Miracle-dz/UniFAR-FTUs huggingface dataset:
Is pulse pressure an independent risk factor for incident acute coronary heart disease events? The REGARDS study.

BACKGROUND Increased attention has been given to pulse pressure (PP) as a potential independent risk factor for cardiovascular disease (CVD). We examined the relationship between the three indices of blood pressure consisting of systolic blood pressure (SBP), diastolic blood pressure (DBP), and PP (= SBP - DBP), respectively, and incident acute coronary heart disease (CHD). METHODS Participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study, a national cohort study of 30,239 black and white participants > 45 years of age, were enrolled between 2003 and 2007. The participants' SBP, DBP, and PP values were separated into the four groups of < 45mm Hg, 45-54.9mm Hg, 55-64.9mm Hg, and ≥ 65mm Hg, and were analyzed on a groupwise basis. Reported CHD events were confirmed by expert adjudication. Cox proportional hazards models were used to examine the association of incident CHD (first acute CHD event) for the four groups of BP measurements with multivariate-adjusted sociodemographic and clinical risk factors. RESULTS Analyses were done for 22,909 men and women (40.4% black, 44.6% male) ≥ 45 years of age (mean age = 64.7±9.4 years) without prevalent CHD at baseline. Associations were found for 681 CHD events, over a mean 3.4 years of follow-up (maximum 6 years), with each unadjusted PP group (hazard ratio [HR] with 95% confidence limits for PP of 45-54.9mm Hg, 55-64.9mm Hg, and ≥ 65mm Hg, respectively, of 3.82, 3.08, and 4.73 as compared with PP < 45mm Hg; P < 0.0001 for linear trend), and this persisted after full adjustment, including that for SBP (1.50, 1.08, 2.09; P trend < 0.01). Subgroup analyses showed no statistically significant differences across age, race, or region of the country, but did suggest the possibility that men were more sensitive to PP than were women. CONCLUSIONS Pulse pressure is positively and independently (particularly so with regard to independence from SBP) associated with incident CHD, and there were no significant racial or regional differences in this association.
Rotation-matrix-based attitude synchronization of multiple spacecraft without velocity measurements

This paper proposes a distributed protocol for multiple spacecraft such that attitude synchronization is achieved in the absence of angular velocity measurements. The attitude of each spacecraft is represented by the rotation matrix. We first introduce two auxiliary systems for each spacecraft in the form of rotation matrix dynamics to deal with the issue caused by unavailability of angular velocities. Here, neither the absolute angular velocity nor the relative angular velocities with respect to the neighboring spacecraft are available. By implementing appropriate inputs into the auxiliary systems, a synchronization protocol is synthesized in terms of local information interaction, where only the information from the neighbors is exchanged for each spacecraft. It is shown that the proposed distributed protocol guarantees velocity-free attitude synchronization of multiple spacecraft given that the communication graph is a tree. Simulations verify the theoretical results.
Evaluation of an avatar-based training program to promote suicide prevention awareness in a college setting

ABSTRACT Objective: Training programs exist that prepare college students, faculty, and staff to identify and support students potentially at risk for suicide. Kognito is an online program that trains users through simulated interactions with virtual humans. This study evaluated Kognito's effectiveness in preparing users to intervene with at-risk students. Participants: Training was completed by 2,727 university students, faculty, and staff from April, 2014 through September, 2015. Methods: Voluntary and mandatory participants at a land-grant university completed Kognito modules designed for higher education, along with pre- and post-assessments. Results: All modules produced significant gains in reported Preparedness, Likelihood, and Self-Efficacy in intervening with troubled students. Despite initial disparities in reported abilities, after training participants reported being similarly capable of assisting at-risk students, including LGBTQ and veteran students. Conclusions: Kognito training appears to be effective, on a large scale, in educating users to act in a facilitative role for at-risk college students.
Music Therapy To Relieve Anxiety In Pregnant Women On Bedrest: A Randomized, Controlled Trial

