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- Creator:
- Heath, Jeffrey and Dicko, Adama
- Description:
- transcription/translation to appear
- Discipline:
- Humanities
-
- Creator:
- Li, Jieming, Zhang, Leyou, Johnson-Buck, Alexander, and Walter, Nils G.
- Description:
- Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we have used deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by thresholding. As a second application, the selector was used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.
- Keyword:
- deep learning, single-molecule fluorescence, total internal reflection microscopy, SiMREPS, smFRET, and Forster resonance energy transfer
- Citation to related publication:
- Li, J., Zhang, L., Johnson-Buck, A., & Walter, N. G. (2020). Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nature Communications, 11(1), 5833. https://doi.org/10.1038/s41467-020-19673-1 and Hayward, S., Lund, P., Kang, Q., Johnson-Buck, A., Tewari, M., Walter, N. (2018). Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z2CZ35DF
- Discipline:
- Science
-
- Creator:
- McCuen, Brett A.
- Description:
- The data were used to study the high-frequency geomagnetic disturbances within the magnetic field data. Included in this repository are the python scripts that perform an identification and classification of high-frequency signals within the magnetometer data that is downloaded from the databases listed in the Methodology section. All analysis and plots were created using subsequent Python libraries. The machine learning study implemented libraries from the sci-kit learn software. All of the specific methodology can be accessed in the readme.txt script.
- Keyword:
- geomagnetic field, high frequency, space weather, transient-large-amplitude, TLA, high frequency dB/dt, and dB/dt search algorithm
- Discipline:
- Science
-
- Creator:
- Nasser, Ahmad and Gumise, Wonder
- Description:
- The work on accelerating authenticated boot for embedded system resulted in designing an algorithm in python to perform the random address generation and cryptographic MAC calculation. The Sampled Boot schemes implemented in this package allow a significant reduction of the time needed to authenticate firmware images during startup, while still retaining a high degree of trust. This is particularly useful for automotive applications in which startup time constraints make secure boot a time prohibitive process. and Citation for this dataset: Nasser, A., Gumise, W. (2019). Authenticated Boot Acceleration Algorithm [Code and data]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/yeh1-1x17
- Keyword:
- Trusted Computing, IOT security, Embedded Security, and Cyber Physical Systems
- Citation to related publication:
- Nasser, A., Gumise, W., and Ma, D., "Accelerated Secure Boot for Real-Time Embedded Safety Systems," SAE Int. J. Transp. Cyber. & Privacy 2(1) : 35-48, 2019, https://doi.org/10.4271/11-02-01-0003
- Discipline:
- Science
-
- Creator:
- Heath, Jeffrey
- Description:
- recordings made in Barato village. Referred to as "text 2021-02" and "text 2021-03." Text 2021-03 is transcribed and annotated at the end of the reference grammar (see link to Deep Blue Documents). Text 2021-02 covers a subset of the same content and has not been transcribed as of late 2022. See also "notes" file inside the work.
- Keyword:
- Bozo, Jenaama, Sorogaama
- Discipline:
- Humanities
-
- Creator:
- Heath, Jeffrey
- Description:
- recording in mp3 format. The reference grammar (see link to Deep Blue Documents) presents transcription and analysis as "text 2021-01."
- Keyword:
- Bozo, Jenaama, Sorogaama
- Discipline:
- Humanities
-
- Creator:
- Heath, Jeffrey
- Description:
- A subset of the Kelenga recordings are being transcribed and will serve as data for the Kelenga reference grammar which, when finished, will be included in the collection "Bozo languages of Mali (documents)" in Deep Blue Documents (see link).
- Keyword:
- Bozo and Kelenga
- Discipline:
- Humanities
-
- Creator:
- Heath, Jeffrey
- Description:
- these and other recordings are data for a reference grammar of Kelenga that, when completed, will be archived in the collection "Bozo languages of Mali (documents)" in Deep Blue Documents. For contents see the "notes" file inside the work. A few of the Kelenga texts are being transcribed, others will be left for others to transcribe or listen to as they wish.
