Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis.

Background: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB.

Methods: Thirteen datasets were downloaded from the gene expression omnibus (GEO) database.

Lightweight deep learning model for underwater waste segmentation based on sonar images.

In recent years, the rapid accumulation of marine waste not only endangers the ecological environment but also causes seawater pollution. Traditional manual salvage methods often have low efficiency and pose safety risks to human operators, making automatic underwater waste recycling a mainstream approach. In this paper, we propose a lightweight multi-scale cross-level network for underwater waste segmentation based on sonar images that provides pixel-level location information and waste categories for autonomous underwater robots.

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several deep learning methods based on RGB images to achieve waste detection and recognition. Considering the limitations of color images, we propose an efficient multi-modal learning solution for pavement waste detection and recognition.

SLC7A11, a potential immunotherapeutic target in lung adenocarcinoma.

SLC7A11 has significant translational value in cancer treatment. However, there are few studies on whether SLC7A11 affects the immune status of lung adenocarcinoma (LUAD). Information on SLC7A11 expression and its impact on prognosis was obtained from the cancer genome atlas and gene expression omnibus databases.

Nitrogen removal efficiency of surface flow constructed wetland for treating slightly polluted river water.

Restoration and water quality improvement of malodorous as well as slightly polluted rivers have been the global focus for environmental protection research and the development and construction of sponge cities. To date, constructed wetlands have been proven to be one of efficient methods to improve water quality. Nitrogen removal efficiency is a crucial indicator for the performance evaluation in slightly polluted river water treatment.