STAT3 signaling is activated in human skeletal muscle following acute resistance exercise

The transcription factor signal transducer and activator of transcription 3 (STAT3) has been identified as a mediator of cytokine signaling and implicated in hypertrophy; however, the importance of this pathway following resistance exercise in human skeletal muscle has not been investigated. In the present study, the phosphorylation and nuclear localization of STAT3, together with STAT3-regulated genes, were measured in the early recovery period following intense resistance exercise. Muscle biopsy samples from healthy subjects (7 males, 23.0 + 0.9 yr) were harvested before and again at 2, 4, and 24 h into recovery following a single bout of maximal leg extension exercise (3 sets, 12 repetitions). Rapid and transient activation of phosphorylated (tyrosine 705) STAT3 was observed at 2 h postexercise. STAT3 phosphorylation paralleled the transient localization of STAT3 to the nucleus, which also peaked at 2 h postexercise. Downstream transcriptional events regulated by STAT3 activation peaked at 2 h postexercise, including early responsive genes c-FOS (800-fold), JUNB (38-fold), and c-MYC (140-fold) at 2 h postexercise. A delayed peak in VEGF (4-fold) was measured 4 h postexercise. Finally, genes associated with modulating STAT3 signaling were also increased following exercise, including the negative regulator SOCS3 (60-fold). Thus, following a single bout of intense resistance exercise, a rapid phosphorylation and nuclear translocation of STAT3 are evident in human skeletal muscle. These data suggest that STAT3 signaling is an important common element and may contribute to the remodeling and adaptation of skeletal muscle following resistance exercise.

Expression of the disintegrin metalloprotease ADAM-10 in prostate cancer and its regulation by dihydrotestosterone, insulin like growth factor -1 and epidermal growth factor in the prostate cell model LNCaP

Purpose: The disintegrin metalloprotease ADAM-10 is a multidomain metalloprotease that is potentially significant in tumor progression due to its extracellular matrix-degrading properties. Previously, ADAM-10 mRNA was detected in prostate cancer (PCa) cell lines; however, the presence of ADAM-10 protein and its cellular localization, regulation, and role have yet to be described. We hypothesized that ADAM-10 mRNA and protein may be regulated by growth factors such as 5α-dihydrotestosterone, insulin-like growth factor I, and epidermal growth factor, known modulators of PCa cell growth and invasion.

Experimental Design: ADAM-10 expression was analyzed by in situ hybridization and immunohistochemistry in prostate tissues obtained from 23 patients with prostate disease. ADAM-10 regulation was assessed using quantitative reverse transcription-PCR and Western blot analysis in the PCa cell line LNCaP.

Results: ADAM-10 expression was localized to the secretory cells of prostate glands, with additional basal cell expression in benign glands. ADAM-10 protein was predominantly membrane bound in benign glands but showed marked nuclear localization in cancer glands. By Western blot, the 100-kDa proform and the 60-kDa active form of ADAM-10 were synergistically up-regulated in LNCaP cells treated with insulin-like growth factor I plus 5α-dihydrotestosterone. Epidermal growth factor also up-regulated both ADAM-10 mRNA and protein.

Conclusions: This study describes for the first time the expression, regulation, and cellular localization of ADAM-10 protein in PCa. The regulation and membrane localization of ADAM-10 support our hypothesis that ADAM-10 has a role in extracellular matrix maintenance and cell invasion, although the potential role of nuclear ADAM-10 is not yet known.

Establishment of HIV-1 latency in resting CD4+ T cells depends on chemokine-induced changes in the actin cytoskeleton

Eradication of HIV-1 with highly active antiretroviral therapy (HAART) is not possible due to the persistence of long-lived, latently infected resting memory CD4+ T cells. We now show that HIV-1 latency can be established in resting CD4+ T cells infected with HIV-1 after exposure to ligands for CCR7 (CCL19), CXCR3 (CXCL9 and CXCL10), and CCR6 (CCL20) but not in unactivated CD4+ T cells. The mechanism did not involve cell activation or significant changes in gene expression, but was associated with rapid dephosphorylation of cofilin and changes in filamentous actin. Incubation with chemokine before infection led to efficient HIV-1 nuclear localization and integration and this was inhibited by the actin stabilizer jasplakinolide. We propose a unique pathway for establishment of latency by direct HIV-1 infection of resting CD4+ T cells during normal chemokine-directed recirculation of CD4+ T cells between blood and tissue.

