Miracle-dz/UniFAR-FTUs huggingface dataset:
Animal Models of IgE Anaphylaxis

Simple Summary Anaphylaxis is the most severe form of allergic reactions and can be life-threatening. It is very difficult to study the mechanisms underlying anaphylaxis in humans since these events are rare and often lethal. Therefore, animal models have been established. Mice and rats are mostly used since their biological parameters, such as temperature drop, behavioral changes, and blood or cell biomarkers, can be easily measured in the laboratory. These animals can also be genetically modified to express human proteins and cell functions. Different animal models have been established to replicate as closely as possible the natural route of sensitization to the allergen and to trigger anaphylaxis in animals. These animal models have deepened our knowledge on human anaphylaxis with certain limitations, as discussed in this review. Abstract Allergies and atopy have emerged as significant public health concerns, with a progressively increasing incidence over the last two decades. Anaphylaxis is the most severe form of allergic reactions, characterized by a rapid onset and potentially fatal outcome, even in healthy individuals. Due to the unpredictable nature and potential lethality of anaphylaxis and the wide range of allergens involved, clinical studies in human patients have proven to be challenging. Diagnosis is further complicated by the lack of reliable laboratory biomarkers to confirm clinical suspicion. Thus, animal models have been developed to replicate human anaphylaxis and explore its pathophysiology. Whereas results obtained from animal models may not always be directly translatable to humans, they serve as a foundation for understanding the underlying mechanisms. Animal models are an essential tool for investigating new biomarkers that could be incorporated into the allergy workup for patients, as well as for the development of novel treatments. Two primary pathways have been described in animals and humans: classic, predominantly involving IgE and histamine, and alternative, reliant on IgG and the platelet-activating factor. This review will focus essentially on the former and aims to describe the most utilized IgE-mediated anaphylaxis animal models, including their respective advantages and limitations.
Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model

In today’s world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.
Coupled core-crust-magnetosphere oscillations of magnetars

In this thesis we describe a model which can explain the quasi-periodic oscillations observed in gamma-ray flares of soft gamma-ray repeaters. These oscillations are assumed to originate from the magneto-elastic pulsations of a highly magnetized neutron star. These modulate the magnetic field in the exterior of the star and thus create electric currents in the magnetosphere. Photons emitted from the stellar surface or by the flare can scatter resonantly (cyclotron scattering) off the charge carriers, which are mostly electrons and positrons. Thereby, the intensity of the radiation changes in certain frequency bands and the observed light curve is modulated. In analogy to seismology for the earth or for the sun, it is possible to learn about the interior magnetic field of the neutron star and about its interior structure.
On the Relationships Among Probabilistic Extensions of Answer Set Semantics

Answer Set Programming (ASP) is one of the main formalisms in Knowledge Representation (KR) that is being widely applied in a large number of applications. While ASP is effective on Boolean decision problems, it has difficulty in expressing quantitative uncertainty and probability in a natural way. Logic Programs under the answer set semantics and Markov Logic Network (LP) is a recent extension of answer set programs to overcome the limitation of the deterministic nature of ASP by adopting the log-linear weight scheme of Markov Logic. This thesis investigates the relationships between LP and two other extensions of ASP: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. The studied relationships show how different extensions of answer set programs are related to each other, and how they are related to formalisms in Statistical Relational Learning, such as Problog and MLN, which have shown to be closely related to LP. The studied relationships compare the properties of the involved languages and provide ways to compute one language using an implementation of another language. This thesis first presents a translation of LP into programs with weak constraints. The translation allows for computing the most probable stable models (i.e., MAP estimates) or probability distribution in LP programs using standard ASP solvers so that the well-developed techniques in ASP can be utilized. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl’s Causal Models, that are shown to be translatable into LP. This thesis also presents a translation of P-log into LP. The translation tells how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LP, which yields a way to compute P-log using standard ASP solvers or MLN solvers.
Self-diffusion measurements in isotopic heterostructures of undoped and in situ doped ZnO: Zinc vacancy energetics

Alexander Azarov,1,* Vishnukanthan Venkatachalapathy,1 Zengxia Mei,2 Lishu Liu,2 Xiaolong Du,2 Augustinas Galeckas,1 Edouard Monakhov,1 Bengt G. Svensson,1 and Andrej Kuznetsov1,† 1University of Oslo, Department of Physics, Centre for Materials Science and Nanotechnology, PO Box 1048 Blindern, N-0316 Oslo, Norway 2Institute of Physics, The Chinese Academy of Sciences, Beijing 100190, China (Received 3 May 2016; revised manuscript received 17 October 2016; published 17 November 2016)
Recent Trends in Domination in Graph Theory

For the last thirty years, due to the involvement in areas such as computer science, electrical and computer engineering and operations research, graph theory has grown exponentially. Study of domination is one of the important sub-areas of graph theory and seen enormous growth due to its varied applications. The study on domination not only restricted to domination parameters, but also relates to the other parameters like independence number, covering number and others. This book deals with certain new domination parameters and their properties with other existing parameters of graphs.
AN OPTIMIZATION MODEL TO MINIMIZE THE EXPECTED END-TO-END TRANSMISSION TIME IN WIRELESS MESH NETWORKS

Time metrics are extremely important to evaluate the transmission performance on Wireless Mesh Networks (WMNs), whose main characteristic is to use multihop technology to extend the network coverage area. One of such metrics is WCETT (Weighted Cumulative Expected Transmission Time), in which transmission times per hop are weighted for both proactive and reactive conditions. Furthermore, such metrics are able to detect delays that can degrade some network services. This paper presents an optimization model to minimize WCETT in a WMN, subject to constraints grouped by bandwidth, flow control and power control. As the model includes nonlinear constraints, we propose a heuristic to solve it, which divides the problem in two subproblems. The first subproblem maximizes the network link capacity and a Simulated Annealing algorithm is used to solve it. Considering the link capacities obtained, the second subproblem minimizes the WCETTs, which is formulated as a linear programming model. Some numerical results are presented, based on instances of WMNs randomly generated. Some of these results are compared with the results obtained by a commercial simulator in order to verify the coherence of the proposed heuristic for realistic scenarios.
Chemical Composition, Analgesic and Anti-Inflammatory Activity of Pelargonium peltatum Essential Oils from Eastern Cape, South Africa

Pelargonium species are native to South Africa, and they have a long history in medicinal use. This study aimed to extract essential oils from different parts of P. peltatum, determine the chemical composition of the essential oils, and assess the essential oils’ biological potential as analgesic and anti-inflammatory agents. The essential oils were obtained by hydro-distilling different parts of P. peltatum, and the essential profile was determined by GC-FID and GC-MS. The analgesic activity of the essential oil was determined by using a tail immersion in hot water method in rats, whereas the anti-inflammatory activity of the essential oils was assessed according to right hind paw oedema induced by egg albumin; the three doses selected for each experiment were 100, 200, and 400 mg/kg. According to the GC-FID and GC-MS analysis, camphene (3.6–33.4%), α-terpineol (4.8–19.1%), α-thujone (1.5–15.6%), piperitone (0.9–12.2%), linalool (1.6–11.7%), myrcene (5.2–10.7%), germacrene D (3.7–10.4%), β-caryophyllene (1.2–9.5%), β-cadinene (3.4–6.7%), and β-bourbonene (4.2–6.2%) were some of the major compounds identified in the oil. P. peltatum essential oils demonstrated analgesic activity by increasing pain latency in hot water; furthermore, in an inflammation test, the essential oil reduced the egg-albumin-induced paw oedema in both the first and second phases. Therefore, the current findings suggest that P. peltatum essential oils have analgesic and anti-inflammatory properties.
Reanalysis of Chromosomal Microarray Data Using a Smaller Copy Number Variant Call Threshold Identifies Four Cases with Heterozygous Multiexon Deletions of ARID1B, EHMT1, and FOXP1 Genes

Introduction: Chromosomal microarray (CMA) is a highly accurate and established method for detecting copy number variations (CNVs) in clinical genetic testing. CNVs are important etiological factors for disorders such as intellectual disability, developmental delay, and multiple congenital anomalies. Recently developed analytical methods have facilitated the identification of smaller CNVs. Therefore, reanalyzing CMA data using a smaller CNV calling threshold may yield useful information. However, this method was left to the discretion of each institution. Methods: We reanalyzed the CMA data of 131 patients using a smaller CNV call threshold: 50 kb 50 probes for gain and 25 kb 25 probes for loss. We interpreted the reanalyzed CNVs based on the most recently available information. In the reanalysis, we filtered the data using the Clinical Genome Resource dosage sensitivity gene list as an index to quickly and efficiently check morbid genes. Results: The number of copy number loss was approximately 20 times greater, and copy number gain was approximately three times greater compared to those in the previous analysis. We detected new likely pathogenic CNVs in four participants: a 236.5 kb loss within ARID1B, a 50.6 kb loss including EHMT1, a 46.5 kb loss including EHMT1, and an 89.1 kb loss within the FOXP1 gene. Conclusion: The method employed in this study is simple and effective for CMA data reanalysis using a smaller CNV call threshold. Thus, this method is efficient for both ongoing and repeated analyses. This study may stimulate further discussion of reanalysis methodology in clinical laboratories.
Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control.
Examining eXtension: Diffusion, Disruption, and Adoption Among Iowa State University Extension and Outreach Professionals

As eXtension unveils its new membership model, Iowa State University Extension and Outreach must determine how best to support professionals and clientele using the technology. This article reports on a study that used the diffusion of innovations and disruptive innovation theories to assess Iowa Extension professionals' adoption and perceptions of eXtension. One quarter of Iowa Extension professionals had no knowledge of eXtension, and 25% of respondents reported using the technology. Respondents perceived that eXtension exhibits a relative advantage and some of the attributes—accessibility and capacity—needed to become a disruptive innovation. These findings provide a basis for studying disruptive innovations in Cooperative Extension.
Study of the extreme gamma-ray emission from supernova remnants and the crab pulsar

Esta tesis tiene como objetivo el estudio de la emision extrema de rayos gamma proveniente de remanentes de supernova y el pulsar del Cangrejo. Dos remanentes de supernova han sido observados tanto con Fermi, como con MAGIC; Cassiopeia A y SNRG24.7+0.6. Cassiopeia A, el principal candidato de su clase a revelarse como PeVatron, ha sido descartado como tal, al haberse detectado un corte en el espectro de rayos gamma en torno a 3TeV, lo cual implica que, la emision observada es producida por el decaimiento de piones neutros, originados en colisiones proton-proton por una poblacion de protones que presenta un corte exponencial en su espectro en torno a una energia de 10TeV. Esta energia maxima a la que son acelerados estos rayos cosmicos pone en serias dudas la existencia de remanentes de supernova que se comporten como PeVatrones, y por tanto, la teoria de que estos son la fuente principal de rayos cosmicos galacticos. En el caso de SNRG24.7+0.6, con las observaciones llevadas a cabo, hemos conseguido detectar por primera vez la contrapartida de la emision radio a energias desde 60MeV hasta 5TeV, MAGIC J1835–069, producida por protones que escapan del remanente de supernova y que interaccionan con una nube molecular cercana. En estas mismas observaciones, hemos podido detectar otra fuente nueva, MAGIC J1837–073, la cual esta asociada, muy probablemente, con un cumulo estelar tal y como sugiere su localizacion. Su emision puede ser explicada asumiendo una inyeccion casi continua de rayos cosmicos durante la totalidad de la vida estimada del cumulo estelar. Por otra parte, el estudio del pulsar del Cangrejo, llevado a cabo con MAGIC ha resultado en el descubrimiento de emision pulsada hasta 1.5TeV, refutando cualquiera de los modelos presentados hasta el momento. Ademas, la curva de luz que caracteriza la emision proveniente del pulsar por encima de 400 GeV, presenta dos picos sincronizados en fase con los picos hallados a energias mas bajas. Esta emision, extremadamente energetica, unicamente puede ser producida por electrones acelerados hasta factores de Lorentz muy altos en regiones cercanas al cilindro de luz, ya sea en su interior o en su exterior, y que posteriormente colisionan y transfieren su energia a fotones termicos.
The Comparison Study between Tuberculin Skin Test and Interferon Gamma Release Assay in BCG-Vaccinated Healthy Donors

The incidence of tuberculosis (TB) in the Republic of Korea remains high when compared to the incidence in other Organization for Economic Cooperation and Development (OECD) countries. The prompt diagnosis and effective treatment of latent TB infection (LTBI) are very important in terms of controlling the burden of TB. The tuberculin skin test (TST) has long been the "gold standard" assay for the diagnosis of LTBI. However, it can show false positive results due to Bacille Calmette-Guérin (BCG) vaccination and infection with many environmental nontuberculous mycobacteria (NTM). The interferon gamma release assay (IGRA) using Mycobacterium tuberculosis (MTB)-specific antigens, was developed for the detection of LTBI. The QuantiFERON-TB Gold In-Tube assay is one of the most commonly used forms of the IGRA. In order to compare the diagnostic efficacy of the TST and IGRA in relation to LTBI among BCG-vaccinated healthy donors, whole blood samples were collected from 51 participants, and the results of the TST and IGRA were compared. Of the 51 cases, 18 cases (35.3%) were positive and 33 cases (64.7%) were negative when using the TST, while four cases (7.8%) were positive and 47 cases (92.2%) negative when using the IGRA. There was no correlation between the size of the induration in the TST and the IFN-γ protein level. In conclusion, the TST showed higher cross-reactivity among the BCG-vaccinated healthy participants, therefore, the IGRA might be the most suitable assay for the rapid screening of LTBI in BCG-vaccinated healthy population, or for TB contact investigation.
A Bivariate Generalized Bernstein Operator and its application to Fredholm Integral Equations

being Bm(f) the m−th Bernstein polynomial of f ∈ C([0, 1]). In this talk we introduce a bivariate Generalized Bernstein Operator, on the space of continuous functions defined on the square S = [0, 1]× [0, 1], as a tensor product of two univariate GB operators, i.e. Bm,s := Bm,s ⊗Bm,s. A remarkable approximation property of the sequence {Bm,s(f)}m is the improvement of the rate of convergence to the function f ∈ C(S), when the smoothness of f increases. By means of {Bm,s(f)}m,s we obtain a cubature rule based on grids of equally spaced points in S. This cubature formula is used in constructing a Nyström method for bivariate Fredholm Integral Equations of the following type:
Exploring decision processes behind food choices: An eye tracking approach

This doctoral dissertation makes a twofold contribution to the understanding of psychological processes behind food choice. First, it explores whether cognitive shortcuts, known as heuristics, based on seemingly irrational beliefs can lead to rational behaviours when applied in the right context. One such heuristic, the organic = healthful heuristic, is explored. It is the belief that organic products are more healthful than conventional products. There is no conclusive evidence supporting this belief, also known as the halo effect, where positive attitudes towards organic products transfer to beliefs about specific properties such as healthfulness. Here I propose statistical learning as an alternative explanation to the halo effect, and test this in three studies. Study 1 shows that food products from healthful food categories are more likely to be organic. Study 2 shows that consumer perceptions of the healthfulness and the number of organic products across food categories are accurate. Study 3 shows that consumers perceive organic products as more healthful when the statistical structure justifies this inference. These findings show that consumers correctly use organic products as a cue for healthfulness because they are, on average, 30% more healthful than conventional products. Second, this doctoral dissertation develops a new information search measure which complements existing measures to better describe consumer search processes. One area, which is currently not covered by existing measures, is when information search consists of equal amounts of attribute- and alternative-wise search sequences. I propose a new measure, the Systematicity of Search Index (SSI), which explores information search in terms of systematicity or the proportion of non-random search. Study 4 demonstrates the usefulness of the measure and shows that the SSI can shed light on processes not captured by the existing measures for analysing information search.
Wearable-based Frozen Shoulder Rehabilitation Exercise Recognition using Machine Learning Approaches

Frozen shoulder is a disease that causes shoulder pain and stiffness. It limits the range of movement of the shoulder and has a great impact on the quality of daily life. One of the common treatment methods is to do frozen shoulder rehabilitation exercises. However, patients often fail to follow the instructions of the physical therapists during the program of home-based rehabilitation. Furthermore, clinical professionals are unavailable to track and monitor the home-based rehabilitation exercise performance of patients. To support clinical monitoring, we develop a wearable-based frozen shoulder rehabilitation exercise recognizer using different machine learning models and deep learning models. The proposed methods can automatically identify movement/silence segments from continuous signals and classify types of frozen shoulder rehabilitation exercises. Besides, we propose a finite state machine and fragmentation revision mechanism for error correction. Twenty subjects are invited to perform six types of rehabilitation exercises. The proposed methods achieve the best result of 95.6% accuracy, 95.83% F-score for the identification of movement/silence and 95.58% accuracy, 95.49% F-score for classification of exercise type, respectively. The results demonstrate the feasibility of the proposed method to automatically monitor the frozen shoulder rehabilitation exercise, which has the potential to provide objective, continuous and quantitative information for telerehabilitation.