Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images.

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg).

Hierarchical cumulative network for unsupervised medical image registration.

Unsupervised deep learning techniques have gained increasing popularity in deformable medical image registration However, existing methods usually overlook the optimal similarity position between moving and fixed images To tackle this issue, we propose a novel hierarchical cumulative network (HCN), which explicitly considers the optimal similarity position with an effective Bidirectional Asymmetric Registration Module (BARM). The BARM simultaneously learns two asymmetric displacement vector fields (DVFs) to optimally warp both moving images and fixed images to their optimal similar shape along the geodesic path. Furthermore, we incorporate the BARM into a Laplacian pyramid network with hierarchical recursion, in which the moving image at the lowest level of the pyramid is warped successively for aligning to the fixed image at the lowest level of the pyramid to capture multiple DVFs.

Design and Strengthening of Superhydrophobic Coatings: The Influence of Intermediate Layers.

The employment of intermediate layer technology to improve the mechanical stability of superhydrophobic coatings (SHCs) is an acknowledged tool, but the mechanism by which intermediate layers, especially different ones, affect superhydrophobic composite coatings is not clear. In this work, a series of SHCs based on the strengthening of the intermediate layer were fabricated by employing polymers with different elastic moduli such as polydimethylsiloxane (PDMS), polyurethane (PU), epoxy (EP) resin, as well as graphite/SiO hydrophobic components. Following that, the effect of different elastic modulus polymers as an intermediate layer on the durability of SHCs was investigated.

Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT.

Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges.

Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions.

The optimal neoadjuvant chemotherapy regimen for locally advanced gastric and gastroesophageal junction adenocarcinoma: a systematic review and Bayesian network meta-analysis.

Background: Neoadjuvant chemotherapy (NAC) for locally advanced gastric and gastroesophageal junction adenocarcinoma (LAGC) has been recommended in several guidelines. However, there is no global consensus about the optimum of NAC regimens. We aimed to determine the optimal NAC regimen for LAGC.

Resection of Scar Tissue in Rats With Spinal Cord Injury Can Promote the Expression of βⅢ-tubulin in the Injured Area.

Background: Previous research shows that scar tissue formed in the injured area after spinal cord injury blocks nerve regeneration and functional recovery. However, those researchers tried to prevent the formation of scar after spinal cord injury to promote nerve regeneration, but it ran counter to their desire, indicating that the formation of scar might play a role in functional recovery after spinal cord injury.

Methods: To investigate roles of scar formation on functional repair after spinal cord injury, we selected several different key time points to resect the scar tissue formed after spinal cord injury based on the rat models of the T8-T9 transection injury of spinal cord.

Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation.

Background And Objective: Accurate lung tumor segmentation from computed tomography (CT) is complex due to variations in tumor sizes, shapes, patterns and growing locations. Learning semantic and spatial relations between different feature channels, image regions and positions is critical yet challenging.

Methods: We propose a new segmentation method, PRCS, by learning and integrating multi-channel contextual relations, and spatial and position dependencies across image regions.

Near-Infrared Responsive Smart Superhydrophobic Coating with Self-Healing and Robustness Enhanced by Disulfide-Bonded Polyurethane.

Synergistic self-healing materials and inorganic particles to create self-healing superhydrophobic surfaces for improving their robustness is a common technique, but the suitability between the two is rarely mentioned. In this work, we developed a multifunctional superhydrophobic coating with room-temperature stability, mechanical stability, self-healing, and NIR stimuli response, in which self-healing polyurethane (PU) serves as the interface reinforcement layer and poly(dopamine) (PDA)-coated flower-like ZnO composite particles serve as the hydrophobic layer. A series of temperature-dependent self-healing PU materials were designed and synthesized by regulating the ratio of hard and soft chain segments in PU, and the relationship between the healing temperature of PU and the hydrophobic stability of the composite coatings was investigated.

Network pharmacology study of Citrus reticulata and Pinellia ternata in the treatment of non-small cell lung cancer.

It has been recognized that Citrus reticulata and Pinellia ternata have a good therapeutic effect on NSCLC. However, the potential mechanism of C. reticulata and P.

Association of 25-hydroxyvitamin D level with survival outcomes in female breast cancer patients: A meta-analysis.

Conflicting results have been reported on the association of blood vitamin D level with prognosis in women with breast cancer. This meta-analysis aimed to evaluate the association between blood 25-hydroxyvitamin D level and survival outcomes in female breast cancer patients. Two authors independently searched PubMed and Embase databases from their inception to August 25, 2020.

Nano-realgar suppresses lung cancer stem cell growth by repressing metabolic reprogramming.

Background: Recent studies in cancer biology suggest that metabolic glucose reprogramming is a potential target for cancer treatment. However, little is known about drug intervention in the glucose metabolism of cancer stem cells (CSCs) and its related underlying mechanisms.

