Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Obesity Activity of the Marine Carotenoid Fucoxanthin

Nowadays the global tendency towards physical activity reduction and an augmented dietary intake of fats, sugars and calories is leading to a growing propagation of overweight, obesity and lifestyle-related diseases, such diabetes, hypertension, dyslipidemia and metabolic syndrome. In particular, obesity, characterized as a state of low-level inflammation, is a powerful determinant both in the development of insulin resistance and in the progression to type 2 diabetes. A few molecular targets offer hope for anti-obesity therapeutics. One of the keys to success could be the induction of uncoupling protein 1 (UCP1) in abdominal white adipose tissue (WAT) and the regulation of cytokine secretions from both abdominal adipose cells and macrophage cells infiltrated into adipose tissue. Anti-obesity effects of fucoxanthin, a characteristic carotenoid, exactly belonging to xanthophylls, have been reported. Nutrigenomic studies reveal that fucoxanthin induces UCP1 in abdominal WAT mitochondria, leading to the oxidation of fatty acids and heat production in WAT. Fucoxanthin improves insulin resistance and decreases blood glucose levels through the regulation of cytokine secretions from WAT. The key structure of anti-obesity effect is suggested to be the carotenoid end of the polyene chromophore, which contains an allenic bond and two hydroxyl groups. Fucoxanthin, which can be isolated from edible brown seaweeds, recently displayed its many physiological functions and biological properties. We reviewed recent studies and this article aims to explain essential background of fucoxanthin, focusing on its promising potential anti-obesity effects. In this respect, fucoxanthin can be developed into promising marine drugs and nutritional products, in order to become a helpful functional food.
Contour Completion Without Region Segmentation

Contour completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. Most existing methods for contour completion have focused on pixelwise detection accuracy. In contrast, fewer methods have addressed the global contour closure effect, despite psychological evidences for its importance. This paper proposes a purely contour-based higher order CRF model to achieve contour closure, through local connectedness approximation. This leads to a simplified problem structure, where our higher order inference problem can be transformed into an integer linear program and be solved efficiently. Compared with the methods based on the same bottom-up edge detector, our method achieves a superior contour grouping ability (measured by Rand index), a comparable precision-recall performance, and more visually pleasing results. Our results suggest that contour closure can be effectively achieved in contour domain, in contrast to a popular view that segmentation is essential for this purpose.
A Dynamic Fuzzy Neural Adaptive Control algorithm and its application

The uncertainties coursing by the changing of modeling parameters should be considered when modeling and controlling a kind of nonlinear dynamic systems. A Dynamic Fuzzy Neural Adaptive Control (DFNAC) algorithm is presented in the paper. The DFNAC combines a Dynamic Fuzzy Neural Networks (DFNN) with a PID controller. DFNN adjusts its structure and parameters online, and generates the fuzzy rules automatically when being trained. The algorithm conquers the disadvantage of either overfitting or overtraining in traditional static FNN-based control methods. Simulation results of the course control of a container ship validate the effectiveness of the proposed algorithm.
2016 Ieee International Conference on Big Data (big Data) Phishing Detection Based on Newly Registered Domains

—Phishing is a security attack that involves the creation of websites that mimic legitimate websites, and these fraud websites bring Internet users a lot of loss. Traditional anti-phishing methods usually worked in a passive way by receiving report data of user. Due to the growing shorter survival time of phishing, this kind of methods is not efficient enough to find and take down new phishing attacks. In this paper, we propose an Intelligent Phishing Detection (IPD) system to address phishing detection problem actively. Specially, IPD first generates the detection dataset from the global massive domain name registration data automatically; then it applies the Naïve Bayes algorithm which is optimized by position-based features to achieve the high precision detection; finally, in order to find more phishing websites, IPD expands detection dataset by generating Uniform Resource Locator (URL) templates based on the detection results. The experimental results of IPD demonstrate the effectiveness and timeliness in detection phishing websites.
Range based sensor node localization in the presence of unknown clock skews

We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. To avoid the drawbacks in solving the optimal estimator, we use approximations and derive linear models, which facilitate efficient solutions. In particular, we employ the least squares method and solve a general trust region subproblem to find a coarse estimate. To further refine the estimate, we linearize the measurements and obtain a linear model which can be solved using regularized least squares. Simulation results illustrate that the proposed approaches asymptotically attain the Cramér-Rao lower bound.
Class specific dictionary learning for face recognition

