” data is stored in BrewerRatan2019Metrics○Analyzed “Reverse” data is stored in BrewerRatan2019MetricsRThe provided.sql queries in csv tool  for Gini4.The data were downloaded from Google BigQuery into the files shown in Note: The.sql queries can be executed directly against the data by anyone with access to Google's BigQuery web console   occur in the npm module ‘market-agents’.d.Deciding what to log and when occurs in single-market-robot-simulator but the logging itself is delegated to the npm module ‘simple-isomorphic-logger’.e.Other functionality takes place as referenced in each module's package.json file.JavaScript software modules critical to operating the simulation are posted as open source on GitHub and NPM repositories. Specialization and delegation occur through this modular design.2.Separation of these tasks into different modules allows for independent testing. Each module includes numerous software tests that exercise important functionality through a sequence of examples. These tests are located in the “test” subdirectory of each module.3.isomorphic JavaScript. This reduces bugs and differences that might occur from mismatched code. A simulation running in Google Cloud should produce the same mathematical operations seen on the web browser. The cloud can run multiple simulations in parallel and for more periods or otherwise at larger scale.Modules are designed to run both on a server (with nodejs) or on a researcher's PC in their web browser from the same lines of code, also known as 4.Because these modules are open source, they qualify for free automated testing by the testing service Travis-CI. Reports from Travis-CI are available by clicking on the “build” badge in each module repository's documentation.5.drpaulbrewer@eaftc.com). This aids in detecting unforeseen issues when restructuring code or adding new features.When a change to the software is posted to the GitHub repository, Travis-CI receives a notification, runs the automated testing, changes the “build” badge to passing/green or failing/red, and sends a test report to the software author  meeting the market improvement rule, a row is recorded in the simulator raw output data in file buyorder.csv. These rows are also combined into the buyorder tables in Google BigQuery. The relevant columns for recovering the original unit value parameters are ‘id’ and ‘buyerValue’. To scan and collate all of buy orders, periods, and treatments, a single query is sufficient.2.For buyer values/primary/market1, execute in Google BigQuery console :SELECT DISTINCT id,buyerValueFROM ‘eaftc-free.BrewerRatan2019Market1.buyorder'ORDER BY id asc, buyerValue desc;3.Confirm that all values (there should be at most 3 per buyer agent) agree with the buyer unit values in the research design and no extraneous unit values are reported.4.Repeat for market2, substituting “Market2” for “Market1” in step 2 above.5.Repeat steps 2–4 for the reverse data, substituting Market1R or Market 2R6.For seller costs, execute in Google BigQuery console:SELECT DISTINCT id,sellerCostFROM ‘eaftc-free.BrewerRatan2019Market1.sellorder'ORDER BY id asc, sellerCost asc;7.Confirm that all costs (there should be at most 3 per seller agent) agree with the seller unit costs in the research design and no extraneous unit costs are reported. Transform the id number reported here to the “Seller Id” by subtracting 20. For example, the seller id number for the first seller is 21 in the software . The first seller is described as “Seller 1” in the research design.8.Repeat steps 6–7 for Market2, Market1R, Market2RThis procedure is a list of explicit steps and was performed as part of checking that the software performed as expected in reading the market parameters, particularly the robot traders value and cost parameters, and including them in relevant output in buy order and sell order raw data.2.2.4This section describes installing and using simulation software from the internet on Windows, using Docker Desktop. While we expect the download archives to be well maintained, there are always security risks that you can mitigate by asking for local technical assistance.For research transparency and provenance, it is desirable for other researchers to have the capability to replicate simulations with a tool similar to or part of the original software. Ideally, a version of the tool should be frozen and remain fixed over time and be independent of the latest changes and improvements. It should be convenient to run on commonly available computers, i.e. Windows, without overly complex installation. Ideally, the tool should be free – with source code available - and not involve permission from or tracking by the original researchers. Although source code should be available for techs to review, only “average researcher” knowledge should be required to run the tool to replicate a simulation.These requirements are satisfied by a container technology called Docker that allows packaging up entire Linux software systems and easily running them elsewhere.1.Find an appropriate computer.Consider potential for abusive users or intruders to use Docker Desktop to run unauthorized spy software or other unwanted software, access to sensitive information , and local rules about installing software.At least 2 CPU cores, 8 GB Ram, 20GB free disk space.A copy of the dataset should be downloaded and unzipped.Mac or Linux: see special section below for minor changes to these proceduresNot compatible: Windows 10 for Home; older version of Windows; most early Chromebooks2.Install Docker DesktopThis is only required once per computer.Install Docker Desktop  by visit3.Open Command Prompt. Type “command prompt” in the search box at the lower left of Windows 10 to find the app that creates (black background) command windows.4.docker run hello-worldentered into the command window will test your docker installation and internet connection to Docker Hub. You should see some messages, including “Hello from Docker! This message shows that your installation appears to be working correctly.” See also Docker's “Get Started” page .5.mkdir C:∖workentered into the command window creates a folder for containing the input and output files. You can choose a different folder location - but then you must change subsequent steps to match.6.cd C:∖workentered into the command window changes directory to C:∖workThe prompt should change to indicate the command window is working with C:∖work. If there is an error, check step #5.7.Go to the file manager and find the dataset folder and the sim.json file for the treatment you wish to replicate. Copy the sim.json file to C:∖work.This example uses the dataset file /primary/market1/data/10/sim.json8.type sim.jsonentered into the command window displays the sim.json file for confirmation.Note: this command is “type sim.json”, not merely the act of typing “sim.json” alone.If the sim.json file does not exist, check step #7.Confirm you can scroll the screen to see the appropriate buyerValues, sellerCosts, and robot types in the output. Optionally compare values and costs with the design specified in Ref.  for mark9.docker run -it -v c:∖work:/work drpaulbrewer/single-market-robot-simulator:4.3.0entered into the command window runs the simulation treatment defined in C:∖work∖sim.jsonYou may be asked to allow access to “C:∖work” by Docker Desktop. The simulator will not run properly if you refuse permission to read and write files in C:∖work.The simulation runs without any user interface or indication of progress. Depending on the speed of your computer, it could take anywhere from a few minutes to 30 minutes. If you get an immediate error, check the docker command carefully for minor errors.During the simulation, if you open another window and look in your work directory (C:∖work), you will find a number of.csv files have been created and are filling up with data. The file “period” contains the current period number. When it reaches 10000, the simulation will stop and the command window will provide a new input line.To replicate an individual simulation treatment with the single-market-robot-simulator software on Windows 10 Pro or Windows 10 For Education, follow these steps:•Step 1: Linux based Docker can be run on weaker systems  because Docker will run natively on Linux.•Step 2: Docker Desktop exists for Mac. On Linux, use a recent version of Docker CE.•Step 3: Mac and Linux have a “terminal” app for command input.•File locations are specified using “/” on Mac and Linux instead of “∖” for Windows files.•Step 8: “more sim.json” is the command to view the file•Step 9: Depending on installation, Mac or Linux users may require “root” or “sudo.” Docker can be set up to be accessible to ordinary users, but that is not always the case.•Step 9: A more convenient specification of the “-v” option is available : "-v $PWD:/work" indicates that the current directory is to be linked to the /work container directory.Mac and Linux users (not Windows users) should modify the above procedure as follows:On completing the simulation, you should have a work directory with various.csv files containing market data.As a next step in this replication, you may calculate the Gini coefficient from total profit in the simulation treatment aggregated over 10,000 periods.The Gini calculation procedure involves entering the following docker command in a command window, from the work directory:-This is a single long command. Check for errors before pressing enter.-Check gini-csv is repeated twice, but in different contexts. This is necessary because docker must first fetch and use the drpaulbrewer/gini-csv container and then run the gini-csv command.-Check that after “gini-csv” each of n, s, m, r has a dash in front. The y and 4 do not have a dash.docker run -it -v c:∖work:/work drpaulbrewer/gini-csv gini-csv -n -s -m y -r 4 work/profit.csvThe output, the Gini coefficient for total profits, is sent to the command window.•Windows 10 Pro, Intel i7•Mac Mini, Intel i5.•Linux, using a remote virtual machine running on Free Google Cloud Shell .•Linux, using a remote virtual machine running on Amazon AWS, type ‘t2.micro’ This replication procedure has been tested on:Gini Coefficients obtained from these four replications ranged from 0.2368 to 0.2379.


