NYCSEA

Abstract: 

Meteorites can carry important information about the solar system and its origins. Unfortunately, there is no official network in the United States dedicated to finding meteorites after observing meteors. The availability of low-cost computers and cameras such as the Raspberry Pi has made it possible to create affordable meteor observing station networks that could be set up by Citizen Scientists. We investigate the basic design of such a meteor station and will use it to test the hypothesis that it is possible to identify materials that arrive at Earth from other planetary systems. Specifically, we will look for extrasolar meteorite candidates because they can offer pristine information about our area of the universe before the formation of the Solar System. At the present time, we have calculated the expected luminosities of fireballs in optical, infrared, and radio wavelengths and are in the process of building and testing elements of the system.

Keywords:  Meteorites, Citizen Scientists, luminosities of fireballs in optical, infrared, and radio wavelengths, elements of the system

References

1. Appleton, E., Naismith, R. Radar Detection of Meteor Trails. Nature 158, 936–938 (1946). https://doi.org/10.1038/158936a0

2. Bartos, I., Marka, S. A nearby neutron-star merger explains the actinide abundances in the early Solar System. Nature 569, 85–88 (2019). https://doi.org/10.1038/s41586-019-1113-

3. Burbidge, E., Burbidge G., Fowler, W., and Hoyle, F. Rev. Mod. Phys. 29, 547 (1 October 1957).
   https://doi.org/10.1103/RevModPhys.29.54

4. Emily Clay, 2020. “Perseids Peak: Watch Best Meteor Shower of the Year!” NASA. [August 12,
   2020]https://blogs.nasa.gov/Watch_the_Skies/tag/perseids/#:~:text=NASA%20All%20Sky%20F
   ireball%20Network%20Cameras%20Catch%20Perseids&text=The%20Perseids%20have
   %20been%20observed,meteor%20showers%20of%20the%20year.
   International Meteor Organization, 2020. “Fireballs.” International Meteor Organization. [August 27, 2020]
   https://www.imo.net/observations/fireballs/fireballs/#:~:text=The%20brightness%20is%20difficult%20to,shadow%20under%20the%20darkest%20conditions.

5. Jack AlmaPhoto. September 10, 2014, “How Many Photons on a Pixel.” Exposure, Sensors. Strolls with my Dog. [August 28, 2020] https://www.strollswithmydog.com/photons-on-a-pixel/

6. Lossing, F. How Few Photons Per Second Can You See? Journal of the Royal Astronomical Society of Canada Newsletter, Vol. 78, p.L11 (1984).
   http://adsabs.harvard.edu/full/1984JRASC..78L..11L

7. M.N. ElGabry, I.M. Korrat, H.M. Hussein, I.H. Hamama, Infrasound detection of meteors,
   NRIAG Journal of Astronomy and Geophysics, Volume 6, Issue 1, 2017, Pages 68-80,
   ISSN 2090-9977, https://doi.org/10.1016/j.nrjag.2017.04.004.
   (http://www.sciencedirect.com/science/article/pii/S209099771630075X)

8. Obenberger, K. & Taylor, G. & Hartman, J. & Dowell, J. & Ellingson, S. & Helmboldt, J. &
   Henning, P. & Kavic, Michael & Schinzel, F. & Simonetti, J. & Stovall, Kevin & Wilson,
   T.. (2014). Detection of Radio Emission from Fireballs. The Astrophysical Journal
   Letters. 788. L26. 10.1088/2041-8205/788/2/L26.

9. Richard C. Greenwood, Thomas H. Burbine, Martin F. Miller, Ian. A. Franchi, Melting and
   differentiation of early-formed asteroids: The perspective from high precision oxygen
   isotope studies, Geochemistry, Volume 77, Issue 1, 2017, Pages 1-43, ISSN 0009-2819,
   https://doi.org/10.1016/j.chemer.2016.09.005.
   (http://www.sciencedirect.com/science/article/pii/S0009281916301994)
   RTL-SDR Admin, January 22, 2018, “ECHOES: AN RTL-SDR TOOL FOR METEOR
   SCATTER DETECTION.” RTL-SDR.COM

   https://www.rtl-sdr.com/echoes-an-rtl-sdr-tool-for-meteor-scatter-detection/ September
   12, 2020

10. Segon, Damir & Andreić, Željko & Vida, Denis & Novoselnik, Filip & Korlević, Korado.
    (2013). Meteors in the near-infrared. 111-114.
    Swinburne Astronomy Online, 2020. “Fireball.” COSMOS — The Sao Encylopedia of
    Astronomy. Swinburne University of Technology [August 24, 2020].
    https://astronomy.swin.edu.au/cosmos/F/Fireball

11. Ulrich Ott, Presolar grains in meteorites: an overview and some implications, Planetary and
    Space Science, Volume 49, Issue 8, 2001, Pages 763-767, ISSN 0032-0633,
    https://doi.org/10.1016/S0032-0633(01)00025-3.
    (http://www.sciencedirect.com/science/article/pii/S0032063301000253)

