Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell Stress Induces Upregulation of Osteopontin via the ERK Pathway in Type II Alveolar Epithelial Cells

Osteopontin (OPN) is a multifunctional protein that plays important roles in cell growth, differentiation, migration and tissue fibrosis. In human idiopathic pulmonary fibrosis and murine bleomycin-induced lung fibrosis, OPN is upregulated in type II alveolar epithelial cells (AEC II). However, the mechanism of OPN induction in AEC II is not fully understood. In this study, we demonstrate the molecular mechanism of OPN induction in AEC II and elucidate the functions of OPN in AEC II and lung fibroblasts. Human lung adenocarcinoma cells (A549) and mouse alveolar epithelial cells (MLE12), used as type II alveolar epithelial cell lines for in vitro assays, and human pulmonary alveolar epithelial cells (HPAEpiC) were treated with either bleomycin, doxorubicin or tunicamycin. The mechanism of OPN induction in these cells and its function as a pro-fibrotic cytokine on A549 and lung fibroblasts were analyzed. The DNA damaging reagents bleomycin and doxorubicin were found to induce OPN expression in A549, MLE12 and HPAEpiC. OPN expression was induced via activation of the extracellular signal-regulated protein kinase (ERK)-dependent signaling pathway in A549 and MLE12. The endoplasmic reticulum (ER) stress-inducing reagent tunicamycin induced OPN mRNA expression in A549, MLE12 and HPAEpiC, and OPN mRNA expression was induced via activation of the ERK-dependent signaling pathway in A549 and MLE12. Another ER stress-inducing reagent thapsigargin induced the expression of OPN mRNA as well as the subsequent production of OPN in A549 and MLE12. Furthermore, OPN promoted the proliferation of A549 and the migration of normal human lung fibroblasts. Inhibition of OPN by small interference RNA or neutralizing antibody suppressed both of these responses. The results of this study suggest that cell stress induces the upregulation of OPN in AEC II by signaling through the ERK pathway, and that upregulated OPN may play a role in fibrogenesis of the lung.
South Carolina Preschool Teachers’ Perceptions And Experiences Implementing Critical Literacy In The Preschool Classroom

iv CHAPTER I: INTRODUCTION.1 CHAPTER II: REVIEW OF THE LITERATURE.23 CHAPTER III: METHODS OF RESEARCH.39 CHAPTER IV: RESEARCH FINDINGS AND ANAYLSIS.58 CHAPTER V: DISCUSSION AND CONCLUSION.140 REFERENCES.168 APPENDIX A: INTERVIEW PROTOCOL.183 APPENDIX B: INFORMED CONSENT FORM.185
Matrix Metalloproteinase-2 Degrades Fibrillin-1 and Fibrillin-2 of Oxytalan Fibers in the Human Eye and Periodontal Ligaments In Vitro

Oxytalan fibers are distributed in the eye and periodontal ligaments (PDL). The ciliary zonule, known as Zinn’s zonule, in the eye is composed of oxytalan fibers, which are bundles of microfibrils consisting mainly of fibrillin-1 and fibrillin-2. As turnover of oxytalan fibers is slow during life, their degradation mechanism remains unclarified. This study was performed to examine degradation pattern of fibrillin-1 and fibrillin-2 by experimental MMP activation. We cultured human non-pigmented ciliary epithelial cells (HNPCEC) and PDL fibroblasts for 7 days, then treated them with concanavalin A to activate matrix metalloproteinase (MMP)-2, and examined the degradation of fibrillin-1 and fibrillin-2 for 72 hr using immunofluorescence. At 7 days of HNPCEC culture, fibrillin-1-positive fibers were observed, some of which merged with fibrillin-2. After MMP-2 activation, fibrillin-1-positive fibers became thin and disappeared by 72 hr, while fibrillin-2-positive fibers disappeared almost completely within 24 hr. At 7 days of PDL fibroblast culture, fibrillin-1-positive fibers were mostly merged with fibrillin-2. After MMP-2 activation, fibrillin-1-positive fibers became thin by 24 hr and had almost disappeared by 48 hr, while fibrillin-2-positive fibers decreased constantly after 24 hr. A MMP-2 inhibitor completely suppressed these degradations. These results suggest that the patterns of fibrillin-1 and fibrillin-2 degradation differ between the eye and the PDL, possibly reflecting the sensitivity of fibrillin-1 and fibrillin-2 of each type of oxytalan fiber against MMP-2.
Internally- and externally-driven network transitions as a basis for automatic and strategic processes in semantic priming: theory and experimental validation

For the last four decades, semantic priming—the facilitation in recognition of a target word when it follows the presentation of a semantically related prime word—has been a central topic in research of human cognitive processing. Studies have drawn a complex picture of findings which demonstrated the sensitivity of this priming effect to a unique combination of variables, including, but not limited to, the type of relatedness between primes and targets, the prime-target Stimulus Onset Asynchrony (SOA), the relatedness proportion (RP) in the stimuli list and the specific task subjects are required to perform. Automatic processes depending on the activation patterns of semantic representations in memory and controlled strategies adapted by individuals when attempting to maximize their recognition performance have both been implicated in contributing to the results. Lately, we have published a new model of semantic priming that addresses the majority of these findings within one conceptual framework. In our model, semantic memory is depicted as an attractor neural network in which stochastic transitions from one stored pattern to another are continually taking place due to synaptic depression mechanisms. We have shown how such transitions, in combination with a reinforcement-learning rule that adjusts their pace, resemble the classic automatic and controlled processes involved in semantic priming and account for a great number of the findings in the literature. Here, we review the core findings of our model and present new simulations that show how similar principles of parameter-adjustments could account for additional data not addressed in our previous studies, such as the relation between expectancy and inhibition in priming, target frequency and target degradation effects. Finally, we describe two human experiments that validate several key predictions of the model.
A taxonomy of parallel sorting

In this paper, we propose a taxonomy of parallel sorting that includes a broad range of array and file sorting algorithms. We analyze the evolution of research on parallel sorting, from the earliest sorting networks to the shared memory algorithms and the VLSI sorters. In the context of sorting networks, we describe two fundamental parallel merging schemes the odd-even and the bitonic merge. Sorting algorithms have been derived from these merging algorithms for parallel computers where processors communicate through interconnection networks such as the perfect shuffle, the mesh and a number of other sparse networks. After describing the network sorting algorithms, we show that, with a shared memory model of parallel computation, faster algorithms have been derived from parallel enumeration sorting schemes, where keys are first ranked and then rearranged according to their rank. Parallel sorting algorithms are evaluated according to a number of criteria, related not only to their time complexity, but also to their feasibility from a computer architecture point of view. We show that in addition to their attractive communication schemes, network sorting algorithms have non-adaptive schedules that make them suitable for implementation. In particular, they are easily generalized to block-sorting algorithms, that utilize limited parallelism to solve large sorting problems. We also address the problem of sorting large mass-storage files in parallel, using modified disk devices or intelligent bubble memories. Finally, the newer area of VLSI sorting is mentioned as an active and promising direction of research on parallel sorting.
New Evidence of Technical Trading Profitability

We developed profitable foreign exchange forecasts by applying a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm to five-minute high frequency data of six of the most traded currency pairs. We examined the out-of-sample performance of these intraday technical trading models based on STGP and optimised linear forecasting. We found evidence of economically and statistically significant out-of-sample excess returns, after taking into account appropriate transaction costs.
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates

Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual series. Furthermore, our algorithm is extended to take into account individual autocorrelation to provide better estimation, using a recent convex relaxation of quadratically constrained quadratic program. Extensive experiments on synthetic and real-world electricity consumption datasets illustrate the effectiveness of our matrix recovery algorithms.
Effects of Glucomannan-Enriched, Aronia Juice-Based Supplement on Cellular Antioxidant Enzymes and Membrane Lipid Status in Subjects with Abdominal Obesity

The aim of this study was to analyze the effects of a 4-week-long consumption of glucomannan-enriched, aronia juice-based supplement on anthropometric parameters, membrane fatty acid profile, and status of antioxidant enzymes in erythrocytes obtained from postmenopausal women with abdominal obesity. Twenty women aged 45–65 with a mean body mass index (BMI) of 36.1 ± 4.4 kg/m2 and waist circumference of 104.8 ± 10.1 cm were enrolled. Participants were instructed to consume 100 mL of supplement per day as part of their regular diet. A significant increase in the content of n-3 (P < 0.05) polyunsaturated fatty acids in membrane phospholipids was observed, with a marked increase in the level of docosahexaenoic fatty acid (P < 0.05). Accordingly, a decrease in the n-6 and n-3 fatty acids ratio was observed (P < 0.05). The observed effects were accompanied with an increase in glutathione peroxidase activity (P < 0.05). Values for BMI (P < 0.001), waist circumference (P < 0.001), and systolic blood pressure (P < 0.05) were significantly lower after the intervention. The obtained results indicate a positive impact of tested supplement on cellular oxidative damage, blood pressure, and anthropometric indices of obesity.
Tiagabine treatment in kainic acid induced cerebellar lesion of dystonia rat model

Dystonia is a neurological disorder characterized by excessive involuntary muscle contractions that lead to twisting movements. The exaggerated movements have been studied and have implicated basal ganglia as the point of origin. In more recent studies, the cerebellum has also been identified as the possible target of dystonia, in the search for alternative treatments. Tiagabine is a selective GABA transporter inhibitor, which blocks the reuptake and recycling of GABA. The study of GABAergic drugs as an alternative treatment for cerebellar induced dystonia has not been reported. In our study, tiagabine was i.p. injected into kainic acid induced, cerebellar dystonic adult rats, and the effects were compared with non-tiagabine injected and sham-operated groups. Beam walking apparatus, telemetric electromyography (EMG) recording, and histological verification were performed to confirm dystonic symptoms in the rats on post-surgery treatment. Involuntary dystonic spasm was observed with repetitive rigidity, and twisting movements in the rats were also confirmed by a high score on the dystonic scoring and a high amplitude on the EMG data. The rats with tiagabine treatment were scored based on motor amelioration assessed via beam walking. The result of this study suggests and confirms that low dose of kainic acid microinjection is sufficient to induce dystonia from the cerebellar vermis. In addition, from the results of the EMG recording and the behavioral assessment through beam walking, tiagabine is demonstrated as being effective in reducing dystonic spasm and may be a possible alternative therapeutic drug in the treatment of dystonia.
Decision Algorithms for Multiplayer Non-Cooperative Gamesof Incomplete

Extending the complexity results of Reif 1, 2] for two player games of incomplete information, this paper (also see 3]) presents algorithms for deciding the outcome for various classes of multiplayer games of incomplete information, i.e. deciding whether or not a team has a winning strategy for a particular game. Our companion paper (Peter-son, Reif, and Azhar 4]) shows that these algorithms are indeed asymptotically optimal by providing matching lower bounds. The classes of games to which our algorithms are applicable include games which were not previously known to be decidable. We apply our algorithms to provide alternative upper bounds, and new time-space trade-oos on the complexity of multi-person alternating Turing machines 3]. We analyze the algorithms to characterize the space complexity of multiplayer games in terms of the complexity of deterministic computation on Turing machines. In hierarchical multiplayer games each additional clique (subset of players with the same information) increases the complexity of the outcome problem by a further exponential. We show that an S(n) space bounded k-player game of incomplete information has a deterministic time upper bound of k + 1 repeated exponentials of S(n). Furthermore , S(n) space bounded k-player blindfold games have a deterministic space upper bound of k repeated exponentials of S(n). This paper proves that this exponential blow-up can occur. We also show that time bounded games do not exhibit such hierarchy. A T (n) time bounded blindfold multiplayer game, as well as a T (n) time bounded multiplayer game of incomplete information, has a deterministic space bound of T (n).
"DreamHouse": NUI-based Photo-realistic AR Authoring System for Interior Design

This paper proposes a system which enables users to have enhanced interior deigning and authoring tool through augmented reality (AR). The proposed system, which we refer to as DreamHouse, focuses on providing natural user interaction and realistic AR experience by enabling following features: 1) allowing users to utilize bare-hand interaction with an attached egocentric RGB-D camera when trying to make detail adjustments to virtual objects' size, location and orientation, 2) rendering every virtual object in consideration of an environment and lighting conditions, creating photo-realistic scenes to help users to have immersive and realistic interior designing experience. As a result, DreamHouse allows users to freely and easily interact in physical space with virtual objects using bare hands and gives immersive and realistic AR authoring experience to users through photo-realistic rendering.
Pattern Templates for Automating Business Choreography on Web Services

Integrating and automating workflow processes have been investigated to decrease resources and increase the efficiency of e-business processes. Researchers have analyzed patterns for workflow, but little has been done to make the patterns into a systematically deployable application. In this research, we proposed templates to support established workflow patterns, and developed a framework to accommodate these templates. The proposed system analyzes pattern type from a work flow diagram, collects the necessary information for the process, and makes a connection to appropriate application system. We developed a simple prototype to show the effectiveness of our system for automated business choreography.
A Framework for Analyzing Resource/Quality Tradeo s in Real-Time AI

Many researchers currently seek to combine the predictability of real-time systems with the intelligence of AI planning systems. We develop a framework for describing various approaches to this problem, by distinguishing strategic and tactical methods for meeting real-time performance guarantees. We introduce the any-dimension algorithm concept to clarify the fundamental connict between real-time and AI, showing that existing real-time systems techniques are incompatible with the goal of intelligent behavior. Using this framework, we examine several existing approaches to real-time AI, and motivate our hybrid approach| the Cooperative Intelligent Real-Time Control Architecture (CIRCA)| which combines aspects of strategic and tactical real-time methods to provide the exible yet predictable performance required for real-time AI. The opinions, ndings, and recommendations expressed in this publication are those of the authors, and do not necessarily reeect the views of the funding agencies. David Musliner is also aaliated with the UM Institute for Systems Research (NSF Grant NSFD CDR-88003012). ABSTRACT Many researchers currently seek to combine the predictability of real-time systems with the intelligence of AI planning systems. We develop a framework for describing various approaches to this problem, by distinguishing strategic and tactical methods for meeting real-time performance guarantees. We introduce the any-dimension algorithm concept to clarify the fundamental connict between real-time and AI, showing that existing real-time systems techniques are incompatible with the goal of intelligent behavior. Using this framework, we examine several existing approaches to real-time AI, and motivate our hybrid approach| the Cooperative Intelligent Real-Time Control Architecture (CIRCA)| which combines aspects of strategic and tactical real-time methods to provide the exible yet predictable performance required for real-time AI.
Ectopically Expressed Variant Form of Sperm Mitochondria-Associated Cysteine-Rich Protein Augments Tumorigenicity of the Stem Cell Population of Lung Adenocarcinoma Cells

Cancer stem-like cells (CSCs)/cancer-initiating cells (CICs) are defined as a small population of cancer cells that have self-renewal ability, differentiation ability and high tumor-initiating ability. CSCs/CICs are resistant to cancer therapies including chemotherapy and radiotherapy. Therefore, CSCs/CICs are thought to be responsible for cancer recurrence and distant metastasis after treatment. However, the molecular mechanisms of CSCs/CICs are still elusive. In this study, we isolated CSCs/CICs as side population (SP) cells from lung carcinoma, colon carcinoma and breast carcinoma cells and analyzed the molecular mechanisms of CSCs/CICs. cDNA micro-array screening and RT-PCR analysis revealed that sperm mitochondria-associated cysteine-rich protein (SMCP) is ectopically expressed in SP cells. 5′-Rapid amplification of cDNA end (RACE) analysis revealed that the SMCP transcript in SP cells was a variant form (termed vt2) which is composed from only one exon. SMCP vt2 was detected in only cancer cells, whereas the wild-type (vt1) form of SMCP was expressed in the testis. SMCP was shown to have a role in tumor initiation by SMCP overexpression and SMCP knockdown using siRNAs in lung cancer cells. Taken together, the initiation results indicate that an ectopically expressed variant form of SMCP has a role in tumor initiation of CSCs/CICs and that the variant form of SMCP might be a novel CSC/CIC marker and a potential and promising target of CSC/CIC-targeting therapy.
Biologically plausible models of homeostasis and STDP: Stability and learning in spiking neural networks

