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Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.

Background: Patients often misuse a combination of prescription drugs including opioids; however, the relationship between a controlled substance (CS) score and health outcomes is unknown., Objective: To examine the association between a CS scoring algorithm and health care use, specifically total cost of care, hospitalizations, and emergency room (ER) visits., Methods: This analysis was a retrospective cohort study using administrative claims data from a large U.S. health insurer. Included in the analysis were 999,852 members with a minimum CS score of 2.5 in the fourth quarter (4Q) of 2012, who were continuously enrolled from January 1, 2012, to December 31, 2013, and who were aged 18 years or older. A CS score was calculated using 4Q 2012 (3 months) prescription claims data and divided into 3 components: (1) number of CS claims, (2) number of unique pharmacies and unique prescribers, and (3) evidence of increasing CS use. The primary outcomes were total cost of care (pharmacy and medical costs), all-cause hospitalizations, and ER visits in 2013. We also quantified what a 1-point change in CS score meant for the primary outcomes., Results: 47% of members had a CS score of 2.5, indicating a single CS claim, and 51% of members had a score between 3 and less than 12. The remaining 2% (20,858 members) had a score of 12 or more. There was a statistically significant and consistently increasing association between the 4Q 2012 CS score and hospitalizations, ER visits, and total costs of care in 2013. A 1-point change in CS score was associated with a $1,488 change in total cost of care, 0.9% change in the hospitalization rate, and 1.5% change in the ER visit rate., Conclusions: There is a linear association between increasing CS score and negative health outcomes. Insurers should consider interventions to lower member CS scores., Disclosures: This study was funded internally by Prime Therapeutics. Starner, Qiu, and Gleason are employees of Prime Therapeutics, a pharmacy benefits management company. Karaca-Mandic is an employee of the University of Minnesota and did not receive any compensation related to this work. The results of this study were presented as a poster at the Academy of Managed Care Pharmacy's 27th Annual Meeting and Expo; San Diego, California; April 7-10, 2015. Study concept and design were contributed by Starner, Gleason, and Qiu. Qiu took the lead in data collection, assisted by Starner and Gleason. Data interpretation was performed by all the authors. Starner primarily wrote and revised the manuscript, along with the other authors.

Purpose: Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images., Methods: Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices., Results: Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4., Conclusion: Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically., (Copyright © 2013 Wiley Periodicals, Inc.)

Objective: This study was to assess the diagnostic value of strain index (SI) for transrectal real-time tissue elastography (TRTE) on differentiating malignant from benign lesions in the prostate peripheral zone., Methods: 83 patients suspected of having prostate cancer (PCa) underwent transrectal ultrasonography (TRUS) and TRTE examinations. The lesions in the prostate peripheral zone detected by TRTE were set as the regions of interest (ROI) for strain ratio (SR) measurement (SRA). The moderate texture tissues without lesion were set as the reference ROI for SR measurement (SRB). Then, SI (SRB/SRA) of total lesions (ASI) and local lesion (PSI) were calculated, and the diagnostic values of ASI and PSI on differentiating benign from malignant lesions were assessed respectively., Results: The range of PSI was 2.23-67.21 (29.97 ± 15.58) in malignant tumors and 0. 4-43.6 (7.79 ± 8.75) in benign lesions (AUC=0.90), while the range of ASI was 2.84-47.9 (8.38 ± 12.20) in malignant tumors and 0.4 -2.79 (5.85 ± 7.29) in benign lesions (AUC=0.62). There was significant difference of PSI values between the benign and malignant lesions (P<0.01). At the cutoff value of 17.44, PSI yielded the highest sensitivity (74.5%) and specificity (83.3%) for discriminating PCa from benign lesions. The capability of PSI in the diagnosis of PCa improved with the increase of Gleason scores., Conclusion: PSI is one of the elasticity parameters obtained easily by TRTE, it can provide more information in the differentiation of prostate peripheral zone lesions., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Background. Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear. Method. Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database. Results. A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs. Conclusion. Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm called the partial-failure segregated multicasting routing and spectrum assignment (PFS MRSA) algorithm to improve the service blocking performance of the multicast transmission in flex-grid optical networks (FGONs). By segregating one failure destination leaf-node from a blocked multicast request and accommodating the failure destination leaf-node and the remaining multicast request independently, the success probability of accommodating the originally blocked multicast request can be greatly increased. In this way, the proposed PFS MRSA algorithm can effectively reduce the service blocking probability for the multicast services in FGONs. Simulation results show that the proposed PFS MRSA algorithm achieves significant reduction in service blocking probability when compared with the conventional MRSA algorithms, and such reduction can even reach 100% in some scenarios with low traffic load. [ABSTRACT FROM AUTHOR]

Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

By employing sophisticated routing and spectrum assignment (RSA) algorithms together with a finer spectrum granularity (namely frequency slot) in resource allocation procedures, flex-grid optical networks can accommodate diverse kinds of services with high spectrum-allocation flexibility and resource-utilization efficiency. However, the continuity and the contiguity constraints in spectrum allocation procedures may always induce some isolated, small-sized, and unoccupied spectral blocks (known as spectrum fragments) in flex-grid optical networks. Although these spectrum fragments are left unoccupied, they can hardly be utilized by the subsequent service requests directly because of their spectral characteristics and the constraints in spectrum allocation. In this way, the existence of spectrum fragments may exhaust the available spectrum resources for a coming service request and thus worsens the networking performance. Therefore, many reactive defragmentation algorithms have been proposed to handle the fragmented spectrum resources via re-optimizing the routing paths and the spectrum resources for the existing services. But the routing-path and the spectrum-resource re-optimization in reactive defragmentation algorithms may possibly disrupt the traffic of the existing services and require extra components. By comparison, some proactive defragmentation algorithms (e.g. fragmentation-aware algorithms) were proposed to suppress spectrum fragments from their generation instead of handling the fragmented spectrum resources. Although these proactive defragmentation algorithms induced no traffic disruption and required no extra components, they always left the generated spectrum fragments unhandled, which greatly affected their efficiency in spectrum defragmentation. In this paper, by comprehensively considering the characteristics of both the reactive and the proactive defragmentation algorithms, we proposed a time-aware service-classified (TASC) spectrum defragmentation algorithm, which simultaneously employed proactive and reactive mechanisms in suppressing spectrum fragments with the awareness of services’ types and their duration times. By dividing the spectrum resources into several flexible groups according to services’ types and limiting both the spectrum allocation and the spectrum re-tuning for a certain service inside one specific spectrum group according to its type, the proposed TASC defragmentation algorithm cannot only suppress spectrum fragments from generation inside each spectrum group, but also handle the fragments generated between two adjacent groups. In this way, the proposed TASC algorithm gains higher efficiency in suppressing spectrum fragments than both the reactive and the proactive defragmentation algorithms. Additionally, as the generation of spectrum fragments is retrained between spectrum groups and the defragmentation procedure is limited inside each spectrum group, the induced traffic disruption for the existing services can be possibly reduced. Besides, the proposed TASC defragmentation algorithm always re-tunes the spectrum resources of the service with the maximum duration time first in spectrum defragmentation procedure, which can further reduce spectrum fragments because of the fact that the services with longer duration times always have higher possibility in inducing spectrum fragments than the services with shorter duration times. The simulation results show that the proposed TASC defragmentation algorithm can significantly reduce the number of the generated spectrum fragments while improving the service blocking performance. [ABSTRACT FROM AUTHOR]

Flex-grid optical networks (FONs) employ dynamic routing and spectrum assignment (RSA) algorithms to support diverse services with heterogeneous bit-rates. Although these RSA algorithms can increase the flexibility and efficiency of spectrum allocation, they may also possibly induce spectrum fragments when allocating spectrum to accommodate different service requests. The generated spectrum fragments may exhaust the available spectrum resources and degrade networking performances. Therefore, many schemes referred as defragmentation algorithms have been proposed to reduce spectrum fragments at the cost of traffic disruption or extra components, which are usually required to handle the fragmented spectrum via resource re-optimization. Although some grouping RSA algorithms have been proposed to prevent the generation of spectrum fragments with no traffic disruption or extra components, the pre-divided fixed spectrum groups reduce the flexibility in a dynamic spectrum assignment and thus degrade the blocking performance of the network. In this paper, we propose a RSA algorithm based on variable-grouping mechanism, namely Minimized Variable Grouping (MVG) RSA algorithm, which can not only efficiently restrain the generation of spectrum fragments, but also improve the blocking performance compared to the typical ungrouped and the conventional grouping RSA algorithms. By employing variable grouping mechanism, the proposed algorithm sorts services into different kinds according to their bandwidth requirements and then divides the spectrum resources into several variable groups according to the kinds of services. And the generation of the spectrum fragments is restricted in the spectrum intervals between two adjacent groups. In this way, the proposed algorithm can dramatically restrain the generation of fragments. In addition, the proposed algorithm can reduce the blocking probability by minimizing the grouped spectrum resources to maximize the remained vacant spectrum resources in the spectrum intervals between two adjacent groups for future service requests and thus helps improve the blocking performance. The simulation results show that the proposed MVG RSA algorithm can dramatically reduce the fragments with no traffic disruption while exhibiting a low blocking probability. [ABSTRACT FROM AUTHOR]