12. Vítek, S., & Nasyrova, M. (2017). Real-Time Detection of Sporadic Meteors in the Intensified
    TV Imaging Systems. Sensors (Basel, Switzerland), 18(1), 77.
    https://doi.org/10.3390/s18010077

13. Watson, T. France launches massive meteor-spotting network. Nature Pages Used (10 June
    2016). doi:10.1038/nature.2016.20070

Abstract: African Americans are often viewed as a monolithic group in the United States because Black people generally have been subjected to the same racism and prejudice throughout American society. While African Americans have had many similar experiences in the United States, their opinions on the current political, social, and economic worldview may differ based on ethnic groups. The author chose to closely examine the extent to which family history and decade of one's arrival (or one's family's arrival) to the United States, and the region from which one (or one's family) originated, might influence the current political, social and economic worldview of adolescent and adult Americans who self-identify as Black. In order to study the effects of these variables, I administered surveys to 146 African American adults in suburban New York City. The online survey consisted of four parts. These parts included views on economic success, law enforcement, current events, specifically the Black Lives Matter Movement, and Black representation in American society. Ultimately the study found statistically significant differences between region/decade of arrival and societal world views. There were also gender gaps.

Keywords: African-American, representation, BLM, Afro-Caribbean, African, economic success

References

1. Bunyasi, T. L. (2019, February 6). Do All Black Lives Matter Equally to Black People? Respectability Politics and the Limitations of Linked Fate | Journal of Race, Ethnicity, and Politics. Cambridge Core. https://www.cambridge.org/core/journals/journal-of-race-ethnicity-and-politics/article/do-all-black-lives-matter-equally-to-black-people-respectability-politics-and-the-limitations-of-linked-fate/CBC842CABC6F8FAA6C892B08327B09DA
2. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2019, December 26). Race and Economic Opportunity in the United States: an Intergenerational Perspective*. OUP Academic. https://academic.oup.com/qje/article/135/2/711/5687353?login=true
3. Davis, R., & Hendricks, N. (2007, January 1). Immigrants and Law Enforcement: A Comparison of Native-Born and Foreign-Born Americans’ Opinions of the Police. International Review of Victimology. https://journals.sagepub.com/doi/abs/10.1177/026975800701400105
4. Fan, Y. (2019, February 13). Gender and cultural bias in student evaluations: Why representation matters. Plos One.

Abstract: Current estimates reveal that approximately 1.2 billion people reside in areas susceptible to flooding. However, due to human-inflicted changes to the environment, it is predicted that within the next 30 years, this number will increase by at least 400 million. Despite the prevailing belief that the effects of flooding are diminutive, catastrophic destruction is possible, especially when victims belong to vulnerable populations. Aside from physical damage, severe flooding often prevents individuals from securing the bare necessities- water, food, shelter, and medical attention- leading to health crises and social segregation. Following Hurricane Sandy, these adverse effects devastated communities on the East Coast, namely those in New York City and Long Island. To mitigate complications during recuperation, researchers proposed updating strategies and policies to take into account factors such as social capital and economic vulnerability. Doing so may ensure that all communities have equal access to ample resources and services, regardless of demographic composition. Therefore, this study investigated the role of community support, as opposed to socioeconomic status, in the vulnerability and resiliency of New York residents to flooding from Hurricane Sandy.

Those who are more engaged in politics tend to be more vigilant about the efforts of their local government. If local politicians are unjustly favoring a certain demographic and neglecting the needs of others, people who pay attention to politics are able to identify the problem and understand how it can be rectified. Furthermore, people who pay attention to the workings of their government are more inclined to address social issues. For vulnerable families, this is relevant because an unsupportive, inept government is frequently the root of problems including forced evacuation/homelessness, poverty, inaccessible resources, etc. If political attentiveness could be quantified, policymakers and community organizations would be able to ascertain which populations are less educated about flooding preparation/reconstruction and which populations can assist the former.

