Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of PDGF, IL-1alpha, and BMP2/4 on corneal fibroblast chemotaxis: expression of the platelet-derived growth factor system in the cornea.

PURPOSE The purpose of this study was to examine expression of platelet-derived growth factor (PDGF) and PDGF receptors in the human cornea and to study the effects of the PDGF isotypes on proliferation and chemotaxis of human corneal fibroblasts. The effects of interleukin (IL)-1alpha, bone morphogenic protein (BMP)2, and BMP4 on chemotaxis of human corneal fibroblasts were also studied. METHODS mRNA expression was monitored with reverse transcription-polymerase chain reaction (RT-PCR) in primary cultured cells. Protein expression in fresh-frozen human corneal sections was studied with immunocytology. Chemotaxis was measured using a modified Boyden chamber, and proliferation was quantitated by cell counting. RESULTS PDGF A, PDGF B, PDGF receptor alpha, and PDGF receptor beta mRNAs were detected in corneal epithelial cells, fibroblasts, and endothelial cells in culture. The proteins were expressed in each major cell type in human corneal sections, with PDGF A and PDGF B detected at high levels in the epithelial basement membrane. PDGF, BMP2, and BMP4 had attractive chemotactic effects on corneal fibroblasts, with the PDGF BB dimer having a significantly greater positive chemotactic effect than the other PDGF isotypes. Interleukin-1alpha had a repulsive chemotactic effect on corneal fibroblasts. PDGF AA, AB, and BB stimulated proliferation of human corneal fibroblasts. CONCLUSIONS The PDGF growth factor receptor system is expressed in the human cornea. PDGF, BMP2, BMP4, and IL-1alpha may modulate keratocyte chemotaxis and proliferation during homeostasis and wound healing.
ORGANIZATIONAL JUSTICE: LOOKING BACK, LOOKING FORWARD

We provide a brief review of how the concept of justice has evolved over time from a single construct (distributive justice) to one represented by four constructs (distributive, procedural, interpersonal, and informational justice). We then compare and contrast two recent meta‐analytic views of organizational justice, focusing on the relationships each documents between justice constructs and organizational outcomes. We conclude by arguing that the justice literature needs to focus on identifying moderators of the justice‐outcome relations noted in the meta‐analyses.
New Measure of Perceived Motor Competence for Children Ages 4 to 6 Years

The purpose of this study was to develop and validate a test for the assessment of perceived motor competence in young children ages 4 to 6 years old. The structure of the Children's Perception of Motor Competence Scale was analyzed through exploratory and confirmatory factor analysis with 495 children between 4 and 6 years of age. A second-order model was selected and consisted of one scale of Perceived General Motor Competence and two subscales, Perceived Gross Motor Competence and Perceived Fine Motor Competence. The number of items was 22. This test showed acceptable internal reliability: global scale (α = .81), Perceived Gross Motor Competence (α = .80), and Perceived Fine Motor Competence (α = .65). Children manifested accuracy in the assessment of the competence. Perceived motor competence was related to actual motor competence as measured by the Movement ABC Test and by an observational scale used by Physical Education teachers. There were no sex differences. The Children's Perception of Motor Competence Scale could be considered an interesting assessment test for identifying current self-perceptions of motor competence in young children.
Knowledge transfer in IT offshoring relationships: the roles of social capital, efficacy and outcome expectations

Information technology (IT) development in global organisations relies heavily on the transfer of tacit and complex knowledge from onshore units to offshore subsidiaries. A central concern of such organisations is the development of social capital, which is known to facilitate the smooth transfer of knowledge. However, only a few studies in IS research have explicitly examined the role of social capital for knowledge transfer in an IT offshoring context. In this paper, we argue that such knowledge transfer mechanisms can be understood better by considering social capital in concert with knowledge senders' efficacy and outcome expectations, two of the potentially key motivational drivers of knowledge transfer. We develop our arguments through a qualitative case study of a large German multinational company. German IT developers in this firm provided in‐depth accounts of their experience with offshore colleagues in an Indian captive subsidiary unit. Drawing on our analysis, we develop a model that depicts the influence of social capital, efficacy and outcome expectations on onshore IT developers' ability and willingness to transfer knowledge to offshore colleagues. Through the model, we also explain how social capital, efficacy and outcome expectations are interrelated and generate three interlocked, self‐reinforcing circles of knowledge transfer success in IT offshoring relationships.
F-structure transfer-based statistical machine translation

In this paper, we describe a statistical deep syntactic transfer decoder that is trained fully automatically on parsed bilingual corpora. Deep syntactic transfer rules are induced automatically from the f-structures of a LFG parsed bitext corpus by automatically aligning local f-structures, and inducing all rules consistent with the node alignment. The transfer decoder outputs the n-best TL f-structures given a SL f-structure as input by applying large numbers of transfer rules and searching for the best output using a log-linear model to combine feature scores. The decoder includes a fully integrated dependency-based tri-gram language model. We include an experimental evaluation of the decoder using different parsing disambiguation resources for the German data to provide a comparison of how the system performs with different German training and test parses.
Flash visual evoked potentials in diurnal birds of prey

The objective of this pilot study was to evaluate the feasibility of Flash Visual Evoked Potentials (FVEPs) testing in birds of prey in a clinical setting and to describe the protocol and the baseline data for normal vision in this species. FVEP recordings were obtained from 6 normal adult birds of prey: n. 2 Harris’s Hawks (Parabuteo unicinctus), n. 1 Lanner Falcon (Falco biarmicus), n. 2 Gyrfalcons (Falco rusticolus) and n. 1 Saker Falcon (Falco cherrug). Before carrying out VEP tests, all animals underwent neurologic and ophthalmic routine examination. Waveforms were analysed to identify reproducible peaks from random variation of baseline. At least three positive and negative peaks were highlighted in all tracks with elevated repeatability. Measurements consisted of the absolute and relative latencies of these peaks (P1, N1, P2, N2, P3, and N3) and their peak-to-peak amplitudes. Both the peak latency and wave morphology achieved from normal animals were similar to those obtained previously in other animal species. This test can be easily and safely performed in a clinical setting in birds of prey and could be useful for an objective assessment of visual function.
Use of the DNA Flow-Thru Chip, a Three-Dimensional Biochip, for Typing and Subtyping of Influenza Viruses

ABSTRACT Influenza A viruses, which are further subtyped on the basis of antigenic differences in external hemagglutinin and neuraminidase glycoproteins, and influenza B viruses are prominent among the viral causes of respiratory diseases and can cause a wide spectrum of illness. Each year these viruses are responsible for recurrent epidemics, frequently in association with genetic variation. There is a requirement for sensitive and rapid diagnostic techniques in order to improve both the diagnosis of infections and the quality of surveillance systems. A new three-dimensional biochip platform (Flow-Thru Chip; MetriGenix) was used to develop a rapid and reliable molecular method for the typing and subtyping of influenza viruses. Oligonucleotide probes immobilized in microchannels of a silicon wafer were selected to recognize multiple fragments of the influenza A virus matrix protein gene; the influenza B virus NS gene; the H1, H3, and H5 hemagglutinin genes; and the N1 and N2 neuraminidase genes. Biotinylated amplicons resulting from either multiplex or random reverse transcription-PCR were hybridized to arrayed oligonucleotides on the influenza virus chip before they were stained with horseradish peroxidase-streptavidin and were imaged by use of a chemiluminescent substrate. The chip analysis procedure, from the time of pipetting of the sample into the chip cartridge to the time of analysis of the results, was performed in less than 5 h. The random PCR exhibited a higher level of performance than the multiplex PCR in terms of the specificity of product hybridization to the influenza virus chip. Analysis of influenza A viruses (H1N1, H3N2, H1N2, and H5N1) and influenza B viruses showed that this microarray-based method is capable of the rapid and unambiguous identification of all types and subtypes of viruses by use of random PCR products. The redundancy of the probes designed for each gene selected yielded an additional criterion of confidence for the subtyping of viruses which are known for antigenic variations in some of their components.
Analytic bias and phonological typology*

Two factors have been proposed as the main determinants of phonological typology: channel bias, phonetically systematic errors in transmission, and analytic bias, cognitive predispositions making learners more receptive to some patterns than others. Much of typology can be explained equally well by either factor, making them hard to distinguish empirically. This study presents evidence that analytic bias is strong enough to create typological asymmetries in a case where channel bias is controlled. I show that (i) phonological dependencies between the height of two vowels are typologically more common than dependencies between vowel height and consonant voicing, (ii) the phonetic precursors of the height-height and height-voice patterns are equally robust and (iii) in two experiments, English speakers learned a height-height pattern and a voice-voice pattern better than a height-voice pattern. I conclude that both factors contribute to typology, and discuss hypotheses about their interaction.
Dendritic Alterations of the Pyramidal Cell of Reil’s Insula in Alzheimer’s disease

Alzheimer’s disease is an insidiously progressive severe presenile and senile dementia, involving a number of cellular and biochemical mechanisms. It is characterized by cognitive decline affecting memory primarily, associated frequently with behavioral and mood disorders, which increasingly appear as the disease advances. Despite the strong clinical rationale and many laboratory hints, the insula may be an important target of Alzheimer’s disease, neuropathologic alterations of Reil’s insula have not been described extensively In the present study, we attempt to describe the morphometric and morphological changes of the neurons of insula in Alzheimer’s disease in comparison to normal ageing, senile plaque deposition and neurofibrillary degeneration using the silver impregnation technique. Each one of the selected cells was traced using the Neuromantic application and the tracing was quantitatively analyzed with Image J program based on Sholl analysis method of concentric circles. Statistical analysis was based on the Student’s test on the basis of 360 cells in SPSS v.17. 0 and significance was taken as p.
What Can Learning Styles Research Learn from Cognitive Psychology

Abstract People differ from each other in the manner in which they process information from the world. These individual differences are called learning styles’. The purpose of this paper is to explore how research in this field can benefit from certain findings in cognitive psychology. Following a review of some difficulties which afflict research on learning styles (e.g. inadequate construct validation of measures), attention is drawn to two relevant findings in cognitive research: the influence of prior knowledge on learning and the potential value to learners of ‘metacognitive’ awareness. Some implications of these findings for learning styles research are then discussed.
Multi-level Speech Emotion Recognition Based on HMM and ANN

This paper proposes a new approach for emotion recognition based on a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN), using both utterance and segment level information from speech. To combine the advantage on capability to dynamic time warping of HMMs and pattern recognition of ANN, the utterance is viewed as a series of voiced segments, and feature vectors extracted from the segments are normalized into fixed coefficients using orthogonal polynomials methods, and then, distortions are calculated as an input of ANN. Meanwhile, the utterance as a whole is modeled by HMMs, and likelihood probabilities derived from the HMMs are normalized to be another input of ANN. Adopting Beihang University Database of Emotional Speech (BHUDES) and Berlin database of emotional speech, comparison between isolated HMMs and hybrid of HMMs/ANN proves that the approach introduced in this paper is more effective, and the average recognition rate of five emotion states has reached 81.7%.
Online Submodular Maximization with Preemption

Submodular function maximization has been studied extensively in recent years under various constraints and models. The problem plays a major role in various disciplines. We study a natural online variant of this problem in which elements arrive one by one and the algorithm has to maintain a solution obeying certain constraints at all times. Upon arrival of an element, the algorithm has to decide whether to accept the element into its solution and may preempt previously chosen elements. The goal is to maximize a submodular function over the set of elements in the solution. We study two special cases of this general problem and derive upper and lower bounds on the competitive ratio. Specifically, we design a 1/e-competitive algorithm for the unconstrained case in which the algorithm may hold any subset of the elements, and constant competitive ratio algorithms for the case where the algorithm may hold at most k elements in its solution.
Applying the harmonic plus noise model in concatenative speech synthesis

This paper describes the application of the harmonic plus noise model (HNM) for concatenative text-to-speech (TTS) synthesis. In the context of HNM, speech signals are represented as a time-varying harmonic component plus a modulated noise component. The decomposition of a speech signal into these two components allows for more natural-sounding modifications of the signal (e.g., by using different and better adapted schemes to modify each component). The parametric representation of speech using HNM provides a straightforward way of smoothing discontinuities of acoustic units around concatenation points. Formal listening tests have shown that HNM provides high-quality speech synthesis while outperforming other models for synthesis (e.g., TD-PSOLA) in intelligibility, naturalness, and pleasantness.
An Economy Driven Resource Management Architecture for Global Computational Power Grids

The growing computational power requirements of grand challenge applications has promoted the need for linking highperformance computational resources distributed across multiple organisations. This is fueled by the availability of the Internet as a ubiquitous commodity communication media, low cost high-performance machines such as clusters across multiple organisations, and the rise of scientific problems of multi-organisational interest. The availability of expensive, special class of scientific instruments or devices and data sources in few organisations has increased the interest in offering a remote access to these resources. The recent popularity of coupling (local and remote) computational resources, special class of scientific instruments, and data sources across the Internet for solving problems has led to the emergence of a new platform called “Computational Grid”. This paper identifies the issues in resource management and scheduling driven by computational economy in the emerging grid computing context. They also apply to clusters of clusters environment (known as federated clusters or hyperclusters) formed by coupling multiple (geographically distributed) clusters located in the same or different organisations. We discuss our current work on the Nimrod/G resource broker, whose scheduling mechanism is driven by a user supplied application deadline and a resource access budget. However, current Grid access frameworks do not provide the dynamic resource trading services that are required to facilitate flexible application scheduling. In order to overcome this limitation, we have proposed an infrastructure called GRid Architecture for Computational Economy (GRACE). In this paper we present the motivations for grid computing, resource management architecture, Nimrod/G resource broker, computational economy, and GRACE infrastructure and its APIs along with future work.
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data

