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This study aims to evaluate the potential of percutaneous patent foramen ovale (PFO) closure to improve the headache in patients with migraine and PFO, and discuss the difference between the randomized controlled trials (RCTs) and the single-center studies. Patients of migraine with a large shunt of PFO, who experienced ≥2 headache attacks per month and failed ≥2 categories of standardized medication, underwent PFO closure in First Affiliated Hospital of Xi'an Jiao Tong University. The clinical outcomes, including frequency and duration of headache attacks, Headache Impact Test (HIT-6) score, and Visual Analogue Scale (VAS) score, were evaluated at 3, 6, and 12 months of follow-up after the PFO closure. The different efficacies of the clinical outcomes between patients with and without aura as well as different grades of PFO were also evaluated, respectively. 134 patients with migraine (39 male and 95 female) with PFO were enrolled, whose average age was 39.21±11.37 years. After PFO closure, there was a significant reduction in frequency and duration of headache attacks, HIT-6 score, and VAS score at 3, 6, and 12 months’ follow-up (p50% in 44 (32.84%) patients at 3 months’ follow-up and increased to 48 (35.82%) at 12 months’ follow-up. 31.03% patients remained residual shunt after 6 months of closure with varying improvements of headache. This study confirmed that PFO closure can effectively reduce frequency and duration of migraine and improve quality of life, but the definitive indications and long-term effect still need further research.

Read-ahead schemes have been widely used in page cache to improve read performance of Linux systems. As the Android system inherits the Linux kernel, the traditional read-ahead scheme is directly transplanted to mobile devices. However, request sizes and page cache sizes on mobile devices are much smaller, which may degrade read-ahead efficiency and therefore hurt user experience. This article first observes that many pages pre-fetched by read-ahead are unused, which causes frequent page cache eviction. And these evict operations could induce extra access latency, especially when write-back is conducting. Then, this article proposes a new analysis model to characterize the factors that closely relate to the access latency. It is found that there exists a trade-off between read-ahead size and access latency. Finally, this article proposes two optimized read-ahead schemes to exploit this trade-off under different situations. Size-tuning scheme aims to find the proper maximum size of read-ahead according to the characteristics of mobile devices. While MobiRA scheme improves the read-ahead efficiency by dynamically tuning read-ahead size and stop-settings. Experimental results on real mobile devices show that the proposed schemes can increase the efficiency of read-ahead scheme and improve the overall performance of mobile devices.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Read-ahead schemes of page cache have been widely used to improve read performance of Linux systems. As Android system inherits the Linux kernel, traditional read-ahead schemes are directly applied in mobile devices. However, read request sizes and page cache sizes in mobile devices are much smaller than other platforms, which may decrease read-ahead efficiency and hurt user experience. The read-ahead efficiency is defined as hit pages / all pre-fetched pages in a sequential read. To study the efficiency of traditional read-ahead in mobile devices, this paper first observes that many pre-fetched pages are unused in page cache, which causes high page cache eviction ratio with high extra access latency. Then, this paper analyzes the factors that closely relate to the access latency. It is found that there exists a trade-off between read-ahead size and access latency. A size-tuning scheme is then proposed to explore this trade-off. Experimental results on real mobile devices have shown that the proposed scheme can reduce the number of pre-fetched pages and improve the efficiency of read-ahead without decreasing the page cache hit ratio.

Numerous published studies have investigated the relationship between the CD14-260C>T (rs2569190) polymorphism and the risk of myocardial infarction (MI). However, the results are still conflicting and inconclusive. Potentially eligible published articles were searched in four databases including PubMed, Web of Science, EMBASE and Chinese Biomedical Database (CBM). The odds ratio (OR) with its 95% confidence interval (CI) was used to estimate the strength of the associations. Thirteen papers including 17 case-control studies were included, reporting a total of 6,443 MI patients and 6,315 controls. A significant increase in overall MI susceptibility was identified in the homozygote model. In the subgroup analysis, with respect to the type of MI, a significantly increasing acute MI susceptibility was found in the homozygote model. In the subgroup analysis for ethnicity, a significant increased susceptibility was found in Asian populations in allele, homozygote, recessive and dominant models. However, no significant association was found among Caucasian populations. In conclusion, there may be a moderate association between the CD14-260C>T polymorphism and acute MI susceptibility. This association may be different between ethnicities with the CD14-260C>T polymorphism being a risk factor for myocardial infarction in Asian populations.