Purpose:To explore the effect of music therapy on anxiety alleviation for antepartal women on bedrest in China. Design and Methods:One hundred and twenty patients recruited from one tertiary hospital in Changsha city, China were enrolled in a randomized controlled trial. Women in the experimental group received music therapy for 30 minutes on 3 consecutive days. Usual care participants had a 30-minute rest on 3 consecutive days. Variables included anxiety (State-Trait Anxiety Inventory), and physiological responses (vital signs, fetal heart rate). Descriptive statistics, t tests, χ2 tests, Wilcoxon rank sum tests, and Pearson correlation analyses were used to analyze the data. Results:Anxiety levels decreased and physiological responses improved significantly in the intervention group, which was provided with music therapy while on bedrest. Clinical Implications:Carefully selected music that incorporates a patient's own preferences may offer an inexpensive and effective method to reduce anxiety for antepartal women with high risk pregnancies who are on bedrest.
Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement

This paper proposes a method based on the active shape model (ASM) to segment the prostate in magnetic resonance (MR) images. Due to the great variability in appearance among different boundaries of the prostate and among subjects, the traditional ASM is weak in MR image prostate segmentation. To address these limitations, we investigated a novel ASM-based method by incorporating deep feature learning into our previous liver segmentation framework. First, an adaptive feature learning probability boosting tree (AFL-PBT) based on both simple handcrafted features and deep learned features was developed for prostate pre-segmentation and for further shape model initialization. The proposed AFL-PBT classifier also provided a boundary searching band, which made the ASM less sensitive to model initialization. Then, the convolutional neutral network (CNN) deep learning method was used to train a boundary model, which separated voxels into three types: near, inside, and outside the boundary. A three-level ASM based on the CNN boundary model was employed for the final segmentation refinement. On MICCAI PROMISE12 test data sets, the proposed method yielded a mean Dice score of 84% with a standard deviation of 4%. The experimental results demonstrated that the proposed method outperformed other ASM-based prostate MRI segmentation methods and achieved a level of accuracy comparable to that of state-of-the-art methods.
In Situ Error Detection Techniques in Ultralow Voltage Pipelines: Analysis and Optimizations

In order to achieve high tolerance against process, voltage, and temperature variations in the ultralow voltage (ULV) circuits, in situ error detection and correction (EDAC) techniques were presented. However, circuits adding the capability of error detection incur large hardware overhead, especially in ULV due to larger delay variability. In this paper, we analyze the hardware overhead of error detection techniques in pipelines based on three different sequential elements: flip-flops, two-phase latches, and pulsed latches. By exploiting the cycle-borrowing ability, we propose a technique called sparse insertion of error detecting registers on the two-phase latch-based and pulsed-latch-based pipelines to reduce the sequential logic area. Furthermore, we propose a delay-padding methodology using a multi-<inline-formula> <tex-math notation="LaTeX">$V_{t}$ </tex-math></inline-formula> cell library in ULV circuits to reduce EDAC hardware overhead. The proposed techniques are applied on a benchmark six-stage pipeline operating at 0.35 V in a 65-nm CMOS. The analysis results show that our proposed techniques can reduce the total area by 26%–33% and the error detecting register count by 2.9–<inline-formula> <tex-math notation="LaTeX">$4.3\times $ </tex-math></inline-formula> compared with conventional EDAC techniques.
Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.
Outage probability analysis of a two-hop Rayleigh-Lognormal relay network

In next generation wireless technologies such as 5 G systems, heterogeneous relay networks are crucial to connect different wireless environments. In this paper, we analyze the end-to-end outage probability of two-hop relay networks over heterogeneous wireless networks. The setup incorporates Lognormal and Rayleigh distributions to model small-scale fading of some indoor and outdoor wireless channels, respectively. We derive an analytic expression for the outage probability of the amplify-and-forward transmission scheme over two-hop heterogeneous (Rayleigh-Lognormal) relay networks. In particular, we derive closed-form expressions for lower and upper bounds on the end-to-end outage probability. Numerical results validate the tightness of the proposed bounds. We also provide quantitative comparisons of the outage performance of two-hop relay networks in Rayleigh-Lognormal environments and the outage performance of two-hop relay networks in Rayleigh-Rayleigh fading. Numerical examples show that the former has lower outage probability over the entire signal-to-noise ratio range.
Single round access privacy on outsourced storage

