Miracle-dz/UniFAR-FTUs huggingface dataset:
Aortic valve replacement with sutureless and rapid deployment aortic valve prostheses

Aortic valve stenosis is the most common valve disease in the western world. Over the past few years the number of aortic valve replacement (AVR) interventions has increased with outcomes that have been improved despite increasing age of patients and increasing burden of comorbidities. However, despite such excellent results and its well-established position, conventional AVR has undergone great development over the previous two decades. Such progress, by way of less invasive incisions and use of new technologies, including transcatheter aortic valve implantation and sutureless valve prostheses, is intended to reduce the traumatic impact of the surgical procedure, thus fulfilling lower risk patients' expectations on the one hand, and extending the operability toward increasingly high-risk patients on the other. Sutureless and rapid deployment aortic valves are biological, pericardial prostheses that anchor within the aortic annulus with no more than three sutures. The sutureless prostheses, by avoiding the passage and the tying of the sutures, significantly reduce operative times and may improve outcomes. However, there is still a paucity of robust, evidence-based data on the role and performance of sutureless AVR. Therefore, strongest long-term data, randomized studies and registry data are required to adequately assess the durability and long-term outcomes of sutureless aortic valve replacement.
Symmetry breaking in reconstituted actin cortices

The actin cortex plays a pivotal role in cell division, in generating and maintaining cell polarity and in motility. In all these contexts, the cortical network has to break symmetry to generate polar cytoskeletal dynamics. Despite extensive research, the mechanisms responsible for regulating cortical dynamics in vivo and inducing symmetry breaking are still unclear. Here we introduce a reconstituted system that self-organizes into dynamic actin cortices at the inner interface of water-in-oil emulsions. This artificial system undergoes spontaneous symmetry breaking, driven by myosin-induced cortical actin flows, which appears remarkably similar to the initial polarization of the embryo in many species. Our in vitro model system recapitulates the rich dynamics of actin cortices in vivo, revealing the basic biophysical and biochemical requirements for cortex formation and symmetry breaking. Moreover, this synthetic system paves the way for further exploration of artificial cells towards the realization of minimal model systems that can move and divide. DOI: http://dx.doi. org/10.7554/eLife.01433. 001
Shared binary decision diagram with attributed edges for efficient Boolean function manipulation

The efficiency of Boolean function manipulation depends on the form of representation of Boolean functions. Binary decision diagrams (BDDs) are graph representations proposed by S.B. Akers (1978) and R.E. Bryant (1985). BDDs have some properties which can be used to enable efficient Boolean function manipulation. The authors describe a technique of more efficient Boolean function manipulation that uses shared binary decision diagrams (SBDDs) with attributed edges. The implements include an ordering algorithm of input variables and a method of handling 'don't care'. A Boolean function manipulator using the above methods is developed and it is shown that the manipulator is very efficient in terms of speed and storage. <<ETX>>
Bounded-Distortion Metric Learning

Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical inaccuracy. This paper presents {\it bounded-distortion metric learning} (BDML), a new metric learning framework which amounts to finding an optimal Mahalanobis metric space with a bounded-distortion constraint. An efficient solver based on the multiplicative weights update method is proposed. Moreover, we generalize BDML to pseudo-metric learning and devise the semidefinite relaxation and a randomized algorithm to approximately solve it. We further provide theoretical analysis to show that distortion is a key ingredient for stability and generalization ability of our BDML algorithm. Extensive experiments on several benchmark datasets yield promising results.
A new framework for Euclidean summary statistics in the neural spike train space

Statistical analysis and inference on spike trains is one of the central topics in the neural coding. It is of great interest to understand the underlying structure of given neural data. Based on the metric distances between spike trains, recent investigations have introduced the notion of an average or prototype spike train to characterize the template pattern in the neural activity. However, as those metrics lack certain Euclidean properties, the defined averages are nonunique, and do not share the conventional properties of a mean. In this article, we propose a new framework to define the mean spike train where we adopt a Euclidean-like metric from an $L^p$ family. We demonstrate that this new mean spike train properly represents the average pattern in the conventional fashion, and can be effectively computed using a theoretically-proven convergent procedure. We compare this mean with other spike train averages and demonstrate its superiority. Furthermore, we apply the new framework in a recording from rodent geniculate ganglion, where background firing activity is a common issue for neural coding. We show that the proposed mean spike train can be utilized to remove the background noise and improve decoding performance.
Purifying Selection Can Obscure the Ancient Age of Viral Lineages