Recently, sparse representation based classification (SRC) has been successfully used for visual recognition and showed impressive performance. Given a testing sample, SRC computes its sparse linear representation with respect to all the training samples and calculates the residual error for each class of training samples. However, SRC considers the training samples in each class contributing equally to the dictionary in that class, i.e., the dictionary consists of the training samples in that class. This may lead to high residual error and instability. In this paper, a class specific dictionary learning algorithm is proposed. First, by introducing the dual form of dictionary learning, an explicit relationship between the bases vectors and the original image features is represented, which enhances the interpretability. SRC can be thus considered to be a special case of the proposed algorithm. Second, blockwise coordinate descent algorithm and Lagrange multipliers are then applied to optimize the corresponding objective function. Extensive experimental results on three benchmark face recognition datasets demonstrate that the proposed algorithm has achieved superior performance compared with conventional classification algorithms.
Do Antenatal Parasite Infections Devalue Childhood Vaccination?

On a global basis, both potent vaccine efficacy and high vaccine coverage are necessary to control and eliminate vaccine-preventable diseases. Emerging evidence from animal and human studies suggest that neglected tropical diseases (NTDs) significantly impair response to standard childhood immunizations. A review of efficacy and effectiveness studies of vaccination among individuals with chronic parasitic infections was conducted, using PUBMED database searches and analysis of data from the authors' published and unpublished studies. Both animal models and human studies suggest that chronic trematode, nematode, and protozoan infections can result in decreased vaccine efficacy. Among pregnant women, who in developing countries are often infected with multiple parasites, soluble parasite antigens have been shown to cross the placenta and prime or tolerize fetal immune responses. As a result, antenatal infections can have a significant impact on later vaccine responses. Acquired childhood parasitic infections, most commonly malaria, can also affect subsequent immune response to vaccination. Additional data suggest that antiparasite therapy can improve the effectiveness of several human vaccines. Emerging evidence demonstrates that both antenatal and childhood parasitic infections alter levels of protective immune response to routine vaccinations. Successful antiparasite treatment may prevent immunomodulation caused by parasitic antigens during pregnancy and early childhood and may improve vaccine efficacy. Future research should highlight the varied effects that different parasites (alone and in combination) can have on human vaccine-related immunity. To optimize vaccine effectiveness in developing countries, better control of chronic NTDs may prove imperative.
Hybrid Neuro Fuzzy Approach for Automatic Generation Control of Two -Area Interconnected Power System

The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
A Survey of Vision-Based Architectures for Robot Learning by Imitation

Learning by imitation is a natural and intuitive way to teach social robots new behaviors. While these learning systems can use different sensory inputs, vision is often their main or even their only source of input data. However, while many vision-based robot learning by imitation (RLbI) architectures have been proposed in the last decade, they may be difficult to compare due to the absence of a common, structured description. The first contribution of this survey is the definition of a set of standard components that can be used to describe any RLbI architecture. Once these components have been defined, the second contribution of the survey is an analysis of how different vision-based architectures implement and connect them. This bottom–up, structural analysis of architectures allows to compare different solutions, highlighting their main advantages and drawbacks, from a more flexible perspective than the comparison of monolithic systems.
Multimodality Imaging in Cardiooncology

Cardiotoxicity represents a rising problem influencing prognosis and quality of life of chemotherapy-treated patients. Anthracyclines and trastuzumab are the drugs most commonly associated with development of a cardiotoxic effect. Heart failure, myocardial ischemia, hypertension, myocarditis, and thrombosis are typical manifestation of cardiotoxicity by chemotherapeutic agents. Diagnosis and monitoring of cardiac side-effects of cancer treatment is of paramount importance. Echocardiography and nuclear medicine methods are widely used in clinical practice and left ventricular ejection fraction is the most important parameter to asses myocardial damage secondary to chemotherapy. However, left ventricular ejection decrease is a delayed phenomenon, occurring after a long stage of silent myocardial damage that classic imaging methods are not able to detect. New imaging techniques including three-dimensional echocardiography, speckle tracking echocardiography, and cardiac magnetic resonance have demonstrated high sensitivity in detecting the earliest alteration of left ventricular function associated with future development of chemotherapy-induced cardiomyopathy. Early diagnosis of cardiac involvement in cancer patients can allow for timely and adequate treatment management and the introduction of cardioprotective strategies.
Thyroid fine needle aspiration biopsy: do nodule volume and cystic degeneration ratio affect specimen adequacy and cytological diagnosis time?

