Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating Vascular Growth Factors and Central Hemodynamic Load in the Community

Mean and pulsatile components of hemodynamic load are related to cardiovascular disease. Vascular growth factors play a fundamental role in vascular remodeling. The links between growth factors and hemodynamic load components are not well described. In 3496 participants from the Framingham Heart Study third generation cohort (mean age: 40±9 years; 52% women), we related 4 tonometry-derived measures of central arterial load (carotid femoral pulse wave velocity and forward pressure wave, mean arterial pressure, and the global reflection coefficient) to circulating concentrations of angiopoietin 2, its soluble receptor; vascular endothelial growth factor, its soluble receptor; hepatocyte growth factor; insulin-like growth factor 1; and its binding protein 3. Using multivariable linear regression models, adjusted for standard cardiovascular risk factors, serum insulin-like growth factor 1 concentrations were negatively associated with carotid femoral pulse wave velocity, mean arterial pressure, and reflection coefficient (P⩽0.01 for all), whereas serum vascular endothelial growth factor levels were positively associated with carotid femoral pulse wave velocity and mean arterial pressure (P<0.04). Serum insulin-like growth factor binding protein 3 and soluble angiopoietin 2 receptor levels were positively related to mean arterial pressure and to forward pressure wave, respectively (P<0.05). In our cross-sectional study of a large community-based sample, circulating vascular growth factor levels were related to measures of mean and pulsatile hemodynamic load in a pattern consistent with the known physiological effects of insulin-like growth factor 1 and vascular endothelial growth factor.
Inconformity of CXCL3 Plasma Level and Placenta Expression in Preeclampsia and Its Effect on Trophoblast Viability and Invasion

As a member of the chemokine family, CXCL3 was previously known to participate in many pathophysiological events. However, whether CXCL3 stimulates trophoblast invasion as a key process of preeclampsia pathogenesis remains largely unknown. Therefore, the aim of this study was to investigate this hypothesis and determine the effect of CXCL3 on the first trimester trophoblast. Seventy gravidas were included in this study. ELISA was used to detect CXCL3 plasma levels on preeclampsia and normal pregnant groups. CXCL3 protein and mRNA levels were detected via Western blot and real-time quantitative PCR analysis after immunolocalized in human placenta. Moreover, the CXCL3 function in HTR-8/Svneo was analyzed via WST-1 assay, flow cytometry and invasion test. The plasma CXCL3 level in preeclampsia was significantly higher than that in normal pregnancy. CXCL3 expression was observed in the cytoplasm of placental trophoblasts and vascular endothelium in all groups without significant difference between maternal and fetal sides. In addition, placenta CXCL3 expression in severe preeclampsia was significantly lower than those in normal and mild PE groups. Moreover, exogenous CXCL3 can promote the proliferation and invasion of HTR-8/Svneo; however, its effect on apoptosis remains unclear. In summary, a significant abnormality of plasma CXCL3 level and placental CXCL3 expression was discovered in severe preeclampsia; CXCL3 had a function in trophoblast invasion, which indicated its participation in shallow implantation. Therefore CXCL3 might be involved in severe preeclampsia pathogenesis.
Quality of Matching in large scale scenarios

Matching Techniques are becoming a very attractive research topic. With the development and the use of a large variety of data (e.g. DB schemas, XML schemas, ontologies), in many domains (e.g. semantic web, E-business, etc), matching techniques are called to overcome the challenge of aligning these different data. In this paper, we are interested in studying the quality of large scale matching systems. We define and propose a quality of Matching (QoM) that can be used to evaluate large scale matching systems. We survey the techniques, called optimization techniques, used in existing matching approaches to improve this quality. One can acknowledge that this domain is on top of effervescence and large scale matching need much more advances. So, we demonstrate how quality evaluation can be integrated in our scalable matching system PLASMA.
Subclass-based multi-task learning for Alzheimer's disease diagnosis

In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively.
Emotion Processes in Knowledge Revision

