Hierarchical Encoder-Decoder With Soft Label-Decomposition for Mitochondria Segmentation in EM Images.

Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information.

LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.

Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing.

HIVE-Net: Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images.

Background And Objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics.

Case Report: Opportunities for Treatment of Severe COVID-19 Patients-Lessons From a Death Case.

With the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the incidence of coronavirus disease (COVID-19) increases each day. To date, there is no specific anti-SARS-CoV-2 drug. The usual approach to treating COVID-19 is treating its symptoms.

HDC-Net: Hierarchical Decoupled Convolution Network for Brain Tumor Segmentation.

Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for clinical treatment decision and surgical planning. Due to the large diversity of the tumors and complex boundary interactions between sub-regions, it is of a great challenge. Besides accuracy, resource constraint is another important consideration.

Mitochondria Segmentation From EM Images via Hierarchical Structured Contextual Forest.

Delineation of mitochondria from electron microscopy (EM) images is crucial to investigate its morphology and distribution, which are directly linked to neural dysfunction. However, it is a challenging task due to the varied appearances, sizes and shapes of mitochondria, and complicated surrounding structures. Exploiting sufficient contextual information about interactions in extended neighborhood is crucial to address the challenges.

Nourseothricin N-acetyl transferase (NAT), a new selectable marker for nuclear gene expression in .

Background: is a unicellular green alga, which is a most commonly used model organism for basic research and biotechnological applications. Generation of transgenic strains, which usually requires selectable markers, is instrumental in such studies/applications. Compared to other organisms, the number of selectable markers is limited in this organism.

Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis.

Multimodal data fusion has shown great advantages in uncovering information that could be overlooked by using single modality. In this paper, we consider the integration of high-dimensional and data for Alzheimer's disease (AD) diagnosis. With a focus on taking advantage of both phenotype and genotype information, a novel structured sparsity, defined by -norm ( > 1), regularized multiple kernel learning method is designed.

Effects of terrestrial and marine organic matters on deposition of dechlorane plus (DP) in marine sediments from the Southern Yellow Sea, China: Evidence from multiple biomarkers.

As an emerging halogenated organic contaminant, Dechlorane Plus (DP) was scarcely reported in marine environments, especially in China. In this work, 35 surface sediments and a sediment core were collected across the Southern Yellow Sea (SYS) to comprehensively explore the spatio-temporal distribution and possible migration pathway of DP. DP concentrations ranged from 14.

Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.

Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label.

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

Purpose: Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images.

Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.

The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution.

Distribution and region-specific sources of Dechlorane Plus in marine sediments from the coastal East China Sea.

Dechlorane Plus (DP) is a highly chlorinated flame retardant and found to be ubiquitously present in the environment. We reported here the first record of DP in sediments from the coastal East China Sea (ECS). DP was detected in most of the surface sediments, and the concentrations ranged from 14.

Multi-surface analysis for human action recognition in video.

The majority of methods for recognizing human actions are based on single-view video or multi-camera data. In this paper, we propose a novel multi-surface video analysis strategy. The video can be expressed as three-surface motion feature (3SMF) and spatio-temporal interest feature.

Characterizing distributions, composition profiles, sources and potential health risk of polybrominated diphenyl ethers (PBDEs) in the coastal sediments from East China Sea.

Sediment samples (n = 20) were collected from Yangtze River Estuary (YRE) and the adjacent East China Sea (ECS) inner shelf to explore spatial and temporal distributions, environmental fate, sources and potential health risk of polybrominated diphenyl ethers (PBDEs). Concentrations of BDE-209 and total 7 PBDEs (without BDE-209; ∑7PBDEs) ranged from 62.3 to 1758 pg g(-1) and from 36.

[Determination of eight polybrominated diphenyl ethers in marine sediments by ultrasonically assisted alkaline degradation extraction and gas chromatography-electron capture detection].

For determination of the eight polybrominated diphenyl ethers (PBDEs) in marine sediments based on gas chromatography-electron capture detection (GC-ECD), a rapid and effective method for simultaneous sample extraction and purification was developed, in which ultrasonically assisted alkaline hydrolysis was combined with solvent extraction. The sediment sample was processed in an ultrasonic bath in 2. 00 mol/L NaOH-methanol solution for 30 min, and subsequently extracted by n-hexane.

3D liver segmentation using multiple region appearances and graph cuts.

Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty.

Current levels, composition profiles, source identification and potentially ecological risks of polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) in the surface sediments from Bohai Sea.

Polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were analyzed to assess the environmental quality in the surface sediments from Bohai Sea (BS), China. Concentrations of ∑37PCBs, ∑7PBDEs and BDE-209 were 0.157-1.

Polybrominated diphenyl ethers in sediments from the Southern Yellow Sea: Concentration, composition profile, source identification and mass inventory.

The Southern Yellow Sea (SYS) is believed to be influenced by the contaminants from mainland China and the Korean peninsula. Here we report the first record about concentrations of polybrominated diphenyl ethers (PBDEs) in the sediments of the SYS. The concentrations of ∑(7)PBDEs (BDE-28, 47, 99, 100, 153, 154, 183) and BDE-209 were 0.

A region-appearance-based adaptive variational model for 3D liver segmentation.

Purpose: Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge- and region-based information.

Methods: In this semiautomatic method, the evolving surface is principally attracted to strong edges but is facilitated by the region-based information where edge information is missing.

Development of hyperbranched polymers with non-covalent interactions for extraction and determination of aflatoxins in cereal samples.

A novel approach for assembling homogeneous hyperbranched polymers based on non-covalent interactions with aflatoxins was developed; the polymers were used to evaluate the extraction of aflatoxins B1, B2, G1 and G2 (AFB1, AFB2, AFG1 and AFG2) in simulant solutions. The results showed that the extraction efficiencies of three kinds of synthesized polymers for the investigated analytes were not statistically different; as a consequence, one of the representative polymers (polymer I) was used as the solid-phase extraction (SPE) sorbent to evaluate the influences of various parameters, such as desorption conditions, pH, ionic strength, concentration of methanol in sample solutions, and the mass of the sorbent on the extraction efficiency. In addition, the extraction efficiencies for these aflatoxins were compared between the investigated polymer and the traditional sorbent C18.

[Nano-silver colloids prepared by electrolysis and research on its SERS activity].

Three kinds of nano-silver colloids have been prepared by electrolysis of silver rod using sodium citrate solution and AgNO3 mixed with polyvinyl alcohol solution as electrolyte and applying 7 V direct current for one hour. Nano-silver colloids have been investigated by means of TEM, absorption spectrum, electrophoresis experiment and SERS. The particle size ranges roughly from 20 nm to 25 nm (spheroid) for sample 1, from 20 nm to 35 nm (spheroid) for sample 2, and from 30 to 80 (many-sided) for sample 3, featuring absorption maximum at 404, 421 and 434 nm, respectively.