Miracle-dz/UniFAR-FTUs huggingface dataset:
Priming of CD4+ and CD8+ T cell responses using a HCV core ISCOMATRIX™ vaccine: A phase I study in healthy volunteers

The disease burden and public health impact of chronic HCV infection continues to be a major problem globally. Current treatment for chronic HCV infection is not effective in all patients and is frequently associated with unacceptable side effects. Clearly a need exists for improved treatments and one such strategy is the use of therapeutic vaccines. Although still not completely understood, emerging data indicate that the generation of CD4+ and CD8+ T cells are important for the clearance of HCV. We have developed a prototype vaccine with the HCV Core protein and ISCOMATRIX™ adjuvant (HCV Core ISCOMATRIX™ vaccine). ISCOMATRIX™ vaccines have been shown to induce CD4+ and CD8+ T cell responses to a range of antigens in both animal models and in human studies. Additionally, ISCOMATRIX™ vaccines have been shown to be safe and generally well tolerated in several clinical trials. Preliminary studies demonstrated that the prototype HCV Core ISCOMATRIX™ vaccine induced strong CD4+ and CD8+ T cell responses in monkeys following immunisation. Here we show the results of a Phase I placebo controlled, dose escalation clinical study designed to evaluate the safety, tolerability and immunogenicity of the HCV Core ISCOMATRIX™ vaccine in healthy individuals. The 30 subjects received 3 immunisations of HCV Core ISCOMATRIX™ vaccines or placebo vaccine on days 0, 28 and 56. The HCV Core ISCOMATRIX™ vaccines contained 5, 20 or 50 μg HCV Core protein with 120 μg ISCOMATRIX™ adjuvant. The adverse events reported were generally mild to moderate in severity, of short duration and self-limiting. The most common adverse events were injection site reactions such as pain and redness as well as myalgia. Antibody responses were detected in all but one of the participants receiving the HCV Core ISCOMATRIX™ vaccine and there was no indication of a dose response. CD8+ T cell responses were only detected in 2 of the 8 participants receiving the highest dose. T cell cytokines were detected in 7 of the 8 participants in the highest dose group. The results of this study support the further evaluation of this prototype HCV Core ISCOMATRIX™ vaccine in HCV infected subjects.
Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.
IN VITRO CONSTRUCTION OF COMPLEX STEM CELL MICROENVIRONMENTS BY STENCIL-AND- FLIP CELL PATTERNING (SAF-CP)

Embryonic development is a complex dynamic process where spatial organization of molecular signals direct stem cells into the 3 germ layers. Recapitulating this process in vitro will greatly facilitate the mechanistic understanding of embryonic development. However, current models are unable to reproduce the complex developmental environment adequately. To realize the aim of building a complex developmental model, we developed the Stencil-and-Flip Cell Patterning (SAF-CP) technique to present multiple spatially organized microenvironments to a single population of stem cells.
The Maternal Adversity, Vulnerability and Neurodevelopment Project: Theory and Methodology

Objective: To describe the theory and methodology of the multi-wave, prospective Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) study. The goal of MAVAN is to examine the pre- and postnatal influences, and their interaction, in determining individual differences in mental health. Method: MAVAN is a community-based, birth cohort study of pregnant Canadian mothers and their offspring. Dyads are assessed longitudinally, with multiple assessments of both mother and child in home and laboratory across the child's development. Study measures, including assessments of cognitive and emotional function, are described. The study uses a candidate gene approach to examine gene–environment interdependence in specific developmental outcomes. Finally, the study includes measures of both brain-based phenotypes and metabolism to explore comorbidities associated with child obesity. One of the unique features of the MAVAN protocol is the extensive measures of the mother–child interaction. The relation between these measures will be discussed. Results: Evidence from the MAVAN project shows interesting results about maternal care, families, and child outcomes. In our review, preliminary analyses showing the correlations between measures of maternal care are reported. As predicted, early evidence suggests that maternal care measures are positively correlated, over time. Conclusions: This review provides evidence for the feasibility and value of laboratory-based measures embedded within a longitudinal birth cohort study. Though retention of the samples has been a challenge of MAVAN, they are within a comparable range to other studies of this nature. Indeed, the trade-off of somewhat greater participant burden has allowed for a rich database. The results yielded from the MAVAN project will not only describe typical development but also possible targets for intervention. Understanding certain endophenotypes will shed light on the pathogenesis of various mental and physical disorders, as well as their interrelation.
Design Technologies for Low Power VLSI

