Investigating the relationships between unfavourable habitual sleep and metabolomic traits:evidence from multi-cohort multivariable regression and Mendelian randomization analyses

BACKGROUND: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease.METHODS: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions.RESULTS: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures.CONCLUSIONS: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.</p

Vigilance cue sensitivity Cahill et al., 2024 Raw Data

Excell file with raw data values for figures of Cahill et al., 2024

CFC OXT Sherman 2024 Raw Data

Excell file with raw data values for figures of Sherman et al., 2024

Cumulative culture in artificial navigators

This project is on how cumulative culture can spontaneously emerge in agents who are bound by just four simple rules: Goal direction: Having a sense of (roughly) where the goal is. Social proximity: Aiming to stay close to other agents by moving in the direction they are expected to be next. Route memory: Agents remember landmarks along the route, and aim to follow along these landmarks. Their memory precision improves over several journeys. Continuity: To avoid erratic/jerky movements, agents aim to move mostly in the direction that they are currently travelling in. Despite the lack of explicit social transmission or evaluation of outcomes, pairs of agents with generational turnover show gradual improvements in route efficiency (they converge on the direct line between start and goal). For more information, please read the manuscript on arXiv (linked below). Current version Due to uploading restrictions on Zenodo, included here are the reduced format data files. This is easier, as they can be readily used with the scripts from the linked GitHub repository. Files Each of the files below contains data for 50 repeats of the following parameter ranges: p_goal_range: [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35] p_social_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35] p_memory_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5] sd_goal_range: [1.0] sd_memory_max_range: [0.9] simulation_types: ["experimental", "solo", "pair"] n_repetitions: 50 n_agents: 2 n_generations: 5 n_flights_per_generation: 12 max_path_length: 200 Note that the maximum path length for the reduced format is limited to 200 steps, whereas the simulation's maxmimum path length is a lot longer. This thus cuts a (very limited) number of true paths short. This only impacts the (x,y) coordinates in the x.dat and y.dat files, but not the efficiency data (which was computed using the full path). efficiency.dat This file contains the efficiency computed from full paths from each condition, repeat, and set of parameters. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, and n_flights_per_generation. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12). Values coded as "nan" exist, but they are rare. This occurs if the goal is never reached, and in generations after a generation in which the goal was never reached. (Because these never fly in the first place, as their predecessors never made it to the goal.) x.dat This file contains the horizontal (x) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200). Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this. y.dat This file contains the vertical (y) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200). Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this. Past versions Version 1 There are two data archives in this set: data_simulation_narrow.zip (41 GB; 2680 folders containing a total of 80399 subfolders for a total of 5225935 CSV files) and data_simulation_wide.zip (7 GB; 557 folders containing a total of 16710 subfolders for a total of 1086150 CSV files). Each archive contains subfolders, each of which represent a unique combination of simulation parameters. These are named with the following naming scheme: "Pgoal-{w}_SDgoal-{sd}_Pcontinuity-{p}_SDcontinuity-{sd}_Psocial-{p}_SDsocial-{sd}_Pmemory-{p}_SDmemoryMax-{sd}_SDmemoryMin-{sd}_SDmemorySteps-5", where {p} is 1000 times the weight parameter, {sd} is 1000 times the equivalent standard deviation for the kappa parameter, and both are rounded to the nearest integer. Example: "Pgoal-100_SDgoal-1000_Pcontinuity-100_SDcontinuity-350_Psocial-100_SDsocial-800_Pmemory-700_SDmemoryMax-2000_SDmemoryMin-400_SDmemorySteps-5" Within each simulation folder, a number of subfolders can be found. This should normally be 30. Each of these represents a single run of a simulation within a condition. The naming convention is "{condition}_run-{run_counter}", where {condition} is the name of the condition ("experimental", "pair", or "solo"), and {run_counter} is a counter that starts at 1 and goes up from there (should be 1-10 in the current set). Within each simulation run subfolder, there are CSV files. These hold the actual data from journeys by artificial navigators. There are two types of data file, one for efficiency, and one for the travelled path. Both types of CSV have a header row with the names of the columns, followed by data rows. Efficiency files are named "efficiency_gen-{gen_nr}.csv", with {gen_nr} indicating the generation number. This starts at 0, ends at 4 (inclusive), and increments by 1. Efficiency files have three columns: "flight" for the flight counter (int, starts at 1), "efficiency_agent1" for the efficiency for the first agent's efficiency (float, between 0 and 1), and "efficiency_agent2" (float, between 0 and 1). Flight path files are named "xy_gen-{gen_nr}_flight-{flight_nr}.csv", with {gen_nr} being the same as above, and {flight_nr} being the journey number. This starts at 0, ends at 11 (inclusive), and increments by 1. Path files have four columns: "x_agent1" for the first agent's horizontal coordinate (float), "y_agent1" for the first agent's vertical coordinate (float), "x_agent2" for the second agent's horizontal coordinate (float), and "y_agent2" for the second agent's vertical coordinate (float). From generation 2 in the experimental condition, the first is the experienced agent, and the second is the naive agent. Float values coded as "nan" reflect there is no data. This occurs for e.g. the second agent in the solo condition and the first-generation experimental condition