Statistical methods for molecular dating of viral origins have been used extensively to infer the time of most common recent ancestor for many rapidly evolving pathogens. However, there are a number of cases, in which epidemiological, historical, or genomic evidence suggests much older viral origins than those obtained via molecular dating. We demonstrate how pervasive purifying selection can mask the ancient origins of recently sampled pathogens, in part due to the inability of nucleotide-based substitution models to properly account for complex patterns of spatial and temporal variability in selective pressures. We use codon-based substitution models to infer the length of branches in viral phylogenies; these models produce estimates that are often considerably longer than those obtained with traditional nucleotide-based substitution models. Correcting the apparent underestimation of branch lengths suggests substantially older origins for measles, Ebola, and avian influenza viruses. This work helps to reconcile some of the inconsistencies between molecular dating and other types of evidence concerning the age of viral lineages.
Task–specific impairments and enhancements induced by magnetic stimulation of human visual area V5

Transcranial magnetic stimulation (TMS) can be used to simulate the effects of highly circumscribed brain damage permanently present in some neuropsychological patients, by reversibly disrupting the normal functioning of the cortical area to which it is applied. By using TMS we attempted to recreate deficits similar to those reported in a motion–blind patient and to assess the specificity of deficits when TMS is applied over human area V5. We used six visual search tasks and showed that subjects were impaired in a motion but not a form ‘pop–out’ task when TMS was applied over V5. When motion was present, but irrelevant, or when attention to colour and form were required, TMS applied to V5 enhanced performance. When attention to motion was required in a motion––form conjunction search task, irrespective of whether the target was moving or stationary, TMS disrupted performance. These data suggest that attention to different visual attributes involves mutual inhibition between different extrastriate visual areas.
Ultraviolet fixed point and generalized flow equation of quantum gravity

A new exact renormalization group equation for the effective average action of Euclidean quantum gravity is constructed. It is formulated in terms of the component fields appearing in the transverse-traceless decomposition of the metric. It facilitates both the construction of an appropriate infrared cutoff and the projection of the renormalization group flow onto a large class of truncated parameter spaces. The Einstein-Hilbert truncation is investigated in detail and the fixed point structure of the resulting flow is analyzed. Both a Gaussian and a non-Gaussian fixed point are found. If the non-Gaussian fixed point is present in the exact theory, quantum Einstein gravity is likely to be renormalizable at the nonperturbative level. In order to assess the reliability of the truncation a comprehensive analysis of the scheme dependence of universal quantities is performed. We find strong evidence supporting the hypothesis that 4-dimensional Einstein gravity is asymptotically safe, i.e. nonperturbatively renormalizable. The renormalization group improvement of the graviton propagator suggests a kind of dimensional reduction from 4 to 2 dimensions when spacetime is probed at sub-Planckian length scales.
Electrospun silk fibroin nanofibers promote Schwann cell adhesion, growth and proliferation

In this study, Schwann cells, at a density of 1 × 105 cells/well, were cultured on regenerated silk fibroin nanofibers (305 ± 84 nm) prepared using the electrospinning method. Schwann cells cultured on the silk fibroin nanofibers appeared more ordered, their processes extended further, and they formed more extensive and complex interconnections. In addition, the silk fibroin nanofibers had no impact on the proliferation of Schwann cells or on the secretion of ciliary neurotrophic factor, brain-derived neurotrophic factor or nerve growth factor. These findings indicate that regenerated electrospun silk fibroin nanofibers can promote Schwann cell adhesion, growth and proliferation, and have excellent biocompatibility.
Low-Complexity Multisampling Multiuser Detector for Time-Hopping UWB Systems

Multiple access interference (MAI) in time hopping (TH) ultra-wideband (UWB) systems is known to be non-Gaussian. Much previous research in TH-UWB receiver design has attempted to propose better single-user UWB detectors by introducing more accurate models for the distribution of the MAI. Recently, it was shown that some of the single-user receivers track closely the optimum achievable single-user performance. Although, these receivers are simple, all suffer from error rate floors, and hence limited user capacity. Multiuser detection (MUD) is considered for offering high performance at the cost of complexity that grows exponentially with the number of users. Thought to be too complex for low-cost UWB receivers, MUD applied in TH-UWB systems benefits from the low duty cycle implying that the number of effective interfering users is small compared to the number of active users. A novel low-complexity multisampling multiuser detector inspired by the inferiority of single-user receivers and the small number of effective interfering users in TH-UWB systems is proposed. Simulation results show that this detector achieves the performance of the conventional matched filter receiver operating in a single-user system.
A flexible rank-based framework for detecting copy number aberrations from array data

