Miracle-dz/UniFAR-FTUs huggingface dataset:
Hippocampal GluA1-Containing AMPA Receptors Mediate Context-Dependent Sensitization to Morphine

Glutamatergic systems, including AMPA receptors (AMPARs), are involved in opiate-induced neuronal and behavioral plasticity, although the mechanisms underlying these effects are not fully understood. In the present study, we investigated the effects of repeated morphine administration on AMPAR expression, synaptic plasticity, and context-dependent behavioral sensitization to morphine. We found that morphine treatment produced changes of synaptic AMPAR expression in the hippocampus, a brain area that is critically involved in learning and memory. These changes could be observed 1 week after the treatment, but only when mice developed context-dependent behavioral sensitization to morphine in which morphine treatment was associated with drug administration environment. Context-dependent behavioral sensitization to morphine was also associated with increased basal synaptic transmission and disrupted hippocampal long-term potentiation (LTP), whereas these effects were less robust when morphine administration was not paired with the drug administration environment. Interestingly, some effects may be related to the prior history of morphine exposure in the drug-associated environment, since alterations of AMPAR expression, basal synaptic transmission, and LTP were observed in mice that received a saline challenge 1 week after discontinuation of morphine treatment. Furthermore, we demonstrated that phosphorylation of GluA1 AMPAR subunit plays a critical role in the acquisition and expression of context-dependent behavioral sensitization, as this behavior is blocked by a viral vector that disrupts GluA1 phosphorylation. These data provide evidence that glutamatergic signaling in the hippocampus plays an important role in context-dependent sensitization to morphine and supports further investigation of glutamate-based strategies for treating opiate addiction.
Network Aggregative Games and Distributed Mean Field Control via Consensus Theory

We consider network aggregative games to model and study multi-agent populations in which each rational agent is influenced by the aggregate behavior of its neighbors, as specified by an underlying network. Specifically, we examine systems where each agent minimizes a quadratic cost function, that depends on its own strategy and on a convex combination of the strategies of its neighbors, and is subject to personalized convex constraints. We analyze the best response dynamics and we propose alternative distributed algorithms to steer the strategies of the rational agents to a Nash equilibrium configuration. The convergence of these schemes is guaranteed under different sufficient conditions, depending on the matrices defining the cost and on the network. Additionally, we propose an extension to the network aggregative game setting that allows for multiple rounds of communications among the agents, and we illustrate how it can be combined with consensus theory to recover a solution to the mean field control problem in a distributed fashion, that is, without requiring the presence of a central coordinator. Finally, we apply our theoretical findings to study a novel multi-dimensional, convex-constrained model of opinion dynamics and a hierarchical demand-response scheme for energy management in smart buildings, extending literature results.
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most “useful” or “interesting”. The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.
Complicity in games of chase and complexity thinking: Emergence in curriculum and practice-based research

i Complicity in games of chase and complexity thinking: Emergence in curriculum and practice-based research Abstract This thesis explores how the discourse of complexity thinking can be used to foster emergence in curriculum and practice-based research. The curriculum-related exploration focused specifically on games of chase as one facet of early childhood curriculum. It investigated using complexity thinking firstly, to occasion emergence (that is, create a new phenomenon) in children‟s games of chase at an early childhood centre and secondly, to describe this emergence. The research-related exploration focused on creating an emergent methodology which is underpinned by complexity thinking. In this thesis report, I present a series of emergent curriculum-related phenomena that arose during the explorations, that is, an emergent game, a local curriculum theory for games of chase, the concepts of local curriculum theory, curriculum design and curriculum dynamics, and a curriculum vision. I also present an understanding of emergent methodology and two methodological innovations in the form of the Research Data Management System and the Visual Summary. This research involved taking the role of a volunteer teacher-researcher-curriculum designer at an early childhood centre to play games of chase with children. This role was informed by and contributed to a curriculum design that focused on designing the teaching and learning environment to occasion emergence in learning and curriculum. The games of chase curriculum contributed to children‟s learning, my own learning and the general rhythm of life at the centre. The children learnt to distinguish between children who were playing and those who were not. They also learnt different ways to tag people in a game. In addition, the children and I developed a game playing routine before playing each game. This routine involved putting on tag belts, discussing what game we were playing and how we were going to play it. We played three different games of chase, starting with tag, followed by What is the time Mr(s) Wolf? , and finally the emergent game Big A, Little A. The stories of emergence are described in visual, descriptive and narrative texts organised into curriculum stories, teaching stories and children‟s learning stories. Curriculum stories describe the activities that unfolded. Teaching stories present stories of teaching while learning stories are stories of children‟s learning. These stories represent views of the enacted curriculum as activity, teaching and learning respectively. Taken together, the stories present a description of the curriculum dynamics that unfolded at the centre in relation to games of chase.
Depression in children with cancer.

The results of a standardized evaluation of the occurrence of depression in a group of 34 children suffering from cancer are presented. The children (13 male, 21 female) were admitted to the oncological unit of the Department of Pediatrics at the University of Graz, Austria, during a 14 months-course (January 1987-March 1988). We rated each child 4 times in 4 weekly intervals after admission and start of induction chemotherapy. To access the occurrence and severity of depression and to monitor progress during treatment we performed a psychiatric interview and also applied the psychiatric rating scale CDRS (Children's Depression Rating Scale) by Poznanski et al 1979 and SCMDD (Self-Report Symptom Checklist for Major Depressive Disorders) by Kashani et al 1985 derived from DSM III. 340 diagnostic ratings were performed by 3 raters; two of them were psychotherapeutically trained pediatricians (M. D., P. J. S.) and one psychologist (L. W.). A "clearcut" depression was found in 25 of the 340 ratings i.e. in 6 of the 34 children, demonstrating that the occurrence of depression in pediatric cancer patients was lower than primarily assumed.
A CCG-based Approach to Fine-Grained Sentiment Analysis

In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.
Tumor Epidermal Growth Factor Receptor and EGFR PY1068 Are Independent Prognostic Indicators for Head and Neck Squamous Cell Carcinoma

Purpose: To assess the prognostic value of epidermal growth factor receptor (EGFR) molecular characteristics of head and neck squamous cell carcinoma (HNSCC). Patients and Methods: HNSCC tumors from patients prospectively enrolled in either an Early Detection Research Network (EDRN) study and treated with surgery without an EGFR-targeted agent (N = 154) or enrolled in a chemoradiation trial involving the EGFR-targeted antibody cetuximab (N = 39) were evaluated for EGFR gene amplification by FISH and EGFR protein by immunohistochemical staining. Fresh-frozen tumors (EDRN) were also evaluated for EGFR protein and site-specific phosphorylation at Y992 and Y1068 using reverse-phase protein array (n = 67). Tumor (n = 50) EGFR and EGFRvIII mRNA levels were quantified using real-time PCR. Results: EGFR expression by immunohistochemistry (IHC) was significantly higher in the EDRN tumors with EGFR gene amplification (P < 0.001), and a similar trend was noted in the cetuximab-treated cohort. In the EDRN and cetuximab-treated cohorts elevated EGFR by IHC was associated with reduced survival (P = 0.019 and P = 0.06, respectively). Elevated expression of total EGFR and EGFR PY1068 were independently significantly associated with reduced progression-free survival in the EDRN cohort [HR = 2.75; 95% confidence interval (CI) = 1.26–6.00 and HR = 3.29; 95% CI = 1.34–8.14, respectively]. Conclusions: In two independent HNSCC cohorts treated with or without cetuximab, tumor EGFR levels were indicative of survival. Tumor EGFR PY1068 levels provided prognostic information independent of total EGFR. Clin Cancer Res; 18(8); 2278–89. ©2012 AACR.
High-affinity triplex targeting of double stranded DNA using chemically modified peptide nucleic acid oligomers

While sequence-selective dsDNA targeting by triplex forming oligonucleotides has been studied extensively, only very little is known about the properties of PNA–dsDNA triplexes—mainly due to the competing invasion process. Here we show that when appropriately modified using pseudoisocytosine substitution, in combination with (oligo)lysine or 9-aminoacridine conjugation, homopyrimidine PNA oligomers bind complementary dsDNA targets via triplex formation with (sub)nanomolar affinities (at pH 7.2, 150 mM Na+). Binding affinity can be modulated more than 1000-fold by changes in pH, PNA oligomer length, PNA net charge and/or by substitution of pseudoisocytosine for cytosine, and conjugation of the DNA intercalator 9-aminoacridine. Furthermore, 9-aminoacridine conjugation also strongly enhanced triplex invasion. Specificity for the fully matched target versus one containing single centrally located mismatches was more than 150-fold. Together the data support the use of homopyrimidine PNAs as efficient and sequence selective tools in triplex targeting strategies under physiological relevant conditions.
A molecular time-scale for eukaryote evolution recalibrated with the continuous microfossil record

Recent attempts to establish a molecular time-scale of eukaryote evolution failed to provide a congruent view on the timing of the origin and early diversification of eukaryotes. The major discrepancies in molecular time estimates are related to questions concerning the calibration of the tree. To limit these uncertainties, we used here as a source of calibration points the rich and continuous microfossil record of dinoflagellates, diatoms and coccolithophorids. We calibrated a small-subunit ribosomal RNA tree of eukaryotes with four maximum and 22 minimum time constraints. Using these multiple calibration points in a Bayesian relaxed molecular clock framework, we inferred that the early radiation of eukaryotes occurred near the Mesoproterozoic–Neoproterozoic boundary, about 1100 million years ago. Our results indicate that most Proterozoic fossils of possible eukaryotic origin cannot be confidently assigned to extant lineages and should therefore not be used as calibration points in molecular dating.
Butyrophilin-Like 9 (BTNL9) Suppresses Invasion and Correlates with Favorable Prognosis of Uveal Melanoma

Background Uveal melanoma (UM) is the most common intraocular malignancy, and the prognosis of patients with advanced stage of UM is very dismal. The T cell receptor ectopic expression of butyrophilin-like 9 (BTNL9) has been observed in several types of cancers, but the expression and clinical significance of BTNL9 in UM is unclear. Material/Methods In our study, we detected the expression of BTNL9 in 6 pairs of UM tissues and adjacent tissues using quantitative real-time polymerase chain reaction (qRT-PCR), and further investigated BTNL9 expression with immunohistochemistry (IHC) in a retrospective cohort consisted of 62 UM patients. The correlations between BNTL9 expression and clinicopathological factors were analyzed with Fisher’s test, and the prognostic significance of BTNL9 was evaluated with univariate analysis and multivariate analysis. Using experiments in vitro, we investigated the function of BTNL9 in UM proliferation and invasion. Results BTNL9 mRNAs in adjacent tissues were remarkably higher than in UM tissues. The percentages of BTNL9 low expression and high expression were 56.45% and 43.55%, respectively. High expression of BTNL9 was significantly associated with favorable prognosis of UM. BTNL9 expression was identified as a prognostic biomarker predicting better outcome of UM patients. Moreover, BTNL9 could suppress invasion instead of proliferation in melanoma cell line. Conclusions BTNL9 was a favorable prognostic factor of UM and it could suppress invasion of UM, suggesting that BTNL9 detection could help stratify high-risk patients with UM after operation and guide more precise surveillance and treatment.
Hormonal correlates of pathogen disgust: Testing the Compensatory Prophylaxis Hypothesis

Raised progesterone during the menstrual cycle is associated with suppressed physiological immune responses, reducing the probability that the immune system will compromise the blastocyst’s development. The Compensatory Prophylaxis Hypothesis proposes that this progesterone-linked immunosuppression triggers increased disgust responses to pathogen cues, compensating for the reduction in physiological immune responses by minimizing contact with pathogens. Although a popular and influential hypothesis, there is no direct, within-woman evidence for correlated changes in progesterone and pathogen disgust. To address this issue, we used a longitudinal design to test for correlated changes in salivary progesterone and pathogen disgust (measured using the pathogen disgust subscale of the Three Domain Disgust Scale) in a large sample of women (N=375). Our analyses showed no evidence that pathogen disgust tracked changes in progesterone, estradiol, testosterone, or cortisol. Thus, our results provide no support for the Compensatory Prophylaxis Hypothesis of variation in pathogen disgust.
The Effect of Self-Service Technology, Service Quality, and Relationship Marketing on Customer Satisfaction and Loyalty

This research was motivated by the increasing number of people who use banking services and the fact that the society is increasingly critical in choosing a bank, especially banking services. Bank seeks to improve the range of facilities and marketing activities in order to attract customers to avail banking services. Every bank needs to give high attention to customer loyalty factors due to the high competition within banking industries. The purpose of this research was to examine the relationship among self-service technology (SST), service quality, and relationship marketing on customer satisfaction and loyalty. Samples of the study were 201 respondents of a state bank in East Java-Indonesia who have experienced using self-service technology. The Partial Least Square (PLS) was used to analyze the data and the hypotheses. Results of this study indicate that self-service technology, service quality and relationship marketing significantly effected on customer satisfaction. Furthermore, relationship marketing significantly effected on customer loyalty. However this study could not prove the effect of the self-service technology and service quality on customer loyalty.
Evaluation of Commercially Available Reagents for Diagnosis of Histoplasmosis Infection in Immunocompromised Patients

ABSTRACT Urinary histoplasma antigen measurement can be useful for diagnosing systemic histoplasmosis and for monitoring treatment response, especially in immunocompromised patients. However, testing has traditionally been limited to specialized reference laboratories, as immunoassay reagents for the antigen were not widely available. Recently, a polyclonal-antibody-based in vitro diagnostic (IVD) kit for histoplasma antigen detection was released, as well as monoclonal-antibody reagents against the target. We evaluated the analytical and clinical performance of the two reagents. Both assays were capable of detecting histoplasma antigen in urine samples over a wide dynamic range, although the monoclonal assay showed improved precision and analytical sensitivity relative to the polyclonal IVD. In a test set of clinically characterized patient samples, the monoclonal laboratory-developed test (LDT) demonstrated 90.5% sensitivity and 96.3% specificity versus 61.9% sensitivity and 79.3% specificity for the polyclonal IVD, with areas under the curve (AUCs) of 0.987 and 0.754, respectively. The major differences between the two assays were higher background reactivity in healthy donors with the polyclonal assay and an increased signal response in positive samples for the monoclonal assay. The impact of these differences on monitoring treatment response was evaluated in a series of patients undergoing treatment for histoplasmosis. While all the assays gave similar qualitative estimates of treatment response, responses were more evident using the monoclonal assay. In summary, we conclude that while multiple assays are available for measuring histoplasma antigen in urine, a monoclonal-antibody-based assay appears to provide improved analytical performance for management of immunocompromised histoplasmosis patients.
Force Estimation and Prediction from Time-Varying Density Images

We present methods for estimating forces which drive motion observed in density image sequences. Using these forces, we also present methods for predicting velocity and density evolution. To do this, we formulate and apply a Minimum Energy Flow (MEF) method which is capable of estimating both incompressible and compressible flows from time-varying density images. Both the MEF and force-estimation techniques are applied to experimentally obtained density images, spanning spatial scales from micrometers to several kilometers. Using density image sequences describing cell splitting, for example, we show that cell division is driven by gradients in apparent pressure within a cell. Using density image sequences of fish shoals, we also quantify 1) intershoal dynamics such as coalescence of fish groups over tens of kilometers, 2) fish mass flow between different parts of a large shoal, and 3) the stresses acting on large fish shoals.
Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R2 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R2 0.85; Polysorbate 80, R2 0.90; Cremophor EL, R2 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R2 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R2 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.