Key-value stores based on log-structured merge-tree (LSM-tree), e.g. LevelDB by Google and RocksDB by Facebook, have been widely used in a lot of cyber-physical systems scenarios to provide flexible data access and high performance. Compaction is a necessary operation to delete some invalid data, reduce the data size and improve the efficiency of reading and writing. However, compaction would stall write requests, which significantly degrades system performance. This paper studies the effect of compaction on tail latency that shows frequent latency spikes. These spikes would cause poor user experience and have already concerned by industrial researchers. Furthermore, the number of involved SSTables proportionally decides the height of latency spikes. We propose the limited compaction to only allow a part of SSTables involved in the compaction. We implement the limited compaction with a basic method to choose the SSTables randomly and a selective method to choose the SSTables according to their overlapped ranges with the next level. Experimental results on LevelDB with comprehensive benchmarks have shown that the proposed methods can effectively reduce the tail latency, while inducing an acceptable write amplification.

Higher mortality in asthmatics has been shown previously. However, evidence on different asthma phenotypes on long-term mortality risk is limited. The aim was to evaluate the impact of asthma phenotypes on mortality in general population. Data from the National Health and Nutrition Examination Survey from 2001–2002 to 2013–2014 linked mortality files through December 31, 2015, were used (N = 37,015). Cox proportional hazards regression was used to estimate the risk of all-cause and cause-specific mortality adjusting for sociodemographic characteristics, smoking, body mass index, and chronic conditions. During the mean follow-up time of 7.5 years, 4326 participants died from a variety of causes. Current asthma, but not former asthma was associated with increased all-cause mortality (current asthma: HR = 1.37; 95% CI 1.20–1.58; Former asthma: HR = 0.93; 95% CI 0.73–1.18); as well as mortality from cardiovascular disease (HRCurrent = 1.41; 95% CI 1.08–1.85) and chronic lower respiratory diseases (HRCurrent = 3.17; 95% CI 1.96–5.14). In addition, we found that the HR for cardiovascular disease (CVD) mortality was slightly greater in people with childhood-onset asthma than those with adult-onset asthma. The HR for chronic lower respiratory diseases (CLRD) mortality was greater in people with adult-onset asthma than those with childhood-onset asthma. However, the differences were not statistically significant. Our study suggested that current asthma but not former asthma was associated with increased all-cause, CLRD and CVD mortality. Future well-designed studies with larger sample are required to demonstrate the association and clarify the potential mechanisms involved.

Objective: To identify feature autophagy-related genes (ARGs) in patients with acute myocardial infarction (AMI) and further investigate their value in the diagnosis of AMI. Methods: Gene microarray expression data of AMI peripheral blood samples were downloaded from the GSE66360 dataset. The data were randomly classified into a discovery cohort (21 AMI patients and 22 healthy controls) and a validation cohort (28 AMI patients and 28 healthy controls). Differentially expressed ARGs between patients with AMI and healthy controls in the discovery cohort were identified using a statistical software package. Feature ARGs were screened based on support vector machine-recursive feature elimination (SVM-RFE), and an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value of the classifier, which was further verified in an independent external cohort. Results: A total of seven genes were identified based on SVM-RFE. The SVM classifier had an excellent discrimination ability in both the discovery cohort (area under the curve [AUC] = 0.968) and the validation cohort (AUC = 0.992), which was further confirmed in the GSE48060 dataset (AUC = 0.963). Furthermore, the SVM classifier showed outstanding discrimination between AMI patients with and without recurrent events in the independent external cohort (AUC = 0.992). The identified genes are mainly involved in the cellular response to autophagy, macroautophagy, apoptosis, and the FoxO signaling pathway. Conclusion: Our study identified feature ARGs and indicated their potential roles in AMI diagnosis to improve our understanding of the molecular mechanism underlying the occurrence of AMI.