ABSTRACT In recent years, a number of insights have been gained into the cognitive processes that explain how individuals overcome misconceptions and revise their previously acquired incorrect knowledge. The current study complements this line of research by investigating the moment-by-moment emotion processes that occur during knowledge revision using a think-aloud methodology. Undergraduate students read both refutation and nonrefutation texts and reported out loud their thoughts, which were coded along valence and activation dimensions of emotions. Results showed that at key points during reading, emotions differed within and between experimental text conditions. Further, exploratory mediational analysis showed that surprise was an influential emotion for learning. Findings are discussed in terms of theoretical contributions to our basic understanding of the role of emotions during knowledge revision.
Practical Shadow Mapping

Abstract In this paper, we present several methods that can greatly improve image quality when using the shadow mapping algorithm. Shadow artifacts introduced by shadow mapping are mainly due to low resolution shadow maps and/or the limited numerical precision used when performing the shadow test. These problems especially arise when the light source's viewing frustum, from which the shadow map is generated, is not adjusted to the actual camera view. We show how a tight-fitting frustum can be computed such that the shadow mapping algorithm concent rates on the visible parts of the scene and takes advantage of nearly the full available precision. Furthermore, we recommend uniformly spaced depth values in contrast to perspectively spaced depths in order to equally sample the scene seen from the light source.
Application-driven energy-efficient architecture explorations for big data

Building energy-efficient systems is critical for big data applications. This paper investigates and compares the energy consumption and the execution time of a typical Hadoop-based big data application running on a traditional Xeon-based cluster and an Atom-based (Micro-server) cluster. Our experimental results show that the micro-server platform is more energy-efficient than the Xeon-based platform. Our experimental results also reveal that data compression and decompression accounts for a considerable percentage of the total execution time. More precisely, data compression/decompression occupies 7-11% of the execution time of the map tasks and 37.9-41.2% of the execution time of the reduce tasks. Based on our findings, we demonstrate the necessity of using a heterogeneous architecture for energy-efficient big data processing. The desired architecture takes the advantages of both micro-server processors and hardware compression/decompression accelerators. In addition, we propose a mechanism that enables the accelerators to perform more efficient data compression/decompression.
Short Radius Centrifuges - A New Approach for Life Science Experiments under Hyper-g Conditions for Applications in Space and Beyond

A broad variety of countermeasures on the effects of weightlessness on human physiology have been developed and applied in the course of space exploration. Devices like treadmills, stretch ropes etc. have several disadvantages in common: they require a significant amount of crew time and they may not efficiently counteract the degradation of physiological structures and cellular functions. Some methods even include potentially painful or uncomfortable procedures for the astronauts. Thus, the application of Artificial Gravity (AG) generated by short radius centrifuges (they fit into space vessels) has been discussed and proposed by a number of scientists and space agencies as an alternative countermeasure during long-term space missions. Although there is a profound knowledge concerning, e.g., the cardiovascular system and immune responses acquired on long radius centrifuges, there is a remarkable lack of knowledge concerning the same issues on devices operating with short radius. In strict contrast to long radius centrifuges, there is a significant gravity gradient in the head-to-toe axis which comes along with the short radius and higher relative rotation velocity. Thus it is of utmost importance to continue investigating the effects of AG, especially by use of short radius centrifuges. The Short Arm Human Centrifuge (SAHC) at the German Aerospace Center (DLR) in Cologne, Germany, is the most advanced type of short radius centrifuges presently commercially available. Experience gained so far using the SAHC at DLR revealed that future projects on centrifuge devices with short radius should aim at a clear identification of the threshold level of the g-load, which is necessary to efficiently counteract the degradation of physical structures and an efficient support of cellular functions. A satisfying result would be combined countermeasure methods applied at a threshold concerning g-load and exposition time in the course of long-term sojourn in microgravity. Another future control or monitoring method to exactly dose AG training is heart rate variability, which offers an insight into neurovegetative and cardiovascular regulation. Centrifuges like the SAHC are also useful platforms to accommodate small biological experiments, e.g., experiments addressing the response of cultured cells to hypergravity. Here, we briefly review the issue of short radius centrifuges and also address our experience hitherto gained during a number of scientific projects carried out at the SAHC at DLR.
Study on Automatic Scoring of Descriptive Type Tests using Text Similarity Calculations