Cumulative culture in artificial navigators

This project is on how cumulative culture can spontaneously emerge in agents who are bound by just four simple rules: Goal direction: Having a sense of (roughly) where the goal is. Social proximity: Aiming to stay close to other agents by moving in the direction they are expected to be next. Route memory: Agents remember landmarks along the route, and aim to follow along these landmarks. Their memory precision improves over several journeys. Continuity: To avoid erratic/jerky movements, agents aim to move mostly in the direction that they are currently travelling in. Despite the lack of explicit social transmission or evaluation of outcomes, pairs of agents with generational turnover show gradual improvements in route efficiency (they converge on the direct line between start and goal). For more information, please read the manuscript on arXiv (linked below). Data archives There are two data archives in this set: data_simulation_narrow.zip (41 GB; 2680 folders containing a total of 80399 subfolders for a total of 5225935 CSV files) and data_simulation_wide.zip (7 GB; 557 folders containing a total of 16710 subfolders for a total of 1086150 CSV files). Each archive contains subfolders, each of which represent a unique combination of simulation parameters. These are named with the following naming scheme: "Pgoal-{w}_SDgoal-{sd}_Pcontinuity-{p}_SDcontinuity-{sd}_Psocial-{p}_SDsocial-{sd}_Pmemory-{p}_SDmemoryMax-{sd}_SDmemoryMin-{sd}_SDmemorySteps-5", where {p} is 1000 times the weight parameter, {sd} is 1000 times the equivalent standard deviation for the kappa parameter, and both are rounded to the nearest integer. Example: "Pgoal-100_SDgoal-1000_Pcontinuity-100_SDcontinuity-350_Psocial-100_SDsocial-800_Pmemory-700_SDmemoryMax-2000_SDmemoryMin-400_SDmemorySteps-5" Within each simulation folder, a number of subfolders can be found. This should normally be 30. Each of these represents a single run of a simulation within a condition. The naming convention is "{condition}_run-{run_counter}", where {condition} is the name of the condition ("experimental", "pair", or "solo"), and {run_counter} is a counter that starts at 1 and goes up from there (should be 1-10 in the current set). Within each simulation run subfolder, there are CSV files. These hold the actual data from journeys by artificial navigators. There are two types of data file, one for efficiency, and one for the travelled path. Both types of CSV have a header row with the names of the columns, followed by data rows. Efficiency files are named "efficiency_gen-{gen_nr}.csv", with {gen_nr} indicating the generation number. This starts at 0, ends at 4 (inclusive), and increments by 1. Efficiency files have three columns: "flight" for the flight counter (int, starts at 1), "efficiency_agent1" for the efficiency for the first agent's efficiency (float, between 0 and 1), and "efficiency_agent2" (float, between 0 and 1). Flight path files are named "xy_gen-{gen_nr}_flight-{flight_nr}.csv", with {gen_nr} being the same as above, and {flight_nr} being the journey number. This starts at 0, ends at 11 (inclusive), and increments by 1. Path files have four columns: "x_agent1" for the first agent's horizontal coordinate (float), "y_agent1" for the first agent's vertical coordinate (float), "x_agent2" for the second agent's horizontal coordinate (float), and "y_agent2" for the second agent's vertical coordinate (float). From generation 2 in the experimental condition, the first is the experienced agent, and the second is the naive agent. Float values coded as "nan" reflect there is no data. This occurs for e.g. the second agent in the solo condition and the first-generation experimental condition

