Miracle-dz/UniFAR-FTUs huggingface dataset:
Folate Receptor-β Imaging Using 99mTc-Folate to Explore Distribution of Polarized Macrophage Populations in Human Atherosclerotic Plaque

In atherosclerotic plaques, the risk of rupture is increased at sites of macrophage accumulation. Activated macrophages express folate receptor-β (FR-β), which can be targeted by folate coupled to radioactive ligands to visualize vulnerability. The aim of this study was to explore the presence of activated macrophages in human atherosclerotic plaques by 99mTc-folate imaging and to evaluate whether this technique can discriminate between an M1-like and M2-like macrophage phenotype. Methods: Carotid endarterectomy specimens of 20 patients were incubated with 99mTc-folate, imaged using micro-SPECT, and divided into 3-mm slices. The mean accumulation was calculated per slice, and the distribution of M1-like and M2-like macrophages per slice was quantified by immunohistochemical staining for CD86 as well as inducible nitric oxide synthase (iNOS) for M1 and CD163 and FR-β for M2 macrophages. Monocytes from healthy donors were differentiated toward M1-like or M2-like phenotype by in vitro culturing. Messenger RNA levels of specific M1 and M2 markers were measured by reverse-transcription polymerase chain reaction and expression of FR-β, CD86, and CD163 by flow cytometry. Results: There was a heterogeneous accumulation of 99mTc-folate in plaques (median, 2.45 [0.77–6.40] MBq/g). Slices with the highest 99mTc-folate accumulation of each plaque showed significantly more expression of FR-β and CD163, compared with slices with the lowest 99mTc-folate accumulation, which showed significantly more expression of iNOS. In in vitro polarized macrophages, messenger RNA expression of FR-β, mannose receptor, IL-10, and matrix metalloproteinase-9 was significantly increased in M2-like macrophages, compared with M1-like macrophages. On a receptor level, CD86 was shown to be overexpressed on M1-like macrophages whereas FR-β and CD163 were overexpressed on M2-like macrophages measured by flow cytometry. Conclusion: Higher numbers of M2-like macrophages were present in areas of high 99mTc-folate accumulation than areas with low accumulation. It is anticipated that 99mTc-folate imaging using SPECT as a marker for M2-like macrophages in atherosclerosis might be a good indicator for plaque vulnerability.
Sources of Lower Financial Decision-Making Ability at Older Ages

After middle age, further aging is associated with lower levels of many cognitive abilities, some of which could influence import economic decisions. Our prior research (Choi et al., 2014) shows a substantial negative relationship between age and the consistency of choices with economic rationality (decisionmaking quality). This paper investigates the sources of that negative correlation using panel data on more than 4,000 members of a panel study in the Netherlands. The analysis finds no evidence that the correlation between age and rationality is, in fact, a just a cohort, not an age effect. Similarly, there is little evidence that the correlation is due to other forms of cognitive or health declines. Rather, the findings indicate that age has an independent and negative effect on economic rationality.
Episodic grammar: a computational model of the interaction between episodic and semantic memory in language processing

We present a model of the interaction of semantic and episodic memory in language processing. Our work shows how language processing can be understood in terms of memory retrieval. We point out that the perceived dichotomy between rule-based versus exemplar-based language modelling can be interpreted in a neuro-biological perspective in terms of the interaction between a semantic memory system that encodes linguistic knowledge in the form of abstract rules, and an episodic memory that stores concrete linguistic events. We implement the idea of a semantic-episodic memory integration in a probabilistic grammar, and evaluate its performance as a syntactic parser on corpora of natural language. Our labeled precision and recall results are competitive with state-of-the-art syntactic parsers, with F-scores up to 90.68 on section 22 of the Penn WSJ corpus.
Fast and Adaptive Indexing of Multi-Dimensional Observational Data

Sensing devices generate tremendous amounts of data each day, which include large quantities of multi-dimensional measurements. These data are expected to be immediately available for real-time analytics as they are streamed into storage. Such scenarios pose challenges to state-of-the-art indexing methods, as they must not only support efficient queries but also frequent updates. We propose here a novel indexing method that ingests multi-dimensional observational data in real time. This method primarily guarantees extremely high throughput for data ingestion, while it can be continuously refined in the background to improve query efficiency. Instead of representing collections of points using Minimal Bounding Boxes as in conventional indexes, we model sets of successive points as line segments in hyperspaces, by exploiting the intrinsic value continuity in observational data. This representation reduces the number of index entries and drastically reduces "over-coverage" by entries. Experimental results show that our approach handles real-world workloads gracefully, providing both low-overhead indexing and excellent query efficiency.
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles

In this paper we present a new beat tracking algorithm which extends an existing state-of-the-art system with a multi-model approach to represent different music styles. The system uses multiple recurrent neural networks, which are specialised on certain musical styles, to estimate possible beat positions. It chooses the model with the most appropriate beat activation function for the input signal and jointly models the tempo and phase of the beats from this activation function with a dynamic Bayesian network. We test our system on three big datasets of various styles and report performance gains of up to 27% over existing stateof-the-art methods. Under certain conditions the system is able to match even human tapping performance.
Changing Perceptions of Cycling in the African American Community to Encourage Participation in a Sport that Promotes Health in Adults

This study introduces two interventions designed to influence perceptions of cycling among African Americans. Results from the 2001 National Household Transportation Survey reveal that African Americans cycle at two-thirds the rate of White and Hispanic Americans (Pucher and Renne, 2003). Moreover, African Americans are less likely to possess alternative transportation modes like a bicycle (Royal and Miller-Steiger, 2008). Researchers suggest that cycling disparities are linked to negative perceptions among inexperienced cyclists and non-cyclists –including African Americans (McCray et al, 2010). An important consideration in analyzing why African Americans generally do not cycle is that of perception. The purpose of this study is to address negative perceptions of cycling that inhibit bicycle use, including a lack of experience, knowledge, and safety. Few studies exist that explore race or ethnic-specific reasons for low levels of physical activity and this information is needed to increase physical activity among minority groups (Rogers, et al. 2007). By examining perceptions of cycling among African Americans, this study builds on existing literature and fills a significant void in addressing the lack of bicycle ridership in the African-American community.
Temporal structure methods for image-based change analysis

This paper addresses the exploitation of massive numbers of image-derived change detections. We use the term ldquochange analysisrdquo to emphasize the intelligence value obtained from large numbers of change detection over long time intervals, rather than the emphasis by most researchers to date on ldquochange detectionrdquo methods and small numbers of change detections. Our methods emphasize local temporal descriptions of activities and include minimal spatial information about activities. Our three methods adapt and extend: (1) classic unsupervised pattern recognition operating on bag-of-words features; (2) Latent Semantic Analysis (LSA); and (3) probabilistic LSA (PLSA). These methods allow us to: (a) Detect and describe anomalous activities; (b) Discover categories of activity, describe a category of activity, and assign an activity to a category; (c) Retrieve similar activities from a historical database. We present experimental results that compare our methods (1)-(3) for performing functions (a)-(c), using webcam images of a town market square collected every few minutes over 74 days. We discuss how our techniques are equally applicable for change analysis using wide-area sensors.
A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices

We present a Bayesian scheme for the approximate diagonalisation of several square matrices which are not necessarily symmetric. A Gibbs sampler is derived to simulate samples of the common eigenvectors and the eigenvalues for these matrices. Several synthetic examples are used to illustrate the performance of the proposed Gibbs sampler and we then provide comparisons to several other joint diagonalization algorithms, which shows that the Gibbs sampler achieves the state-of-the-art performance on the examples considered. As a byproduct, the output of the Gibbs sampler could be used to estimate the log marginal likelihood, however we employ the approximation based on the Bayesian information criterion (BIC) which in the synthetic examples considered correctly located the number of common eigenvectors. We then succesfully applied the sampler to the source separation problem as well as the common principal component analysis and the common spatial pattern analysis problems.
Assessing sequence comparison methods with the average precision criterion

MOTIVATION Comprehensive performance assessment is important for improving sequence database search methods. Sensitivity, selectivity and speed are three major yet usually conflicting evaluation criteria. The average precision (AP) measure aims to combine the sensitivity and selectivity features of a search algorithm. It can be easily visualized and extended to analyze results from a set of queries. Finally, the time-AP plot can clearly show the overall performance of different search methods. RESULTS Experiments are performed based on the SCOP database. Popular sequence comparison algorithms, namely Smith-Waterman (SSEARCH), FASTA, BLAST and PSI-BLAST are evaluated. We find that (1) the low-complexity segment filtration procedure in BLAST actually harms its overall search quality; (2) AP scores of different search methods are approximately in proportion of the logarithm of search time; and (3) homologs in protein families with many members tend to be more obscure than those in small families. This measure may be helpful for developing new search algorithms and can guide researchers in selecting most suitable search methods. AVAILABILITY Test sets and source code of this evaluation tool are available upon request.
Impact of Audit Evidence on Auditor’S Report

This study seeks to emphasize the importance of audit evidences, their quality characteristics and the professional judgment used to measure and to evaluate them in order to express their final audit opinion. There is no mathematical formula, neither a specific model in order to evaluate the quality of audit evidences. Their quality depends upon the professional judgment concerning the audit technical standards, the accounting references and nevertheless upon the auditor’s ethics. This is one of the reasons for which the financial audit is one of the edges of economical research, highlighting the credibility of financial statements. This study employ primary and secondary source of data. Questionnaires were administered to obtain information about audit evidence and auditor report was obtained through the secondary source. The empirical findings from the binary logistic regression result revealed that sufficiency of audit evidence had a negative but insignificant and reliability of audit evidence had a positive coefficient sign but insignificant. The low value of the Mcfadden R-squared indicated that the null hypotheses were rejected and acceptance of the alternative hypotheses. The study suggested that further empirical work should be conducted in this area. Keywords: Auditor report, Audit evidence, Audit opinion and Auditing standard.
Summarizing Drug Information in Medline Citations

Adverse drug events and drug-drug interactions are a major concern in patient care. Although databases exist to provide information about drugs, they are not always up-to-date and complete (particularly regarding pharmacogenetics). We propose a methodology based on automatic summarization to identify drug information in Medline citations and present results to the user in a convenient form. We evaluate the method on a selection of citations discussing ten drugs ranging from the proton pump inhibitor lansoprazole to the vasoconstrictor sumatriptan. We suggest that automatic summarization can provide a valuable adjunct to curated drug databases in supporting quality patient care.
Helicobacter pylori Impairs Murine Dendritic Cell Responses to Infection

Background Helicobacter pylori, a human pathogen associated with chronic gastritis, peptic ulcer and gastric malignancies, is generally viewed as an extracellular microorganism. Here, we show that H. pylori replicates in murine bone marrow derived-dendritic cells (BMDCs) within autophagosomes. Methodology/Principal Findings A 10-fold increase of CFU is found between 2 h and 6 h p.i. in H. pylori-infected BMDCs. Autophagy is induced around the bacterium and participates at late time points of infection for the clearance of intracellular H. pylori. As a consequence of infection, LC3, LAMP1 and MHC class II molecules are retained within the H. pylori-containing vacuoles and export of MHC class II molecules to cell surface is blocked. However, formalin-fixed H. pylori still maintain this inhibitory activity in BMDC derived from wild type mice, but not in from either TLR4 or TLR2-deficient mice, suggesting the involvement of H. pylori-LPS in this process. TNF-alpha, IL-6 and IL-10 expression was also modulated upon infection showing a TLR2-specific dependent IL-10 secretion. No IL-12 was detected favoring the hypothesis of a down modulation of DC functions during H. pylori infection. Furthermore, antigen-specific T cells proliferation was also impaired upon infection. Conclusions/Significance H. pylori can infect and replicate in BMDCs and thereby affects DC-mediated immune responses. The implication of this new finding is discussed for the biological life cycle of H. pylori in the host.
Iterative Designed Experiment Analysis (IDEA)

In the traditional use of design of experiments (DOE), the analysis of a designed experiment proceeds only when the designed plan has been fully carried out and the response values are all available, a standard practice that could be inefficient in time and cost and inadequate when quick indicative results are needed. A procedure referred to as ‘iterative designed experiment analysis (IDEA)’ is proposed for suggesting results progressively and giving early indications in situations where response values are available only gradually. The procedure is expected to be useful in the early stages of experimental discovery, especially in factor screening situation. Two case studies illustrate and suggest the potential of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
Serendipitous Exploration of Large-scale Product Catalogs

Online shopping has developed to a stage where catalogs have become very large and diverse. Thus, it is a challenge to present relevant items to potential customers within a very few interactions. This is even more so when users have no defined shopping objectives but operate in an opportunistic mindset. This problem is often tackled by recommender systems. However, these systems rely on consistent user interaction patterns to predict items of interest. In contrast, we propose to adapt the classical information retrieval (IR) paradigm for the purpose of accessing catalog items in a context of un-predictable user interaction. Accordingly, we present a novel information access strategy based on the notion of interest rather than relevance. We detail the design of a scalable browsing system including learning capabilities joint with a limited-memory model. Our approach enables locating interesting items within a few steps while not requiring good quality descriptions. Our system allows customer to seamlessly change browsing objectives without having to start explicitly a new session. An evaluation of our approach based on both artificial and real-life datasets demonstrates its efficiency in learning and adaptation.
Catalytic Activities of Tumor-Specific Human Cytochrome P450 CYP2W1 Toward Endogenous Substrates

