k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

Effect of histone deacetylase inhibitor, trichostatin A, on cartilage regeneration from free perichondrial grafts in rabbits

Purpose: To evaluate the effect of histone deacetylase (HDAC) inhibitor, trichostatin A (TCA), on cartilage regeneration in a rabbit perichondrial graft model.Methods: Perichondrial grafts (20 × 20 mm2) were derived from the ears of New Zealand rabbits and transplanted onto the paravertebral muscle of the face of each rabbit. The rabbits were separated into three groups: non-treated control group, vehicle-treated control group that received 0.3 mL of saline, and TCA-treated group administered 0.3 mL of TCA (500 ng/mL). Rabbits in all three groups were further divided into subgroups according to the duration of treatment after transplantation: 2, 4, 6, and 8 weeks (n = 12 rabbits each). The effect of TCA on cartilage regeneration was determined histologically by evaluating the thickness of the cartilage plate in the grafted rabbits.Results: TCA increased the amount of immature cartilage 4 and 6 weeks after perichondrial graft implantation. Mature cartilage was seen in the TCA-treated rabbits 8 weeks after transplantation. The thickness of the cartilage plate was significantly (p < 0.01) higher in TCA group (905 ± 36) than in either the non-treated (632 ± 22) or the vehicle-treated control (639 ± 22) group.Conclusion: Treatment with trichostatin A, an HDAC inhibitor, enhances cartilage regeneration in rabbit recipients of a perichondrial graft. Furthermore, the findings of this study should be helpful in exploring the clinical use of trichostatin A.Keywords: Histone deacetylase inhibitor, Perichondrial graft, TrichostatinA, Cartilage regeneration, Transplantatio

DeepEP: A Deep Learning Framework for Identifying Essential Proteins

Background: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion: We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods

Towards the Identification of Protein Complexes and Functional Modules by Integrating PPI Network and Gene Expression Data

Background: Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules. Results: In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using “Biological Process” annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of “Biological Process”. More importantly, the relationship between protein complexes and functional modules are studied. Conclusions: The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index

A diagnostic challenge for schistosomiasis japonica in China: consequences on praziquantel-based morbidity control

Worldwide schistosomiasis continues to be a serious public health problem. Over the past five decades, China has made remarkable progress in reducing Schistosoma japonicum infections in humans to a relatively low level. Endemic regions are currently circumscribed in certain core areas where re-infection and repeated chemotherapy are frequent. At present, selective chemotherapy with praziquantel is one of the main strategies in China's National Schistosomiasis Control Program, and thus diagnosis of infected individuals is a key step for such control. In this paper we review the current status of our knowledge about diagnostic tools for schistosomiasis japonica. A simple, affordable, sensitive, and specific assay for field diagnosis of schistosomiasis japonica is not yet available, and this poses great barriers towards full control of schistosomiasis. Hence, a search for a diagnostic approach, which delivers these characteristics, is essential and should be given high priority