Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Oral Gabapentin, Local Bupivacaine and Intravenous Pethidine on Post Tonsillectomy Pain

Introduction: Tonsillectomy is one of the most common surgeries performed worldwide. Post-operative pain arising from tonsillectomy is one of the earliest complications that can postpone oral nutrition and increase the hospitalization period. Administration of opioids via injection is usually preferred to relieve pain in these patients. However, the side effects of this approach prompted us to seek alternative treatments. In this study, the effectiveness of oral gabapentin is compared with an intravenous (IV) injection of pethidine and a local injection of bupivacaine in the control of pain after tonsillectomy. Materials and Methods: This clinical trial was performed on 7-15 year-old patients who were candidates for tonsillectomy at Shahrekord Kashani hospital from 2012–2013. The patients were divided into three groups at random. Group 1 was give 20 mg/kg oral gabapentin 1 hour before anesthesia. In Group 2, 2.5 ml bupivacaine 0.25% was injected into each tonsil bed by a surgeon. In Group 3,1 mg/kg pethidine was injected intravenously after intubation. To assess post-operative pain, the Oucher scale was used in recovery as well as 3,6,12, and 24 hours after surgery. Results: The pain score was lowest in the gabapentin group and highest in the bupivacaine group during the study. The pain score in the gabapentin group was significantly lower than that in the bupivacaine group (P<0.05). No statistically significant difference was found between the pain score of the Pethidine group and that of the Bupivacaine group (P>0.05). Conclusion: Gabapentin, with its antihyperalgesic properties and other unknown properties, is a convenient drug for controlling pain following tonsillectomy.
A system of argumentation for reasoning about trust

Trust is a mechanism for managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a formal system of argumentation that can be used to reason about trust. We explore some of the simple properties of the system and we illustrate its application on a benchmark problem from the trust literature.
Automatic ontology generation from Web tabular structures

Efficient automatic information handling has become increasingly important in information society. Most information on the Web is presented in the form of semistructured or unstructured documents, encoded as a mixture of loosely structured natural language text and template units. The lack of metadata, which would precisely annotate the structure and semantics of documents, and ambiguity of natural language makes automatic computer processing very complex. The Semantic Web aims to overcome this bottleneck. The central contribution of the dissertation presents a novel method for automatic generation of knowledge models such as ontologies from arbitrary tabular structures found on the Web. An accompanying implementation is reflected in a system named TARTAR (Transforming ARbitrary TAbles into fRames) which is a component of the multi-agent system OntoGeMS (Ontology Generation Multi-agent System). The method is based on a grounded cognitive table model introduced by Hurst. The methodology is stepwise instantiated in four steps. In the first step, a table is transformed into regular matrix form. In the following two steps the table is handled from a structural and functional, and in the last step from a semantic point of view. The outcome of the method is threefold: a knowledge frame, an ontology, and a knowledge base, all encoded in an F-Logic representation language. The frame makes explicit the meaning of cell contents, the functional dimension of the table which is comparable to the relational schema, and the meaning of the table based on its structure. In the ontology the concepts are arranged into a directed acyclic graph, where the arcs represent relations among concepts and also the types of relations. The table content is formalized according to the frame into the knowledge base. The empirical evaluation is performed from four perspectives. The efficiency of the method is measured according to the portion of correctly transformed tables belonging to two domains, tourist and geopolitical, enabling us to prove the domain independency of the approach. Usability of the approach clearly shows the syntactic and semantic correctness of generated frames that are compared to the manually annotated frames. Approach applicability is shown from two views. By querying the content of tables encoded in the knowledge base, it is shown that returned answers are true and complete in all cases. The querying is enabled by the use of the inference engine OntoBroker. In
Rad23 Provides a Link between the Png1 Deglycosylating Enzyme and the 26 S Proteasome in Yeast*

In addition to a role in DNA repair events in yeast, several lines of evidence indicate that the Rad23 protein (Rad23p) may regulate the activity of the 26 S proteasome. We report evidence that a de-N-glycosylating enzyme, Png1p, may be involved in the proteasomal degradation pathway via its binding to Rad23p. Interaction of Rad23p and Png1p was first detected by two-hybrid screening, and this interaction in vivo was confirmed by biochemical analyses. The Png1p-Rad23p complex was shown to be distinct from the well established DNA repair complex, Rad4p-Rad23p. We propose a model in which Rad23p functions as an escort protein to link the 26 S proteasome with proteins such as Rad4p or Png1p to regulate their cellular activities.
Image Restoration by Variable Splitting based on Total Variant Regularizer

The aim of image restoration is to obtain a higher quality desired image from a degraded image. In this strategy, an image inpainting method fills the degraded or lost area of the image by appropriate information. This is performed in such a way so that the obtained image is undistinguishable for a casual person who is unfamiliar with the original image. In this paper, different images are degraded by two procedures; one is to blur and to add noise to the original image, and the other one is to lose a percentage of the pixels belonging to the original image. Then, the degraded image is restored by the proposed method and also two state-of-art methods. For image restoration, it is required to use optimization methods. In this paper, we use a linear restoration method based on the total variation regularizer. The variable of optimization problem is split, and the new optimization problem is solved by using Lagrangian augmented method. The experimental results show that the proposed method is faster, and the restored images have higher quality compared to the other methods.
PyRates—A Python framework for rate-based neural simulations

In neuroscience, computational modeling has become an important source of insight into brain states and dynamics. A basic requirement for computational modeling studies is the availability of efficient software for setting up models and performing numerical simulations. While many such tools exist for different families of neural models, there is a lack of tools allowing for both a generic model definition and efficiently parallelized simulations. In this work, we present PyRates, a Python framework that provides the means to build a large variety of neural models as a graph. PyRates provides intuitive access to and modification of all mathematical operators in a graph, thus allowing for a highly generic model definition. For computational efficiency and parallelization, the model graph is translated into a tensorflow-based compute graph. Using the example of two different neural models belonging to the family of rate-based population models, we explain the mathematical formalism, software structure and user interfaces of PyRates. We then show via numerical simulations that the behavior shown by the model implementations in PyRates is consistent with the literature. Finally, we demonstrate the computational capacities and scalability of PyRates via a number of benchmark simulations of neural networks differing in size and connectivity.
EPGTOP: A tool for continuous monitoring of a distributed system

Monitoring is fundamental to provide operational support for online systems and has been an integral part of most computer systems for decades. Kernel-level counters maintaining statistics such as the number of accepted/dropped packets are examples for classic monitoring. The ever-increasing number of connected devices has affected the scale of computer systems. Distributed systems are now inherent to most largescale computer systems and require adjustments to existing monitoring algorithms since monitoring statistics are no longer retained locally and must be communicated over a network. Evolved Packet Gateway (EPG) is a performance-critical distributed system responsible for processing mobile broadband data. An EPG system contains cards to process requests and can scale up to thousands of worker processes when running in production. The amount of data generated and transmitted to monitor these processes using traditional methods can overload the network cards in EPG use to communicate with one another and adversely affect the system’s performance. This thesis provides an overview of continuous distributed monitoring and evaluates continuous monitoring algorithms for distributed systems. The thesis presents EPGTOP, a monitoring service developed for continuous monitoring of EPG to asses communication-efficiency of monitoring algorithms. EPGTOP provides two modes of operation: basic and approximate. When running in the basic mode, monitoring data is periodically transmitted to a designed management node. To improve communication-efficiency of monitoring, the approximate mode allows an error threshold to be configured. The threshold is used to adjust the accuracy of system statistics continuously reported by the management node. Furthermore, the thesis discusses adjustments required to monitoring algorithms to integrate them into EPG and provides results for EPGTOP to compare and analyse trade-offs between accuracy and communication-efficiency when continuously monitoring distributed systems. Our results demonstrate that continuous distributed monitoring algorithms are able to improve the efficiency of monitoring significantly by reducing communication costs. Additionally, utilizing larger error thresholds leads to far less monitoring data to be generated, albeit at the expense of accuracy.
Human parvovirus B19 non-structural protein (NS1) induces apoptosis through mitochondria cell death pathway in COS-7 cells

