Clinical and Genetic Risk Factors Associated with Psoriatic Arthritis among Patients with Psoriasis.

IntroductionPsoriatic arthritis (PsA) is a chronic, inflammatory arthritis that affects an estimated 30% of patients with psoriasis. PsA is underdiagnosed in primary care and dermatology clinics due to a variety of reasons, including failure of healthcare providers to ask about symptoms, overlap of symptoms and signs with other rheumatologic conditions, and lack of a specific diagnostic test. A delay in PsA diagnosis and treatment, even as short as 6 months, can lead to decreased quality of life, increased joint damage, and worse long-term physical function. In this study, we sought to identify the clinical and genetic factors that help discriminate patients with PsA from those with cutaneous psoriasis only.MethodsWe analyzed a cohort of 974 psoriasis patients at an academic medical center, of whom 175 had confirmed PsA, and performed univariate, multivariate, and predictive modeling to determine factors associated with PsA.ResultsThe univariate analysis revealed significant positive associations of PsA with age, nail involvement, scalp involvement, skin fold involvement, elbow/knee involvement, psoriasis severity, plaque subtype, erythrodermic subtype, hypertension, type 2 diabetes, and coronary artery disease, and a significant negative association of PsA with the human leukocyte antigen (HLA)-C*06:02 allele. In the multivariate analysis, nail involvement, type 2 diabetes, and pustular psoriasis remained significantly associated with PsA, while HLA-C*06:02 positivity remained protective. There was a trend towards an association of PsA with older age, younger age of psoriasis onset, and skin fold involvement, while there was protective trend for smoking. A predictive model including both clinical and genetic factors showed reasonable discriminative ability between psoriasis and PsA, with an area under the curve of 0.87 for a receiver operating characteristic curve.ConclusionThis study identified a number of clinical and genetic features that could help stratify patients who are at higher risk for having PsA and for whom rheumatology referral may be beneficial

An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection

Sophisticated automatic incident detection (AID) technology plays a key role in contemporary transportation systems. Though many papers were devoted to study incident classification algorithms, few study investigated how to enhance feature representation of incidents to improve AID performance. In this paper, we propose to use an unsupervised feature learning algorithm to generate higher level features to represent incidents. We used real incident data in the experiments and found that effective feature mapping function can be learnt from the data crosses the test sites. With the enhanced features, detection rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are significantly improved in all of the three representative cases. This approach also provides an alternative way to reduce the amount of labeled data, which is expensive to obtain, required in training better incident classifiers since the feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation Systems (ITSC 2012

Generalized visual information analysis via tensorial algebra

High order data is modeled using matrices whose entries are numerical arrays of a fixed size. These arrays, called t-scalars, form a commutative ring under the convolution product. Matrices with elements in the ring of t-scalars are referred to as t-matrices. The t-matrices can be scaled, added and multiplied in the usual way. There are t-matrix generalizations of positive matrices, orthogonal matrices and Hermitian symmetric matrices. With the t-matrix model, it is possible to generalize many well known matrix algorithms. In particular, the t-matrices are used to generalize the SVD (Singular Value Decomposition), HOSVD (High Order SVD), PCA (Principal Component Analysis), 2DPCA (two Dimensional PCA) and GCA (Grassmannian Component Analysis). The generalized t-matrix algorithms, namely TSVD, THOSVD, TPCA, T2DPCA and TGCA, are applied to low-rank approximation, reconstruction and supervised classification of images. Experiments show that the t-matrix algorithms compare favourably with standard matrix algorithms

Diving beetle (Coleoptera: Dytiscidae) community dissimilarity reveals how low landscape connectivity restricts the ecological value of urban ponds.

Context: Structural and functional connectivity, as subconcepts of landscape connectivity, are key factors in biodiversity conservation and management. Previous studies have focused on the consequences of connectivity for populations of terrestrial organisms, which may not be appropriate for aquatic organisms. Objectives: As landscape connectivity critically affects the potential value of ponds for biodiversity, here we used diving beetles (Dytiscidae), an indicator taxon of wetland biodiversity, to investigate how structural connectivity affects functional connectivity to aquatic invertebrates in an urban landscape. Methods: We assessed pairwise similarities of dytiscid community, i.e. the variation of species composition between clustered and isolated ponds in the Helsinki Metropolitan Area, Finland. We investigated how dytiscid community similarity is affected by Euclidean distances between ponds, as an indicator of structural connectivity. Results: We found that clustered ponds shared more species than isolated ponds. Dytiscid species community similarity responded negatively to increasing Euclidean distance between ponds. Effectively dispersing species were widely distributed across the landscape, while poor dispersers were scarcely distributed in the same landscape. Conclusions: Structural connectivity determines which species are able to disperse successfully, with poor dispersers restricted to well-connected ponds. The different responses of effective dispersers and poor dispersers to the same structural connectivity indicate that functional connectivity determines species composition. We recommend providing well-connected aquatic habitats in urban landscapes and the implementation of measures to reduce isolation of wetland assemblages. Even clustered ponds need dispersal from other habitats to ensure their contribution to urban biodiversity.Peer reviewe

The Importance of Personalization in Affecting Consumer Attitudes toward Mobile Advertising in China

Empowered by the Web’s interactive and quick-response capabilities, mobile marketing is a very promising direct marketing channel. The present research investigates consumer attitudes toward mobile advertising in China. The results of a survey indicate that (1) consumers in China generally have slightly negative attitudes toward receiving mobile advertising (2) there is a direct relationship between consumer attitudes and consumer intention in receiving mobile advertising. (3) Personalization plays an important role in affecting consumers’ attitude toward receiving mobile advertising. Thus the designers and marketers should better strategize their advertising designs by considering the personalization factor

Microhabitats with emergent plants counterbalance the negative effects of fish presence on diving beetle (Coleoptera: Dytiscidae) diversity in urban ponds

Aquatic plants are important prey refuges for aquatic organisms, and their species richness is positively related with aquatic invertebrate species richness. Yet, it is unclear how the quantity of refuges, i.e. aquatic vegetation cover, affect aquatic invertebrate assemblages and their habitat use under different levels of predation risk (e.g. in the presence or absence of fish), nor at different scales (i.e. microhabitat and pond scales). Here, we investigated how provision of refuges affects diving beetle (Dytiscidae) species richness and abundance in the presence and absence of fish predators. We sampled diving beetles (Dytiscidae) with 1-L activity traps and estimated emergent plant cover at both the pond and microhabitat scales. We found that plant cover affected dytiscid populations differently: At the pond scale, dytiscid presence was positively correlated with increasing plant cover, both with and without fish, indicating the importance of emergent plants for aquatic biodiversity. At the microhabitat scale, dytiscid species richness and abundance were positively correlated with increasing plant cover in ponds with fish, but there was no such relationship in ponds without fish, emphasizing that the level of predation risk can alter prey species’ use of prey refuges. Our findings provide evidence that the availability of both vegetated and non-vegetated microhabitats can benefit aquatic invertebrates. We suggest maintaining variation in the provision of emergent plant cover, to retain high habitat heterogeneity in urban ponds and to enhance freshwater biodiversity.Peer reviewe