We present SR-ORAM1, the first single-round-trip polylogarithmic time Oblivious RAM that requires only logarithmic client storage. Taking only a single round trip to perform a query, SR-ORAM has an online communication / computation cost of O(log n log log n), and an offline, overall amortized per-query communication cost of O(log2 n log log n), requiring under 2 round trips. The client folds an entire interactive sequence of Oblivious RAM requests into a single query object that the server can unlock incrementally, to satisfy a query without learning its result. This results in an Oblivious RAM secure against an actively malicious adversary, with unprecedented speeds in accessing large data sets over high-latency links. We show this to be the most efficient storage-free-client Oblivious RAM to date for today's Internet-scale network latencies.
Exploring the Potential of Instruction-Level Parallelism of Exposed Datapath Architectures with Buffered Processing Units

Recent processor architectures expose their datapaths to the compiler so that the compiler not only takes care of scheduling instructions to the available processing units but also of scheduling the data transports between the processing units. Bypassing register usage this way generally allows the compiler to improve the degree of instruction-level parallelism. However, the current compiler technology is still based on code generation done by a depth-first traversal on the syntax trees that makes use of as few registers as possible. Code generators inspired from queue machines can better utilize the register bypassing capability of exposed datapath architectures with buffered processing units. In this paper, we encode the decision version of optimal code generation as a satisfiability modulo theories (SMT) problem and use SMT solvers to generate optimal code that maximizes the instruction-level parallelism (ILP) for a given number of processing units. Our experimental results clearly demonstrate the potential of exposed datapath architectures to utilize ILP contained in basic blocks to the fullest by relying on our recently suggested queue-based code generation. Second, it is also shown that the queue-based code generation technique produces more efficient code than classic compiler techniques.
X-ray phase-contrast CO2 angiography for sub-10 μm vessel imaging

X-ray in-line phase contrast has recently been combined with CO2 angiography for high-resolution small-animal vascular imaging at low radiation dose. In this paper we further investigate the potential and limitations of this method and demonstrate observation of vessels down to 8 μm in diameter, considerably smaller than the 60 μm previously reported. Our in-line phase-contrast imaging system is based on a liquid-metal-jet-anode x-ray source and utilizes free-space propagation to convert phase shifts, caused by refractive index variations, into intensity differences. Enhanced refractive index variations are obtained through injection of CO2 gas into the vascular system to replace the blood. We show rat-kidney images with blood vessels down to 27 μm in diameter and mouse-ear images with vessels down to 8 μm. The minimum size of observable blood vessels is found to be limited by the penetration of gas into the vascular system and the signal-to-noise ratio, i.e. the allowed dose. The diameters of vessels being gas-filled depend on the gas pressure and follow a simple model based on surface tension. A theoretical signal-to-noise comparison shows that this method requires 1000 times less radiation dose than conventional iodine-based absorption contrast for observing sub-50 μm vessels.
Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances

With the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multimachine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.
Nonholonomic control of distance-based cyclic polygon formation

Inter-agent formation is the interesting issue and coped within previous literatures. We propose a control strategy based on inter-agent cyclic formulation of nonholonomic model. Graph is directed. Each agent maintains a desired distance with a neighbor agent. Under sliding control technique, sequence of control input forces configuration to equilibrium manifold. Unicycle-like model is used as nonholonomic agent model.
Development of self learning system using 3D image sensor for novice drummer

Drumming motion of percussion is important to play music, and it can learn efficiently by coaching from expert. However, if novice drummer cannot get coaching from expert, they practice percussion by oneself with use of book. Then, it is difficult for novice drummer to measure of practice result. We have been developing self learning system for novice drummer. Using this system, novice drummer can check percentage of a much between instruction and practice.
Probabilistic Cost-Effectiveness Analysis of Vaccination for Mild or Moderate Alzheimer’s Disease

