Three Jericho Students Named Intel Semifinalists
Three Jericho Students Named Intel Semifinalists
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Crystal Zheng, Ien Li, and Steve Zheng, seniors at Jericho High School, were recently named semifinalists in the prestigious Intel Science Competition. These students are three of only 300 selected from more than 1,800 entrants from 460 high schools.

Each semifinalist receives a $1,000 award from Intel with an additional $1,000 going to his or her school. On Jan. 21, 40 of the 300 semifinalists will be named Intel Science Talent Search finalists. Finalists will receive an all-expenses-paid trip to Washington, D.C. from March 5-11, where they will compete for more than $1 million in awards. Information about the projects is below.

Crystal Zheng: BDNF VAL66MET induces endocytosis-dependent dendritic spine collapse via proNGF-like collapse mechanism.

Summary: Found in 20-30% of the population, the MET mutation in the Brain-Derived Neurotrophic Factor (BDNF) gene increases susceptibility to various fear-related disorders by impairing fear extinction circuitry. Previously, MET was found to induce axonal growth cone retraction in DIV3 hippocampal neurons, tri-complexing with SorCS2 and p75. However, METinduced collapse mechanism and MET function in mature neurons remained unknown. In proNGF, another neurotrophin precursor, collapse mechanism, fascin and Trio were identified as key actin stabilizers. Thus, this study investigated receptor-complex endocytosis and the underlying molecular mechanism in MET-induced dendritic remodeling. Prodomain-treated and dynasore/prodomain-treated DIV18-21 hippocampal neurons were imaged with SIM-TIRF microscopy. Nikon NIS Elements and BitPlane Imaris reconstructed and quantified images were statistically analyzed with Student’s t-test. MET significantly decreased mushroom spine number, increased thin spine number and average spine length (p<0.0001), while total spine number remained unchanged. MET significantly decreased p-fascin (p<0.05), Trio (p<0.0001), and fascin (p<0.0001) positive spines and increased Trio, p-fascin, and fascin (p<0.0001) in the dendritic shaft. Control DYNA and MET DYNA-treated neurons had similar mushroom, thin, spine length, total spine numbers, p-fascin and Trio puncta in dendritic shaft and positive-spines. Overall, a novel endocytosis-dependent MET-induced collapse mechanism in mature hippocampal neurons was revealed. Future investigations include elucidating MET/SorCS2/p75 tri-complex sorting (recycling or degradation pathways) to further understanding molecular mechanisms underlying MET prodomain function, more effective targeted treatments for fearrelated disorders production.

Ien Li:
Statistical Modeling of Major Depression: Bridging the Gap between Brain and Behavior

Summary: Despite decades of research, the pathophysiological processes of major depressive disorder (MDD), as well as its laterality effects, are not well characterized. Further, response and remission rates to antidepressant medications are low. Development of new medication targeting specific neural substrates of MDD requires greater understanding of the neurological correlates of depression’s clinical presentations. The present project identified the biological underpinnings of specific depressive symptoms deconstructed from the 17-item Hamilton Depression Rating Scale (HAM17), through the integration of neuroanatomical and psychological measures. Magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and clinical data (n=46) were obtained from the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study. Using the Statistical Package for Social Sciences (SPSS), 6 statistically significant (p≤.002) linear regression models were developed to predict severity of MDD symptoms, calculated as factors of HAM17. Number of major depressive episodes (p=.001) and left-handedness (p=.047) positively correlated with total HAM17 score, though biological measures did not emerge as important HAM17 predictors. Yet different neurobiological anomalies were associated with distinct MDD-related symptom clusters, which may underlie the heterogeneity of MDD biosignatures. It is posited that HAM17, a sum score of symptoms, may fail to reflect MDD-related brain changes as a result of different psychological manifestations. Due to the polymorphic nature of depression, the deconstruction of HAM17 into multiple dimensions may be essential for determining the structural correlates of specific MDD symptoms. Integration of neuroimaging and clinical observation may aid in the future recruitment of more anatomically and symptomatically homogeneous cohorts to reveal optimal treatments for different MDD syndromes. It was demonstrated that identifying anatomical biomarkers of distinct depressive phenotypes may guide the development of novel therapeutics, establish systematic treatment algorithms based on both psychological and biological evidence, and elucidate the relationship between brain and behavior in complex mental disorders, which still eludes the field of psychiatry today.


Steve Zheng: Synthesis of a novel MOF for CO2 separation in Carbon Capture and Storage


The atmospheric release of CO2 is strongly correlated with climate change. Metal organic frameworks (MOFs), porous, uniform crystalline structures, have demonstrated high promises for CO2 separation in Carbon Capture and Storage. This study reports a novel MOF, C24H24CuF6N4O3Si (X), synthesized solvothermally with its respective physical and chemical properties analyzed via thermogravimetric analysis (TGA), powder diffraction, and single crystal X-Ray diffraction. An ASAP 2020 analyzed gas sorption behaviors. TGA revealed that X is thermally stable up to 190°C while powder diffraction demonstrated X remained chemically stable in the presence of CO2, CH4, and N2. X’s 77K N2 adsorption revealed a BET surface area 934 m²/g. Gas sorption analyses indicate that X adsorbs 27cm³/g of CO2, significantly higher than that of other published adsorbents such as Mg-MOF-1, MOF-2, and ZIF- 8. X’s binding energy to CO2 is 27kJ/mol, highly optimum for CO2 separation, as its regenerative costs will be lower than current chemisorbents. X’s IAST selectivity of CO2 over N2 at 15% and 85% flue gas composition was calculated at approximately 20, which is attributed to the electrostatic interactions between the CO2 and SiF6 anion. X provides a new platform for post-synthetic modification to potentially develop more effective adsorbents for separation technology.


Pictured: Congratulations to Intel Semifinalists (L-R) Ien Li, Steve Zheng and Crystal Zheng. They are pictured here with Dr. Serena McCalla, Jericho High School Science Research Coordinator.