Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of immunoglobulin E-binding epitopes in the characterization of food allergy

Purpose of reviewAllergen-specific immunoglobulin E (IgE) antibodies play a central role in food allergic reactions. IgE epitope mapping of food allergens may provide information regarding patient's clinical history and contribute to food allergy diagnosis/prognosis. The goal of this article is to review recent developments in the methods for IgE epitope mapping and the role of IgE-binding epitopes in the characterization of food allergy. Recent findingsRecent studies have suggested a potential role for sequential IgE-binding epitopes as biomarkers for characterizing various phenotypes of food allergy. Studies of allergens in milk, peanut, egg and wheat have shown a correlation between IgE sequential epitope diversity and patients' allergy severity or persistence. Several informative epitopes in milk allergens have been identified as candidate biomarkers to predict the development of tolerance to milk. However, limitations with current methods of IgE-binding epitope identification need to be addressed before they can be applied in the diagnosis/prognosis of food allergy. SummaryIgE epitope mapping has the potential to become an additional tool for the diagnosis/prognosis of food allergy and lead to a better understanding of the pathogenesis and tolerance induction of food allergy.
Fundamental Dynamical Modes Underlying Human Brain Synchronization

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.
Database Models for Computational Lexicography

This paper examines, from the perspective ofcomputalional lexicography/lexicology, lhe range of options for representation and storage of lexical data. The discussion is limited to the particular context of extraction of such data, insofar as it is of utility lo natural language processing, from machine-readable dictionary sources. Wc thus ignore, for instance, considerations of building on-line dictionaries for human use, or designing software systems for compiling dictionary entries for inclusion in human dictionaries. Instead, we concentrate on lhe predominant paradigm for acquisition ol lexical information and look at the suitability of existing data models for gcnericlasks like identification, discovery, extraction and representation of lexical properties of words on lhe basis of studying large dictionaries available in electronic form. Л growing number of projects in computational lexicography and lexicology use machine-readable forms of published dictionaries ( M R D s ) as their starling point. Individual goals may vary substantially; they might include, for instance, linguistic analysis of dictionary definitions, statistically-based search for regular patterns across a dictionary source| or semi-automatic derivation of computational lexicons from existing dictionaries. However, a common characteristic of such projects is their shared requirement for a framework which facililales lhe mapping from source form (typically a typesetting tape) to a lexical database ( L D B ) . There are at least llircc aspects to such frameworks, falling in lhe general categories of transduction, representation, and query. Oulside of lhe specifics of any Particular approach, lhe ultimate use of a machine-readable dictionary is forextraclion of (fragments of) lexical entries. Access to the lexical content of an on-line dic­ tionary is typically provided by a query mechanism; the expressive power ol lhe query ! anguage determines the range and granularity of lexical properlies exlraclable from lhe source. To a certain extent, granularity also depends on the representational sche­ me employed by the on-line dictionary model. More importantly, however, thc nature of dictionary representation crucially constrains the kinds of observations lhat can be made regarding both implicitly encoded information in lhe dictionary and thc linguistic generalisations reflected in il. Finally, independent of thc representation itself, some kind of computational support is required for transducing the raw tape formal into a set of dictionary entrv records and fields. These issues arc orthogonal; in particular, lhc choice of mechanism for parsing the dictionary source can be largely independenl of lhe nature of representation adopted for holding dictionary data. Still, work in this area lends to blur thc line between parsing and loading; consequently there is no principled distinction between the processes involved in source transduction and those underlying lhe functionality required from an on-line dictionary. The development of a general purpose appara-
Derivation of a Markovian Controller for an exo-skeleton by overcome the benchmarks of a single and double inverted pendulum

We discuss the application of a Markovian Jump linear System (MJLS) for the control of the single and double inverted pendula. We discuss the fundamental behavior of such a controller and take a closer look at robustness and performance and compare this controller with a Linear Quadratic Regulator (LQR). The results included serve the purpose for further investigation of this controller in a real application, such as an exoskeleton for rehabilitation treatment.
Research of Multi-robot Parallel Assembly Optimization Base on PSO-SS

