972 resultados para animal identification


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Experts’ views and commentary have been highly respected in every discipline. However, unlike traditional disciplines like medicine, mathematics and engineering, Information System (IS) expertise is difficult to define. Despite seeking expert advice and views is common in the areas of IS project management, system implementations and evaluations. This research-in-progress paper attempts to understand the characteristics of IS-expert through a comprehensive literature review of analogous disciplines and then derives a formative research model with three main constructs. A validated construct of expertise of IS will have a wide range of implications for research and practice.

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Two representations have dominated public perceptions of the largest living marsupial carnivore, the Tasmanian devil. One is the voracious, hurricane-like innocent savage Taz of Looney Tunes cartoon fame. The other, familiar in nineteenth- and twentieth-century rural Tasmania, is the ferocious predator and scavenger that wantonly kills livestock — and perhaps even people, should they become immobilized in the wilderness at night. Devils can take prey nearly three times their size and eat more than a third of their body weight in a sitting. Even so, it is hard to imagine how this species, being only slightly larger than a fox terrier, could be so maligned in name and image...

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The time consuming and labour intensive task of identifying individuals in surveillance video is often challenged by poor resolution and the sheer volume of stored video. Faces or identifying marks such as tattoos are often too coarse for direct matching by machine or human vision. Object tracking and super-resolution can then be combined to facilitate the automated detection and enhancement of areas of interest. The object tracking process enables the automatic detection of people of interest, greatly reducing the amount of data for super-resolution. Smaller regions such as faces can also be tracked. A number of instances of such regions can then be utilized to obtain a super-resolved version for matching. Performance improvement from super-resolution is demonstrated using a face verification task. It is shown that there is a consistent improvement of approximately 7% in verification accuracy, using both Eigenface and Elastic Bunch Graph Matching approaches for automatic face verification, starting from faces with an eye to eye distance of 14 pixels. Visual improvement in image fidelity from super-resolved images over low-resolution and interpolated images is demonstrated on a small database. Current research and future directions in this area are also summarized.

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Mesenchymal stem cells (MSCs) are undifferentiated, multi-potent stem cells with the ability to renew. They can differentiate into many types of terminal cells, such as osteoblasts, chondrocytes, adipocytes, myocytes, and neurons. These cells have been applied in tissue engineering as the main cell type to regenerate new tissues. However, a number of issues remain concerning the use of MSCs, such as cell surface markers, the determining factors responsible for their differentiation to terminal cells, and the mechanisms whereby growth factors stimulate MSCs. In this chapter, we will discuss how proteomic techniques have contributed to our current knowledge and how they can be used to address issues currently facing MSC research. The application of proteomics has led to the identification of a special pattern of cell surface protein expression of MSCs. The technique has also contributed to the study of a regulatory network of MSC differentiation to terminal differentiated cells, including osteocytes, chondrocytes, adipocytes, neurons, cardiomyocytes, hepatocytes, and pancreatic islet cells. It has also helped elucidate mechanisms for growth factor–stimulated differentiation of MSCs. Proteomics can, however, not reveal the accurate role of a special pathway and must therefore be combined with other approaches for this purpose. A new generation of proteomic techniques have recently been developed, which will enable a more comprehensive study of MSCs. Keywords

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This study proposes a full Bayes (FB) hierarchical modeling approach in traffic crash hotspot identification. The FB approach is able to account for all uncertainties associated with crash risk and various risk factors by estimating a posterior distribution of the site safety on which various ranking criteria could be based. Moreover, by use of hierarchical model specification, FB approach is able to flexibly take into account various heterogeneities of crash occurrence due to spatiotemporal effects on traffic safety. Using Singapore intersection crash data(1997-2006), an empirical evaluate was conducted to compare the proposed FB approach to the state-of-the-art approaches. Results show that the Bayesian hierarchical models with accommodation for site specific effect and serial correlation have better goodness-of-fit than non hierarchical models. Furthermore, all model-based approaches perform significantly better in safety ranking than the naive approach using raw crash count. The FB hierarchical models were found to significantly outperform the standard EB approach in correctly identifying hotspots.

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This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.

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Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems.

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A combination of enzymatic digestion and electrospray ionisation mass spectrometry (ESI-MS) was used to characterise bifunctional adducts in which cisplatin is bound to GA base sequences in 8mer and 16mer oligonucleotides that do not contain other, higher affinity binding sites. The extent of formation of bifunctional adducts with GA base sequences was significant, but less than that seen with similar oligonucleotides containing either AG or GG sequences.

