Mesenchymal Stem Cells Improve Wound Healing In Vivo via Early Activation of Matrix Metalloproteinase-9 and Vascular Endothelial Growth Factor

We investigated the effects of mesenchymal stem cells (MSCs) on wound healing using a three-dimensional (3D) collagen gel scaffold. Three circular full-thickness skin defects were created on the back of Sprague-Dawley rats. One site was covered with a 3D collagen gel containing 2 × 106 MSCs (MSCs+/3D collagen+). Another site was replaced with a 3D collagen gel without MSCs and the third site was left empty. The wound size was significantly reduced in the MSCs+/3D collagen+ sites. MSCs+/3D collagen+ sites exhibited the most neovascularization. FISH showed that Y-chromosome possessing cells were found within the dermis of MSCs+/3D collagen+ sites. Gelatin zymography revealed that the most intense expression of MMP-9 was detected early in the MSCs+/3D collagen+ sites. Our results indicate that MSCs upregulate the early expression of MMP-9 which induces the early mobilization of VEGF. Thus, MSCs appear to accelerate significantly wound healing via early activation of MMP-9 and VEGF

Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires

Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization

Abnormal driver states, particularly have been major concerns for road safety, emphasizing the importance of accurate drowsiness detection to prevent accidents. Electroencephalogram (EEG) signals are recognized for their effectiveness in monitoring a driver's mental state by monitoring brain activities. However, the challenge lies in the requirement for prior calibration due to the variation of EEG signals among and within individuals. The necessity of calibration has made the brain-computer interface (BCI) less accessible. We propose a practical generalized framework for classifying driver drowsiness states to improve accessibility and convenience. We separate the normalization process for each driver, treating them as individual domains. The goal of developing a general model is similar to that of domain generalization. The framework considers the statistics of each domain separately since they vary among domains. We experimented with various normalization methods to enhance the ability to generalize across subjects, i.e. the model's generalization performance of unseen domains. The experiments showed that applying individual domain-specific normalization yielded an outstanding improvement in generalizability. Furthermore, our framework demonstrates the potential and accessibility by removing the need for calibration in BCI applications.Comment: Submitted to 2024 12th IEEE International Winter Conference on Brain-Computer Interfac

Coherence of a field-gradient-driven singlet-triplet qubit coupled to many-electron spin states in 28Si/SiGe

Engineered spin-electric coupling enables spin qubits in semiconductor nanostructures to be manipulated efficiently and addressed individually. While synthetic spin-orbit coupling using a micromagnet is widely used for driving qubits based on single spins in silicon, corresponding demonstration for encoded spin qubits is so far limited to natural silicon. Here, we demonstrate fast singlet-triplet qubit oscillation (~100 MHz) in a gate-defined double quantum dot in $^{28}$Si/SiGe with an on-chip micromagnet with which we show the oscillation quality factor of an encoded spin qubit exceeding 580. The coherence time $\textit{T}_{2}$* is analyzed as a function of potential detuning and an external magnetic field. In weak magnetic fields, the coherence is limited by fast noise compared to the data acquisition time, which limits $\textit{T}_{2}$* < 1 ${\mu}$s in the ergodic limit. We present evidence of sizable and coherent coupling of the qubit with the spin states of a nearby quantum dot, demonstrating that appropriate spin-electric coupling may enable a charge-based two-qubit gate in a (1,1) charge configuration

Erratum

"Erratum on the article ‘A bi-level framework for pricing of high-occupancy toll lanes’." Transport, 29(4), p. 46