References

1. Becker, J. S., Taylor, H. L., Doody, B. J., Wright, K. C., Gruntfest, E., & Webber, D. (2015). A Review of People's Behavior in and around Floodwater. Weather, Climate, and Society, 7(4), 321-332. https://doi.org/10.1175/WCAS-D-14-00030.1
2. Bukvic, A., Zhu, H., Lavoie, R., & Becker, A. (2018). The role of proximity to waterfront in residents' relocation decision-making post-Hurricane Sandy. Ocean & Coastal Management, 154. https://doi.org/10.1016/j.ocecoaman.2018.01.002
3. Campbell, K. A., Laurien, F., Czajkowski, J., Keating, A., Hochrainer-Stigler, S., & Montgomery, M. (2019). First insights from the Flood Resilience Measurement Tool: A large-scale community flood resilience analysis. International Journal of Disaster Risk Reduction, 40, 101257. https://doi.org/10.1016/j.ijdrr.2019.101257
4. Chakraborty, L., Rus, H., Henstra, D., Thistlethwaite, J., & Scott, D. (2020). A place-based socioeconomic status index: Measuring social vulnerability to flood hazards in the context of environmental justice. International Journal of Disaster Risk Reduction, 43, 101394. https://doi.org/10.1016/j.ijdrr.2019.101394
5. Clay, P. M., Colburn, L. L., & Seara, T. (2016). Social bonds and recovery: An analysis of Hurricane Sandy in the first year after landfall. Marine Policy, 74, 334-340. https://doi.org/10.1016/j.marpol.2016.04.049
6. Deria, A., Ghannad, P., & Lee, Y.-C. (2020). Evaluating implications of flood vulnerability factors with respect to income levels for building long-term disaster resilience of low-income communities. International Journal of Disaster Risk Reduction, 48, 101608. https://doi.org/10.1016/j.ijdrr.2020.101608
7. Flores, A. B., Collins, T. W., Grineski, S. E., & Chakraborty, J. (2020). Social vulnerability to Hurricane Harvey: Unmet needs and adverse event experiences in Greater Houston, Texas. International Journal of Disaster Risk Reduction, 46. https://doi.org/10.1016/j.ijdrr.2020.101521
8. Fujimi, T., & Fujimura, K. (2020). Testing public interventions for flash flood evacuation through environmental and social cues: The merit of virtual reality experiments. International Journal of Disaster Risk Reduction, 50, 101690. https://doi.org/10.1016/j.ijdrr.2020.101690
9. Gibbens, S. (2019, February). Hurricane Sandy, explained. In National Geographic. https://www.nationalgeographic.com/environment/natural-disasters/reference/hurricane-sandy/#close
10. Graham, L., Debucquoy, W., & Anguelovski, I. (2016). The influence of urban development dynamics on community resilience practice in New York City after Superstorm Sandy: Experiences from the Lower East Side and the Rockaways. Global Environment Change, 40. https://doi.org/10.1016/j.gloenvcha.2016.07.001
11. Hamilton, K., Demant, D., Peden, A. E., & Hagger, M. S. (2020). A systematic review of human behaviour in and around floodwater. International Journal of Disaster Risk Reduction, 47, 101561. https://doi.org/10.1016/j.ijdrr.2020.101561
12. Maantay, J., & Maroko, A. (2009). Mapping urban risk: Flood hazards, race, & environmental justice in New York. Applied Geography, 29(1). https://doi.org/10.1016/j.apgeog.2008.08.002
13. Martins, V. N., Nigg, J., Louis-Charles, H. M., & Kendra, J. M. (2019). Household preparedness in an imminent disaster threat scenario: The case of superstorm sandy in New York City. International Journal of Disaster Risk Reduction, 34, 316-325. https://doi.org/10.1016/j.ijdrr.2018.11.003
14. McGuire, A. P., Gauthier, J. M., Anderson, L. M., Hollingsworth, D. W., Tracy, M., Galea, S., & Coffey, S. F. (2018). Social Support Moderates Effects of Natural Disaster Exposure on Depression and Posttraumatic Stress Disorder Symptoms: Effects for Displaced and Nondisplaced Residents. Journal of Traumatic Stress, 31(2), 223-233. https://doi.org/10.1002/jts.22270
15. Morss, R. E., Mulder, K. J., Lazo, J. K., & Demuth, J. L. (2016). How do people perceive, understand, and anticipate responding to flash flood risks and warnings? Results from a public survey in Boulder, Colorado, USA. Journal of Hydrology, 541, 649-664. http://dx.doi.org/10.1016/j.jhydrol.2015.11.047
16. Ntontis, E., Drury, J., Amlôt, R., Rubin, G. J., & Williams, R. (2020). Endurance or decline of emergent groups following a flood disaster: Implications for community resilience. International Journal of Disaster Risk Reduction, 45, 101493. https://doi.org/10.1016/j.ijdrr.2020.101493
17. Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37. https://doi.org/10.1016/j.ijdrr.2019.101176
18. Rezende, O. M., Ribeiro da Cruz de Franco, A. B., Beleño de Oliveira, A. K., Miranda, F. M., Pitzer Jacob, A. C., Martins de Sousa, M., & Miguez, M. G. (2020). Mapping the flood risk to Socioeconomic Recovery Capacity through a multicriteria index. Journal of Cleaner Production, 255, 120251. https://doi.org/10.1016/j.jclepro.2020.120251
19. Thistlethwaite, J., Henstra, D., Brown, C., & Scott, D. (2017). How Flood Experience and Risk Perception Influences Protective Actions and Behaviours among Canadian Homeowners. Environmental Management, 61(2), 197-208. https://doi.org/10.1007/s00267-017-0969-2
20. Wang, Z., Lam, N. S.N., Obradovich, N., & Ye, X. (2019). Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data. Applied Geography, 108, 1-8. https://doi.org/10.1016/j.apgeog.2019.05.