Methods: The crude realgar powder was Nano-grinded to meets the requirements of Nano-pharmaceutical preparations, and Nano-realgar solution (NRS) was prepared for subsequent experiments.

degradation and cytocompatibility of a low temperature grown self-healing Mg-Al LDH coating on MAO-coated magnesium alloy AZ31.

Basically, Mg-Al layered double hydroxide (LDH) coatings are prepared on the surface of micro-arc oxidation (MAO) coated magnesium (Mg) alloys at a high temperature or a low pH value. This scenario leads to the growth rate of LDH coating inferior to the dissolution rate of the MAO coating. This in turn results in limited corrosion resistance of the composite coating.

corrosion resistance of a TaO nanofilm on MAO coated magnesium alloy AZ31 by atomic layer deposition.

Micro-arc oxidation (MAO) coating with outstanding adhesion strength to Mg alloys has attracted more and more attention. However, owing to the porous structure, aggressive ions easily invaded the MAO/substrate interface through the through pores, limiting long-term corrosion resistance. Therefore, a dense and biocompatible tantalum oxide (TaO) nanofilm was deposited on MAO coated Mg alloy AZ31 through atomic layer deposition (ALD) technique to seal the micropores and regulate the degradation rate.

Apoptosis induction by alantolactone in breast cancer MDA-MB-231 cells through reactive oxygen species-mediated mitochondrion-dependent pathway.

Alantolactone is a sesquiterpene lactone isolated from Inula helenium L. Although alantolactone possesses anti-inflammation and apoptosis-induction activities, the underlying mechanism of anti-cancer effect on human breast cancer cells remains largely unknown. In this study, we explored the possibility of alantolactone as an apoptosis-inducing cytotoxic agent using MDA-MB-231 cells as in vitro model.

Reversion Correction and Regularized Random Walk Ranking for Saliency Detection.

In recent saliency detection research, many graph-based algorithms have applied boundary priors as background queries, which may generate completely "reversed" saliency maps if the salient objects are on the image boundaries. Moreover, these algorithms usually depend heavily on pre-processed superpixel segmentation, which may lead to notable degradation in image detail features. In this paper, a novel saliency detection method is proposed to overcome the above issues.

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).

Automatic prostate segmentation on MR images with deep network and graph model.

Automated prostate diagnoses and treatments have gained much attention due to the high mortality rate of prostate cancer. In particular, unsupervised (automatic) prostate segmentation is an active and challenging research. Most conventional works usually utilize handcrafted (low-level) features for prostate segmentation; however they often fail to extract the intrinsic structure of the prostate, especially on images with blurred boundaries.

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

Background: With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer types/subtypes, which may contribute to more accurate somatic point mutation based cancer classification (SMCC). However in existing SMCC methods, issues like high data sparsity, small volume of sample size, and the application of simple linear classifiers, are major obstacles in improving the classification performance.

Results: To address the obstacles in existing SMCC studies, we propose DeepGene, an advanced deep neural network (DNN) based classifier, that consists of three steps: firstly, the clustered gene filtering (CGF) concentrates the gene data by mutation occurrence frequency, filtering out the majority of irrelevant genes; secondly, the indexed sparsity reduction (ISR) converts the gene data into indexes of its non-zero elements, thereby significantly suppressing the impact of data sparsity; finally, the data after CGF and ISR is fed into a DNN classifier, which extracts high-level features for accurate classification.

Earthquake Safety Training through Virtual Drills.

Recent popularity of consumer-grade virtual reality devices, such as the Oculus Rift and the HTC Vive, has enabled household users to experience highly immersive virtual environments. We take advantage of the commercial availability of these devices to provide an immersive and novel virtual reality training approach, designed to teach individuals how to survive earthquakes, in common indoor environments. Our approach makes use of virtual environments realistically populated with furniture objects for training.

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images.

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion.

A combinatorial Bayesian and Dirichlet model for prostate MR image segmentation using probabilistic image features.

Blurred boundaries and heterogeneous intensities make accurate prostate MR image segmentation problematic. To improve prostate MR image segmentation we suggest an approach that includes: (a) an image patch division method to partition the prostate into homogeneous segments for feature extraction; (b) an image feature formulation and classification method, using the relevance vector machine, to provide probabilistic prior knowledge for graph energy construction; (c) a graph energy formulation scheme with Bayesian priors and Dirichlet graph energy and (d) a non-iterative graph energy minimization scheme, based on matrix differentiation, to perform the probabilistic pixel membership optimization. The segmentation output was obtained by assigning pixels with foreground and background labels based on derived membership probabilities.

Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets.

We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: nonuniform density; missing boundaries; and strong speckle noise. We introduced a "guitar" structure that represents the negative space surrounding the thalamic regions.