Miracle-dz/UniFAR-FTUs huggingface dataset:
Virotherapy of Canine Tumors with Oncolytic Vaccinia Virus GLV-1h109 Expressing an Anti-VEGF Single-Chain Antibody

Virotherapy using oncolytic vaccinia virus (VACV) strains is one promising new strategy for cancer therapy. We have previously reported that oncolytic vaccinia virus strains expressing an anti-VEGF (Vascular Endothelial Growth Factor) single-chain antibody (scAb) GLAF-1 exhibited significant therapeutic efficacy for treatment of human tumor xenografts. Here, we describe the use of oncolytic vaccinia virus GLV-1h109 encoding GLAF-1 for canine cancer therapy. In this study we analyzed the virus-mediated delivery and production of scAb GLAF-1 and the oncolytic and immunological effects of the GLV-1h109 vaccinia virus strain against canine soft tissue sarcoma and canine prostate carcinoma in xenograft models. Cell culture data demonstrated that the GLV-1h109 virus efficiently infect, replicate in and destroy both tested canine cancer cell lines. In addition, successful expression of GLAF-1 was demonstrated in virus-infected canine cancer cells and the antibody specifically recognized canine VEGF. In two different xenograft models, the systemic administration of the GLV-1h109 virus was found to be safe and led to anti-tumor and immunological effects resulting in the significant reduction of tumor growth in comparison to untreated control mice. Furthermore, tumor-specific virus infection led to a continued production of functional scAb GLAF-1, resulting in inhibition of angiogenesis. Overall, the GLV-1h109-mediated cancer therapy and production of immunotherapeutic anti-VEGF scAb may open the way for combination therapy concept i.e. vaccinia virus mediated oncolysis and intratumoral production of therapeutic drugs in canine cancer patients.
Using Expressive Trends for Identifying Violin P erformers

This paper presents a new approach for identifying professional performers in commercial recordings. We propose a Trend-based model that, analyzing the way Narmour’s Implication-Realization patterns are played, is able to characterize performers. Concretely, starting from automatically extracted descriptors provided by state-of-the-art extraction tools, the system performs a mapping to a set of qualitative behavior shapes and constructs a collection of frequency distributions for each descriptor. Experiments were conducted in a data-set of violin recordings from 23 different performers. Reported results show that our approach is able to achieve high identification rates.
Multilevel Models for the Analysis of Angle-Specific Torque Curves with Application to Master Athletes

Abstract The aim of this paper was to outline a multilevel modeling approach to fit individual angle-specific torque curves describing concentric knee extension and flexion isokinetic muscular actions in Master athletes. The potential of the analytical approach to examine between individual differences across the angle-specific torque curves was illustrated including between-individuals variation due to gender differences at a higher level. Torques in concentric muscular actions of knee extension and knee extension at 60°·s-1 were considered within a range of motion between 5°and 85° (only torques “truly” isokinetic). Multilevel time series models with autoregressive covariance structures with standard multilevel models were superior fits compared with standard multilevel models for repeated measures to fit anglespecific torque curves. Third and fourth order polynomial models were the best fits to describe angle-specific torque curves of isokinetic knee flexion and extension concentric actions, respectively. The fixed exponents allow interpretations for initial acceleration, the angle at peak torque and the decrement of torque after peak torque. Also, the multilevel models were flexible to illustrate the influence of gender differences on the shape of torque throughout the range of motion and in the shape of the curves. The presented multilevel regression models may afford a general framework to examine angle-specific moment curves by isokinetic dynamometry, and add to the understanding mechanisms of strength development, particularly the force-length relationship, both related to performance and injury prevention.
Psychosocial health mediates the gratitude-physical health link

ABSTRACT There is now a growing body of research demonstrating the physical health benefits of being grateful. However, research has only just began to explore the mechanisms accounting for this gratitude-health relationship. This study examines the relationship between dispositional gratitude and self-reported physical health symptoms, and explores whether this relationship is explained through reduced levels of perceived loneliness and stress. This study employed a cross-sectional design with a sample of 607 healthy adults. Serial mediation analysis revealed that the positive effect of gratitude on physical health was significantly mediated by lower reported levels of perceived loneliness and stress. These findings are important given evidence that gratitude can be cultivated, and may serve to buffer against stress and loneliness and improve somatic health symptoms in the general population.
Acquisiton of process skills by IV standard pupils through an instructional programme in environmental studies

2. For Mathematics Education a) The goal of Mathematics teaching is to develop in children mathematical thinking, teach them procedures and also create a positive attitude towards Maths. There is a feeling that the current curriculum focuses mainly on the second of these (i.e. teaching Maths procedures). One way of encouraging Mathematical thinking is to build concepts gradually clearly showing the links and patterns underlying different concepts.
Balancing domain decomposition for mixed finite elements

The rate of convergence of the Balancing Domain Decomposition method applied to the mixed finite element discretization of second order elliptic equations is analyzed. The Balancing Domain Decomposition method, introduced by Mandel in [24], is a substructuring method that involves at each iteration the solution of a local problem with Dirichlet data, a local problem with Neumann data, and a "coarse grid" problem to propagate information globally and to insure the consistency of the Neumann problems. It is shown that the condition number grows at worst like the logarithm squared of the ratio of the subdomain size to the element size, in both two and three dimensions and for elements of arbitrary order. The bounds are uniform with respect to coefficient jumps of arbitrary size between subdomains. The key component of our analysis is the demonstration of an equivalence between the norm induced by the bilinear form on the interface and the H^{1/2}-norm of an interplant of the boundary data. Computational results from a message passing parallel implementation on an INTEL-Delta machine demonstrate the scalability properties of the method and show almost optimal linear observed speed-up for up to 64 processors.
Incidence and Basket Size Response to the Price War in Dutch Grocery Retailing

Acknowledgements: The authors gratefully acknowledge the support of Aimark and Publi-info, which provided the data for this study. The assistance of Alfred Dijs, Dick Valstar, Peter Gouw, Ton Luyten and Vincent van Witteloostuyn (Europanel) is especially acknowledged. For their constructive comments and feedback, we thank Marnik Dekimpe, Scott Neslin, Laurens Sloot and seminar participants at the Marketing Dynamics Conference, the Marketing Science Conference and North-East Marketing Consortium. Research funding was provided by the Marketing Science Institute and by the Netherlands Organization for Scientific Research (for the first author).
Ethnic Differences in Survival after Breast Cancer in South East Asia

Background The burden of breast cancer in Asia is escalating. We evaluated the impact of ethnicity on survival after breast cancer in the multi-ethnic region of South East Asia. Methodology/Principal Findings Using the Singapore-Malaysia hospital-based breast cancer registry, we analyzed the association between ethnicity and mortality following breast cancer in 5,264 patients diagnosed between 1990 and 2007 (Chinese: 71.6%, Malay: 18.4%, Indian: 10.0%). We compared survival rates between ethnic groups and calculated adjusted hazard ratios (HR) to estimate the independent effect of ethnicity on survival. Malays (n = 968) presented at a significantly younger age, with larger tumors, and at later stages than the Chinese and Indians. Malays were also more likely to have axillary lymph node metastasis at similar tumor sizes and to have hormone receptor negative and poorly differentiated tumors. Five year overall survival was highest in the Chinese women (75.8%; 95%CI: 74.4%–77.3%) followed by Indians (68.0%; 95%CI: 63.8%–72.2%), and Malays (58.5%; 95%CI: 55.2%–61.7%). Compared to the Chinese, Malay ethnicity was associated with significantly higher risk of all-cause mortality (HR: 1.34; 95%CI: 1.19–1.51), independent of age, stage, tumor characteristics and treatment. Indian ethnicity was not significantly associated with risk of mortality after breast cancer compared to the Chinese (HR: 1.14; 95%CI: 0.98–1.34). Conclusion In South East Asia, Malay ethnicity is independently associated with poorer survival after breast cancer. Research into underlying reasons, potentially including variations in tumor biology, psychosocial factors, treatment responsiveness and lifestyle after diagnosis, is warranted.
Discrete causal model view of biological networks

Signaling and regulatory pathways coordinate multiple cellular functions in response to environmental variations. Discovering the pathways governing functionally specific responses is essential for understanding of biological systems. It aims at determining the causal cascades of regulations leading to the observed responses. Their characterization by computational methods remains an important and challenging question. The presented cabin (Causal Analysis of Biological Interaction Network) method determines a causal model view composed of a subnetwork and a set of agent states deduced from observations with regards to a model of network dynamics. The validity of the results is ensured by formally checking the conditions of correctness of a model with respect to observations. State-based and symbolic versions of the algorithm have been implemented and used for a biological case study.
Improving Cross-Project Defect Prediction Methods with Data Simplification

Context: Cross-project defect prediction (CPDP) research has been popular and many CPDP methods were proposed. While these methods used cross-project data as is for their inputs, useless or noisy information in the cross-project data can cause the degradation of predictive and computation performance. Removing such information makes the cross-project data simple and it will affect the performance of CPDP methods. Objective: To identify and quantify the effects of the data simplification for CPDP methods. Method: We conducted experiments that compared the predictive performance between CPDP with and without the data simplification. We adopted a data simplification method based on an active learning method proposed for software effort estimation. The experiments adopted 44 versions of OSS projects, four prediction models, and two CPDP methods, namely, Burak-filter and cross-project selection. Results: The data simplification achieved significant improvement in predictive performance for the cross-project selection. It did not improve Burak-filter. Conclusion: The data simplification can be helpful for the cross-project selection in terms of predictive performance and size reduction of cross-project data.
Survival from in-hospital cardiac arrest during nights and weekends.

CONTEXT Occurrence of in-hospital cardiac arrest and survival patterns have not been characterized by time of day or day of week. Patient physiology and process of care for in-hospital cardiac arrest may be different at night and on weekends because of hospital factors unrelated to patient, event, or location variables. OBJECTIVE To determine whether outcomes after in-hospital cardiac arrest differ during nights and weekends compared with days/evenings and weekdays. DESIGN AND SETTING We examined survival from cardiac arrest in hourly time segments, defining day/evening as 7:00 am to 10:59 pm, night as 11:00 pm to 6:59 am, and weekend as 11:00 pm on Friday to 6:59 am on Monday, in 86,748 adult, consecutive in-hospital cardiac arrest events in the National Registry of Cardiopulmonary Resuscitation obtained from 507 medical/surgical participating hospitals from January 1, 2000, through February 1, 2007. MAIN OUTCOME MEASURES The primary outcome of survival to discharge and secondary outcomes of survival of the event, 24-hour survival, and favorable neurological outcome were compared using odds ratios and multivariable logistic regression analysis. Point estimates of survival outcomes are reported as percentages with 95% confidence intervals (95% CIs). RESULTS A total of 58,593 cases of in-hospital cardiac arrest occurred during day/evening hours (including 43,483 on weekdays and 15,110 on weekends), and 28,155 cases occurred during night hours (including 20,365 on weekdays and 7790 on weekends). Rates of survival to discharge (14.7% [95% CI, 14.3%-15.1%] vs 19.8% [95% CI, 19.5%-20.1%], return of spontaneous circulation for longer than 20 minutes (44.7% [95% CI, 44.1%-45.3%] vs 51.1% [95% CI, 50.7%-51.5%]), survival at 24 hours (28.9% [95% CI, 28.4%-29.4%] vs 35.4% [95% CI, 35.0%-35.8%]), and favorable neurological outcomes (11.0% [95% CI, 10.6%-11.4%] vs 15.2% [95% CI, 14.9%-15.5%]) were substantially lower during the night compared with day/evening (all P values < .001). The first documented rhythm at night was more frequently asystole (39.6% [95% CI, 39.0%-40.2%] vs 33.5% [95% CI, 33.2%-33.9%], P < .001) and less frequently ventricular fibrillation (19.8% [95% CI, 19.3%-20.2%] vs 22.9% [95% CI, 22.6%-23.2%], P < .001). Among in-hospital cardiac arrests occurring during day/evening hours, survival was higher on weekdays (20.6% [95% CI, 20.3%-21%]) than on weekends (17.4% [95% CI, 16.8%-18%]; odds ratio, 1.15 [95% CI, 1.09-1.22]), whereas among in-hospital cardiac arrests occurring during night hours, survival to discharge was similar on weekdays (14.6% [95% CI, 14.1%-15.2%]) and on weekends (14.8% [95% CI, 14.1%-15.2%]; odds ratio, 1.02 [95% CI, 0.94-1.11]). CONCLUSION Survival rates from in-hospital cardiac arrest are lower during nights and weekends, even when adjusted for potentially confounding patient, event, and hospital characteristics.
Domain Adaptation For Formant Estimation Using Deep Learning

In this paper we present a domain adaptation technique for formant estimation using a deep network. We first train a deep learning network on a small read speech dataset. We then freeze the parameters of the trained network and use several different datasets to train an adaptation layer that makes the obtained network universal in the sense that it works well for a variety of speakers and speech domains with very different characteristics. We evaluated our adapted network on three datasets, each of which has different speaker characteristics and speech styles. The performance of our method compares favorably with alternative methods for formant estimation.
The impact of brand image fit on attitude towards a brand alliance

Abstract Co-branding has become an increasingly popular strategy over recent decades. Studies have found that the pre-existing attitudes to the parent brands, fit between their product categories and perceived fit in the brands themselves as important drivers of a co-brand success. Despite its importance, most studies have treated brand fit as a simple measure of complementarily and consistency. Recently, a few papers have challenged this view, suggesting that a broader range of brand attributes (such as personality, functional and hedonic characteristics, cultural meaning) should also be considered when investigating brand alliances. The current study draws on these findings, exploring the fit between partners’ brand images and how they influence perceptions of a brand alliance. We treat brand image as a multi-dimensional construct, consisting of economic, symbolic, sensory, futuristic and utilitarian elements. Using an experimental design with nine hypothetical brand pairings with 221 respondents, we find brand image fit provides greater explanatory power over a traditional unidimensional measure of brand fit, with economic, futuristic and utilitarian dimensions having a significant influence on co-brand perceptions.
Glucocorticoids and the preparation for life after birth: are there long-term consequences of the life insurance?

In mammals, survival both before and after birth depends on a number of key physiological processes such as the provision of 02, a supply of oxidative substrates and a means of removing the waste products of metabolism. In adults these processes are carried out by a number of different tissues, whereas in the fetus they are carried out primarily by the placenta. The successful transition from intra- to extra-uterine life, therefore, depends on the ability of specific tissues and organ systems to take over the functions of the placenta at birth. Consequently, organs such as the lungs, liver, kidneys and gut undergo maturational changes during late gestation in preparation for extra-uterine life. Most of these changes are glucocorticoid dependent and can be induced prematurely by exogenous glucocorticoid administration (Silver, As a consequence, synthetic glucocorticoids are now routinely administered to women in threatened preterm labour to improve neonatal viability (NIH Consensus Development Conference,
Nonequilibrium atmospheric secondary organic aerosol formation and growth

Airborne particles play critical roles in air quality, health effects, visibility, and climate. Secondary organic aerosols (SOA) formed from oxidation of organic gases such as α-pinene account for a significant portion of total airborne particle mass. Current atmospheric models typically incorporate the assumption that SOA mass is a liquid into which semivolatile organic compounds undergo instantaneous equilibrium partitioning to grow the particles into the size range important for light scattering and cloud condensation nuclei activity. We report studies of particles from the oxidation of α-pinene by ozone and NO3 radicals at room temperature. SOA is primarily formed from low-volatility ozonolysis products, with a small contribution from higher volatility organic nitrates from the NO3 reaction. Contrary to expectations, the particulate nitrate concentration is not consistent with equilibrium partitioning between the gas phase and a liquid particle. Rather the fraction of organic nitrates in the particles is only explained by irreversible, kinetically determined uptake of the nitrates on existing particles, with an uptake coefficient that is 1.6% of that for the ozonolysis products. If the nonequilibrium particle formation and growth observed in this atmospherically important system is a general phenomenon in the atmosphere, aerosol models may need to be reformulated. The reformulation of aerosol models could impact the predicted evolution of SOA in the atmosphere both outdoors and indoors, its role in heterogeneous chemistry, its projected impacts on air quality, visibility, and climate, and hence the development of reliable control strategies.
Towards a framework for comparing automatic term recognition methods