Low power has emerged as a principal theme in today’s electronics industry. The need for low power has caused a major paradigm shift where power dissipation has become as important a consideration as performance and area. This article reviews various strategies and methodologies for designing low power circuits and systems. It describes the many issues facing designers at architectural, logic, circuit and device levels and presents some of the techniques that have been proposed to overcome these difficulties. The article concludes with the future challenges that must be met to design low power, high performance systems.
Endothelial basement membrane laminin 511 is essential for shear stress response

Shear detection and mechanotransduction by arterial endothelium requires junctional complexes containing PECAM-1 and VE-cadherin, as well as firm anchorage to the underlying basement membrane. While considerable information is available for junctional complexes in these processes, gained largely from in vitro studies, little is known about the contribution of the endothelial basement membrane. Using resistance artery explants, we show that the integral endothelial basement membrane component, laminin 511 (laminin a5), is central to shear detection and mechanotransduction and its elimination at this site results in ablation of dilation in response to increased shear stress. Loss of endothelial laminin 511 correlates with reduced cortical stiffness of arterial endothelium in vivo, smaller integrin b1-positive/vinculin-positive focal adhesions, and reduced junctional association of actin–myosin II. In vitro assays reveal that b1 integrin-mediated interaction with laminin 511 results in high strengths of adhesion, which promotes p120 catenin association with VE-cadherin, stabilizing it at cell junctions and increasing cell–cell adhesion strength. This highlights the importance of endothelial laminin 511 in shear response in the physiologically relevant context of resistance arteries.
Thermal-Constrained Task Allocation for Interconnect Energy Reduction in 3-D Homogeneous MPSoCs

3-D technology that stacks silicon dies with through silicon vias (TSVs) is a promising solution to overcome the interconnect scaling problem in giga-scale integrated circuits (ICs). Thermal dissipation is a major challenge for 3-D integration and prior thermal-balanced task scheduling methods for 3-D multiprocessor system-on-chips (MPSoCs) typically balance power gradient across vertical stacks based on the assumption of strong thermal correlation among processing cores within a stack. On the other hand, 3-D MPSoCs typically employ network-on-chip (NoC) as the communication infrastructure which consumes a large portion of the energy budget. As TSVs consume much less energy than horizontal links in 3-D MPSoCs when transmitting the same amount data due to the reduced interconnect distance between vertical adjacent cores, it motivates to allocate heavily communicating tasks within the same vertical stack as much as possible, and thus traffic is restricted in the third dimension to reduce interconnect energy. However, aggregating active tasks within the same stack probably exacerbates the power density and result in hot spots. In this paper, we explore the tradeoff between thermal and interconnect energy when allocating tasks in 3-D Homogeneous MPSoCs, and propose an efficient heuristic. Experimental results show that the proposed technique can reduce interconnect energy by more than 25% on average with almost the same peak temperature when compared with prior thermal-balanced solutions.
Early dynamic change in high-sensitivity cardiac troponin T in the investigation of acute myocardial infarction.

BACKGROUND The definition of acute myocardial infarction (AMI) requires a rise and/or fall in troponin with 1 or more results ≥99th percentile of the reference range. How much troponin must change has not been specified. We ascertained whether dynamic changes (δ) in high-sensitivity troponin T (hs-TnT) improved diagnostic and prognostic test performance in the emergency department. METHODS We recruited 939 patients with symptoms suggestive of acute coronary syndrome (without ST elevation). hs-cTnT was measured at 0 h and 2 h after presentation. End-points were admission diagnosis of AMI and 1-year adverse events (composite of death, AMI, revascularization). RESULTS Diagnostic specificity of 0-2-h hs-cTnT for AMI (incurred by 200 patients) improved from 79.8% (78.8%-80.5%) by using the 99th percentile alone to 94.2% (92.9%-95.4%) when we also included a δ ≥20%, but diagnostic sensitivity decreased from 94.5% (90.7%-96.9%) to 49.5% (44.6%-53.9%). With the inclusion of those patients with a δ ≥20% when 0-2-h hs-cTnT was <99th percentile, in addition to any with concentrations ≥99th percentile, diagnostic sensitivity increased to 97.5% (94.4%-98.9%). hs-cTnT ≥99th percentile predicted adverse events (incurred by 111 patients), adjusted hazard ratio 1.9 (1.2-2.8), whereas a δ ≥20% did not, hazard ratio 1.1 (0.7-1.7). CONCLUSIONS Diagnostic specificity of hs-cTnT improved with the use of a δ ≥20% in those patients with concentrations ≥99th percentile, but at a cost of a large reduction in sensitivity. Diagnostic sensitivity improved with the use of a δ ≥20% in patients with 0-2-h concentrations <99th percentile. Both approaches may be required for optimum rule-in and rule-out strategies, respectively. The δ criteria seem to be less useful for medium-term risk stratification.
Applying classification techniques to remotely-collected program execution data

