911 resultados para Chimpanzees (Pan troglodytes verus)


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TOD: - A fully planned, mixed use development equipped with good quality transit service and infrastructure for walking and cycling Hypothesis: -TOD will help to reduce urban transport congestion Method: -Comparison of a TOD with non TOD urban environments -Residents’ trip characteristics

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OBJECTIVE: The fibroblast growth factor (FGF) family of signaling molecules has been associated with chemoresistance and poor prognosis in a number of cancer types, including lung, breast, ovarian, prostate, and head and neck carcinomas. Given the identification of activating mutations in the FGF receptor 2 (FGFR2) receptor tyrosine kinase in a subset of endometrial tumors, agents with activity against FGFRs are currently being tested in clinical trials for recurrent and progressive endometrial cancer. Here, we evaluated the effect of FGFR inhibition on the in vitro efficacy of chemotherapy in endometrial cancer cell lines. METHODS: Human endometrial cancer cell lines with wild-type or activating FGFR2 mutations were used to determine any synergism with concurrent use of the pan-FGFR inhibitor, PD173074, and the chemotherapeutics, doxorubicin and paclitaxel, on cell proliferation and apoptosis. RESULTS: FGFR2 mutation status did not alter sensitivity to either chemotherapeutic agent alone. The combination of PD173074 with paclitaxel or doxorubicin showed synergistic activity in the 3 FGFR2 mutant cell lines evaluated. In addition, although nonmutant cell lines were resistant to FGFR inhibition alone, the addition of PD173074 potentiated the cytostatic effect of paclitaxel and doxorubicin in a subset of FGFR2 wild-type endometrial cancer cell lines. CONCLUSIONS: Together these data suggest a potential therapeutic benefit to combining an FGFR inhibitor with standard chemotherapeutic agents in endometrial cancer therapy particularly in patients with FGFR2 mutation positive tumors.

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Aurora Kinase (AK) based therapy targeting AK-A & B is effective against some cancers. We have explored its potential against previously unreported incurable, metastatic androgen depletion independent Prostate Cancer (ADIPC). We used androgen sensitive (AS) and ADI lines derived from Transgenic Adenocarcinoma of the Mouse Prostate (TRAMP) mice. The relevance of this model was unequivocally established through focussed array, quantitative PCR and western blotting studies; significantly greater alteration of genes (fold change and number) representing major cancer pathways was shown in ADI cells compared to AS lines. A marked enhancement of in vivo growth of the ADI subline showing the greatest degree of gene modulations [TRAMP C1 (TC1)-T5: TC1-T5] reflected this. In contrast to the parental AS TC1 line, TC1-T5 cells grew with 100% incidence in the prostate, as lung pseudometastases and migrated to the bone and other soft tissues. The potential involvement of AKs in this transition was indicated by the significant upregulation of AK-A/B and their downstream regulators, survivin and phosphorylated-histone H3 in TC1-T5 cells compared to TC1 cells. This led to enhanced sensitivity of TC1-T5 cells to the pan-AK inhibitor, VX680 and to significant reduction in in vivo tumour growth rates when AK-A and/or B were downregulated in TC1-T5 cells. This cell growth inhibition was markedly enhanced when both AKs were downregulated and also led to substantially greater sensitivity of these cells to docetaxel, the only chemotherapeutic with activity against ADI PC. Finally, use of VX680 with docetaxel led to impressive synergies suggesting promise for treating clinical ADI metastatic PC.

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Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

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The mining environment, being complex, irregular and time varying, presents a challenging prospect for stereo vision. The objective is to produce a stereo vision sensor suited to close-range scenes consisting primarily of rocks. This sensor should be able to produce a dense depth map within real-time constraints. Speed and robustness are of foremost importance for this investigation. A number of area based matching metrics have been implemented, including the SAD, SSD, NCC, and their zero-meaned versions. The NCC and the zero meaned SAD and SSD were found to produce the disparity maps with the highest proportion of valid matches. The plain SAD and SSD were the least computationally expensive, due to all their operations taking place in integer arithmetic, however, they were extremely sensitive to radiometric distortion. Non-parametric techniques for matching, in particular, the rank and the census transform, have also been investigated. The rank and census transforms were found to be robust with respect to radiometric distortion, as well as being able to produce disparity maps with a high proportion of valid matches. An additional advantage of both the rank and the census transform is their amenability to fast hardware implementation.