Previous studies on Chinese semantic role labeling (SRL) have concentrated on a single semantically annotated corpus. But the training data of single corpus is often limited. Whereas the other existing semantically annotated corpora for Chinese SRL are scattered across different annotation frameworks. But still, Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In this paper, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and effectively transfer knowledge between them. We also release a new corpus, Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that our model outperforms state-of-the-art methods.
Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed
The effects of blood vessels on electrocorticography

Electrocorticography, primarily used in a clinical context, is becoming increasingly important for fundamental neuroscientific research, as well as for brain–computer interfaces. Recordings from these implanted electrodes have a number of advantages over non-invasive recordings in terms of band width, spatial resolution, smaller vulnerability to artifacts and overall signal quality. However, an unresolved issue is that signals vary greatly across electrodes. Here, we examine the effect of blood vessels lying between an electrode and the cortex on signals recorded from subdural grid electrodes. Blood vessels of different sizes cover extensive parts of the cortex causing variations in the electrode–cortex connection across grids. The power spectral density of electrodes located on the cortex and electrodes located on blood vessels obtained from eight epilepsy patients is compared. We find that blood vessels affect the power spectral density of the recorded signal in a frequency-band-specific way, in that frequencies between 30 and 70 Hz are attenuated the most. Here, the signal is attenuated on average by 30–40% compared to electrodes directly on the cortex. For lower frequencies this attenuation effect is less pronounced. We conclude that blood vessels influence the signal properties in a non-uniform manner.
Joint Power Allocation for Hybrid Digital-Analog Transmission in Relay Networks

Due to the quantization errors introduced by source coding, the transmission performance in digital communication systems might remain constant when the channel quality gets better, which is referred to as the quality saturation effect. To combat this effect, hybrid digital-analog (HDA) transmission is proposed, where the quantization errors are transmitted in an analog mode to achieve graceful improvement for better channels. In this paper, we focus on HDA transmission in wireless relay networks (WRNs). We propose a power allocation scheme for HDA transmission in a three-node WRN (3-WRN) to eliminate the quality saturation effect and improve the distortion performance. It should be emphasized that HDA transmission involves not only digital-analog power allocation but also source-relay power allocation. Therefore, the joint power allocation is considered. We formulate the joint power allocation problem as a nonlinear fractional programming problem. Then, we propose an effective algorithm to search the solution and obtain the optimal distortion performance. Finally, we conduct simulations to validate our work. The results show that the proposed joint power allocation scheme outperforms existing schemes in terms of end-to-end distortion under various channel conditions.
The role of speed versus working memory in predicting learning new information in multiple sclerosis

The most common cognitive impairments in multiple sclerosis (MS) have been documented in specific domains, including new learning and memory, working memory, and information processing speed. However, little attempt has been made to increase our understanding of their relationship to one another. While recent studies have shown that processing speed impacts new learning and memory abilities in MS, the role of working memory in this relationship has received less attention. The present study examines the relative contribution of impaired working memory versus processing speed in new learning and memory functions in MS. Participants consisted of 51 individuals with clinically definite MS. Participants completed two measures of processing speed, two measures of working memory, and two measures of episodic memory. Data were analyzed via correlational and multiple regression analysis. Results indicate that the variance in new learning abilities in this sample was primarily associated with processing speed, with working memory exerting much less of an influence. Results are discussed in terms of the role of cognitive rehabilitation of new learning and memory abilities in persons with MS.
A practical field extraction method for non-invasive monitoring of hormone activity in the black rhinoceros

A practical field extraction method was developed and rigorously tested under controlled laboratory and simulated field conditions, to allow extracted faecal hormone metabolites to be stored on solid-phase extraction cartridges. Metabolites can be stored at ambient temperatures for 6 months before analysis for reproductive and adrenal hormones in the laboratory by enzyme immunoassay.