Background: Studies on the immunotherapy for Alzheimer’s disease (AD) have increasingly gained attention since 1990s. However, there are pros (preventing of AD) and cons (incurred cost and side effects) regarding the administration of immunotherapy. Up to date, there has been lacking of economic evaluation for immunotherapy of AD. We aimed to assess the cost-effectiveness analysis of the vaccination for AD. Methods: A meta-analysis of randomized control trials after systemic review was conducted to evaluate the efficacy of the vaccine. A Markov decision model was constructed and applied to a 120,000-Taiwanese cohort aged ≥65 years. Person years and quality-adjusted life years (QALY) were computed between the vaccinated group and the the unvaccinated group. Economic evaluation was performed to calculate the incremental cost-effectiveness ratio (ICER) and cost-effectiveness acceptability curve (CEAC). Results: Vaccinated group gained an additional 0.84 life years and 0.56 QALYs over 10-years and an additional 0.35 life years and 0.282 QALYs over 5-years of follow-up. The vaccinated group dominated the unvaccinated group by ICER over 5-years of follow-up. The ICERs of 10-year follow-up for the vaccinated group against the unvaccinated group were $13,850 per QALY and $9,038 per life year gained. Given the threshold of $20,000 of willingness to pay (WTP), the CEAC showed the probability of being cost-effective for vaccination with QALY was 70.7% and 92% for life years gained after 10-years of follow-up. The corresponding figures were 87.3% for QALY and 93.5% for life years gained over 5-years follow-up. Conclusion: The vaccination for AD was cost-effective in gaining QALY and life years compared with no vaccination, under the condition of a reasonable threshold of WTP.
Techniques related to graphene biosensors and their potential combination with optical fibres

Abstract From zero-dimensional fullerene to one-dimensional carbon nanotube and to two-dimensional graphene, nanostructures based on carbon element are always one of the most exciting topics in modern science and technology. The work published in the open literature dealing with the properties, synthesis and sensor applications of graphene is overviewed. The potential techniques to realise optical fibre graphene sensors are presented and discussed.
Could Blobs Fuel Storage-Based Convergence Between HPC and Big Data?

The increasingly growing data sets processed on HPC platforms raise major challenges for the underlying storage layer. A promising alternative to POSIX-IO-compliant file systems are simpler blobs (binary large objects), or object storage systems. They offer lower overhead and better performance at the cost of largely unused features such as file hierarchies or permissions. Similarly, blobs are increasingly considered for replacing distributed file systems for big data analytics or as a base for storage abstractions like key-value stores or time-series databases. This growing interest in such object storage on HPC and big data platforms raises the question: Are blobs the right level of abstraction to enable storage-based convergence between HPC and Big Data? In this paper we take a first step towards answering the question by analyzing the applicability of blobs for both platforms.
A biased random walk recommender based on Rejection Sampling

In this paper, we focus on Recommender Systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the Social Graph, where every step in the walk is chosen almost uniformly at random from the available choices. Even though this strategy yields satisfactory results, it still does not fully exploit the similarity information among users and items. Our work tries to model user-to-user and user-to-item relation as a probability distribution using a novel approach based on Rejection Sampling in order to decide on its next step (biased random walk). Some initial results on reference datasets reveal the potential of this idea.
Stack-based Algorithms for Pattern Matching on DAGs

Existing work for query processing over graph data models often relies on pre-computing the transitive closure or path indexes. In this paper, we propose a family of stack-based algorithms to handle path, twig, and dag pattern queries for directed acyclic graphs (DAGs) in particular. Our algorithms do not precompute the transitive closure nor path indexes for a given graph, however they achieve an optimal runtime complexity quadratic in the average size of the query variable bindings. We prove the soundness and completeness of our algorithms and present the experimental results.
Telepresence Interaction by Touching Live Video Images

This paper presents a telepresence interaction framework based on touchscreen and telepresence-robot technologies. The core of the framework is a new user interface, Touchable live video Image based User Interface, called TIUI. The TIUI allows a remote operator to not just drive the telepresence robot but operate and interact with real objects by touching their live video images on a pad with finger touch gestures. We implemented a telepresence interaction system which is composed of a telepresence robot and tele-interactive objects located in a local space, the TIUI of a pad located in a remote space, and the wireless networks connecting the two spaces. Our system can be a perfect embodiment of a remote operator to do most of daily living tasks, such as opening a door, drawing a curtain, pushing a wheelchair, and other like tasks. The evaluation and demonstration results show the effectiveness and promising applications of our system.