Rapid detection of SMARCB1 sequence variation using high resolution melting

Background : Rhabdoid tumors are rare cancers of early childhood arising in the kidney, central nervous system and other organs. The majority are caused by somatic inactivating mutations or deletions affecting the tumor suppressor locus SMARCB1 [OMIM 601607]. Germ-line SMARCB1 inactivation has been reported in association with rhabdoid tumor, epitheloid sarcoma and familial schwannomatosis, underscoring the importance of accurate mutation screening to ascertain recurrence and transmission risks. We describe a rapid and sensitive diagnostic screening method, using high resolution melting (HRM), for detecting sequence variations in SMARCB1. Methods : Amplicons, encompassing the nine coding exons of SMARCB1, flanking splice site sequences and the 5' and 3' UTR, were screened by both HRM and direct DNA sequencing to establish the reliability of HRM as a primary mutation screening tool. Reaction conditions were optimized with commercially available HRM mixes. Results : The false negative rate for detecting sequence variants by HRM in our sample series was zero. Nine amplicons out of a total of 140 (6.4%) showed variant melt profiles that were subsequently shown to be false positive. Overall nine distinct pathogenic SMARCB1 mutations were identified in a total of 19 possible rhabdoid tumors. Two tumors had two distinct mutations and two harbored SMARCB1 deletion. Other mutations were nonsense or frame-shifts. The detection sensitivity of the HRM screening method was influenced by both sequence context and specific nucleotide change and varied from 1: 4 to 1:1000 (variant to wild-type DNA). A novel method involving digital HRM, followed by re-sequencing, was used to confirm mutations in tumor specimens containing associated normal tissue. Conclusions : This is the first report describing SMARCB1 mutation screening using HRM. HRM is a rapid, sensitive and inexpensive screening technology that is likely to be widely adopted in diagnostic laboratories to facilitate whole gene mutation screening.

Screening and intervention for mental health problems in alcohol and other drug settings: Can training change practitioner behaviour?

Aims: The comorbidity of substance use and mental health problems poses a significant challenge for alcohol and other drug (AOD) treatment services. In many cases, AOD practitioners do not have experience or training in identifying or managing mental health conditions. Methods: This project examined the implementation of screening and intervention practices for mental health disorders among AOD clients. Training and supervision was provided to 20 AOD practitioners across five sites in four agencies with a focus on enhancing skills in detection of, and intervention for, mental health conditions among their clients. A package developed for this purpose, known as PsyCheck, was used. A random file audit was undertaken to examine changes in detection of mental health conditions. Findings: There were significant improvements in detection after training and supervision, with detection rates almost doubling in this time. Conclusions: Training and supervision using the PsyCheck package appears to have the potential to improve mental health detection and intervention in AOD services. This study shows promise for the implementation of mental health intervention in AOD services.

Contextually Learnt Detection of Unusual Motion-Based Behaviour in Crowded Public Spaces

In this paper we are interested in analyzing behaviour in crowded publicplaces at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of ‘‘normal behaviour’’ for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scenespecific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2 h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

A biomimetic sensor for the detection of lead in water.

The monitoring of lead (II) ions (Pb(2+)) in water is essential for both human health and the environment. Herein, a simple yet innovative biosensor for Pb(2+) detection is presented. The sensor is developed by the self-assembly of gold nanoparticles (GNPs) core-satellite structure using naturally occurring tripeptide glutathione (GSH) as linker. The addition of Pb(2+) caused a red-to-blue color change and the localized surface plasmon resonance (LSPR) band was shifted to ca. 650nm. The limit of detection (LOD) is found to be 47.6nM (9.9ppb) by UV-vis spectroscopy with high selectivity against other heavy metals. This method offers a new strategy for heavy metal detection using functionalized GNPs.

