964 resultados para auditory attention detection
Resumo:
The Escherichia coli mu operon was subcloned into a pKK233-2 vector containing rat glutathione S-transferase (GST) 5-5 cDNA and the plasmid thus obtained was introduced into Salmonella typhimurium TA1535. The newly developed strain S.typhimurium NM5004, was found to have 52-fold greater GST activity than the original umu strain S.typhimurium TA1535/pSK1002. We compared sensitivities of these two tester strains, NM5004 and TA1535/ pSK1002, for induction of umuC gene expression with several dihaloalkanes which are activated or inactivated by GST 5-5 activity. The induction of umuC gene expression by these chemicals was monitored by measuring the cellular P-galactosidase activity produced by umuC'lacZ fusion gene in these two tester strains. Ethylene dibromide, 1-bromo-2-chloroethane, 1,2-dichloroethane, and methylene dichloride induced umuC gene expression more strongly in the NM5004 strain than the original strain, 4-Nitroquinoline 1-oxide and N-methyl-N'-nitro-N-nitrosoguanidine were found to induce umuC gene expression to similar extents in both strains. In the case of 1-nitropyrene and 2-nitrofluorene, however, NM5004 strain showed weaker umuC gene expression responses than the original TA1535/ pSK1002 strain, 1,2-Epoxy-3-(4'-nitrophenoxy)propane, a known substrate for GST 5-5, was found to inhibit umuC induction caused by 1-bromo-2-chloroethane. These results indicate that this new tester NM5004 strain expressing a mammalian GST theta class enzyme may be useful for studies of environmental chemicals proposed to be activated or inactivated by GST activity.
Resumo:
Reactive oxygen species are generated during ischaemia-reperfusion of tissue. Oxidation of thymidine by hydroxyl radicals (HO) leads to the formation of 5,6-dihydroxy-5,6-dihydrothymidine (thymidine glycol). Thymidine glycol is excreted in urine and can be used as biomarker of oxidative DNA damage. Time dependent changes in urinary excretion rates of thymidine glycol were determined in six patients after kidney transplantation and in six healthy controls. A new analytical method was developed involving affinity chromatography and subsequent reverse-phase high-performance liquid chromatography (RP-HPLC) with a post-column chemical reaction detector and endpoint fluorescence detection. The detection limit of this fluorimetric assay was 1.6 ng thymidine glycol per ml urine, which corresponds to about half of the physiological excretion level in healthy control persons. After kidney transplantation the urinary excretion rate of thymidine glycol increased gradually reaching a maximum around 48 h. The excretion rate remained elevated until the end of the observation period of 10 days. Severe proteinuria with an excretion rate of up to 7.2 g of total protein per mmol creatinine was also observed immediately after transplantation and declined within the first 24 h of allograft function (0.35 + 0.26 g/mmol creatinine). The protein excretion pattern, based on separation of urinary proteins on sodium dodecyl sulphate-polyacrylamide gel electrophorosis (SDS-PAGE), as well as excretion of individual biomarker proteins, indicated nonselective glomerular and tubular damage. The increased excretion of thymidine glycol after kidney transplantation may be explained by ischaemia-reperfusion induced oxidative DNA damage of the transplanted kidney.
Resumo:
Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.
Resumo:
BACKGROUND: Over the past 10 years, the use of saliva as a diagnostic fluid has gained attention and has become a translational research success story. Some of the current nanotechnologies have been demonstrated to have the analytical sensitivity required for the use of saliva as a diagnostic medium to detect and predict disease progression. However, these technologies have not yet been integrated into current clinical practice and work flow. CONTENT: As a diagnostic fluid, saliva offers advantages over serum because it can be collected noninvasively by individuals with modest training, and it offers a cost-effective approach for the screening of large populations. Gland-specific saliva can also be used for diagnosis of pathology specific to one of the major salivary glands. There is minimal risk of contracting infections during saliva collection, and saliva can be used in clinically challenging situations, such as obtaining samples from children or handicapped or anxious patients, in whom blood sampling could be a difficult act to perform. In this review we highlight the production of and secretion of saliva, the salivary proteome, transportation of biomolecules from blood capillaries to salivary glands, and the diagnostic potential of saliva for use in detection of cardiovascular disease and oral and breast cancers. We also highlight the barriers to application of saliva testing and its advancement in clinical settings. SUMMARY: Saliva has the potential to become a first-line diagnostic sample of choice owing to the advancements in detection technologies coupled with combinations of biomolecules with clinical relevance. (C) 2011 American Association for Clinical Chemistry
Resumo:
Over the past 10 years, the use of saliva as a diagnostic fluid has gained attention and has become a translational research success story. Some of the current nanotechnologies have been demonstrated to have the analytical sensitivity required for the use of saliva as a diagnostic medium to detect and predict disease progression. However, these technologies have not yet been integrated into current clinical practice and work flow. As a diagnostic fluid, saliva offers advantages over serum because it can be collected noninvasively by individuals with modest training, and it offers a cost-effective approach for the screening of large populations. Gland-specific saliva can also be used for diagnosis of pathology specific to one of the major salivary glands. There is minimal risk of contracting infections during saliva collection, and saliva can be used in clinically challenging situations, such as obtaining samples from children or handicapped or anxious patients, in whom blood sampling could be a difficult act to perform. In this review we highlight the production of and secretion of saliva, the salivary proteome, transportation of biomolecules from blood capillaries to salivary glands, and the diagnostic potential of saliva for use in detection of cardiovascular disease and oral and breast cancers. We also highlight the barriers to application of saliva testing and its advancement in clinical settings. Saliva has the potential to become a first-line diagnostic sample of choice owing to the advancements in detection technologies coupled with combinations of biomolecules with clinical relevance.
Resumo:
Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).
Resumo:
Biomolecules are chemical compounds found in living organisms which are the building blocks of life and perform important functions. Fluctuation from the normal concentration of these biomolecules in living system leads to several disorders. Thus the exact determination of them in human fluids is essential in the clinical point of view. High performance liquid chromatography, flow injection analysis, capillary electrophoresis, fluorimetry, spectrophotometry, electrochemical and chemiluminescence techniques were usually used for the determination of biologically important molecules. Among these techniques, electrochemical determination of biomolecules has several advantages over other methods viz., simplicity, selectivity and sensitivity. In the past two decades, electrodes modified with polymer films, self-assembled monolayers containing different functional groups and carbon paste have been used as electrochemical sensors. But in recent years, nanomaterials based electrochemical sensors play an important role in the improvement of public health because of its rapid detection, high sensitivity and specificity in clinical diagnostics. To date gold nanoparticles (AuNPs) have received arousing attention mainly due to their fascinating electronic and optical properties as a consequence of their reduced dimensions. These unique properties of AuNPs make them as an ideal candidate for the immobilization of enzymes for biosensing. Further, the electrochemical properties of AuNPs reveal that they exhibit interesting properties by enhancing the electrode conductivity, facilitating electron transfer and improving the detection limit of biomolecules. In this chapter, we summarized the different strategies used for the attachment of AuNPs on electrode surfaces and highlighted the electrochemical determination of glucose, ascorbic acid (AA), uric acid (UA) and dopamine derivatives using the AuNPs modified electrodes.
Resumo:
This article describes the detection of DNA mutations using novel Au-Ag coated GaN substrate as SERS (surface-enhanced Raman spectroscopy) diagnostic platform. Oligonucleotide sequences corresponding to the BCR-ABL (breakpoint cluster region-Abelson) gene responsible for development of chronic myelogenous leukemia were used as a model system to demonstrate the discrimination between the wild type and Met244Val mutations. The thiolated ssDNA (single-strand DNA) was immobilized on the SERS-active surface and then hybridized to a labeled target sequence from solution. An intense SERS signal of the reporter molecule MGITC was detected from the complementary target due to formation of double helix. The SERS signal was either not observed, or decreased dramatically for a negative control sample consisting of labeled DNA that was not complementary to the DNA probe. The results indicate that our SERS substrate offers an opportunity for the development of novel diagnostic assays.
