This protocol provides instructions on how to run a linear optimization model that determines the cost-optimal supply of coal, from Chinese and foreign mines, to satisfy a given demand for coal in Chinese power and steel plants. We describe how to download the code and required software and detail steps to insert third-party data. We further provide instructions to adjust and define the scenario settings, build and solve the optimization problem file, and visualize the model outcomes.For complete details on the use and execution of this protocol, please refer to Gosens et al. (2022).  •Protocol describes a model to determine the least cost supply of coal to China•Describes how to download all required code and software and insert coal mine data•Provides instructions to adjust scenario settings and run the optimization model•Has instructions to expand node-and-link model and scripts to visualize outcomes Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. This protocol provides instructions on how to run a linear optimization model that determines the cost-optimal supply of coal, from Chinese and foreign mines, to satisfy a given demand for coal in Chinese power and steel plants. We describe how to download the code and required software and detail steps to insert third-party data. We further provide instructions to adjust and define the scenario settings, build and solve the optimization problem file, and visualize the model outcomes. This protocol provides instructions on how to run the “Installation-Level China Coal Model (IL-CCM)”,,,Understanding the Chinese market is of key relevance to understanding international trade in coal. China is the largest importer of both thermal coal (used for power generation) and coking coal (used for steelmaking), being responsible for roughly a quarter of total global imports of both types of coal.,,,,,,,,,,Determining the likely volumes of coal shipped from any mine, or from any country, to the Chinese market, requires understanding the relative competitiveness between these suppliers. A common approach to modelling such relative competitiveness is with so-called “multiregional coal supply models”, with previous applications to global coal markets,The regions, or nodes, in such models can either produce or consume coal (or both), and are connected with other nodes through links, which represent transport infrastructure, and allow one node to supply coal demand in another node. These models include information on coal production capacity and cost for each producing node , transport capacity and cost for each link , and coal demand levels in consuming nodes.In comparison to these previous models, the IL-CCM stands out because of its very high geo-spatial resolution. The most detailed previous analysis with this modelling method represented China’s coal production and demand conflated into provincial-level nodes, and the transport system conflated into inter-provincial links, for a total of 31 nodes and about 150 links.The IL-CCM model uses input data stored in spreadsheet files. Data preparation, processing of the solution file created by the optimization software, and visualization of results is done in R. The file with the linear problem written out as a set of mathematical expressions (a .lp file), for use in linear optimization, is created in Jupyter on a cloud computing instance, whilst solving the problem file is done locally with the CPLEX solver.,We provide all code and data used in this protocol in a public repository, whereas earlier work with similar methods, at best, provided only mathematical descriptions of the optimization model,Timing: 10 min1.https://doi.org/10.5281/zenodo.7222764.Download the repository with fully functional code, data, and folder structure from Zenodo: 2.Extract the repository to any location on your computer.Note: Most of the scripts used below use relative file paths. Any absolute file reference in the below will use “∼/China-coal-model-star-protocol/” to indicate the location of the extracted repository on your machine.The IL-CCM repository includes all the required code and data to run the model. When editing files in this model, only change the values of input data, and do not change the names of files, or sheet or column names in spreadsheet files.Timing: 1–2 h3.a.https://www.woodmac.com/).Option 1: Purchase the “China and Mongolia coal supply” and the “Seaborne Export Thermal Curve” and “Seaborne Export Metallurgical Curve” from Wood Mackenzie  is in for Chinese mines, or what country a mine (cluster) is in for foreign mines.iii.A technical production capacity and total cash cost in US$/t.iv.Coal quality data, specifically: whether it is thermal, hard coking, semi-soft coking, or pulverized coal for injection, as well as calorific value  for thermal coals.Option 2: Use any other data supplier or own assumptions. Data requirements are:Purchase or prepare mine-level coal-mine data.Note: the repository described in the step “Download IL-CCM data and code repository” still includes the coal mine data sheets with the required layout. These files have been censored by replacing data with arbitrarily picked numbers for key technical and economic indicators. That means the model will run fine, but will produce unrealistic results until these coal mine data sheets have been updated with real data.The repository described in the previous step does not include mine-level production costs, production capacity, and coal qualities, as this is proprietary third party data. The user can either use the same third-party data as was originally used in creating this model, or alternatively they may use their own coal mine data, or from any other commercial supplier, although it is substantially more work to insert such data into the model.Timing: 1–3 h4.Note: the versions mentioned in the steps below are the versions as used during the creation of this protocol. Older or newer versions may work as well.a.Download and install R version 4.0.5.b.Download and install R Tools version 4.0.c.i.Open the file “China-coal-model-star-protocol.Rproj” in the data and code repository.ii.>install.packages('renv')Install renv by typing:iii.>renv::restoreInstall all required packages by typing:iv.Type “y” when prompted “Do you want to proceed?”Option 1 (Recommended): Install required packages using the environment file provided with the data and code repository.d.i.>install.packages('devtools')Install devtools by typing:ii.> require(devtools)> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_version> install_versionInstall all required packages by typing:Option 2 (Alternatively): Install required packages manually.Install R and associated software.5.a.Download and install Anaconda 3 v2022.05.b.https://docs.anaconda.com/anaconda/install/verify-install/).Open a Anaconda prompt : Install required software using the environment file provided with the data and code repository, by typing:d.> conda create -n china-coal-imports python=3.6.10 pandas=1.1.1 numpy=1.19.1 xlrd=1.2.0 spyder=4.1.5> conda activate china-coal-imports> conda install -c conda-forge pulp=2.3Option 2 (Alternatively): Install required software manually, by typing:Install Python and associated software.6.a.https://www.ibm.com/products/ilog-cplex-optimization-studio) and click “get the no-cost academic edition”.Browse to the CPLEX website .Timing: 1–2 h7.a.https://azure.microsoft.com/ and click “get started”.Browse to b.Note: A free account will be sufficient for several model runs.Sign up with your details and pick a subscription plan.c.https://portal.azure.com/#home and click “create a resource”.Browse to d.Search for “Azure Machine Learning” in the resources search box, click “create”.e.Choose configuration options , and click “review and create”.Option 1: sign up with MS Azure.8.a.https://aws.amazon.com/sagemaker/ and click “Get started with SageMaker”.Browse to b.Sign up with your details and login.c.Click on “get started” and then “set up sagemaker domain”.d.Choose configuration options , and click “create”.e.Click on “Services”, then “Support”, then “create a case”, “Looking for service limit increases”.f.https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html).Select limit type “Sagemaker studio” form the dropdown, pick a region, and ask for a service limit increase to 1 for an instance  or as gross as received (SE gar), or both. For metallurgical coals define their coking strength after reduction (CSR) number.In the file “selected coal quality data by mine and type -- CENSORED.xlsx”: for every mine, define its name. Define whether its product type is thermal or metallurgical coal.ii.Note: For metallurgical, also define product sub-type  has to list the node that connects these foreign mines to the rest of the node-and-link network. There is one such node for each country, with values provided in the ‘possible values’ sheet. Do the same for all Russian thermal coal mine connected to the Chinese market via rail or road connections, in the sheet “worksheet ovld mines connectors”.In the file “Seaborne Export Thermal - non energy adjusted Worksheet -- CENSORED.xlsx”, in the sheet “worksheet navo mines connectors”: for every thermal coal mine supplying the seaborne market, define its name, and the country and 3 letter iso country code it is located in. Also define its production capacity, production cost, and the calorific value of its output.v.In the file “Seaborne Export Met Worksheet -- CENSORED.xlsx”, do the same as in last step, but for metallurgical coal mines. Define their coal subtype , and not their calorific value. Exclude Australian mines in this sheet.vi.In the file “Australia met coal worksheet -- CENSORED.xlsx”, do the same as in last step, but for Australian metallurgical coal mines.vii.Copy all files in the folder “∼/China-coal-model-star-protocol/1 input/alternative coal data - template” into the folder “∼/China-coal-model-star-protocol/1 input/WoodMac data”, overwriting the files there in the process.Insert coal mine data as follows:b.Note: For this step, we have provided template files with a small set of example entries. Each of these template files contains a sheet named ‘possible values’ to explain restrictions on the values in each column. Adjust as follows:i.Note: the above mentioned file in the code and data repo contains exactly such a list for the node names as used in all of the files described in step a above.From each of the files created in step a above, collect all values in the column ‘orig_node_name’, and create a list with unique values of node names  in the file “∼/China-coal-model-star-protocol/1 input/helper files/mine node list helper.xlsx”.ii.Copy the list of mine node names from step i into the file “∼/China-coal-model-star-protocol/1 input/alternative coal data - template/helper files/node location helper.xlsx”, sheet “loclist”, pasting the list in column A, row 11438 onwards (highlighted in green in the spreadsheet).iii.For each mine node in step ii, add latitude and longitude.iv.Copy the list of mine node names from step i into the file “∼/China-coal-model-star-protocol/1 input/alternative coal data - template/helper files/mine name to location.xlsx”, sheet “Sheet1”, pasting the list in column B, row 2 onwards (highlighted in green in the spreadsheet).v.Note: A list of self-explanatory basin names is provided in the sheet “possible values”.For each mine node in step iv, add the location, which is either the country name, or for mines in China, the name of the province. For mines in China and Mongolia, also select the corresponding basin name.vi.For those mine nodes located in China, copy the list of node names and basins from step iii into the file “∼/China-coal-model-star-protocol/1 input/alternative coal data - template/helper files/basin and mine names.xlsx”, sheet “china mines”, pasting the node name list into column B, row 2 onwards and the corresponding basin names into column C, row 2 onwards (highlighted in green in the spreadsheet). The rest of sheet automatically updates.vii.For those mine nodes located in Mongolia, copy the list of node names from step i into the file “∼/China-coal-model-star-protocol/1 input/alternative coal data - template/network connector files/ international mongolia russia interconnectors cities.xlsx”, sheet “international”, pasting the node name list into column B, row 44 onwards (highlighted in green in the spreadsheet).viii.Note: These destination nodes are all cities or railway stops along the border with Mongolia. More precise information on each is provided in the sheet “dest node description”.For each mine node in step vii, indicate to which node it should be connected (column dest_node_name), picking from the list of nodes provided in the sheet “possible values”.ix.For each mine node in step vii, Also define whether the connection type between the mine and destination nodes is via rail or road, and what distance the link represents.x.Copy the contents of the folder “∼/China-coal-model-star-protocol/1 input/alternative coal data - template” into the folder “∼/China-coal-model-star-protocol/1 input/”, overwriting the files there in the process.Connect the mines defined in the above step to the rest of the node-and-link network.Option 2: when using mine-level data from other sources.The user can also use their own coal mine data, or from any other commercial supplier, but inserting such data into the model will require additional steps, as described below.Timing: 1–2 h3.Open the file “∼/China-coal-model-star-protocol/1 input/input file details.xlsx”.Note: This file contains explanations on the calculations made in each of the spreadsheets in the folder “∼/China-coal-model-star-protocol/1 input”, including on the values to be adjusted for scenario settings.4.Adjust demand for coal through 2030 for different industries using the files in “∼/China-coal-model-star-protocol/1 input/energy demand files”.Note: Demand levels can either be set at national levels, with an automatic distribution to provincial-level demand calculated by these sheets, or the user can adjust provincial-level demand directly.5.Adjust production capacity of coal-fired power plants and steel plants through 2030 using the files in “∼/China-coal-model-star-protocol/1 input/Global Energy Monitor data”.6.Adjust transport capacity of the railway network through 2030 using the file “∼/China-coal-model-star-protocol/1 input/rail network/rail line capacities.xlsx”.7.Adjust annual throughput capacity of coal ports through 2030 using the file “∼/China-coal-model-star-protocol/1 input/port data/port loc and capa consolidated sources.xlsx”.8.Adjust transmission capacity of the UHV network through 2030 using the file “∼/China-coal-model-star-protocol/1 input/network connector files/UHV overview.xlsx”.9.Run the script “∼/China-coal-model-star-protocol/1 Build preparation script.R”; this will take adjusted input files in “∼/China-coal-model-star-protocol/1 input” and summarize these into model build files, which are saved into “∼/China-coal-model-star-protocol/2 build”.These are instructions on how the user may define their own scenario settings for coal production, consumption and transport infrastructure capacity. Running the build preparation script (step 9 below) gathers the scenario settings from their respective spreadsheet files and processes these into model build files which are in the correct format to be read by the optimization script used in the next step.Timing: 10 hhttps://pypi.org/project/PuLP/). You can do this on a desktop machine, but it is recommended to be done using an online cloud computing service. These steps will create a problem file (extension “.lp”) to be used in a solver in the next step.10.a.https://portal.azure.com/#home and click on the Azure Machine Learning instance created under “Browse to b.Click “launch studio”.c.Click “start now” under “Notebooks”.d.In the file folder structure, click on whatever folder you want to run the code in.e.In that folder, create two subfolders called “builds” and “problemfiles”.f.In that same folder, upload the file “∼ /China-coal-model-star-protocol/2c Create problem file on MS Azure ML.ipynb".g.In the folder “builds”, upload all the .csv files from the folder “∼ /China-coal-model-star-protocol/2 build".h.>selected_year = 2025Open the file “2c Create problem file on MS Azure ML.ipynb” on Azure, select the year you want to run the model for and fill that in line 32, i.e.:i.Note: We’d recommend a “compute optimized” instance with at least 16 virtual CPU and 32 GB of memory, i.e., an instance of type “Standard_F16s_v2” or better.Click on “Compute” under “Manage” in the sidebar and click “new”. Choose your options.j.Click on “Notebooks” under “Author” in the sidebar to go back to the optimization model script.k.> pip install pulpClick on “+ Code” under the code cell and type:l.Click “Run cell” next to the cell created in step k.m.Click “Run cell” next to the first cell.n.The problemfile created by these steps will appear in the folder “problemfiles” after about 10 h; download it and copy it to “∼ /China-coal-model-star-protocol/4 solutionfiles".Option 1: Create the problemfile with MS Azure.11.a.https://aws.amazon.com/sagemaker/ and login.Browse to b.Click “Services”, and then “S3”.c.Click “create bucket”, use a bucket name “sagemaker-studio-SOMETHING”, i.e., anything starting with “sagemaker-studio”; this allows easier access to the bucket from sagemaker studio.d.In this bucket, create two subfolders called “builds” and “problemfiles”.e.In the folder “builds”, upload all the .csv files from the folder “∼ /China-coal-model-star-protocol/2 build".f.Click “Services”, “Amazon Sagemaker”, then “Sagemaker dashboard” in the sidebar, then “Open Sagemaker Domain”, then “launch app”, then “Studio” from the dropdown.g.In the Sagemaker studio, click “upload files”, and upload the file “∼ /China-coal-model-star-protocol/2b Create problem file on AWS Sagemaker.ipynb".h.Double click the uploaded file, choose image “Data Science”, Kernel “Python 3”, Start-up script “No script” when promoted, and click select.i.CRITICAL: You must have previously requested access to this type of instance. How to get that access is explained in step 8 under “Click “Select instance” at the top and select your preferred type of instance.j.> pip install pulpClick on the + icon to insert a cell below, and type:k.Click on this cell and click “Run cell” from the menu at the top.l.> bucket ='sagemaker-studio-SOMETHING'Click on the first cell and fill in the bucketname from step c where it reads:m.> selected_year = 2025Select the year you want to run the model for and fill that in where it reads:n.Click “Run cell” in the menu at the top.o.The problemfile created by these steps will appear in the folder “problemfiles” after about 10 h; download it and copy it to “∼ /China-coal-model-star-protocol/4 solutionfiles".Option 2: Create the problemfile with AWS Sagemaker.12.a.> conda activate china-coal-importsLaunch an anaconda prompt and type.b.> spyderLaunch spyder by typing.c.Click open file and browse to “∼ /China-coal-model-star-protocol/2a Create problem file locally.py".d.> currentDirectory = "∼/China-coal-model-star-protocol/"Fill in the correct file location of the code and data repository in line 15, e.g.:e.> selected_year = 2025Select the year you want to run the model for and fill that in line 12, e.g.:f.Press F5 to run the file.g.The problemfile created by these steps will appear in the folder “∼/China-coal-model-star-protocol/3 problemfiles" after a given period of time, depending on the computing power of your desktop machine, between circa 10 h to many days.Option 3: (Not recommended): Create the problemfile on your desktop machine.This step will take your scenario build files, and process it these with the Linear Programming modeler Package “PuLP”  solver, and can be 10–100 faster when compared with other open source solvers. IBM offers a free license for the CPLEX Optimization Studio, but only for academic users, however. The CBC solver is an open source and completely free alternative.13.a.Open CPLEX Optimization Studio.b.> read "∼\China-coal-model-star-protocol\3 problemfiles\prob v13 baseline 2019.lp"> opt> set logfile "∼\China-coal-model-star-protocol\4 solutionfiles\solution v13 baseline 2019.txt"> display solution variables -Type below code, adjusting for the file name for the problem file you created in the previous step:Option 1: with CPLEX Studio.14.https://github.com/coin-or) included in PuLP.a.> conda activate china-coal-importsLaunch an anaconda prompt and type.b.> spyderLaunch spyder by typing.c.Click open file and browse to “∼/China-coal-model-star-protocol/3a Solve with python.py".d.Adjust the problem file and solution file name and location in lines 10 and 12.e.Run the script.Option 2: with python and the Coin-or branch and cut solver Open script “∼/China-coal-model-star-protocol/9 geocoding network additions/geocode locations.R” and copy your Google Maps api credentials into line 19:c.Run this script; this saves location names with latitude and longitude data into the file “∼/China-coal-model-star-protocol/9 geocoding network additions/locations geocoded.xlsx”.Geocode your nodes.19.Note: This step takes the geocoded locations from step 18, and creates a matrix with a combination of each location with all other locations in list of nodes from step 18. This is used to calculate distances from each node to every other node, using the latitude and longitude data of each point determined in step 18, as geodesic or “as the crow flies” distances. For each node, it retains links with the 12 nearest other nodes. For nodes even further away, we consider that these can be reached via other nodes. This ensures that each node is connected, whilst reducing the total number of links.a.Note: The example files currently included with the data and code repository contain a list of Chinese cities and their latitude and longitude.Copy the data (location names with latitude and longitude) in the file “∼/China-coal-model-star-protocol/9 geocoding network additions/locations geocoded.xlsx”, created in step 18, into the files for origins “∼/China-coal-model-star-protocol/9 geocoding network additions/origins.xlsx” and the file for destinations “∼/China-coal-model-star-protocol/9 geocoding network additions/destinations.xlsx”.b.Run the script “∼/China-coal-model-star-protocol/9 geocoding network additions/calculate geodesic distances.R”; this calculates “as the crow flies” distances between all node pairs and saves this info in “∼/China-coal-model-star-protocol/9 geocoding network additions/geodesic distances all.csv”.Determine geodesic distances.20.Note: This step takes the pairs of nodes that are relatively close to each other, as determined with “as the crow flies” distances in step 19, and finds the real world driving distance between these node pairs.a.