In this paper, the auxiliary principle technique is extended to study a system of generalized set-valued strongly nonlinear mixed implicit quasi-variational-like inequalities problem in Hilbert spaces. First, we establish the existence of solutions of the corresponding system of auxiliary variational inequalities problem. Then, using the existence result, we construct a new iterative algorithm. Finally, both the existence of solutions of the original problem and the convergence of iterative sequences generated by the algorithm are proved. We give an affirmative answer to the open problem raised by Noor et al. (Korean J. Comput. Appl. Math. 1:73-89, ; J. Comput. Appl. Math. 47:285-312, ). Our results improve and extend some known results. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the extra-gradient methods for solving split feasibility and fixed point problems involved in pseudo-contractive mappings in real Hilbert spaces. We propose an Ishikawa-type extra-gradient iterative algorithm for finding a solution of the split feasibility and fixed point problems involved in pseudo-contractive mappings with Lipschitz assumption. Moreover, we also suggest a Mann-type extra-gradient iterative algorithm for finding a solution of the split feasibility and fixed point problems involved in pseudo-contractive mappings without Lipschitz assumption. It is proven that under suitable conditions, the sequences generated by the proposed iterative algorithms converge weakly to a solution of the split feasibility and fixed point problems. The results presented in this paper extend and improve some corresponding ones in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and analyze a new general hybrid iterative algorithm for finding a common element of the set of common zeros of two families of finite maximal monotone mappings, the set of fixed points of a nonexpansive mapping and the set of solutions of the variational inequality problem for a monotone, Lipschitz-continuous mapping in a real Hilbert space. Our algorithm is based on four well-known methods: Mann's iteration method, composite method, outer-approximation method and extragradient method. We prove the strong convergence theorem for the proposed algorithm. The results presented in this paper extend and improve the corresponding results of Wei and Tan (Fixed Point Theory Appl. 2014:77, 2014). Some special cases are also discussed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Flex-grid optical network is a promising solution to novel high speed services. [•] Spectrum continuity and contiguity constraints can induce spectrum fragmentation. [•] We propose a group-based spectrum assignment to solve fragmentation problem. [•] Spectrum is divided into groups and assigned to services according to their types. [•] Simulation results verify the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Selecting correct dimensions is very important to subspace clustering and is a challenging issue. This paper studies semi-supervised approach to the problem. In this setting, limited domain knowledge in the form of space level pair-wise constraints, i.e., must-links and cannot-links, are available. We propose a semi-supervised subspace clustering (S3C) algorithm that exploits constraint inconsistence for dimension selection. Our algorithm firstly correlates globally inconsistent constraints to dimensions in which they are consistent, then unites constraints with common correlating dimensions, and finally forms the subspaces according to the constraint unions. Experimental results show that S3C is superior to the typical unsupervised subspace clustering algorithm FINDIT, and the other constraint based semi-supervised subspace clustering algorithm SC-MINER. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a new system of generalized quasi-variational-like inclusion (SGQVLI) in Hilbert spaces. We suggest a new iterative algorithm for finding an approximate solution to SGQVLI, and prove the convergence of this algorithm. Our results improve and extend some known results. [Copyright &y& Elsevier]

