G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification

Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make decisions. However, creating correspondence across various stains is labor-intensive, bringing major difficulties in collecting data and training a vision-based algorithm to assist nephropathy diagnosis. This paper provides an alternative solution for integrating multi-stained visual cues for glomerulus classification. Our approach, named generator-to-classifier (G2C), is a two-stage framework. Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has). We optimize these two stages in a joint manner. To provide a reasonable initialization, we pre-train the generators in an unlabeled reference set under an unpaired image-to-image translation task, and then fine-tune them together with the classifier. We conduct experiments on a glomerulus type classification dataset collected by ourselves (there are no publicly available datasets for this purpose). Although joint optimization slightly harms the authenticity of the generated patches, it boosts classification performance, suggesting more effective visual cues are extracted in an automatic way. We also transfer our model to a public dataset for breast cancer classification, and outperform the state-of-the-arts significantly.Comment: Accepted by AAAI 201

Implications of C1q/TNF-related protein superfamily in patients with coronary artery disease.

The C1q complement/TNF-related protein superfamily (CTRPs) displays differential effects on the regulation of metabolic homeostasis, governing cardiovascular function. However, whether and how they may serve as predictor/pro-diagnosis factors for assessing the risks of coronary artery disease (CAD) remains controversial. Therefore, we performed a clinical study to elaborate on the implication of CTRPs (CTRP1, CTRP5, CTRP7, and CTRP15) in CAD. CTRP1 were significantly increased, whereas CTRP7 and CTRP15 levels were decreased in CAD patients compared to the non-CAD group. Significant differences in CTRP1 levels were discovered between the single- and triple-vascular-vessel lesion groups. ROC analysis revealed that CTRP7 and CTRP15 may serve as CAD markers, while CTRP1 may serve as a marker for the single-vessel lesion of CAD. CTRP1 and CTRP5 can serve as markers for the triple-vessel lesion. CTRP1 may serve as an independent risk predictor for triple-vessel lesion, whereas CTRP15 alteration may serve for a single-vessel lesion of CAD. CTRP1 may serve as a novel superior biomarker for diagnosis of severity of vessel-lesion of CAD patients. CTRP7, CTRP15 may serve as more suitable biomarker for the diagnosis of CAD patients, whereas CTRP5 may serve as an independent predictor for CAD. These findings suggest CTRPs may be the superior predictive factors for the vascular lesion of CAD and represent novel therapeutic targets against CAD

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA

Visualizing Exotic Orbital Texture in the Single-Layer Mott Insulator 1T-TaSe2

Mott insulating behavior is induced by strong electron correlation and can lead to exotic states of matter such as unconventional superconductivity and quantum spin liquids. Recent advances in van der Waals material synthesis enable the exploration of novel Mott systems in the two-dimensional limit. Here we report characterization of the local electronic properties of single- and few-layer 1T-TaSe2 via spatial- and momentum-resolved spectroscopy involving scanning tunneling microscopy and angle-resolved photoemission. Our combined experimental and theoretical study indicates that electron correlation induces a robust Mott insulator state in single-layer 1T-TaSe2 that is accompanied by novel orbital texture. Inclusion of interlayer coupling weakens the insulating phase in 1T-TaSe2, as seen by strong reduction of its energy gap and quenching of its correlation-driven orbital texture in bilayer and trilayer 1T-TaSe2. Our results establish single-layer 1T-TaSe2 as a useful new platform for investigating strong correlation physics in two dimensions

Neurotoxicological effects induced by up-regulation of miR-137 following triclosan exposure to zebrafish (Danio rerio).

Triclosan (TCS) is a prevalent anthropogenic contaminant in aquatic environments and its chronic exposure can lead to a series of neurotoxic effects in zebrafish. Both qRT-PCR and W-ISH identified that TCS exposure resulted in significant up-regulation of miR-137, but downregulation of its regulatory genes (bcl11aa, MAPK6 and Runx1). These target genes are mainly associated with neurodevelopment and the MAPK signaling pathway, and showed especially high expression in the brain. After overexpression or knockdown treatments by manual intervention of miR-137, a series of abnormalities were induced, such as ventricular abnormality, bent spine, yolk cyst, closure of swim sac and venous sinus hemorrhage. The most sensitive larval toxicological endpoint from intervened miR-137 expression was impairment of the central nervous system (CNS), ventricular abnormalities and notochord curvature. Microinjection of microRNA mimics or inhibitors of miR-137 both caused zebrafish malformations. The posterior lateral line neuromasts became obscured and decreased in number in intervened miR-137 groups and TCS-exposure groups. Up-regulation of miR-137 led to more severe neurotoxic effects than its down-regulation. Behavioral observations demonstrated that both TCS exposure and miR-137 over-expression led to inhibited hearing or vision sensitivity. HE staining indicated that hearing and vision abnormalities induced by long-term TCS exposure originated from CNS injury, such as reduced glial cells and loose and hollow fiber structures. The findings of this study enhance our mechanistic understanding of neurotoxicity in aquatic animals in response to TCS exposure. These observations provide theoretical guidance for development of early intervention treatments for nervous system diseases

A study of a clothing image segmentation method in complex conditions using a features fusion model

According to a priori knowledge in complex conditions, this paper proposes an unsupervised image segmentation algorithm to be used for clothing images that combines colour and texture features. First, block truncation encoding is used to divide the traditional three-dimensional colour space into a six-dimensional colour space so that more fine colour features can be obtained. Then, a texture feature based on the improved local binary pattern (LBP) algorithm is designed and used to describe the clothing image with the colour features. After that, according to the statistical appearance law of the object region and background information in the clothing image, a bisection method is proposed for the segmentation operation. Since the image is divided into several subimage blocks, bisection image segmentation will be accomplished more efficiently. The experimental results show that the proposed algorithm can quickly and effectively extract effective clothing regions from complex circumstances without any artificial parameters. The proposed clothing image segmentation method will play an important role in computer vision, machine learning applications, pattern recognition and intelligent systems