CYP2W1 is a recently discovered human cytochrome P450 enzyme with a distinctive tumor-specific expression pattern. We show here that CYP2W1 exhibits tight binding affinities for retinoids, which have low nanomolar binding constants, and much poorer binding constants in the micromolar range for four other ligands. CYP2W1 converts all-trans retinoic acid (atRA) to 4-hydroxy atRA and all-trans retinol to 4-OH all-trans retinol, and it also oxidizes retinal. The enzyme much less efficiently oxidizes 17β-estradiol to 2-hydroxy-(17β)-estradiol and farnesol to a monohydroxylated product; arachidonic acid is, at best, a negligible substrate. These findings indicate that CYP2W1 probably plays an important role in localized retinoid metabolism that may be intimately linked to its involvement in tumor development.
Single Feature Polymorphism Discovery in Rice

The discovery of nucleotide diversity captured as single feature polymorphism (SFP) by using the expression array is a high-throughput and effective method in detecting genome-wide polymorphism. The efficacy of such method was tested in rice, and the results presented in the paper indicate high sensitivity in predicting SFP. The sensitivity of polymorphism detection was further demonstrated by the fact that no biasness was observed in detecting SFP with either single or multiple nucleotide polymorphisms. The high density SFP data that can be generated quite effectively by the current method has promise for high resolution genetic mapping studies, as physical location of features are well-defined on rice genome.
A ug 2 00 4 A MATHEMATICAL THEORY OF THE TOPOLOGICAL VERTEX

We have developed a mathematical theory of the topological vertex—a theory that was originally proposed by M. Aganagic, A. Klemm, M. Mariño, and C. Vafa on effectively computing Gromov-Witten invariants of smooth toric CalabiYau threefolds derived from duality between open string theory of smooth Calabi-Yau threefolds and Chern-Simons theory on three manifolds.
The MPOWER Tool Chain - Enabling Rapid Development of Standards-based and Interoperable Homecare Applications

There is a need for new models for providing care to the increasing number of elderly in the world. Technological solutions are being developed to assist the elderly in their homes, and the caregivers managing the care. However, the development of these solutions is complex and expensive, and new techniques should be applied to reduce the development costs. Reuse of interoperable services is an effective solution that can be realised using a modeldriven development process for the homecare domain. This paper presents the results from the MPOWER project where UML has been used to incorporate domain knowledge into tools that automises the technical implementation through model transformation and code generation.
Kernel Extraction via Voted Risk Minimization

This paper studies a new framework for learning a predictor in the presence of multiple kernel functions where the learner selects or extracts several kernel functions from potentially complex families and finds an accurate predictor defined in terms of these functions. We present an algorithm, Voted Kernel Regularization, that provides the flexibility of using very complex kernel functions such as predictors based on high-degree polynomial kernels or narrow Gaussian kernels, while benefitting from strong learning guarantees. We show that our algorithm benefits from strong learning guarantees suggesting a new regularization penalty depending on the Rademacher complexities of the families of kernel functions used. Our algorithm admits several other favorable properties: its optimization problem is convex, it allows for learning with non-PDS kernels, and the solutions are highly sparse, resulting in improved classification speed and memory requirements. We report the results of some preliminary experiments comparing the performance of our algorithm to several baselines.
Distributed autonomic resource management for network virtualization

Network virtualization is an emerging trend claimed to reduce the costs of future networks. The key strategy in network virtualization is of slicing physical resources (links, routers, servers, etc.) to create virtual networks composed of subsets of these slices. One important challenge on network virtualization is the resource management of the physical or substrate networks. Sophisticated management techniques should be used to accomplish such management. The sophisticated techniques offered by autonomic communications rise as an appropriated alternative to address the challenges of managing the efficient use of substrate resources on network virtualization. Thus, this paper proposes a distributed self-organizing model to manage the substrate network resources. An evaluation scenario is depicted and simulations show that approximately 36.8% of the network traffic load can be spared when the self-organizing model is enabled in the evaluated scenario.
Preconsumption Mood, Causal Explanations, and Postrecovery Reactions

Using two competing affect appraisal models (affect priming and affect-as-information) as frameworks, we examined the joint effect of preconsumption mood and explanations/excuses on customer’s postpurchase satisfaction and loyalty. Two factorial experiments were conducted to test the hypotheses. By manipulating the preconsumption mood and the type of service failure information provided after service failure (explanation in Study 1 and excuse in Study 2), the mechanism through which preconsumption mood impacts customer evaluations in the restaurant context was investigated. The findings lend support to the affect priming model, indicating that preconsumption mood can directly impact consumer satisfaction and loyalty in a mood-congruent fashion. To the best of our knowledge, this study is the first to utilize explanations and excuses to test two competing theories, affect priming and affect-as-information. These results contribute to the hospitality literature by shedding light on mechanism through which preconsumption mood influences customers’ postfailure evaluations.