Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

DNA Binding and Degradation by the HNH Protein ColE7

The bacterial toxin ColE7 bears an HNH motif which has been identified in hundreds of prokaryotic and eukaryotic endonucleases, involved in DNA homing, restriction, repair, or chromosome degradation. The crystal structure of the nuclease domain of ColE7 in complex with a duplex DNA has been determined at 2.5 Å resolution. The HNH motif is bound at the minor groove primarily to DNA phosphate groups at and beyond the 3′ side of the scissile phosphate, with little interaction with ribose groups and bases. This result provides a structural basis for sugar- and sequence-independent DNA recognition and the inhibition mechanism by inhibitor Im7, which blocks the substrate binding site but not the active site. Structural comparison shows that two families of endonucleases bind and bend DNA in a similar way to that of the HNH ColE7, indicating that endonucleases containing a “ββα-metal” fold of active site possess a universal mode for protein-DNA interactions

Chinese Herbal Medicine as an Adjunctive Therapy Ameliorated the Incidence of Chronic Hepatitis in Patients with Breast Cancer: A Nationwide Population-Based Cohort Study

We conducted a National Health Insurance Research Database-based Taiwanese nationwide population-based cohort study to evaluate whether Chinese herbal medicine (CHM) treatment decreased the incidence of chronic hepatitis in breast cancer patients receiving chemotherapy and/or radiotherapy. A total of 81171 patients were diagnosed with breast cancer within the defined study period. After randomly equal matching, data from 13856 patients were analyzed. Hazard ratios of incidence rate of chronic hepatitis were used to determine the influence and therapeutic potential of CHM in patients with breast cancer. The patients with breast cancer receiving CHM treatment exhibited a significantly decreased incidence rate of chronic hepatitis even across the stratification of age, CCI score, and treatments. The cumulative incidence of chronic hepatitis for a period of seven years after initial breast cancer diagnosis was also reduced in the patients receiving CHM treatment. The ten most commonly used single herbs and formulas were effective in protecting liver function in patients with breast cancer, where Hedyotis diffusa and Jia-Wei-Xiao-Yao-San were the most commonly used herbal agents. In conclusion, our study provided information that western medicine therapy combined with CHM as an adjuvant modality may have a significant impact on liver protection in patients with breast cancer

Antihepatoma Activity of Artocarpus communis

Extracts from natural plants have been used in traditional medicine for many centuries worldwide. Artocarpus communis is one such plant that has been used to treat liver cirrhosis, hypertension, and diabetes. To our knowledge, this study is the first to investigate the antihepatoma activity of A. communis toward HepG2 and PLC/PRF/5 cells and the first to explore the relationship between antihepatoma activity and the active compound artocarpin content in different fractions of A. communis. A. communis methanol extract and fractions induced dose-dependent reduction of tumor cell viability. DNA laddering analysis revealed that A. communis extract and fractions did not induce apoptosis in HepG2 and PLC/PRF/5 cells. Instead, acridine orange staining revealed that A. communis triggered autophagic cell death in a dose-dependent manner. The antihepatoma activity of A. communis is attributable to artocarpin. The fractions with the highest artocarpin content were also the fractions with the highest antihepatoma activity in the following order: dichloromethane fraction > methanol extract > ethyl acetate fraction > n-butanol fraction > n-hexane fraction. Taken together, A. communis showed antihepatoma activity through autophagic cell death. The effect was related to artocarpin content. Artocarpin could be considered an indicator of the anticancer potential of A. communis extract

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early; the treatment will be relatively easy; which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However; the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology; this research solves the problem that the original cutting technology cannot extract certain single teeth; and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN); which can identify caries and restorations from the bitewing images. Moreover; it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image; which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization; (2) a dental image cropping procedure to obtain individually separated tooth samples; and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks; namely; AlexNet; GoogleNet; Vgg19; and ResNet50; experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%; respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

Nuclear factor κB-inducing kinase activation as a mechanism of pancreatic β cell failure in obesity

The nuclear factor κB (NF-κB) pathway is a master regulator of inflammatory processes and is implicated in insulin resistance and pancreatic β cell dysfunction in the metabolic syndrome. Whereas canonical NF-κB signaling is well studied, there is little information on the divergent noncanonical NF-κB pathway in the context of pancreatic islet dysfunction. Here, we demonstrate that pharmacological activation of the noncanonical NF-κB-inducing kinase (NIK) disrupts glucose homeostasis in zebrafish in vivo. We identify NIK as a critical negative regulator of β cell function, as pharmacological NIK activation results in impaired glucose-stimulated insulin secretion in mouse and human islets. NIK levels are elevated in pancreatic islets isolated from diet-induced obese (DIO) mice, which exhibit increased processing of noncanonical NF-κB components p100 to p52, and accumulation of RelB. TNF and receptor activator of NF-κB ligand (RANKL), two ligands associated with diabetes, induce NIK in islets. Mice with constitutive β cell-intrinsic NIK activation present impaired insulin secretion with DIO. NIK activation triggers the noncanonical NF-κB transcriptional network to induce genes identified in human type 2 diabetes genome-wide association studies linked to β cell failure. These studies reveal that NIK contributes a central mechanism for β cell failure in diet-induced obesity