The alien A invasive does not infiltrate the native soil rhizobial symbiosis networks.

Using -rhizobia- interaction networks, we address first the soil invasion success of , and second, we report either -rhizobia partnership should form an isolated module within the symbiosis interaction network. Different indexes were used to determine model invasion success and the network topology. Our results indicated that invasion decreased soil microbial biomass, basal respiration, and enzymatic activities.

Integration of multi-omics and benchmark dose modeling to support adverse outcome pathways.

Background: Recent advancements in omics and benchmark dose (BMD) modeling have facilitated identifying the dose required for a predetermined change in a response (e.g. gene or protein change) that can be used to establish acceptable dose levels for hazardous exposures.

Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.

Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events.

Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time.

Background And Objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis.