Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction.

Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets.

Multi-view multi-task spatiotemporal graph convolutional network for air quality prediction.

Accurate air quality prediction is a crucial but arduous task for intelligent cities. Predictable air quality can advise governments on environmental governance and residents on travel. However, complex correlations (i.e., intra-sensor correlation and inter-sensor correlation) make prediction challenging. Previous work considered the spatial, temporal, or combination of the two to model. However, we observe that there are also logical semantic and temporal, and spatial relations. Therefore, we propose a multi-view multi-task spatiotemporal graph convolutional network (M2) for air quality prediction. We encode three views, including spatial view (using GCN to model the correlation between adjacent stations in geographic space), logical view (using GCN to model the correlation between stations in logical space), and temporal view (using GRU to model the correlation among historical data). Meanwhile, M2 chooses a multi-task learning paradigm that includes a classification task (auxiliary task, coarse granularity prediction of air quality level) and a regression task (main task, fine granularity prediction of air quality value) to predict jointly. And the experimental results on two real-world air quality datasets demonstrate our model performances over the state-of-art methods.

Cullin-associated and neddylation-dissociated 1 protein (CAND1) governs cardiac hypertrophy and heart failure partially through regulating calcineurin degradation.

Pathological cardiac hypertrophy is a process characterized by significant disturbance of protein turnover. Cullin-associated and Neddylation-dissociated 1 (CAND1) acts as a coordinator to modulate substrate protein degradation by promoting the formation of specific cullin-based ubiquitin ligase 3 complex in response to substrate accumulation, which thereby facilitate the maintaining of normal protein homeostasis. Accumulation of calcineurin is critical in the pathogenesis of cardiac hypertrophy and heart failure. However, whether CAND1 titrates the degradation of hypertrophy related protein eg. calcineurin and regulates cardiac hypertrophy remains unknown. Therefore, we aim to explore the role of CAND1 in cardiac hypertrophy and heart failure and the underlying molecular mechanism. Here, we found that the protein level of CAND1 was increased in cardiac tissues from heart failure (HF) patients and TAC mice, whereas the mRNA level did not change. CAND1-KO+ /- aggravated TAC-induced cardiac hypertrophic phenotypes; in contrast, CAND1-Tg attenuated the maladaptive cardiac remodeling. At the molecular level, CAND1 overexpression downregulated, whereas CAND1-KO+ /- or knockdown upregulated calcineurin expression at both in vivo and in vitro conditions. Mechanistically, CAND1 overexpression favored the assembly of Cul1/atrogin1/calcineurin complex and rendered the ubiquitination and degradation of calcineurin. Notably, CAND1 deficiency-induced hypertrophic phenotypes were partially rescued by knockdown of calcineurin, and application of exogenous CAND1 prevented TAC-induced cardiac hypertrophy. Taken together, our findings demonstrate that CAND1 exerts a protective effect against cardiac hypertrophy and heart failure partially by inducing the degradation of calcineurin.

The long noncoding RNA lncCIRBIL disrupts the nuclear translocation of Bclaf1 alleviating cardiac ischemia-reperfusion injury.

Cardiac ischemia-reperfusion (I/R) injury is a pathological process resulting in cardiomyocyte death. The present study aims to evaluate the role of the long noncoding RNA Cardiac Injury-Related Bclaf1-Inhibiting LncRNA (lncCIRBIL) on cardiac I/R injury and delineate its mechanism of action. The level of lncCIRBIL is reduced in I/R hearts. Cardiomyocyte-specific transgenic overexpression of lncCIRBIL reduces infarct area following I/R injury. Knockout of lncCIRBIL in mice exacerbates cardiac I/R injury. Qualitatively, the same results are observed in vitro. LncCIRBIL directly binds to BCL2-associated transcription factor 1 (Bclaf1), to inhibit its nuclear translocation. Cardiomyocyte-specific transgenic overexpression of Bclaf1 worsens, while partial knockout of Bclaf1 mitigates cardiac I/R injury. Meanwhile, partial knockout of Bclaf1 abrogates the detrimental effects of lncCIRBIL knockout on cardiac I/R injury. Collectively, the protective effect of lncCIRBIL on I/R injury is accomplished by inhibiting the nuclear translocation of Bclaf1. LncCIRBIL and Bclaf1 are potential therapeutic targets for ischemic cardiac disease.

Inference Attacks and Controls on Genotypes and Phenotypes for Individual Genomic Data.

The rapid growth of DNA-sequencing technologies motivates more personalized and predictive genetic-oriented services, which further attract individuals to increasingly release their genome information to learn about personalized medicines, disease predispositions, genetic compatibilities, etc. Individual genome information is notoriously privacy-sensitive and highly associated with relatives. In this paper, we present an inference attack algorithm to predict target genotypes and phenotypes based on belief propagation in factor graphs. With this algorithm, an attacker can effectively predict the target genotypes and phenotypes of target individuals based on genome information shared by individuals or their relatives, and genotype and phenotype association from genome-wide association study (GWAS). To address the privacy threats resulted from such inference attacks, we elaborate the metrics to evaluate data utility and privacy and then present a data sanitization method. We evaluate our inference attack algorithm and data sanitization method on real GWAS dataset: Age-related macular degeneration (AMD) case/control dataset. The evaluation results show that our work can effectively defense against genome threats while guaranteeing data utility.

Wide-field and full-focus optical microscopic imaging system.

As a precision instrument, the microscope is typically used by researchers in criminal investigation, information forensics, biology, metallography, etc. However, the traditional microscope has a dilemma in that if it uses higher magnification, its field of view is smaller and its depth of field is more limited. Hence, it seriously challenges the endurance and brain of the observer to observe an object thoroughly. This paper proposes a wide-field and full-focus imaging method for solving the above problem. First, a high-precision multi-focus image acquisition platform is improved, and its motion displacement is used directly for image calculation, which greatly reduces the amount of calculation. Second, the focus area of each image is segmented by the mask generation algorithm based on a graph cut. Third, a fusion algorithm, whose contrast pyramid is based on the mask region, is proposed, which utilizes the position of the clear area on the mask pyramid to guide the fusion of the contrast pyramid. Finally, a fast and fault-tolerant stitching algorithm based on mechanical and optical parameters is proposed, which effectively eliminates the interference of the cumulative error and successfully completes hundreds of image-stitching tasks. The experimental results demonstrate that the proposed imaging system is obviously superior to the traditional image fusion algorithms and image-stitching approaches. Both the imaging effect and execution time are satisfactory.

A multi-focus image fusion method via region mosaicking on Laplacian pyramids.

In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.

Multi-focus image fusion algorithm based on Laplacian pyramids.

In this paper, we propose a method named region mosaicking on Laplacian pyramids (RMLP) to fuse multi-focus images that are captured by microscope. First, we apply the sum-modified Laplacian to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has the best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduce the color distortion of the fused images on two datasets.

New applications of Schrödinger type inequalities in the Schrödingerean Hardy space.

As new applications of Schrödinger type inequalities obtained by Jiang (J. Inequal. Appl. 2016: Article ID 247, 2016) in the Schrödingerean Hardy space, we not only obtain the representation of Schrödingerean harmonic functions but also give a sufficient and necessary condition between the Schrödingerean distributional function and its derivative in the Schrödingerean Hardy space.

RiPPAS: A Ring-Based Privacy-Preserving Aggregation Scheme in Wireless Sensor Networks.

Recently, data privacy in wireless sensor networks (WSNs) has been paid increased attention. The characteristics of WSNs determine that users' queries are mainly aggregation queries. In this paper, the problem of processing aggregation queries in WSNs with data privacy preservation is investigated. A Ring-based Privacy-Preserving Aggregation Scheme (RiPPAS) is proposed. RiPPAS adopts ring structure to perform aggregation. It uses pseudonym mechanism for anonymous communication and uses homomorphic encryption technique to add noise to the data easily to be disclosed. RiPPAS can handle both s u m ( ) queries and m i n ( ) / m a x ( ) queries, while the existing privacy-preserving aggregation methods can only deal with s u m ( ) queries. For processing s u m ( ) queries, compared with the existing methods, RiPPAS has advantages in the aspects of privacy preservation and communication efficiency, which can be proved by theoretical analysis and simulation results. For processing m i n ( ) / m a x ( ) queries, RiPPAS provides effective privacy preservation and has low communication overhead.

A game theory-based trust measurement model for social networks.

BACKGROUND: In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. METHODS: We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. RESULTS AND CONCLUSIONS: We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

A Cross Structured Light Sensor and Stripe Segmentation Method for Visual Tracking of a Wall Climbing Robot.

In non-destructive testing (NDT) of metal welds, weld line tracking is usually performed outdoors, where the structured light sources are always disturbed by various noises, such as sunlight, shadows, and reflections from the weld line surface. In this paper, we design a cross structured light (CSL) to detect the weld line and propose a robust laser stripe segmentation algorithm to overcome the noises in structured light images. An adaptive monochromatic space is applied to preprocess the image with ambient noises. In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution. Lastly, the stripe centre points are extracted from the image. In experiments, the CSL sensor and the proposed algorithm are applied to guide a wall climbing robot inspecting the weld line of a wind power tower. The experimental results show that the CSL sensor can capture the 3D information of the welds with high accuracy, and the proposed algorithm contributes to the weld line inspection and the robot navigation.

Brain CT image similarity retrieval method based on uncertain location graph.

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.