Microbial-mineral interaction experiments and density functional theory calculations revealing accelerating effects for the dolomitization of calcite surfaces by organic components.

Carbonates represent major sedimentary rocks in on the continental and oceanic crust of Earth and are often closely related to microbial activities. However, the origin of magnesium-containing carbonates, such as dolomites, has not yet been fully resolved and was debated for many years. In order to reveal the specific role of organic components and microbes on the precipitation of magnesium ions, different dolomitization experiments were carried out with various setups for the presence of eight amino acids and microbes.

KaryoNet: Chromosome Recognition With End-to-End Combinatorial Optimization Network.

Chromosome recognition is a critical way to diagnose various hematological malignancies and genetic diseases, which is however a repetitive and time-consuming process in karyotyping. To explore the relative relation between chromosomes, in this work, we start from a global perspective and learn the contextual interactions and class distribution features between chromosomes within a karyotype. We propose an end-to-end differentiable combinatorial optimization method, KaryoNet, which captures long-range interactions between chromosomes with the proposed Masked Feature Interaction Module (MFIM) and conducts label assignment in a flexible and differentiable way with Deep Assignment Module (DAM).

Trustworthy learning with (un)sure annotation for lung nodule diagnosis with CT.

Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level.

Alleviating Class-Wise Gradient Imbalance for Pulmonary Airway Segmentation.

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels.

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT.

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules.

Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation.

Purpose: Volumetric pancreas segmentation can be used in the diagnosis of pancreatic diseases, the research about diabetes and surgical planning. Since manual delineation is time-consuming and laborious, we develop a deep learning-based framework for automatic pancreas segmentation in three dimensional (3D) medical images.

Methods: A two-stage framework is designed for automatic pancreas delineation.

Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks.

Chromosome classification is critical for karyotyping in abnormality diagnosis. To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks. The approach consists of one global-scale network (G-Net) and one local-scale network (L-Net).

Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.

Purpose: Segmentation of pulmonary nodules is critical for the analysis of nodules and lung cancer diagnosis. We present a novel framework of segmentation for various types of nodules using convolutional neural networks (CNNs).

Methods: The proposed framework is composed of two major parts.

Photo-controlled aptamers delivery by dual surface gold-magnetic nanoparticles for targeted cancer therapy.

Dual surfaced dumbbell-like gold magnetic nanoparticles (Au-FeO) were synthesized for targeted aptamers delivery. Their unique biological properties were characterized as a smart photo-controlled drug carrier. DNA aptamers targeting vascular endothelial growth factor (VEGF) were assembled onto the surface of Au-FeO by electrostatic absorption.