Error-monitoring abnormalities in stimulant-dependent individuals (SDIs) may be due to reduced awareness of committed errors, or to reduced sensitivity upon such awareness. The distinction between these alternatives remains largely undifferentiated, but may have substantial clinical relevance. We sought to better characterize the nature, and clinical relevance, of SDIs' error-monitoring processes by comparing carefully isolated neural responses during the presentation of negative feedback to a) stimulant dependence status and b) lifetime stimulant use. Forty-eight SDIs and twenty-three non-SDIs performed an fMRI-based time-estimation task specifically designed to isolate neural responses associated with the presentation (versus expectation) of contingent negative feedback. SDIs showed reduced dACC response compared to non-SDIs following the presentation of negative feedback, but only when error expectancies were controlled. Moreover, lifetime stimulant use correlated negatively with magnitude of expectancy-controlled dACC attenuation. While this finding was minimized after controlling for age, these results suggest that SDIs may be characterized by a core reduction in neural activity following error feedback, in the context of intact feedback expectancies. Correlations with lifetime stimulant use suggest that this neural attenuation may hold clinical significance. •Examined whether SDIs error-related deficits are due to reduced error-awareness, or reduced sensitivity upon such awareness.•A time-estimation task was used that isolated SDI's and non-SDI's neural responses to the presentation and expectation of negative feedback.•SDIs showed reduced dACC response following the presentation of negative feedback, but only when error-expectancies were controlled.•dACC signal change to the presentation of negative feedback distinguished SDI/non-SDI, and predicted stimulant use history. A growiOf significant inconvenience, precise isolation of neural responses underlying the presentation versus recognition of error-related feedback is not so easily accomplished, as the magnitude of neural response to error is generally reciprocal to that error's prior expectancy ; thus, tPerhaps due to these shortcomings, we are aware of only three studies that have attempted isolation of presentation-phase activity in SDIs, and these have yielded inconsistent results. To these ends, the present study utilized an fMRI-based time-estimation task specifically designed to evaluate neural responses following the presentation of outcome feedback in a way that could control for prior expectancies . Of primAn additional goal of the present study was to serve as a preliminary test of the clinical utility of any witnessed error-related abnormalities in the SDI group. Only a handful of such studies have been conducted to date, and none have attempted to parse expectation/presentation phases of outcome processing . With th22.1Eighty-four participants were recruited through advertisements placed in probation/parole offices throughout the Albuquerque metropolitan area. Advertisements specifically targeted individuals with a criminal history who were currently on probation or parole with or without a history of cocaine use. Participants were phone-screened regarding key inclusion/exclusion components of the study, and were further screened in person to determine eligibility. After obtaining consent, SDI status was determined via the Structured Clinical Interview for DSM-IV Disorders . SDIs mComprehensive substance abuse histories were obtained via a modified version of the Addiction Severity Index – Expanded . All bu2.22.2.1The time estimation task was modified from Participants were instructed to wait for the asterisk to disappear, and to press a button with their index finger when they felt 1 s had transpired from the asterisk's offset. Next, participants rated their level of confidence in their estimate. Finally, following a jittered interval, participants received one of several feedback symbols regarding the accuracy of their estimate. Responses were deemed successful if they fell within a desired window surrounding 1000 ms. This window was initially set at +/−250 ms; thus time estimates between 750 ms and 1250 ms were initially deemed successful, and any estimate outside this window was deemed unsuccessful. The size of the window was adaptive, on a trial-by-trial basis, depending on participants' performance throughout the course of the task. Every time a successful estimate was made, the window decreased by 30 ms; every time an unsuccessful estimate was made, the window increased by 30 ms. Previous work with this type of task has provided substantial evidence of the validity of this adaptive algorithm for balancing trial presentation in both healthy and clinical populations . The tasInformative: participants received a ‘+’ sign to indicate an accurate estimate, or a ‘−’ sign to indicate an inaccurate estimate. On the other half of the trials, feedback was Uninformative: participants received a ‘?’ that provided no accuracy information. The ‘?’ feedback was presented randomly on 50% of accurate trials, and 50% of inaccurate trials; thus, on each trial, participants had an equal expectation of either Informative or Uninformative feedback. The uninformative feedback trials served as a critical design feature: unlike the IncorrectINFORMED > CorrectINFORMED contrast (within which performance and performance feedback are necessarily confounded), the IncorrectINFORMED > IncorrectUNINFORMED contrast differs only in the type of feedback provided. Thus observed differences in neural response in the IncorrectINFORMED > IncorrectUNINFORMED contrast can be specifically attributed to the presentation of the informed feedback.On half of the trials, feedback was 2.2.2All participants completed a urine screen prior to scanning. MRI data was collected on a 3 T Siemens Trio  whole body scanner with gradient-echo pulse sequence  acquired with a 12-channel head coil.2.32.3.1Behavioral measures were collected on each trial, and included estimation accuracy, estimation accuracy on trial n + 1 , and confidence ratings . Time estimations were extracted for each condition of interest. Time estimations >10 s were removed, followed by all estimations >3 standard deviations from individual subject means, to prevent undue influence from outliers. This process resulted in 104 total trials being removed from the analysis .2.3.2All image analyses were undertaken with FMRIB's Software Library (FSL) version 4.1.0 , using sIncorrectINFORMED > CorrectINFORMED (within which performance expectancies necessarily varied), and IncorrectINFORMED > IncorrectUNINFORMED (within which performance expectancies were effectively controlled); each of these contrasts were compared across groups using an independent samples t-test.All analyses were run as part of a 3-stage process. In the first stage, customized regressors were created for each participant for four events: cue, estimate , confidence ratings, and feedback . In addition, six first-order motion parameters were included in the statistical model. Statistical analyses were performed using the general linear model as implemented in FEAT. Time series analyses were conducted using FILM  with locWhole brain group analyses were conducted using permutation testing with the randomise program within FSL. Threshold free cluster enhancement (TFCE) was used to correct for multiple comparisons at p < 0.05.2.3.3IncorrectINFORMED > IncorrectUNINFORMED contrast.Lifetime stimulant use assessment was consistent with general PhenX guidelines , and tot33.1Following exclusions for motion, performance and psychotropic drug use (see Supplement for full details regarding exclusion criteria), 71 participants (48 SDIs and 23 non-SDIs) were included in the final dataset. 