There is an increasing interest in techniques that support measurement and analysis of fielded software systems. One of the main goals of these techniques is to better understand how software actually behaves in the field. In particular, many of these techniques require a way to distinguish, in the field, failing from passing executions. So far, researchers and practitioners have only partially addressed this problem: they have simply assumed that program failure status is either obvious (i.e., the program crashes) or provided by an external source (e.g., the users). In this paper, we propose a technique for automatically classifying execution data, collected in the field, as coming from either passing or failing program runs. (Failing program runs are executions that terminate with a failure, such as a wrong outcome.) We use statistical learning algorithms to build the classification models. Our approach builds the models by analyzing executions performed in a controlled environment (e.g., test cases run in-house) and then uses the models to predict whether execution data produced by a fielded instance were generated by a passing or failing program execution. We also present results from an initial feasibility study, based on multiple versions of a software subject, in which we investigate several issues vital to the applicability of the technique. Finally, we present some lessons learned regarding the interplay between the reliability of classification models and the amount and type of data collected.
Machine Learning for On-Line Hardware Reconfiguration

As computer systems continue to increase in complexity, the need for AI-based solutions is becoming more urgent. For example, high-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems online to optimize for dynamically changing workloads. However, it also introduces the need to decide when and how to reconfigure. This paper presents one approach to solving this online reconfiguration problem. In particular, we learn to identify, from only low-level system statistics, which of a set of possible configurations will lead to better performance under the current unknown workload. This approach requires no instrumentation of the system's middleware or operating systems. We introduce an agent that is able to learn this model and use it to switch configurations online as the workload varies. Our agent is fully implemented and tested on a publicly available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We demonstrate that our adaptive configuration is able to outperform any single fixed configuration in the set over a variety of workloads, including gradual changes and abrupt workload spikes.
'The Italian Protocol': a simplified head-up tilt testing potentiated with oral nitroglycerin to assess patients with unexplained syncope.

Head-up tilt testing potentiated by sublingual nitroglycerin (NTG), advocated by an Italian group, is a simple and safe but still not a standardized, diagnostic tool for the investigation of syncope. In fact, owing to its rapid spread, the original protocol received, often arbitrarily, many subsequent modifications. We now define the best methodology of the test on strictly evidence-based criteria as: stabilization phase of 5 min in the supine position; passive phase of 20 min at a tilt angle of 60 degrees; provocation phase of further 15 min after 400 micrograms NTG sublingual spray. Test interruption is made when the protocol is completed in the absence of symptoms, or there is occurrence of syncope, or occurrence of progressive (> 5 min) orthostatic hypotension. We intend that this protocol, named by us as 'The Italian Protocol', will be accepted as the standard methodology of the tilt test potentiated by sublingual nitrates.
Transient blood flow in elastic coronary arteries with varying degrees of stenosis and dilatations: CFD modelling and parametric study

In this paper, we have analysed pulsatile flow through partially occluded elastic arteries, to determine the haemodynamic parameters of wall shear stress (WSS), wall pressure gradient and pressure drops (ΔP), contributing to enhanced flow resistance and myocardial ischaemic regions which impair cardiac contractility and cause increased work load on the heart. In summary, it can be observed that stenoses in an artery significantly influence the haemodynamic parameters of wall shear stress and pressure drop in contrast to dilatations case. This deduces that stenosis plays a more critical role in plaque growth and vulnerability in contrast to dilatation, and should be the key element in cardiovascular pathology and diagnosis. Through quantitative analysis of WSS and ΔP, we have provided a clearer insight into the haemodynamics of atherosclerotic arteries. Determination of these parameters can be helpful to cardiologists, because it is directly implicated in the genesis and development of atherosclerosis.
2015 Ieee International Conference on Big Data (big Data) Edge Importance Identification for Energy Efficient Graph Processing