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In this issue of Cancer Discovery, Guagnano and colleagues use a large and diverse annotated collection of cancer cell lines, the Cancer Cell Line Encyclopedia, to correlate whole-genome expression and genomic alteration datasets with cell line sensitivity data to the novel pan-fibroblast growth factor receptor (FGFR) inhibitor NVP-BGJ398. Their findings underscore not only the preclinical use of such cell line panels in identifying predictive biomarkers, but also the emergence of the FGFRs as valid therapeutic targets, across an increasingly broad range of malignancies.

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The Chinese Ministry of Education (MOE) initiatives to improve the English competence of college students, as well as increased proficiency level of entering college students (Cheng, 2002) have contributed to greater demands on Teaching English as a Foreign Language (TEFL) academics (MOE, 2004), as “the upgrading of national English proficiency, then, is predicated largely on the professional competence of the teaching force” (Hu, 2005, p. 655). For TEFL academics, one component of this competence is the capacity to conduct research (Shu, 2002), which also reflects other changes in Chinese higher education. The aspirations of higher education institutions at all levels have led to more rigorous recruitment policies and promotion requirements (Che, 2004; Wang, 2007), stressing research as an important indicator of academics’ performance (Shi, 2002; Pan, 2006). These changes highlight the role of research in higher education institutions’ efforts to raise their national status and world ranking (Zhang, Wang, & He, 2006), and have exerted influences on faculty’s academic role. Academics are obliged to engage in research activities, and this has posed challenges to teaching-oriented institutions and disciplines.

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Our everyday environment is full of text but this rich source of information remains largely inaccessible to mobile robots. In this paper we describe an active text spotting system that uses a small number of wide angle views to locate putative text in the environment and then foveates and zooms onto that text in order to improve the reliability of text recognition. We present extensive experimental results obtained with a pan/tilt/zoom camera and a ROS-based mobile robot operating in an indoor environment.

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Infrastructure forms a vital component in supporting today’s way of life and has a significant role or impact on economic, environmental and social outcomes of the region around it. The design, construction and operation of such assets are a multi-billion dollar industry in Australia alone. Another issue that will play a major role in our way life is that of climate change and the greater concept of sustainability. With limited resources and a changing natural world it is necessary for infrastructure to be developed and maintained in a manner that is sustainable. In order to achieve infrastructure sustainability in operations it is necessary for there to be: a sustainability assessment scheme that provides a scientifically sound and realistic approach to measuring an assets level of sustainability; and, systems and tools to support the making of decisions that result in sustainable outcomes by providing feedback in a timely manner. Having these in place will then help drive the consideration of sustainability during the decision making process for infrastructure operations and maintenance. In this paper we provide two main contributions; a comparison and review of sustainability assessment schemes for infrastructure and their suitability for use in the operations phase; and, a review of decision support systems/tools in the area of infrastructure sustainability in operations. For this paper, sustainability covers not just the environment, but also finance/economic and societal/community aspects as well. This is often referred to as the Triple Bottom Line and forms one of the three dimensions of corporate sustainability [Stapledon, 2004].

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Convergence of pervasive technologies, techno-centric customers and the emergence of digitized channels, overabundance of user friendly retail applications are having a profound impact on retail experience, leading to the advent of ‘everywhere retailing’. With the rapid uptake of digital complimentary assets and smart mobile applications are revolutionizing the relationship of retailers with their customers and suppliers. Retail firms are increasingly investing substantial resources on dynamic Customer Relationship Management systems (D-CRM / U-CRM) to better engage with customers to sense and respond quickly (Agility of the firm) to their demands. However, unlike traditional CRM systems, engagement with U-CRM systems requires that firms be hyper sensitive to volatile customer needs and wants. Following the notions of firm agility, this study attempts to develop a framework to understand such unforeseen benefits and issues of U-CRM. This research-in-progress paper reports an a-priory framework including 62 U-CRM benefits derived through an archival analysis of literature.

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Traffic congestion has a significant impact on the economy and environment. Encouraging the use of multimodal transport (public transport, bicycle, park’n’ride, etc.) has been identified by traffic operators as a good strategy to tackle congestion issues and its detrimental environmental impacts. A multi-modal and multi-objective trip planner provides users with various multi-modal options optimised on objectives that they prefer (cheapest, fastest, safest, etc) and has a potential to reduce congestion on both a temporal and spatial scale. The computation of multi-modal and multi-objective trips is a complicated mathematical problem, as it must integrate and utilize a diverse range of large data sets, including both road network information and public transport schedules, as well as optimising for a number of competing objectives, where fully optimising for one objective, such as travel time, can adversely affect other objectives, such as cost. The relationship between these objectives can also be quite subjective, as their priorities will vary from user to user. This paper will first outline the various data requirements and formats that are needed for the multi-modal multi-objective trip planner to operate, including static information about the physical infrastructure within Brisbane as well as real-time and historical data to predict traffic flow on the road network and the status of public transport. It will then present information on the graph data structures representing the road and public transport networks within Brisbane that are used in the trip planner to calculate optimal routes. This will allow for an investigation into the various shortest path algorithms that have been researched over the last few decades, and provide a foundation for the construction of the Multi-modal Multi-objective Trip Planner by the development of innovative new algorithms that can operate the large diverse data sets and competing objectives.

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Production of nanofibrous polyacrylonitrile/calcium carbonate (PAN/CaCO3) nanocomposite web was carried out through solution electrospinning process. Pore generating nanoparticles were leached from the PAN matrices in hydrochloric acid bath with the purpose of producing an ultimate nanoporous structure. The possible interaction between CaCO3 nanoparticles and PAN functional groups was investigated. Atomic absorption method was used to measure the amount of extracted CaCO3 nanoparticles. Morphological observation showed nanofibers of 270–720 nm in diameter containing nanopores of 50–130 nm. Monitoring the governing parameters statistically, it was found that the amount of extraction (ε) of CaCO3was increased when the web surface area (a) was broadened according to a simple scaling law (ε = 3.18 a0.4). The leaching process was maximized in the presence of 5% v/v of acid in the extraction bath and 5 wt % of CaCO3 in the polymer solution. Collateral effects of the extraction time and temperature showed exponential growth within a favorable extremum at 50°C for 72 h. Concentration of dimethylformamide as the solvent had no significant impact on the extraction level.

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In this chapter, we will present a contemporary review of the hitherto numerical characterization of nanowires (NWs). The bulk of the research reported in the literatures concern metallic NWs including Al, Cu, Au, Ag, Ni, and their alloys NWs. Research has also been reported for the investigation of some nonmetallic NWs, such as ZnO, GaN, SiC, SiO2. A plenty of researches have been conducted regarding the numerical investigation of NWs. Issues analyzed include structural changes under different loading situations, the formation and propagation of dislocations, and the effect of the magnitude of applied loading on deformation mechanics. Efforts have also been made to correlate simulation results with experimental measurements. However, direct comparisons are difficult since most simulations are carried out under conditions of extremely high strain/loading rates and small simulation samples due to computational limitations. Despite of the immense numerical studies of NWs, a significant work still lies ahead in terms of problem formulation, interpretation of results, identification and delineation of deformation mechanisms, and constitutive characterization of behavior. In this chapter, we present an introduction of the commonly adopted experimental and numerical approaches in studies of the deformation of NWs in Section 1. An overview of findings concerning perfect NWs under different loading situations, such as tension, compression, torsion, and bending are presented in Section 2. In Section 3, we will detail some recent results from the authors’ own work with an emphasis on the study of influences from different pre-existing defect on NWs. Some thoughts on future directions of the computational mechanics of NWs together with Conclusions will be given in the last section.

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We sought to identify fibroblast growth factor receptor 2 (FGFR2) kinase domain mutations that confer resistance to the pan-FGFR inhibitor, dovitinib, and explore the mechanism of action of the drug-resistant mutations. We cultured BaF3 cells overexpressing FGFR2 in high concentrations of dovitinib and identified fourteen dovitinib-resistant mutations, including the N550K mutation observed in 25% of FGFR2mutant endometrial cancers (EC). Structural and biochemical in vitro kinase analyses, together with BaF3 proliferation assays, showed that the resistance mutations elevate the intrinsic kinase activity of FGFR2. BaF3 lines were used to assess the ability of each mutation to confer cross-resistance to PD173074 and ponatinib. Unlike PD173074, ponatinib effectively inhibited all the dovitinib-resistant FGFR2 mutants except the V565I gatekeeper mutation, suggesting ponatinib but not dovitinib targets the active conformation of FGFR2 kinase. EC cell lines expressing wild-type FGFR2 were relatively resistant to all inhibitors. Whereas EC cell lines expressing mutated FGFR2 showed differential sensitivity. Within the FGFR2mutant cell lines, 3/7 showed marked resistance to PD173074 and relative resistance to dovitinib and ponatinib. This suggests that alternative mechanisms distinct from kinase domain mutations are responsible for intrinsic resistance in these three EC lines. Finally, overexpression of FGFR2N550K in JHUEM-2 cells (FGFR2C383R) conferred resistance (~5 fold) to PD173074, providing independent data that FGFR2N550K can be associated with drug resistance. Biochemical in vitro kinase analyses also shows ponatinib is more effective than dovitinib at inhibiting FGFR2N550K. We propose tumors harboring mutationally activated FGFRs should be treated with FGFR inhibitors that specifically bind the active kinase.