p53 and DNA damage checkpoint responses in the human prostate

Prostate cancer is the most common noncutaneous malignancy and the second leading cause of cancer mortality in men. In 2004, 5237 new cases were diagnosed and altogether 25 664 men suffered from prostate cancer in Finland (Suomen Syöpärekisteri). Although extensively investigated, we still have a very rudimentary understanding of the molecular mechanisms leading to the frequent transformation of the prostate epithelium. Prostate cancer is characterized by several unique features including the multifocal origin of tumors and extreme resistance to chemotherapy, and new treatment options are therefore urgently needed. The integrity of genomic DNA is constantly challenged by genotoxic insults. Cellular responses to DNA damage involve elegant checkpoint cascades enforcing cell cycle arrest, thus facilitating damage repair, apoptosis or cellular senescence. Cellular DNA damage triggers the activation of tumor suppressor protein p53 and Wee1 kinase which act as executors of the cellular checkpoint responses. These are essential for genomic integrity, and are activated in early stages of tumorigenesis in order to function as barriers against tumor formation. Our work establishes that the primary human prostatic epithelial cells and prostatic epithelium have unexpectedly indulgent checkpoint surveillance. This is evidenced by the absence of inhibitory Tyr15 phosphorylation on Cdk2, lack of p53 response, radioresistant DNA synthesis, lack of G1/S and G2/M phase arrest, and presence of persistent gammaH2AX damage foci. We ascribe the absence of inhibitory Tyr15 phosphorylation to low levels of Wee1A, a tyrosine kinase and negative regulator of cell cycle progression. Ectopic Wee1A kinase restored Cdk2-Tyr15 phosphorylation and efficiently rescued the ionizing radiation-induced checkpoints in the human prostatic epithelial cells. As variability in the DNA damage responses has been shown to underlie susceptibility to cancer, our results imply that a suboptimal checkpoint arrest may greatly increase the accumulation of genetic lesions in the prostate epithelia. We also show that small molecules can restore p53 function in prostatic epithelial cells and may serve as a paradigm for the development of future therapeutic agents for the treatment of prostate cancer We hypothesize that the prostate has evolved to activate the damage surveillance pathways and molecules involved in these pathways only to certain stresses in extreme circumstances. In doing so, this organ inadvertently made itself vulnerable to genotoxic stress, which may have implications in malignant transformation. Recognition of the limited activity of p53 and Wee1 in the prostate could drive mechanism-based discovery of preventative and therapeutic agents.

Detecting rare events using Kullback-Leibler divergence: A weakly supervised approach

Video surveillance infrastructure has been widely installed in public places for security purposes. However, live video feeds are typically monitored by human staff, making the detection of important events as they occur difficult. As such, an expert system that can automatically detect events of interest in surveillance footage is highly desirable. Although a number of approaches have been proposed, they have significant limitations: supervised approaches, which can detect a specific event, ideally require a large number of samples with the event spatially and temporally localised; while unsupervised approaches, which do not require this demanding annotation, can only detect whether an event is abnormal and not specific event types. To overcome these problems, we formulate a weakly-supervised approach using Kullback-Leibler (KL) divergence to detect rare events. The proposed approach leverages the sparse nature of the target events to its advantage, and we show that this data imbalance guarantees the existence of a decision boundary to separate samples that contain the target event from those that do not. This trait, combined with the coarse annotation used by weakly supervised learning (that only indicates approximately when an event occurs), greatly reduces the annotation burden while retaining the ability to detect specific events. Furthermore, the proposed classifier requires only a decision threshold, simplifying its use compared to other weakly supervised approaches. We show that the proposed approach outperforms state-of-the-art methods on a popular real-world traffic surveillance dataset, while preserving real time performance.

DNA damage recognition in the normal epithelium of human prostate and seminal vesicles