3.23.2.1F = 414.38, p < 0.001. Importantly, however, the main effect of Information did not reach significance, indicating that performance was similar across Informed and Uninformed trials. Similarly, no group effects, nor interactions, reached significance  = 0.14, p < 0.01. Importantly, this was true for both SDI  and non-SDI  groups (between group t-test: t(67) = 1.61, p = ns); moreover, no significant group differences in confidence were found in any condition .The time estimation task was specifically designed to maximize neural response to informative feedback by minimizing the development of outcome awareness. Consistent with this goal, while participants showed fairly high confidence in their estimation attempts (mean confidence rating = 1.78 (0.87)), the correlation between confidence and accuracy, while significant, was only low-to-moderate, 3.2.3F = 62.94, p < 0.001. While main effects of Information and Group did not reach significance, a significant Accuracy × Information interaction, F = 3.86, p = 0.05, indicated that the difference between performance adjustments following correct/incorrect feedback was greater for Informed compared to Uninformed feedback  × 2 (Information) × 2 (Group) mixed-model ANOVA indicated that participants' estimation adjustments were greater following incorrect compared to correct trials, 3.33.3.1The 2 (Accuracy) × 2 (Information) × 2 (Group) mixed-model ANOVA revealed a main effect of Accuracy, within several clusters including dorsal and pregenual ACC (d/pg ACC), bilateral nucleus accumbens, and bilateral occipital cortex. A main effect of Information was also identified within several regions including d/pg ACC, bilateral anterior insula, and nucleus accumbens. Finally, a main effect of group was identified within right cerebellum and left lingual gyrus. An Accuracy × Feedback interaction revealed significant clusters within bilateral occipital pole, ventral striatum, and left inferior parietal lobe. The Information × Group interaction, Group × Accuracy interaction and three-way Accuracy × Information × Group interaction revealed no significant clusters. See Table S1 in Supplement for all regions reaching significance within these higher-order analyses.3.3.2IncorrectINFORMED > IncorrectUNINFORMED and IncorrectINFORMED > CorrectINFORMED served as primary contrasts of interest. Here we report effects within these contrasts across all participants. Below we present the group-relevant distinctions. Other contrast-level effects can be found in the Supplement.IncorrectINFORMED > IncorrectUNINFORMED. By controlling for expectancy effects related to differential performance, this contrast provides a well-controlled evaluation of the brain's response to the specific presentation of negative feedback. Results revealed greater response following the presentation of IncorrectINFORMED feedback within several regions, including d/pg ACC and bilateral insula  did not alter the direction of these effects (see Supplement for details).A priori hypotheses centered around differences in post-error neural responses between the SDI and non-SDI groups. To this end, we undertook targeted two-sample CorrectINFORMED > IncorrectINFORMED and the CorrectINFORMED > CorrectUNINFORMED contrasts, to evaluate potential differences associated with reward processing. However, only one region within left lateral occipital cortex reached significance in the CorrectINFORMED > IncorrectINFORMED contrast, and no regions reached significance in the CorrectINFORMED > CorrectUNINFORMED contrast. Thus, while broad dACC activation was seen to both positive and error feedback, only error-related responses manifested as group differences.Group differences were also evaluated in the 3.3.4IncorrectINFORMED > IncorrectUNINFORMED contrast, r(53) = −0.33, p < 0.02,  was fully controlled. This effect is subtle, but may provide clarification to a nascent, and currently inconsistent, literature . By contOf equal import, associations with SDIs' lifetime stimulant use were also identified only when performance metrics were held constant. While this effect was somewhat mitigated after controlling for age, it nonetheless suggests that SDIs' attenuated responses following presentation of negative feedback may in fact play an important role in the pathophysiology of the disease state. A handful of previous studies have reported similar associations between error-related responses and clinically-relevant abuse metrics ; howeverIt is relevant to note that both groups showed increased dACC response following presentation of positive feedback, but that these activations did not manifest as group differences. Thus, group differences in d/pgACC response were specific to the presentation of error-related feedback. While coinciding with a growing body of work that has positioned ACC as a core structure within the mesocorticolimbic reward system, it does conflict with some previous work that has reported reward-related associations with drug use behaviors (e.g. ). Much oSome limitations of this study should be noted. First, despite our best attempts, the frequency with which SDI and non-SDI participants expressed comorbid dependencies differed somewhat across the two groups. While such differential comorbidities could have contributed to differences between SDI and non-SDI groups , it is iIn summary, the current study demonstrated that dACC response following presentation of negative feedback, when unconfounded by error expectancies, was 1) attenuated in SDIs and 2) negatively associated with lifetime years of use. Negative feedback-related ACC response may provide a useful biomarker of stimulant dependence, and a potential target for treatments.Drs. Claus and Shane report no financial conflicts of interest.


Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table  training set, χ2-DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers  and takes a short computation time (89 s). χ2-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ2-DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy.Using a short window size of 11 bp, χBased on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions.This article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther.The online version of this article (10.1186/s13062-019-0236-y) contains supplementary material, which is available to authorized users. The amount of genomic sequence data has increased exponentially as a result of the advancement in sequencing technology. Therefore, there is an urgent need to complete genome annotation quickly and reliably. Gene identification is an important task in genome annotation. Most eukaryotic genes consist of protein-coding regions (exons) and noncoding regions (introns), with the exons being separated by intervening introns . The bouk-mers [For splice site prediction based on machine learning approaches, the main steps are feature extraction and classifier selection or design. The extracted features are usually based on nucleotide position information –9, the fk-mers , 6, 10, k-mers –13, RNA k-mers –18, DNA k-mers , and somk-mers –22. The k-mers –25, artik-mers –29, randk-mers .3D dataset has exceeded 90% [3D dataset contains 2796/271,937 true/false donor sites . If all negative samples  are employed for building the prediction model, the huge number of training samples will increase the time complexity of some classifiers  [Although relatively high accuracy has been achieved with the methods currently available eded 90% . Overly and ANN) , and an and ANN) . If onlyand ANN) ) are emp2-DT), extracts the improved positional features based on chi-square tests, combines them with the frequencies of dinucleotides, and then designs a balanced decision table to predict the test samples, which can effectively resolve the imbalanced pattern classification problem. The results show that χ2-DT can achieve high predictive accuracy, high computational speed, and relatively good robustness against DNA sequencing errors (nucleotide insertions and deletions).In this study, we developed a computational approach to predict donor splice sites based on short window size and extremely imbalanced large samples. Our method, named chi-square decision table (χ3D dataset [http://www.sci.unisannio.it/docenti/rampone/) for the experiments, and named them HS3Dall. Each true/false donor splice site-containing sequence has 140 nucleotides, with the conserved dinucleotide GT at the 71st and 72nd positions, and does not contain non-ACGT bases. Setting the positions of the conserved GT as 00, the upstream positions were successively labeled as − 1, − 2, …, − 70, whereas the downstream positions were successively labeled as 1, 2, …, 68. From HS3Dall, we randomly selected 796 true sites and 796 false sites to constitute a balanced testing set, named HS3D-test1:1, and then used the remaining sites to construct the training sets with different ratios of true sites to false sites. Additionally, to compare the performance of χ2-DT with that of other methods, we selected 2796 true sites and different numbers of false sites from HS3Dall to construct four datasets, namely, HS3DI, HS3DII, HS3DIII, and HS3DIV.We collected 2796/271,928 true/false donor splice sites from the publicly available HS dataset  (http://http://genome.crg.es/datasets/genomics96/) contains 570 human genomic DNA sequences and 570 corresponding mutated sequences. The mutated sequences were generated by introducing 1% random frameshift errors (nucleotide insertions and deletions) into the original DNA sequences. Using the BG-570 dataset, we constructed two testing sets (BG-570orig and BG-570muta) to evaluate the robustness of χ2-DT against the frameshift errors. The extracting process of true/false sites in these two testing sets is described in the “The BG-570 dataset  , the corresponding partition is a 2 × 8 grid and the corresponding MIC score is 0.24, which leads to a nontrivial MIC score for independent paired variables under finite samples [r × 2 grid to determine whether the new endpoint should be introduced. If the p-value of the chi-square test is lower than a given threshold, the new endpoint is introduced for partition and ChiMIC continues searching for the next optimal