Comparative phylogeography of alpine invertebrates indicates deep lineage diversification and historical refugia in the Australian Alps

Aim: Comparative phylogeographic analyses of alpine biota from the Northern Hemisphere have linked patterns of genetic diversification to glacial expansion and contraction events in the Pliocene and Pleistocene. Furthermore, the extent of diversification across species groups appears to be associated with vagility. In this study we test whether these patterns apply to a geologically stable system from eastern Australia with comparatively shallow elevational gradients and minimal influence from historical glacial activity. Location: The Australian Alps, Victoria, eastern Australia. Methods: We considered phylogeographic patterns across five alpine invertebrate species based on mitochondrial and nuclear DNA sequence data. Bayesian inference methods were used to estimate species phylogenies and divergence times among lineages. GIS tools were used to map interpopulation genetic divergence and intrapopulation genetic diversity estimates and to visualize spatial patterns across species, providing insights into patterns of endemism and demographic history. Results: Phylogeographic patterns and the timing of lineage diversification were consistent across taxonomic groups. Mountain summits harbour highly differentiated haplogroups, including summits connected by high-elevational plateaus, pointing to diversifications being maintained since the early to mid-Pleistocene. These findings are consistent with previous studies of alpine mammals and reptiles, demonstrating a high degree of endemism in this region, regardless of species vagility. Main conclusions: The fine spatial scales at which deep genetic differentiation among alpine communities was observed in this study are unprecedented. This suggests that glacial periods have had less of an impact on species distributions and genetic diversity than they have in alpine systems in the Northern Hemisphere. Historical gene flow among sky-island populations has been limited despite connecting snowlines during glacial periods, suggesting that factors other than snow cover have influenced patterns of gene flow in this region. These findings emphasize the unique phylogeographic history affecting Victorian alpine biodiversity, and the importance of conserving biodiversity from multiple mountain summits in this region of high endemism.

Learning discriminative sequence models from partially labelled data for activity recognition

Recognising daily activity patterns of people from low-level sensory data is an important problem. Traditional approaches typically rely on generative models such as the hidden Markov models and training on fully labelled data. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support fully supervised learning is often expensive. In this paper, we propose a new approach based on partially-supervised training of discriminative sequence models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that the approach can reduce labelling effort, and at the same time, provides us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart (i.e. the partially hidden Markov model), even when a substantial amount of labels are unavailable.

Molecular recognition of DNA by rigid [n]-polynorbornane-derived bifunctional intercalators: synthesis and evaluation of their binding properties

We have exploited the concept of multivalency in the context of DNA recognition, using novel chemistry to synthesize a new type of bis-intercalator with unusual sequence-selectivity. Bis-intercalation has been observed previously, but design principles for de novo construction of such molecules are not known. Our compounds feature two aromatic moieties projecting from a rigid, polynorbornane-based scaffold. The length and character of the backbone as well as the identity of the intercalators were varied, resulting in mono- or divalent recognition of the double helix with varying affinity. Our lead compound proved to be a moderately sequence-selective bis-intercalator with an unwinding angle of 27 and a binding constant of about 8 M. 9-Aminoacridine rings were preferred over acridine carboxamides or naphthalimides, and a rigid [3]-polynorbornane scaffold was superior to a [5]-polynorbornane. The flexibility of the linker connecting the rings to the scaffold, although less influential, could affect the strength and character of the DNA binding.

Single-walled carbon nanotubes with DNA recognition

Two methods for attaching DNA to oxidized single-walled carbon nanotubes either in organic solvent or aqueous solution are described. The sites of DNA attachment to the nanotubes have been verified by binding gold nanoparticles modified with DNA of complementary sequence to the DNA-modified nanotubes, and imaging with TEM. The gold nanoparticles appear on the tips of the nanotubes, and at isolated positions (defects) on the sidewalls. The methods provide versatility for the modification of nanotubes with DNA for their directed assembly, or for their composites with gold nanoparticles, into nanoscale devices.

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

A smith-waterman local alignment approach for spatial activity recognition

In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

Learning people movement model from multiple cameras for behaviour recognition

In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.

On the recognition of abstract Markov policies

Abstraction plays an essential role in the way the agents plan their behaviours, especially to reduce the computational complexity of planning in large domains. However, the effects of abstraction in the inverse process – plan recognition – are unclear. In this paper, we present a method for recognising the agent’s behaviour in noisy and uncertain domains, and across multiple levels of abstraction. We use the concept of abstract Markov policies in abstract probabilistic planning as the model of the agent’s behaviours and employ probabilistic inference in Dynamic Bayesian Networks (DBN) to infer the correct policy from a sequence of observations. When the states are fully observable, we show that for a broad and often-used class of abstract policies, the complexity of policy recognition scales well with the number of abstraction levels in the policy hierarchy. For the partially observable case, we derive an efficient hybrid inference scheme on the corresponding DBN to overcome the exponential complexity.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

Face Recognition from Video Using the Generic Shape-Illumination Manifold

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence “reillumination” algorithm to achieve robustness to face motion patterns in video. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our system consistently demonstrated a nearly perfect recognition rate (over 99.7%), significantly outperforming state-of-the-art commercial software and methods from the literature