NYCSEA

Abstract: Red 40 dye has been banned in European countries due to rising health concerns. There is evidence that Red 40, also called Allura red AC, can cause hyperactivity in humans along with negatively affecting the colon and causing Early Onset Colorectal cancer in mice. This study aimed to investigate if doses of 0, 15, 30, and 60 microliters of Allura red AC can contribute to negative effects on the phototaxis rate and the rate of regeneration of Dugesia tigrina. It is hypothesized that if Allura red AC is fed to Dugesia tigrina then, it will increase motility, and slow the rate of regeneration. This study was performed using four groups of Dugesia tigrina with 0 µM, 0.125µM, 0.25µM, and 0.5µM of red 40 and measuring the length of the planaria, and the rate of movement of planaria over the course of 31 days. The results of this study show that an increase in Allura red AC exposure causes a decrease in the regeneration rate, an increase in phototaxis, and an increase in mortality. Future research suggests a conversion in dosage and/or form of dye (powder version). Also, recommendations for future research include using a different type of organism such as Drosophila melanogaster. 

References

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CDC. “What Are the Risk Factors for Colorectal Cancer?” Centers for Disease Control and Prevention, 2019, www.cdc.gov/cancer/colorectal/basic_info/risk_factors.htm. Accessed 4 Mar. 2024. 

Center for Food Safety and Applied Nutrition. “Color Additives History.” U.S. Food and Drug Administration, 3 Nov. 2017, www.fda.gov/industry/color-additives/color-additives-history. Accessed 4 Mar. 2024.

Centers for Disease Control and Prevention. “What Is ADHD?” Centers for Disease Control and Prevention, 27 Sept. 2023, www.cdc.gov/ncbddd/adhd/facts.html. Accessed 4 Mar. 2024. Cleveland Clinic. “Is Red Dye 40 Safe?” Cleveland Clinic, 8 Mar. 2023, health.clevelandclinic.org/red-dye-40. Accessed 4 Mar. 2024. 

Issigonis, Melanie. “Could We Use Planarians to Help Us Understand Human Regeneration?” Morgridge Institute for Research, 24 Dec. 2017, morgridge.org/blue-sky/could-we-use-planarians-to-help-us-understand-human-regeneration/#:~:text=But%2C%20unlike%20planarians%2C%20humans. Accessed 4 Mar. 2024.

Kanarek, Robin B. “Artificial Food Dyes and Attention Deficit Hyperactivity Disorder.” Nutrition Reviews, vol. 69, no. 7, 30 June 2011, pp. 385–391, https://doi.org/10.1111/j.1753-4887.2011.00385.x. 

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Paskin, Taylor R., et al. “Planarian Phototactic Assay Reveals Differential Behavioral Responses Based on Wavelength.” PLoS ONE, vol. 9, no. 12, 10 Dec. 2014, p. e114708, https://doi.org/10.1371/journal.pone.0114708. 

“Potential Neurobehavioral Effects of Synthetic Food Dyes in Children.” Ca.gov, 2021, oehha.ca.gov/media/downloads/risk-assessment/report/healthefftsassess041621.p. Accessed 4 Mar. 2024. 

Sagon, Candy. “8 Foods We Eat That Other Countries Ban.” Blogs, 25 June 2013, blog.aarp.org/healthy-living/8-foods-we-eat-that-other-countries-ban. Accessed 4 Mar. 2024.

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Walsh, Caroline J., et al. “Obstetric Complications in Mothers with ADHD.” Frontiers in Reproductive Health, vol. 4, 7 Nov. 2022, https://doi.org/10.3389/frph.2022.1040824. Accessed 4 Mar. 2024. 

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Zhang, Qi, et al. “The Synthetic Food Dye, Red 40, Causes DNA Damage, Causes Colonic Inflammation, and Impacts the Microbiome in Mice.” Toxicology Reports, vol. 11, 1 Dec. 2023, pp. 221–232, www.sciencedirect.com/science/article/pii/S2214750023000926, https://doi.org/10.1016/j.toxrep.2023.08.006. Accessed 19 Sept. 2023.

Abstract: 

The rapid advancement of autonomous driving systems requires robust perception algorithms. Interpreting complex environments and simulation-based testing plays a vital role in validating these algorithms. This research explores the use of the Driving Scenario Designer app in MATLAB® workspace to perform high-fidelity sensor simulation, generate synthetic sensor data, and create dynamic virtual driving scenarios for testing perception systems. To emulate real-world driving conditions, the study focuses on designing scenarios involving multiple actors, including cars, pedestrians, cyclists, and barriers.

We construct and customize scenarios using the app with varying road layouts, traffic patterns, and environmental conditions. Sensor models such as radar, lidar, and cameras are simulated to produce synthetic data that mimics real sensor outputs. The scenarios are exported to the MATLAB® workspace for further analysis, enabling the evaluation of perception algorithms in detecting and tracking objects under different conditions.

Key contributions include a systematic methodology for scenario generation, sensor configuration, and data extraction, along with performance assessments of perception algorithms using synthetic datasets. The results demonstrate the effectiveness of the Driving Scenario Designer in accelerating algorithm development by providing a controlled yet flexible testing environment. This approach reduces reliance on costly physical prototypes while ensuring comprehensive validation across diverse driving conditions. The study highlights the app’s utility in autonomous vehicle research, offering a scalable solution for perception system verification.