>register_googleOpen script “∼/China-coal-model-star-protocol/9 geocoding network additions/geocode distances.R” and copy your Google Maps api credentials into line 21:b.Run this script; this saves location names with geodesic as well as driving distances into the file “∼/distances 12 nearest geodesic and geocoded.csv”.Find driving distances.21.Insert these new connections in the appropriate file in the folder “∼/China-coal-model-star-protocol/1 input/network connector files”.As an example, we have included the exact data and code that we used to identify driving distances between each city and its nearest neighboring cities, in the folder “∼/China-coal-model-star-protocol/9 geocoding network additions”.The expected outcome with each run is 1) a problem file; 2) a solution file; 3) a set of spreadsheets with coal suppliers and coal flows; and 4) a set of visualizations of these model outcomes. The IL-CMM was built specifically to determine levels and origin of coal imports, at given levels of Chinese demand for coal, and with constraints on mine-level production capacity and transport capacity of different types of transport infrastructure. Model outputs are therefore focused on visualizing the origin of supply, the networks used to get to the final source of consumption, and changes in flows of coal under different scenario settings.The code and data repository includes 9 problem files; for each of the years 2015–2019, and a low and high demand scenario for 2025 and 2030. These demand scenarios correspond with the “stated policies” and “sustainable development” scenarios from the IEA’s World Energy Outlook.The current model is a highly detailed model of China’s coal market. The demand and supply conditions in the rest of the world are included in very simplified form only, and cannot be adjusted. The model is also purely focused on coal consumption; it does not include cost-optimization between different sources of electricity and instead uses exogenous input from user defined demand scenarios to determine cost-optimal supply of that demand.A package throws a certain error in R or Python.It is recommended to use the environment files for both R and python provided with the code and data repository. These have been tested to run as a self-contained package. Instructions on how to use either of these environment files are provided under step “The solver reports that the problem is infeasible.•You’ve set demand levels for a certain type of coal at level exceeding the production capacity of all the mines in the model. This is an issue especially with PCI, which has the least supply slack in the model.•You’ve set provincial level demand for coal-fired power or steel at levels exceeding the provincial-level production capacity of coal-fired power plants or steel plants.•You’ve set a reduction in transport network capacities that disconnects different parts of the network from each other. This is unlikely as almost all nodes can also be reached by trucking networks.•A special case is that the provincial-level city of Beijing has no operational power plants in the dataset after 2029; any coal-fired power demand must be delivered via UHV networks. Removing the UHV network whilst simultaneously setting a non-zero coal-fired power demand in Beijing will cause infeasibility.This is a generic issue that could happen when defining problem files. It indicates an incompatibility between different constraints in the model. Investigate by opening the problem text file and finding the definition of the constraint that the CPLEX Optimization Studio reports as causing the infeasibility. A number of likely causes are:The solution is to investigate whether and which demand, supply, and transport constraint cause the infeasibility and adjust scenario settings.When running the script in AWS Sagemaker it reports an error loading the files from the S3 bucket. This is likely due to a version of boto3 , as installed on AWS Sagemaker by default, that is incompatible with the provided script.> pip uninstall -y boto3> pip uninstall -y botocore> pip uninstall -y aiobotocore> pip install boto3==1.17.106 s3fs pulp mipreadUninstall the incompatible version of boto3 and its dependencies, and install a version that does work. To do so, run below code in a new cell in your jupyter notebook:jorrit.gosens@anu.edu.au).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Jorrit Gosens (This study did not generate new unique reagents.


T”. It should be “VKM Ac-2882T”.In the original publication of the article, the number of the strain in VKM was incorrectly published as “VKM 2882And the missing funding information “This research was funded by Sirius University (project BTH-RND-2127) for YVZ and by Ministry of Science and Higher Education of the Russian Federation (Agreement № 075-15-2021-1085) for IAO, MVB” are included.The original article has been corrected.


Hearing a groove rhythm (GR), which creates the sensation of wanting to move to the music, can also create feelings of pleasure and arousal in people, and it may enhance cognitive performance, as does exercise, by stimulating the prefrontal cortex. Here, we examined the hypothesis that GR enhances executive function (EF) by acting on the left dorsolateral prefrontal cortex (l-DLPFC) while also considering individual differences in psychological responses. Fifty-one participants underwent two conditions: 3 min of listening to GR or a white-noise metronome. Before and after listening, participants performed the Stroop task and were monitored for l-DLPFC activity with functional near-infrared spectroscopy. Our results show that GR enhanced EF and l-DLPFC activity in participants who felt a greater groove sensation and a more feeling clear-headed after listening to GR. Further, these psychological responses predict the impact of GR on l-DLPFC activity and EF, suggesting that GR enhances EF via l-DLPFC activity when the psychological response to GR is enhanced. Our recent study using acute exercise with music showed that the key factor of exercise’s effect on prefrontal executive function is a positive affective response3 expressed the two-dimensional axis of pleasure and arousal.A growing amount of evidence shows that physical exercise has beneficial effects on cognitive functions5. GR can be defined by the subjective score of “Wanting to move to the music” and “Good nori”6. The groove sensation can be modulated by syncopation. Syncopation is a method of shifting rhythmic emphasis by manipulating the complexity of a rhythm. Rhythm with low to medium syncopation induces a higher groove sensation than does rhythm with high syncopation as evidenced by averages of mass groups in previous studies9. Low-frequency components, such as the bass drum, induce entrainment of body movement and musical beat10. In several previous studies, drum breaks consisting of hi-hat, snare-drum, and bass-drum sounds were used because they made it easy to control rhythmic factors, syncopation, bass sound, and tempo12.Another potential stimulus which can improve EF through a positive affective response is groove rhythm (GR). GR is a musical rhythm that induces the sensation of “Wanting to move to the music” (groove sensation) accompanied by positive affective responses while listening to music13. Music with groove-inducing characteristics increasing in recent music popularity charts may be indicative of this14. That groove music improves gait performance in Parkinson’s disease (PD) by reducing the cognitive demands of synchronizing to the beat and promoting vigorous movement17 shows that groove music affects the interaction between body movement and brain function. More interestingly, listening to groove music induces entrainment of body movement and musical rhythm together with positive affective responses18 and activates neural networks associated with motor and reward systems20. Since the dopaminergic reward system projects not only to emotion-related brain areas but also to cognition-related areas such as the PFC23, GR could increase PFC activity and lead to improved EF. However, no research has explored the effect of groove music on EF and prefrontal activity to date. The reason for this could be that the effect of GR on EF may have large individual differences because both groove sensation and concurrent positive affective responses to groove music would have many individual differences24 and both responses are associated with reward system activity11. Therefore, we should examine the single effects of GR on EF and its relationship with PFC activity. To examine this, consideration of the psychological responses to listening to GR as influential factors that explain individual differences and creation of an experimental model which can evaluate the effect of GR on EF and how it is related to PFC activity are necessary.Moving the body to music is a universal phenomenon  and one of the main powers of music26, which evaluates inhibitory EF, in order to clarify the effects of an acute bout (10 min) of exercise31. fNIRS is a non-invasive neuroimaging method which can monitor hemodynamic response to neural activation (neurovascular coupling)32 by using near-infrared light passing through tissue. Since fNIRS allows for the least restrictive measuring environment among neuroimaging modalities, it can measure regional cortical activation boosted by listening to music while minimizing possible negative environmental influences on psychological response and cognition. The CWST has been adopted in numerous neuroimaging studies including fNIRS studies35, and the brain regions related with the task are well known. The DLPFC is a key region for inhibitory control of EF36 and responsible for CWST performance38. In addition, the left hemisphere plays a key role in the processing of verbal information42. Therefore, we focused on the left dorsolateral prefrontal cortex (l-DLPFC) as the region of interest (ROI). A previous study indicated that l-DLPFC activity correlated with a positive affective response and that EF changed with a single bout of exercise with music3. Therefore, in the current study, the CWST was performed before and after listening to GR while monitoring l-DLPFC activity using fNIRS. In addition to this acute model, cluster analysis using the subjective senses of both groove sensation and psychological state when listening to GR was introduced, and we tried to reveal the individual differences in the effect of listening to GR on EF and task-related l-DLPFC activity.To that end, in the current study we introduce the combination of an acute experimental model that was used for the detection of exercise’s effect on