Background A fine needle aspiration biopsy (FNAB) of thyroid nodules – the least invasive and most accurate method used to investigate malignant lesions – may yield non-diagnostic specimens even under ultrasonographic guidance. Purpose To evaluate the effects of thyroid nodule volume and extent of cystic degeneration on both the non-diagnostic specimen ratio as well as cytopathologist’s definitive cytological diagnosis time. Material and Methods In this single center study, FNAB was performed on 505 patients with single thyroid nodules greater than 10 mm. Nodule volume was calculated prior to FNAB and cystic degeneration ratio was recorded. All biopsies were performed by a single radiologist who also prepared specimen slides. Specimen adequacy and final diagnosis were made in the pathology laboratory by a single-blinded cytopathologist based on the Bethesda system. Definitive cytological diagnosis time was recorded upon reaching a definitive diagnosis. Results The specimen adequacy ratio was 85.3%. The mean nodule volume of adequate specimens was larger than those of non-diagnostic samples (6.00 mL vs. 3.05 mL; P = 0.001). There was no correlation between nodule volume and cytopathologist’s definitive cytological diagnosis time (r = 0.042). Biopsy of predominantly solid nodules yielded better specimen adequacy ratios compared to predominantly cystic nodules (87.8% vs. 75.3%; P = 0.028). Definitive cytological diagnosis times were longer in predominantly cystic nodules compared to predominantly solid nodules (376 s vs. 294 s; P = 0.019). Conclusion Predominantly cystic nodules are likely to benefit from repeated nodular sampling until the specimen is declared adequate by an on-site cytopathologist. If a cytopathologist is not available, obtaining more specimens per nodule may achieve desired adequacy ratios.
MAXIMAL DIAGNOSIS FOR WIRING NETWORKS

Previous work on the diagnosis of faults in a wiring network has been based on the assumption that both open and short faults do not exist on the same net. When this assumption is relaxed, these results fail to identify all diagnosable faults. The non-diagnosability of these faults, including shorts between nets, represents a deficiency. In this paper we analyze and explain the causes for this deficiency. In addition, we provide new theoretical results and test algorithms, and show how these deficiencies can be overcome. A test set, which is generated based on these results, can identify all diagnosable faults in a wiring network with arbitrary open and short faults. Two adaptive diagnosis algorithms are also presented which can reduce the number of test vectors while retaining the same level of diagnostic resolution. Another algorithm that can reduce the number of vectors by exploring the structure information of the network is also presented.
Identification of Proteins in Human Cytomegalovirus (HCMV) Particles: the HCMV Proteome

ABSTRACT Human cytomegalovirus (HCMV), a member of the herpesvirus family, is a large complex enveloped virus composed of both viral and cellular gene products. While the sequence of the HCMV genome has been known for over a decade, the full set of viral and cellular proteins that compose the HCMV virion are unknown. To approach this problem we have utilized gel-free two-dimensional capillary liquid chromatography-tandem mass spectrometry (MS/MS) and Fourier transform ion cyclotron resonance MS to identify and determine the relative abundances of viral and cellular proteins in purified HCMV AD169 virions and dense bodies. Analysis of the proteins from purified HCMV virion preparations has indicated that the particle contains significantly more viral proteins than previously known. In this study, we identified 71 HCMV-encoded proteins that included 12 proteins encoded by known viral open reading frames (ORFs) previously not associated with virions and 12 proteins from novel viral ORFs. Analysis of the relative abundance of HCMV proteins indicated that the predominant virion protein was the pp65 tegument protein and that gM rather than gB was the most abundant glycoprotein. We have also identified over 70 host cellular proteins in HCMV virions, which include cellular structural proteins, enzymes, and chaperones. In addition, analysis of HCMV dense bodies indicated that these viral particles are composed of 29 viral proteins with a reduced quantity of cellular proteins in comparison to HCMV virions. This study provides the first comprehensive quantitative analysis of the viral and cellular proteins that compose infectious particles of a large complex virus.
Research Online Research

