Miracle-dz/UniFAR-FTUs huggingface dataset:
Involvement of NIPSNAP1, a neuropeptide nocistatin-interacting protein, in inflammatory pain

Background Chronic pain associated with inflammation is an important clinical problem, and the underlying mechanisms remain poorly understood. 4-Nitrophenylphosphatase domain and nonneuronal SNAP25-like protein homolog (NIPSNAP) 1, an interacting protein with neuropeptide nocistatin, is implicated in the inhibition of tactile pain allodynia. Although nocistatin inhibits some inflammatory pain responses, whether NIPSNAP1 affects inflammatory pain appears to be unclear. Here, we examined the nociceptive behavioral response of NIPSNAP1-deficient mice and the expression of NIPSNAP1 following peripheral inflammation to determine the contribution of NIPSNAP1 to inflammatory pain. Results Nociceptive behavioral response increased in phase II of the formalin test, particularly during the later stage (26–50 min) in NIPSNAP1-deficient mice, although the response during phase I (0–15 min) was not significantly different between the deficient and wild-type mice. Moreover, phosphorylation of extracellular signal-related kinase was enhanced in the spinal dorsal horn of the deficient mice. The prolonged inflammatory pain induced by carrageenan and complete Freund’s adjuvant was exacerbated in NIPSNAP1-deficient mice. NIPSNAP1 mRNA was expressed in small- and medium-sized neurons of the dorsal root ganglion and motor neurons of the spinal cord. In the formalin test, NIPSNAP1 mRNA was slightly increased in dorsal root ganglion but not in the spinal cord. In contrast, NIPSNAP1 mRNA levels in dorsal root ganglion were significantly decreased during 24–48 h after carrageenan injection. Prostaglandin E2, a major mediator of inflammation, stimulated NIPSNAP1 mRNA expression via the cAMP-protein kinase A signaling pathway in isolated dorsal root ganglion cells. Conclusions These results suggest that changes in NIPSNAP1 expression may contribute to the pathogenesis of inflammatory pain.
Implementation Tradeoffs in Storage Allocation for Grid Computing

Shared temporary storage space is often the constraining resource for clusters that serve as execution nodes in wide-area distributed systems. At least one large national-scale computing grid has reported a failure rate of as high as thirty percent of submitted jobs, often due to accidentally filled shared storage spaces. Previous systems have attacked this problem by adding space allocation to the distributed system interface. However, these allocations are not enforced at the filesystem level, and thus unexpected or unaccounted uses of storage may cause the system to fail. By adding an inexpensive allocation mechanism to the operating system, we may improve the robustness of such systems at minimal cost. In this paper, we describe an abstract model of space allocation in the file system and explore three implementations of the model: a user-level library, a recursive loopback filesystem, and a modified kernel filesystem. We evaluate the performance and completeness of these implementations and demonstrate that kernel support is essential to keeping the overhead low. Finally, we demonstrate empirically that a cluster under heavy filesystem load can be made more robust by adding allocations to the filesystem.
Idea Generation and Material Consolidation : Tool Use and Intermediate Artefacts in Case Study of Journalistic Writing

We report an in-depth, longitudinal study of a freelance music journalist writing a feature article. Our analysis attends to the participant’s activities from initiation to completion, and the ways in which she established structure using tools and artefacts to support cognitive effort. We observed five work stages: establishing an initial idea; preparing for an interview; interviewing; planning the article; and writing. Each resulted in the production of a working document embodying ideas and commitments which provided a key resource for the next stage. Stages began with phases of idea generation during which ideas were spontaneously triggered through intense engagement with information resources. They finished with phases of material consolidation when intermediate artifacts were configured to facilitate generation during the next stage. We examine these in detail and use our findings to motivate a discussion of working document overview representations and specific requirements related to idea generation and material consolidation.
Blind to Sameness: Sexpectations and the Social Construction of Male and Female Bodies

What is the role of the senses in how we understand the world? Cognitive sociology has long addressed the way we perceive or imagine boundaries in our ordinary lives, but Asia Friedman pushes this question further still. How, she asks, did we come to blind ourselves to sex sameness? Drawing on more than sixty interviews with two decidedly different populations - the blind and the transgendered - "Blind to Sameness" answers provocative questions about the relationships between sex differences, biology, and visual perception. Both groups speak from unique perspectives that magnify the social construction of dominant visual conceptions of sex, allowing Friedman to examine the visual construction of the sexed body and highlighting the processes of social perception underlying our everyday experience of male and female bodies. The result is a notable contribution to the sociologies of gender, culture, and cognition that will revolutionize the way we think about sex.
Application of Multispectral Sensors Carried on Unmanned Aerial Vehicle (UAV) to Trophic State Mapping of Small Reservoirs: A Case Study of Tain-Pu Reservoir in Kinmen, Taiwan

Abstract: Multispectral, as well as multi-temporal, satellite images, coupled with measurements, in situ , have been widely applied to the water quality monitoring of reservoirs. However, the spatial resolutions of the current multispectral satellite imageries are inadequate for trophic state mapping of small reservoirs which merely cover several hectares. Moreover, the temporal gap between effective satellite imaging and measurements, in situ , is usually a few days or weeks; this time lag hampers the establishment of regression models between band ratios and water quality parameters. In this research, the RGB and NIR sensors carried on an unmanned aerial vehicle (UAV) were applied to the trophic state mapping of Tain-Pu reservoir, which is one of the small reservoirs in Kinmen, Taiwan. Due to the limited sampling points and the uncertainty of water fluidity, the average method and the matching pixel-by-pixel (MPP) method were employed to search for the optimal regression models. The experimental results indicate that the MPP method can lead to better regression models than the average method, and the trophic state maps show that the averages of Chl-a, TP, and SD are 179.7 μg·L
Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph

The hierarchical generalized Voronoi graph (HGVG) is a new roadmap developed for sensor-based exploration in unknown environments. This paper defines the HGVG structure: a robot can plan a path between two locations in its work space or configuration space by simply planning a path onto the HGVG, then along the HGVG, and finally from the HGVG to the goal. Since the bulk of the path planning occurs on the one-dimensional HGVG, motion planning in arbitrary dimensioned spaces is virtually reduced to a one-dimensional search problem. A bulk of this paper is dedicated to ensuring the HGVG is sufficient for motion planning by demonstrating the HGVG (with its links) is an arc-wise connected structure. All of the proofs in this paper that lead toward the connectivity result focus on a large subset of spaces in R3, but wherever possible, results are derived in Rm. In fact, under a strict set of conditions, the HGVG (the GVG by itself) is indeed connected, and hence sufficient for motion planning. The chief advantage of the HGVG is that it possesses an incremental construction procedure, described in a companion paper, that constructs the HGVG using only line-of-sight sensor data. Once the robot constructs the HGVG, it has effectively explored the environment, because it can then use the HGVG to plan a path between two arbitrary configurations.
Shifting Innovation to Users via Toolkits

In the traditional new product development process, manufacturers first explore user needs and then develop responsive products. Developing an accurate understanding of a user need is not simple or fast or cheap, however. As a result, the traditional approach is coming under increasing strain as user needs change more rapidly, and as firms increasingly seek to serve "markets of one. "Toolkits for user innovation is an emerging alternative approach in which manufacturers actuallyabandon the attempt to understand user needs in detail in favor of transferringneed-related aspects of product and service development to users. Experience in fields where the toolkit approach has been pioneered show custom products being developed much more quickly and at a lower cost. In this paper we explore toolkits for user innovation and explain why and how they work.
Modeling and simulation of a grid-connected photovoltaic converter with reactive power compensation

