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Deep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFDevelopment and laboratory evaluation of a novel IoT-based electric-driven metering system for high precision garlic planter.
PLoS One
January 2025
College of Engineering, South China Agricultural University, Guangzhou, China.
In order to address many issues, such as the inconsistent and unreliable seeding process in traditional mechanical garlic seed metering systems (SMS), as well as the lack of ability to monitor the effectiveness of the seeding, a highly accurate electric-driven metering system (EDMS) was developed and created specifically for garlic seed planters. This study provided a description of the overall structure and functioning principle, as well as an analysis of the mechanism for smooth transit and delivery. A combination of an infrared (IR) sensor, Arduino Mega board, stepper motor, speed sensor, and a Wi-Fi module was employed to operate the EDMS, as well as monitor and count the quantity of garlic seeds during the planting process and determine the qualified rate (QR) and missing rate (MR).
View Article and Find Full Text PDFField-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.
PLoS One
January 2025
Rice Department, Bangkok, Thailand.
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authorities. This study introduces a new pipeline specifically designed for detecting BLB in rice fields using unmanned aerial vehicle (UAV) imagery.
View Article and Find Full Text PDFImpact of low-carbon economic policies on the corporate environmental responsibility model in China.
PLoS One
January 2025
School of Finance and Economics, Hainan Vocational University of Science and Technology, Haikou, China.
This study investigates the impact of low-carbon economic policies on Corporate Environmental Responsibility (CER) in Chinese A-share listed companies, with a particular focus on the role of financing constraints as a mediating factor. Despite a decrease in environmental pollution incidents in 2022, the economic and social impacts of such incidents remain significant, highlighting the need for stronger environmental governance. Building upon previous research, this study utilizes data from the Shanghai and Shenzhen stock exchanges (2010-2020) and employs a Difference-in-Differences (DID) model to assess the effects of low-carbon economic policies introduced in 2016 on CER.
View Article and Find Full Text PDFNormalized Protein-Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening.
J Chem Inf Model
January 2025
Department of Chemistry, New York University, New York, New York 10003, United States.
Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks.
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