Present advanced precise race detection techniques leverage strict causal relation to locate data races, leading to false negatives and missing many hidden races. In this paper, we present a novel race detection technique, called DigHR, which can detect more hidden races than existing precise techniques. DigHR is based on a weak causal relation, called afterward-confirm (AC) relation, which relaxes restrictions of existing causal relations and is proved to be sound. To represent AC relations between events, we propose a new data structure, called water-flow graph, which enable DigHR to analyze AC relations and detect hidden races at run-time. We have implemented DigHR in C/C++ and evaluated the effectiveness of DigHR by detecting races from real-world benchmarks and applications. The experimental results show that DigHR can precisely detect 21% more races than previous techniques, including FastTrack and CP.

3D-multi-level cell (MLC) NAND flash memory adopts 3D stack technology and stores two bits per cell, leading to improved storage capacities, but sacrificing data reliability. Low-density parity-check (LDPC) codes showing strong error correction capability benefit from their soft decision decoding, which is widely exploited to guarantee data reliability. Nevertheless, adopting LDPC codes can introduce a concern about read performance, because their iterative soft-decision decoding requires Log-Likelihood Ratio (LLR) information, called soft decision information, by applying multi-sensing voltages. This process of obtaining LLR information leads to high sensing and transferring latencies, lowering down 3D-MLC read performance. In particular, when raw bit error rates (RBER) are much higher due to the long retention periods and program/erase (P/E) cycles, this problem becomes more serious. In this paper, we propose a RBER-aware multi-sensing scheme for reducing sensing and transferring latencies, and thus improving read performance. This proposed scheme exploits the variations of RBER in flash pages with the increase of retention time and P/E cycles to dynamically apply sensing voltages. Simulation results show that this scheme significantly decreases the number of required sensing voltages while maintaining LDPC error correction capability, enhancing 3D-MLC read performance.

A potential association between type 1 diabetes mellitus and subsequent epilepsy emerged in recent studies. This study aimed to evaluate the possible relationship between type 1 diabetes mellitus and epilepsy using meta-analysis. Pubmed, ISI Web of Knowledge, Embase and Cochrane Library were searched for potential studies of the association between type 1 diabetes mellitus and epilepsy from inception to February 1, 2017. Two investigators independently screened studies for inclusion and extracted related data; discrepancies were solved by consensus. Random effects model of Hazard Ratio (HR) was used to estimate the strength of association. We identified 13 papers from potentially relevant articles of which 3 cohort studies met the inclusion criteria. Random effects meta-analysis showed that type 1 diabetes mellitus was associated with an increased risk of epilepsy with HR = 3.29 (95% CI: 2.61-4.14; I2 = 0, p = 0.689). Similar results were observed in type 1 diabetes mellitus patents younger than 18-years-old with HR = 2.96 (95% CI: 2.28-3.84; I2 = 0, p = 0.571). Meta-analysis of 2 studies that adjusted for potential confounders yielded an increased risk of epilepsy with HR = 2.89 (95% CI: 2.26-3.70; I2 = 0, p = 0.831). The meta-analysis indicates that type 1 diabetes mellitus is associated with a statistically significant increased risk for epilepsy compared to those without type 1 diabetes mellitus.

Existing studies have uncovered that there exist significant Raw Bit Error Rates (RBERs) variations among different layers of 3D flash memories due to manufacture process variation. These RBER variations would cause significantly diversed read latencies when reading data with traditional Low-Density Parity-Check (LDPC) codes designed for planar flash memories, which induces sub-optimal read performance of flash-based Solid-State Drives (SSDs). To investigate the latency diversity, this paper first performs a preliminary experiment and observes that LDPC read levels proportional to latencies increase in diverse speeds along with data retention. Then, by exploiting the observation results, a Multi-Granularity LDPC (MG-LDPC) read method is proposed to adapt level increase speed for each layer. Five LDPC engines with varied increase granularity are designed to adapt RBER speed requirements. Finally, two implementations for MG-LDPC are applied to assign LDPC engines for each flash layer in a fixed way or dynamically according to prior read levels. Experimental results show that the proposed two implementations can reduce SSD read response time by 21% and 47% on average, respectively.CCS CONCEPTS• Hardware→Temperature optimization; Network on chip; 3D integrated circuits.