endpoint. If the p-value of the chi-square test is greater than the given threshold, the new endpoint is discarded and the process of partition is terminated. For paired independent variables with n = 100, the MIC score calculated by ChiMIC is only about 0.06, and the corresponding partition is a 2 × 2 or 2 × 3 grid, clearly, the grid partition produced by ChiMIC is more reasonable.Just like Pearson correlation coefficient  and mutulgorithm  sets nx  samples . Recentl samples  presente3D-train1:135. Is the 2 × 4 table reasonable? Could it be compressed into a 2 × 3 table, or even a 2 × 2 table? For the local 2 × 2 contingency table . This indicates that the endpoint between A and T should not be introduced according to the ChiMIC algorithm. In other words, that the base at position 6 is A or T cannot provide valuable information for distinguishing positive and negative samples. Similarly, the endpoint between C and G should not be introduced. Finally, the 2 × 4 contingency table of position 6 is compressed into a 2 × 2 contingency table  after compression based on HS3D-train1:135; then, we perform a chi-square test with the 2 × r contingency table and calculate the logarithm of the reciprocal of p-value, here denoted as log(p− 1)  values mean that the corresponding positions are more important for discriminating positives from negatives. Therefore, we determine 11 bp  as the window size for donor splice site prediction. In the following text, the study will be based on the window size of 11 bp unless otherwise specified.For each position in the sequences of 140 bp, we obtain a 2 × r columns of the 2 × r table. For example, based on HS3D-train1:135, position 6 whose original status values are {A, C, G, T} corresponds to a 2 × 2 contingency table after compression, so it has two status values as {AT, CG}; the frequency of dinucleotide AA whose original status values are {0, 1, …, 10} corresponds to a 2 × 3 contingency table after compression, so it has three status values as {0, 1, 2–10}. It should be noted that, for the compositional features, only the adjacent original status values can be merged together in compression because they are sequential.From each sample (a sequence of 11 bp in length), we extract 11 positional features and 16 compositional features. The compositional features are the frequencies of dinucleotides, which range from 0 to 10 because the sequence sample is only 11 bp. For each feature, we can obtain a 2 × r contingency Table (2 ≤ r ≤ 11) after compression; then, the status values of each feature correspond to kth class samples in sample set D is pk ; then, the information entropy of D is defined as:Suppose the proportion of the Xi (1 ≤ i ≤ 27) that has r(2 ≤ r ≤ 11) status values as {s1, s2, …, sj, …, sr}, the information gain [Xi brings for D can be calculated by:Dj represents the samples in D whose Xi takes the status value as sj(1 ≤ j ≤ r), while H(Dj) is the information entropy of Dj.Given a feature ion gain  that Xi Xi is defined as:IV(Xi) is the intrinsic value of Xi.From the features whose information gains are above the average level, we pick out the one that has the highest gain ratio to be the first introduced feature. Here, the gain ratio of r contingency table of each remaining feature, in accordance with the compression process previously described. If the r columns are compressed into one column, the remaining feature cannot be introduced. If the r columns are not compressed into one column, the remaining feature is a candidate feature to be introduced.Step 1: Under the conditions in which the introduced features have existed, further compress the 2 × Step 2: Calculate the information gain of every candidate feature. Then, from the candidate features whose information gains are above the average level, pick out the one with the highest gain ratio to be the next introduced feature.Step 3: Repeat steps 1 and 2 until no feature can be introduced.Next, we introduce the remaining features one by one as follows.3D-train1:135 as an example, 27 introduced features  have formed 201 decision rules ∧(P− 1 = ACT)∧(0 ≤ fGT ≤ 2)” represents position 3 taking a value of A and position − 1 taking a value of ACT, while the frequency of dinucleotide GT takes values from 0 to 2. Other decision rules have similar representations. Given that the number of negative samples far exceeds that of positive samples, to resolve the imbalanced pattern classification problem, we adjust the decision weight of negative samples in each column of Table θ , and then get a 2 × 201 balanced decision table ∧(P− 1 = ACT)∧(0 ≤ fGT ≤ 2)”, first we assume that it is positive, replace 5 with 5 + 1, and calculate the corresponding chi-square value When using the balanced decision table for making decisions, suppose a test sample meets the decision rule “(Sensitivity (SN), specificity (SP), and the Matthew correlation coefficient (MCC) as common measures for evaluating binary classifications are defined as follows:Here, TP, FP, TN, and FN denote the numbers of true positives, false positives, true negatives, and false negatives, respectively. SN represents the percentage of positive samples correctly predicted as true. SP represents the percentage of negative samples correctly predicted as false. MCC takes into account true and false positives and negatives, and is generally regarded as a balanced measure. However, when the class distribution of the testing set is imbalanced, the MCC value will become relatively small, which cannot really reflect the performance of a classification model.9 [9 as the measure of global accuracy to assess predictive performance in the case of an imbalanced testing set. Q9 is defined as follows:The global accuracy index Q9  is indepwhereThe receiver operating characteristic (ROC) curve, which is widely used in evaluating the predictive accuracy of statistical predictors, is given by SN against 1 − SP. When dealing with highly skewed datasets, the Precision–Recall (PR) curve can provide better insight into an algorithm’s performance . The are3D-train1:1 and HS3D-test1:1, the independent tests were performed to compare the performance of χ2-DT using various window sizes. The results  Short window size reduces feature dimension and saves computational time. For example, using the window size of 138 bp, the feature dimension is 154 ; however, using the window size of 11 bp, the feature dimension drops to 27 , and there is about a 96% decrease in the elapsed time, running in the same computer system (Intel Core i5-3320M 2.6 GHz/8 GB RAM). Therefore, we have more confident on the short window size of 11 bp. The follow-up results are all based on 11-bp-long window size.Based on HSts Table  show the3D-test1:1, we respectively used imbalanced and balanced decision tables that were built based on various HS3D training sets to make decisions. The independent test results are given in Table For HS3D-train1:1) to 0.694 (HS3D-train1:135). Therefore, the follow-up results are produced by using balanced decision Tables. The results indicate the following: 1) When training sets are imbalanced, a balanced decision table can accurately predict donor splice sites. For a balanced decision table, MCC remains stable (0.847–0.867) with training sets having different positive-to-negative ratios. By contrast, for an imbalanced decision table, MCC continually drops with an increase in negative training samples, and declines from 0.847  [https://www.cs.waikato.ac.nz/ml/weka/index.html) and the neural network toolbox [3D-test1:1, HS3D-train1:1, and HS3D-train1:135; the corresponding results are given in Table Based on the same input features , χ (RVKDE) . We sele toolbox  of Matla2-DT outperforms all of the other classifiers. As Table 3D-train1:1, MCC of χ2-DT is 0.847, which is comparable to those of RF, ANN, and RVKDE. In contrast, based on HS3D-train1:135, MCC of χ2-DT rises to 0.867, and is significantly higher than those of the other classifiers (0.248–0.353). 