Human parvovirus B19 has been found in various tissues in addition to erythroid lineage cells, and non-structural protein (NS1) is reported to induce cytotoxicity and apoptosis in erythroid lineage cells, but the mechanism in non-permissive cells is still unclear. To address this issue, we have constructed the NS1 gene in a cytomegalovirus episomal vector, pEGFP-C1 and transfected it into monkey epithelial cells, COS-7. EGFP-NS1 expression in transfected cells was monitored and assessed by fluorescence microscopy, RT-PCR and Western blot. The flow cytometric analysis showed that the NS1-transfected cells were arrested at G1 phase by paclitaxel treatment and there was increased apoptosis. The expression of p53, an important molecule in apoptosis and cell cycle regulation, and its downstream cell cycle kinase inhibitors p16INK4 and p21WAF1/CIP1 were up-regulated in the NS1-transfected cells. Also, increased expression of the pro-apoptotic Bcl-2 members Bax, Bad and activation of caspase 3 and caspase 9, but not the activation of caspase 8 or Fas were detected in the NS1-transfected cells. p53-induced Bax expression and subsequent activation of caspase 9 is probably the apoptotic pathway in NS1-transfected cells since activation of the caspase 9 was suppressed by the p53 inhibitor and apoptosis was significantly inhibited by the caspase 9 inhibitor. Our results suggest that the cell death of the NS1-transfected cells is associated with mitochondria related apoptosis. These findings might provide alternative information for further study and characterization of B19 NS1 protein in B19 non-permissive cells.
A Comprehensive Analysis on Data Hazard for RISC32 5-Stage Pipeline Processor

This paper describes the verification plan on data hazard detection and handling for a 32-bit MIPS ISA (Microprocessor without Interlocked Pipeline Stages Instruction Set Architecture) compatible 5-stage pipeline processor, RISC32. Our work can be used as a reference for RISC32 processordevelopers to identify and resolve every possible data hazard that might arise during execution phase within the range of the basic MIPS core instruction set. The techniques used to resolvedata hazard in this paper are data forwarding and pipeline stages stalling. When data hazard arises, it is first resolve by using data forwarding. If the problem persists, we use pipeline stages stalling then only follow by another data forwarding to resolve the data hazard. This combination will reduce the impact of data hazard on the processor throughput, instead of only using the pipeline stages stalling. This paper delivers a comprehensive analysis and the development of the data hazard resolving blocks that are able to resolve data hazard arises.
Uncertainty in dual permeability model parameters for structured soils

Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore‐ and matrix‐macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to date. Therefore, this study quantifies uncertainty in dual permeability model parameters of experimental soil columns with different macropore distributions (single macropore, and low‐ and high‐density multiple macropores). Uncertainty evaluation is conducted using adaptive Markov chain Monte Carlo (AMCMC) and conventional Metropolis‐Hastings (MH) algorithms while assuming 10 out of 17 parameters to be uncertain or random. Results indicate that AMCMC resolves parameter correlations and exhibits fast convergence for all DPM parameters while MH displays large posterior correlations for various parameters. This study demonstrates that the choice of parameter sampling algorithms is paramount in obtaining unique DPM parameters when information on covariance structure is lacking, or else additional information on parameter correlations must be supplied to resolve the problem of equifinality of DPM parameters. This study also highlights the placement and significance of matrix‐macropore interface in flow experiments of soil columns with different macropore densities. Histograms for certain soil hydraulic parameters display tri‐modal characteristics implying that macropores are drained first followed by the interface region and then by pores of the matrix domain in drainage experiments. Results indicate that hydraulic properties and behavior of the matrix‐macropore interface is not only a function of saturated hydraulic conductivity of the macroporematrix interface (Ksa) and macropore tortuosity (lf) but also of other parameters of the matrix and macropore domains.
Modeling Energy Consumption of High-Performance Applications on Heterogeneous Computing Platforms

MODELING ENERGY CONSUMPTION OF HIGH-PERFORMANCE APPLICATIONS ON HETEROGNEOUS COMPUTING PLATFORMS Gary D. Lawson Jr. Old Dominion University, 2017 Director: Masha Sosonkina Director: Yuzhong Shen Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit. Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship between workload and power is less understood. Thus, investigation and analysis of power measurements has been a focal point in this work with the aim to improve the general understanding of energy consumption in the context of HPC. This dissertation investigates power and energy consumption of many different parallel applications on several hardware platforms while varying a number of execution characteristics. Multicore and manycore hardware devices are investigated in homogeneous and heterogeneous computing environments. Further, common techniques for reducing power and energy consumption are employed to each of these devices. Well-known power and performance models have been combined to form the
Improving PET-based physiological quantification through methods of wavelet denoising

The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via redundant multiscale representations. In analyzing dynamic PET data of healthy volunteers, a multiscale method improved the estimate-to-error ratio of flows fivefold without loss of detail. This technique also maintained accuracy of flow estimates in comparison with the "gold standard," using dynamic PET with O15-water. In addition, in studies of coronary disease patients, flow patterns were preserved and infarcted regions were well differentiated from normal regions. The results show that a wavelet-based noise-suppression method produced reliable approximations of salient underlying signals and led to an accurate quantification of myocardial perfusion. The described protocol can be generalized to other temporal biomedical imaging modalities including functional magnetic resonance imaging and ultrasound.
Noise-robust Binary segmentation based on Ant Colony System and Modified Fuzzy C-Means algorithm

The wide application of Binary segmentation for grayscale images could be found in computer vision and pattern recognition, especially for the purpose of object identification and recognition with industry and military images. This paper proposes a noise robust binary segmentation approach which incorporates Ant Colony System (ACS) with the modified Fuzzy C-Means (FCM) clustering algorithm. The ACS first survey the whole image, adding an additional pheromone dimension other than grayscale on each pixel. The modified FCM then deems every pixel a 2-dimensional vector and classifies all image pixels into two categories. Experiments have demonstrated better segmentation results and the advantage of robustness against noise using this method.
Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.
Granulocyte-macrophage colony-stimulating factor and interleukin-3 signaling pathways converge on the CREB-binding site in the human egr-1 promoter

Granulocyte-macrophage colony-stimulating factor (GM-CSF) stimulates myeloid progenitor cell proliferation and enhances the function of terminally differentiated effector cells. Interleukin-3 (IL-3) stimulation results in the proliferation and maturation of early bone marrow progenitor cells. These activities are mediated by non-tyrosine kinase-containing receptors which consist of ligand-specific alpha subunits that complex with a common beta subunit required for signal transduction. Both GM-CSF and IL-3 rapidly and transiently induce expression of early growth response gene 1 (egr-1) in the human factor-dependent cell line TF-1. To define the mechanism of early response gene induction by GM-CSF and IL-3, growth factor- and serum-starved TF-1 cells transfected with recombinant constructs containing sequences of the human egr-1 promoter were stimulated with GM-CSF or IL-3. A 116-nucleotide (nt) region of the egr-1 promoter which contains sequences inducible by GM-CSF and IL-3 was defined. DNase I footprint analysis identified a 20-nt region, including nt -57 to -76, which contains a potential cyclic AMP (cAMP) response element (CRE). Electrophoretic mobility shift assays performed with CREB antibody confirmed the presence of CREB in the DNA-binding complex. Mutational analysis of the cytokine-responsive region of the egr-1 promoter revealed that both the cAMP response and serum response elements are required for induction by GM-CSF and IL-3. Nuclear extracts from GM-CSF- or IL-3-stimulated but not unstimulated TF-1 cells contain factors which specifically bind to the Egr-1-binding site in the nt -600 to -480 region of the promoter. Electrophoretic mobility shift assays were performed with antibodies against the Egr-1 protein to demonstrate the presence of the protein product in the shifted complex. Our studies suggest that the Egr-1 protein may further stimulate transcription of the egr-1 gene in response to GM-CSF as a secondary event.
Methods Population samples The analysis for the present paper is based on data from the cross-sectional North / South Ireland Food

Recommendations limiting the intake of total fat, SFA, MUFA and PUFA have been established in several countries with the aim of reducing the risk of chronic diseases such as CVD. Studies have shown that intakes of total fat and SFA are above desired recommended intake levels across a wide range of age and sex groups. In addition, intakes of PUFA and MUFA are often reported to be less than the desired recommended intake levels. The aims of the present paper are to provide the first data on estimates of current intakes and main food sources of SFA, MUFA and PUFA in Irish children (aged 5–12 years), teenagers (aged 13–17 years) and adults (aged 18–64 years) and to analyse compliance with current dietary recommendations. Data for this analysis were based on the North/ South Ireland Food Consumption Survey (n 1379, 18–64 years), the National Children’s Food Survey (n 594, 5–12 years) and the National Teen Food Survey (n 441, 13–17 years). Results showed that SFA intakes in Irish children, teenagers and adults are high, with only 6 % of children, 11 % of teenagers and 21 % of adults in compliance with the recommended daily intake. The main food groups that contributed to SFA intakes were whole milk; fresh meat; meat products; biscuits, cakes, buns and pastries; and sugars, confectionery and preserves.