References

1. MathWorks. (2023). Driving Scenario Designer User’s Guide. 

2. Ulbrich, S., Menzel, T., Reschka, A., Schuldt, F., & Maurer, M. (2015). Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving. IEEE Intelligent Vehicles Symposium (IV).

3. Dosovitskiy, A., Ros, G., Codevilla, F., López, A., & Koltun, V. (2017). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning (CoRL).

4. Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020). A Survey of Deep Learning Techniques for Autonomous Driving. Journal of Field Robotics, 37(3), 362-386.

5. Bansal, M., Krizhevsky, A., & Ogale, A. (2018). Chauffeurnet: Learning to Drive by Imitating the Best and Synthesizing the Worst. Robotics: Science and Systems (RSS).

6. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

7. Rong, G., Shin, B. H., Tabatabaee, H., et al. (2020). LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving. IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

8. Janai, J., Güney, F., Behl, A., & Geiger, A. (2020). Computer Vision for Autonomous Vehicles: Problems, Datasets, and State-of-the-Art. Foundations and Trends in Computer Graphics and Vision, 12(1-3), 1-308.

9. Kato, S., Tokunaga, S., Maruyama, Y., et al. (2018). Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems. ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

10. Pendleton, S. D., Andersen, H., Du, X., et al. (2017). Perception, Planning, Control, and Coordination for Autonomous Vehicles. Machines, 5(1), 6.

11. Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. IEEE International Conference on Computer Vision (ICCV).

12. Li, Y., Ibanez-Guzman, J., & Ng, H. K. (2020). Lidar for Autonomous Driving: The Principles, Challenges, and Trends. IEEE Signal Processing Magazine, 37(4), 50-61.

13. Bojarski, M., Del Testa, D., Dworakowski, D., et al. (2016). End to End Learning for Self-Driving Cars. arXiv preprint arXiv:1604.07316.

14. Kong, J., Pfeiffer, M., Schildbach, G., & Borrelli, F. (2015). Kinematic and Dynamic Vehicle Models for Autonomous Driving Control Design. IEEE Intelligent Vehicles Symposium (IV).

15. NHTSA. (2021). Automated Driving Systems: A Vision for Safety. National Highway Traffic Safety Administration.

     

     

Abstract: 

Green roofs (GRs) are typically used to retain stormwater and are increasingly being used to produce food by growing edible vegetation, such as Mad Hatter Peppers (Capsicum baccatum). However, there have been conflicting studies on whether GRs can feasibly produce Capsicum baccatum in GRs compared to in-ground production. To test this, water retention was compared among small-scale models of three different vegetation types: two Sedum setups, two Capsicum baccatum setups, and one bare setup. The models used water storage compartments and moisture retention fabric to increase water retention and to reduce the need for irrigation. There was not a statistically significant difference in water retention between the different vegetation types, and the Capsicum baccatum wilted by the end of the study, so it did not produce food. These results indicate that Sedum should be used in future GRs because they can provide many benefits other than water retention, whereas Capsicum baccatum may not be healthy enough to provide other benefits.

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Berardi, U., GhaffarianHoseini, A. H., & GhaffarianHoseini, A. (2014). State-of-the-art analysis of the environmental benefits of green roofs. Applied Energy, 115, 411–428. https://doi.org/10.1016/j.apenergy.2013.10.047 

Cristiano, E., Deidda, R., & Viola, F. (2021). The role of green roofs in urban water-energy-food-ecosystem nexus: A Review. Science of The Total Environment, 756, 143876. https://doi.org/10.1016/j.scitotenv.2020.143876 

Davitt, J. (2022, August 1). Looking back on a record hot July. Spectrum News NY1. Retrieved October 6, 2022, from https://www.ny1.com/nyc/all-boroughs/weather/2022/07/30/looking-back-on-a-record-hot-july-for-nyc 

Eksi, M., Rowe, D. B., Fernández-Cañero, R., & Cregg, B. M. (2015). Effect of substrate compost percentage on green roof vegetable production. Urban Forestry & Urban Greening, 14(2), 315–322. https://doi.org/10.1016/j.ufug.2015.03.006 

Eksi, M., Sevgi, O., Akburak, S., Yurtseven, H., & Esin, İ. (2020). Assessment of recycled or locally available materials as green roof substrates. Ecological Engineering, 156, 105966. https://doi.org/10.1016/j.ecoleng.2020.105966 

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Abstract: 

The CDC reports that over two million people in the United States alone become infected with antibiotic resistant bacteria. This paper studies one of the primary mechanisms of antibiotic resistance, efflux, in Escherichia coli. EmrA and EmrB proteins, belonging to the efflux pump EmrAB-TolC were purified in two types of competent cells to evaluate the methodology of the protein purification procedure. Analysis of each purified protein was completed using a variety of standard wet lab techniques. The structure of the complex was determined using artificial intelligence-based structure prediction software. This project presents effective methods for the purification of EmrAB and presents a model that reveals C9 homo-oligomeric arrangement of subunit EmrA in complex with a dimeric EmrB. Basic ligand binding assays were completed with known substrates in silico. Confirming protein purification methods allow future research to continue in vitro, with the eventual goal of experimentally determining the complex at a high resolution and aid in the future identification of drug targets and development of EmrAB-TolC protein inhibitors.

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