Accounting for uncertainty in volumes of seabed change measured with repeat multibeam sonar surveys

Seafloors of unconsolidated sediment are highly dynamic features; eroding or accumulating under the action of tides, waves and currents. Assessing which areas of the seafloor experienced change and measuring the corresponding volumes involved provide insights into these important active sedimentation processes. Computing the difference between Digital Elevation Models (DEMs) obtained from repeat Multibeam Echosounders (MBES) surveys has become a common technique to identify these areas, but the uncertainty in these datasets considerably affects the estimation of the volumes displaced. The two main techniques used to take into account uncertainty in volume estimations are the limitation of calculations to areas experiencing a change in depth beyond a chosen threshold, and the computation of volumetric confidence intervals. However, these techniques are still in their infancy and, as a result, are often crude, seldom used or poorly understood. In this article, we explored a number of possible methodological advances to address this issue, including: (1) using the uncertainty information provided by the MBES data processing algorithm CUBE, (2) adapting fluvial geomorphology techniques for volume calculations using spatially variable thresholds and (3) volumetric histograms. The nearshore seabed off Warrnambool harbour - located in the highly energetic southwest Victorian coast, Australia - was used as a test site. Four consecutive MBES surveys were carried out over a four-months period. The difference between consecutive DEMs revealed an area near the beach experiencing large sediment transfers - mostly erosion - and an area of reef experiencing increasing deposition from the advance of a nearby sediment sheet. The volumes of sediment displaced in these two areas were calculated using the techniques described above, both traditionally and using the suggested improvements. We compared the results and discussed the applicability of the new methodological improvements. We found that the spatially variable uncertainty derived from the CUBE algorithm provided the best results (i.e. smaller confidence intervals), but that similar results can be obtained using as a fixed uncertainty value derived from a reference area under a number of operational conditions.

Detection of anomalous crowd behaviour using hyperspherical clustering

Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect anomalous events and objects, two types of feature coding has been proposed: spatial features and spatio-temporal features. Spatial features comprises of contrast, correlation, energy and homogeneity, which are derived from Gray Level Co-occurrence Matrix (GLCM). Spatio-temporal feature includes the time spent by an object at different locations in the scene. Hyperspherical clustering has been employed to detect the anomalies. Spatial features revealed the anomalous frames by using contrast and homogeneity measures. Loitering behaviour of the people were detected as anomalous objects using the spatio-temporal coding.

Profiling pedestrian distribution and anomaly detection in a dynamic environment

Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (Hy-CARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.

A visual-numeric approach to clustering and anomaly detection for trajectory data

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.

Self-assembled core-satellite gold nanoparticle networks for ultrasensitive detection of chiral molecules by recognition tunneling current

Chirality sensing is a very challenging task. Here, we report a method for ultrasensitive detection of chiral molecule l/d-carnitine based on changes in the recognition tunneling current across self-assembled core-satellite gold nanoparticle (GNP) networks. The recognition tunneling technique has been demonstrated to work at the single molecule level where the binding between the reader molecules and the analytes in a nanojunction. This process was observed to generate a unique and sensitive change in tunneling current, which can be used to identify the analytes of interest. The molecular recognition mechanism between amino acid l-cysteine and l/d-carnitine has been studied with the aid of SERS. The different binding strength between homo- or heterochiral pairs can be effectively probed by the copper ion replacement fracture. The device resistance was measured before and after the sequential exposures to l/d-carnitine and copper ions. The normalized resistance change was found to be extremely sensitive to the chirality of carnitine molecule. The results suggested that a GNP networks device optimized for recognition tunneling was successfully built and that such a device can be used for ultrasensitive detection of chiral molecules.

CCTV scene perspective distortion estimation from low-level motion features

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to use low-level spatiotemporally local motion features only. This particular challenge arises in many semiautomatic surveillance systems that alert a human operator to potential abnormalities in the scene. Low-level spatiotemporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions. First, we describe a dense algorithm for perspective estimation, which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate. Second, we also present an alternative coarse algorithm that subdivides the image frame into blocks and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme. Third, we report the results of an evaluation using nine large sets acquired using existing closed-circuit television cameras, not installed specifically for the purposes of this paper. Our findings demonstrate that both proposed methods are successful, their accuracy matching that of human labeling using complete visual data (by the constraints of the setup unavailable to our algorithms).