Abstract: Extreme weather events are becoming more prevalent with increasing global temperatures. In the Northeastern U.S., spring frost events are destroying forest ecosystems by defoliating newly budded trees. In order to grasp a better understanding of community dynamics and carbon fluxes, it is imperative to understand more about species-specific phenological and physiological responses to these events. This study aimed to investigate the legacy effects of a spring frost event in Black Rock Forest on the specific photosynthetic and intrinsic water use efficiency responses within unaffected red maples and sugar maples alongside defoliated red oaks. A LI-6800 machine conducted gas exchange measurements in the north, south, valley, and headquarter sites for each species. The new flush of red oak leaves portrayed

the greatest amount of photosynthetic productivity and efficiency while red maples and sugar maples retained their original characteristics with increased sensitivities. Hence, the defoliated tree species had a competitive advantage with shifted phenological patterns. Future research can be conducted several growing seasons after the frost event to determine the extent to which these events impact species dynamics, including DBH tree growth. New predicative carbon models can also be formed to create new management for tree implantation’s that maximize sequestration rates.

Keywords: spring frost event, defoliation, photosynthetic productivity, water use efficiency, sequestration

References

1. Anderson, R., & Ryser, P. (2015). Early Autumn Senescence in Red Maple (Acer rubrum L.) Is Associated with High Leaf Anthocyanin Content. Plants (Basel, Switzerland), 4(3), 505–522. https://doi.org/10.3390/plants4030505
2. Andrew D. Richardson, David Y. Hollinger, D. Bryan Dail, John T. Lee, J. William Munger, John O'keefe, Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests, Tree Physiology, Volume 29, Issue 3, March 2009, Pages 321–331, https://doi.org/10.1093/treephys/tpn040
3. Augspurger, C.K. (2009), Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest. Functional Ecology, 23: 1031-1039. https://doi:10.1111/j.1365-2435.2009.01587.x
4. Bascietto, Bajocco, Mazzenga, & Matteucci. (2018). Assessing spring frost effects on beech forests in Central Apennines from remotely-sensed data. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2017.10.007
5. Bassow, S.L. and Bazzaz, F.A. (1998), HOW ENVIRONMENTAL CONDITIONS AFFECT CANOPY LEAF‐LEVEL PHOTOSYNTHESIS IN FOUR DECIDUOUS TREE SPECIES. Ecology, 79: 2660-2675. https://doi:10.1890/0012-9658(1998)079[2660:HECACL]2.0.CO;2
6. Bielczynski, L. W., Łącki, M. K., Hoefnagels, I., Gambin, A., & Croce, R. (2017). Leaf and Plant Age Affects Photosynthetic Performance and Photoprotective Capacity. Plant physiology, 175(4), 1634–1648. https://doi.org/10.1104/pp.17.00904
7. Blunden, J., D. S. Arndt, and M. O. Baringer, 2011: State of the Climate in 2010. Bull. Amer. Meteor. Soc., 92, S1–S236, https://doi.org/10.1175/1520-0477-92.6.S1.
8. Bodnaruk, Yang, Kroll, & Hirabayashi. (n.d.). Where to plant urban trees? A spatially explicit methodology to explore ecosystem service tradeoffs. Landscape and Urban Planning. https://doi.org/10.1016/j.landurbplan.2016.08.016
9. Diffenbaugh, Singh, & Mankin. (2018). Unprecedented climate events: Historical changes, aspirational targets, and national commitments. Science Advances, 4. https://doi.org/10.1126/sciadv.aao3354
10. Fitchett, J. M., Grab, S. W., & Thompson, D. I. (2015). Plant phenology and climate change: Progress in methodological approaches and application. Progress in Physical Geography: Earth and Environment, 39(4), 460–482. https://doi.org/10.1177/0309133315578940
11. Goldblum, & Kennett. (n.d.). Geographical variation in the photosynthesis characteristics of lab- and field-grown sugar maple (Acer saccharum) seedlings. In Geographical Bulletin - Gamma Theta Upsilon.
12. Hänninen, H., & Tanino, K. (2011). Tree seasonality in a warming climate. Trends in plant science, 16(8), 412–416. https://doi.org/10.1016/j.tplants.2011.05.001
13. Hatfield, & Dold. (2019). Water-Use Efficiency: Advances and Challenges in a Changing Climate. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2019.00103
14. Hufkens, Keenan, Sonnentag, O'Keefe, Friedl, Bailey, & Richardson. (2012). Article Ecological Impacts of a Widespread Frost Event Following Early Spring Leaf-Out. Global Change Biology. https://doi.org/10.1111/j.1365-2486.2012.02712.x
15. Jennifer M. Nagel, Kevin L. Griffin, William S. F. Schuster, David T. Tissue, Matthew H. Turnbull, Kim J. Brown, David Whitehead, Energy investment in leaves of red maple and co-occurring oaks within a forested watershed, Tree Physiology, Volume 22, Issue 12, August 2002, Pages 859–867, https://doi.org/10.1093/treephys/22.12.859