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Abstract Genome-wide association studies (GWAS) have identified more than 30 prostate cancer (PrCa) susceptibility loci. One of these (rs2735839) is located close to a plausible candidate susceptibility gene, KLK3, which encodes prostate-specific antigen (PSA). PSA is widely used as a biomarker for PrCa detection and disease monitoring. To refine the association between PrCa and variants in this region, we used genotyping data from a two-stage GWAS using samples from the UK and Australia, and the Cancer Genetic Markers of Susceptibility (CGEMS) study. Genotypes were imputed for 197 and 312 single nucleotide polymorphisms (SNPs) from HapMap2 and the 1000 Genome Project, respectively. The most significant association with PrCa was with a previously unidentified SNP, rs17632542 (combined P = 3.9 × 10−22). This association was confirmed by direct genotyping in three stages of the UK/Australian GWAS, involving 10,405 cases and 10,681 controls (combined P = 1.9 × 10−34). rs17632542 is also shown to be associated with PSA levels and it is a non-synonymous coding SNP (Ile179Thr) in KLK3. Using molecular dynamic simulation, we showed evidence that this variant has the potential to introduce alterations in the protein or affect RNA splicing. We propose that rs17632542 may directly influence PrCa risk.

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Cameron (2004) proposed a three-dimensional model and measure of social identification consisting of cognitive centrality, in-group affect, and in-group ties. This approach has received growing theoretical and empirical support; however, little research has examined how these dimensions of social identification may relate differentially to intergroup outcome behaviors. The current research sought to address this question by examining the possible mediating role the dimensions of social identification on the relationship between prototypicality of group members and the intergroup outcome behaviors of in-group favoritism, out-group derogation, and collective self-esteem. The current study examined university students’ (N = 235) feelings towards students from their own and another local university. Structural equation modeling was used to identify the most appropriate and parsimonious models of these pathways. The results showed support for the utility of measuring social identification using a multidimensional approach with unique meditational pathways emerging for the distinct intergroup behaviors.

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We have previously reported the presence of a 70 kDa insulin-like growth factor (IGF)-II-specific binding protein in chicken serum using Western ligand blotting approaches. In order to ascertain the identity of this 70 kDa IGF-II binding species, the protein has been purified from chicken serum using a combination of ion-exchange and gel-permeation chromatography. Interestingly, amino acid sequencing of the purified protein revealed that it has the same N-terminal sequence as chicken vitronectin (VN). The protein has the ability to specifically bind IGF-II and not IGF-I as determined by ligand blotting, cross-linking and competitive binding assay approaches. In addition, the protein binds 125I-des(l-6)-IGF-II, suggesting that the interaction with IGF-II is different to those with other characterized IGF-binding proteins. Importantly, we have ascertained that both human and bovine VN also specifically bind IGF-II. These results are particularly relevant in the light of the recent report that the urokinase-type plasminogen activator receptor, a protein that also binds VN, has been shown to associate with the cation-independent mannose-6-phosphate/GF-II receptor and suggest a possible role for IGF-II in cell adhesion and invasion.

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Metastatic melanoma, a cancer historically refractory to chemotherapeutic strategies, has a poor prognosis and accounts for the majority of skin cancer related mortality. Although the recent approval of two new drugs combating this disease, Ipilimumab and Vemurafenib (PLX4032), has demonstrated for the first time in decades an improvement in overall survival; the clinical efficacy of these drugs has been marred by severe adverse immune reactions and acquired drug resistance in patients, respectively. Thus, understanding the etiology of metastatic melanoma will contribute to the improvement of current therapeutic strategies while leading to the development of novel drug approaches. In order to identify recurrently mutated genes of therapeutic relevance in metastatic melanoma, a panel of stage III local lymph node melanomas were extensively characterised using high-throughput genomic technologies. This led to the identification of mutations in TFG in 5% of melanomas from a candidate gene sequencing approach using SNP array analysis, 24% of melanomas with mutations in MAP3K5 or MAP3K9 though unbiased whole-exome sequencing strategies, and inactivating mutations in NF1 in BRAF/NRAS wild type tumours though pathway analysis. Lastly, this thesis describes the development of a melanoma specific mutation panel that can rapidly identify clinically relevant mutation profiles that could guide effective treatment strategies through a personalised therapeutic approach. These findings are discussed in respect to a number of important issues raised by this study including the current limitation of next-generation sequencing technology, the difficulty in identifying ‘driver’ mutations critical to the development of melanoma due to high carcinogenic exposure by UV radiation, and the ultimate application of mutation screening in a personalised therapeutic setting. In summary, a number novel genes involved in metastatic melanoma have been identified that may have relevance for current therapeutic strategies in treating this disease.