Miracle-dz/UniFAR-FTUs huggingface dataset:
Some aspects of the statistical analysis of the 'mixed model'.

This paper deals with the statistical analysis (both parametric and non-parametric) of 'mixed model' experiments. The general structure of such experiments involves n randomly chosen subjects who respond once to each of p distinct treatments. Thus the subject or block effects are random and treatment effects are fixed. The hypothesis of no treatment effects is considered under several different combinations of assumptions concerning the joint distribution of the observations corresponding to each of the particular subjects. For each situation, an appropriate test procedure is discussed and its properties studied. The different methods considered in the paper are illustrated in detail in two numerical examples. These examples have been chosen to illustrate the relative performances of the different test criteria for a situation in which the null hypothesis is essentially true (Example 1) and for a situation in which the null hypothesis is essentially false (Example 2). The reader may wish to begin by studying these examples for a better understanding of the theory. Finally, the section on examples contains algorithms for the efficient computation of the various test criteria. A computer program based on these algorithms has been written and can be made available to any interested persons.
Experiments on Adaptive Soft-Copy Color Reproduction

The perceived colors of an image seen on a self-luminous display are affected by viewing flare and perceptual phenomena, both of which vary with the ambient illumination. A framework for a display system that is capable of adapting its output to varying viewing environments is introduced. The proposed system uses a light sensor such as a CCD camera to estimate properties of ambient illumination around a display and then adjusts display colorimetry for those lighting conditions. The emphasis of this paper is on accounting for various illumination-dependent visual effects through color appearance modeling. The effects of different parameter choices in CIECAM97s were investigated in a series of visual experiments. Paired comparison technique was used to subjectively evaluate the appearance of pictorial images adjusted using CIECAM97s and viewed on an LCD display under three different ambient illumination conditions.
Epistemic Cognition During Problem Solving

Students’ beliefs about knowledge and knowing (personal epistemologies) have been shown to influence various aspects of learning including, self-regulated learning, metacognition, and problem solving achievement (Bromme, Pieschl, & Stahl, 2009; Muis & Franco, 2009b, 2009a). Additionally, these beliefs have been shown to be discipline and context specific (Hofer, 2006; Hofer & Pintrich, 1997; Liu, 2011; Chinn, Buckland, & Samarapungavan, 2011). Multiple studies have investigated students’ personal epistemologies across disciplines and how these beliefs influence aspects of learning; however, little work has investigated the connections between students’ personal epistemologies and the processes they utilize during problem solving. Through a three phase study, this work sought to answer the overall research question, “How do undergraduate engineering students’ epistemic beliefs and need for cognitive closure relate to their activation of components of epistemic cognition during problem solving? ” This study investigated epistemic cognition in two unique problem solving contexts, the classroom and research environment. Throughout this work, engineering epistemic beliefs are defined based on Hofer and Pintrich’s (1997) conceptualization and include students’ beliefs about the source, structure, and certainty of knowledge. Epistemic cognition is defined based on Chinn and colleagues’ (2011; 2014) proposed framework that brings work from philosophy and psychology together. This framework suggests considering the nature of students’ aims when approaching a task to determine if they are epistemic (related to gaining knowledge or understanding), as well as the processes students use to accomplish these goals. Phase I (Chapter 2) of the study focused on assessing the reliability and validity of a survey instrument designed to measure students’ engineering epistemic beliefs and need for cognitive closure. During this phase, the internal consistency reliability of each construct was assessed and students’ open-ended responses to items were analyzed to further understand their beliefs. Phase II (Chapter 3) aimed to understand the connections between students’ engineering epistemic beliefs, need for cognitive closure, and activation of
Soliton solutions for Q 3

We construct N-soliton solutions to the equation called Q3 in the recent Adler-Bobenko-Suris classification. An essential ingredient in the construction is the relationship of (Q3) δ=0 to the equation proposed by Nijhoff, Quispel and Capel in 1983 (the NQC equation). This latter equation has two extra parameters, and depending on their sign choices we get a 4-to-1 relationship from NQC to (Q3) δ=0 . This leads to a four-term background solution, and then to a 1-soliton solution using a Bäcklund transformation. Using the 1SS as a guide allows us to get the N-soliton solution in terms of the tau-function of the Hirota-Miwa equation.
Solid Particle Erosion of Thermal Sprayed Coatings

