Scarsdale Public Schools

The Val30Met (V30M) and Val122Ile (V122I) transthyretin (TTR) mutations often beget hereditary amyloid transthyretin amyloidosis (hATTR). Since symptoms are progressively debilitating and potentially fatal if untreated, low survival rates result from late diagnoses of hATTR patients. This retrospective analysis of microarray and biobank data helped establish clinical biomarkers for early hATTR detection. In a Portuguese sample of V30M carriers (n=183), gene profiling identified dysregulated immune markers. Among African Americans (AA) and Hispanic/Latinx Americans (HA) from the Mount Sinai BioMe Biobank (n=28,718), a case-control style Phenome-Wide Association Study (PheWAS; odds ratio [95% confidence interval]) of V122I for phenotypic and echocardiogram traits (β coefficients [95% CI]) determined gene pleiotropy. Among V30M profiles, 96 (52.4%) were symptomatic, expressing upregulated neutrophil activity (p<10-16), IL-6/JAK/STAT3 signaling (p<10-3), and downregulated CD4+T cell expression (p=0.009), compared to their asymptomatic counterparts. In BioMe, 562 (2.0%) were V122I carriers, demonstrating associations with heart failure (1.71 [1.23-2.39]; p=0.0014), amyloidosis (20.79 [8.42-51.31]; p=4.67×10-11), secondary/extrinsic cardiomyopathies (17.73 [7.25-43.37]; p=2.97×10-10), peripheral nerve disorders (4.14 [2.42-7.09]; p=2.26×10-7), primary angle-closure glaucoma (8.03 [3.15-20.46]; p=1.27×10-5), malignant neoplasm of the female breast (4.48 [2.23-9.00]; p=2.48×10-5), fracture of tibia and fibula (8.42 [3.25-21.89]; p=1.19×10-5), and Carpal tunnel syndrome (2.62 [1.68-4.11]; p=2.44×10-5). Echocardiographic presentations included higher LVEDV (15.87 [9.63-22.10]; p=6.04×10-7) and LA length (1.52 [0.69-2.35]; p=3.31×10-4). Race-stratified associations identified that AA presented more severe cardiac abnormalities than HA. This study identified inflammatory biomarkers upregulated in symptomatic V30M carriers and phenotypic/echocardiographic traits associated with V122I, representing comorbidities of hATTR pathology. Such markers can provide the basis for future improvements in diagnostic regimes to deliver early therapies.

The increasing intensity and frequency of water scarcity, carbon emissions, and climate risks pose critical challenges necessitating increased uptake of and a paradigm shift to energy- and climate-smart water desalination processes. This study employs metrics and a decision framework to enable and accelerate the energy efficiency, decarbonization, and cost-effectiveness of water desalination processes. As an essential step, we analyze various Renewable Energy (RE) sources, such as photovoltaic, wind, concentrated solar power, geothermal, and hydro energy; in addition, we examine battery storage systems to address the intermittency challenges associated with solar and wind energy. The feasibility of these diverse RE systems was assessed at four (4) mid-to-large scale U.S. desalination plants using operating plant and weather/environmental data, establishing optimization functions and constraints. In this research, to facilitate a comprehensive Energy Management System (EMS), we align RE generation with the anticipated energy demand of the plants. Machine Learning (ML) models, including SARIMA, Random Forest, XGBoost, and Gradient Boosting, are employed for forecasting water production, energy consumption, and long-term weather. The results show that Artificial Intelligence (AI) models, notably Gradient Boosting and an innovative XGBoost average method, demonstrated high accuracy in forecasting critical variables for RE systems in water desalination, with a normalized Root Mean Square Error of less than 10% for key metrics. This study can serve as a basis to optimize the mix of hybrid RE systems to minimize cost and carbon emissions.

Alzheimer’s disease (AD) is the most common neurodegenerative disease in the United States (US). Animal models, specifically mouse models have been developed to better elucidate disease mechanisms and test therapeutic strategies for AD. A large portion of effort in the field was focused on developing transgenic (Tg) mouse models through over-expression of genetic mutations associated with familial AD (FAD) patients. Newer generations of mouse models through knock-in (KI)/knock-out (KO) or CRISPR gene editing technologies, have been developed for both familial and sporadic AD risk genes with the hope to more accurately model proteinopathies without over-expression of human AD genes in mouse brains. In this review, we summarized the phenotypes of a few commonly used as well as newly developed mouse models in translational research laboratories including the presence or absence of key pathological features of AD such as amyloid and tau pathology, synaptic and neuronal degeneration as well as cognitive and behavior deficits. In addition, advantages and limitations of these AD mouse models have been elaborated along with discussions of any sex-specific features. More importantly, the omics data from available AD mouse models have been analyzed to categorize molecular signatures of each model reminiscent of human AD brain changes, with the hope to guide future selection of most suitable models for specific research questions to be addressed in the AD field.

ABSTRACT Multi-styles string education includes musical practices from close to 30 identified styles. Adding the multi-styles approach into curriculum may not be easy: Pedagogies may be different from those needed for Western classical music, the primary style most practicing teachers have studied. As such, the decision to start incorporating multi-styles into curriculum can present a challenge as an unsupported leap in an unknown direction. This study examined how string teachers implement a multi-styles approach into their P-12 string class curricula. Questions guiding this study are: (1) How do school orchestra teachers describe their visions for a multi-styles approach to curriculum? (2) What planning and action is taken in order to enact their visions, including programme and infrastructure changes, additional resources, professional development? Data were collected through a qualitative interview design and analysed through a queer theory lens. Themes include blurred visions of multi-styles, stepwise shifts in curriculum and pedagogy through a both/and student-centred approach, and the utilisation of human resources. We found that multi-styles is less a curriculum and more a philosophical approach. However, the name itself of this approach caused dissonance, leading towards a vague future. Implications provide new avenues of research and practice in this line of inquiry.

Desalination, when combined with energy-efficient operations and clean energy, has significant potential to address water security, resilience, and costs. Energy demands of desali-nation must be met, yet current inefficiencies increase costs, and the use of non-renewable sources exacerbates climate change. This research seeks to fill these gaps by advancing integrated water-energy system decarbonization, using data from multiple U.S. desalination plants while defining optimization functions and constraints to reduce energy costs and carbon emissions. A framework is designed for the optimal sizing of grid-connected hybrid renewable energy and storage systems using Artificial Intelligence algorithms to utilize at least 50% renewables.