cognition and prefrontal activity and a grouping analysis to explore the psychological response to GR. In our previous research, we used functional near-infrared spectroscopy (fNIRS) with the color-word-matching Stroop task (CWST)GR elicits groove sensation and concurrent positive affective response, but it is not known whether it enhances inhibitory EF with l-DLPFC activity as a result. The purpose of this study is to determine whether GR enhances EF and l-DLPFC activity, focusing on individual differences in psychological responses to GR. Our working hypothesis is that GR presented as drum breaks with low to medium syncopation enhances CWST performance with task-related l-DLPFC activation. Furthermore, the effects can be remarkable in participants who experience a higher groove sensation and positive psychological state. This study will allow us to look ahead to new aspects of the effect of GR, for example a potential cumulative effect with exercise.t tests were conducted over condition . We confirmed that there were no differences in HR between conditions (t(48) = − 0.983, P = 0.33). GR elicited significantly higher scores compared to WM in these items: “Good nori” (t(50) = − 11.99, P < 0.001), “Wanting to move to the music” (t(50) = − 9.17, P < 0.001), “Feeling like my body is resonating with the rhythm” (t(50) = − 5.62, P < 0.001), “Having fun” (t(50) = − 11.62, P < 0.001), “Excited” (t(50) = − 9.00, P < 0.001), and “Feeling clear-headed” (t(50) = − 4.16, P < 0.001). WM elicited significantly higher scores compared to GR in these items: “Struggling to synchronize with the beat” (t(50) = 4.35, P < 0.001), “Bored” (t(50) = 10.73, P < 0.001), “Wanting to stop listening” (t(50) = 6.48, P < 0.001), and “Feeling discomfort” (t(50) = 5.18, P < 0.001).Physical load and psychological measures for each experimental condition for all participants are shown in Table P < 0.001, Fig. P < 0.001, Fig. We confirmed whether the Stroop interference could be observed in this experiment Fig. A-C. Reacneutral, RTincongruent, RTinterference, l-DLPFC oxy-Hbneutral, l-DLPFC oxy-Hbincongruent, and l-DLPFC oxy-Hbinterference were subjected to repeated measures two-way ANOVA with condition  and time  as within-subject factors  = 25.02, P < 0.001 and F = 15.18, P < 0.001, respectively). There was no significant change of RTinterference  = 1.33, P = 0.25), l-DLPFC oxy-Hbneutral  = 0.89, P = 0.34), l-DLPFC oxy-Hbincongruent  = 0.78, P = 0.37), or l-DLPFC oxy-Hbinterference  = 0, P = 0.99) before or after listening for either of the GR or WM conditions.Stroop RTors Fig. D, E. Fornori”  and “Feeling clear-headed” , respectively. In subsequent analyses,“Good nori” and “Feeling clear-headed” were used as representative items for psychological responses to listening to GR.To account for individual differences, we objectively classified the participants into groups using cluster analysis based on their psychological responses to listening to GR and compared the effects between inter/intra clusters. Pearson correlation coefficient between Stroop performance and all psychological variables are shown in Table In the two-cluster solutions, the ratio [inter-cluster variance/total variance] was 0.41 and there was overlapping of two clusters in the plot  was characterized by high groove and high feeling clear-headed and named the “Groove-familiar” cluster. The second cluster (n = 14) was characterized by high groove and low feeling clear-headed and named the “Low feeling clear-headed” cluster. The third cluster (n = 21) was characterized by low groove and low feeling clear-headed and named the “Groove-unfamiliar” cluster.nori” (GR-WM) than the “Groove-unfamiliar” cluster  = 46.15, P < 0.001). The “Groove-familiar” cluster had higher “Feeling clear-headed” (GR-WM) than the “Low feeling clear-headed” and the “Groove-unfamiliar” clusters  = 36.44, P < 0.001). The “Groove-unfamiliar” cluster had higher “Feeling clear-headed” (GR-WM) than the “Low feeling clear-headed” cluster (P < 0.05). In the comparison of basic individual specifications of music/dance between the clusters with a repeated measures one-way ANOVA, no significant differences were revealed (Supplementary Table In the comparison of condition differences (GR-WM) between clusters, a repeated measures one-way ANOVA with cluster  was conducted Fig. . The “Grt test  = 2.22, P < 0.05). Δl-DLPFC oxy-Hbinterference in the GR condition was significantly increased compared to the WM condition (t(15) = − 2.16, P < 0.05). In the “Low feeling clear-headed” cluster, there were no significant differences between conditions for both CWST performance and l-DLPFC activity. In the “Groove-unfamiliar” cluster, ΔStroop RTinterference in the WM condition was significantly shorter than in the GR condition (t (20) = − 3.52, P < 0.01). There were no significant differences between conditions for l-DLPFC activity.Next, comparisons between condition  within each cluster were conducted using the paired est Fig. . In the nori”, “Feeling clear-headed”), Δl-DLPFC oxy-Hbinterference, and ΔStroop RTinterference were used. The first model, χ2 = 0.01, CFI = 0.69, and RMSEA = 0.22, yielded a very poor fit to the data. The relative effects were as follows ; “Feeling clear-headed” to Δl-DLPFC oxy-Hbinterference ; and Δl-DLPFC oxy-Hbinterference to ΔStroop RTinterference . The second model, χ2 = 0.12, CFI = 0.91, and RMSEA = 0.14, yielded a poor fit to the data. The relative effects were as follows ; “Feeling clear-headed” to Δl-DLPFC oxy-Hbinterference ; “Feeling clear-headed” to ΔStroop RTinterference ; and Δl-DLPFC oxy-Hbinterference to ΔStroop RTinterference . The third model, χ2 = 0.24, CFI = 0.98, and RMSEA = 0.08, yielded a satisfactory fit to the data. The relative effects were as follows ; “Good nori” to Δl-DLPFC oxy-Hbinterference ; “Feeling clear-headed” to Δl-DLPFC oxy-Hbinterference ; “Feeling clear-headed” to ΔStroop RTinterference ; and Δl-DLPFC oxy-Hbinterference to ΔStroop RTinterference . The final model showed both “Good nori” inducing “Feeling clear-headed” and Δl-DLPFC oxy-Hbinterference as starting factors and that “Feeling clear-headed” affected both Δl-DLPFC oxy-Hbinterference and ΔStroop RTinterference.In all models, the variables which were employed in cluster analysis  and positive affective response  compared with the WM by comparing average values. This agreed with previous studies showing that drum breaks with a low to medium degree of syncopation induce higher groove sensation9. To make the rhythm more interesting to listeners and to thus induce groove sensation and the related positive affective responses, a balance between expectation and violation of the rhythm is thought to be important12. This is supported by the hypothesis that prediction error (deviation from predicted rhythm) is a requisite for music to activate the reward system in the brain45. Since the validity of our experimental design was confirmed, we proceeded to the detection of the effect of GR on cognitive function.First, as a precondition of the experiment, we confirmed that drum breaks with a low to medium degree of syncopation (GR) induced groove sensation , listening to GR significantly enhanced EF and l-DLPFC activity compared to the WM condition  could be among the important factors for promoting the positive effects of GR. Generally, music induces positive affective responses and concurrently increases dopamine release and brain activation related to the reward system including the basal ganglia (BG), midbrain, and orbitofrontal cortex54. In the current experiment, the average scores for the beat-processing ability test among participants in both the “Low feeling clear-headed” and "Groove-unfamiliar" clusters were relatively low compared to the participants in the "Groove-familiar" cluster, but not significantly so (Supplementary Table 55) are needed to detect the influence of inhibiting factors.Conversely, the participants grouped in the “Low feeling clear-headed” and “Groove-unfamiliar” clusters experienced no or negative effects of GR on EF and l-DLPFC activity. One potential inhibiting factor is low beat-processing ability in those individuals. It is possible that participants who have a low beat-processing ability were forced to pay extra attention to the beat while listening to the GR. This may have resulted not only in inducing failure to become entrained, but also in reduced mental resources, which limited attention, motivation, and cognitive performance58. Though more than 50 students participated in the current experiment, the sample size was insufficient for conducting sub-analyses to seek the effects of these potential influential factors. Future studies should plan to compare the effects between participants grouped by these potential influential factors. Second, in the auditory stimulus, we used rhythm with a low to medium degree of syncopation as the GR stimulus and a white-noise metronome as the control stimulus. We could control tempo, but could not control the type and number of sounds. Further studies are needed to try to detect the influence of other acoustic specifications by using different patterns for the GR and control stimuli. Third, in the measurement of the brain’s neural activation, the current study focused on only cortical, specifically the l-DLPFC, activity. We should use fMRI and PET to examine brain activity in broader and deeper regions that are involved with motor, reward, and cognitive systems, their networks, and released neurotransmitters.This study has several limitations. First, widespread individual differences in psychological responses to the rhythm were demonstrated in the current results. Various potential factors such as musical training experience, music reward sensitivity, beat processing ability, familiarity with groove music/dance, body morphology and cultural background could influence psychological responses to the rhythmIn conclusion, listening to a rhythm with low to medium syncopation enhanced EF and l-DLPFC activity in only groove-familiar participants, which supports our hypothesis. These results suggest that individual differences in psychological responses to GR are one of the key factors in predicting the effects of listening to GR on prefrontal EF. This study raises the potential that GR can enhance human cognitive performance like exercise.28 showed that the effect sizes of exercise were Cohen's d = 0.5 ~ 0.7. In the current study, we estimated a slightly lower effect of GR than of exercise and conducted a Power Analysis with Cohen's d = 0.4. A Power Analysis using the sample size determination software G-power showed that 52 subjects would be considered sufficient to detect a significant difference (dz = 0.9) between groups on a two-sided, 0.05 test of proportions (difference between two dependent means [matched pairs]) with > 80% power. All participants were recruited from the student population of the University of Tsukuba through ethics board approved flyers that were displayed across the campus. No subject reported a history of neurological or psychiatric disorders or had a disease requiring medical care. All participants were Japanese native speakers and