2) With the large training set, χ2-DT has an advantage with regard to computational speed. We ran all of the simulations on an Intel Core i5-3320M 2.6 GHz/8 GB RAM system. For HS3D-train1:135, the elapsed time of χ2-DT was just 89 s, while RVKDE took more than 32 h. This speed of χ2-DT is due to the fact that no parameters need to be optimized.The results indicate the following: 1) Using the extremely imbalanced training set, χorig, and extract 2081/149,572 true/false donor splice site-containing sequences from 570 mutated DNA sequences to constitute another testing set called BG-570muta. Based on HS3D-train1:135, the independent test results respectively employing the positional features and the combination of positional and compositional features are shown in Table In BG-570 dataset, setting the window size as 11 bp , we can extract 2127/149,039 true/false donor splice site-containing sequences from 570 original DNA sequences to constitute a testing set called BG-5709, which is invariant to class skew, is added for evaluation purposes. The comparative results demonstrate that: 1) The compositional features have tolerance to frameshift errors of DNA sequencing. Based on the positional features, Q9obtained under the testing of BG-570muta is 0.9114, lower than that under BG-570orig (0.9258). However, after adding compositional features, Q9 rises back to 0.9239 when still tested by BG-570muta. 2) Whether or not there are frameshift errors in testing sets, χ2-DT can achieve satisfactory performance (Q9 ≥ 0.92).The MCC values in Table 2-DT, with the aim of comparing it with existing methods. To perform 10-fold cross validation, the dataset was randomly divided into ten non-overlapping subsets of equal size. In each repetition, one subset was used as a testing set and the remaining nine subsets were used as a training set. Based on each training set, we built a balanced decision table independently. The average of ten values of predictive accuracy was used as the final accuracy. All comparisons were carried out in the HS3D datasets, and the 10-fold cross accuracy values of the methods for comparison were obtained directly from the corresponding references.10-fold cross validation was applied to assess the predictive performance of χ2-DT was compared with the methods using longer window size (≥100 bp), including a first-order Markov model combined with a dinucleotide-based hidden Markov model (MM1-H2MM) [http://cabgrid.res.in:8080/maldoss. The 10-fold cross accuracy of χ2-DT was calculated based on HS3Dall. Table 2-DT with much shorter window size can achieve better predictive performance, despite the degree of imbalance of the training set being higher.On the one hand, χM1-H2MM) , SVM witM1-H2MM) , and MehM1-H2MM) . The web2-DT was compared with the methods using short window size (9 bp). Maximum entropy model (MEM) [http://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html. The web server based on SAE is available at http://cabgrid.res.in:8080/sspred. Based on the HS3D datasets with different ratios of positive-to-negative samples , the AUC-ROC and AUC-PR values of SAE, MEM, WMM, MDD and first-order Markov model (MM1) were calculated by MaxEntScan, employing 9-bp-long window size. For comparison, we also calculated the AUC-ROC and AUC-PR values of χ2-DT based on HS3DI, HS3DII, HS3DIII, and HS3DIV. The results , AUC-PRs of the other methods were around 0.68, with decline of up to 28%, while the AUC-PR value of χ2-DT was 0.85, representing a decline of only about 10%.On the other hand, χel (MEM)  and SAE el (MEM)  are the dow size  bp. Maxi2-DT, was proposed. In terms of accuracy, χ2-DT is clearly superior to the methods for comparison. With regard to computational speed, χ2-DT is fast, even when using a large training set with more than 270,000 samples, because no parameters need to be optimized during model training. Furthermore, the independent test results based on the BG-570 dataset indicate that χ2-DT has relatively good robustness against frameshift errors in DNA sequencing, due to the addition of compositional features.Based on the short window size of 11 bp, a high-performance method for predicting donor splice sites, called χ2-DT is applied to predicting acceptor splice sites, it does not further improve the predictive accuracy of existing methods, so it is necessary to devise another optimal model for acceptor sites. 3) The detection of splice sites ultimately involves identifying genes, so our overall goal is to constantly improve the proposed splice site predictor, and then use it to find genes.In future research, we plan to focus on the following: 1) We will attempt to combine more valuable features  for characterizing the candidate splice sites, in pursuit of better predictive performance. 2) When χRyan McGinty, Ph.D.Reviewer summary:Zeng, et al. have created a new computational method, X2-DT, for predicting gene splice donor sites that uses a very small window size (11 bp), is robust to a very low true/false ratio in their training data set, and runs efficiently. This method appears to perform as well or better than previous methods which were compared in this study. It would benefit the manuscript for the authors to make a clearer case for the usefulness and applicability of their tool.Reviewer recommendations to authors:Major suggestions: Clarify how X2-DT could be used by others and why it would be useful. The authors state that X2-DT can be used for the “prediction of splice sites in short reads generated by next-generation sequencing.” However, it is never stated whether it should be applied to short reads generated from genomic sequencing or transcriptome sequencing. From the context, it would appear to be the latter, as genomic short read sequences are assembled into longer fragments and therefore the read length is irrelevant to the window size. Incidentally, there also exists a field dedicated to predicting splice site strength from genomic sequences, rather than RNA sequences. Assuming the short reads in question here are from transcriptome sequencing, the issue that the authors propose to solve can be described more clearly. In this case, the short reads should contain spliced mRNA sequences, and so the issue becomes whether there is enough sequence context on either side of the splice site to unambiguously map the splice junction without the need for computational prediction. The authors suggest that “high-throughput DNA sequencing technologies produce billions of short reads with lengths of about 50 bp , while mWe appreciate the detailed recommendations made by the reviewer. This study is limited to DNA sequence data generated from genomic sequencing. As mentioned by the reviewer, genomic short read sequences are assembled into longer fragments before splice site prediction, so it is inappropriate to highlight the argument that χ2-DT can predict the splice sites in short reads generated by next-generation sequencing. In the revised manuscript, we removed this inappropriate argument.Authors’ response: 3D-train1:1 and HS3D-test1:1, the independent tests were performed to compare the performance of χ2-DT using various window sizes. The results  Short window size reduces feature dimension and saves computational time. For example, based on 138-bp-long window size, the feature dimension is 154 ; while, as for 11 bp-long window size, the feature dimension drops to 27 , and there is about a 96% decrease in the elapsed time, running in the same computer system.However, for our method, the use of short window size (11 bp) is necessary as far as improving prediction accuracy and simplifying the prediction model. In the revised manuscript, we discussed the benefits that 11-bp-long window size brought. Based on HSts Table  show the2-DT using 11-bp-long window size was compared with several existing approaches that used longer window sizes . The results  that are inputted the same feature vectors, our method obtains higher prediction accuracy , page 5 (line 123), page 8 (lines 200–201), page 12 lines 294–307), page 15 , page 16 (line 401), titles of Tables –110, pag4–307, paMinor issues:The authors compare their work to several existing methods. While these methods are categorized and listed by their method or strategy, it would be of some practical use to know the name of each tool being compared in each table. Stylistically, I would prefer more detailed explanations of the methods that might help the study be understood by a broader audience. As written, there is a heavy prerequisite for knowledge of statistical and computational methods, including much undefined terminology. This knowledge is likely not shared by many readers interested in the biology of splicing, or with a practical need to employ the best splicing prediction program.SVM-B, WMM, MDD, MM1 and MEM are the conventional models for predicting splice sites, and they are often employed for comparing with new presented methods. In Table, the results of MEM, MDD, WMM and MM1 were obtained by executing the MaxEntScan (a web server) which is available athttp://genes.