Solid state drives (SSDs) widely used in cyber-physical systems have to use block cleaning operations to reclaim storage space because of the out-of-place update characteristics. As these operations often take high time costs and do harm to flash lifetime because of write amplification, SSD system performance has been largely decreased. Garbage collection and wear leveling, as two typical block cleaning techniques, are invoked without enough communication between each other, which may induce one block repeatedly cleaned within a short period, leading to worse performance and lifetime of SSDs. This paper proposes an unified block cleaning method called UniBC by considering the two invoking conditions unitedly to reduce the cost of overall block cleaning. In details, UniBC makes garbage collection able to consider the wear degree of victim blocks by utilizing constrained victim block selection techniques and wear leveling be aware of the utilization degree of victim blocks. By exploiting UniBC, repetitive block cleaning operations can be avoided and overall system performance can be improved. Experimental results show that UniBC can improve 10% block cleaning performance on average.

Purpose. To compare the sensitivity and specificity of modified and traditional methods of contrast echocardiography of the right portion of the heart in patients with a suspicion of patent foramen ovale (PFO). Methods. The study population consisted of 506 patients with high clinical suspicion of PFO. The traditional Valsalva maneuver consists of expiration against a closed glottis after a full inspiration. A modified Valsalva maneuver was performed with a handmade pressure monitoring device, which measured pressure during performance of the Valsalva maneuver. Modified and traditional methods of contrast echocardiography were performed among all patients. Contrast transesophageal echocardiography (TEE) was regarded as the gold standard. Results. A total of 279 patients with PFO were confirmed by TEE. 259 cases (sensitivity: 92.83%) were detected by a modified method of contrast echocardiography of the right portion of the heart, while 234 cases were detected using the traditional method (sensitivity: 83.87%). The sensitivity of modified contrast echocardiography of the right portion of the heart was significantly higher than that of the traditional method (92.83% vs. 83.87%, P=0.001). However, there was no significant difference in the specificity of the two methods for the diagnosis of PFO (97.35% vs. 96.03%, P=0.431). Additionally, the results of semiquantitative evaluation of PFO using modified method failed to show a more positive rate than shown by the traditional method (Z=−1.782, P=0.075). Conclusions. Modified contrast echocardiography of the right portion of the heart yielded a higher sensitivity than the traditional method, which contributed to the diagnosis of cardiac PFO. The research was a part of a register study (https://register.clinicaltrials.gov/ ClinicalTrials ID: NCT02777359).

With the development of bit density and technology scaling, the process variation (PV) has become much severe on NAND flash memory. As PV presents reliability among flash blocks, which causes read performance variation to read data on different blocks. This paper proposes to improve read performance of LDPC based flash memory by exploiting the reliability characteristics of PV. First, a block grouping approach is proposed to classify the flash blocks based on their reliability. Then, a read data placement scheme is proposed, which is designed to place read-hot data on flash blocks with high reliability and move read-cold data to blocks with low reliability. Experiment results show that, with negligible overhead, the proposed scheme is able to significantly improve the read performance.

This paper proposes to improve read performance of LDPC based flash memory by exploiting process variation (PV). The work includes three parts. First, a block grouping approach is proposed to classify the flash blocks based on their reliability. Second, based on the grouping approach, a read data placement scheme is proposed, which is designed to place read-hot data on flash blocks with high reliability. However, simply placing read-hot data to high reliable blocks conflicts with recent PV-aware wear leveling schemes. In the third part of the work, a grouping partition scheme is proposed to limit the number of high reliable blocks for read-hot data. In this case, read performance can be well improved with little impact on the lifetime improvement.