right-handed. All experimental protocols were approved by the Institutional Ethics Committee of the University of Tsukuba. The experiment protocols were in accordance with the guidelines of the latest version of the Helsinki Declaration. Written informed consent was obtained from all participants after being given complete information about this study. Before all statistical processes, seven participants were excluded from the analysis for the following reasons: reported short sleep duration the night before the experiment (N = 1), had a pre-session CWST correct-answer rate equal to 80% or less (N = 1), had a pre-session Stroop RTinterference with a negative value or 2SD greater than the average (N = 3), or had missing fNIRS data (N = 2). As a result, we analyzed the data of fifty-one participants. Demographics of the participants are presented in Supplementary Table Fifty-eight healthy young adults  took part in this study. Our previous fNIRS studies examining the effect of very light to moderate intensity exercise on CWST performance59, and they rated their dance familiarity by responding to the phrase, “I often start to dance when music plays” on an eleven-point scale ranging from 0 = “Not at all”, 5 = “Neutral” and 10 = “Extremely”. On the third and fourth days, they underwent two experimental conditions: listening to groove rhythm (GR condition) or listening to a white-noise metronome (WM condition). The participants listened to auditory stimuli played through studio speakers  for 3 min in a standing position. To increase the groove sensation18, participants were instructed to “please beat the rhythm freely in your upper body, for example, tapping, nodding, and swaying”. However, dynamic physical movement such as stepping was discouraged. In order to confirm the physiological load during listening, heart rate (HR) was measured using a wireless sports heart rate meter . Before and after listening, the CWST was conducted while hemodynamic changes in the l-DLPFC were monitored using fNIRS. After listening, participants answered a questionnaire about their psychological responses to the stimuli   by referencing a previous studynori”, “Wanting to move to the music”, “Feeling like my body is resonating with the rhythm”, “Easy to synchronize with the beat”, and “Struggling to synchronize with the beat”. We employed several terms closely related to groove as suggested in previous studies61. “Good nori” is a Japanese term similar to the concept of groove6. These items were also shown to be highly correlated with groove. The second category was about psychological state and included descriptors such as “Having fun”, “Excited”, “Bored”, “Wanting to stop listening”, “Feeling clear-headed”, and “Feeling discomfort”. “Having fun” and “Excited” have been shown to correlate with groove in previous studies62. “Bored”, “Wanting to stop listening”, and “Feeling discomfort” were the reverse. “Feeling clear-headed” was set as an item which expresses cognitive alertness64 to detect responses to GR based on the preliminary study. The eleven-point (0–10) scale ranged from 0 = “Not at all”, 5 = “Neutral” to 10 = “Extremely”.To evaluate the potential for a psychological response to listening to the rhythm to mediate the effects on EF, we created two categories each consisting of eleven questions. The first category was about the sensation of groove including “Good 28. The CWST is a task in which participants are presented with two rows of letters and they must determine whether the top row’s color corresponds to the bottom row’s color name. Subjects answered by pressing the "yes" or "no" buttons on a keyboard  with the index finger of their right and left hands.The Japanese version of the CWST, which has been validated in previous studies, was used to assess inhibitory control of a core component of executive function Fig. B3,27,28.This task consists of three conditions: neutral, congruent, and incongruent. In the lower part of the screen, one of the four color-name words "RED", "BLUE", "GREEN", and "YELLOW" were displayed, in Japanese, for all trials. However, the characters displayed in the upper row of the screen were different for each condition: the symbol "xxxx" was presented in the neutral condition, a color word written in an ink color matching its meaning  was presented in the congruent condition, and a color word displayed in a color that did not match its meaning  was presented in the incongruent condition. The incongruent condition causes a cognitive conflict between the meaning and color of the upper row’s word and induces a delay in reaction time (RT). We randomly displayed 30 trials including 10 neutral, 10 congruent, and 10 incongruent.interference), which represents inhibitory executive function. Every participant practiced the CWST at least three times before starting the first CWST session.To force the subjects to look from the upper row to the lower row, the upper row was displayed 100 ms earlier than the lower row. After 2 s of the upper characters being displayed, the screen returned to the fixation screen, which was centered on a "+" mark (gazing point). The inter-stimulus interval was randomized (10–12 s) for each subject to prevent them from predicting the timing. We evaluated RT for the incongruent task minus that for the neutral task to determine Stroop interference time  was placed over FT7, with the medial edge of the probe column parallel to the medial line , which is equipped with two wavelengths of near-infrared light (785 and 830 nm), was used. To cover the l-DLPFC using an fNIRS probe holder, the composition and placement of the fNIRS probe holder followed the same procedure as described in our previous studiesine Fig. A. Likewi65. The oxy-Hb changes in the l-DLPFC were preprocessed with a band pass filter . The differences of the oxy-Hb signal between peak  periods and baseline  periods were calculated as the oxy-Hb change  space67. In brief, this method allows us to place a virtual probe holder on the scalp using a simulation of the holder’s deformation and to register the probes and channels on reference brains in the MRI database71. ROIs are created by combining four neighboring channels, and anatomically labeled using LBPA40, which is a widely used method . The l-DLPFC as a ROI accounts for channels 13, 14, 16, and 17.The oxygenated hemoglobin (oxy-Hb) was analyzed and calculated in units of millimolar-millimeter (mM⋅mm)interference in the categories of “Sensation of groove” and “Psychological state” . Regarding HR data, two datasets were missing because of slippage of the sensor band. For Stroop task performance, RT and ER were subjected to repeated-measures three-way ANOVA with condition , time , and task condition  as within-subject factors to examine whether the general tendencies for the Stroop task could be reproduced in all conditions. This analysis was limited to the main effect of the task condition because the purpose of the ANOVA was to examine the occurrence of Stroop interference. Subsequently, Stroop RTneutral, RTincongruent, and RTinterference [Incongruent—Neutral] were calculated, and repeated-measures two-way ANOVA were performed with condition  and time  as within-subject factors. When there were significant interaction or main effects in the two-way ANOVA, post-hoc tests using the Bonferroni method were carried out. For l-DLPFC activity, in order to confirm whether there was a Stroop effect (l-DLPFC oxy-Hbinterference), l-DLPFC oxy-Hb changes were subjected to repeated-measures three-way ANOVA with condition , time , and task condition  as within-subject factors as for the Stroop task performance analysis. Subsequently, l-DLPFC oxy-Hbinterference [Incongruent − Neutral] was calculated, and a two-way ANOVA was performed with condition  and time  as within-subject factors. When there were significant interactions or main effects in the two-way ANOVA, post-hoc tests using the Bonferroni method were carried out.For HR and each psychological measure, paired interference and interference were calculated for the following contrast: [post − pre]. Condition differences of psychological measures, interference, and interference were calculated for the following contrast: [GR-WM]. Pearson correlation analysis between all psychological measures as independent variables and interference as a dependent variable was carried out.Subsequently, 72, representative variables which had the highest correlation with ΔStroop RTinterference in each category of “Sensation of groove” and “Psychological state” were used for cluster analysis. As a result, “Good nori” and “Feeling clear-headed” were used. Regarding determination of the optimal number of clusters, we used the ratio of inter-cluster variance and total variance (R script: km$betweenss/km$totss)73 and visual inspection of the scatterplots. The ratio shows aggregability of intra-cluster and separation of inter-cluster. We determined the cluster number which showed a greater ratio of inter-cluster variance and total variance not exceeding three cluster numbers to avoid the difficulty of defining the clusters.The K-means method was used to identify relatively homogenous participant groups on R software. As with a previous study using the k-means methodinterference and interference between clusters, and the comparison of basic individual features of music/dance between clusters were conducted using a repeated measures one-way ANOVA with clusters. When there were significant main effects in the one-way ANOVA, a post-hoc test using the Gabriel method was carried out. Then, to detect the effect of listening to GR by cluster, the comparison of psychological measures, interference, and interference between conditions within each cluster were conducted using the paired t test over condition .After clustering, to reveal the specification of clusters, the comparison of condition differences [GR-WM] in psychological measures, 2 goodness of fit, the root mean square error of approximation (RMSEA), and the Comparative Fit Index (CFI). The criteria were as follows: CFI ≥ 0.90, RMSEA < 0.10, and χ2 ≥ 0.05. Regarding RMSEA values, 0.05 is often used as the cutoff; however, this rejects too many valid models with small sample sizes (n ≤ 100)74. Conversely, RMSEA values greater than or equal to 0.10 suggest a poor fit75. Thus, we set the criteria as RMSEA < 0.10. The variables were “Feeling clear-headed”, "Good nori", interference, and interference, and they were reflected in differences between conditions [GR – WM].To address a possible causal relationship, path analysis was conducted using the “lavaan” package in R software. Model fit was evaluated with the χinterference as an indirect effect on EF and to interference as a direct effect on EF. The third model was a modified version of the second model which included the influence of “Good nori” on Δl-DLPFC oxy-Hbinterference.Three models were tested to examine the causal relationship between the variables depending on the hypothesis. The first model assumed that all variables have only a direct effect. The second model allowed that “Feeling clear-headed” has two paths: to Δl-DLPFC oxy-HbSupplementary Information.


