The Southern University System is a system of public historically black universities in the U.S. state of Louisiana. Its headquarters are at the Joseph Samuel Clark Administration Building on the Southern University campus in Baton Rouge. The Southern University System is the only historically black college system in the United States.
This document presents the initial scientific case for upgrading the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) to 22 GeV. It is the result of a community effort, incorporating insights from a series of workshops conducted between March 2022 and April 2023. With a track record of over 25 years in delivering the world's most intense and precise multi-GeV electron beams, CEBAF's potential for a higher energy upgrade presents a unique opportunity for an innovative nuclear physics program, which seamlessly integrates a rich historical background with a promising future. The proposed physics program encompass a diverse range of investigations centered around the nonperturbative dynamics inherent in hadron structure and the exploration of strongly interacting systems. It builds upon the exceptional capabilities of CEBAF in high-luminosity operations, the availability of existing or planned Hall equipment, and recent advancements in accelerator technology. The proposed program cover various scientific topics, including Hadron Spectroscopy, Partonic Structure and Spin, Hadronization and Transverse Momentum, Spatial Structure, Mechanical Properties, Form Factors and Emergent Hadron Mass, Hadron-Quark Transition, and Nuclear Dynamics at Extreme Conditions, as well as QCD Confinement and Fundamental Symmetries. Each topic highlights the key measurements achievable at a 22 GeV CEBAF accelerator. Furthermore, this document outlines the significant physics outcomes and unique aspects of these programs that distinguish them from other existing or planned facilities. In summary, this document provides an exciting rationale for the energy upgrade of CEBAF to 22 GeV, outlining the transformative scientific potential that lies within reach, and the remarkable opportunities it offers for advancing our understanding of hadron physics and related fundamental phenomena.
Ischemic stroke is the leading cause of serious long-term disability and the 5th leading cause of death in the United States. Revascularization of the occluded cerebral artery, either by thrombolysis or endovascular thrombectomy, is the only effective, clinically-approved stroke therapy. Several potentially neuroprotective agents, including glutamate antagonists, anti-inflammatory compounds and free radical scavenging agents were shown to be effective neuroprotectants in preclinical animal models of brain ischemia. However, these compounds did not demonstrate efficacy in clinical trials with human patients following stroke. Proposed reasons for the translational failure include an insufficient understanding on the cellular and molecular pathophysiology of ischemic stroke, lack of alignment between preclinical and clinical studies and inappropriate design of clinical trials based on the preclinical findings. Therefore, novel neuroprotective treatments must be developed based on a clearer understanding of the complex spatiotemporal mechanisms of ischemic stroke and with proper clinical trial design based on the preclinical findings from specific animal models of stroke. We and others have demonstrated the clinical potential for neuregulin-1 (NRG-1) in preclinical stroke studies. NRG-1 significantly reduced ischemia-induced neuronal death, neuroinflammation and oxidative stress in rodent stroke models with a therapeutic window of >13 h. Clinically, NRG-1 was shown to be safe in human patients and improved cardiac function in multisite phase II studies for heart failure. This review summarizes previous stroke clinical candidates and provides evidence that NRG-1 represents a novel, safe, neuroprotective strategy that has potential therapeutic value in treating individuals after acute ischemic stroke.
Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.
