Recently, several approaches simultaneously exploiting ratings and review texts have been proposed for personalized recommendations. These approaches apply topic modeling techniques on review texts to mining major latent aspects of the item (or the user) and align them with collaborative filtering algorithms to increase the accuracy and interpretability of rating prediction. However, they learn the topics for each item (or user) by harnessing all reviews related to it, which is not intuitive or in line with users' rating and review behavior. In this paper, we propose a Fine-grained Latent Aspects Model (FLAM), which learns the topics for each review with the corresponding latent aspect ratings of the user and the item. FLAM is an united model of Latent Factor Model (LFM) and Latent Dirichlet Allocation (LDA). LFM, well-known for its high prediction accuracy, is employed to predict latent aspect ratings of the user and the item. LDA, a classical topic model, is used to extract latent aspects in the reviews. Our experiment results on 25 real-world datasets show the proposed model has superiority over state-of-the-art methods and can learn the latent topics that are interpretable. Furthermore, our model can alleviate the cold-start problem.

Background: It has been reported that ventricular repolarization dispersion resulting from transmural, apicobasal and interventricular action potential duration (APD) gradients makes the T wave concordant with the QRS complex.Method and results: A whole-heart model integrating transmural, apicobasal, interventricular and anteroposterior APD gradients was used, and the corresponding electrocardiograms were simulated to study the influence of these APD gradients on the T-wave amplitudes. The simulation results showed that changing a single APD gradient (e.g., interventricular APD gradient alone) only made substantial changes to the T-wave amplitudes in a limited number of leads and was not able to generate T waves with amplitudes comparable with clinical findings in all leads. A combination of transmural, apicobasal and interventricular APD gradients could simulate T waves with amplitudes similar to clinical values in the limb leads only. Adding the anteroposterior APD gradient into the model greatly improved the consistency between the simulated T-wave amplitudes and the clinical values.Conclusion: The simulation results support that the transmural, apicobasal, interventricular and the anteroposterior APD gradient are all essential to the genesis of the clinical T wave. (C) 2016 Elsevier Inc. All rights reserved.

This paper proposes an algorithm that allows fully utilize the Central Processing Unit-Graphics Processing Unit (CPU-GPU) hybrid architecture to conduct parallel computation and reasonable scheduling for computer simulation of electrocardiogram (ECG). This algorithm is realized by accelerating calculation speed and increasing platform adaptability of the parallel algorithm.Today, many algorithms have been proposed to dynamically schedule a set of tasks in CPU-GPU hybrid environments. Among these scheduling algorithms, only Pure Self-Scheduling (PSS) algorithm can achieve load balancing in such an extremely heterogeneous environment. However, Pure Self-Scheduling can neither fully exploit the advantages of GPU performance, nor efficiently minimize the dynamic scheduling overhead. In this paper, Load-Prediction Scheduling (LPS) has been introduced to solve the aforementioned problems. Furthermore, to meet the demand for the best performance in a hybrid environment, which is formed by many heterogeneous computers, we propose an approach to adjust scheduling parameters dynamically. In order to validate our parallel algorithm and scheduling approach, we performed ECG simulation to confirm the efficiency and accuracy of ECG simulation algorithms based on the proposed method. At first, LPS predicts the workloads of each step in the simulation. The prediction results help to schedule heavy workloads to components with strong computational ability and light workloads to components with weak computational ability. LPS also synthesizes dynamic-scheduling and static-scheduling methods to minimize the disadvantages of these two scheduling methods. In the meantime, a Sliding Window Mechanism (SWM) adjusts the boundary between dynamic-scheduling and static-scheduling to make LPS perform better in hybrid environments. Experimental results of LPS on the computer simulation of ECG show that the LPS algorithm is more efficient than PSS. The ECG simulation is improved by about 20 times by using our proposed method. The ECG simulation of LPS with SWM is about 21% faster than that without SWM. (C) 2015 Elsevier Inc. All rights reserved.

Provides a listing of current committee members and society officers.

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder. It has been reported that approximately 40% of patients with moderate or severe OSAS die within the first eight years of disease. In hospitals, OSAS is inspected using polysomnography, which uses a number of sensors. Because of the cumbersome nature of this polysomnography, an initial OSAS screening is usually conducted. In recent years, OSAS screening techniques using Holter electrocardiogram (ECG) have been reported. However, the techniques so far reported cannot perform an OSAS severity assessment. The present study presents a new method to distinguish the obstructive sleep apnea (OSA) and non-OSA epochs at one-second intervals based on the Apnea Hypopnea Index assessment, defined as the duration of continuous apnea. In the proposed method, the time-frequency components of the heart rate variability and three ECG-derived respiration signals calculated by the complex Morlet wavelet transformation are adopted as features. A support vector machine is employed for classification. The proposed method is evaluated using three eight-hour ECG recordings containing OSA episodes from three subjects. As a result, the sensitivity and specificity of classification are found to reach approximately 90%, a level suitable for OSAS screening in clinical settings.