It perturbs data nonlinearly while accommodating the requirement of strict privacy protection, for instance, differential privacy. We highlight multiple facets of data flush through examples.Data perturbation is a technique for generating synthetic data by adding “noise” to raw data, which has an array of applications in science and engineering, primarily in data security and privacy. One challenge for data perturbation is that it usually produces synthetic data resulting in information loss at the expense of privacy protection. The information loss, in turn, renders the accuracy loss for any statistical or machine learning method based on the synthetic data, weakening downstream analysis and deteriorating in machine learning. In this article, we introduce and advocate a fundamental principle of data perturbation, which requires the preservation of the distribution of raw data. To achieve this, we propose a new scheme, named  The explosive growth of large volumes of data with complex structures demands the wide usage of data in applied sciences. In privacy protection, data perturbation is an effective technique. For instance, it privatizes the U.S. Decennial Census Data to protect the confidentiality of individuals by the standard of differential privacy  In the ε-differential privacy  from a cumulative distribution function (CDF) F, we perturb the raw sample to follow a prespecified target distribution R. For example, R can be a standard normal distribution or a uniform distribution. But more commonly, R = F if F is known and Given an independent sample  from Uniform and relabel them so that the rank of Ui in  remains the same as that of Zi in . This transformation from Zi to Ui encodes a positive (Spearman’s rank) correlation between the perturbed and the original samples, c.f., m perturbed samples. We add independent continuous noise eij, j = 1, . . .,m, to Ui independently. Then, we map Ui + eij to yield a perturbed sample following the target distribution R:G is the CDF of Ui + eij.First, we sample  following a p-dimensional continuous distribution F, we apply Rl)-differential privacy  distribution to guarantee ε-differential privacy  for a Gaussian sample . Data flush provides a means of evaluating the model sensitivity for various data.To quantify the impact of model selection on estimation, Data-flush in 3.θ.This section develops a data perturbation tool for pivotal inference based on raw data without privacy concerns. We apply the data-flush scheme Z = , with each Zi following a probability distribution F(θ), and F is known but θ is unknown. The distribution of T is independent of θ, which requires a Monte-Carlo resampling method such as bootstrap to estimate, as its analytic form is often unavailable. However, such a resampling method may suffer the difficulty of inference after model selection. As pointed out in T.Let Z =  according to Zi follows a target distribution Z∗ by applying the same statistical procedure for Data flush generates a pseudo sample T∗ given Z, which can substitute an unknown distribution of T for pivotal inference. Note that the former can be computed but not the latter.(Distribution preservation) The conditional distribution of T∗givenZremains the same as the distribution of T for anyZ. Hence, any test or a confidence interval on the conditional distribution of T∗givenZis exactly as if the distribution of T would have been used.The proof is given in the T∗ given Z via a Monte-Carlo approximation while correcting bias through data perturbation to improve the finite-sample performance. Data perturbation permits estimation of the bias of a statistical procedure through repeated experiments as in simulations, as illustrated in a subsequent data example. The following data-flush Monte-Carlo method summarizes this proposal.For an exact or asymptotic pivotal, we may compute the conditional distribution of D independent perturbed samples d = 1, . . .,D, m = D. Note that we may choose any continuous unbounded distribution of eij in ε-differential privacy). In what follows, D refers to as a Monte-Carlo size. Compute the perturbed pivotal d = 1, . . .,D. Compute the empirical distribution of T as D → ∞.Generate Compute the bias estimate Use Next, we illustrate this data-flush inference method by two examples.θ of a normal distribution with unknown σ2. The pivotal is of the form S is the sample standard deviation. Here, we apply the data-flush inference scheme to simulate the distribution of perturbed pivotal T∗ and compare it with the bootstrapped pivotal  distribution with ε = 0.01 and R being the CDF of Z.The first example concerns the distribution of a pivotal quantity for construction of a confidence interval of the population mean  pivotal  and the T, whereas nonparametric bootstrap differs substantially for a small sample size n = 5. In other words, nonparametric bootstrap’s approximation accuracy depends highly on the sample size n. Indeed, data flush yields an exact distribution of a pivotal as the Monte-Carlo size D → ∞. This observation agrees with the result of p predictors:p could be substantially larger than the sample size n, β =  is a vector of regression coefficients, Xi =  ~ N is a vector of predictors that are independent of the error εi, and the -th element of the covariance matrix Σ is ρj−k||, and σ2 is an unknown error variance. Our goal is to construct a confidence interval for an individual coefficient βl with other covariates involving model selection.Our second example focuses on the construction of a confidence interval in linear regression on a vector of L1-penalty  constraint approximates the L0-constraint for computation. Towards this end, we apply the data-flush Monte-Carlo inference method based on L0-norm regression (CTLP) estimate To construct a confidence interval for parameter gression  to selec/SE(β^l) , where Seij is independently sampled from the Laplace distribution and ε = 0.01, Then, ε-differential privacy for any j, where σ2 based on a holdout sample that is independent of the inference sample, R is the CDF of G is the CDF of Ui +eij with Ui following the Uniform distribution.To replicates β1 = β2 = β3 = 1 and βj = 0 otherwise, with σ = 0.5 and ρ = 0.5. Then, we apply m = D/n and D = 10p to construct a 95% confidence interval for each βj based on CTLP. The results for β1 and β4 are representative and are presented. Specifically, we use the glmtlp package in R to compute the CTLP estimate We perform simulations with the true parameters β1 and β4 are close to the nominal level 95% in each scenario. The discrepancy between the empirical converge and its target 95% is because the asymptotic pivotal may suffer from the bias in the finite-sample situation. Overall, the data-flush Monte-Carlo inference scheme yields a credible confidence interval for a non-smooth problem involving model selection.4.This section applies the data-flush scheme The American Community Survey collects demographic data from 3.24 million persons nation-wide, roughly 1% of the population in the Year 2019 . StatistAlternatively, an investigator can apply data flush to privatize survey data like ACS data without incurring information loss when the data-flush scheme preserves the distribution of raw data. For the ACS dataset, we use In particular, we investigate the impact of privatization by ε-differential privacy, we apply eij following a Laplace distribution to preserve the joint distribution of 16 covariates and 1 response variable across common data types. In this fashion, privatization protects each individual’s information. To illustrate this point, we scrutinize the histogram of the variable AGE before and after privatization in To satisfy We apply the data-flush Monte-Carlo method to construct confidence intervals for raw and privatized data. In particular, for each replication, we only perturb the linear regression residuals and follow the high-dimensional regression example in ε, is 1 for ε-differential privacy, which requires a stricter level of privacy protection 1/17 for each of the 17 variables. It is equivalent to that each variable requires independent Laplace, where the noise variance greatly exceeds the ranges of many variables in the ACS data, especially for binary dummy variables.Privacy loss usually occurs for high-dimensional data, which is an inherent challenge for any method in differential privacy. In particular, to maintain the same accuracy level, the overall level of privacy protection for each variable tends to decay as the number of variables increases. In our situation, the overall level of privacy protection, defined by the privacy factor 5.Data perturbation has its great potential as an effective tool for replicating a sample, which can apply to data security, statistical inference, data integration, among others. The fundamental principle, distribution preservation for data perturbation, that we described in this article allows users to design data perturbation schemes, such as data flush, to satisfy task-specific requirements, as we showcase for statistical inference with differential private data in Several future directions of research include a more flexible model-based estimation  for high-dimensional target distributions and a compatible data perturbation scheme, as well as generalizations to independent but non-identically distributed data, time-series data, and unstructured data.