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html. Meher’s method and SAE are recently developed methods for donor splice site prediction. Based on Meher’s method, a web server (MaLDoSS) has been developed, and is available athttp://cabgrid.res.in:8080/maldoss. The web server based on SAE can be available athttp://cabgrid.res.in:8080/sspred. As per suggestion, we supplement the above contents in the revised manuscript for the convenience of practical application. As for written, we have checked and revised carefully, to avoid undefined terminology, in the hope that many readers interested in this study can understand.Authors’ response: Necessary changes have been made in the revised manuscript: page 15 , page 16 (lines 384–388).Dirk WaltherReviewer summary:Prediction of splice-sites has been a long-standing problem in Bioinformatics and many algorithms have been developed, essentially exhausting all possible ways to formulate and solve the computational problem. Despite the many methods and their reasonable success, and despite the increased availability of transcript sequencing data which allow determining splices sites based on experimental information, this reviewer is willing to be open to new in-silico methods. Clearly, correct splice site prediction would help tremendously for genome annotation purposes.Reviewer recommendations to authors:(1) The authors highlight as an advantage and pose as a need to base predictions on short sequence motifs (11mers) as necessitated by the short available sequence reads from DNAseq data. Though, I would think, splices site predictions would always be applied to assembled genomes or genes, not individual reads. So for me, this is not an argument at all. The length of the k-mer should reflect what is truly necessary for correct identifications. That aside, I still believe it is interesting to see how well methods based on short k-mers can work.We agree with the recommendations given by the reviewer. In the revised manuscript, we removed the inappropriate argument that χ2-DT can predict the splice sites in short reads generated by next-generation sequencing. And we changed “short sequence” in the title to “short window size” which we thought may be more appropriate.Authors’ response: 3D-train1:1 and HS3D-test1:1, the independent tests were performed to compare the performance of χ2-DT using various window sizes. The results  Short window size reduces feature dimension and saves computational time. For example, based on 138-bp-long window size, the feature dimension is 154 ; while, as for 11-bp-long window size, the feature dimension drops to 27 , and there is about a 96% decrease in the elapsed time, when running in the same computer system.In this study, the use of short window size 11 bp) is necessary as far as improving prediction accuracy and simplifying the prediction model. In the revised manuscript, we discussed the benefits that 11-bp-long window size brought. Based on HS bp is ne2-DT using 11-bp-long window size was compared with several existing approaches that used long window sizes . The results , page 5 (line 123), page 8 (lines 200–201), page 12 lines 294–307), page 15 , page 16 (line 401), titles of Tables –110, pag4–307, pa(2) The study reports results on donor sites only. The authors state that with regard to acceptor sites, no performance gain has been achieved, leading me to believe that performance was at least comparable. This should be discussed more - why gain for donor sites, not acceptor sites. Also, this point should be mentioned much sooner in the manuscript than in the very last paragraph. Furthermore, the equal performance of their method relative to others should be documented.We determined the 18-bp-long window size (− 17~ + 1) by chi-square test, for predicting acceptor splice sites. Using 2880/28,800 true/false acceptor splice sites from HS3D dataset, 10-fold cross validation was applied to assess the performance of χ2-DT, and the predictive accuracy is: SN = 0.8901, SP = 0.8751, Q9 = 0.8826. Based on the same dataset, Q9achieved by SVM-B and MM1-H2MM are 0.8951 and 0.9057, respectively, which are slightly higher than that of our method.Authors’ response: 2-DT employs positional features and compositional features. While, as for acceptor sites, we found positional features and compositional features were not enough to characterize the candidate samples, maybe some other valuable features, such as DNA structural properties [χoperties , should (3) The method section needs a better introduction/motivation. I had difficulties grasping the basic rationale of the method. In fact, I am not sure, I did. I could not follow the arguments with regard to “compressing that tables” at all. More explanation is needed.Let’s begin with maximal information coefficient (MIC). Just like Pearson correlation coefficientand mutual information estimatorsthat are used for identifying relationships between variables, MIC is a novel measure proposed to capture dependences between paired variables. Giving an independent paired variables {xi, yi}, xi, yi∈, as shown in following:Authors’ response: nt (MIC). Just lifficientand mututimatorsthat arex and y, a maximum grid solution  with the highest induced mutual information will be searched. And a 2 × 9 table  will achieve 1, clearly, it is illogical, because MIC value should be tend to 0 for statistically independent variables. Thus, to avoid producing the nontrivial MIC values due to excessive grid partitions, ApproxMaxMI algorithm [n0.6 as the maximal grid size restriction, here, n is the sample size. Then, a 2 × 3 grid would be generated to partition data, and the corresponding MIC value falls to 0.31. So the 2 × 9 table is compressed into a 2 × 3 table  is reasonable.Recently, our research group presented the ChiMIC algorithm  for calcSimilarly, for each position in donor splice site-containing sequences, we can build a 2 × 4 contingency table to respectively count the frequencies of four bases in positive and negative samples. Is the 2 × 4 table reasonable? Could it be compressed into a 2 × 3 table, or even a 2 × 2 table? Taking position 6 as an example, its 2 × 4 contingency table is finally compressed into a 2 × 2 contingency table, according to the ChiMIC algorithm see Fig. .11 decision table after introducing 11 positional features, and with the further introduction of features, the number of columns in decision table will be grow exponentially, then the decision table would be quite sparse.Moreover, if do not compress the 2 × 4 contingency table of each position, we will get a 2 × 4Therefore, we compressed the 2 × r contingency table of each feature, including positional and compositional features. And the results indicate the compression strategy is effective for correct prediction.Necessary changes have been made in the revised manuscript: page 6 (lines 155–157), page 7 (lines 172–176); we add references , 38.(4) Despite trying, I had difficulties understanding, where and how the imbalance was tested (during training or during testing or both?) Try to be more clear about it. So, in essence, I was not able to assess whether the claimed improved performance on this imbalanced problem was, in fact, achieved.The number of false donor sites far exceeds that of true donor sites, e.g., the HS3D dataset contains 2796/271,937 true/false donor sites. If all negative samples  are employed for building the prediction model, the extremely imbalanced large training samples will lead to poor predictive results for many methods.Authors’ response: We give an example to explain how we resolve the imbalanced pattern classification problem. Suppose there are 87/1687 positive/negative training samples, if only 2 positional features (position − 1 and 3) are introduced and have formed 4 decision rules, we separately count the numbers of positive and negative samples that conform to the decision rules, then get a 2 × 4 imbalanced decision table Table .Table 14P− 1 = G)∧(P3 = GC)”. In Table Giving a positive testing sample, suppose its position −1 and 3 both take a value of G, the testing sample will conform to the decision rule “, and use 796/796 positive/negative samples to generate a balanced testing set (HS3D-test1:1). The independent testing results  is about 0.25–0.35. Further, it is found in Table In this study, we use 2000/271,132 positive/negative samples to generate an extremely imbalanced training set (HSNecessary changes have been made in the revised manuscript: page 10 (lines 251–253).Minor issuesGenerally, the article is well written (English-wise). Some minor mistakes need correcting. For example, use present tense in the Abstract when talking about your method and results (“The proposed method presents” (not “presented”). Check use of the definite articles.Authors’ response: Following the suggestions of reviewer, we have made language corrections, including tense and use of the definite articles.Necessary changes have been made in the revised manuscript: Abstract, page 14 (line 346).Additional file 1:Table S1. The table lists 201 decision rules obtained based on HS3D-train1:135, and lists the number of positive and negative training samples conforming to the decision rules. (XLSX 38 kb)


