is a Potential Biomarker and Therapeutic Target for Glioblastoma.

Glioblastoma (GBM) is a type of central nervous system malignancy. In our study, we determined the effect of in GBM patients through The Cancer Genome Atlas (TCGA) data analysis, and studied the effects of on GBM cell function to estimate its potential as a therapeutic target. Gene expression profiles of glioblastoma cohort were acquired from TCGA database and analyzed to look for central genes that may serve as GBM therapeutic targets.

ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement.

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage.

Effects of metformin on Sonic hedgehog subgroup medulloblastoma progression: and studies.

Metformin is a first-line drug for type 2 diabetes, and its anticancer effects have also been widely studied in recent years. The Sonic hedgehog (Shh) signaling pathway is involved in the initiation and progression of medulloblastoma. In order to develop a new treatment strategy for medulloblastoma (MB), this study investigated the inhibitory effect of metformin on MB and the underlying mechanism of metformin on the Shh signaling pathway.

Effects of bis (2-butoxyethyl) phthalate on adrenocortical function in male rats in puberty partially via down-regulating NR5A1/NR4A1/NR4A2 pathways.

Phthalates may interfere with the biosynthesis of steroid hormones in the adrenal cortex. Bis (2-butoxyethyl) phthalate (BBOP) is a phthalate containing oxygen atoms in the alcohol moiety. In this study, 35-day-old male Sprague-Dawley rats were daily gavaged with BBOP (0, 10, 100, 250, and 500 mg/kg body weight) for 21 days.

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location.

Effects of bis(2-butoxyethyl) phthalate exposure in utero on the development of fetal Leydig cells in rats.

Phthalates are plasticizers widely found in the environment. They are potential endocrine disruptors. Bis(2-butoxyethyl) phthalate (BBOP) is a unique phthalate that contains oxygen atoms in the carbon backbone.

In utero exposure to dipentyl phthalate disrupts fetal and adult Leydig cell development.

Phthalates as plasticizers are widely used in many consumer products. Dipentyl phthalate (DPeP) is one of phthalates. However, there are currently few data on whether DPeP exposure affects rat Leydig cell development.

Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs.

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency.

Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning.

Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality.