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IntroductionAs a malignant hematological disease, the incidence of acute myeloid leukemia (AML) has exhibited an upward trend in recent years. Nevertheless, certain limitations persist in the treatment of AML. Sperm-associated antigen 6 (SPAG6) has been implicated in the onset and progression of various human cancers, with its expression levels significantly elevated in AML. Consequently, we undertook a series of experiments to investigate the role and underlying mechanisms of SPAG6 in AML cell lines.MethodsIn the in vitro experiments of this study, DEPs and GO and KEGG enrichment analysis subsequent to SPAG6 down-regulation were detected by TMT. CCK8 was employed to determine cell viability. The levels of apoptosis and ROS were measured by flow cytometry. In the in vivo experiments, a xenografted tumor model was constructed, and the expression of SPAG6 and GSTP1 in tumor tissues was detected by IHC.ResultsUltimately, our findings indicated that over-expression of SPAG6 promoted cell growth and decreased reactive oxygen species (ROS) and malondialdehyde levels. Furthermore, SPAG6 knockdown was found to diminish mitochondrial membrane potential and facilitate cell apoptosis. In vivo, SPAG6 could also promote tumor growth, suggesting that SPAG6 may serve as a pro-tumor factor. In addition, daunorubicin (DNR) may cause oxidative stress and initiate apoptosis, resulting in oxidative damage to AML cells. However, the overexpression of SPAG6 may attenuate the efficacy of DNR. This was due to SPAG6 promoted GSTP1 expression, thereby reducing ROS levels. Simultaneously, the elevation of GSTP1 and JNK complex may reduce the expression of p-JNK and inhibit the activation of JNK pathway, which might inhibit cell apoptosis.DiscussionIn conclusion, our experiments suggested that upregulated SPAG6 might mitigate the pro-apoptotic effects of DNR through ROS/JNK MAPK axis in a GSTP1-dependent manner.

Abstract Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Abstract Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, and so on. With the large number of participants and real-time property, social networks increasingly demonstrate their strength in information dissemination. Social computing has become a promising research area and attracts lots of attention. Analyzing and mining human behaviors, topological structure, and information diffusion in social networks can help to understand the essential mechanism of macroscopic phenomena, discover potential public interest, and provide early warnings of collective emergencies.

Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. Many computational methods have been developed to predict potential drug-target interactions based on known drug-target interactions, but the accuracy of these methods still needs to be improved. In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse drug-related and target-related datasets. Its encoder consists of two components: a graph convolutional network (GCN) and a random walk with restart (RWR). And the decoder is DistMult, a matrix factorization model, using embedding vectors from encoder to discover potential DTIs. The combination of GCN and RWR can provide nodes with more information through a larger neighborhood, and it can also avoid over-smoothing and computational complexity caused by multi-layer message passing. Based on the 10-fold cross-validation, we conduct three experiments in different scenarios. The results show that GADTI is superior to the baseline methods in both the area under the receiver operator characteristic curve and the area under the precision–recall curve. In addition, based on the latest Drugbank dataset (V5.1.8), the case study shows that 54.8% of new approved DTIs are predicted by GADTI.

circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.

Big Data is defined as an emerging paradigm that includes complex and large-scale information beyond the processing capability of conventional tools. Traditional data analytics methods have been commonly used for many applications, such as text classfication, image recognition, and video tracking. For analysis purposes, these data often need to be represented as vectors. However, many other types of data objects in real-world applications contain rich feature vectors and structure information, such as chemical com pounds in bio-pharmacy, brain regions in brain networks and users in social networks. Unfortunately, vector representa tions are very simple features that do not inherently contain the object’s structure information. In reality, objectsmay have complicated characteristics depending on how the objects are assessed and characterized. Data may also reside in het erogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, and semi-structured data. As a result, novel data analytics methods are needed to discover meaningful knowledge in advanced applications from objects with large-scale com plex characteristics. In total, there were 104 submissions to this IEEE ACCESS Special Section, with 39 accepted after rigorous peer review.

With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

With the development of the mobile phone industry, mobile applications market becomes thriving. However, the immature information technology and unfriendly interface bring negative user experience (UX) to the mobile users and thus affect the service life of mobile applications, especially for the most concerned entertaining applications, mobile games. As a result, the evaluating UX and finding crucial factors of UX become a challenge. Over the last decades, numerous researches have tried to deal with this issue, but none of them has clearly identified the relations among positive-negative UX, sufficient human characteristics and specific events of applications. This paper proposes a subjective-objective evaluation method. Subjective UX of mobile games is sufficiently obtained and objective UX is verified through the electrocardiogram signals and heart rate variability. In order to reveal distinct relations among UX factors, sufficient user characteristics and categories of game events, an improved RIPPER algorithm is proposed by this paper to obtain the relations. Experiments are performed with 300 testers who played mobile parkour games for at least five minutes. The accuracy and efficiency of the proposed method have been verified through real experiments and objective measures. In addition, this paper provides an effective sampling method and a data analysis algorithm to obtain crucial UX factors for mobile applications.

Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, interrelationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.

Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter.

Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC's primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting.