Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of fatty acids on uncoupling protein‐2 expression in the rat heart

Fatty acids are thought to play a role in the activity of uncoupling proteins (UCP) and have been shown to regulate the expression of genes encoding proteins involved in fatty acid handling. Therefore, we investigated whether fatty acids, which are the main substrates for the heart, affect rat cardiac UCP‐2 expression in vivo and in vitro. After birth, when the contribution of fatty acid oxidation to cardiac energy conversion increases, UCP‐2 expression enhanced rapidly. In the adult heart, however, UCP‐2 mRNA levels did not alter during conditions associated with either enhanced (fasting, diabetes) or decreased (hypertrophy) fatty acid utilization. Exposure of neonatal cardiomyocytes and embryonic rat heart‐derived H9c2 cells to fatty acids (palmitic and oleic acid) for 48 h strongly induced UCP‐2 expression. Stimulation of neonatal cardio‐myocytes with triiodothyronine also increased UCP‐2 mRNA levels, though only in the presence of fatty acids. Ligands specific to the fatty acid‐activated transcription factor PPARa, but not to PPAR7, acted as inducers of cardiomyocyte UCP‐2 expression. It is concluded that fatty acids promote UCP‐2 expression in neonatal cardiomyocytes, which might explain the rapid increase in UCP‐2 mRNA in the postnatal heart. However, UCP‐2 mRNA levels in the adult heart appear to be insensitive to changes in cardiac fatty acid handling under various pathological conditions. —van der Lee, K. A. J. M. , Willemsen, P. H. M. , van der Vusse, G. J., van Bilsen, M. Effects of fatty acids on uncoupling protein‐2 expression in the rat heart. FASEB J. 14, 495–502 (2000)
The impact of potential flexibility gains and losses on the intention to outsource business processes

In an ever accelerating world demanding fast adjustment to changing business environments, organizational flexibility becomes increasingly important. By outsourcing business processes (BPO), there are both potential flexibility losses (e.g. loss of control) and potential flexibility gains (e.g. the transformation of fixed to variable costs). Firms have to balance this trade-off to retain sufficient flexibility or even enlarge their strategic and operational flexibility. Based on an empirical study with Germany’s Top 200 Banks it is shown that the perception of both flexibility gains and losses have a profound impact on the outsourcers’ attitude towards BPO. In particular, potential flexibility losses have a higher impact on outsourcers’ attitude than potential flexibility gains. Subsequently, in accordance with the theory of reasoned action it turns out that attitude is an antecedent of the intention towards outsourcing. Therefore we argue that flexibility-related issues should be explicitly considered in outsourcing evaluations and service providers should emphasize on contractual means to demonstrate that organizational flexibility will not be negatively affected.
Community systems research at Yahoo!

The web and its continued evolution present unprecedented opportunities for database researchers and practitioners to deliver unique user experiences that are not possible traditionally, e.g., mass collaborations through (automatically) established online communities and exploration of large scale structured information. Along with these opportunities, however, come significant challenges. The challenges are two-fold: systems, the infrastructures that allow us to deliver information at scale; and community, the applications that deliver the next generation of web experiences centered around people and social networks. In this paper, we describe the ongoing research efforts within the Community Systems group here at Yahoo! Research to address these challenges.
Identifying Important Nodes in Heterogenous Networks

This is a position paper that presents a new approach to identifying important nodes or entities in a complex heterogeneous network. We provide a novel definition of an importance score based on a statistical model: An individual is important to the extent that including an individual explicitly in the model improves the data fit of the model more than it increases the model's complexity. We apply techniques from statistical-relational learning, a recent field that combines AI and machine learning, to identify statistically important individuals in a scalable manner. We investigate empirically our approach with the OPTA soccer data set for the English premier league.
Random Forest Segregation of Drug Responses May Define Regions of Biological Significance

The ability to assess brain responses in unsupervised manner based on fMRI measure has remained a challenge. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment with an analgesic drug (buprenorphine) as compared to control (saline). Three groups of animals were studied: two groups treated with different doses of the opioid buprenorphine, low (LD), and high dose (HD), and one receiving saline. PhMRI responses were evaluated in 45 brain regions and RF analysis was applied to allocate rats to the individual treatment groups. RF analysis was able to identify drug effects based on differential phMRI responses in the hippocampus, amygdala, nucleus accumbens, superior colliculus, and the lateral and posterior thalamus for drug vs. saline. These structures have high levels of mu opioid receptors. In addition these regions are involved in aversive signaling, which is inhibited by mu opioids. The results demonstrate that buprenorphine mediated phMRI responses comprise characteristic features that allow a supervised differentiation from placebo treated rats as well as the proper allocation to the respective drug dose group using the RF method, a method that has been successfully applied in clinical studies.
The Analysis and Summarizing System of Thai Hotel Reviews Using Opinion Mining Technique

This paper proposes a development of a new opinion mining framework for analyzing and summarizing the hotel reviews on Thai language. This framework uses a hybrid concept between a keyword knowledge and a classification technique to identify a opinion feature of hotel reviews into 3 aspects (location, service and worthiness) and to classify the opinion polarity of hotel reviews into 2 classes (positive and negative). The research methodology consists of 4 steps as 1) preprocessing 2) hotel feature identification 3) opinion polarity classification and 4) hotel review summarization. Experiments showed that the accuracy of the opinion feature identification is 83.33% and the accuracy of the opinion polarity classification is 81.47% with a testing data which be manually collected from a hotel review on the agoda website.
Behavioural Aspects of BP-SOMTon Weijters

Back-propagation learning (bp) is known for its serious limitations in generalising knowledge from certain types of learning material. In this paper we describe a new learning algorithm, bp-som, which overcomes some of these limitations as is shown by its application to four benchmark tasks. bp-som is a combination of a multi-layered feed-forward network (mfn) trained with bp, and Kohonen's self-organising maps (soms). During the learning process, hiddenunit activations of the mfn are presented as learning vectors to self-organising maps trained in parallel. Information on classi cation errors contained in the maps is used when updating the connection weights of the network in addition to standard error-back-propagation. The additional update leads to an increasing similarity of hidden-unit activation patterns associated with the same output class. In a number of experiments, bp-som is shown (i) to improve generalisation performance over that of bp in tasks for which bp frequently exhibit over tting; (ii) to provide indications of the number of hidden layers needed to learn a task; (iii) to increase the amount of hidden units that can be pruned without loss of generalisation performance, and (iv) to provide a means for automatic rule extraction from trained networks. From these experiments, we conclude that the hybrid bp-som architecture and learning algorithm successfully combines advantages of the bp and som learning algorithms. 2
Access to the Published Version May Require Journal Subscription. Published with Permission From: Elsevier Calcium-induced Generation of Reactive Oxygen Species in Brain Mitochondria Is Mediated by Permeability Transition

This is an author produced version of a paper published in Free radical biology & medicine. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. "Calcium-induced generation of reactive oxygen species in brain mitochondria is mediated by permeability transition. " ABSTRACT Mitochondrial uptake of calcium in excitotoxicity is associated with subsequent increase in reactive oxygen species (ROS) generation and delayed cellular calcium deregulation in ischemic and neurodegenerative insults. The mechanisms linking mitochondrial calcium uptake and ROS production remain unknown but activation of the mitochondrial permeability transition (mPT) may be one such mechanism. In the present study, calcium increased ROS generation in isolated rodent brain and human liver mitochondria undergoing mPT despite an associated loss of membrane potential, NADH and respiration. Unspecific permeabilization of the inner mitochondrial membrane by alamethicin likewise increased ROS independently of calcium, and the ROS increase was further potentiated if NAD(H) was added to the system. Importantly, calcium per se did not induce a ROS increase unless mPT was triggered. Twenty-one cyclosporin A analogs were evaluated for inhibition of calcium-induced ROS and their efficacy clearly paralleled their potency of inhibiting mPT-mediated mitochondrial swelling. We conclude that while intact respiring mitochondria possess powerful antioxidant capability, mPT induces a dysregulated oxidative state with loss of GSH-and NADPH-dependent ROS detoxification. We propose that mPT is a significant cause of pathological ROS generation in excitotoxic cell death. Calcium and Mitochondrial ROS 2 INTRODUCTION An excessive or sustained increase of reactive oxygen species (ROS) has been implicated in the pathogenesis of diseases as diverse as cancer, diabetes mellitus and atherosclerosis as well as in the mechanisms of senescence [1]. Mitochondrial generation of ROS participates in cell death cascades of both acute brain injuries and chronic neurodegenerative disease [2, 3]. Reactive oxygen species may play a physiological role in intracellular signaling, and function as second messengers in e.g. ischemic preconditioning [1, 4, 5], but are potentially harmful as they are capable of oxidative modifications of proteins, nucleic acids, polysaccharides and lipid membranes [6]. Excitotoxicity is a pathological feature of both acute and chronic neurodegenerative conditions [7-9]. Increased ROS production from mitochondria has been demonstrated following NMDA receptor stimulation in models of excitotoxicity and has also been coupled to mitochondrial loading of calcium [10-15]. However, the mechanism linking mitochondrial calcium loading and ROS production is currently unresolved [15-17], and studies in isolated brain mitochondria have yielded diverging results …
Central Angiotensin-(1–7) Improves Vagal Function Independent of Blood Pressure in Hypertensive (mRen2)27 Rats

Hypertensive transgenic (mRen2)27 rats with overexpression of the mRen2 gene have impaired baroreflex sensitivity for heart rate control and high nicotinamide adenine dinucleotide phosphate oxidase and kinase-to-phosphatase signaling activity in medullary tissue compared with normotensive Hannover Sprague-Dawley control rats. They also exhibit insulin resistance at a young age. To determine whether blocking angiotensin II actions, supplementing angiotensin-(1–7), or scavenging reactive oxygen species in brain differentially alters mean arterial pressure, baroreflex sensitivity, or metabolic function, while altering medullary signaling pathways in these animals, we compared intracerebroventricular infusions of the angiotensin II type 1 receptor antagonist candesartan (4 &mgr;g/5 &mgr;L/h), angiotensin-(1–7) (0.1 &mgr;g/5 &mgr;L/h), a reactive oxygen species scavenger tempol (25 &mgr;g/5 &mgr;L/h), or artificial cerebrospinal fluid (5 &mgr;L/h) for 2 weeks. Mean arterial pressure was reduced in candesartan-treated rats without significantly improving the vagal components of baroreflex function or heart rate variability. In contrast, angiotensin-(1–7) treatment significantly improved the vagal components of baroreflex function and heart rate variability at a dose that did not significantly lower mean arterial pressure. Tempol significantly reduced nicotinamide adenine dinucleotide phosphate oxidase activity in brain dorsal medullary tissue but had no effect on mean arterial pressure or autonomic function. Candesartan tended to reduce fat mass, but none of the treatments significantly altered indices of metabolic function or mitogen-activated protein kinase signaling pathways in dorsal medulla. Although additional dose response studies are necessary to determine the potential maximal effectiveness of each treatment, the current findings demonstrate that blood pressure and baroreflex function can be essentially normalized independently of medullary nicotinamide adenine dinucleotide phosphate oxidase or mitogen-activated protein kinase in hypertensive (mRen2)27 rats.
Molecular modeling of S-RNases involved in almond self-incompatibility

Gametophytic self-incompatibility (GSI) is a mechanism in flowering plants, to prevent inbreeding and promote outcrossing. GSI is under the control of a specific locus, known as the S-locus, which contains at least two genes, the RNase and the SFB. Active S-RNases in the style are essential for rejection of haploid pollen, when the pollen S-allele matches one of two S-alleles of the diploid pistil. However, the nature of their mutual interactions at genetic and biochemical levels remain unclear. Thus, detailed understanding of the protein structure involved in GSI may help in discovering how the proteins involved in GSI may function and how they fulfill their biological roles. To this end, 3D models of the SC (Sf) and two SI (S8 and S23) S-RNases of almond were constructed, using comparative modeling tools. The modeled structures consisted of mixed α and β folds, with six helices and six β-strands. However, the self-compatible (Sf) RNase contained an additional extended loop between the conserved domains RC4 and C5, which may be involved in the manifestation of self-compatibility in almond.
An Algorithm of Extracting Features from Point Set Models

Our feature extraction is a multi-step refinement method, we use principle curvatures to flag potential feature points and develop a new approach to detect potential feature curves by employing our improved weight sensitive moving least squares. Then the potential feature points are enhanced by projecting onto the local potential feature curves. Using an optimized principal covariance analysis approach, we smooth the projected points. Finally smooth feature curves are obtained after resolving gaps and relaxing the results. I. INTRODUCTION
Passively self-error-rejecting qubit transmission over a collective-noise channel

We propose a passively self-error-rejecting single-qubit transmission scheme for an arbitrary polarization state of a single qubit over a collective-noise channel, without resorting to additional qubits and entanglement. By splitting a single qubit into some wavepackets with some Mach-Zehnder interferometers, we can obtain an uncorrupted state with a success probability approaching 100% via postselection in different time bins, independent of the parameters of collective noise. It is simpler and more flexible than the schemes utilizing decoherence-free subspace and those with additional qubits. One can directly apply this scheme to almost all quantum communication protocols based on single photons or entangled photon systems against a collective noise.
Techniques and tools for dynamic optimization

