Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions.

Deep learning models can predict uptake of emerging contaminants in plants with improved accuracy because they leverage advanced data-driven approaches to capture non-linear relationships that traditional models struggle to address. Traditional models suffer from low accuracy in predicting transpiration stream concentration factor (TSCF) and root concentration factor (RCF). This study applied deep neural networks (DNN), recurrent neural networks (RNN), and long short-term memory (LSTM) to enhance the accuracy of predictive models for TSCF and RCF.

An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio.

Background: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams.

Purpose: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study.

Materials And Methods: In SECT study, phantoms with effective diameters of 9.

Selective suppression of de novo SARS-CoV-2 vaccine antibody responses in patients with cancer on B cell-targeted therapy.

We assessed vaccine-induced antibody responses to the SARS-CoV-2 ancestral virus and Omicron variant before and after booster immunization in 57 patients with B cell malignancies. Over one-third of vaccinated patients at the pre-booster time point were seronegative, and these patients were predominantly on active cancer therapies such as anti-CD20 monoclonal antibody. While booster immunization was able to induce detectable antibodies in a small fraction of seronegative patients, the overall booster benefit was disproportionately evident in patients already seropositive and not receiving active therapy.

COVID-19 Case Investigation and Contact Tracing in New York City, June 1, 2020, to October 31, 2021.

Importance: Contact tracing is a core strategy for preventing the spread of many infectious diseases of public health concern. Better understanding of the outcomes of contact tracing for COVID-19 as well as the operational opportunities and challenges in establishing a program for a jurisdiction as large as New York City (NYC) is important for the evaluation of this strategy.

Objective: To describe the establishment, scaling, and maintenance of Trace, NYC's contact tracing program, and share data on outcomes during its first 17 months.