Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming mutations of RAC guanosine triphosphatases in human cancers

Members of the RAS superfamily of small guanosine triphosphatases (GTPases) transition between GDP-bound, inactive and GTP-bound, active states and thereby function as binary switches in the regulation of various cellular activities. Whereas HRAS, NRAS, and KRAS frequently acquire transforming missense mutations in human cancer, little is known of the oncogenic roles of other small GTPases, including Ras-related C3 botulinum toxin substrate (RAC) proteins. We show that the human sarcoma cell line HT1080 harbors both NRAS(Q61K) and RAC1(N92I) mutant proteins. Whereas both of these mutants were able to transform fibroblasts, knockdown experiments indicated that RAC1(N92I) may be the essential growth driver for this cell line. Screening for RAC1, RAC2, or RAC3 mutations in cell lines and public databases identified several missense mutations for RAC1 and RAC2, with some of the mutant proteins, including RAC1(P29S), RAC1(C157Y), RAC2(P29L), and RAC2(P29Q), being found to be activated and transforming. P29S, N92I, and C157Y mutants of RAC1 were shown to exist preferentially in the GTP-bound state as a result of a rapid transition from the GDP-bound state, rather than as a result of a reduced intrinsic GTPase activity. Activating mutations of RAC GTPases were thus found in a wide variety of human cancers at a low frequency; however, given their marked transforming ability, the mutant proteins are potential targets for the development of new therapeutic agents.
The method for solving two types of errors in customer segmentation on unbalanced data

Two types of errors of "rejecting true and accepting false" are inevitable in customer value segmentation. The traditional data mining method which is on the total accuracy rate can not reflect the influence caused by the great difference of misclassification costs and unbalanced quantity distribution of customers who have various values. The thesis proposes the cost-sensitive SVM (support vector machine) classifier by presenting misclassification cost function based on customer value, which is evaluated by the function of the exceptional lost. The data test result proves that the method can control the different types of errors distribution with various cost of misclassification accurately, reduce the total misclassification cost, and distinguish the customer value effectively.
EVOLUTION OF PARAMETERS OF NONLINEAR POSITION CONTROL FOR DYNAMIC MODEL OF MOBILE ROBOT WITH FRICTION

Abstract In this paper, we propose two level control system for a mobile robot. The first level subsystem deals with the control of the linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot represents the second level control, which is nonlinear. The nonlinear control design is implemented by a modified backstepping algorithm whose parameters are adjusted by a genetic algorithm, which is a robust nonlinear optimization method. The performance of the proposed system is investigated using a dynamic model of a nonholonomic mobile robot with friction. We present a new dynamic model in which the angular velocities of wheels are main variables. Simulation results show the good quality of position tracking capabilities a mobile robot with the various viscous friction torques.
Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures

Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without and one with performance feedback. Analysis was performed off-line. A ratio of the canonical correlation analysis (CCA) coefficients was used to construct a Bayesian probability model and a thresholding method for the ratio of the posterior probability of the target frequency over maximal posterior probability of non-target frequencies was used as classification criteria. Moreover, the probability thresholds were optimized for each frequency, subject to maximizing the ITR. We achieved a maximum ITR of 39.82 bit/min. Although the performance feedback did not improve the overall ITR, it did improve the accuracy measure. Possible applications in the medical industry are discussed.
Analysis of DNA methylation patterns associated with the gastric cancer genome

The objective of the current study was to investigate the characteristics of DNA methylation patterns associated with the gastric cancer genome and to identify clinically useful diagnostic markers and therapeutic targets for gastric cancer. The Infinium 450K methylation microarray was used to compare differential DNA methylation sites of gastric cancer tissue with that of normal gastric tissue. The results of the DNA microarray analysis were confirmed by pyrosequencing. Functional analysis of the differential genes was performed using the GO software. The effect of candidate site methylation on gene expression was monitored using quantitative polymerase chain reaction analysis. Of the 2,645 differential methylation sites identified in gastric cancer tissues, 2,016 were hypermethylated sites, 629 were hypomethylated sites, 826 were located in promoter regions and 1,024 were located within genes. These differential sites were associated with 1,352 genes. In total, five sites were selected and pyrosequencing verified the results of the microarray analysis in five of the sites. Change in gastric cancer DNA methylation pattern was a common occurrence. Differential methylation sites appeared more often in non-promoter regions. The associated genes were involved in multiple signaling pathways, and hypermethylated and hypomethylated sites were involved in roughly the same signaling pathways. Methylation of the genome promoted gene expression. TRIM15, ITGAM, MSX2 and FAM38A may be candidate genes for diagnosing gastric cancer.
Energy-efficient connected coverage of discrete targets in wireless sensor networks

A major concern in wireless sensor networks is to maximize network lifetime (in terms of rounds) while maintaining a high quality of services (QoS) at each round such as target coverage and network connectivity. Due to the power scarcity of sensors, a mechanism that can efficiently utilize energy has a great impact on extending network lifetime. Most existing works concentrate on scheduling sensors between sleep and active modes to maximize network lifetime while maintaining target/area coverage and network connectivity. This paper generalizes the sleep/active mode by adjusting sensing range to maximize total number of rounds and presents a distributed heuristic to address this problem.
Memory as discrimination : strategic processing of retrieval cues

This thesis investigates the validity of a memory retrieval view that insists on the diagnostic relationship between retrieval cues and target memories and examines how the diagnostic value of a cue can influence the processing it receives. As a concept, the encoding-retrieval match is a widely accepted explanation of retrieval performance. According to this view, performance relates to the overlap between the retrieval information and the to-be-recalled information [see Tulving (1979, 1983) for a discussion]. The cue overload effect is another well-established phenomenon. It stipulates that retrieval performance will decrease as the number of potential targets in memory subsumed under a retrieval cue increases (Watkins & Watkins, 1975). Assuming these two factors can interact, a variety of outcomes are possible. The memory-as-discrimination view, investigated as part of this thesis, suggests that factors such as cue overload, distinctiveness, and potentially others contribute to a process of memory retrieval where diagnosticity is central (Nairne, 2002). Under such a system, retrieval is most likely to be successful when a cue specifies a target in memory to the exclusion of other potential candidates. In the second chapter, this idea is tested via three studies that use a cued-recognition paradigm. Results support the memory-as-discrimination view. However, this set of experiments brought to light another interesting phenomenon: participants appeared to prioritise the processing of the more diagnostic cues, perhaps in an effort to maximise the discrimination power of the available cue constellations. Support for this hypothesis is provided in the third chapter through a set of three experiments calling upon eye-tracking and other measures. Results from all three studies suggest that people preferentially process the more discriminative cues. How well a cue specifies a retrieval target appears to have two related effects: diagnosticity has a causal relationship with retrieval performance and it determines which cues are prioritised / processed with more emphasis.
Combining sequence and structural profiles for protein solvent accessibility prediction.

Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein. We then integrate the estimated solvent accessibility and the position specific scoring matrix of the query protein using a neural network. We tested our method on a large data set consisting of 3386 non-redundant proteins. The comparison with other methods show slightly improved prediction accuracies with our method. The resulting system does need not be re-trained when new data is available. We incorporated our method into the MUPRED system, which is available as a web server at http://digbio.missouri. edu/mupred.
Local regularity of fitness landscapes and the performance of a simple ES

We present a theoretical and experimental analysis of the influence of the local irregularity of the fitness function on the behavior of an (1+1)ES. Previous work on this subject suggests that the performance of an EA strongly depends on the irregularity of the fitness function. Several irregularity measures have been derived for discrete search spaces, in order to numerically characterize this type of difficulty for EA. These characterizations are mainly based on Holder exponents. Previous studies used however a global characterization of fitness regularity (the global Holder exponent), with experimental validations being conducted on test functions with uniform regularity. This work is extended here in two ways: Results are now stated for continuous search spaces, and pointwise instead of global irregularity is considered. In addition, we present a way to modify the genetic topology to accommodate for variable regularity: the mutation radius, which controls the size of the neighbourhood of a point, is allowed to vary according to the local irregularity of the fitness function. These results are explained through a simple theoretical analysis which gives a relation between the pointwise Holder exponent and the optimal mutation radius. Several questions connected to on-line measurements and usage of regularity in EAs are raised.
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

We present a new dependency parsing method for Korean applying cross-lingual transfer learning and domain adaptation techniques. Unlike existing transfer learning methods relying on aligned corpora or bilingual lexicons, we propose a feature transfer learning method with minimal supervision, which adapts an existing parser to the target language by transferring the features for the source language to the target language. Specifically, we utilize the Triplet/Quadruplet Model, a hybrid parsing algorithm for Japanese, and apply a delexicalized feature transfer for Korean. Experiments with Penn Korean Treebank show that even using only the transferred features from Japanese achieves a high accuracy (81.6%) for Korean dependency parsing. Further improvements were obtained when a small annotated Korean corpus was combined with the Japanese training corpus, confirming that efficient crosslingual transfer learning can be achieved without expensive linguistic resources.
Rab27a and Rab27b are involved in stimulation‐dependent RANKL release from secretory lysosomes in osteoblastic cells