In this paper, we evaluate the automatic scoring of a descriptive type test. In the experiments, three test similarity measures are compared in terms of automatic scoring quality. Two of them are BLEU and RIBES, which are n -gram and word-level matching processes respectively, originally used for automatic evaluation of machine translation output. The other similarity process is Doc2Vec, which utilizes distributed representation to calculate the cosine distance. It was finally found that, according to the experimental results, the most efficient process used to calculate the text similarity depends on the type of the question.
GPU-accelerated feature point matching using extended ColourFAST descriptors

A real-time feature point matching algorithm is introduced. It extracts vector-based ColourFAST feature strength and direction measures from the colour channels of the pixels in an image. This information is combined with the relative locations of the feature points to provide frame-by-frame scale and rotation invariant matching. The resulting algorithm is specifically designed for high throughput and optimised for GPU pipelining on embedded devices. Results are given showing high-framerate matching is achieved on 720p resolution images.
Microtask Crowdsourcing for Disease Mention Annotation in PubMed Abstracts

Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language processing (BioNLP) projects attempt to address this challenge, but the state of the art still leaves much room for improvement. Progress in BioNLP research depends on large, annotated corpora for evaluating information extraction systems and training machine learning models. Traditionally, such corpora are created by small numbers of expert annotators often working over extended periods of time. Recent studies have shown that workers on microtask crowdsourcing platforms such as Amazon's Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in PubMed abstracts. We used the NCBI Disease corpus as a gold standard for refining and benchmarking our crowdsourcing protocol. After several iterations, we arrived at a protocol that reproduced the annotations of the 593 documents in the 'training set' of this gold standard with an overall F measure of 0.872 (precision 0.862, recall 0.883). The output can also be tuned to optimize for precision (max = 0.984 when recall = 0.269) or recall (max = 0.980 when precision = 0.436). Each document was completed by 15 workers, and their annotations were merged based on a simple voting method. In total 145 workers combined to complete all 593 documents in the span of 9 days at a cost of $. 066 per abstract per worker. The quality of the annotations, as judged with the F measure, increases with the number of workers assigned to each task; however minimal performance gains were observed beyond 8 workers per task. These results add further evidence that microtask crowdsourcing can be a valuable tool for generating well-annotated corpora in BioNLP. Data produced for this analysis are available at http://figshare.com/articles/Disease_Mention_Annotation_with_Mechanical_Turk/1126402.
Rapid Synthesis of Gold Nanoparticles from Quercus incana and Their Antimicrobial Potential against Human Pathogens

In current study, bioreduction of tetrachloroauric acid (HAuCl4·3H2O) was carried out using leaves extract of Quercus incana for nanoparticle synthesis. The nanoparticles were characterized by ultraviolet visible spectrum (UV), Fourier-transform infrared (FT-IR), and transmission electron microscopy (TEM) analysis. The gold nanoparticles (GNPs) were generally clumpy agglomerates of polydispersed particles, with an average size in the range 5.5–10 nm. The Gas chromatography–mass spectrometry (GC–MS) qualitative analysis and FT-IR data supported the presence of bioactive compounds, which are responsible for the metal reduction and nanoparticles stabilization. The biocompatibility of synthesized GNPs was evaluated via antibacterial activity by using human bacterial pathogens. The results showed that synthesized GNPs showed enhanced antibacterial activity against all bacterial pathogens.
A quaternion-based unscented Kalman filter for orientation tracking

This paper describes a Kalman filter for the real-time estimation of a rigid body orientation from mea- surements of acceleration, angular velocity and magnetic field strength. A quaternion representation of the orienta- tion is computationally effective and avoids problems with singularities. The nonlinear relationship between estimated Orientation and expected measurement prevent the usage of a classical Kalman filter: This problem is solved by an Un- scented Kalman filter which allows nonlinear process and measurement models and is more accurate and less costly than the common Extended Kalman filter: Several exten- sions to the original Unscented Kalman filter are necessary to treat the inherent properties of unit quaternions. Results with simulated and measured data are discussed.
Selective Sampling for Beat Tracking Evaluation