This paper presents a new methodology for the deformable object modelling by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by the non-linear CNN activity. An improved autonomous CNN model is developed for propagating the energy generated by the external force on the object surface in the natural manner of heat conduction. A heat flux based method is presented to derive the internal forces from the potential energy distribution established by the CNN. The proposed methodology models non-linear materials with non-linear CNN rather than geometric non-linearity in the most existing deformation methods. It can not only deal with large-range deformations due to the local connectivity of cells and the CNN dynamics, but it can also accommodate both isotropic and anisotropic materials by simply modifYing conductivity constants. Examples are presented to demonstrate the efficacy of the proposed methodology.
Cross-Layer Optimization of Correlated Data Gathering in Wireless Sensor Networks

We consider the problem of gathering correlated sensor data by a sink node in a wireless sensor network. We design efficient distributed protocols to maximize the network lifetime subject to nodal energy constraints. Many existing approaches address the routing layer only, but the routing often interacts with physical-layer power control and MAC-layer link access. We present a first effort to maximize the network lifetime by jointly considering the three layers. We first solve the joint power control and routing problem, by assuming that the link access probabilities are known. We show that the problem is convex and propose a distributed algorithm, JRPA, as solution. When the link access probabilities are unknown, we then generalize the problem to encompass all three layers of routing, power control, and link random access. The general problem is non-convex; a duality gap exists when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem. Numerical results show that JRPRA is highly effective; particularly, even without the best link access probabilities pre-determined for JRPA, JRPRA achieves extremely competitive performance. Our results also show the convergence of the algorithms and their advantages over existing solutions.
Evidence that children with special needs all require visual assessment

Design A protocol-based ophthalmological assessment was performed on-site by a skilled investigator. Patients Children attending schools for special needs in Glasgow were offered eye care within their school. Outcomes for the first 240 participants are reported. Outcome measures Number of children for whom visual acuity could be measured and the results of refraction. Results 228/240 (95%) children were able to co-operate in a complete or nearly complete assessment of visual function. Visual acuity could be reliably assessed in 190 children using a range of tests from preferential looking to logMAR charts. 23/190 (12.1%) were found to be visually impaired according to WHO criteria. 105/228 (46.1%) subjects were found to have a refractive error which required correction. 50/105 subjects were wearing an adequate correction (ie, difference of less than 0.75 D sphere or cylinder compared with the retinoscopy result obtained on screening) and 55 (24.1%) children were prescribed a new correction. The prevalence of all types of refractive error was found to be significantly higher in the population with special needs, in particular the learning disabilities subgroup, compared with normal children. High hyperopia and astigmatism were common. Conclusion With patience, suitably adapted methods, a familiar environment and skill, visual assessment is feasible in the majority of children with special needs. The prevalence of reduced visual acuity is high and unaddressed correctable refractive error is a major cause.
Hippocampal but not amygdalar volume affects contextual fear conditioning in humans

Both animal and human studies have identified a critical role of the hippocampus in contextual fear conditioning. In humans mainly functional magnetic resonance imaging has been used. To extend these findings to volumetric properties, 58 healthy participants underwent structural magnetic resonance imaging and participated in a differential fear conditioning paradigm with contextual stimuli. Ratings of emotional valence, arousal, and contingency as well as skin conductance responses (SCRs) were used as indicators of conditioning. Twenty‐nine participants with the smallest hippocampal volumes were compared with 29 persons with the largest hippocampal volumes. Persons with larger hippocampal volume (especially on the right side) learned to discriminate between two conditioned contexts, whereas those with small hippocampal volumes did not, as indicated by SCRs. Further analyses showed that these results could not be explained by amygdalar volumes. In contrast, the participant answers on the self‐report measures were not significantly influenced by hippocampal or amygdalar, but by total brain volume, suggesting a role of cortical structures in these more cognitive evaluation processes. Reanalysis of the self‐report data using partial hippocampal volumes revealed a significant influence of the posterior but not anterior subvolumes, which is in accordance with theories and empirical findings on hippocampal functioning. This study shows the relevance of hippocampal volume for contextual fear conditioning in healthy volunteers and may have important implications for anxiety disorders. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.