Cumulative culture in artificial navigators

This project is on how cumulative culture can spontaneously emerge in agents who are bound by just four simple rules: Goal direction: Having a sense of (roughly) where the goal is. Social proximity: Aiming to stay close to other agents by moving in the direction they are expected to be next. Route memory: Agents remember landmarks along the route, and aim to follow along these landmarks. Their memory precision improves over several journeys. Continuity: To avoid erratic/jerky movements, agents aim to move mostly in the direction that they are currently travelling in. Despite the lack of explicit social transmission or evaluation of outcomes, pairs of agents with generational turnover show gradual improvements in route efficiency (they converge on the direct line between start and goal). For more information, please read the manuscript on arXiv (linked below). Current version Due to uploading restrictions on Zenodo, included here are the reduced format data files. This is easier, as they can be readily used with the scripts from the linked GitHub repository. Files Each of the files below contains data for 50 repeats of the following parameter ranges: p_goal_range: [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35] p_social_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35] p_memory_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5] sd_goal_range: [1.0] sd_memory_max_range: [0.9] simulation_types: ["experimental", "solo", "pair"] n_repetitions: 50 n_agents: 2 n_generations: 5 n_flights_per_generation: 12 max_path_length: 200 Note that the maximum path length for the reduced format is limited to 200 steps, whereas the simulation's maxmimum path length is a lot longer. This thus cuts a (very limited) number of true paths short. This only impacts the (x,y) coordinates in the x.dat and y.dat files, but not the efficiency data (which was computed using the full path). efficiency.dat This file contains the efficiency computed from full paths from each condition, repeat, and set of parameters. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, and n_flights_per_generation. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12). Values coded as "nan" exist, but they are rare. This occurs if the goal is never reached, and in generations after a generation in which the goal was never reached. (Because these never fly in the first place, as their predecessors never made it to the goal.) x.dat This file contains the horizontal (x) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200). Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this. y.dat This file contains the vertical (y) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200). Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this. Past versions Version 1 There are two data archives in this set: data_simulation_narrow.zip (41 GB; 2680 folders containing a total of 80399 subfolders for a total of 5225935 CSV files) and data_simulation_wide.zip (7 GB; 557 folders containing a total of 16710 subfolders for a total of 1086150 CSV files). Each archive contains subfolders, each of which represent a unique combination of simulation parameters. These are named with the following naming scheme: "Pgoal-{w}_SDgoal-{sd}_Pcontinuity-{p}_SDcontinuity-{sd}_Psocial-{p}_SDsocial-{sd}_Pmemory-{p}_SDmemoryMax-{sd}_SDmemoryMin-{sd}_SDmemorySteps-5", where {p} is 1000 times the weight parameter, {sd} is 1000 times the equivalent standard deviation for the kappa parameter, and both are rounded to the nearest integer. Example: "Pgoal-100_SDgoal-1000_Pcontinuity-100_SDcontinuity-350_Psocial-100_SDsocial-800_Pmemory-700_SDmemoryMax-2000_SDmemoryMin-400_SDmemorySteps-5" Within each simulation folder, a number of subfolders can be found. This should normally be 30. Each of these represents a single run of a simulation within a condition. The naming convention is "{condition}_run-{run_counter}", where {condition} is the name of the condition ("experimental", "pair", or "solo"), and {run_counter} is a counter that starts at 1 and goes up from there (should be 1-10 in the current set). Within each simulation run subfolder, there are CSV files. These hold the actual data from journeys by artificial navigators. There are two types of data file, one for efficiency, and one for the travelled path. Both types of CSV have a header row with the names of the columns, followed by data rows. Efficiency files are named "efficiency_gen-{gen_nr}.csv", with {gen_nr} indicating the generation number. This starts at 0, ends at 4 (inclusive), and increments by 1. Efficiency files have three columns: "flight" for the flight counter (int, starts at 1), "efficiency_agent1" for the efficiency for the first agent's efficiency (float, between 0 and 1), and "efficiency_agent2" (float, between 0 and 1). Flight path files are named "xy_gen-{gen_nr}_flight-{flight_nr}.csv", with {gen_nr} being the same as above, and {flight_nr} being the journey number. This starts at 0, ends at 11 (inclusive), and increments by 1. Path files have four columns: "x_agent1" for the first agent's horizontal coordinate (float), "y_agent1" for the first agent's vertical coordinate (float), "x_agent2" for the second agent's horizontal coordinate (float), and "y_agent2" for the second agent's vertical coordinate (float). From generation 2 in the experimental condition, the first is the experienced agent, and the second is the naive agent. Float values coded as "nan" reflect there is no data. This occurs for e.g. the second agent in the solo condition and the first-generation experimental condition

