5 datasets found
  1. Data from: Maps made with smartphones highlight lower noise pollution during...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    bin, csv
    Updated Mar 26, 2024
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    Alyssa Helmling; Alyssa Helmling; Carina Terry; Richard Primack; Carina Terry; Richard Primack (2024). Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston [Dataset]. http://doi.org/10.5061/dryad.ncjsxkt35
    Explore at:
    csv, binAvailable download formats
    Dataset updated
    Mar 26, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Alyssa Helmling; Alyssa Helmling; Carina Terry; Richard Primack; Carina Terry; Richard Primack
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Boston
    Measurement technique
    <p>We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative data. We followed these procedures for both collection cycles (2020 during lockdown and 2021 after lockdown had been lifted). The 2017 data were collected for an unrelated noise pollution project conducted by previous members of the Primack Lab and were not collected with the exact parameters established for the 2020 and 2021 collections. However, we found these noise data to be valuable given that they could be used to compare lockdown sound levels to the soundscape before the COVID-19 pandemic.</p> <p>We used R Studio to create sound maps from the individual data points in a way that allows for spatial visualization of the soundscape before, during, and after the pandemic lockdown. To test for statistically significant differences in sound level between years, we performed Welch's t-tests on the raw data for all sites comparing lockdown (2020) measurements to pre (2017) and post (2021) lockdown measurements. Given the hypothesis that 2020 would have lower sound levels at each site, we report the results of one-tailed t-tests. </p>
    Description

    Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.

  2. Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity...

    • data.niaid.nih.gov
    • ebi.ac.uk
    • +1more
    xml
    Updated Dec 4, 2020
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    Christoph Schlaffner; Judith A. Steen (2020). Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity and Define Critical Steps in Alzheimer’s Disease Progression - MC1-isolated Tau [Dataset]. https://data.niaid.nih.gov/resources?id=pxd020482
    Explore at:
    xmlAvailable download formats
    Dataset updated
    Dec 4, 2020
    Dataset provided by
    Boston Children's Hospital; Harvard Medical School
    Department of Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
    Authors
    Christoph Schlaffner; Judith A. Steen
    Variables measured
    Proteomics
    Description

    To elucidate the role of Tau isoforms and PTM stoichiometry in Alzheimer’s disease (AD), we generated a high resolution quantitative proteomic map of 88 PTMs on multiple isoforms of Tau isolated from the post-mortem human tissue from 49 AD and 42 control subjects. While Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and patient frequency for AD suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by the analysis of size fractionated Tau. To summarize, critical features within the Tau protein for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C-terminal, an increase in negative charge in the PRR and a decrease in positive charge in the MBD.

  3. Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity...

    • data.niaid.nih.gov
    • ebi.ac.uk
    xml
    Updated Dec 4, 2020
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    Christoph Schlaffner; Judith A. Steen (2020). Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity and Define Critical Steps in Alzheimer’s Disease Progression - angular gyrus [Dataset]. https://data.niaid.nih.gov/resources?id=pxd020517
    Explore at:
    xmlAvailable download formats
    Dataset updated
    Dec 4, 2020
    Dataset provided by
    Boston Children's Hospital; Harvard Medical School
    Department of Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
    Authors
    Christoph Schlaffner; Judith A. Steen
    Variables measured
    Proteomics
    Description

    To elucidate the role of Tau isoforms and PTM stoichiometry in Alzheimer’s disease (AD), we generated a high resolution quantitative proteomic map of 88 PTMs on multiple isoforms of Tau isolated from the post-mortem human tissue from 49 AD and 42 control subjects. While Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and patient frequency for AD suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by the analysis of size fractionated Tau. To summarize, critical features within the Tau protein for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C-terminal, an increase in negative charge in the PRR and a decrease in positive charge in the MBD.

  4. Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity...

    • data.niaid.nih.gov
    • ebi.ac.uk
    xml
    Updated Dec 4, 2020
    + more versions
    Share
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    Christoph Schlaffner; Judith A. Steen (2020). Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity and Define Critical Steps in Alzheimer’s Disease Progression - high/low molecular weight tau [Dataset]. https://data.niaid.nih.gov/resources?id=pxd020483
    Explore at:
    xmlAvailable download formats
    Dataset updated
    Dec 4, 2020
    Dataset provided by
    Boston Children's Hospital; Harvard Medical School
    Department of Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
    Authors
    Christoph Schlaffner; Judith A. Steen
    Variables measured
    Proteomics
    Description

    To elucidate the role of Tau isoforms and PTM stoichiometry in Alzheimer’s disease (AD), we generated a high resolution quantitative proteomic map of 88 PTMs on multiple isoforms of Tau isolated from the post-mortem human tissue from 49 AD and 42 control subjects. While Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and patient frequency for AD suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by the analysis of size fractionated Tau. To summarize, critical features within the Tau protein for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C-terminal, an increase in negative charge in the PRR and a decrease in positive charge in the MBD.

  5. Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity...

    • data.niaid.nih.gov
    • ebi.ac.uk
    xml
    Updated Dec 4, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Christoph Schlaffner; Judith A. Steen (2020). Quantitative PTM Maps of Human Pathologic Tau Identify Patient Heterogeneity and Define Critical Steps in Alzheimer’s Disease Progression - frontal gyrus (sarkosyl soluble) [Dataset]. https://data.niaid.nih.gov/resources?id=pxd020717
    Explore at:
    xmlAvailable download formats
    Dataset updated
    Dec 4, 2020
    Dataset provided by
    Boston Children's Hospital; Harvard Medical School
    Department of Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
    Authors
    Christoph Schlaffner; Judith A. Steen
    Variables measured
    Proteomics
    Description

    To elucidate the role of Tau isoforms and PTM stoichiometry in Alzheimer’s disease (AD), we generated a high resolution quantitative proteomic map of 88 PTMs on multiple isoforms of Tau isolated from the post-mortem human tissue from 49 AD and 42 control subjects. While Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and patient frequency for AD suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by the analysis of size fractionated Tau. To summarize, critical features within the Tau protein for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C-terminal, an increase in negative charge in the PRR and a decrease in positive charge in the MBD.

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Alyssa Helmling; Alyssa Helmling; Carina Terry; Richard Primack; Carina Terry; Richard Primack (2024). Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston [Dataset]. http://doi.org/10.5061/dryad.ncjsxkt35
Organization logo

Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

Explore at:
csv, binAvailable download formats
Dataset updated
Mar 26, 2024
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Alyssa Helmling; Alyssa Helmling; Carina Terry; Richard Primack; Carina Terry; Richard Primack
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Area covered
Boston
Measurement technique
<p>We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative data. We followed these procedures for both collection cycles (2020 during lockdown and 2021 after lockdown had been lifted). The 2017 data were collected for an unrelated noise pollution project conducted by previous members of the Primack Lab and were not collected with the exact parameters established for the 2020 and 2021 collections. However, we found these noise data to be valuable given that they could be used to compare lockdown sound levels to the soundscape before the COVID-19 pandemic.</p> <p>We used R Studio to create sound maps from the individual data points in a way that allows for spatial visualization of the soundscape before, during, and after the pandemic lockdown. To test for statistically significant differences in sound level between years, we performed Welch's t-tests on the raw data for all sites comparing lockdown (2020) measurements to pre (2017) and post (2021) lockdown measurements. Given the hypothesis that 2020 would have lower sound levels at each site, we report the results of one-tailed t-tests. </p>
Description

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.

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