Solid particle erosion is an important material degradation mechanism. Although various methods of coating are tried and used for protection against erosion, thermal sprayed coating for such purpose is the most widely used method. In this chapter, evolution of thermal sprayed coating, erosion testing methods, and erosive wear of thermal sprayed coatings are discussed extensively with emphasis on recent developments. It is generally found that erosion of thermal sprayed coatings depends on erosion test conditions, microstructural features, and mechanical properties of the coating materials. Most thermal sprayed coatings respond in brittle manner having maximum erosion rate at oblique impact and velocity exponent in excess of 3.0. Erosion rate is also dependent on thermal spraying techniques and post coating treatment. However, little work is done on dependence of erosion rate on coating techniques and coating conditions. Future direction of work is also reported. S. G. Sapate Visvesvaraya National Institute of Technology, India Manish Roy Defence Metallurgical Research Laboratory, India Solid Particle Erosion of Thermal Sprayed Coatings
Matrix Metalloproteinase-3: A Novel Signaling Proteinase from Apoptotic Neuronal Cells That Activates Microglia

Microglial activation and inflammation are associated with progressive neuronal apoptosis in neurodegenerative human brain disorders. We sought to investigate molecular signaling mechanisms that govern activation of microglia in apoptotic neuronal degeneration. We report here that the active form of matrix metalloproteinase-3 (MMP-3) was released into the serum-deprived media (SDM) of PC12 cells and other media of apoptotic neuronal cells within 2-6 h of treatment of the cells, and SDM and catalytic domain of recombinant MMP-3 (cMMP-3) activated microglia in primary microglia cultures as well as BV2 cells, a mouse microglia cell line. Both SDM and cMMP-3 induced generation of tumor necrosis factor α (TNF-α), interleukin-6 (IL-6), IL-1β, and interleukin-1 receptor antagonist but not IL-12 and inducible nitric oxide synthase, which are readily induced by lipopolysaccharide, in microglia, suggesting that there is a characteristic pattern of microglial cytokine induction by apoptotic neurons. Neither glial cell line-derived neurotrophic factor nor anti-inflammatory cytokines, such as IL-10 and transforming growth factor-β1, were induced. SDM and cMMP-3 extensively released TNF-α from microglia and activated the nuclear factor-κB pathway, and these microglial responses were totally abolished by preincubation with an MMP-3 inhibitor, NNGH [N-isobutyl-N-(4-methoxyphenylsulfonyl)-glycylhydroxamic acid]. MMP-3-mediated microglial activation mostly depended on ERK (extracellular signal-regulated kinase) phosphorylation but not much on either JNK (c-Jun N-terminal protein kinase) or p38 activation. Conditioned medium of SDM- or cMMP-3-activated BV2 cells caused apoptosis of PC12 cells. These results strongly suggest that the distinctive signal of neuronal apoptosis is the release of active form of MMP-3 that activates microglia and subsequently exacerbates neuronal degeneration. Therefore, the release of MMP-3 from apoptotic neurons may play a major role in degenerative human brain disorders, such as Parkinson's disease.
Dialogic Reading in the Home Environment: A Multiple Case Study of Six Families

DIALOGIC READING IN THE HOME ENVIRONMENT: A MULTIPLE CASE STUDY OF SIX FAMILIES Christy K. Irish, Ph.D. George Mason University, 2016 Dissertation Director: Dr. Seth Parsons The purpose of this multiple case study was to describe the experiences of six families of varying ethnicities and social classes being taught to use dialogic reading (DR) with their young children at home and to assess students’ vocabulary development. Using a Comprehensive Model of Emergent Literacy (Rhode, 2015) and an Ecological Model of Children’s Developmental Pathways (Weisner, 2002). Each case was analyzed and the family’s experiences contributed to the knowledge of how DR was implemented in the home. The families were taught to use DR using an instructional video and then observed over a six-week time frame. The preschoolers participating in the study were tested using the Expressive Vocabulary Test2 (EVT-2) (Williams, 2007) before and after the DR implementation. Data collection included interviews, direct observations, and video taping of dialogic reading in the home, field notes, and the EVT-2 (Williams, 2007). Data analysis consisted of initial and axial coding to form themes and the cases were compared using Stake’s (2006) multiple case analysis. This study found the families implemented DR to various degrees, while using some prompts more than others. The families experienced increased levels of interest, engagement, and bonding during shared readings. While there was little change in expressive vocabulary development, one preschooler’s scores increased using DR. Implications of this research include expanding the Comprehensive Emergent Literacy Model to include motivational factors, as well as the need for exploring further development in the use of recall prompts.
Parser-Based Retraining for Domain Adaptation of Probabilistic Generators

While the effect of domain variation on Penn-treebank-trained probabilistic parsers has been investigated in previous work, we study its effect on a Penn-Treebank-trained probabilistic generator. We show that applying the generator to data from the British National Corpus results in a performance drop (from a BLEU score of 0.66 on the standard WSJ test set to a BLEU score of 0.54 on our BNC test set). We develop a generator retraining method where the domain-specific training data is automatically produced using state-of-the-art parser output. The retraining method recovers a substantial portion of the performance drop, resulting in a generator which achieves a BLEU score of 0.61 on our BNC test data.
Cell cloning-based transcriptome analysis in Rett patients: relevance to the pathogenesis of Rett syndrome of new human MeCP2 target genes

More than 90% of Rett syndrome (RTT) patients have heterozygous mutations in the X‐linked methyl‐CpG binding protein 2 (MECP2) gene that encodes the methyl‐CpG‐binding protein 2, a transcriptional modulator. Because MECP2 is subjected to X chromosome inactivation (XCI), girls with RTT either express the wild‐type or mutant allele in each individual cell. To test the consequences of MECP2 mutations resulting from a genome‐wide transcriptional dysregulation and to identify its target genes in a system that circumvents the functional mosaicism resulting from XCI, we carried out gene expression profiling of clonal populations derived from fibroblast primary cultures expressing exclusively either the wild‐type or the mutant MECP2 allele. Clonal cultures were obtained from skin biopsy of three RTT patients carrying either a non‐sense or a frameshift MECP2 mutation. For each patient, gene expression profiles of wild‐type and mutant clones were compared by oligonucleotide expression microarray analysis. Firstly, clustering analysis classified the RTT patients according to their genetic background and MECP2 mutation. Secondly, expression profiling by microarray analysis and quantitative RT‐PCR indicated four up‐regulated genes and five down‐regulated genes significantly dysregulated in all our statistical analysis, including excellent potential candidate genes for the understanding of the pathophysiology of this neurodevelopmental disease. Thirdly, chromatin immunoprecipitation analysis confirmed MeCP2 binding to respective CpG islands in three out of four up‐regulated candidate genes and sequencing of bisulphite‐converted DNA indicated that MeCP2 preferentially binds to methylated‐DNA sequences. Most importantly, the finding that at least two of these genes (BMCC1 and RNF182) were shown to be involved in cell survival and/or apoptosis may suggest that impaired MeCP2 function could alter the survival of neurons thus compromising brain function without inducing cell death.
DISCRIMINATIVE CLASSIFIERS WITH GENERATIVE KERNELS FOR NOISE ROBUST SPEECH RECOGNITION

Discriminative classifiers are a popular approach to solving classification problems. However one of the problems with these approaches, in particular kernel based classifiers such as Support Vector Machines (SVMs), is that they are hard to adapt to mismatches between the training and test data. This paper describes a scheme for overcoming this problem for speech recognition in noise by adapting the kernel rather than the SVM decision boundary. Generative kernels, defined using generative models, are one type of kernel that allows SVMs to handle sequence data. By compensating the parameters of the generative models for each noise condition noise-specific generative kernels can be obtained. These can be used to train a noise-independent SVM on a range of noise conditions, which can then be used with a test-set noise kernel for classification. The noise-specific kernels used in this paper are based on Vector Taylor Series (VTS) model-based compensation. VTS allows all the model parameters to be compensated and the background noise to be estimated in a maximum likelihood fashion. A brief discussion of VTS and the optimisation of the mismatch function representing the impact of noise on the clean speech, is also included. Experiments using these VTS-based test-set noise kernels were run on the AURORA 2 continuous digit task. The proposed SVM rescoring scheme yields large gains in performance over the VTS compensated models.
An Approach towards Characterization of Ultrasound Renal Stone Images Using Wavelet Transforms

This paper describes an approach for texture characterization based on various wavelet decomposition transforms and its application for the discrimination of visually similar renal stone images. The proposed feature extraction algorithm applies Daubechies, Symlet, Coieflet, orthogonal and reverse biorthogonal wavelet transforms and uses approximation and detail coefficients to characterize renal stones images. This Classification is experimented on a set of twenty five (25) different stones which includes five different type shows that first order statistical, second order statistical, fast fourier transform in combination with wavelet decomposition could be a reliable method for a texture characterization. Key WordsRenal Calculi, Classification, Statistical Analysis, FFT, Texture Characterization, Wavelet Decomposition
A dimension reduction model for sparse hyperspectral target detection with weighted ℓ1 minimization

Target detection for hyperspectral image is an important application in many given areas. As natural signals can always be represented sparsely using a given dictionary, some effective sparse-based algorithms are developed for target detection. However, this kind of method is extremely dependent on the spectra library, a huge dictionary data set from which useful information is extracted. This provides mathematical challenge for its efficiency of programming. Under such circumstances, we propose a novel algorithm called dimension reduction based sparse detector (DRSD), which aims to eliminate the calculation process by building a smaller new spectra library. In addition, as the detection result is based on the sparse reconstruction the algorithm provide, we utilize effective approximate solver to find sparse reconstruction within the new spectra library. The experimental results demonstrate that the proposed algorithm is more effective than current applied sparse algorithms.
Unraveling the impact of IL1RAPL1 mutations on synapse formation : towards potential therapies for intellectual disability

Preserving the integrity of neuronal synapses is important for the development and maintenance of cognitive capacities. Mutations on a growing number of genes coding for synaptic proteins are associated with intellectual disability (ID), a neurodevelopmental disease characterized by deficits in adaptive and intellectual functions. The present work is dedicated to the study of one of those genes, IL1RAPL1, and the role of its encoding protein in synapse formation and function. IL1RAPL1 is a trans-membrane protein that is localized at excitatory synapses, where it interacts with the postsynaptic proteins PSD-95, RhoGAP2 and Mcf2l. Moreover, the extracellular domain of IL1RAPL1 interacts trans-synaptically with the presynaptic phosphatase PTPd. We studied the functional consequences of two novel mutations identified in ID patients affecting this IL1RAPL1 domain. Those mutations lead either to a decrease of the protein expression or of its interaction with PTPd, affecting in both cases the IL1RAPL1-mediated excitatory synapse formation. In the absence of IL1RAPL1, the number or function of excitatory synapses is perturbed, leading to an imbalance of excitatory and inhibitory synaptic transmissions in specific brain circuits. In particular, we showed that this imbalance in the lateral amygdala results in associative memory deficits in mice lacking Il1rapl1. Altogether, the results included in this work show that IL1RAPL1/PTPd interaction is essential for synapse formation and suggest that the cognitive deficits in ID patients with mutations on IL1RAPL1 result from the imbalance of the excitatory and inhibitory transmission. These observations open therapeutic perspectives aiming to reestablish this balance in the affected neuronal circuits.
A GENETIC ALGORITHM BASED DESIGN APPROACH FOR THE PROPERTIES OF A GAUSSIAN PROCESS FOR TIME SERIES FORECASTING

: In this work autoregressive filters based on Gaussian processes are used to forecast time series. As a benchmark function the Mackey-Glass delay differential equation is used. The time lagged inputs of the filters and their covariance functions are determined via a multicriteria, genetic algorithm – namely the NSGA-II. The used optimization criteria are the number of inputs and the prediction error of the filters on the known data which leads to Pareto optimal solutions. The obtained filters are validated with out-of-sample data and their properties are discussed in the context of the given problem.
Using Vector Clocks to Visualize Communication Flow

Given a dataset comprising a temporal sequence of communications between actors, how can we visualize the ‘flow’ of communication over time? Current practice transforms the dataset into a dynamic graph – vertices represent the actors and directed edges represent the communications. The directed edges are added and removed over time. There are then several approaches to visualizing dynamic graphs that optimize aesthetic criteria, most producing animated node-link diagrams. However, dynamic graphs are not the only way to model this problem. One alternative from the field of distributed computing is vector clocks. Recent work employed vector clocks to analyze communication flow in social networks with much effect, arguing that they provide new insights into the problem. In this paper, we use vector clocks as a basis for visualizing communication flow. We show that communication patterns, e.g., random, partitioned and core-periphery, are easily discernible in the resulting visualizations. We also argue that, in the cases where vector clocks are used to analyze communication flow, it is most natural to base the accompanying visualizations on vector clocks also.
Prognostic significance of L1CAM in ovarian cancer and its role in constitutive NF-κB activation.

BACKGROUND Overexpression of L1-cell adhesion molecule (L1CAM) has been observed for various carcinomas and correlates with poor prognosis and late-stage disease. In vitro, L1CAM enhances proliferation, cell migration, adhesion and chemoresistance. We tested L1CAM and interleukin-1 beta (IL-1β) expression in tumor samples and ascitic fluid from ovarian carcinoma patients to examine its role as a prognostic marker. PATIENTS AND METHODS We investigated tumor samples and ascitic fluid from 232 serous ovarian carcinoma patients for L1CAM by enzyme-linked immunosorbent assay. L1CAM expression was correlated with pathoclinical parameters and patients' outcome. IL-1β levels were measured in tumor cell lysates. Ovarian cancer cell lines were analyzed for the contribution of L1CAM to IL-1β production and nuclear factor 'kappa-light-chain-enhancer' of activated B-cells (NF-κB) activation. RESULTS We observed that L1CAM-expressing tumors show a highly invasive phenotype associated with restricted tumor resectability at primary debulking surgery and increased lymphogenic spread. Soluble L1CAM proved to be a marker for poor progression-free survival and chemoresistance. In ovarian carcinoma cell lines, the specific knock-down of L1CAM reduces IL-1β expression and NF-κB activity. CONCLUSIONS L1CAM expression contributes to the invasive and metastatic phenotype of serous ovarian carcinoma. L1CAM expression and shedding in the tumor microenvironment could contribute to enhanced invasion and tumor progression through increased IL-1β production and NF-κB activation.
Morphological Control of Inositol-1,4,5-Trisphosphate–Dependent Signals

Inositol-1,4,5-trisphosphate (InsP3)-mediated calcium signals represent an important mechanism for transmitting external stimuli to the cell. However, information about intracellular spatial patterns of InsP3 itself is not generally available. In particular, it has not been determined how the interplay of InsP3 generation, diffusion, and degradation within complex cellular geometries can control the patterns of InsP3 signaling. Here, we explore the spatial and temporal characteristics of [InsP3]cyt during a bradykinin-induced calcium wave in a neuroblastoma cell. This is achieved by using a unique image-based computer modeling system, Virtual Cell, to integrate experimental data on the rates and spatial distributions of the key molecular components of the process. We conclude that the characteristic calcium dynamics requires rapid, high-amplitude production of [InsP3]cyt in the neurite. This requisite InsP3 spatiotemporal profile is provided, in turn, as an intrinsic consequence of the cell's morphology, demonstrating how geometry can locally and dramatically intensify cytosolic signals that originate at the plasma membrane. In addition, the model predicts, and experiments confirm, that stimulation of just the neurite, but not the soma or growth cone, is sufficient to generate a calcium response throughout the cell.