Traditional code optimizers have produced significant performance improvements over the past forty years. While promising avenues of research still exist, traditional static and profiling techniques have reached the point of diminishing returns. The main problem is that these approaches have only a limited view of the program and have difficulty taking advantage of the actual run-time behavior of a program. We are addressing this problem through the development of a dynamic optimization system suited for aggressive optimization - using the full power of the most beneficial optimizations. We have designed our optimizer to operate using a software dynamic translation (SDT) execution system. Difficult challenges in this research include reducing SDT overhead and determining what optimizations to apply and where in the code to apply them. Another challenge is having the necessary tools to ensure the reliability of software that is dynamically optimized. In this paper, we describe our efforts in reducing overhead in SDT and efficient techniques for instrumenting the application code. We also describe our approach to determine what and where an optimization should be applied. We discuss other fundamental issues in developing a dynamic optimizer and finally present a basic debugger for SDT systems
Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia

The recent spread of the so called Web of Data has made available a vast amount of interconnected data, paving the way to a new generation of ubiquitous applications able to exploit the information encoded in it. In this paper we present Cinemappy, a location-based application that computes contextual movie recommendations. Cinemappy refines the recommendation results of a content-based recommender system by exploiting contextual information related to the current spatial and temporal position of the user. The content-based engine leverages DBpedia, one of the best-known datasets publicly available in the Linked Open Data (LOD) project.
Integrated speech and gaze control for realistic desktop environments

Nowadays various are the situations in which people need to interact with a Personal Computer without having the possibility to use traditional pointing devices, such as a keyboard or a mouse. In the latest years, various alternatives to the classical input devices like keyboard and mouse and novel interaction paradigms have been proposed. Particularly, multimodal interactions have been proposed to overcome the limit of each input channel take alone. In this paper we propose a multimodal system based on the integration of speech- and gaze-based inputs for interaction with a real desktop environment. A real-time grammar is generated to limit the vocal vocabulary basing on the fixated area. A disambiguation method is used for inherently ambiguous vocal commands, and the performed tests show its efficiency.
Evolutionary community discovery in dynamic networks based on leader nodes

Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader nodes (EvoLeaders) is proposed to cluster the dynamic network. Compared with the static community discovery algorithm based on leader nodes (the Top Leaders algorithm), experimental results over two real-world datasets demonstrate that the EvoLeaders is more suitable for dynamic scenarios.
Genetic contributions to white matter architecture revealed by diffusion tensor imaging in Williams syndrome

Little is known about genetic regulation of the development of white matter. This knowledge is critical in understanding the pathophysiology of neurodevelopmental syndromes associated with altered cognition as well as in elucidating the genetics of normal human cognition. The hemideletion of ≈25 genes on chromosome 7q11.23 that causes Williams syndrome (WS) includes genes that regulate cytoskeletal dynamics in neurons, especially LIMK1 and CYLN2, and therefore offers the opportunity to investigate the role of these genes in the formation of white matter tracts. We used diffusion tensor imaging to demonstrate alteration in white matter fiber directionality, deviation in posterior fiber tract course, and reduced lateralization of fiber coherence in WS. These abnormalities are consistent with an alteration of the late stages of neuronal migration, define alterations of white matter structures underlying dissociable behavioral phenotypes in WS, and provide human in vivo information about genetic control of white matter tract formation.
Hybrid BDI agents with improved learning capabilities for adaptive planning in a container terminal application

Vessel berthing system in a container terminal is regarded as a very complex dynamic application in today's business world. We propose a new extended BDI framework with an intelligent module for handling complex situations. Change rate of beliefs (/spl Psi/) and expected cost of reaching the final goal state from different states in the plan hierarchy have been considered by the agent in the proposed architecture. This would enable agents to identify the alternative plans in the intention structure with the change of the environment. Dynamic selection of plans and expected cost of achieving the final goal state from various plan paths are modeled with the use of a supervised neural network. Adaptive neuro fuzzy inference system (ANFIS) has been incorporated in making the final rational decisions in the agent model.
Analysis of user behavior on multilingual tagging of learning resources

Although social, collaborative classification through tagging has been the focus of recent research, the effect of multilingual tags is often overlooked. This work presents an early exploratory study of the production and consumption of multilingual tags in a European educational K-12 context. The data, produced by teachers bookmarking and tagging learning resources during three month period, was analysed. Thereafter, a focus group of teachers evaluated a sample of learning resources with metadata records containing both thesaurus terms and multilingual tags. The results of this early study suggest that some tags are found as useful as thesaurus terms and that users are divided about the benefits of multilingual tags. As some tags are useful for some users, “hiding all but the right tags” becomes crucial for the success of a multilingual collaborative tagging system.
Design of Jitter Compensation Algorithm for Robot Vision Based on Optical Flow and Kalman Filter

Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.