87value/wos11967 huggingface dataset:
Dietary composition has been shown to influence metabolism and to impact on the prevalence and risk for certain diseases, but hitherto, there have been no systematic studies on the effects of dietary modulation of human metabolic phenotype (metabotype). Here, we have applied H-1 NMR spectroscopy in combination with multivariate statistical analysis to characterize the effects of three diets: vegetarian, low meat, and high meat on the metabotype signature of human participants. Twelve healthy male participants (age range of 25-74 years) consumed each of these diets, in a randomized order, for continuous 15-day-periods with an intervening washout period between each diet of 7 days duration. Each participant provided three consecutive 24-hour urine collections on days 13, 14, and 15 of each dietary period, and H-1 NMR spectra were acquired on all samples. Pattern recognition analysis allowed differentiation of the characteristic metabolic signatures of the diets with creatine, carnitine, acetylcarnitine, and trimethylamine-N-oxide (TMAO) being elevated in the high-meat consumption period. Application of orthogonal projection to latent structure discriminant analysis (O-PLS-DA) allowed the low-meat diet and vegetarian diet signatures to be characterized, and p-hydroxyphenylacetate (a microbial mammalian cometabolite) was higher in the vegetarian than meat diet samples, signaling an alteration of the bacterial composition or metabolism in response to diet. This work shows the potential for the routine use of metabonomics in nutritional and epidemiological studies, in characterizing and predicting the metabolic effects and the influence of diet on human metabotypes.
Combined with Fast Fourier Transform (FFT), a mathematical hybrid method for accurately computing the lightning current flowing along the grounding grid buried in multilayered earth model has been developed in this paper. In the hybrid method, electrical circuits consist with T typical elementary element. To accelerate the calculation, both dynamic state and quasi-static complex image methods, and closed form of Green's function were introduced into this model. The model can be used for study performance of transient lightning response to grounding grid.
This paper describes the successful application of a pilot with sensors in a groyne alongside the Yellow River near Dayulan. The aim of this innovative application of the sensors is to create a real time monitoring system. The used monitoring system was designed based on the knowledge gained in the Dutch IJkdijk project (IJkdijk is the Dutch word for Calibration levee). The deployed sensors are tri - axialy MEMS (Micro - Electrical - Mechanical Systems) and an optical glass fibre system in a geotextile. The management system, ReadyMIND, links the sensor output to a dike stability analysis model and provides a GIS based interface offering continuous status information on the dike stability. This system also provides sophisticated simulation capability, and operational support, offering benefits in areas of emergency response planning and crisis management.
Obesity and insulin resistance have been recognised as leading causes of major health issues, particularly diabetes type 2 and metabolic syndrome. Although obesity, defined as excess body fat, is frequently accompanied by insulin resistance, diabetes, metabolic syndrome and cardiovascular diseases, the molecular basis for the link between obesity and those diseases has not yet been clarified. Adipose tissue expresses various secretory proteins, including leptin, tumour necrosis factor-alpha and adiponectin, which may be involved in the regulation of energy expenditure, lipid metabolism and insulin resistance. The aim of this study is to provide an overview of the metabolic alterations occurring in insulin resistance as well as to review the biological roles of adiponectin, particularly in the regulation of fatty acid oxidation and insulin action. Adiponectin is the most abundant gene product in adipose tissue and accounts for 0.01% of total plasma protein. Plasma adiponectin level is decreased in obesity, both in children and adults, and it is negatively associated to plasma insulin and positively associated to plasma triglycerides. Low levels of adiponectin decreases fatty acid oxidation in muscle. Recent data have demonstrated that adiponectin effects are mediated by the interaction with muscle and hepatic receptors through activation of AMP kinase, the cellular fuel gauge, which in turn inhibits acetyl CoA carboxylase and increases fatty acid beta-oxidation. Since there is no available recombinant adiponectin for human use, its direct effects on human metabolism remain unknown, but this hormone appears to be promising in the treatment of obesity an related metabolic disorders. (C) 2004 Elsevier Ltd. All rights reserved.
The strategy games have been traditionally applied in the teaching of subjects related with decisions making, principally in the university studies related with the company. Nevertheless, the potential of this methodology makes also applicable to studies that lead to applicable practices in companies or factories, like happens with the technical educations. So, the present paper shows the methodology applied in the teaching of Engineering Plant, inside the Official Master of Manufacturing Engineering in the University of Cadiz, circumscribing it in a context of a material forming machine shop by chip removal. During the experience, distributions by process and by product have been simulated, determining the fictitious domain to pass from one to another. This was preceded by the determination of the. Finally, the discussion has driven to emphasize the importance of the reduction of dead-times due to the movements.
One of major ideas to design a multivariate public key cryptosystem (MPKC) is to generate its quadratic forms by a polynomial map over an extension field. In fact, Matsumoto-Imai's scheme (1988), HFE (Patarin, 1996), MFE (Wang et al., 2006) and multi-HFE (Chen et al., 2008) are constructed in this way and Sflash (Akkar et al., 2003), Quartz (Patarin et al., 2001), Gui (Petzoldt et al, 2015) are variants of these schemes. An advantage of such extension field type MPKCs is to reduce the numbers of variables and equations to be solved in the decryption process. In the present paper, we study the security of MPKCs whose quadratic forms are derived from a quadratic map over an extension field and propose a new attack on such MPKCs. Our attack recovers partial information of the secret affine maps in polynomial time when the field is of odd characteristic. Once such partial information is recovered, the attacker can find the plain-text for a given cipher-text by solving a system of quadratic equations over the extension field whose numbers of variables and equations are same to those of the system of quadratic equations used in the decryption process.
This article addresses mainly questions about the roles of fluid systems, in particular of digital ones, in an Industrie 4.0 (I4.0) environment. There are three roles: the general machines' subsystems/components' role in machines of an I4.0 production system, the role of subsystems/components of machines which are produced under I4.0 conditions, and the manufacturing of fluid systems' components in an I4.0 setting. These roles are discussed and examples of digital hydraulic solutions which fulfill these roles excellently are presented. They feature simplicity and robustness of hardware components and transfer of functionality to software. It is concluded that components and subsystems can support the successful realization of I4.0-type production systems decisively.
Objective. Hypersensitivity reactions (HSR) are frequently reported in patients rechallenged with carboplatin for recurrent ovarian cancer (ROC) and represent a critical issue, since discontinuation of the platinum-based therapy could affect prognosis. Several strategies to allow platinum rechallenge have been described, with controversial outcomes. The aim of this study is to illustrate a 10-year experience with cisplatin in patients with a previous HSR to carboplatin or at risk for allergy. Methods. A retrospective review of all patients with platinum sensitive ROC retreated with carboplatin was performed between January 2007 and May 2016 at the Istituto Nazionale Tumori, Fondazione G, Pascale, Naples. Results. Among 183 patients, 49 (26.8%) presented HSR to carboplatin, mainly during second line therapy. Mean number of cycles before HSR was 8 (range 3-17). G2, G3 and G4 reaction were detected in 83%, 15% and 2% of patients, respectively. In a multivariate analysis including age, hystotype, BRCA status, previous known HSR, and combination drug administered, only the type of carboplatin-based doublet used as 2nd line therapy was found to significantly affect HSR development, with a protective effect of PLD (pegylated liposomal doxorubicin) (p = 0.014, OR = 0.027). Thirty seven patients (77%) with a previous HSR to carboplatin were rechallenged with cisplatin. Treatment was generally well tolerated. 5 patients (13.1%) experienced mild HSR to cisplatin, successfully managed in all cases. 14 patients were treated with cisplatin even without a carboplatin-related HSR due to other allergies, Among these, only one developed HSR (7.1%), Conclusions. Cisplatin rechallenge is a feasible approach in patients experiencing I-JSR to carboplatin to maintain the beneficial effect of platinum while reducing hypersensitivity-related risks. (C) 2016 Elsevier Inc. All rights reserved.
As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects from a group of images. On one hand, traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to possibly reflect the faithful properties of the co-salient regions. Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications. On the other hand, most current methods pursue co-saliency detection in unsupervised fashions. This, however, tends to weaken their performance in real complex scenarios because they are lack of robust learning mechanism to make full use of the weak labels of each image. To alleviate these two problems, this paper proposes a new SP-MIL framework for co-saliency detection, which integrates both multiple instance learning (MIL) and self-paced learning (SPL) into a unified learning framework. Specifically, for the first problem, we formulate the co-saliency detection problem as a MIL paradigm to learn the discriminative classifiers to detect the co-saliency object in the instance-level. The formulated MIL component facilitates our method capable of automatically producing the proper metrics to measure the intra-image contrast and the inter-image consistency for detecting co-saliency in a purely self-learning way. For the second problem, the embedded SPL paradigm is able to alleviate the data ambiguity under the weak supervision of co-saliency detection and guide a robust learning manner in complex scenarios. Experiments on benchmark datasets together with multiple extended computer vision applications demonstrate the superiority of the proposed framework beyond the state-of-the-arts.
Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed hyperscanning. Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
In this paper, we focus on interconnected trajectory optimization of two sets of solenoid actuated butterfly valves dynamically coupled in series. The system undergoes different approach angles of a pipe contraction as a typical profile of the so-called Smart Valves network containing tens of actuated valves. A high fidelity interconnected mathematical modeling process is derived to reveal the expected complexity of such a multiphysics system dealing with electromagnetics, fluid mechanics, and nonlinear dynamic effects. A coupled operational optimization scheme is formulated in order to seek the most efficient trajectories of the interconnected valves minimizing the energy consumed enforcing stability and physical constraints. We examine various global optimizationmethods including Particle Swarm, Simulated Annealing, Genetic, and Gradient based algorithms to avoid being trapped in several possible local minima. The effect of the approach angles of the pipeline contraction on the amount of energy saved is discussed in detail. The results indicate that a substantial amount of energy can be saved by an intelligent operation that uses flow torques to augment the closing efforts.