Fragranced consumer products have been associated with adverse effects on human health. Babies are exposed to a variety of fragranced consumer products, which can emit numerous volatile organic compounds (VOCs), some considered potentially hazardous. However, fragranced baby products are exempt from disclosure of all ingredients. Consequently, parents and the public have little information on product emissions. This study investigates VOCs emitted from a range of fragranced baby products, including baby hair shampoos, body washes, lotions, creams, ointments, oils, hair sprays, and fragrance. The products were analysed using gas chromatography/mass spectrometry (GC/MS) headspace analysis. Of the 42 baby products tested, 21 products made claims of green, organic, or all-natural. Results of the analysis found 684 VOCs emitted collectively from the 42 products, representing 228 different VOCs. Of these 684 VOCs, 207 are classified as potentially hazardous under federal regulations, representing 43 different VOCs. The most common VOCs emitted were limonene, acetaldehyde, ethanol, alpha-pinene, linalool, beta-myrcene, acetone, and beta-pinene. A comparison between ingredients emitted and ingredients listed reveals that only 5% of the 684 VOCs, including 12% of 207 potentially hazardous VOCs, were listed on the product label, safety data sheet, or website. More than 95% of both green and regular products emitted one or more potentially hazardous VOCs. Further, emissions of the most prevalent VOCs from green, organic, or all-natural products were not significantly different from regular products. Results from this study can help improve public awareness about emissions from baby products, with the aim to reduce pollutant exposure and potential adverse effects on babies.The online version of this article (10.1007/s11869-018-0593-1) contains supplementary material, which is available to authorized users. Volatile organic compounds (VOCs) are a major category of pollutants, associated with adverse effects on human health  Exposure to fragranced consumer products is widespread. For instance, an estimated 98.5% of Australians are exposed to fragrance products at least once a week from their own use, others’ use, or both  products, assuming they may contain fewer chemicals of concern Klaschka . HoweverPrevious research on VOC emissions from fragranced consumer products found the most common compounds were ethanol and limonene , 9 lotions , 2 hair sprays, and 1 fragrance spray. The green category includes 12 shampoos , 8 lotions , and 1 anti-bug spray. All of these 42 products were purchased from stores in Australia, including groceries, pharmacies, and organic food stores.“Green” products are defined in this study as the products with the claim of “certified organic,” “certified green,” “green,” “organic,” “natural,” “no petrochemicals,” “non-toxic,” “plant-based,” and “essential oils” for the whole products or their ingredients on their product label, safety data sheet, or website. “Regular” products are the products not in the green category.“Baby” products are defined in this study as products containing specific designations, such as wording  or illustrations , that they are intended for use on infants and toddlers.“Fragranced” products are identified in this study as products containing fragrance, parfum, aromatic extracts, or essential oils.Headspace GC/MS of the baby products was performed using a Shimadzu GC/MS-QP2010 Plus instrument coupled to an automated Shimadzu AOC-5000 sample injection system. Approximately 2 g of each baby product was weighed into a 10-mL amber vial. Each vial was then tightly sealed with a magnetic screw cap with a PTFE/silicone septum. Samples were incubated at 40 °C for 1 h immediately prior to injection of 2.5 mL of the headspace into the injection port heated at 240 °C (split ratio 25). Separation was performed on a BPX-VOL capillary column  using helium as the carrier gas (flow rate 30 cm/s). The oven temperature was kept at 35 °C for 3 min, then increased by 5 °C/min to 220 °C and held at this temperature for 5 min. The total run time was 45 min. The mass spectrometer ion source and interface temperatures were maintained at 200 and 240 °C, respectively. The mass spectrometer was operated in full scan mode between m/z 25 and 400. Blanks were analysed periodically each day to account for any background impurities. The volatile sample components were identified based on the mass spectral library of the National Institute of Standards and Technology NIST Version 2.0 Stein .Two regulatory analyses were performed to identify whether chemicals emitted from baby products could be classified as (i) potentially hazardous under Australian regulations or (ii) potentially carcinogenic under the World Health Organization. For (i), Safe Work Australia (SWA) maintains a Hazardous Chemical Information System (HCIS) that permits identification of chemicals with hazard classifications  A summary of all VOCs emitted from “green” and “regular” baby products is provided in Table Among the 42 products, the most prevalent VOCs (in at least 40% of the products) were limonene, acetaldehyde, ethanol, alpha-pinene, linalool, beta-myrcene, acetone, and beta-pinene. In regular products, the six most prevalent VOCs were ethanol, limonene, acetaldehyde, alpha-pinene, ethyl butyrate, and phenoxyethanol. In green products, the six most prevalent VOCs were limonene, linalool, acetaldehyde, beta-myrcene, alpha-pinene, and acetone. Among all the identified VOCs, limonene was the most common, found in 67% of the products .Among the green products, 20 emitted at least 1 VOCs classified as potentially hazardous  could also be generated. Further, the study focused on VOCs, even though other chemical classes (such as semi-volatile or non-volatile organic compounds) could also pose potential hazards. Finally, the relative safety or hazard of products depends on many factors , and this study focused exclusively on identifying VOC emissions.This study provides the results of VOCs emitted from 42 common fragranced baby products, finding a total of 684 VOCs emitted (representing 228 different VOCs), with 207 VOCs emitted (representing 43 different VOCs) classified as potentially hazardous. However, only 5% of all VOCs and 12% of potentially hazardous VOCs were listed on any product label, safety data sheet, or website. Moreover, emissions of the most prevalent VOCs from green, organic, and natural fragranced baby products were not significantly different from regular baby products. The results of this study can contribute to improved awareness of product ingredients and to reduce the risk of exposure for babies.ESM 1(DOC 366 kb)ESM 2(DOC 284 kb)ESM 3(DOC 21 kb)ESM 4(DOC 19 kb)ESM 5(DOC 18 kb)


Aroma volatiles, especially esters, were significantly increased, both qualitatively and quantitatively, in pear fruits fed on fatty acid metabolic precursors. Cultivars having different flavor characteristics had distinctly different aroma volatile metabolisms. More esters were formed in fruity-flavored “Nanguoli” fruits than in green-flavored “Dangshansuli” fruits fed on the same quantities of linoleic acid and linolenic acid. Hexanal and hexanol were more efficient metabolic intermediates for volatile synthesis than linoleic acid and linolenic acid. Hexyl esters were the predominant esters produced by pear fruits fed on hexanol, and their contents in “Dangshansuli” fruits were higher than in “Nanguoli” fruits. Hexyl esters and hexanoate esters were the primary esters produced in pear fruits fed on hexanal, however the content of hexyl ester in “Dangshansuli” was approximately three times that in “Nanguoli”. The higher contents of hexyl esters in “Dangshansuli” may have resulted from a higher level of hexanol derived from hexanal. In conclusion, the synthesis of aroma volatiles was largely dependent on the metabolic precursors presented.To examine the biochemical metabolism of aroma volatiles derived from fatty acids, pear fruits were incubated  Fruit aromas consist of many aroma volatiles, and their formation involves several biosynthetic pathways. Fatty acid metabolism is an important metabolic pathway involved in the biosynthesis of aroma volatiles in fruits. Fatty acids liberated by lipase activity and those further metabolized by β-oxidative enzymes and/or lipoxygenase (LOX) are generally regarded as the initial precursors of straight-chain esters, alcohols, and aldehydes produced in fruits during development and maturation ,2,3.Transgenic modification of fatty acid metabolism in plant tissues resulted in significant changes in aroma compound profiles . For fruin vitro aroma volatile biosynthesis modifications.Studies examining aroma production in fruits feeding on short or long-chain fatty acids (FA) up to 16 C atoms) ,6,7 have C atoms Pyrus bretschneideri Dangshansuli is a pear cultivar widely planted in China for its great size, rich juice and sweet, crisp flesh. The ripe fruits contain many straight-chain aldehydes and alcohols, giving them a “green” flavor note. Pyrus ussuriensis Nanguoli is another cultivar widely planted in the northern China for its attractive color, exquisite flesh and pleasant flavor. Esters are the important volatiles of the ripe fruits, endowing them a “fruity” flavor note [vor note . The twovor note . Most arIn vitro cultures are useful for examining the biosynthetic pathways of aroma volatiles in fruits [n fruits ,12,13. TLinoleic acid content increased by 7.98% and 3.13% for “Dangshansuli” and “Nanguoli” fruits fed on linoleic acid, respectively. The content of linoleic acid in “Dangshansuli” fruits fed on linoleic acid was 2.19-fold that of the control, while it was 1.23-fold that of the control for “Nanguoli” fruits.The content of linolenic acid increased for pear fruits fed on linolenic acid, however the increase was not significant for “Dangshansuli” fruits. Linolenic acid content increased by 0.62% and 3.27% for “Dangshansuli” and “Nanguoli” fruits fed on linolenic acid, respectively. These results show that “Dangshansuli” fruits absorbed linoleic acid much more effectively than linolenic acid, while “Nanguoli” fruits had similar absorption capacities for linolenic acid and linoleic acid.