Search

Periostin is a secreted extracellular matrix protein preferentially expressed in bone by osteocytes and periosteal osteoblasts. Reduced periostin expression may affect osteoblast differentiation and collagen type I synthesis and predispose to osteoporosis and increased fracture risk. We aimed to evaluate circulating periostin levels in postmenopausal women with low bone mass, their possible correlations with clinical and laboratory parameters, as well as the 3-month effect of zoledronic acid. Serum samples for periostin, 25-hydroxyvitamin D, parathyroid hormone (PTH), C-terminal telopeptide of type I collagen (CTx), and total alkaline phosphatase (tALP) were obtained from 46 postmenopausal women with low bone mass at baseline and 3 months after zoledronic acid infusion and from 30 age-matched, postmenopausal controls with normal bone mass at baseline. There was no difference in periostin levels between women with normal and low bone mass (250±15 vs. 272±14 ng/ml, respectively; p=0.279). Periostin remained essentially unchanged after zoledronic acid infusion (262±18 ng/ml; p=0.130). Serum periostin levels at baseline were not affected by previous bisphosphonate treatment, and were correlated only to tALP (rs=0.351; p=0.018). In multiple linear regression analysis, tALP (B=3.17; 95% CI=0.59–5.79; p=0.018) was associated with serum periostin levels at baseline, independently from previous anti-osteoporotic treatment, age, body mass index, and 25-hydroxyvitamin D. In conclusion, serum periostin levels do not differ between postmenopausal women with normal and low bone mass and are not affected by zoledronic acid treatment. Women with higher tALP have independently higher periostin levels.

Activin-A is expressed in bone and seems to regulate osteoclastogenesis. In this study, serum activin-A was increased in postmenopausal women with low bone mass and was positively correlated to age and negatively to lumbar spinal bone mineral density (BMD). Serum activin-A levels did not change 3 months after zoledronic acid infusion.The aims of the study were to evaluate prospectively the circulating activin-A levels in postmenopausal women with low bone mass and explore possible correlations with clinical and laboratory data, as well as the 3-month effect of zoledronic acid infusion.Postmenopausal women with low bone mass assigned to receive zoledronic acid infusion (Patients, n = 47) and age-matched, postmenopausal women with normal bone mass (Controls, n = 27) were recruited on an outpatient basis. Main outcome measurement was serum activin-A levels.Serum activin-A was higher in patients at baseline compared to controls (p 0.001) and activin-A in the serum of patients and controls was positively correlated with age (Spearman's coefficient of correlation [rs] = 0.325; p = 0.005) and negatively with lumbar spinal (LS) BMD (rs = -0.425; p 0.001). In multiple linear regression analysis, only age (B = 8.93; 95 % CI = 4.39-13.46; p 0.001) was associated with serum activin-A levels at baseline, independent from group (patients or controls), previous anti-osteoporotic treatment, LS BMD and follicle-stimulating hormone. Circulating activin-A levels were not affected 3 months after zoledronic acid infusion.Serum activin-A is increased in postmenopausal women with low bone mass compared with postmenopausal women with normal bone mass and is positively correlated to age and negatively to LS BMD.

Facial images change appearance due to multiple factors such as different poses, lighting variations, and facial expressions. Tensors are higher order extensions of vectors and matrices, which make it possible to analyze different appearance factors of facial variation. Using higher order tensors, we can construct a multilinear structure and model the multiple factors of face variation. In particular, among the appearance factors, the factor of a person's identity modeled by a tensor structure can be used for face recognition. However, this tensor-based face recognition creates difficulty in factorizing the unknown parameters of a new test image and solving for the person-identity parameter. In this paper, to break this limitation of applying the tensor-based methods to face recognition, we propose a novel tensor approach based on an individual-modeling method and nonlinear mappings. The proposed method does not require the problematic tensor factorization and is more efficient than the traditional TensorFaces method with respect to computation and memory. We set up the problem of solving for the unknown factors as a least squares problem with a quadratic equality constraint and solve it using numerical optimization techniques. We show that an individual-multilinear approach reduces the order of the tensor so that it makes face-recognition tasks computationally efficient as well as analytically simpler. We also show that nonlinear kernel mappings can be applied to this optimization problem and provide more accuracy to face-recognition systems than linear mappings. In this paper, we show that the proposed method, individual kernel TensorFaces, produces the better discrimination power for classification. The novelty in our approach as compared to previous work is that the Individual Kernel TensorFaces method does not require estimating any factor of a new test image for face recognition. In addition, we do not need to have any a priori knowledge of or assumption about the factors of a test image when using the proposed method. We can apply individual kernel TensorFaces even if the factors of a test image are absent from the training set. Based on various experiments on the Carnegie Mellon University Pose, Illumination, and Expression database, we demonstrate that the proposed method produces reliable results for face recognition.

We present a real-time face recognition system that we implemented on a cell-phone using computationally efficient minimum average correlation energy filters. We examine the challenges faced in realizing such a practical system that can perform both enrollment and verification in real-time on the cell-phone using its limited memory and computational resources. Due to the speed and memory constraints of the cellphone, we optimize the correlation filters using a fast fixed-point implementation of 2D Fourier transform optimal for the cell-phone platform. We also detail an efficient algorithm for synthesizing these filters efficiently in order to minimize computation load and making the system practical with real-time enrollment of users.

This article describes a system for interfacing items of computing equipment in circum-stances where the short-term data rate of the source exceeds that of the receiver device. Outline circuitry is given for an application permitting high-speed input to an event-driven time and event data logger.