Spiking neural network (SNN) simulations with spike-timing dependent plasticity (STDP) often experience runaway synaptic dynamics and require some sort of regulatory mechanism to stay within a stable operating regime. Previous homeostatic models have used L1 or L2 normalization to scale the synaptic weights but the biophysical mechanisms underlying these processes remain undiscovered. We propose a model for homeostatic synaptic scaling that modifies synaptic weights in a multiplicative manner based on the average postsynaptic firing rate as observed in experiments. The homeostatic mechanism was implemented with STDP in conductance-based SNNs with Izhikevich-type neurons. In the first set of simulations, homeostatic synaptic scaling stabilized weight changes in STDP and prevented runaway dynamics in simple SNNs. During the second set of simulations, homeostatic synaptic scaling was found to be necessary for the unsupervised learning of V1 simple cell receptive fields in response to patterned inputs. STDP, in combination with homeostatic synaptic scaling, was shown to be mathematically equivalent to non-negative matrix factorization (NNMF) and the stability of the homeostatic update rule was proven. The homeostatic model presented here is novel, biologically plausible, and capable of unsupervised learning of patterned inputs, which has been a significant challenge for SNNs with STDP.
Feasibility and efficacy of a computer-based intervention aimed at preventing reading decoding deficits among children undergoing active treatment for medulloblastoma: results of a randomized trial.

OBJECTIVE To investigate the feasibility of a computer-based reading intervention completed by patients diagnosed with a brain tumor. METHODS Patients were randomized to the intervention (n = 43) or standard of care group (n = 38). The intervention consisted of 30 sessions using Fast ForWord® exercises in a game-like format. Change in reading decoding scores over time since diagnosis was examined. Gender, race, parent education, parent marital status, and age at diagnosis were examined as covariates. RESULTS 17 patients (39.5%) were able to complete the target goal of 30 intervention sessions. Females had significantly greater training time than males (p = .022). Age at diagnosis was associated with average training time/session for females (r = .485, p = .041). No significant differences were found in reading scores between the randomized groups. CONCLUSIONS The study was well accepted by families and adherence by patients undergoing radiation therapy for medulloblastoma was moderate. Suggestions for improved methodology are discussed.
Changes in cardiac aldosterone and its synthase in rats with chronic heart failure: an intervention study of long-term treatment with recombinant human brain natriuretic peptide

The physiological mechanisms involved in isoproterenol (ISO)-induced chronic heart failure (CHF) are not fully understood. In this study, we investigated local changes in cardiac aldosterone and its synthase in rats with ISO-induced CHF, and evaluated the effects of treatment with recombinant human brain natriuretic peptide (rhBNP). Sprague-Dawley rats were divided into 4 different groups. Fifty rats received subcutaneous ISO injections to induce CHF and the control group (n=10) received equal volumes of saline. After establishing the rat model, 9 CHF rats received no further treatment, rats in the low-dose group (n=8) received 22.5 μg/kg rhBNP and those in the high-dose group (n=8) received 45 μg/kg rhBNP daily for 1 month. Cardiac function was assessed by echocardiographic and hemodynamic analysis. Collagen volume fraction (CVF) was determined. Plasma and myocardial aldosterone concentrations were determined using radioimmunoassay. Myocardial aldosterone synthase (CYP11B2) was detected by quantitative real-time PCR. Cardiac function was significantly lower in the CHF group than in the control group (P<0.01), whereas CVF, plasma and myocardial aldosterone, and CYP11B2 transcription were significantly higher than in the control group (P<0.05). Low and high doses of rhBNP significantly improved hemodynamics (P<0.01) and cardiac function (P<0.05) and reduced CVF, plasma and myocardial aldosterone, and CYP11B2 transcription (P<0.05). There were no significant differences between the rhBNP dose groups (P>0.05). Elevated cardiac aldosterone and upregulation of aldosterone synthase expression were detected in rats with ISO-induced CHF. Administration of rhBNP improved hemodynamics and ventricular remodeling and reduced myocardial fibrosis, possibly by downregulating CYP11B2 transcription and reducing myocardial aldosterone synthesis.