−1·FW and 1104.4 ng·g−1 FW for “Dangshansuli” and “Nanguoli” fruits fed on linoleic acid, respectively, representing 2.07-fold and 1.66-fold increases, respectively, over control levels.The absorbed linoleic acid and linolenic acid were used for the synthesis of straight-chain esters, alcohols and aldehydes. Esters were the class of volatile with the greatest increases in pear fruits. Methyl acetate, methyl hexanoate, methyl 2-hexenoate, ethyl hex-3-enoate, hexyl acetate, and methyl octanoate were all increased in “Dangshansuli” fruits fed on linoleic acid. The esters increased in “Nanguoli” fruits fed on linoleic acid were ethyl acetate, ethyl butanoate, butyl acetate, methyl hexanoate, and some long-chain ester compounds . The totZ)-hexen-2-ol acetate, were significantly increased in fruits fed on linolenic acid. More than 10 aroma volatiles, including several long-chain esters were synthesized in pear fruits fed on linolenic acid. The total quantity of esters increased to 285.1 ng·g−1·FW and 1,163.4 ng·g−1 FW for “Dangshanli” and “Nanguoli” fruits fed on linolenic acid, respectively, representing 1.44-fold and 1.75-fold increases over control fruits. The increases in esters corresponded to the increases of linoleic acid and linolenic acid  ,15. The Z)-2-hexenyl acetate was detected only in “Dangshansuli” fruits fed on linolenic acid, ethyl (Z)-4-octenoate was detected only in “Nanguolii” fruits fed on linolenic acid, methyl tetradecanoate was the only ester detected in “Dangshansuli” and “Nanguolii” fruits fed on linolenic acid. The result may due to the specific recognition of key enzymes.As intermediate of fatty acid metabolism, the direct precursors of ester formation are also affected by the key enzymes catalyzing their production. The activities of these key enzymes may be affected by cultivar, maturity stage, and environmental conditions, either on or off of the plant ,19,20,21E)-2-hexen-1-ol, 1-hexanol, 1-heptanol, 1-octanol and octen-2-ol, while 1-hexanol and 1-octanol increased in “Nanguoli” fruits. The total quantity of alcohol was increased for pear fruits fed on linoleic acid, while the increase was greater for “Dangshansuli” than “Nanguoli” fruits. The total quantity of alcohol was increased in “Dangshansuli” fruits fed on linolenic acid, but decreased in “Nanguoli” fruits given this compound. The contents of aldehydes were decreased in pear fruits fed on linoleic acid and linolenic acid.The different characters of the key enzymes and the different content of linoleic acid and linolenic acid maybe explain the varying volatile ester-forming capacities in different pear cultivars. The enhancement of precursor contents may be a more feasible method of increasing volatile esters in pear fruits for a cultivar. The content of alcohols also changed significantly. The main alcohols increased in “Dangshansuli” fruits were . Twenty ripe fruits of uniform size and color and free of visible defects or decay were harvested for each cultivar and stored under refrigeration.In this study, “−1. After incubation, the discs were blotted using a filter paper, frozen in liquid nitrogen, and stored at −80 °C until analysis.Each precursor standard  was dissolved in 0.4 mM sorbitol, and several drops of Tween-20 were added to promote dissolution. Individual 1 mM solutions of linoleic acid, linolenic acid, hexanal and hexanol solution were obtained individually for incubation in this study. Linoleic acid, linolenic acid, hexanal and hexanol standards were all of analytical grade and purchased from Sigma-Aldrich . Sorbitol was chemically pure and purchased from Sinopharm Chemical Reagent Co., Ltd . Fruits were removed from cold storage and incubated at room temperature for 24 h, then cut into discs with a diameter of 1 cm and a thickness of 0.2 cm. Incubation of fruits with the precursor substrates was conducted as described by Zhang . Fruit dg for 7 min. The resulting extract was again subjected to extraction with chloroform/methanol  and chloroform (4 mL) was then used to purge. All of the resultant extracts were combined, 0.76% NaCl (w/v) (20 mL) was added, and the samples were shaken on a shaking table  for 15 min. The aqueous layer was removed and the residue was vaporized using a rotary evaporator  to obtain the polar lipids. The polar lipids were dissolved in petroleum ether (90–120 °C boiling point range) with saturated methanol (6 mL), and methanol saturated with petroleum ether (6 mL) was then used to purify them. This purification process was repeated three times. Finally, the pure polar lipid was dissolved in methanol  grade) for esterification.Fruit tissue (2 g) was ground in an ice bath and chloroform/methanol  was added in the flesh. The tissue was extracted for 20 min at 4 °C, then centrifuged at 3000× The polar lipids were then methyl esterified. The polar lipid was redissolved in 0.4 M KOH in methanol (2 mL) and benzene-petroleum ether , vibrated, and then allowed to rest for 15 min. Distilled water (16 mL) was added to the vials, and the upper layer of the extracts was withdrawn and vaporized using a vacuum machine, and then redissolved in hexane (1 mL) for gas chromatography analysis.−1) in constant flow mode, with a split ratio of 1:10. Injector and detector temperatures were set at 250 °C and 280 °C, respectively. For the temperature gradient, the oven was set at an initial temperature of 50 °C for 1 min, which was increased to 200 °C at a rate of 25 °C·min−1, then raised to 230 °C at a rate of 3 °C·min−1, and held at 230 °C for 18 min.Fatty acids were analyzed using an Agilent 7820 gas chromatograph  equipped with flame ionization detector (FID) detector and DB-WAX column, . A volume of 5 µL of extract was injected for analyses. Helium was used as the carrier gas , and quantified using normalization methods. Fatty acid content is denoted as a percent.−1 3-nonanone . The SPME fiber was exposed to the head space of the sample for 30 min to adsorb the analyte, then introduced into the heated injector port of the chromatography apparatus for desorption at 250 °C for 5 min in splitless mode.Head space solid-phase microextraction (HS-SPME) was used to extract and concentrate the aroma volatiles as described by Qin . The volm/z range 33–350 amu at intervals of 1 s.Volatile constituents were analyzed using an Agilent 5973B mass selective detector coupled to an Agilent 7890A gas chromatograph equipped with a 30 m × 0.25 mm × 1.0 mm HP-5 MS (5% phenyl-polymethylsiloxane) capillary column. A constant column flow of 1.0 mL/min helium was used as carrier gas. The injector and detector temperatures were 250 °C and 280 °C, respectively. The oven temperature program was 35 °C for 8 min, an increase of 2 °C/min to 140 °C, 140 °C for 2 min, and an increase of 10 °C/min to 260 °C, which was held for 5 min. Mass spectra were recorded at 70 eV in electron impact (EI) ionization mode. The temperatures of the quadrupole mass detector and ion source were 150 °C and 230 °C, respectively. The temperature of the transfer line was 280 °C. Mass spectra were scanned in the E,Z)-2,4-decadienoate, 1-hexanol and hexanal were used in the experiment. Quantification was conducted using the internal standard method, where the concentration of each aroma volatile was normalized to that of 3-nonanone.Tentative identification of aroma volatiles was conducted by comparing the mass spectra of the samples with the data system library (NIST 98). Whenever possible, MS identification was confirmed using authentic references for ethyl acetate, butyl acetate, ethyl hexanoate, hexyl acetate, ethyl octanoate, ethyl  and Excel were used for statistical analysis. All data were generated from triplicate experiments and reported as the average of three replicates. Analysis of variance (ANOVA) was calculated and the significance of differences was detected by using Duncan’s test or Tukey’s test.Aroma volatiles, particularly volatile esters, were significantly increased both qualitatively and quantitatively in pear fruits fed on fatty acid metabolic precursors. The quantity of aroma volatiles was largely dependent on the quantity of precursors presented. Hexanal and hexanol were more efficient for promoting volatile synthesis than linoleic acid and linolenic acid. More volatile esters were synthesized by “Dangshansuli” fruits than by “Nanguoli” fruits fed on the same quantity of hexanal and hexanol, suggesting that the green-flavored “Dangshansuli” fruits had less endogenous hexanal and hexanol to form volatile esters. Hexanal and hexanol contents and their formation rates may be rate-limiting factors for volatile ester production in pear fruits.