• We propose a multiple leaf-ringing based protection algorithm with spectrum defragmentation. • The proposed algorithm sorts the destination nodes of a multicast service into groups. • The proposed algorithm constructs one leaf ring for all the destination nodes in one group. • The proposed algorithm constructs two dedicated trunk paths from the source to each constructed ring. • The proposed algorithm triggers spectrum defragmentation to re-optimize resource utilization. Elastic optical networks (EONs) have realized high-speed data transmission with attractively efficiency and flexibility in resource utilization. In order to guarantee the reliability and increase the efficiency of the multicast transmission in EONs, we propose a multiple leaf-ringing based protection algorithm with spectrum defragmentation (MLRP-SD) in this article. The proposed algorithm firstly sorts the destination nodes of a multicast service into multiple groups. Then, it constructs one leaf ring for all the destination nodes in one group and two dedicated trunk paths (including one protection trunk path) from the source to each constructed ring. Since a leaf ring can provide protection for the destination nodes in its corresponding group, the reliability of multicast traffic can be improved. And the use of leaf rings can reduce the spectrum consumed in service protection compared to the traditional dedicated path protection (DPP) algorithms. In order to further improve the efficiency, the proposed MLRP-SD algorithm triggers spectrum defragmentation to re-optimize resource utilization in the network when sufficient available spectrum resources cannot be found along the sought protection trunk paths. The simulation results show that the proposed MLRP-SD algorithm can guarantee the reliability of multicast traffic with low service blocking probability. [ABSTRACT FROM AUTHOR]

• We propose a time-based composite-consumption-aware (TCCA) MRSA for the multicast traffic in EONs. • The proposed algorithm considers the holding time of each multicast service. • The proposed algorithm optimizes the multicasting composite resource consumption (MCRC) for multicast services. • The proposed algorithm reduces the MCRC with low service blocking probability. Given the importance of multicast traffic in current optical networks, many effective multicast routing and spectrum assignment (MRSA) algorithms have been proposed to achieve efficient multicast transmission in elastic optical networks (EONs). However, most of these MRSA algorithms were focus on optimizing only the spectrum resources allocated to multicast service requests, but ignored some fragmented spectrum resources remained after spectrum allocation. Although some fragment-aware MRSA algorithms were proposed for EONs to restrain the fragmented spectrum generated in provisioning multicast transmission, they could hardly precisely compute the amount of the fragmented resources while optimizing the allocated resources for the multicast transmission in EONs. This is because that the fragmented and the allocated spectrum resources always have different duration characteristics. In this paper, we propose a time-based composite-consumption-aware (TCCA) MRSA algorithm to optimize both the fragmented and the allocated resources consumed by the multicast transmission in EONs. By additionally taking the holding time of each multicast service request into account, the proposed algorithm can comprehensively optimize both the fragmented and the allocated resource consumption for the multicast transmission in EONs. Since the comprehensive resource consumption, including both the fragmented and the allocated resource consumption, is optimized by the proposed TCCA algorithm, more available resources can be remained for the subsequent multicast requests, which can increase the request blocking performance in EONs. Simulation results verify that the proposed algorithm can reduce the comprehensive resource consumption for the multicast transmission in EONs with low request blocking probability. [ABSTRACT FROM AUTHOR]

Elastic optical networks (EONs) can make service accommodation more flexible and precise by employing efficient routing and spectrum allocation (RSA) algorithms. In order to improve the efficiency of RSA algorithms, the advanced-reservation technique was introduced into designing RSA algorithms. However, few of these advanced-reservation-based RSA algorithms were focused on the unavailable spectrum resources in EONs. In this paper, we propose an Advanced-Reservation-based Invalid-Spectrum-Aware (AR-ISA) resource allocation algorithm to improve the networking performance and the resource alignment of EONs. By employing a new index, Invalid Spectrum Rate (ISR), to record the proportion of unavailable spectrum resources in EONs, the proposed AR-ISA algorithm set a network load threshold to trigger the postponement of an arriving service. Compared with the traditional slack-based AR mechanism, the proposed algorithm has more concerns about the current spectrum usage of the path designated by the service than the conflicts between AR services and other existing services. To further increase the networking performance, the proposed algorithm adopts defragmentation to increase the number of available spectrum resources when postponing a service. Theoretical analysis and simulation results show that the proposed AR-ISA algorithm has obvious effectiveness in reducing the service blocking rate and increasing the spectrum alignment rate. [ABSTRACT FROM AUTHOR]