16. JEONG, S.‐J., HO, C.‐H., GIM, H.‐J. and BROWN, M.E. (2011), Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Global Change Biology, 17: 2385-2399. https://doi:10.1111/j.1365-2486.2011.02397.x
17. Kim, Kimball, Didan, & Henebry. (n.d.). Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2014.04.001
18. Lahr, E. C., Dunn, R. R., & Frank, S. D. (2018). Variation in photosynthesis and stomatal conductance among red maple (Acer rubrum) urban planted cultivars and wildtype trees in the southeastern United States. PloS one, 13(5), e0197866. https://doi.org/10.1371/journal.pone.0197866
19. Lavergne, Sandoval, Hare, Graven, & Prentice. (n.d.). Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: Insights from stable carbon isotope data. Global Change Biology. https://doi.org/10.1111/gcb.1536
20. Marc D. Abrams, The Red Maple Paradox: What explains the widespread expansion of red maple in eastern forests?, BioScience, Volume 48, Issue 5, May 1998, Pages 355–364, https://doi.org/10.2307/1313374
21. Medrano, Tomas, Martorell, & Flexas. (n.d.). From leaf to whole-plant water use efficiency (WUE) in complex canopies: Limitations of leaf WUE as a selection target. The Crop Journal. https://doi.org/10.1016/j.cj.2015.04.002
22. Morin, Roy, Sonie, & Chuine. (n.d.). Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytologist. https://doi.org/10.1111/j.1469-8137.2010.03252.x
23. Nolè, A., Rita, A., Ferrara, A.M.S. et al. Effects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI. Annals of Forest Science 75, 83 (2018). https://doi.org/10.1007/s13595-018-0763-1
24. Príncipe A, van der Maaten E, van der Maaten-Theunissen M, Struwe T,Wilmking M, Kreyling J (2017) Low resistance but high resilience in growth of a major deciduous forest tree (Fagus sylvatica L.) in response to late spring frost in southern Germany. Trees 31(2):743–751. https://doi.org/10.1007/s00468-016-1505-3
25. RICHARDSON, A.D., BAILEY, A.S., DENNY, E.G., MARTIN, C.W. and O'KEEFE, J. (2006), Phenology of a northern hardwood forest canopy. Global Change Biology, 12: 1174-1188. https://doi.org/10.1111/j.1365-2486.2006.01164.x
26. Schuster. (2011). Age-related decline of stand biomass accumulation is primarily due to mortality and not to reduction in NPP associated with individual tree physiology, tree growth or stand structure in a Quercus-dominated forest. Journal of Ecology. https://doi.org/10.1111/j.1365-2745.2011.01933.x
27. Tkemaladze, & Makhashvili. (2016). Climate Changes and Photosynthesis. Annals of Agrarian Science. https://doi.org/10.1016/j.aasci.2016.05.012
28. Vitasse, Y., Lenz, A., Hoch, G. and Körner, C. (2014), Earlier leaf‐out rather than difference in freezing resistance puts juvenile trees at greater risk of damage than adult trees. J Ecol, 102: 981-988. https://doi.org/10.1111/1365-2745.12251
29. Vitasse, Signarbieux,, & Fu. (2018). Global warming leads to more uniform spring phenology across elevations. PNAS. https://doi.org/10.1073/pnas.1717342115
30. Xu, C.‐Y., Turnbull, M.H., Tissue, D.T., Lewis, J.D., Carson, R., Schuster, W.S.F., Whitehead, D., Walcroft, A.S., Li, J. and Griffin, K.L. (2012), Age‐related decline of stand biomass accumulation is primarily due to mortality and not to reduction in NPP associated with individual tree physiology, tree growth or stand structure in a Quercus‐dominated forest. Journal of Ecology, 100: 428-440. https://doi/10.1111/j.1365-2745.2011.01933.x

Abstract: Though sharp-wave ripples have been recorded in the EEG data of the hippocampus of mammals for years, it remains unclear how ripples can contribute to memory for different behaviors.. Sharp wave ripples are one of the most synchronous patterns in the mammalian brain. These waves are most common during non-REM sleep, although they can also be associated with consummatory behaviors. In EEG recordings, these occurrences can be seen as large amplitude negative polarity deflections (40–100 ms) in CA1 stratum radiatum that are associated with a short-lived fast oscillatory pattern of the LFP in the CA1 pyramidal layer, known as “ripples.” The purpose of this study was to investigate the distinction between sleep and awake ripples along with the connection between sharp-wave ripples and specific mammalian behaviors during memory tasks. The hypothesis tested was that SPW-Rs occur when the animal has an experience that will help guide subsequent successful task completion that results in obtaining a desired reward. To conduct the experiment electrophysiological signals were collected from a rat’s hippocampus during various tasks. The data were then analyzed using Neuroscope and compared to a visual recording of the rat’s actions. The data suggest that sharp wave ripples are more likely to occur close to a reward, most often before the reward, and do not have a higher tendency to occur early or late in learning. Future research can further clarify these results and investigate the process by which these ripples occur.