- Keyword:
- Bozo and Kelenga
- Discipline:
- Humanities
-
- Creator:
- Heath, Jeffrey
- Description:
- For content see the "notes" file inside the work. Most of the recordings are translated and annotated at the end of the reference grammar (see link to Deep Blue Documents).
- Keyword:
- Bozo, Jenaama, Cliffs
- Discipline:
- Humanities
-
- Creator:
- Keppel-Aleks, Gretchen and Liptak, Jessica
- Description:
- -CESM_bdrd _NEP_pulse_response_CO2.nc contains time series from the ‘FullyCoupled’ simulation -CESM_bdrcs_NEP_pulse_response_CO2.nc contains time series from the ‘NoRad’ simulation -CESM_bdrd_pftcon_NEP_pulse_response_CO2.nc contains data from the ‘NoLUC’ simulation -CESM_bdrd_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the FullyCoupled simulation - CESM_bdrcs_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the CESM ‘NoRad’ simulation - CESM_bdrd_pftcon_Regional_Fluxes_NEP.nc contains NEP time series for each terrestrial source region from the CESM ‘NoLUC’ simulation The 3-letter station IDs, latitudes, and longitudes of the sample locations are: ID Latitude (ºN) Longitude (ºE) 1. BRW 71.3 203.4 2. ZEP 78.9 11.9 3. SHM 52.7 174.1 4. THD 41.1 235.8 5. TAP 36.7 126.1 6. BMW 32.3 295.1 7. MLO 19.5 204.4 8. POCN15 15.0 215.0 9. ALT 82.5 297.5 10. BHD -41.4 174.9 11. EIC -27.2 250.6 12. GMI 13.4 144.7 13. HUN 47.0 16.7 14. IZO 28.3 343.5 15. LLN 23.5 120.9 16. NAT -5.8 324.7 17. WLG 36.3 100.9 18. HBA -75.6 333.8 19. BKT -0.20 100.3 20. UUM 44.5 111.1 21. CGO -40.7 144.5 22. SDZ 40.7 117.1 23. ASC -8.0 345.6 24. SEY -4.7 55.5 25. POCS20 -20.0 186.0 26. POCS35 -35.0 180.0 27. PSA -64.9 296.0 28. SYO -69.0 39.6 29. CHR 1.7 202.8 30. KEY 25.7 279.8 31. BAL 55.4 17.2 32. HPB 47.8 11.0 33. LMP 35.5 12.6 34. NMB -23.6 15.0 35. RPB 13.2 300.2 36. WIS 30.0 35.1 37. POCS10 -10.0 199.0 38. POCN10 10.0 211.0 39. MID 28.2 182.6 40. SMO -14.2 189.4 41. SPO -90.0 335.2 The terrestrial CO2 source region abbreviations are: 1. NBNA 2. SBNA 3. ETNA 4. WTNA 5. CNAM 6. AMZN 7. EASA 8. WESA 9. EURO 10. SAME 11. MDAF 12. AFRF 13. SOAF 14. EABA 15. WEBA 16. SOBA 17. CNAS 18. SEAS 19. EQAS 20. AUST 21. GNLD 22. ATCA
- Keyword:
- atmospheric CO2 annual cycle amplitude and CESM extended concentration pathway
- Citation to related publication:
- Hornick, T., Bach, L. T., Crawfurd, K. J., Spilling, K., Achterberg, E. P., Woodhouse, J. N., Schulz, K. G., Brussaard, C. P. D., Riebesell, U., & Grossart, H.-P. (2017). Ocean acidification impacts bacteria–phytoplankton coupling at low-nutrient conditions. Biogeosciences, 14(1), 1–15. https://doi.org/10.5194/bg-14-1-2017
- Discipline:
- Science