Miracle-dz/UniFAR-FTUs huggingface dataset:
Specificity of RSG-1.2 Peptide Binding to RRE-IIB RNA Element of HIV-1 over Rev Peptide Is Mainly Enthalpic in Origin

Rev is an essential HIV-1 regulatory protein which binds to the Rev responsive element (RRE) present within the env gene of HIV-1 RNA genome. This binding facilitates the transport of the RNA to the cytoplasm, which in turn triggers the switch between viral latency and active viral replication. Essential components of this complex have been localized to a minimal arginine rich Rev peptide and stem IIB region of RRE. A synthetic peptide known as RSG-1.2 binds with high binding affinity and specificity to the RRE-IIB than the Rev peptide, however the thermodynamic basis of this specificity has not yet been addressed. The present study aims to probe the thermodynamic origin of this specificity of RSG-1.2 over Rev Peptide for RRE-IIB. The temperature dependent melting studies show that RSG-1.2 binding stabilizes the RRE structure significantly (ΔT m = 4.3°C), in contrast to Rev binding. Interestingly the thermodynamic signatures of the binding have also been found to be different for both the peptides. At pH 7.5, RSG-1.2 binds RRE-IIB with a Ka = 16.2±0.6×107 M−1 where enthalpic change ΔH = −13.9±0.1 kcal/mol is the main driving force with limited unfavorable contribution from entropic change TΔS = −2.8±0.1 kcal/mol. A large part of ΔH may be due to specific stacking between U72 and Arg15. In contrast binding of Rev (Ka = 3.1±0.4×107 M−1) is driven mainly by entropy (ΔH = 0 kcal/mol and TΔS = 10.2±0.2 kcal/mol) which arises from major conformational changes in the RNA upon binding.
Machine Transliteration Based on Error-Driven Learning

Transliteration is a common translation method when named entities are introduced into another language. Direct orthographical mapping (DOM) approach is successfully applied in machine transliteration by segmenting a word according to syllables and then mapping them directly into target language without considering its pronunciation. The paper studies the performance of two-stage machine transliteration based on Conditional Random Fields. To reduce the amount of computation in model training, we propose an error-driven learning by dividing the training data into several groups and training the transliteration model step by step based on the error prediction data until the performance doesnât increase or the limitation of the computer. Experiments on data of NEWS2011 show that error-driven model training reduces computational complexity and saves the time of model training. Compared to the combining transliteration model, our transliteration system increases the accuracy of top-1 output with 0.06, reaching 0.652.
Specific micro-RNA expression patterns distinguish the basal and luminal subtypes of muscle-invasive bladder cancer

The roles of non-coding RNAs in controlling clinical and biological heterogeneity in bladder cancer remain unclear. We used TCGA's published dataset (n = 405 tumors) as a discovery cohort and created a new validation cohort to define the miRNA expression patterns in the basal and luminal molecular subtypes of muscle-invasive bladder cancer (MIBC). We identified 63 miRNAs by PAM, which optimally identified basal and luminal tumors. The targets of the top luminal miRNAs were activators of EMT (ZEB1, ZEB2) and basal subtype transcription (IL-6, EGFR, STAT3), whereas the targets of the top basal miRNAs were involved in adipogenesis pathways and luminal breast cancer (ERBB2, ERBB3). We also identified a 15-miRNA signature that identified stromally infiltrated basal and luminal MIBCs corresponding to the “cluster IV/immune undifferentiated/claudin-low” and “cluster II/luminal immune” subtypes identified previously, which likely contain samples with higher infiltration rates. Using the 63-miRNA signature, we accurately assigned MIBCs to the basal and luminal subtypes and confirmed that patients with basal tumors had shorter overall survival. The results strongly suggest that miRNAs contribute to the control of the gene expression patterns observed in basal and luminal MIBCs and that they can be used as biomarkers and candidate therapeutic targets.
A Multilevel scheme for the Travelling Salesman Problem

I would like to thank my tutors Fredrik Manne and Noureddine Bhoumala for support and help. I would also like to thank my girlfriend Line Carlsen for her support and patience, and Per Otto Hjertenes for his assistance in proofreading my texts. This text is about the Travelling Salesman Problem. An observant reader would already now have discovered that I am using travelling instead of traveling. This spelling difference comes from the subtle differences between U.K English and U.S English. If you are interested in more facts about the name travelling and traveling confusion I would like to refer to the following web site [29], or to [30] where it is also mentioned. The picture on the front page is by Applegate, Bixby, Chvátal, and Cook , and is collected from [31]. The picture shows the optimal tour of the tsp instance d15112 from TSPLIB [25], depicting the shortest tour to travel, if you want to visit all German cities.
Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas

Antibiotic resistance often evolves by mutations at conserved sites in essential genes, resulting in parallel molecular evolution between divergent bacterial strains and species. Whether these resistance mutations are having parallel effects on fitness across bacterial taxa, however, is unclear. This is an important point to address, because the fitness effects of resistance mutations play a key role in the spread and maintenance of resistance in pathogen populations. We address this idea by measuring the fitness effect of a collection of rifampicin resistance mutations in the β subunit of RNA polymerase (rpoB) across eight strains that span the diversity of the genus Pseudomonas. We find that almost 50% of rpoB mutations have background-dependent fitness costs, demonstrating that epistatic interactions between rpoB and the rest of the genome are common. Moreover, epistasis is typically strong, and it is the dominant genetic determinant of the cost of resistance mutations. To investigate the functional basis of epistasis, and because rpoB plays a central role in transcription, we measured the effects of common rpoB mutations on transcriptional efficiency across three strains of Pseudomonas. Transcriptional efficiency correlates strongly to fitness across strains, and epistasis arises because individual rpoB mutations have differential effects on transcriptional efficiency in different genetic backgrounds.
Using constraint techniques for a safe and fast implementation of optimality-based reduction

Optimality-based reduction attempts to take advantage of the known bounds of the objective function to reduce the domain of the variables, and thus to speed up the search of a global optimum. However, the basic algorithm is unsafe, and thus, the overall process may no longer be complete and may not reach the actual global optimum. Recently, Kearfott has proposed a safe implementation of optimality-based reduction. Unfortunately, his method suffers from some limitations and is rather slow. In this paper, we show how constraint programming filtering techniques can be used to implement optimality-based reduction in a safe and efficient way.
A review of calibrated blood oxygenation level‐dependent (BOLD) methods for the measurement of task‐induced changes in brain oxygen metabolism

The dynamics of the blood oxygenation level‐dependent (BOLD) response are dependent on changes in cerebral blood flow, cerebral blood volume and the cerebral metabolic rate of oxygen consumption. Furthermore, the amplitude of the response is dependent on the baseline physiological state, defined by the haematocrit, oxygen extraction fraction and cerebral blood volume. As a result of this complex dependence, the accurate interpretation of BOLD data and robust intersubject comparisons when the baseline physiology is varied are difficult. The calibrated BOLD technique was developed to address these issues. However, the methodology is complex and its full promise has not yet been realised. In this review, the theoretical underpinnings of calibrated BOLD, and issues regarding this theory that are still to be resolved, are discussed. Important aspects of practical implementation are reviewed and reported applications of this methodology are presented. Copyright © 2012 John Wiley & Sons, Ltd.
Cognitive Rehabilitation in Patients with Gliomas and Other Brain Tumors: State of the Art

Disease prognosis is very poor in patients with brain tumors. Cognitive deficits due to disease or due to its treatment have an important weight on the quality of life of patients and caregivers. Studies often take into account quality of life as a fundamental element in the management of disease and interventions have been developed for cognitive rehabilitation of neuropsychological deficits with the aim of improving the quality of life and daily-life autonomy of patients. In this literature review, we will consider the published studies of cognitive rehabilitation over the past 20 years.
Automatically Tuning Control Systems for Simulated Legged Robots

Rather than create a control system from scratch each time we build a new robot creature, we would like to generate control systems automatically. I have implemented an algorithm which, given a control system that works well for one creature, automatically tunes it to work for a new, similar creature. Using this approach, the control system for a horse might be adjusted for use with elephants, giraffes, and dogs. The adjustment is accomplished by gradually altering the original creature to make it like the new one and repeatedly tuning the control system as these changes are made. Because the creature's alteration is gradual, the control system can be tuned using a local search such as gradient descent. In simulation tests, the tuning algorithm has successfully tuned the control system of a planar quadruped simulation to accommodate a reduction in leg length by a factor of two, an increase in body mass by a factor of three, and changes in the commanded speed while trotting.
The Aid of Colour on Spatial Navigation: A Study in a Virtual Hospital Environment

The aim of this study is to explore the effects of colour as visuo-spatial cue on spatial navigation and developing a cognitive map in elderly in a simulated virtual hospital environment. The study further purports to explore whether it is possible to improve elders' spatial navigation and cognitive mapping performances as well as younger-aged group, by the aid of coloured visuo-spatial cues.
Emotional feeling and the orbitomedial prefrontal cortex: Theoretical and empirical considerations

Emotional feeling can be defined as the affective constituent of emotions representing a subjective experience such as, for example, feeling love or hate. Several recent neuroimaging studies have focused on this affective component of emotions thereby aiming to characterise the underlying neural correlates. These studies indicate that the orbitomedial prefrontal cortex is crucially involved in the processing of emotional feeling. It is the aim of this paper to analyse the extent to which the present state of the art in neuroscience enables emotional feeling to be related to specific brain regions. In the first step, methodological and theoretical problems in the investigation of emotional feeling will be discussed leading to the characterisation of a “twofold gap. ” This gap represents (a) the theoretical difficulties encountered in transforming vivid subjective experience into a theoretical psychological concept, and (b) the problems of implementing such a concept by performing empirical studies. Based on these considerations we suggest approaches for future empirical studies. In the second step, a group of functional neuroimaging studies focusing on the affective constituent of emotions will be discussed in detail with regard to the theoretical problems outlined in the first step.
Evolution of SINE Sl Retroposons in Crucifeme Plant Species

The Sl element is a plant short interspersed element (SINE) that was first described and studied in Brussica nupus. In this work, we investigated the distribution and the molecular phylogeny of the Sl element within the Cruciferae (=Brassicaceae). Sl elements were found to be widely distributed within the Cruciferae, especially in species of the tribe Brassiceae. The molecular phylogeny of Sl elements in eight Cruciferae species (Brussicu oleruceu, Brussicu rupu, Brussicu nupus, Brussicu n&-u, Sinupis urvensis, Sinupis pubescens, Coincyu monensis, and Vellu spinosu) was inferred using 14-36 elements per species. Significant neighbor-joining and maximum-parsimony phylogenetic clusters, supported by high bootstrap P values and/or represented in 100% of the most-parsimonious trees, were observed for each species. Most of these clusters probably correspond to recent species-specific bursts of Sl amplification. Since these species diverged recently, S 1 amplification in Cruciferae plants is proposed to be a highly dynamic process that could contribute to genome rearrangements and eventually lead to reproductive isolation. S 1 sequence analysis also revealed putative gene conversion events that occurred between different Sl elements of a given species. These events suggest that gene conversion is a minor but significant component of the molecular drive governing Sl concerted evolution.
Image Segmentation based on Histogram Analysis and Soft Thresholding

researched area in the field of object oriented image processing procedure is efficient and effective image segmentation. Segmentation is a process of partitioning a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. In this paper, we introduce a spatial domain segmentation framework based on the histogram analysis and soft threshold. The histogram analysis uses discontinuity and similarity properties of image statistics in tandem with distribution of pixels to define the binary label for a homogenous region. The soft threshold used for classification is determined based on the localized statistics of the image in consideration for merging of the regions. Simulation results and analysis would verify that the proposed algorithm shows good performance in image segmentation without choosing the region of interest.
The MET13 Methylenetetrahydrofolate Reductase Gene Is Essential for Infection-Related Morphogenesis in the Rice Blast Fungus Magnaporthe oryzae

Methylenetetrahydrofolate reductases (MTHFRs) play a key role in the biosynthesis of methionine in both prokaryotic and eukaryotic organisms. In this study, we report the identification of a novel T-DNA-tagged mutant WH672 in the rice blast fungus Magnaporthe oryzae, which was defective in vegetative growth, conidiation and pathogenicity. Analysis of the mutation confirmed a single T-DNA insertion upstream of MET13, which encodes a 626-amino-acid protein encoding a MTHFR. Targeted gene deletion of MET13 resulted in mutants that were non-pathogenic and significantly impaired in aerial growth and melanin pigmentation. All phenotypes associated with Δmet13 mutants could be overcome by addition of exogenous methionine. The M. oryzae genome contains a second predicted MTHFR-encoding gene, MET12. The deduced amino acid sequences of Met13 and Met12 share 32% identity. Interestingly, Δmet12 mutants produced significantly less conidia compared with the isogenic wild-type strain and grew very poorly in the absence of methionine, but were fully pathogenic. Deletion of both genes resulted in Δmet13Δmet12 mutants that showed similar phenotypes to single Δmet13 mutants. Taken together, we conclude that the MTHFR gene, MET13, is essential for infection-related morphogenesis by the rice blast fungus M. oryzae.
MMSE estimation with CFAR detection for underwater communication channel

Various channel estimation methods have been proposed for shallow underwater communication channel with sparse channel type. A matching pursuit (MP), which is one of typical sparse channel estimation methods, can detect dominant components of sparse channel sequentially as a portion of matching. Mean squared error of MP, however, converges to fixed value even though signal-to-noise ratio (SNR) increases. This paper proposes a minimum mean squared error (MMSE) estimation method with constant false alarm (CFAR) detection for sparse channel estimation. Unlike MP, the mean squared error of the proposed method decreases as SNR increases and is lower than that of conventional MMSE estimators on the sparse channel. Experimental results show that the proposed method estimates sparse underwater communication channel efficiently.
Journal of Internet Banking and Commerce Factors Influencing the Adoption Behavior of Mobile Banking: a South Korean Perspective

University where she got her degrees of MS and PhD. Her current research interests are impacts of IT applications on business including mobile telecommunication industry.Abstract The objective of this study was to identify factors influencing the adoption of mobile banking service. We specifically focused our attention on perceived risk, perceived usefulness, and trust in mobile banking adoption. This study modified the concept of a technology acceptance model (TAM) within the context of mobile banking. It introduced " perceived risk " and " trust " in a proposed model to reflect consumers' needs to use mobile banking. In addition to the satisfactory fit level of our proposed model, we concluded that perceived risk indirectly influences adoption behavior but only when it was via trust. Using the mobile banking service context, we also obtained strong empirical evidence for measuring perceived risks' dimensions. Evidence for a composite perceived risk variable was identified. We found the strong inhibiting effect of perceived risk on trust. This result encouraged the decomposition of the perceived risk variable into its theorized dimensions. The financial-performance risk dimension proved to be the most salient concern for this sample and its context. Trust also had stronger influence on the adoption behavior of mobile banking than perceived usefulness, which was used as an important variable in the traditional TAM variables.
Exploring nonprofits in the social economy: The social enterprises phenomena

x Chapter 1: Introduction and overview 1 1.1 The Nonprofit dichotomy 1 1.2 The Social economy dilemma 2 1.3 The role of the SBDC 7 1.4 The role of the social economy 9 1.5 Problem Statement 14 1.6 Research Questions & Hypothesis 16 Chapter 2: Literature Review 18 2.
Automated Inspection of E-Shaped Magnetic Core Elements Using K-tSL-Center Clustering and Active Shape Models

Automated optical inspection (AOI) has been widely used in industrial Quality Assurance (QA) procedures. Multi-task inspection in high-speed AOI systems is becoming a significant problem in the design. In this paper, the design of an AOI system for E-shaped magnetic core elements is briefly described and several novel algorithms are proposed to realize defects detection by this system. First, this paper proposes a robust k-tSL-center clustering method to classify the interfaces of the element into normal and damaged areas. Second, a modified Active Shape Model (ASM) method is adopted to perform shape distortion detection in real-time. Performance evaluations are carried out on an E-shaped Magnetic Core Image Database, in which all images are captured by the designed AOI system. Experimental results show that the proposed methods are more efficient, robust and accurate than state-of-the-art methods in this application.
Maximum likelihood estimation of mixed C-vines with application to exchange rates

Multivariate copulas are commonly used in economics, finance and risk management. They allow for very flexible dependency structures, even though they are applied to transformed financial data after marginal time dependencies are removed. This is necessary to facilitate statistical parameter estimation. In this paper we consider a very flexible class of mixed C-vines, which allows the variables to be ordered according to their influence. Vines are built from bivariate copulas only and the term ‘mixed’ refers to allowing the pair-copula family to be chosen individually for each term. In addition there are many C-vine structure specifications possible and therefore we propose a novel data driven sequential selection procedure, which selects both the C-vine structure and its attached pair-copula families with parameters. After the model selection maximum likelihood (ML) estimation of the parameters is facilitated using the sequential estimates as starting values. An extensive simulation study shows a satisfactory performance of ML estimates in small samples. Finally an application involving US-exchange rates demonstrates the need for mixed C-vine models.
Intelligent strategies for DAG scheduling optimization in Grid environments

The paper presents a solution to the dynamic DAG scheduling problem in Grid environments. It presents a distributed, scalable, efficient and fault-tolerant algorithm for optimizing tasks assignment. The scheduler algorithm for tasks with dependencies uses a heuristic model to optimize the total cost of tasks execution. Also, a method based on genetic algorithms is proposed to optimize the procedure of resources assignment. The experiments used the MonALISA monitoring environment and its extensions. The results demonstrate very good behavior in comparison with other scheduling approaches for this kind of DAG scheduling algorithms.