Light Science & ApplicationsCorrection to: 10.1038/s41377-022-00864-y published online 10 June 20221, it was brought to our attention that the Supplementary Information was attached by mistake, the article is a review article and it doesn’t have Supplementary Information. The original publication has been corrected.After publication of this article


The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applications that it is being used in. Quality assurance of VO is imperative to ensure that it provides accurate domain knowledge to these downstream tasks. Manual review to identify and fix quality issues  is challenging given the complexity of the ontology. Automated approaches are highly desirable to facilitate the quality assurance of VO.is-a relations in VO. First, we construct two types of VO concept-pairs: (1) linked; and (2) unlinked. Each concept-pair further derives an Acquired Term Pair (ATP) based on their lexical features. If the same ATP is obtained by a linked concept-pair and an unlinked concept-pair, this is considered to indicate a potentially missing is-a relation between the unlinked pair of concepts.We developed an automated lexical approach that identifies potentially missing is-a relations. A manual review by a VO domain expert on a random sample of 70 potentially missing is-a relations revealed that 65 of the cases were valid missing is-a relations in VO (a precision of 92.86%).Applying this approach on the 1.1.192 version of VO, we were able to identify 232 potentially missing is-a relation in VO.The results indicate that our approach is highly effective in identifying missing  A vaccine as defined by the Centers for Disease Control and Prevention (CDC) is a preparation that is used to stimulate the body’s immune response against diseases and is usually administered via needles while oral and nasal sprays are also available . VaccineDue to the clinical usage and extensive research regarding vaccines, it has become necessary to standardize vaccine annotation, combine vaccine information from disparate sources, and support machine-readability. To address such challenges, the Vaccine Ontology (VO) has been developed , 4. VO finfectious bursal disease virus vaccine” (with VO concept identifier VO:0001497) is claimed as a subconcept of “viral vaccine” (VO:0000609), but “live attenuated infectious bursal disease virus vaccine” (VO:0000961) is not claimed as a subconcept of “live attenuated viral vaccine” (VO:0001220). It is imperative that VO is audited so that quality issues can be identified and addressed especially because it has been used in a multitude of applications including vaccine data integration [Being a community-based ontology in the domain of rapidly evolving biomedical knowledge, VO may suffer from incomplete knowledge and inconsistent modelling. For example, “e” VO:00020. It isegration , 14. SucOntology development tools such as Protégé , 16 ensuis-a relations and missing concepts in SNOMED CT, NCIt, and GO [is-a relations [is-a relations in VO.The principal objective of automated and semi-automated methods for quality analysis of biomedical ontologies is to uncover concepts with high likelihood of being problematic, which can then be reviewed by a subject domain expert for verification. Various approaches have been investigated for identifying different quality issues of biomedical ontologies , 23. For, and GO –33. Non-, and GO . Additioelations , 36. To is-a relation.To demonstrate our approach, we used the 1.1.192 version (released on 03/19/2022) of VO in Web Ontology Language (OWL). Utilizing the OWLReady2 python library , we obtaHepatitis B Surface Antigen Vaccine 0.01 MG/ML” (VO:0003423). This concept would be represented as {hepatitis, b, surface, antigen, vaccine, 0.01, mg/ml}.Our approach requires a concept to be represented as a set of its features. In this work, we obtain the features from concept names as follows. We first convert the name of a concept to lowercase. Then we tokenize the concept name to words and remove duplicate words. The result would be a set of words which can be considered as the lexical features corresponding to the name of the concept. For example, consider the concept “C and A would form a linked concept-pair L if the following constraints are satisfied: A is an ancestor of C; andif C and A have at least a single common lexical feature.if We leverage the ancestor information for each concept obtained through OWLReady2 to construct a set of linked concept-pairs as follows. A given concept-pair L indicate that C is the descendant and A is the ancestor. This means that L and L are different pairs. However, usually L and L would not both exist in an ontology as they would form a cycle.Note that linked concept-pairs are ordered pairs. That is, infectious bursal disease virus vaccine” (VO:0001497) and “viral vaccine” (VO:0000609) in Fig. vaccine}. Similarly, considering Fig. Bovine rotavirus” (NCBITaxon:10927) and “Rotavirus” (NCBITaxon:10912) form a linked concept-pair.For example, the concepts “e” VO:000497 and “We iterate through ancestors of all the concepts and construct a set of all linked concept-pairs.C and D would form an unlinked concept-pair U only if the following conditions are satisfied: C≠D;if D is not an ancestor of C and C is not an ancestor of D;if C and D have at least a single common lexical feature;if C and D both belong to the same ontology ; andif C and D fall within the same subhierarchy out of the 19 different subhierarchies under concept “material entity” (BFO:0000040) of VO.if We leverage the hierarchical information of VO to construct a set of unlinked concept-pairs. A given concept-pair material entity” for the following reasons: (1) the vast majority of VO concepts (including vaccines) are under “material entity”; and (2) the subhierarchies under “material entity” model different domains of VO.Here the fifth condition requires the unlinked concept-pair to be in the same subhierarchy of “U is considered to be different from U. However, in certain situations, one of them could form a linked concept-pair. For instance, if U is an unlinked concept-pair but L is a linked concept-pair, then we do not include U in the unlinked concept-pair set. Otherwise, both are included.Note that unlinked concept-pairs are ordered as well. That is, live attenuated infectious bursal disease virus vaccine” (VO:0000961) and “live attenuated viral vaccine” (VO:0001220) in Fig. processed material” (OBI:0000047) which is a subhierarchy under “material entity” (BFO:0000040) and both the concepts have common lexical features {live, attenuated, vaccine}. Similarly, in Fig. Bovine Respiratory Syncytial Virus vaccine” (VO:0001507) and “Respiratory syncytial virus vaccine” (VO:0000753) form an unlinked-pair.As an example, the concepts “VO:000096 and “liv” VO:000120 in Figmaterial entity” (BFO:0000040), we iterate through all combinations of concept-pairs and construct a set of all unlinked concept-pairs.For each subhierarchy under “C1 and C2 be F(C1) and F(C2) respectively. Then the ATP generated by the concepts is defined as: infectious bursal disease virus vaccine” (VO:0001497) and “viral vaccine” (VO:0000609) in Fig. Bovine Respiratory Syncytial Virus vaccine” (VO:0001507) and “Respiratory syncytial virus vaccine” (VO:0000753) in Fig. A linked or unlinked concept-pair derives an ATP which emphasises the unique lexical features of each concept. Let the lexical features of a concept-pair C1,C2) would generate an ATP that is different from an ATP generated by concept-pair .Note that a concept-pair  and an unlinked concept-pair U, if ATP=ATP, then we suggest a potentially missing is-a relation: C3is-aC4. In other words, if an ATP derived by a linked concept-pair can also be derived by an unlinked concept-pair, this is considered to indicate a potentially missing is-a relation among the unlinked concept-pair.Given a linked concept-pair infectious bursal disease virus vaccine” (VO:0001497) and “viral vaccine” (VO:0000609) derive the ATP , which can also be derived by the unlinked concept-pair “live attenuated infectious bursal disease virus vaccine” (VO:0000961) and “live attenuated viral vaccine” (VO:0001220). Hence, this denotes a potentially missing is-a relation: VO:0000961 is-a VO:0001220.For example, in Fig. Bovine rotavirus” (NCBITaxon:10927) and “Rotavirus” (NCBITaxon:10912) as well as the unlinked concept-pair “Bovine Respiratory Syncytial Virus vaccine” (VO:0001507) and “Respiratory syncytial virus vaccine” (VO:0000753), derive the same ATP , and thus indicate a potentially missing is-a relation: VO:0001507 is-a VO:0000753. Note that as in this example, the linked or unlinked concept-pairs may originate from external ontologies as VO reuses concepts from external ontologies.Similarly, in Fig. is-a relations.Given the unlinked-pairs and linked-pairs, Algorithm 1 shows the procedure that was used to extract such potentially missing is-a relation C3is-aC4 may be obtained leveraging multiple linked concept-pairs L and L if they derive the same ATP. We remove such duplicate cases from our final set of potentially missing is-a relations.Note that the same potentially missing is-a relations as described below. For an unlinked concept-pair U and a linked concept-pair L generating the same ATP, if the name of the concept C3 is the same as the concept C1 or the name of the concept C4 is the same as the concept C2, then we do not suggest a potentially missing is-a relation between C3 and C4. This is because two concepts with the same name but different identifiers may reveal a different type of quality issues  rather than a missing is-a relation. For example, the linked concept-pair “F fusion protein” (VO:0011167) and “Measles virus protein” (VO:0010784) and the unlinked concept-pair “F fusion protein” (VO:0011208) and “Measles virus protein” (VO:0010784) generate the same ATP: . However, VO:0011167 and VO:0011208 have the same name: “F fusion protein”. Hence, we do not suggest a missing is-a relation between