This paper researches into multi-robot parallel assembly optimization problem. The concepts of product parts system assembly proportion and assembly complexity are introduced with the parallel robot involved in product assembly process. A nonlinear optimization model is proposed to minimize the assembly complexity of product system. An improved PSO-SS co-evolution algorithm is presented, and a constraint handling mechanism based on the tolerance is provided in the PSO-SS algorithm. A case study of automobile car body is investigated. The experimental data are randomly generated from the range of parameters, and then the experiment result is described according to product assembly process, showing that the model and PSO-SS co-evolution algorithm is suitable for parallel assembly task allocation in automobile industry.
Contextual advertising by combining relevance with click feedback

Contextual advertising supports much of the Web's ecosystem today. User experience and revenue (shared by the site publisher and the ad network) depend on the relevance of the displayed ads to the page content. As with other document retrieval systems, relevance is provided by scoring the match between individual ads (documents) and the content of the page where the ads are shown (query). In this paper we show how this match can be improved significantly by augmenting the ad-page scoring function with extra parameters from a logistic regression model on the words in the pages and ads. A key property of the proposed model is that it can be mapped to standard cosine similarity matching and is suitable for efficient and scalable implementation over inverted indexes. The model parameter values are learnt from logs containing ad impressions and clicks, with shrinkage estimators being used to combat sparsity. To scale our computations to train on an extremely large training corpus consisting of several gigabytes of data, we parallelize our fitting algorithm in a Hadoop framework [10]. Experimental evaluation is provided showing improved click prediction over a holdout set of impression and click events from a large scale real-world ad placement engine. Our best model achieves a 25% lift in precision relative to a traditional information retrieval model which is based on cosine similarity, for recalling 10% of the clicks in our test data.
The Mixed Leucocyte Reaction: Studies Using Leucocytes From Unrelated Human Subjects

Summary. The MLR has been studied in 87 culture combinations assessed by morphological techniques. The upper limit of negativity in these cultures was taken as the mean index of transformation found in all control cultures plus 2 standard deviations. Two sampling times (4 days and 6 days) were used. 6/87 cultures were within the negative range on both occasions. Of the positive cultures, 4 different patterns of response were observed and are described. The significance of these patterns is not yet clear. Similar results were found in those 69 cultures which were assessed by the degree of incorporation of I125 iododeoryuridine. This method is more sensitive than the morphological technique and fewer cultures could be regarded as within the negative range (mean of controls ± 2 standard deviations). The same patterns of response were observed.
Gangliosides do not affect ABC transporter function in human neuroblastoma cells Published, JLR Papers in Press, March 18, 2006.

Previous studies have indicated a role for glucosylceramide synthase (GCS) in multidrug resistance (MDR), either related to turnover of ceramide (Cer) or generation of gangliosides, which modulate apoptosis and/or the activity of ABC transporters. This study challenges the hypothesis that gangliosides modulate the activity of ABC transporters and was performed in two human neuroblastoma cell lines, expressing either functional P-glycoprotein (Pgp) or multidrug resistance-related protein 1 (MRP1). Two inhibitors of GCS, d,l-threo-1-phenyl-2-hexadecanoylamino-3-pyrrolidino-1-propanol (t-PPPP) and N-butyldeoxynojirimycin (NB-dNJ), very efficiently depleted ganglioside content in two human neuroblastoma cell lines. This was established by three different assays: equilibrium radiolabeling, cholera toxin binding, and mass analysis. Fluorescence-activated cell sorting (FACS) analysis showed that ganglioside depletion only slightly and in the opposite direction affected Pgp- and MRP1-mediated efflux activity. Moreover, both effects were marginal compared with those of well-established inhibitors of either MRP1 (i.e., MK571) or Pgp (i.e., GF120918). t-PPPP slightly enhanced cellular sensitivity to vincristine, as determined by 3-[4,5-dimethylthiazol-2-yl]2,5-diphenyl tetrazolium bromide analysis, in both neuroblastoma cell lines, whereas NB-dNJ was without effect. MRP1 expression and its localization in detergent-resistant membranes were not affected by ganglioside depletion. Together, these results show that gangliosides are not relevant to ABC transporter-mediated MDR in neuroblastoma cells.
Bluetooth-enabled in-home patient monitoring system: Early detection of Alzheimer's disease

As the baby boom generation is aging, more and more people are diagnosed with Alzheimer's disease, early detection of which is shown to be vital and necessary for better medical treatments and prolonging life expectancies. In this article we propose a Bluetooth-enabled in-home patient monitoring system, facilitating early detection of Alzheimer's disease. We take advantage of shortrange Bluetooth communications for in-home patient location tracking, and the location information can then be recorded in a local database. With knowledge of the movement pattern of a patient, a medical practitioner is more likely to be able to determine whether a target patient is developing Alzheimer's disease. We also conduct a feasibility study, and our study shows that the proposed in-home patient monitoring system is feasible and can be applied in practice. Our proposed e-healthcare solution is expected to facilitate medical treatments, improve the quality of life of senior people, and reduce healthcare costs.
Nearly generalized Jordan derivations

Let A be an algebra and let X be an A-bimodule. A ∂-linear mapping d: A → X is called a generalized Jordan derivation if there exists a Jordan derivation (in the usual sense) δ: A → X such that d(a2) = ad(a)+δ(a)a for all a ∈ A. The main purpose of this paper is to prove the Hyers-Ulam-Rassias stability and superstability of the generalized Jordan derivations.
Drug-drug interaction through molecular structure similarity analysis

BACKGROUND Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. METHODS We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates. RESULTS The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. CONCLUSION The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.
A discriminative tracklets representation for crowd analysis

In this work, we propose a discriminative tracklets representation for motion pattern extraction from crowded scene. The representation incorporates relative position, velocity, and direction information of tracklet into one compact form by shaping it within a rectangle. We adopt deep belief networks to extract low-dimensional features from this representation. It not only reduces the computational complexity for the following clustering, but also achieves more discriminative tracklets representation which is invariant to noises brought by tracking failures. To determine the spatio-temporal distribution of each motion pattern, a robust clustering scheme composed of three clustering procedures is proposed. Comprehensive experiments in multiple datasets validate the effectiveness of our approach.
Planning in Stochastic Domains: Problem Characteristics and Approximation

This paper is about planning in stochastic domains by means of partially observable Markov decision processes POMDPs POMDPs are di cult to solve and approxima tion is a must in real world applications Approximation methods can be classi ed into those that solve a POMDP directly and those that approximate a POMDP model by a simpler model Only one previous method falls into the second category It approximates POMDPs by using fully observable Markov decision processes MDPs We propose to approximate POMDPs by using what we call region observable POMDPs Region ob servable POMDPs are more complex than MDPs and yet still solvable They have been empirically shown to yield signi cantly better approximate policies than MDPs In the process of designing an algorithm for solving region observable POMDPs we also propose a new method for attacking the core problem known as dynamic programming updates that one has to face in solving POMDPs We have shown elsewhere that the new method is signi cantly more e cient that the best previous method
Survey of Channel and Radio Propagation Models for Wireless MIMO Systems

This paper provides an overview of the state-of-the-art radio propagation and channel models for wireless multiple-input multiple-output (MIMO) systems. We distinguish between physical models and analytical models and discuss popular examples from both model types. Physical models focus on the double-directional propagation mechanisms between the location of transmitter and receiver without taking the antenna configuration into account. Analytical models capture physical wave propagation and antenna configuration simultaneously by describing the impulse response (equivalently, the transfer function) between the antenna arrays at both link ends. We also review some MIMO models that are included in current standardization activities for the purpose of reproducible and comparable MIMO system evaluations. Finally, we describe a couple of key features of channels and radio propagation which are not sufficiently included in current MIMO models.
EVIDENCE FOR A ROLE OF NITRIC OXIDE IN THE NEUROTRANSMITTER SYSTEM MEDIATING RELAXATION OF THE RAT ANOCOCCYGEUS MUSCLEC

1. The nitric oxide (NO) synthesis inhibitor NG‐monomethyl‐l‐arginine (l‐NMMA), but not d‐NMMA, inhibited the NANC‐mediated relaxations of the rat anococcygeus muscle, but did not affect the relaxation produced by sodium nitroprusside.
Algorithm for answer graph construction for keyword queries on RDF data

RDF and RDFS have recently become very popular as frameworks for representing data and meta-data in form of a domain description, respectively. RDF data can also be thought of as graph data. In this paper, we focus on keywordbased querying of RDF data represented as a graph. Existing approaches for answering such keyword queries, identies connected trees with minimal cost in the labeled graph as answers. In this paper we present an elegant algorithm for keyword query processing on RDF data that not only identies trees but also more meaningful graph structures including cycles. The approach adopts a pruned exploration mechanism where closely related nodes are identied, sub-graphs are pruned and joined using suitable hook nodes. The system also exploits Type/SubClassOf relationship during the construction of the answer graph. The working of the algorithm is illustrated using a fragment of AIFB institute data represented as an RDF graph.
A randomised controlled trial of endoscopic sphincterotomy in acute cholangitis without common bile duct stones

Background: Biliary decompression with endoscopic sphincterotomy (EPT) is beneficial in patients with biliary obstruction due to common bile duct (CBD) stones. However, it is not known whether EPT with decompression of the bile duct is beneficial in patients with acute cholangitis and gall bladder stones but without evidence of CBD stones. Aim: A randomised controlled study to assess the effect of EPT on the outcome of patients suffering from acute cholangitis with gall bladder stones but with no CBD stones on initial endoscopic retrograde cholangiopancreatography. Patients: A total of 111 patients were recruited into the study. Methods and results: Fifty patients were randomised to receive EPT while 61 patients received no endoscopic intervention. There was a significant difference in the duration of fever in the EPT and non-EPT groups (mean (SD): 3.2 (2.2) days v 4.3 (2.1) days; p<0.001). Duration of hospital stay was also shorter in the EPT group than in the non-EPT group (mean (SD): 8.1 (3.0) v 9.1 (3.2) days; p=0.04). Patients were followed up for a mean (SD) of 42.4 (11.1) months. Twenty three patients (20.3%) developed recurrent acute cholangitis (RAC): 14 patients (12.6%) in the EPT group and nine patients (8.1%) in the non-EPT group (p=0.09). Conclusion: EPT in patients with acute cholangitis without CBD stones decreased the duration of acute cholangitis and reduced hospital stay but it did not decrease the incidence of RAC.
How supportive Transfer Climate Affects Individual’s Motivation to Training Transfer

Training transfer is an important concern for researchers and practitioners due to the huge budget on training but lower estimation of transfer in practical domains. Researches revealed training design, trainees’ characteristics and work environment characteristics are the most important sets of variables in a training context. This study intended to examine individuals’ cognition of motivation with perceived organizational transfer climate by structural equation modeling (SEM) from broad industries in Taiwan. 500 questionnaires were sent to participants and 316 usable returns in 63.2% return rate were computed. The results revealed the predictor role of the positive organizational transfer climate to both motivation to learn and motivation to transfer. Both motivation to learn and motivation to transfer predict training transfer and played mediator roles of transfer climate to training transfer. Moreover, motivation to transfer also mediated motivation to learn to training transfer. In addition to confirm the prediction role of positive organizational transfer climate, this study contributed to reinforce the relationship among motivation to learn, motivation to transfer, and training transfer.
The Dramatic Presentation of News and Its Effects on Cognitive Complexity

Television news stories tend to be structured to resemble drama or, more specifically, melodrama. What is the effect of such news presentation on the complexity with which viewers think about the political issues raised in the stories? Socialpsychological research on the effects of schema activation (Milburn, 1987; Milburn & Fay-Dumaine, 1988) indicates that such activation may reduce individuals' cognitive complexity. To test the effect of the dramatic presentation of television news, dramatic news stories were taped and edited to remove the highly dramatic scenes. The edited and nonedited stories were then presented to subjects, and subjects' recall and cognitive complexity were measured. We found that, controlling for various background differences, exposure to the dramatic news stories significantly decreased subjects' recall of the information in the stories and reduced the complexity with which individuals thought about the events reported. The results raise important concerns about the contribution of the news to the public's information level and political sophistication.
Empirical models of spiking in neural populations

Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling. We test this claim by comparing a latent dynamical model with realistic spiking observations to coupled generalised linear spike-response models (GLMs) using cortical recordings. We find that the latent dynamical approach outperforms the GLM in terms of goodness-of-fit, and reproduces the temporal correlations in the data more accurately. We also compare models whose observations models are either derived from a Gaussian or point-process models, finding that the non-Gaussian model provides slightly better goodness-of-fit and more realistic population spike counts.