MOTIVATION DNA copy number aberration--both inherited and sporadic--is a significant contributor to a variety of human diseases. Copy number characterization is therefore an area of intense research. Probe hybridization-based arrays are important tools used to measure copy number in a high-throughput manner. RESULTS In this article, we present a simple but powerful nonparametric rank-based approach to detect deletions and gains from raw array copy number measurements. We use three different rank-based statistics to detect three separate molecular phenomena-somatic lesions, germline deletions and germline gains. The approach is robust and rigorously grounded in statistical theory, thereby enabling the meaningful assignment of statistical significance to each putative aberration. We demonstrate the flexibility of our approach by applying it to data from three different array platforms. We show that our method compares favorably with established approaches by applying it to published well-characterized samples. Power simulations demonstrate exquisite sensitivity for array data of reasonable quality. CONCLUSIONS Our flexible rank-based framework is suitable for multiple platforms including single nucleotide polymorphism arrays and array comparative genomic hybridization, and can reliably detect gains or losses of genomic DNA, whether inherited, de novo, or somatic. AVAILABILITY An R package RankCopy containing the methods described here, and is freely available from the author's web site (http://mendel.gene.cwru.edu/laframboiselab/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Query Workload-based RDF Graph Fragmentation and Allocation

As the volume of the RDF data becomes increasingly large, it is essential for us to design a distributed database system to manage it. For distributed RDF data design, it is quite common to partition the RDF data into some parts, called fragments, which are then distributed. Thus, the distribution design consists of two steps: fragmentation and allocation. In this paper, we propose a method to explore the intrinsic similarities among the structures of queries in a workload for fragmentation and allocation, which aims to reduce the number of crossing matches and the communication cost during SPARQL query processing. Specifically, we mine and select some frequent access patterns to reflect the characteristics of the workload. Here, although we prove that selecting the optimal set of frequent access patterns is NP-hard, we propose a heuristic algorithm which guarantees both the data integrity and the approximation ratio. Based on the selected frequent access patterns, we propose two fragmentation strategies, vertical and horizontal fragmentation strategies, to divide RDF graphs while meeting different kinds of query processing objectives. Vertical fragmentation is for better throughput and horizontal fragmentation is for better performance. After fragmentation, we discuss how to allocate these fragments to various sites. Finally, we discuss how to process a query based on the results of fragmentation and allocation. Extensive experiments confirm the superior performance of our proposed solutions.
GIRL POWER? AN ANALYSIS OF PEER EFFECTS USING EXOGENOUS CHANGES IN THE GENDER MAKE-UP OF THE PEER GROUP

The effect of a child’s peers has long been regarded as an important factor in affecting their educational outcomes. I use exogenous changes in the proportion of girls within English school cohorts to estimate the causal effect of a more female peer group. I find significant negative effects of a more female peer group on boys’ outcomes in English, particularly at age 7. Much, but not all, of this deficit is caught up by age 11, but there is still a significant negative effect present. In maths and science, all pupils benefit from a more female peer group in primary schools.
A non-monotonic approach to periodic event-triggered control with packet loss

This paper treats the topic of periodic event-triggered control (PETC) for Networked Control Systems in a setup where the network provides a maximum sampling rate and a maximum number of successive packet losses. The packet loss is tackled with a non-monotonic approach, meaning that the trigger rules are designed such that a Lyapunov function is guaranteed to decrease only between two successful triggering instants. The stability properties of the resulting continuous-time sampled-data system are analyzed in two ways. The first one uses different restricted dynamical systems and the second one applies theory on non-monotonic Lyapunov functions. Based on this theory an example for another trigger rule is given that can be derived using this approach. The theoretical results are demonstrated through a known numerical example.
Dynamic expression of N‐myc in mouse embryonic development using an enhanced green fluorescent protein reporter gene in the N‐myc locus

N‐myc belongs to the Myc oncogene family and plays an essential role in mammalian embryonic development. The expression of N‐myc is dynamically regulated during embryonic development; however, its expression pattern has not been well characterized due to the lack of a suitable animal model. In this paper, a genetically modified mouse model was generated in which the enhanced green fluorescent protein (EGFP) coding sequence was inserted into the N‐myc locus, so that endogenous N‐myc expression could be traced by the signal of EGFP. The EGFP signal in the transgenic mouse was confirmed to be consistent with the expression pattern of endogenous N‐myc by fluorescence microscopy and immunohistochemical staining. Furthermore, the spatial and temporal expression of EGFP was observed in the central and peripheral nervous system, heart, lung and kidney, given the known indispensable role of N‐myc in their formation. EGFP was also strongly detected in the liver, paranephros and the epithelium of the intestine. The EGFP signal can be used to trace N‐myc expression in this transgenic mouse model. N‐myc expression was observed in specific locations and cell lineages, and dynamically changed during embryonic development. The changing N‐myc expression pattern seen in mouse embryonic development and the animal model described in this paper provide important insights and a new tool to research N‐myc function.
Full-Duplex Cloud Radio Access Networks: An Information-Theoretic Viewpoint