References

1. Bartsch, T., & Wulff, P. (2015, November 19). The hippocampus in aging and disease: From plasticity to vulnerability. Neuroscience, 309, 1-16. ScienceDirect. https://doi.org/10.1016/j.neuroscience.2015.07.084
2. Bragin, A., Engel Jr, J., Wilson, C. L., Fried, I., & Buzsáki, G. (1999, April 15). High‐frequency oscillations in human brain. Hippocampus, 9(2), 137-142. Wiley Online Library. https://doi.org/10.1002/(SICI)1098-1063(1999)9:2%3C137::AID-HIPO5%3E3.0.CO;2-0
3. Buzsáki, G. (2015, September 26). Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning. Hippocampus, 25(10), 1073-1188. Wiley Online Library. https://doi.org/10.1002/hipo.22488
4. Buzsáki, G., Leung, L. W., & Vanderwolf, C. H. (1983, October). Cellular bases of hippocampal EEG in the behaving rat. Brain Research, 287(2), 139-171. PubMed Central. https://doi.org/10.1016/0165-0173(83)90037-1
5. Buzsáki, G., Royer, S., Belluscio, M., Berényi, A., Diba, K., Fujisawa, S., Grosmark, A., Mao, D., Mizuseki, K., Patel, J., Stark, E., Sullivan, D., Watson, B., & Vandecasteele, M. (2012, March 04). Large-scale Recording of Neurons by Movable Silicon Probes in Behaving Rodents. Journal of Visualized Experiments, 61. PubMeb Central. https://dx.doi.org/10.3791%2F3568
6. G Buzsáki 1 , Z Horváth, R Urioste, J Hetke, K Wise, G., Urioste, R., Hetke, J., & Wise, K. (1992, May 15). High-frequency network oscillation in the hippocampus. Science, 256(5059), 1025-1027. American Association for the Advancement of Science. https://doi.org/10.1126/science.1589772
7. Jouvet, M., Michel, F., & Courjon, J. (1959). L'activite electrique du rhinencephale au cours du sommeil chez le chat. Comptes rendus des seances de la Societe de biologie et de ses filiales, 153(1), 98-101.
8. Kanamori, N. (1985, May). A spindle-like wave in the cat hippocampus: a novel vigilance level-dependent electrical activity. Brain Research, 334(1), 180-182. ScienceDirect. https://doi.org/10.1016/0006-8993(85)90584-0
9. Keefe, J. O. (1976). Place units in the hippocampus of the freely moving rat. Experimental Neurology, 51(1), 78-109. ScienceDirect. https://doi.org/10.1016/0014-4886(76)90055-8
10. Keefe, J. O., & Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford University Press.
11. Knierim, J. J. (2015, December 07). The hippocampus. Primer, 25(23), PR1116-R1121. Current Biology. https://doi.org/10.1016/j.cub.2015.10.049
12. Kropotov, J. D. (2009). Quantitative EEG, Event-Related Potentials and Neurotherapy. Elsevier. https://doi.org/10.1016/B978-0-12-374512-5.X0001-1
13. Le Van Quyen, M., Staba, R., Bragin, A., Dickson, C., Valderrama, M., Fried, I., & Engel, J. (2010, June 9). Large-Scale Microelectrode Recordings of High-Frequency Gamma Oscillations in Human Cortex during Sleep. The Journal of Neuroscience, 30(23), 7770-7782. PubMed Central. https://dx.doi.org/10.1523%2FJNEUROSCI.5049-09.2010
14. Lopes da Silva, F. (2013, December 4). EEG and MEG: Relevance to neuroscience. Neuron, 80, 1112-1128. PubMed Central. https://doi.org/10.1016/j.neuron.2013.10.017
15. Moser, M.-B., & Moser, E. I. (January, 1999). Functional differentiation in the hippocampus. Hippocampus, 8(6), 608-619. Wiley Online Library. https://doi.org/10.1002/(SICI)1098-1063(1998)8:6%3C608::AID-HIPO3%3E3.0.CO;2-7
16. Singh, K. A., & Dhikav, V. (2012, Oct-Dec). Hippocampus in health and disease: An overview. Indian Academy of Neurology, 15(4), 239-246. PubMed Central. https://dx.doi.org/10.4103%2F0972-2327.104323
17. Steriade, M., Gloor, P., Llinas, R. R., Lopes de Silva, F. H., & Mesulam, M. M. (1990). Report of IFCN Committee on Basic Mechanisms. Basic mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology, 76, 481-508. PubMed Central. https://doi.org/10.1016/0013-4694(90)90001-z
18. Suzuki, S. S., & Smith, G. K. (1987, October). Spontaneous EEG spikes in the normal hippocampus. Electroencephalography and Clinical Neurophysiology, 67(4), 348-359. PubMed Central. https://doi.org/10.1016/0013-4694(87)90123-4
19. Vanderwolf, C. H. (1969, April). Hippocampal electrical activity and voluntary movement in the rat. Electroencephalography and Clinical Neurophysiology, 26(4), 407-418. PubMed Central. https://doi.org/10.1016/0013-4694(69)90092-3
20. Zhang, H., & Jacobs, J. (2015, September 09). Traveling Theta Waves in the Human Hippocampus. The Journal of Neuroscience, 35(36), 12477-12487. PubMed Central. https://dx.doi.org/10.1523%2FJNEUROSCI.5102-14.2015

Abstract: Pneumonia, a fatal lung disease, is caused by infection of Streptococcus pneumoniae; it is detected by chest x-rays that reveal inflammation of the alveoli. However, the efficiency by which it is diagnosed can be improved through the use of artificial intelligence. Convolutional neural networks (CNNs), a form of artificial intelligence, have recently demonstrated enhanced accuracy when classifying images. This study used CNNs to analyze chest x-rays and predict the probability the patient has pneumonia. Furthermore, a comprehensive investigation was conducted, examining the function of various components of the CNN, in the context of pneumonia x-rays. This study was able to achieve significantly high performance, making it viable for clinical implementation. Furthermore, the architecture of the proposed model is applicable to various other diseases, and can thus be used to optimize the disease diagnosis industry.

Keywords: artificial intelligence, disease diagnosis, pneumonia, convolutional neural networks, machine learning

References

1. Albawi,  S.,  Mohammed,  T.  A.,  &  Al-Zawi,  S.   (2017).   Understanding  of  a  convolutionalneural network.  In 2017 international conference on engineering and technology (icet) (p. 1-6).  doi:  10.1109/ICEngTechnol.2017.8308186
2. Bebis,  G., & Georgiopoulos,  M.  (1994).  Feed-forward neural networks. IEEE Potentials, 13(4), 27-31.  doi:  10.1109/45.329294
3. Bjorck,  J.,  Gomes,  C.,  Selman,  B.,  &  Weinberger,  K.  Q.   (2018).   Understanding  batch normalization. arXiv preprint arXiv:1806.02375.
4. Eckle, K., & Schmidt-Hieber, J. (2019). A comparison of deep networks with relu activation function and linear spline-type methods. Neural Networks,110, 232–242.
5. Himavathi,  S.,  Anitha,  D., & Muthuramalingam,  A.  (2007).  Feedforward neural network implementation in fpga using layer multiplexing for effective resource utilization. IEEE Transactions on Neural Networks,18(3), 880-888.  doi:  10.1109/TNN.2007.891626
6. Ho, Y., & Wookey, S.  (2019).  The real-world-weight cross-entropy loss function:  Modeling the costs of mislabeling. IEEE Access,8, 4806–4813.
7. Huss-Lederman, S., Jacobson, E. M., Johnson, J. R., Tsao, A., & Turnbull, T.  (1996).  Implementation of strassen’s algorithm for matrix multiplication.  In Supercomputing’96:Proceedings of the 1996 acm/ieee conference on supercomputing(pp. 32–32).
8. Kermany, D., Zhang, K., Goldbaum, M., et al. (2018). Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data,2(2).
9. LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y.  (1999).  Object recognition with gradient-based  learning. In Shape, contour and grouping in computer vision (pp.  319–345). Springer.
10. Liu, K., Kang, G., Zhang, N., & Hou, B. (2018). Breast cancer classification based on fully-connected layer first convolutional neural networks. IEEE Access,6, 23722-23732. doi:10.1109/ACCESS.2018.2817593
11. Nagi, J., Ducatelle, F., Di Caro, G. A., Cire ̧san, D., Meier, U., Giusti, A., . . .  Gambardella, L. M.  (2011).  Max-pooling convolutional neural networks for vision-based hand gesture recognition.  In 2011 ieee international conference on signal and image processing applications (icsipa) (p. 342-347).  doi: 10.1109/ICSIPA.2011.6144164
12. Ruder, S.  (2016).  An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
13. Yu,  D.,  Wang,  H.,  Chen,  P.,  &  Wei,  Z.   (2014).   Mixed  pooling  for  convolutional  neural networks.   In International conference on rough sets and knowledge technology(pp.364–375).

Abstract: Coronavirus disease -19 (COVID-19) has gained widespread interest in the field of mathematical epidemiology in order to inform the public on basic statistics surrounding COVID-19. However, the age-standardized mortality rates (ASMRs), which adjust age and population discrepancies between different regions by comparing a subpopulation to a standard population, have not been shown publicly. Usually, COVID-19 ASMRs have not been calculated due to the lengthy process required to calculate them; however, ASMRs for COVID-19 have occasionally been calculated, but their effectiveness have been hindered due to the use of a hand-written formula and graphical manual methods. My study involved the development of a deep learning algorithm to calculate ASMR and to instantly graph the ASMR of a subpopulation versus the crude mortality rate of the standard population. This algorithm was used to compare the ASMRs for COVID-19 in American states to the crude mortality rate of the standard population, America. In this study, the algorithm shows efficiency with a consistent runtime of time≤5seconds, within 95% confidence interval error bars among trials. ASMRs show statistically significant differences in expected COVID-19 deaths among most populations. There is at least 95% confidence (p≤0.05) that differences in ASMR are independent of age and population distributions. These findings suggest that there are more factors than just age discrepancy that affect COVID-19 mortality rates.

Keywords: COVID-19, Age-Standardization, Mortality Rate, Algorithm, Deep Learning

References

1. Wang, D., Li, Z., & Liu, Y. (2020). An overview of the safety, clinical application and antiviral research of the COVID-19 therapeutics. Journal of Infection and Public Health. doi:10.1016/j.jiph.2020.07.004
2. Brown, S. M., Doom, J. R., Lechuga-Peña, S., Watamura, S. E., & Koppels, T. (2020). Stress and parenting during the global COVID-19 pandemic. Child Abuse & Neglect. doi:10.1016/j.chiabu.2020.104699
3. Overton, C. E., Stage, H. B., Ahmad, S., Curran-Sebastian, J., Dark, P., Das, R., . . . Webb, L. (2020). Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. Infectious Disease Modelling, 5, 409-441. doi:10.1016/j.idm.2020.06.008
4. Tiirinki, H., Tynkkynen, L., Sovala, M., Atkins, S., Koivusalo, M., Rautiainen, P., . . . Keskimäki, I. (2020). COVID-19 pandemic in Finland – preliminary analysis on health system response and economic consequences. Health Policy and Technology. doi:10.1016/j.hlpt.2020.08.005
5. Russell, T. W., Hellewell, J., Jarvis, C. I., Zandvoort, K. V., Abbott, S., Ratnayake, R., . . . Kucharski, A. J. (2020). Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Eurosurveillance, 25(12). doi:10.2807/1560-7917.es.2020.25.12.2000256
6. Bernardino, G., Benkarim, O., Garza, M. S., Prat-Gonzàlez, S., Sepulveda-Martinez, A., Crispi, F., . . . Ballester, M. A. (2020). Handling confounding variables in statistical shape analysis - application to cardiac remodelling. Medical Image Analysis, 65. doi:10.1016/j.media.2020.101792
7. Xu, L., Polya, D. A., Li, Q., & Mondal, D. (2020). Association of low-level inorganic arsenic exposure from rice with age-standardized mortality risk of cardiovascular disease (CVD) in England and Wales. Science of The Total Environment, 743. doi:10.1016/j.scitotenv.2020.140534
8. Shende, R., Gupta, G., & Macherla, S. (2019). Determination of an inflection point for a dosimetric analysis of unflattened beam using the first principle of derivatives by python code programming. Reports of Practical Oncology & Radiotherapy, 24(5), 432-442. doi:10.1016/j.rpor.2019.07.009
9. Mohamed, M. O., Gale, C. P., Kontopantelis, E., Doran, T., Belder, M. D., Asaria, M., . . . Mamas, M. A. (2020). Sex-differences in mortality rates and underlying conditions for COVID-19 deaths in England and Wales. Mayo Clinic Proceedings. doi:10.1016/j.mayocp.2020.07.009
10. Kavadi, D. P., Patan, R., Ramachandran, M., & Gandomi, A. H. (2020). Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons & Fractals, 139. doi:10.1016/j.chaos.2020.110056
11. Minicozzi, P., Cassetti, T., Vener, C., & Sant, M. (2018). Analysis of incidence, mortality and survival for pancreatic and biliary tract cancers across Europe, with assessment of influence of revised European age standardisation on estimates. Cancer Epidemiology, 55, 52-60. doi:10.1016/j.canep.2018.04.011
12. Bosch, Jaume, et al. “Asynchronous Runtime with Distributed Manager for Task-Based Programming Models.” Parallel Computing, vol. 97, 2020, p. 102664., doi:10.1016/j.parco.2020.102664.
13. Rodriguez-Diaz, Carlos E., et al. “Risk for COVID-19 Infection and Death among Latinos in the United States: Examining Heterogeneity in Transmission Dynamics.” Annals of Epidemiology, 23 July 2020, doi:10.1016/j.annepidem.2020.07.007.
14. Wiemers, Emily, et al. “Disparities in Vulnerability to Severe Complications from COVID-19 in the United States.” Research in Social Stratification and Mobility, vol. 69, 2020, doi:10.3386/w27294.
15. Etkin, Yana, et al. “Acute Arterial Thromboembolism in Patients with COVID-19 in the New York City Area.” Annals of Vascular Surgery, 28 Aug. 2020, doi:10.1016/j.avsg.2020.08.085.
16. Centers for Disease Control and Prevention. www.cdc.gov/.
17. 2020 World Population by Country, worldpopulationreview.com/.