Numerous reports have been done to seek the relationship between sleep apnea hypopnea syndrome (SAHS) and the risk of atrial fibrillation (AF). However, definite conclusion has not yet been fully established. We examined whether SAHS increases AF incidence in common population and summarized all existing studies in a meta-analysis. We summarized the current studies by searching related database for potential papers of the association between SAHS and the risk of AF. Studies that reported original data or relative risks (RRs) with 95% confidence intervals (CIs) for the associations were included. Sensitivity analyses were performed by omitting each study iteratively and publication bias was detected by Begg’s tests. Eight eligible studies met the inclusion criteria. Fixed effects meta-analysis showed that SAHS increased AF risk in the common population (RR = 1.70, 95% CI: 1.53–1.89, P=0.002, I2=69.2%). There was a significant association between mild SAHS and the risk of AF (RR = 1.52, 95% CI: 1.28–1.79, P=0.01, I2=78.4%), moderate SAHS (RR = 1.88, 95% CI: 1.55–2.27, P=0.017, I2=75.6%), and severe SAHS (RR = 2.16, 95% CI: 1.78–2.62, P<0.001, I2=91.0%). The results suggest that sleep apnea hypopnea syndrome could increase the risk of AF, and the higher the severity of SAHS, the higher risk of atrial fibrillation.

With the technology scaling of solid state drives (SSDs), data reliability has to be ensured by advanced Low-Density Parity-Check (LDPC) codes. As LDPC reads often take high latency, read performance involved in traditional garbage collection, called external GC, is largely decreased, which affects system performance. GC can choose not to correct data with LD-PC codes but just copy valid pages into registers of NAND flash, which we call internal GC. Although this method avoids long latency in LDPC reads, the data integrity is sacrificed because of error aggregation in valid pages. To balance performance and reliability, this paper proposes a new garbage collection method, named as LDPC-aware Garbage Collection (LaGC), to selectively choose blocks with high error rates to perform external GC. These blocks are often close to uncorrectable error rates, which take a small portion during a long flash life period. Thus, most of blocks perform internal GC and keep advantage of low read latency. Experimental results show that LaGC can reduce 78% of read latency in external GC and achieve about 2% system performance improvement.

3D NAND flash memory has been well developed due to its high density and decreasing cost compared with planar flash. However, one issue for 3D NAND flash, which has not been well solved, is its worse read disturb. The worse read disturb of 3D NAND flash stems from its much more word lines in a block. In this case, it receives much more read operations, leading to increased read disturb. Previous work proposed to relax the read disturb on planar flash through reducing the pass-through voltage, V pass , on the unread word lines. However, this is not viable for 3D NAND flash with the increased number of word lines in a block. In this work, a new read disturb reduction scheme is proposed for 3D NAND flash. First, a read error model is presented, which demonstrates that selective V pass reduction is a viable approach. Then, a read-hotness aware V pass reduction scheme is proposed to improve performance without violating the reliability requirement. Simulation shows that the proposed scheme achieves encouraging performance improvement.

In order to relieve reliability problem caused by technology scaling, LDPC codes have been widely applied in flash memories to provide high error correction capability. However, LDPC read performance slowdown along with data retention largely weakens the access speed advantage of flash memories. This paper considers to apply the concept of refresh, that were used for flash lifetime improvement, to optimize flash read performance. Exploiting data read characteristics, this paper proposes LDR, a lightweight data refresh method, that aggressively corrects errors in read-hot pages with long read latency and reprograms error-free data into new pages. Experimental results show that LDR can achieve 29% read performance improvement with only 0.2% extra P/E cycles on average, which causes negligible overhead on flash lifetime.

This paper proposes a cascading algorithm (CSR) based on compressed sensing, which aims to reduce intensive computations in super-resolution imaging of fluorescence microscopy. Performance of existing algorithms such as CVX and L1H drop sharply when applied to obtain finer images with high density molecules. CSR fully exploits the extreme sparsity property of molecules in the compressed sensing model and progressively restricts solution space stage by stage. We perform a comprehensive study of existing algorithms and the proposed algorithm under different resolutions and molecules’ densities. Simulation and experimental results confirm the performance advantage of CSR when applied to recover dense molecules.