Prostate cancer is one of the most prevalent cancer types in men. The development of prostate tumors is known to require androgen exposure, and several pathways governing cell growth are deregulated in prostate tumorigenesis. Recent genetic studies have revealed that complex gene fusions and copy - number alterations are frequent in prostate cancer, a unique feature among solid tumors. These chromosomal aberrations are though to arise as a consequence of faulty repair of DNA double strand breaks (DSB). Most repair mechanisms have been studied in detail in cancer cell lines, but how DNA damage is detected and repaired in normal differentiated human cells has not been widely addressed. The events leading to the gene fusions in prostate cancer are under rigorous studies, as they not only shed light on the basic pathobiologic mechanisms but may also produce molecular targets for prostate cancer treatment and prevention. Prostate and seminal vesicles are part of the male reproductive system. They share similar structure and function but differ dramatically in their cancer incidence. Approximately fifty primary seminal vesicle carcinomas have been reported worldwide. Surprisingly, only little is known on why seminal vesicles are resistant to neoplastic changes. As both tissues are androgen dependent, it is a mystery that androgen signaling would only lead to tumors in prostate tissue. In this work, we set up novel ex vivo human tissue culture models of prostate and seminal vesicles, and used them to study how DNA damage is recognized in normal epithelium. One of the major DNA - damage inducible pathways, mediated by the ATM kinase, was robustly activated in all main cell types of both tissues. Interestingly, we discovered that secretory epithelial cells had less histone variant H2A.X and after DNA damage lower levels of H2AX were phosphorylated on serine 139 (γH2AX) than in basal or stromal cells. γH2AX has been considered essential for efficient DSB repair, but as there were no significant differences in the γH2AX levels between the two tissues, it seems more likely that the role of γH2AX is less important in postmitotic cells. We also gained insight into the regulation of p53, an important transcription factor that protects genomic integrity via multiple mechanisms, in human tissues. DSBs did not lead to a pronounced activation of p53, but treatments causing transcriptional stress, on the other hand, were able to launch a notable p53 response in both tissue types. In general, ex vivo culturing of human tissues provided unique means to study differentiated cells in their relevant tissue context, and is suited for testing novel therapeutic drugs before clinical trials. In order to study how prostate and seminal vesicle epithelial cells are able to activate DNA damage induced cell cycle checkpoints, we used primary cultures of prostate and seminal vesicle epithelial cells. To our knowledge, we are the first to report isolation of human primary seminal vesicle cells. Surprisingly, human prostate epithelial cells did not activate cell cycle checkpoints after DSBs in part due to low levels of Wee1A, a kinase regulating CDK activity, while primary seminal vesicle epithelial cells possessed proficient cell cycle checkpoints and expressed high levels of Wee1A. Similarly, seminal vesicle cells showed a distinct activation of the p53 - pathway after DSBs that did not occur in prostate epithelial cells. This indicates that p53 protein function is under different control mechanisms in the two cell types, which together with proficient cell cycle checkpoints may be crucial in protecting seminal vesicles from endogenous and exogenous DNA damaging factors and, as a consequence, from carcinogenesis. These data indicate that two very similar organs of male reproductive system do not respond to DNA damage similarly. The differentiated, non - replicating cells of both tissues were able to recognize DSBs, but under proliferation human prostate epithelial cells had deficient activation of the DNA damage response. This suggests that prostate epithelium is most vulnerable to accumulating genomic aberrations under conditions where it needs to proliferate, for example after inflammatory cellular damage.

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

H. 264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.

Online Estimation of Evolving Human Visual Interest

Regions in video streams attracting human interest contribute significantly to human understanding of the video. Being able to predict salient and informative Regions of Interest (ROIs) through a sequence of eye movements is a challenging problem. Applications such as content-aware retargeting of videos to different aspect ratios while preserving informative regions and smart insertion of dialog (closed-caption text) into the video stream can significantly be improved using the predicted ROIs. We propose an interactive human-in-the-loop framework to model eye movements and predict visual saliency into yet-unseen frames. Eye tracking and video content are used to model visual attention in a manner that accounts for important eye-gaze characteristics such as temporal discontinuities due to sudden eye movements, noise, and behavioral artifacts. A novel statistical-and algorithm-based method gaze buffering is proposed for eye-gaze analysis and its fusion with content-based features. Our robust saliency prediction is instantiated for two challenging and exciting applications. The first application alters video aspect ratios on-the-fly using content-aware video retargeting, thus making them suitable for a variety of display sizes. The second application dynamically localizes active speakers and places dialog captions on-the-fly in the video stream. Our method ensures that dialogs are faithful to active speaker locations and do not interfere with salient content in the video stream. Our framework naturally accommodates personalisation of the application to suit biases and preferences of individual users.