Resumo:
Legacies of the Global Financial Crisis and major domestic corporate collapses – such as HIH Insurance Pty Ltd and One.Tel Ltd (telecommunications) – have significantly changed Australia‟s financial regulatory landscape. Legal requirements for auditors have attracted particular attention as have practice standards more broadly around disclosure and conflict of interest. Conversely, although successful detection and prosecution of breaches may rest in significant part on forensic accounting activities, Australia‟s practitioners in this field have no minimum training or qualifications standards other than the baseline requirements mandated by the country‟s three professional accounting bodies. For those unaffiliated with these organizations, no professional oversight exists. In Australia, growth in the forensic accounting industry has been in direct response to public demand for expertise in a broad range of fraud, forensic and business analytics areas in order to improve the corporate governance practices of Australian organizations. During the 1990s, Australian forensic accounting firms expanded and diversified into a number of different areas going well beyond just the examination of financial documents and involvement in financial litigation disputes. “Big 4” accounting firms such as PriceWaterhouseCoopers, KPMG, Deloitte and Ernst and Young formed independent forensic accounting or forensic services units; a number of mid-tier and „boutique‟ forensic accounting firms similarly expanded into forensic investigative, analytical and advisory services. By 2008, 800 forensic accountants were registered with the country‟s largest specialist forensic accounting group, the Forensic Accounting Special Interest Group (FASIG) of the ICAA1. Currently, obtaining more precise figures on numbers of forensic accounting practitioners is problematic: professional accounting bodies either do not keep a register or have ceased registering their forensic accounting members; lack of formal recognition, admission or certification processes complicate identification of candidates; and diversity of the skills sets the industry requires has meant the influx of non-accounting based specialists.
Resumo:
Pavlovian fear conditioning is an evolutionary conserved and extensively studied form of associative learning and memory. In mammals, the lateral amygdala (LA) is an essential locus for Pavlovian fear learning and memory. Despite significant progress unraveling the cellular mechanisms responsible for fear conditioning, very little is known about the anatomical organization of neurons encoding fear conditioning in the LA. One key question is how fear conditioning to different sensory stimuli is organized in LA neuronal ensembles. Here we show that Pavlovian fear conditioning, formed through either the auditory or visual sensory modality, activates a similar density of LA neurons expressing a learning-induced phosphorylated extracellular signal-regulated kinase (p-ERK1/2). While the size of the neuron population specific to either memory was similar, the anatomical distribution differed. Several discrete sites in the LA contained a small but significant number of p-ERK1/2-expressing neurons specific to either sensory modality. The sites were anatomically localized to different levels of the longitudinal plane and were independent of both memory strength and the relative size of the activated neuronal population, suggesting some portion of the memory trace for auditory and visually cued fear conditioning is allocated differently in the LA. Presenting the visual stimulus by itself did not activate the same p-ERK1/2 neuron density or pattern, confirming the novelty of light alone cannot account for the specific pattern of activated neurons after visual fear conditioning. Together, these findings reveal an anatomical distribution of visual and auditory fear conditioning at the level of neuronal ensembles in the LA.
Resumo:
This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.
Resumo:
The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.
Deterrence of drug driving : the impact of the ACT drug driving legislation and detection techniques
Resumo:
Overarching Research Questions Are ACT motorists aware of roadside saliva based drug testing operations? What is the perceived deterrent impact of the operations? What factors are predictive of future intentions to drug drive? What are the differences between key subgroups
Resumo:
This paper outlines the approach taken by the Speech, Audio, Image and Video Technologies laboratory, and the Applied Data Mining Research Group (SAIVT-ADMRG) in the 2014 MediaEval Social Event Detection (SED) task. We participated in the event based clustering subtask (subtask 1), and focused on investigating the incorporation of image features as another source of data to aid clustering. In particular, we developed a descriptor based around the use of super-pixel segmentation, that allows a low dimensional feature that incorporates both colour and texture information to be extracted and used within the popular bag-of-visual-words (BoVW) approach.