Automatic Term Recognition focuses on the extraction of words and multi-word expressions that are significant for a given domain. There is a considerable interest in using ATR for automatic metadata generation, creation of thesauri and terminological glossaries, keyword extraction, ontology building, etc. In this paper, we build upon the work done at the University of Sheffield, where a library with a few algorithms for ATR was recently developed. We enrich this library with new ATR algorithms and tools for evaluation. Our aim is to perform an experimental study comparing the base ATR methods as well as their combinations under various conditions. The results of the study indicate that better precision can be usually reached by combining ATR methods using foreground and ATR methods using background knowledge. The created platform is freely available and prepared for extensions by other researchers.
What makes students satisfied? A discussion and analysis of the UK’s national student survey

Abstract This paper analyses data from the National Students Survey, determining which groups of students expressed the greatest levels of satisfaction. We find students registered on clinical degrees and those studying humanities to be the most satisfied, with those in general engineering and media studies the least. We also find contentment to be higher among part-time students, and significantly higher among Russell group and post-1992 universities. We further investigate the sub-areas that drive overall student satisfaction, finding teaching and course organisation to be the most important aspects, with resources and assessment and feedback far less relevant. We then develop a multi-attribute measure of satisfaction which we argue produces a more accurate and more stable reflection of overall student satisfaction than that based on a single question.
Stock Markets, Banks, and Economic Growth

Using data on 49 countries from 1976 to 1993, the authors investigate whether measures of stock market liquidity, size, volatility, and integration in world capital markets predict future rates of economic growth, capital accumulation, productivity improvements, and private savings. They find that stock market liquidity-as measured by stock trading relative to the size of the market and economy - is positively and significantly correlated with current and future rates of economic growth, capital accumulation, productivity growth, even after controlling for economic and political factors. Stock market size, volatility, and integration are not robustly linked with growth. Nor are financial indicators closely associated with private savings rates. Significantly, banking development -as measured by bank loans to private enterprises divided by GDP -when combined with stock market liquidity predicts future rates of growth, capital accumulation, and productivity growth when entered together in regressions. The authors determine that these results are consistent with views that (1)financial markets and institutions provide important services for long-run growth, and (2)stock markets and banks provide different financial services.
Efficient Algorithms and Cost Models for Reverse Spatial-Keyword k-Nearest Neighbor Search

Geographic objects associated with descriptive texts are becoming prevalent, justifying the need for spatial-keyword queries that consider both locations and textual descriptions of the objects. Specifically, the relevance of an object to a query is measured by spatial-textual similarity that is based on both spatial proximity and textual similarity. In this article, we introduce the Reverse Spatial-Keyword k-Nearest Neighbor (RSKkNN) query, which finds those objects that have the query as one of their k-nearest spatial-textual objects. The RSKkNN queries have numerous applications in online maps and GIS decision support systems. To answer RSKkNN queries efficiently, we propose a hybrid index tree, called IUR-tree (Intersection-Union R-tree) that effectively combines location proximity with textual similarity. Subsequently, we design a branch-and-bound search algorithm based on the IUR-tree. To accelerate the query processing, we improve IUR-tree by leveraging the distribution of textual description, leading to some variants of the IUR-tree called Clustered IUR-tree (CIUR-tree) and combined clustered IUR-tree (C2IUR-tree), for each of which we develop optimized algorithms. We also provide a theoretical cost model to analyze the efficiency of our algorithms. Our empirical studies show that the proposed algorithms are efficient and scalable.