Expression and activity profiles of DPP IV/CD26 and NEP/CD10 glycoproteins in the human renal cancer are tumor-type dependent

Background: Cell-surface glycoproteins play critical roles in cell-to-cell recognition, signal transduction and regulation, thus being crucial in cell proliferation and cancer etiogenesis and development. DPP IV and NEP are ubiquitous glycopeptidases closely linked to tumor pathogenesis and development, and they are used as markers in some cancers. In the present study, the activity and protein and mRNA expression of these glycoproteins were analysed in a subset of clear-cell (CCRCC) and chromophobe (ChRCC) renal cell carcinomas, and in renal oncocytomas (RO). Methods: Peptidase activities were measured by conventional enzymatic assays with fluorogen-derived substrates. Gene expression was quantitatively determined by qRT-PCR and membrane-bound protein expression and distribution analysis was performed by specific immunostaining. Results: The activity of both glycoproteins was sharply decreased in the three histological types of renal tumors. Protein and mRNA expression was strongly downregulated in tumors from distal nephron (ChRCC and RO). Moreover, soluble DPP IV activity positively correlated with the aggressiveness of CCRCCs (higher activities in high grade tumors). Conclusions: These results support the pivotal role for DPP IV and NEP in the malignant transformation pathways and point to these peptidases as potential diagnostic markers.

Skeleton based visual pattern recognition. Applications to tabletop interaction

211 p. :il.

Computer Recognition of Three-Dimensional Objects in a Visual Scene

Methods are presented (1) to partition or decompose a visual scene into the bodies forming it; (2) to position these bodies in three-dimensional space, by combining two scenes that make a stereoscopic pair; (3) to find the regions or zones of a visual scene that belong to its background; (4) to carry out the isolation of objects in (1) when the input has inaccuracies. Running computer programs implement the methods, and many examples illustrate their behavior. The input is a two-dimensional line-drawing of the scene, assumed to contain three-dimensional bodies possessing flat faces (polyhedra); some of them may be partially occluded. Suggestions are made for extending the work to curved objects. Some comparisons are made with human visual perception. The main conclusion is that it is possible to separate a picture or scene into the constituent objects exclusively on the basis of monocular geometric properties (on the basis of pure form); in fact, successful methods are shown.

Recognition of Human Action Using Moment-Based Feature

The performance of different classification approaches is evaluated using a view-based approach for motion representation. The view-based approach uses computer vision and image processing techniques to register and process the video sequence. Two motion representations called Motion Energy Images and Motion History Image are then constructed. These representations collapse the temporal component in a way that no explicit temporal analysis or sequence matching is needed. Statistical descriptions are then computed using moment-based features and dimensionality reduction techniques. For these tests, we used 7 Hu moments, which are invariant to scale and translation. Principal Components Analysis is used to reduce the dimensionality of this representation. The system is trained using different subjects performing a set of examples of every action to be recognized. Given these samples, K-nearest neighbor, Gaussian, and Gaussian mixture classifiers are used to recognize new actions. Experiments are conducted using instances of eight human actions (i.e., eight classes) performed by seven different subjects. Comparisons in the performance among these classifiers under different conditions are analyzed and reported. Our main goals are to test this dimensionality-reduced representation of actions, and more importantly to use this representation to compare the advantages of different classification approaches in this recognition task.

Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.

Multimodal Biometric Human Recognition for Perceptual Human-Computer Interaction

In this paper, a novel video-based multimodal biometric verification scheme using the subspace-based low-level feature fusion of face and speech is developed for specific speaker recognition for perceptual human--computer interaction (HCI). In the proposed scheme, human face is tracked and face pose is estimated to weight the detected facelike regions in successive frames, where ill-posed faces and false-positive detections are assigned with lower credit to enhance the accuracy. In the audio modality, mel-frequency cepstral coefficients are extracted for voice-based biometric verification. In the fusion step, features from both modalities are projected into nonlinear Laplacian Eigenmap subspace for multimodal speaker recognition and combined at low level. The proposed approach is tested on the video database of ten human subjects, and the results show that the proposed scheme can attain better accuracy in comparison with the conventional multimodal fusion using latent semantic analysis as well as the single-modality verifications. The experiment on MATLAB shows the potential of the proposed scheme to attain the real-time performance for perceptual HCI applications.

AVEC 2011–The First International Audio/Visual Emotion Challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. This paper first describes the challenge participation conditions. Next follows the data used – the SEMAINE corpus – and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are introduced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition.

Adaptive Fusion Of Particle Filtering And Spatio-Temporal Motion Energy For Human Tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.