—Modern graphs are large, often containing billions of nodes and edges that demand huge amount of processing for analysis purposes. The algorithms processing these graphs often run for long time and consume substantial amount of energy. However, not all edges in the graphs are equally important. Some edges play critical role in maintaining the community and other interesting structures in the graph, while the rest are less important for analysis. Identifying edges as important and unimportant allows one to apply elastic fidelity computing when processing edges of low importance, hence saving significant amount of energy while processing large graphs. In this paper we propose a novel technique for identifying important edges in a graph using a fast method that exploits locality sensitive hashing. We then propose a framework for energy-efficient computing that applies elastic fidelity computing when processing edges of low importance and applies full fidelity computing when processing important edges. This allows the framework to deliver good results while saving energy when processing a large number of low-importance edges. Our proposed technique reduces the power consumption by 3–30% while still producing results that are within acceptable range of the full-accuracy results.
Format preserving speech encryption for secured voice service on 3G/LTE network

The speech encryption for secured voice service on 3G/LTE network is proposed. In the presented method, AES cryptography is applied to the payload part of the speech codec bit-stream. With this approach, the speech information is fully protected while the bit-stream format of the standard codec is maintained after encryption. When the format of the encrypted bit-stream is preserved as same as the standard one, the secured bit-stream can travel to the other side without any trouble even on the commercial 3G/LTE network. The key protocol developed under FIPS standards and NIST guide is used to ensure the "Confidentiality" and "Integrity" during the key exchange process. For the usability assessment, interoperability test across the carriers, compatibility test between 3G and LTE network, call latency test, and speech quality test are performed.
Study on Depression Evaluation Indicator in the Elderly using Sensibility Technology

Depression is important issue with aging of global population. Previously we have proposed a method to evaluate the mental health status of a person by his or her voice and developed a smartphone-based system to monitor mental health from voice during a call. Although the system has excellent continuous monitoring capability, it has not enough specificity for screening. Therefore, in this study we propose an evaluation indicator to assess depression status in the elderly, based on multivariate analysis using the emotional components of the voice data collected in the aforementioned system and the BDI score. The voice emotion data on subjects was divided into two groups according to BDI score, one where doctor’s diagnosis was deemed necessary and the other not so. A significant difference between the two groups was observed in t-test when the mean of the evaluation indicator estimated using data of each group and applying logistic regression prediction equation was compared. Moreover, a performance corresponding to AUC of approximately 0 . 75 was achieved in the ROC curve of the derived evaluation indicator. The results suggest that a new method to evaluate depression using voice has likely been developed.
Analysis of a New Single-Stage Soft-Switching Power-Factor-Correction LED Driver With Low DC-Bus Voltage

A new isolated single-stage soft-switching power-factor-correction (S4 PFC) driver for supplying light-emitting diodes (LEDs) is introduced in this paper. In the proposed LED driver, the switches voltage stress and also the dc-bus voltage is limited to the peak of the line voltage. Hence, low voltage rated mosfets and diodes can be used. The efficiency is improved because the switches are turned on under zero current switching (ZCS) condition and turned off under zero voltage zero current switching (ZVZCS) condition. Also, no current feedback is required since the converter provides an output current independent of output voltage. In this paper, operating principle of the proposed LED driver is presented and design considerations are discussed. To verify the theoretical analysis, a laboratory prototype of the proposed converter for supplying a 50 W/70 V LED module from 220 Vrms/50 Hz ac mains is implemented, and the experimental results are presented. Since current feedback is not used, dimming property and operation for universal voltage range is not achievable.
A new approach to apply texture features in minerals identification in petrographic thin sections using ANNs

Identification of minerals in petrographic thin sections using intelligent methods is very complex and challenging task which, mineralogists and computer scientists are faced with it. Textural features have very important role to identify minerals, and undoubtedly without using these features, recognition minerals in thin sections yield to many miss classification results. Thin sections have been studied applying plane-polarized and cross-polarized lights. In this paper, in order to extract textural features of minerals in thin section, co-occurrence matrix is used, and six features as Entropy, Homogeneity, Energy, Correlation and Maximum Probability are extracted from each image. Then, ANNs are used for identifying in complex situation and experimental results have shown that using textural features in mineral identification, significant improve classification result in petrographic thin sections.
Focused Web Crawling of Relevant Pages on e-Shops

E-shop data extraction requires all detail pages of products to be crawled. To maintain extracted data actual, the crawling needs to be periodical. E-shops contain many irrelevant pages such as ads, basket or contact information that are good to avoid during a crawling process. This paper presents a focused web crawling method based on an analysis of a previous initial crawling that eliminates irrelevant paths from the following crawls of the e-shop. The method preserves the ability to collect all products of all product domains, even the new ones.