Generative adversarial local density-based unsupervised anomaly detection.

Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.

Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.

Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton.

A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects.

The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios.

promotes synchronous multiple primary lung cancer progression through apoptosis regulation.

Background: Dysbiosis of the lung microbiome can contribute to the initiation and progression of lung cancer. Synchronous multiple primary lung cancer (sMPLC) is an increasingly recognized subtype of lung cancer characterized by high morbidity, difficulties in early detection, poor prognosis, and substantial clinical challenges. However, the relationship between sMPLC pathogenesis and changes in the lung microbiome remains unclear.

Microstructural and functional connectivity changes of decision-related brain networks in end-stage kidney disease patients undergoing peritoneal dialysis.

Background: We aimed to explore changes in decision-related brain microstructure, brain functional activities, and functional connectivity, and their correlations with cognitive function in end-stage kidney disease (ESKD) patients undergoing peritoneal dialysis (PD). Furthermore, the impact of dialysis on these changes was examined.

Methods: Thirty ESKD patients undergoing PD, 20 chronic kidney disease (CKD) stage 5 patients without dialysis (predialysis CKD stage 5), and 30 healthy controls (HC) were recruited for the study.