Tris(2-ethylhexyl) phosphate induces cytotoxicity in TM3 Leydig cells by modulating autophagy and endoplasmic reticulum stress.

Tris (2-ethylhexyl) phosphate (TEHP) is a frequently used organophosphorus flame retardant with significant ecotoxicity and widespread human exposure. Recent research indicates that TEHP has reproductive toxicity. However, the precise cell mechanism is not enough understood.

Perfluorooctanoic acid induces Leydig cell injury via inhibition of autophagosomes formation and activation of endoplasmic reticulum stress.

Perfluorooctanoic acid (PFOA) is a man-made chemical broadly distributed in various ecological environment and human bodies, which poses potential health risks. Its toxicity, especially the male reproduction toxicity has drawn increasing attention due to declining birth rates in recent years. However, how PFOA induces male reproductive toxicity remains unclear.

Insight into Tetrabromobisphenol A-Associated Liver Transcriptional Landscape via Single Cell RNA Sequencing.

In recent years, there has been growing concern over the rising incidence of liver diseases, with increasing exposure to environmental toxins as a significant contributing factor. However, the mechanisms of liver injury induced by environmental pollutants are largely unclear. Here, using tetrabromobisphenol A (TBBPA), a widely used brominated flame retardant, as an example, environmental toxin-induced liver toxicity in mice is characterized via single-cell sequencing technology.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding.

Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. This task has achieved significant momentum in the computer vision community as it enables activity grounding beyond pre-defined activity classes by utilizing the semantic diversity of natural language descriptions. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization).

Personalized Latent Structure Learning for Recommendation.

In recommender systems, users' behavior data are driven by the interactions of user-item latent factors. To improve recommendation effectiveness and robustness, recent advances focus on latent factor disentanglement via variational inference. Despite significant progress, uncovering the underlying interactions, i.

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression.

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis.

Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice.

Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation.

Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI.

Background: Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.

Purpose: To develop a DL model for automated classification of MESCC on MRI.

Design and Analysis of a 2-DOF Electromagnetic Actuator with an Improved Halbach Array for the Magnetic Suspension Platform.

For large bearing capacity and low current consumption of the magnetic suspension platform, a 2-DOF electromagnetic actuator with a new structure of halbach array is proposed to improve driving force coefficients. The structure and the working principle are introduced. An accurate sub domain model of the new structure is established to accurately and rapidly calculate the magnetic field distribution for obtaining the parameters and performance of the electromagnetic actuators.

A Novel 2-DOF Lorentz Force Actuator for the Modular Magnetic Suspension Platform.

The modular magnetic suspension platform depends on multi degree of freedom of Lorentz force actuators for large bearing capacity, high precision positioning and structure miniaturization. To achieve the integration of vertical driving force and horizontal driving force, a novel 2- (two degrees-of-freedom) DOF Lorentz force actuator is developed by designing the pose of the windings and permanent magnets (PMs). The structure and the working principle are introduced.

VPModel: High-Fidelity Product Simulation in a Virtual-Physical Environment.

In the development of a new product, the design team must describe the expected effects of the final products to potential users and stakeholders. However, existing prototyping tools can only present a product imperfectly, due to limitations at different levels. Specifically, the physical product model, which may be the product of 3D printing, could lack a visual interface; the presentation of the product through modeling software such as Rhinoceros 3D does not provide good realistic tactile perception; or the interface platforms, such as Axure RP, used to display the interactive effects differ from those to be used in the actual operation.