In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method “selective sampling,” is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of different evaluation measures. Using our approach we demonstrate how to compile a new evaluation dataset comprised of difficult excerpts for beat tracking and examine this difficulty in the context of perceptual and musical properties. Based on tag analysis we indicate the musical properties where future advances in beat tracking research would be most profitable and where beat tracking is too difficult to be attempted. Finally, we demonstrate how our mutual agreement method can be used to improve beat tracking accuracy on large music collections.
An optimal trade-off between content freshness and refresh cost

Caching is an effective mechanism for reducing bandwidth usage and alleviating server load. However, the use of caching entails a compromise between content freshness and refresh cost. Constant refreshing allows a high degree of content freshness at a greater cost of system resource. Conversely, too little refreshing reduces content freshness but saves the cost of resource usage. To address the freshness-cost problem, we formulate the refresh scheduling problem with a generic cost model and use this cost model to determine an optimal refresh frequency that gives the best trade-off between refresh cost and content freshness. We prove the existence and uniqueness of an optimal refresh frequency under the assumptions that the arrival of updated content is Poisson and the age-related cost monotonically increases with decreasing freshness. In addition, we provide an analytic comparison of system performance under fixed refresh scheduling and random refresh scheduling, showing that two refresh schedulings with the same average refresh frequency are mathematically equivalent in terms of the long-run average cost.
Enhanced centroid localization of wireless sensor nodes using linear and neighbor weighting mechanisms

The drop in cost and reduction in size of sensor nodes has eased the development of wireless sensor networks (WSNs) applications. However, the noise and disturbing nature of most sensing environments require accurate algorithms that can overcome these difficulties. Nodes' localization is one of the basic activity a WSN can perform to make other network's functionalities, such as routing easy to tackle. Nowadays there exists many localization methods, however many pose computational and/or accuracy issues. Centroid is a localization algorithm by which an unknown node's coordinates are estimated as the centroid of anchors' coordinates. Its implementation is simple but it has a high error rate. In this paper, two methods are proposed to enhance the centroid localization algorithm. The first, Linear Weighting Centroid (LWC) uses the distance between the anchor and the unknown nodes to linearly weight each anchor's coordinates. The second, the Neighbor Weighting Centroid (NWC) uses the number of intersect nodes between an unknown node and its neighbor anchors to estimate the degree of proximity of the anchor nodes. Both methods assign larger weights to closer anchors and lesser weights to remote anchors to improve centroid accuracy while keeping computation almost at the same level. Simulation is used to study the performance of both mechanisms. The results show that for a large WSN, both methods localize unknown nodes with better position accuracy than centroid, with LWC performing better than NWC.
Image prior combination in space-variant blur deconvolution for the dual exposure problem

In this paper we propose a space-variant blur estimation and effective deconvolution method when combining a long exposure blurry image with a short exposure noisy one. The blur in the long exposure shot is mainly caused by camera shake or object motion, and the noise of the underexposed image is introduced by the gain factor applied to the sensor when the ISO is set to a high value. The image pair is divided in overlapping patches for processing. The main idea in this work is to incorporate a combination of prior image models to a spatially-varying deblurring/denoising framework which is applied to each patch. The method exploits kernel and parameters estimation to choose between denoise or deblur each patch. In addition, the proposed approach estimates all necessary parameters automatically without user supervision. Experiments on both synthetic and real images validate the used approach.
Rate-Distortion Optimized Video Streaming with Multiple Deadlines for Low Latency Applications

We extend a recently-proposed framework for the rate-distortion optimized transmission of packetized media. The original framework assumed that media packets each have a single arrival deadline and that a packet is useless if it arrives after its deadline. In practice, however, packets may be associated with multiple deadlines. One example is the case of decoders that can recover from late packet arrivals through the accelerated retroactive decoding (ARD) of the dependency chain. We extend the original framework to consider multiple deadlines. Results of experiments in which packet transmission channels were simulated as iid delay channels with high packet loss rates and experiments in which packet transmission channels were simulated according measured Internet delay and loss traces show that the multiple deadline formulation yields rate-distortion performance improvement that increases as latency constraints grow tighter, with PSNR gains of more than 3 dB and bit-rate savings of up to 47% observed at low latencies.