With the increasing penetration of photovoltaic generation systems in the grid, the use of grid-connected photovoltaic converters for reactive power compensation has been proposed to increase the utilization of the system's voltage source converter. Power factor correction and voltage regulation at the point of common coupling are among the proposed methods. This paper presents the modeling and simulation of a photovoltaic converter with reactive power compensation connected to the distribution grid. The system is based on a single-stage PV converter controlled using direct power control with space vector modulation (DPC-SVM). Results for day-time and night-time operation are presented.
Memory reactivation during rest supports upcoming learning of related content

Significance How our brains capture and store new information is heavily influenced by what we already know. While prior work demonstrates that existing memories are spontaneously reactivated and strengthened in the brain during passive rest periods, the prospective benefits of spontaneous offline reactivation for future learning remain unknown. Here, we use functional MRI to interrogate how reactivation and interregional coupling support the ability to learn related content in later situations. We find that offline processing of prior memories is associated with better subsequent learning. Our results provide a mechanistic account of the circumstances under which prior knowledge can come to facilitate—as opposed to interfere with—new learning, serving as a strong foundation upon which new content is encoded. Although a number of studies have highlighted the importance of offline processes for memory, how these mechanisms influence future learning remains unknown. Participants with established memories for a set of initial face–object associations were scanned during passive rest and during encoding of new related and unrelated pairs of objects. Spontaneous reactivation of established memories and enhanced hippocampal–neocortical functional connectivity during rest was related to better subsequent learning, specifically of related content. Moreover, the degree of functional coupling during rest was predictive of neural engagement during the new learning experience itself. These results suggest that through rest-phase reactivation and hippocampal–neocortical interactions, existing memories may come to facilitate encoding during subsequent related episodes.
A Randomized , Double-Blind , Placebo-Controlled Trial to Determine the Effectiveness of Botanically Derived Inhibitors of 5 AR in the Treatment of Androgenetic Alopecia

Background Androgenetic alopecia (AGA) is characterized by the structural miniaturization of androgen-sensitive hair follicles in susceptible individuals and anatomically defined within a given pattern of the scalp. Biochemically, one contributing factor of this disorder is the endogenous conversion of testosterone (T) to dihydrotestosterone (DHT) via the enzyme 5-alpha reductase (5AR). This metabolism is also key to the onset and progression of benign prostatic hyperplasia (BPH). Further, AGA has also been shown to be responsive to drugs and agents used to treat BPH. Of note, certain botanical compounds have previously demonstrated efficacy against BPH. Here, we report the first example of a placebocontrolled, double-blind study undertaken in order to examine the benefit of these botanical substances in the treatment of AGA.
Local Integral Statistics for Monochromatic Random Waves

This paper concerns local universality and global variance estimates for a wide class of integral statistics of monochromatic random waves on a compact smooth Riemannian manifold $(M,g)$. Such statistics include, but are not limited to, Hausdorff measures of zero sets and numbers of critical points. Our results hold under conditions about the structure of geodesics on M that are generic in the space of all metrics on $M$.
Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data

Existing work on detecting deceptive reviews primarily focuses on feature engineering and applies off-the-shelf supervised classiﬁcation algorithms to the problem. Then, one real challenge would be to manually recognize plentiful ground truth spam review data for model building, which is rather difﬁcult and often requires domain expertise in practice. In this paper, we propose to exploit the relatedness of multiple review spam detection tasks and readily available unlabeled data to address the scarcity of labeled opinion spam data. We ﬁrst develop a multi-task learning method based on logistic regression (MTL-LR), which can boost the learning for a task by sharing the knowledge contained in the training signals of other related tasks. To leverage the unlabeled data, we introduce a graph Laplacian regularizer into each base model. We then propose a novel semi-supervised multi-task learning method via Laplacian regularized logistic regression (SMTL-LLR) to further improve the review spam detection performance. We also develop a stochastic alternating method to cope with the optimization for SMTL-LLR. Experimental results on real-world review data demonstrate the beneﬁt of SMTL-LLR over several well-established baseline methods.
Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles

The proliferation of online communities has attracted much attention to modelling user behaviour in terms of social interaction, language adoption and contribution activity. Nevertheless, when applied to large-scale and cross-platform behavioural data, existing approaches generally suffer from expressiveness, scalability and generality issues. This paper proposes trans-dimensional von Mises-Fisher (TvMF) mixture models for L2 normalised behavioural data, which encapsulate: (1) a Bayesian framework for vMF mixtures that enables prior knowledge and information sharing among clusters, (2) an extended version of reversible jump MCMC algorithm that allows adaptive changes in the number of clusters for vMF mixtures when the model parameters are updated, and (3) an online TvMF mixture model that accommodates the dynamics of clusters for time-varying user behavioural data. We develop efficient collapsed Gibbs sampling techniques for posterior inference, which facilitates parallelism for parameter updates. Empirical results on simulated and real-world data show that the proposed TvMF mixture models can discover more interpretable and intuitive clusters than other widely-used models, such as k-means, non-negative matrix factorization (NMF), Dirichlet process Gaussian mixture models (DP-GMM), and dynamic topic models (DTM). We further evaluate the performance of proposed models in real-world applications, such as the churn prediction task, that shows the usefulness of the features generated.
Models in health psychology: an introduction

Health belief model The health belief model (HBM) is a cognition model, i.e. a model that emphasises the way an individual provides a rationale for their behaviour without particular reference to a social context. The HBM was conceived by Rosenstock (1966) and has undergone revision and development, primarily by Becker (1974). It has made an important contribution to the prediction of health What makes some people more able than others to lose weight? Why are some people better motivated to perform blood glucose monitoring? What makes some people more able to cope with distressing situations? What factors contribute to the development of depression? How can one possibly predict whether a person newly diagnosed with diabetes will manage the demands of the condition? These are the type of questions typically addressed in health psychology — the study of how psychological factors such as beliefs, values, thoughts, feelings and attitudes impact on health and illness and health-related behaviours. Health psychology is a relatively new discipline, emerging in the context of a challenge to traditional biomedical models of illness. Briefly, these traditional models see illness as arising from external forces (e.g. bacteria) over which the individual has no control. There is no continuum between mental and physical aspects of illness and the two are regarded as entirely separate. Treatment is solely in the hands of the healthcare professional. By contrast, health psychology views mental processes as not only intertwined with physical processes in illness, but also as a potential contributory factor to both health and illness. The individual is seen as having an active, rather than passive, role in the cause, progression and outcome of illness. The biopsychosocial model (Table 1) illustrates how these various components interact. Introduction This article, the first in a series on health psychology, introduces the subject and some of the models which seem particularly relevant to diabetes care. These include the health belief model, the health locus of control model, the theory of planned behaviour, and the transtheoretical theory of behaviour change. Examples of how these theories apply directly to the care of patients with diabetes are explored. The intention is to promote greater insight, further reading and, hopefully, future application of the theories in research studies.
Improving Education Outcomes in Germany

Improving education outcomes is important for Germany’s long-term economic performance and social cohesion. While student achievement is above the OECD average in science and at the OECD average in reading and mathematics according to the 2006 OECD PISA study, weaker students tend to do badly by international comparison and socio-economic and/or immigrant backgrounds have a large impact. Another problem is that the proportion of younger people that completes tertiary education is relatively low. The authorities are undertaking wide ranging reforms touching all levels of education to tackle these problems. Nevertheless, there is scope to go further by: increasing participation in early childhood education and care of children from less advantaged socio-economic backgrounds and improving the quality of such education; improving teaching quality; reducing stratification in the school system; and making tertiary education more attractive and responsive to labour-market requirements. With the reforms underway or suggested, Germany would be able to look forward to higher education achievement and attainment and, especially, greater equality of education opportunity. Ameliorer les resultants de l'enseignement en Allemagne Il importe d’ameliorer les resultats de l’enseignement pour les performances economiques a long terme et pour la cohesion sociale de l’Allemagne. Si les eleves reussissent mieux que la moyenne de l’OCDE en sciences et atteignent la moyenne en comprehension de l’ecrit et en mathematiques selon l’enquete PISA 2006 de l’OCDE, les eleves en difficulte ont generalement des resultats faibles par rapport a ceux des autres pays et l’influence du milieu socio-economique et/ou de l’origine est forte. Autre probleme : la proportion des jeunes qui achevent leurs etudes superieures est relativement faible. Les autorites ont entrepris une vaste reforme de l’ensemble du systeme educatif afin de resoudre ces difficultes. Neanmoins, il est possible d’aller plus loin, notamment en augmentant le nombre d’enfants de familles defavorisees inscrits dans les services d’education et d’accueil des jeunes enfants et en ameliorant la qualite de ces services, en rehaussant la qualite de l’enseignement, en reduisant la stratification du systeme scolaire, et en rendant l’enseignement superieur plus avantageux et plus reactif face aux exigences du marche du travail. Avec les reformes en cours ou proposees, l’Allemagne pourrait esperer des resultats scolaires et des niveaux de formation plus eleves et surtout, une plus grande egalite des chances dans le domaine de l’education.
A Sublinear Bipartiteness Tester for Bounded Degree Graphs

d, and the testing algorithm can perform queries of the form: ``who is the ith neighbor of vertex v''. The tester should determine with high probability whether the graph is bipartite or ε-far from bipartite for any given distance parameter ε. Distance between graphs is defined to be the fraction of entries on which the graphs differ in their incidence-lists representation. Our testing algorithm has query complexity and running time where N is the number of graph vertices. It was shown before that queries are necessary (for constant ε), and hence the performance of our algorithm is tight (in its dependence on N), up to polylogarithmic factors.In our analysis we use techniques that were previously applied to prove fast convergence of random walks on expander graphs. Here we use the contrapositive statement by which slow convergence implies small cuts in the graph, and further show that these cuts have certain additional properties. This implication is applied in showing that for any graph, the graph vertices can be divided into disjoint subsets such that: (1) the total number of edges between the different subsets is small; and (2) each subset itself exhibits a certain mixing property that is useful in our analysis.
Aberrant Promoter Methylation of Caveolin-1 Is Associated with Favorable Response to Taxane-Platinum Combination Chemotherapy in Advanced NSCLC

Purpose Aberrant promoter DNA methylation can serve as a predictive biomarker for improved clinical responses to certain chemotherapeutics. One of the major advantages of methylation biomarkers is the ease of detection and clinical application. In order to identify methylation biomarkers predictive of a response to a taxane-platinum based chemotherapy regimen in advanced NSCLC we performed an unbiased methylation analysis of 1,536 CpG dinucleotides in cancer-associated gene loci and correlated results with clinical outcomes. Methods We studied a cohort of 49 patients (median age 62 years) with advanced NSCLC treated at the Atlanta VAMC between 1999 and 2010. Methylation analysis was done on the Illumina GoldenGate Cancer panel 1 methylation microarray platform. Methylation data were correlated with clinical response and adjusted for false discovery rates. Results Cav1 methylation emerged as a powerful predictor for achieving disease stabilization following platinum taxane based chemotherapy (p = 1.21E-05, FDR significance = 0.018176). In Cox regression analysis after multivariate adjustment for age, performance status, gender, histology and the use of bevacizumab, CAV1 methylation was significantly associated with improved overall survival (HR 0.18 (95%CI: 0.03–0.94)). Silencing of CAV1 expression in lung cancer cell lines(A549, EKVX)by shRNA led to alterations in taxane retention. Conclusions CAV1 methylation is a predictor of disease stabilization and improved overall survival following chemotherapy with a taxane-platinum combination regimen in advanced NSCLC. CAV1 methylation may predict improved outcomes for other chemotherapeutic agents which are subject to cellular clearance mediated by caveolae.