The neonicotinoid insecticide imidacloprid disrupts bumblebee foraging rhythms and sleep. Tasman et al.

Neonicotinoids have been implicated in the large declines observed in insects such as bumblebees, an important group of pollinators. Neonicotinoids are agonists of nicotinic acetylcholine receptors that are found throughout the insect central nervous system and are the main mediators of synaptic neurotransmission. These receptors are important for the function of the insect central clock and circadian rhythms. The clock allows pollinators to coincide their activity with the availability of floral resources and favourable flight temperatures, as well as impacting learning, navigation and communication. Here we show that exposure to the field relevant concentration of 10 µg/L imidacloprid caused a reduction in bumblebee foraging activity, locomotion and foraging rhythmicity. Foragers showed an increase in daytime sleep and an increase in the proportion of activity occurring at night. This could reduce foraging and pollination opportunities, reducing the ability of the colony to grow and reproduce, endangering bee populations and crop yields. Tasman et al Figure 1 contains the activity counts for each individual forager for 5 days of LD (collected using the LAM), in 30 minute bins. Each .txt file is for a single bee, first line shows the run name, followed by the monitor number and then the channel number, e.g. 2015CtM022C08. Here run name is 2015Ct, monitor is number 22 and channel is number 8. This is followed by the date on which the run started. The second line shows the number of bins, the third line shows the length of these bins in minutes and the 4th line shows the time of the first bin, e.g. 09:00. The activity count for each bin is then listed. The file contains the data for colonies 1-3, which were combined for analysis of activity levels and circadian rhythms. Each folder contains a text file detailing which individual received which treatment, e.g. control, IM 1 ug/L or IM 10 ug/L. Tasman et al Figure 2 contains the same type of data as in Figure 1 but for 5 days of DD. Tasman et al Figure 3 contains the same type of data as in Figure 1, for 5 days of LD in both 1 min and 30 mins bins, which was used for sleep analysis. Tasman et al Figure 4 contains excel spreadsheets of the readout for the RFID reader, for the 5 days of LD. There is a folder for each of the three runs, within which there are folders for each day of the run and a spreadsheet for each 30 min bin within that day. These spreadsheets show each read from the RFID reader, listing (from left to right) the date and time of the read, the reader number and the ID of the bee being read. Consecutive reads less than 1 min apart from the same bee were counted as one activity bout. Days start at 9am except Day 1 which starts at 10am. There is a text file showing the colony number and treatment for each reader in each run. This was analysed for foraging activity and rhythmicity. Tasman et al Figure 5 shows the same type of data as in Figure 4 but for 5 days of DD

Systematic Review of Depression in Paediatric CFS/ME

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Reducing social media engagement with affectively polarised content via inoculation

Datasets for validation study and experiments one two and three

Debunking Handbook 2020

This handbook compiles expert data about debunking misinformation. You may download a PDF of the handbook in the "Highlights" section below