The aim of this study is to present a method, using 3 computer-aided design/computer-aided manufacturing (CAD/CAM) surgical guides, to accurately obtain the desired bone reduction followed by immediate implant placements and loading for patients diagnosed with terminal dentition. Patients who had bone reduction, implants placed, and immediate loading using Anatomage Invivo 5 CAD/CAM surgical guides between the period 2013 and 2015 were evaluated retrospectively. Patients diagnosed with terminal dentition and treated using the 3-guide technique were identified. Pre-and postsurgical images were superimposed to evaluate deviations of the bone reduction and deviations at the crest, apex, and angle of implants placed. Twenty-six implants placed in 5 patients were included in this study. The overall deviation means measured for bone reduction was 1.98 mm. The overall deviation means measured for implant placement at the crest, apex, and angle were 1.43 mm, 1.90 mm, and 4.14 degrees, respectively. The CAD/CAM surgical guide fabrication is an emerging tool that may facilitate the surgical process and aid in safe and predictable execution of bone reduction and immediate implant placement. Using 3 CAD/CAM surgical guides, a method is presented to obtain the desired bone reduction followed by immediate implant placement and loading for patients diagnosed with terminal dentition. This method may improve guide stability for patients with terminal dentition undergoing complete implant-supported treatment by taking advantage of the teeth to be extracted.
The changing manufacturing environment characterized by aggressive competition on a global scale and rapid changes in manufacturing processes and related technologies requires creation of education systems that are easily upgradable and into which new syllabi and functions can be readily integrated, according to a report on next-generation manufacturing. This new environment calls for hands-on experiences in laboratories that have modularity, integrability, customization, and open architecture as key features. There is also an emphasis on teamwork, innovation, and leadership as a part of new curricula in manufacturing engineering education. This paper discusses these concepts and presents ideas for the design of courses and labs to support new curricula.
Commercial anti-virus software traditionally memorizes specific byte sequences (known as signatures) in the file contents of previously encountered malware. However, malware authors can evade signature based detection in many ways; for instance, by using obfuscation techniques such as packing (encryption or compression) to hide snippets of malicious code; by writing metamorphic malware; or by tampering with existing malware. We hypothesize that certain evasion techniques can leave traces in the file's entropy signal, revealing either similarities to known malware or the presence of tampering per se. To this end, we present SUSPEND (SUSPicious ENtropy signal Detector), an expert system which evaluates the suspiciousness of an executable file's entropy signal in order to subserve malware classification. Whereas traditionally, entropy analysis has been used for the goal of packer detection (and therefore entropy-based features often merely comprise mean entropy or the entropy of a few file subcomponents), SUSPEND applies non-stationary time series modeling to aid in malware detection. In particular, SUSPEND (a) quantifies the amount of structure in the entropy signal (through detrended fluctuation analysis), (b) finds the location and size of sudden jumps in entropy (through mean change point modeling), and (c) computes the distribution of entropic variation across multiple spatial scales (through wavelet decomposition). In addition, SUSPEND (d) summarizes the entropy signal's empirical probability distribution. Because SUSPEND's run time can be made to scale linearly in file size, it is well-suited for large-scale malware analysis. We apply SUSPEND to a large-scale malware detection task with 500,000 heterogeneous real-world samples and over 1 million features. We find that SUSPEND boosts the predictive performance of traditional entropy analysis (as found in packer detectors) from 77.02% to 96.62%. Moreover, SUSPEND's focus on entropy signals makes it a natural candidate for combining with other types of features; for instance, combining SUSPEND with a strings-based feature set boosts predictive accuracy from 97.18% to 98.62%. Thus, whereas traditionally, entropy analysis has focused on detecting that a file is packed, SUSPEND's more comprehensive representation of the entropy signal helps to determine that a file is malicious. We illustrate the application of SUSPEND by studying 18 pieces of VirRansom, a family of viral ransomware which could cost millions to large organizations. SUSPEND is able to detect 100% of the studied files with over 99% confidence, whereas a more traditional strings-based model was very close to undecided and represents the entire family with a single string. (C) 2016 Elsevier Ltd. All rights reserved.
Background: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment -including a-amyloid depostion, vascular disease, network breakdown and atrophy -to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. Methods/design: This paper outlines the clinical, cognitive and imaging protocol of Insight 46, a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that similar to 1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, beta-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). Discussion: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
Under increasing penetration of distributed resources, regulators and electricity distribution utilities face greater uncertainty regarding the evolution of network uses and efficient system costs. This uncertainty can threaten revenue adequacy and challenges both cost of service/rate of return and incentive/performance-based approaches to the remuneration of distribution utilities. To address these challenges, this paper proposes a novel methodology to establish allowed utility revenues over a multi-year regulatory period. This method combines several state of the art regulatory tools designed to overcome information asymmetries, manage uncertainty, and align incentives for utilities to cost-effectively integrate distributed energy resources while taking advantage of opportunities to reduce system costs and improve performance. We use a reference network model to simulate a large-scale urban distribution network, demonstrate the practical application of this regulatory method, and illustrate its performance in the face of both benchmark and forecast errors.