VO:0011208 and VO:0010784.We further perform a filtration step on the set of potentially missing is-a relations identified by this method need to be manually reviewed for validation and confirmation before their adoption to VO. We randomly selected a subset of suggested potentially missing is-a relations for domain expert evaluation. For each missing is-a relation in the subset, the names of the two concepts together with their identifiers were provided to a domain expert . The domain expert examined whether the suggested relation is valid; not only theoretically, but also in terms of its suitability to current modeling practices of VO.Potentially missing is-a relations in VO. Table is-a relations identified by our approach, as well as their ATPs and corresponding linked concept-pairs. As an example, our method suggested that “inactivated acellular pertussis vaccine” (VO:0003196) is a descendant of “inactivated vaccine” (VO:0000315). The 232 potentially missing relations were based on 120 distinct ATPs. Table is-a relations. For instance, the ATP  accounted for 12.07% (28/232) potentially missing is-a relations.Table e” VO:000196 is a is-a relations, we constructed a random sample of 70 potentially missing is-a relations for manual evaluation by the VO domain expert. The 70 cases were selected so that no two cases correspond to the same ATP to avoid reviewing similar cases. The evaluation revealed that 65 of our suggestions represent valid missing is-a relations in VO. Therefore, the overall precision of the method is 92.86% (65/70).To assess the effectiveness of our approach in discovering valid missing is-a relation inconsistencies from the Vaccine Ontology. We constructed two types of concept-pairs: linked and unlinked. For the unlinked concept-pairs, we focused on the different subhierarchies under the “material entity” (BFO:0000040) concept. This is because we needed to pair unrelated but relevant concepts as unlinked concept-pairs. Each subhierarchy under “material entity” (BFO:0000040) models different domains such as “anatomical entity”, “antigen”, “chemical entity”, “gene”, etc. Therefore, concepts under each of these subhierarchies have a certain degree of relevance with each other. This is important as pairing irrelevant concepts may not only lead to the extraction of invalid missing is-a relations, but would also increase the running time of the method. In fact, VO currently does not have any is-a relations across these subhierarchies. It should also be noted that out of the 6,883 concepts in the 1.1.192 version of VO used in this work, 5,672 concepts (82.4%) were in these subhierarchies.In this work, we investigated a lexical approach to extract is-a relations uncovered in this work. For example, 80 missing is-a relation suggestions had ATPs of size , while only 16 had size . The analysis of the ATP sizes as displayed in Fig. is-a relations (74.57%) correlate with situations where the second set in the ATP is an empty set. This happens when the lexical features of the potential ancestor form a subset of the lexical features of the potential descendant. In fact, the most number of is-a relations were suggested by the following ATP size: , , , and .We denote the size of an ATP by a pair of integers corresponding to the sizes of the two sets the ATP contains. For instance, the size of the ATP  is 3, 1). Figure , 1. Figuis-a relations decreases. For instance,  was observed in 80 potentially missing is-a relations, while  was observed in 16, and  was observed in 5. Since the ATP contains unique words in each concept, this shows that in a majority of missing is-a relations, more unique words can be found on the descendant concept than the ancestor concept. This is expected as descendant concepts are supposed to be more specific concepts than ancestors.In addition, it can be seen that for a certain size of the first set of the ATP, when the size of the second set increases, the number of suggested missing is-a suggestions were invalid. For example, as displayed in Fig. is-a relation between concepts “smallpox vaccine” (VO:0004613) and “Smallpox virus vaccine” (VO:0000651). However, these concepts were found to be synonyms that need to be merged into one concept. Therefore, in this instance, while our suggestion is invalid, it has lead into the identification of a different inconsistency in VO.Based on the evaluation by the VO domain expert, it was seen that 5 of our missing is-a relation between concepts “Brucella canis” (NCBITaxon:36855) and “Canis” (NCBITaxon:9611), which was found to be invalid. This is because brucella canis is a bacterium, while canis is a mammal. Our method also suggested an invalid missing is-a relation between concepts “Corynebacterium pseudotuberculosis” (NCBITaxon:1719) and “Corynebacterium diphtheriae” (NCBITaxon:1717). These terms are actually siblings under the parent concept “Corynebacterium” (NCBITaxon:144193) and do not form a valid is-a relation. Note that these two false positives were missing is-a suggestions among concept-pairs in the NCBI Taxonomy [is-a suggestion for an external ontology was valid, the actual fix would need to be handled by the external ontology so that VO could re-import and re-use the corrected version.In another example, our method suggested a potentially missing Taxonomy , an exteis-a suggestions. Regarding the suggested is-a relation between “Varicella-Zoster Virus Vaccine Live (Oka-Merck) strain 29800 UNT/ML” (VO:0003279) and “Varicella-Zoster Virus Vaccine Live (Oka-Merck) strain Injection” (VO:0003274), the expert was uncertain whether the former concept always corresponds to an injection. For the suggested is-a relation between concepts “toxoid vaccine” (VO:0000561) and “toxoid” (VO:0001252), the expert was uncertain about the validity as the latter concept corresponds to a component of the former. We categorized both these cases as false positives.The expert was indecisive about two of the missing A major distinction between our approach and most other ontology quality assurance approaches is its capability of identifying the exact quality issues, as a result of which less manual review effort is needed by domain experts. For example, abstraction networks based approaches , 25 onlyNote that due to the discovery nature of the ontology quality assurance task , differen different words . In this work, we generalized the approach without such limitations, and applied it to the Vaccine Ontology, for which according to our knowledge, systematic automated approaches have not being developed for auditing purposes. Additional improvement of the approach in this work is requiring the unlinked concept-pair to be within the same subhierarchy, which was not leveraged in the prior work and without which invalid cross-hierarchical missing is-a relations would be more likely to be suggested.In prior work, we introduced a similar approach leveraging word differences between concepts to audit the Gene Ontology , 36. Howis-a discovered and the precision of the method. The features may also be imported from mapped concepts in external ontologies. Additionally, the only normalization that was performed in this work was to convert the lexical features to lowercase. Leveraging other strategies like lemmatization and synonym identification may help identify additional potentially missing is-a relations.One limitation of this work is that our concepts were represented by lexical features obtained from concept names. An interesting future direction is to investigate whether incorporating additional attributes of concepts such as other lexical metadata , ancestor lexical features, attribute relations, etc. would improve the overall results in terms of the number of potentially missing is-a relations identified by this method are only between concept-pairs with common words. In the future, we will explore other strategies that can also handle cases between concepts without any common words. A particularly interesting direction is to investigate whether machine learning could be of help. To train a machine learning model, the lexical features of concepts will need to be represented numerically using techniques such as word embedding. These representations would embed the meaning of the lexical features and therefore, through machine learning it might be possible to learn complex relationships between lexical features of the concept-pairs. Hence, such an approach may have the potential to predict a missing is-a relation between any concept-pair, not restricting to concept-pairs with common words.Another limitation of the method is that the missing is-a suggestions were evaluated by one expert. The precision of the method may be different if the size of the evaluation samples varies or multiple experts were involved. However, it should be noted that our expert (YH) is currently leading the development of VO. Since only 70 out of 232 missing is-a suggestions were evaluated (30.17%), a future work would be getting the entire set of 232 evaluated and incorporated into a future release of VO.A limitation of our evaluation is that only a subset of the missing is-a relations in the Vaccine Ontology, by leveraging term differences between concept-pairs. A total of 232 potentially missing is-a relations were suggested by our approach applied on the 1.1.192 version of the ontology. A random sample of 70 potentially missing is-a relations was evaluated by a Vaccine Ontology domain expert and 65 of them were confirmed as valid cases. The results revealed that our lexical approach is highly effective in identifying missing is-a relations in the Vaccine Ontology.In this paper, we presented an automated lexical approach to discover potentially missing


Cell Research 10.1038/s41422-022-00646-6, published online 29 March 2022Correction to: In the initial published version of this article, there is an error in the These corrections do not affect the major findings and conclusions of this work. We deeply apologize for this oversight.Supplementary information