The conventional design of cellular systems prescribes the separation of uplink and downlink transmissions via time-division or frequency-division duplex. Recent advances in analog and digital domain self-interference interference cancellation challenge the need for this arrangement and open up the possibility to operate base stations, particularly low-power ones, in a full-duplex mode. As a means to cope with the resulting downlink-to-uplink interference among base stations, this letter investigates the impact of the Cloud Radio Access Network (C-RAN) architecture. The analysis follows an information theoretic approach based on the classical Wyner model. The analytical results herein confirm the significant potential advantages of the C-RAN architecture in the presence of full-duplex base stations, as long as sufficient fronthaul capacity is available and appropriate mobile station scheduling, or successive interference cancellation at the mobile stations, is implemented.
Effect of Layer Thickness and Printing Orientation on Mechanical Properties and Dimensional Accuracy of 3D Printed Porous Samples for Bone Tissue Engineering

Powder-based inkjet 3D printing method is one of the most attractive solid free form techniques. It involves a sequential layering process through which 3D porous scaffolds can be directly produced from computer-generated models. 3D printed products' quality are controlled by the optimal build parameters. In this study, Calcium Sulfate based powders were used for porous scaffolds fabrication. The printed scaffolds of 0.8 mm pore size, with different layer thickness and printing orientation, were subjected to the depowdering step. The effects of four layer thicknesses and printing orientations, (parallel to X, Y and Z), on the physical and mechanical properties of printed scaffolds were investigated. It was observed that the compressive strength, toughness and Young's modulus of samples with 0.1125 and 0.125 mm layer thickness were more than others. Furthermore, the results of SEM and μCT analyses showed that samples with 0.1125 mm layer thickness printed in X direction have more dimensional accuracy and significantly close to CAD software based designs with predefined pore size, porosity and pore interconnectivity.
Scalable Detection of Sentiment-Based Contradictions

The analysis of user opinions expressed on the Web is becomin g increasingly relevant to a variety of applications. It allows us to track the evolution of opinions or discussions in the blogosphere , or perform product surveys. The aggregation of sentiments and ana lysis of contradictions is another important application, which becomes effective since we are able to capture the diversity in senti ments on different topics with more precision and on a large scale. Th oug , there is still a need for a scalable way of sentiment aggregat ion with respect to the time dimension, which preserves enough infor mation to capture contradictions. In this paper, we are focusing on the problem of finding sentim entbased contradictions at a large scale. First, we define two ty pes of contradictions, depending on the distributions of oppos ite entiments over time. Second, we introduce a novel measure of co ntradiction based on the mean value and the variance of sentim ts among different texts. Third, we propose a scalable method f or identifying both types of contradictions at different time scales. We evaluate the performance of our method using synthetic and r ealworld datasets, as well as a user-study. The experiments dem onstrate the effectiveness of the proposed method in capturin g co tradictions in a scalable manner.
Low-power gated and buffered clock network construction

We propose an efficient algorithm to construct a low-power zero-skew gated clock network, given the module locations and activity information. Unlike previous works, we consider masking logic insertion and buffer insertion simultaneously, and guarantee to yield a zero-skew clock tree. Both the logical and physical information of the modules are carefully taken into consideration when determining where masking logic should be inserted. We also account for the power overhead of the control signals so that the total average power consumption of the constructed zero-skew gated clock network can be minimized. To this end, we present a recursive approach to compute the effective switched capacitance of a general gated and buffered clock network, accounting for both the clock tree's and controller tree's switched capacitance. The power consumptions of the gated clock networks constructed by our algorithm are 20 to 36% lower than those reported in the best previous work in the literature.
Averaging Model of a Three-Phase Controlled Rectifier Feeding an Uncontrolled Buck Converter

Dynamic models of power converters are normally time-varying because of their switching actions. Several approaches are applied to analyze the power converters to achieve the timeinvariant models suitable for system analysis and design via the classical control theory. The paper presents how to derive dynamic models of the power system consisting of a three-phase controlled rectifier feeding an uncontrolled buck converter by using the combination between the well known techniques called the DQ and the generalized state-space averaging methods. The intensive timedomain simulations of the exact topology model are used to support the accuracies of the reported model. The results show that the proposed model can provide good accuracies in both transient and steady-state responses. Keywords—DQ method, Generalized state-space averaging method, Three-phase controlled rectifier, Uncontrolled buck converter, Averaging model, Modeling, Simulation.