The quantity of the receptor activator of NF‐κB ligand (RANKL) expressed at the cell surface of osteoblastic cells is an important factor regulating osteoclast activation. Previously, RANKL was found to be localized to secretory lysosomes in osteoblastic cells and to translocate to the cell surface in response to stimulation with RANK‐Fc‐conjugated beads. However, the in vivo significance of stimulation‐dependent RANKL release has not been elucidated. In this study we show that small GTPases Rab27a and Rab27b are involved in the stimulation‐dependent RANKL release pathway in osteoblastic cells. Suppression of either Rab27a or Rab27b resulted in a marked reduction in RANKL release after stimulation. Slp4‐a, Slp5, and Munc13‐4 acted as effector molecules that coordinated Rab27a/b activity in this pathway. Suppression of Rab27a/b or these effector molecules did not inhibit accumulation of RANKL in lysosomal vesicles around the stimulated sites but did inhibit the fusion of these vesicles to the plasma membrane. In osteoblastic cells, suppression of the effector molecules resulted in reduced osteoclastogenic ability. Furthermore, Jinx mice, which lack a functional Munc13‐4 gene, exhibited a phenotype characterized by increased bone volume near the tibial metaphysis caused by low bone resorptive activity. In conclusion, stimulation‐dependent RANKL release is mediated by Rab27a/b and their effector molecules, and this mechanism may be important for osteoclast activation in vivo. © 2011 American Society for Bone and Mineral Research.
On optimization of the measurement matrix for compressive sensing

In this paper the problem of Compressive Sensing (CS) is addressed. The focus is on estimating a proper measurement matrix for compressive sampling of signals. The fact that a small mutual coherence between the measurement matrix and the representing matrix is a requirement for achieving a successful CS is now well known. Therefore, designing measurement matrices with smaller coherence is desired. In this paper a gradient descent method is proposed to optimize the measurement matrix. The proposed algorithm is designed to minimize the mutual coherence which is described as absolute off-diagonal elements of the corresponding Gram matrix. The optimization is mainly applied to random Gaussian matrices which is common in CS. An extended approach is also presented for sparse signals with respect to redundant dictionaries. Our experiments yield promising results and show higher reconstruction quality of the proposed method compared to those of both unoptimized case and previous methods.
A Re-ordering Strategy for Accelerating Index-based Audio Fingerprinting

The Haitsma/Kalker audio fingerprinting system [4] has been in use for years, but its search algorithm’s scalability has not been researched very well. In this paper we show that by simple re-ordering of the query fingerprint’s subprints in the index-based retrieval step, the overall search performance can be increased significantly. Furthermore, we show that combining longer fingerprints with re-ordering can lead to even higher performance gains, up to a factor of 9.8. The proposed re-ordering scheme is based on the observation that sub-prints, which are elements of n-runs of identical consecutive sub-prints, have a higher survival rate in distorted copies of a signal (e.g. after mp3 compression) than other sub-prints.
Principles and Pitfalls of Nerve Conduction Studies

~~ ~ This report reviews the fundamental principles and the changing concepts of nerve stimulation techniques, and discusses the proper application of these techniques in the differential diagnosis of peripheral nerve disorders. Nerve conduction stlldies help delineate the extent and distribution of the neural lesion and distinguish two major categories of peripheral nerve disease: demyelination and axonal degeneration. Although the method is based on simple principles, pitfalls abound in practice. Variability in nerve conduction measurement may result from temperature change, variations among nerve segments, and the effects of age. Other sources of error include excessive spread of stimulation current, anomalous innervation, temporal dispersion, and inaccuracy of surface measurement. Unlike a bipolar derivation, which selectively records near-field potentials, a referential recording may give rise to stationary far-field peaks from a moving source. Overlooking this possibility can lead to an incorrect interpretation of findings. Conventional nerve conduction studies deal primarily with measurements of the distal nerve segments in an extremity. More recent techniques are applicable to less accessible anatomical regions, as illustrated by elicitation of the blink reflex, F wave, and H reflex, and the use of the inching technique. Other methods used to assess special aspects of nerve conduction include the ischemic test and studies of slow-conducting fibers. lmura J. Principles and pitfalls of nerve conduction studies. Ann Neurol 16:415-429, 1984
Gene Discovery for Comparative Biology of Parasitic and Non-parasitic Plants. a Five-week Molecular Research Immersion

As part of an NSF-funded initiative, biology students, primarily those in the early stages of their degree program, were recruited to participate in ongoing research alongside principle investigators. In this example, ten students were given five weeks of intensive training in molecular research methods, with the objective of cloning genes involved in core metabolic processes in the parasitic plant Cuscuta pentagona (dodder) and its non-parasitic relative Ipomoea hederacea (ivy-leaf morning glory). During the session, students used bioinformatic tools to search gene databases, designed PCR primers, isolated genomic DNA for use in amplification and molecular cloning, mapped candidate clones, and prepared templates for sequencing. The students were provided resulting sequence data and asked to determine whether the effort had been successful. Each of these activities provided an opportunity for students to learn biological principles through their application towards a research objective.
Realistic Safety Cases for the Timing of Systems

Timing is often seen as the most important property of systems after function, and safety-critical systemsarenoexception.Inthispaper,weconsiderhowtimingistypicallytreatedinsafetyassurance and, in particular, the safety arguments being proposed by industry and academia.A critique of these arguments is performed based on how systems are generally developed and how evidence is gathered. Significant weaknesses are exposed resulting in a more appropriate safety argument being proposed. As part of this work techniques for identifying relationships, in the form of contracts, between parts of the argument and the strength of evidence are used. The work is demonstrated using a ComputerAssistedBrakingexample,specificallyanAnti-LockBrakingSystemforacar,asitisaclassicexample of a component that may be used ‘Out of Context’, as discussed in a number of safety standards, and may also be reused across a number of systems as well as part of a product line.
A Comparison of Various Methods for Concept Tagging for Spoken Language Understanding

The extraction of flat concepts out of a given word sequence is usually one of the first steps in building a spoken language understanding (SLU) or dialogue system. This paper explores five different modelling approaches for this task and presents results on a French state-of-the-art corpus, MEDIA. Additionally, two log-linear modelling approaches could be further improved by adding morphologic knowledge. This paper goes beyond what has been reported in the literature. We applied the models on the same training and testing data and used the NIST scoring toolkit to evaluate the experimental results to ensure identical conditions for each of the experiments and the comparability of the results. Using a model based on conditional random fields, we achieve a concept error rate of 11.8% on the MEDIA evaluation corpus.
DMA analysis of the structure of crosslinked poly(methyl methacrylate)s.

PURPOSE This paper presents the study aimed at the development of crosslinked poly(methyl methacrylate)s (X-PMMA) of varied crosslink density and the investigation of the relationships between the polymer network structure and dynamic mechanical properties. METHODS A series of model X-PMMA networks were crosslinked by the introduction of: 1, 2, 5, 10 and 20% of triethylene glycol dimethacrylate (TEGDMA). The copolymerizations led to various glass-rubber relaxation properties of the polymer networks, as revealed by dynamic-mechanical analysis (DMA). Glass temperature (Tg) and storage modulus above the Tg (Erubbery ) were a sensitive function of network architecture. DMA data were used for calculating the network parameter (Mc), crosslink density (q) and its alternative measure - the degree of crosslinking (DX). RESULTS The viscoelastic properties as well as structural parameters calculated from those showed correlation with the amount of the crosslinker. The increase in TEGDMA content resulted in the Tg, q and DX increases, whereas Mc decrease. The possible incomplete conversion of double bonds was detected in the DMA analysis, which was confirmed by the degree of conversion (DC), measured by FTIR spectroscopy. Additionally, some amount of sol fraction was found by 1H NMR experiments. CONCLUSIONS The structure-property relationships developed for the system presented in this work could be useful in tissue engineering, where X-PMMA is applied. The direct measure of storage modulus values before and above glass transition may serve as a simple and fast indicator of the X-PMMA crosslink density.
Traffic classification method of P2P streaming application based on sampling technology

With the continuous development of high-speed network technology, this paper presents a traffic classification method of P2P streaming application based on sampling technology. By comparing the statistics data before and after the sampling, we find a group of statistical flow characteristics which are easy to be acquired and implemented. Using these characteristics and classification method proposed by this paper, we obtained a higher recognition rate of P2P streaming application. More importantly, traffic of P2P streaming with different sampling rate could be effectively identified by using unified classification model. The accuracy rate of classification is more than 92% when sampling rate was not less than 0.01. It is qualified for highspeed network measurement.