BACKGROUND:The action potential duration (APD) and the conduction velocity (CV) restitution have been reported to be important in the maintenance and conversion of ventricular fibrillation (VF), whose mechanisms remain poorly understood. Multiple-wavelet and/or mother-rotor have been regarded as the main VF mechanisms, and APD restitution (APDR) and CV restitution (CVR) properties are involved in the mutual conversion or transition between VF and ventricular tachycardia (VT).METHODS AND RESULTS:The effects of APDR (both its slope and heterogeneity) and CVR on VF organization and conversion were examined using a "rule-based" whole-heart model. The results showed that different organizations of simulated VF were manifestations of different restitution configurations. Multiple-wavelet and mother-rotor VF mechanisms could recur in models with steep and heterogeneous APDR, respectively. Suppressing the excitability either decreased or increased the VF complexity under the steep or shallow APDR, respectively. The multiple-wavelet VF changed into a VT in response to a flattening of the APDR, and the VT degenerated into a mother-rotor VF due to the APDR heterogeneity.CONCLUSIONS:Our results suggest that the mechanisms of VF are tightly related to cardiac restitution properties. From a viewpoint of the "rule-based" whole-heart model, our work supports the hypothesis that the synergy between APDR and CVR contributes to transitions between multiple-wavelet and mother-rotor mechanisms in the VF.

The distributed database HBase has become an effective solution of storing massive data. But there are still no standard methods of designing HBase tables from a conceptual model for an application. This paper presents a mechanism for transforming an E-R model into HBase tables. It defines four types of transforming processes for entities, one-to-one relationship, one-to-many relationship and many-to-many relationship respectively, and corresponding strategies for the organization of column families for more efficient queries. This mechanism was effectively applied to implement the Hbase based Document Registry for the Cross-Enterprise Document Exchange, a profile from Integrating the Healthcare Enterprise.

Spin-torque oscillator (STO) with reference layer (REF) has been proposed to be an efficient way to excite oscillation at low current density. A well-controlled STO is the center part of microwave assisted magnetic recording (MAMR); however, in the recording process, the STO is exposed in an alternative external head field in addition to the applied current. To assist the writing in MAMR, the STO should switch and change the oscillation chirality when the head pole flips, and the switching time needs to be shorter than 0.2 ns so as to follow the write field flipping. In this work, we would like to find the suitable design of Co/Pt REF to achieve this target.

We performed the computer simulation of cathode ablation for a trial fibrillation based on the standard and improved Cox maze III procedure with transmural ablation or not. At first, a computational a trial model was built from MRI images of a male volunteer, and was discretized into spherical units in a spherical coordinate. Then, the anisotropy of conduction velocity and electrical conductivity were incorporated into the special conduction system of this model. The action potential of each cell was simulated based on the Nygren cell model. Furthermore, action potential and conduction velocity restitutions were also introduced to the model. The simulation results demonstrated that non-transmural maze III ablation procedure can achieve the same effect as the transmural ablation if the positions of ablation lines are reasonable. This study may provide theoretical hints and evidence for the usefulness of maze III ablation procedure in the treatment of chronic a trial fibrillation.

In order to study the performance of MIC-GPU-CPU hybrid computing platform, this paper conduct a whole heart simulation model on this platform. And to corroborate the advantages of this heterogeneous architecture environment, we parallelize the electrocardiograms simulation algorithm. Electrocardiograms simulation algorithm is a recommendable case to study the ability of MIC-GPU-CPU architecture for its great deal of computation. The paper addresses the heart modeling program performs in the MIC-GPU-CPU architecture, such architecture performs well in electrocardiograms simulation application, and the bottleneck in this platform with a set of parameter-various benchmarks. The heart simulation results on CPU, MIC, GPU, and MIC-GPU-CPU are exhibited at last.

