Identifying risk clusters for African swine fever in Korea by developing statistical models.

Introduction: African swine fever (ASF) is a disease with a high mortality rate and high transmissibility. Identifying high-risk clusters and understanding the transmission characteristics of ASF in advance are essential for preventing its spread in a short period of time. This study investigated the spatial and temporal heterogeneity of ASF in the Republic of Korea by analyzing surveillance data on wild boar carcasses.

Corrigendum: Detection of COVID-19 epidemic outbreak using machine learning.

[This corrects the article DOI: 10.3389/fpubh.2023.

Deciphering the History of ERK Activity from Fixed-Cell Immunofluorescence Measurements.

The Ras/ERK pathway drives cell proliferation and other oncogenic behaviors, and quantifying its activity is of high interest in cancer diagnosis and therapy. Pathway activation is often assayed by measuring phosphorylated ERK. However, this form of measurement overlooks dynamic aspects of signaling that can only be observed over time.

Detection of COVID-19 epidemic outbreak using machine learning.

Background: The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.

Objective: In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.

siVAE: interpretable deep generative models for single-cell transcriptomes.

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference.

Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data.

We motivate and present , which combines the variational autoencoder framework of with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions.

The economic impact of COVID-19 interventions: A mathematical modeling approach.

Prior to vaccination or drug treatment, non-pharmaceutical interventions were almost the only way to control the coronavirus disease 2019 (COVID-19) epidemic. After vaccines were developed, effective vaccination strategies became important. The prolonged COVID-19 pandemic has caused enormous economic losses worldwide.

Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach.

Coronavirus disease 2019 (COVID-19) vaccination has recently started worldwide. As the vaccine supply will be limited for a considerable period of time in many countries, it is important to devise the effective vaccination strategies that reduce the number of deaths and incidence of infection. One of the characteristics of COVID-19 is that the symptom, severity, and mortality of the disease differ by age.

Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach.

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020. The first case of COVID-19 was reported in December 2019 in Wuhan, China. Since then, there have been more than 21 million incidences and 761 thousand casualties worldwide as of 16 August 2020.

Systems-Level Properties of EGFR-RAS-ERK Signaling Amplify Local Signals to Generate Dynamic Gene Expression Heterogeneity.

Intratumoral heterogeneity is associated with aggressive tumor behavior, therapy resistance, and poor patient outcomes. Such heterogeneity is thought to be dynamic, shifting over periods of minutes to hours in response to signaling inputs from the tumor microenvironment. However, models of this process have been inferred from indirect or post-hoc measurements of cell state, leaving the temporal details of signaling-driven heterogeneity undefined.

Mild weight loss reduces inflammatory cytokines, leukocyte count, and oxidative stress in overweight and moderately obese participants treated for 3 years with dietary modification.

Obesity-induced oxidative stress and inflammation are involved in the pathogenesis of cardiovascular disease. We investigated whether diet-induced, long-term, mild weight loss improved proinflammatory cytokine levels, leukocyte count, and oxidative stress. Overweight/obese participants (25 ≤ body mass index < 34 kg/m(2), N = 122, 30-59 years) joined a 3-year-long clinical intervention involving daily 100-kcal calorie deficits.