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Perhaps one of the worst disasters in United States history, Hurricane Katrina is expected to have a lasting impact on the economies of Louisiana, Mississippi, Alabama, and Florida with losses in the billions of dollars. Given that the economic foundation of the approximately 600 schools and libraries affected was far from ideal before the hurricane, the prospect of recovery for technology and media infrastructures seemed dim. The authors set about this research with little or no knowledge of the impact of Katrina on technology and media infrastructures. It was the authors' intention to provide a descriptive snapshot of the damage done and the relief received. To accomplish this goal, an online form was created, and the technology contacts for school districts listed in the Federal Emergency Management Agency's (FEMA) zones for individual assistance were contacted. In these zones, FEMA, along with other federal, state, and local agencies, was providing aid to individual citizens and households in the form of grants, loans, tax relief, counseling, as well as emergency food and shelter. The link for the survey was mailed out to the technology contacts for each of the parishes/districts, and each district was contacted by phone and urged to participate. A total of 77 parishes were targeted, and of these, 41 responded. (Contains 4 figures.)

Introduction: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. Methods: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Results: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. Discussion: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment. [ABSTRACT FROM AUTHOR]

Abstract: Lactoferrin (LF) is a mammalian host defense glycoprotein with diverse biological activities. Peptides derived from the cationic region of LF possess cytotoxic activity against cancer cells in vitro and [...]

Large scientific data centers have recently begun providing a number of different types of data storage in order to satisfy the various needs of their users. Users with interactive accounts, for example, might want a POSIX interface for easy access to the data from their interactive machines. Grid computing sites, on the other hand, likely need to provide an X509-based storage protocol, like SRM and GridFTP, since the data management system is built upon them. Meanwhile, an experiment producing large amounts of data typically demands a service that provides archival storage for the safe keeping of their unique data. To access these various types of data, users must use specific sets of commands tailored to their respective storage, making access to their data complex and difficult. BNLBox is an attempt to provide a unified and easy to use storage service for all BNL users, to store their important documents, code and data. It is a cloud storage system with an intuitive web interface for novice users. It provides an automated synchronization feature that enables users to upload data to their cloud storage without manual intervention, freeing them to focus on analysis rather than data management software. It provides a POSIX interface for local interactive users, which simplifies data access from batch jobs as well. At the same time, it also provides users with a straightforward mechanism for archiving large data sets for later processing. The storage space can be used for both code and data within the compute job environment. This paper will describe various aspects of the BNLBox storage service.