A distributed simulation method of electric field based on the atrial defibrillation of the heart modeling and finite element solution is proposed in this study. In order to solve the problem that ordinary clinical trials could not measure the actual distribution of the defibrillation electric field in the heart accurately, this method provides a research tool for electrical defibrillation. A complete atrial anatomical structure in the heart model is used in the research, the finite element method is proceeded to solve; Three parameters: defibrillation threshold voltage, the high field strength rate and the defibrillation threshold energy are set to evaluate the effect of defibrillation. The heart electric field distributions of transvenous atrial defibrillation with different electrode locations or sizes are simulated. The simulation results and the reported results match fairly well, which initially verify the feasibility of this method.

In this paper, through the inertial sensors or a smart phone to collect human walking gait data and then with periodic gait analysis to establish personal health counseling application. Based on regularly gait data analysis, it could find some special features of the person, such as dynamic symmetry of gait, cyclic stability of gait, and the walking patterns, also their changes with weekly or monthly. If the irregular or abnormal walking patterns like asymmetry or skew, stumbles or slip could be often detected, it may make early warning to the person that he or she may has the problem of body, possibly the falls risk.

Biological computations like Electrocardiological modeling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of Electrocardiograms (ECGs). We realized the parallel computation for computer simulation of ECGs on a CPU-GPU cluster using a hybrid parallel algorithm with the parallel program development tools-MPI, OpenMP, and CUDA. Furthermore, we proposed a load-prediction static scheduling and load-prediction dynamic scheduling to achieve efficient process-level and thread-level scheduling, respectively. Compared with traditional static and dynamic scheduling, our scheduling schemes are more efficient. In our research, we achieved a speedup of 55.1 using four PCs for the computer simulation of ECGs. This study demonstrates that the cluster can provide a cheap and efficient environment for parallel computations in biological modeling and simulation studies.

Obstructive sleep apnea syndrome is the most common type of sleep apnea, characterized by repetitive pauses in breathing during sleep. Recent studies have investigated screening methods based only on an electrocardiogram (ECG). Generally, in ECG-based screening, the ECG-derived respiration (EDR) is often used, which is caused by the variance in the cardiac electrical axis due to the chest movement associated with the respiration itself. This method might be effective for diagnosing the sleep apnea severity, called the apnea-hypopnea index, which is defined by the duration and occurrence rate of apnea episodes. However, conventional ECG lead systems are not necessarily optimized for this purpose. In this study, nine bipolar electrodes located across the entire ventral thoracic region were devised, based on the conventional lead system, to effectively measure thoracic breathing. To evaluate the most effective electrode placements, two eupneic and three apneic tasks with nine electrodes were conducted, and EDRs were calculated. Then, the respiratory rates were estimated from the EDRs, and the eupnea and apnea groups were classified using features of the EDRs. Consequently, it was found that three electrodes located at the lower thoracic region yielded accurate estimations of the respiratory rate and discrimination rate.

A cosine-function-based approximation method is proposed to reconstruct atrial fibrillatory wave in QRST intervals for estimating dominant frequency of atrial fibrillation (AF) using single-lead ECG. It is hypothesized that atrial fibrillatory waves in two adjacent QRST intervals can be approached by a cosine function and direct component. As harmonics of dominant frequency are neglected, the determination of harmonics’ number and Tikhonov regularization are avoided compared with previous methods. Initially, we build a learning data set composed of at most 3 TQ intervals neighboring to the two QRST intervals. Next, the optimum frequency of cosine is determined in an enumeration way using the learning data set, and the corresponding amplitude, phase, and direct component are estimated in a least square method. Finally, the atrial fibrillatory waves in the two adjacent QRST intervals are calculated based on the cosine function and direct component. This method is simple and convenient, and the atrial fibrillatory waves in ectopic QRST intervals can also be easily estimated. Through evaluation with artificial and clinical AF ECG, we proved that frequency analysis of atrial fibrillation based on this method can obtain a dominant frequency profile with less standard deviation than that based on ABS method.

Multi-core CPU and GPU technologies upgrades a PC to a personal supercomputer. Many algorithms have been proposed to achieve dynamic scheduling in CPU-GPU hybrid environments. Among them, only pure self-scheduling (PSS) can achieve perfect load balancing in this extremely heterogeneous environment. But PSS can not take full advantage of GPU performance, reduce the overhead of the tail problem, and reduce the dynamic scheduling overhead. In this paper, load-prediction scheduling (LPS) was introduced to solve the above problems. To demonstrate the efficiency of LPS in practical applications, it was implemented to parallelize computer simulation of electrocardiogram. Experimental results of LPS on the computer simulation of electrocardiogram (ECG) show that the LPS algorithm is more efficient than PSS.