Upregulated MT1-MMP/TIMP-2 axis in the TSU-Pr1-B1/B2 model of metastatic progression in transitional cell carcinoma of the bladder

Muscle invasive transitional cell carcinoma (TCC) of the bladder is associated with a high frequency of metastasis, resulting in poor prognosis for patients presenting with this disease. Models that capture and demonstrate step-wise enhancement of elements of the human metastatic cascade on a similar genetic background are useful research tools. We have utilized the transitional cell carcinoma cell line TSU-Pr1 to develop an in vivo experimental model of bladder TCC metastasis. TSU-Pr1 cells were inoculated into the left cardiac ventricle of SCID mice and the development of bone metastases was monitored using high resolution X-ray. Tumor tissue from a single bone lesion was excised and cultured in vitro to generate the TSU-Pr1-B1 subline. This cycle was repeated with the TSU-Pr1-B1 cells to generate the successive subline TSU-Pr1-B2. DNA profiling and karyotype analysis confirmed the genetic relationship of these three cell lines. In vitro, the growth rate of these cell lines was not significantly different. However, following intracardiac inoculation TSU-Pr1, TSU-Pr1-B1 and TSU-Pr1-B2 exhibited increasing metastatic potential with a concomitant decrease in time to the onset of radiologically detectable metastatic bone lesions. Significant elevations in the levels of mRNA expression of the matrix metalloproteases (MMPs) membrane type 1-MMP (MT1-MMP), MT2-MMP and MMP-9, and their inhibitor, tissue inhibitor of metalloprotease-2 (TIMP-2), across the progressively metastatic cell lines, were detected by quantitative PCR. Given the role of MT1-MMP and TIMP-2 in MMP-2 activation, and the upregulation of MMP-9, these data suggest an important role for matrix remodeling, particularly basement membrane, in this progression. The TSU-Pr1-B1/B2 model holds promise for further identification of important molecules.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

Identification of genes important for growth of asymptomatic bacteriuria Escherichia coli in urine

Escherichia coli is the most important etiological agent of urinary tract infections (UTIs). Unlike uropathogenic E. coli, which causes symptomatic infections, asymptomatic bacteriuria (ABU) E. coli strains typically lack essential virulence factors and colonize the bladder in the absence of symptoms. While ABU E. coli can persist in the bladder for long periods of time, little is known about the genetic determinants required for its growth and fitness in urine. To identify such genes, we have employed a transposon mutagenesis approach using the prototypic ABU E. coli strain 83972 and the clinical ABU E. coli strain VR89. Six genes involved in the biosynthesis of various amino acids and nucleobases were identified (carB, argE, argC, purA, metE, and ilvC), and site-specific mutants were subsequently constructed in E. coli 83972 and E. coli VR89 for each of these genes. In all cases, these mutants exhibited reduced growth rates and final cell densities in human urine. The growth defects could be complemented in trans as well as by supplementation with the appropriate amino acid or nucleobase. When assessed in vivo in a mouse model, E. coli 83972carAB and 83972argC showed a significantly reduced competitive advantage in the bladder and/or kidney during coinoculation experiments with the parent strain, whereas 83972metE and 83972ilvC did not. Taken together, our data have identified several biosynthesis pathways as new important fitness factors associated with the growth of ABU E. coli in human urine.

Phage library screening for the rapid identification and in vivo testing of candidate genes for a DNA vaccine against Mycoplasma mycoides subsp. mycoides small colony biotype

A new strategy for rapidly selecting and testing genetic vaccines has been developed, in which a whole genome library is cloned into a bacteriophage λ ZAP Express vector which contains both prokaryotic (Plac) and eukaryotic (PCMV) promoters upstream of the insertion site. The phage library is plated on Escherichia coli cells, immunoblotted, and probed with hyperimmune and/or convalescent-phase antiserum to rapidly identify vaccine candidates. These are then plaque purified and grown as liquid lysates, and whole bacteriophage particles are then used directly to immunize the host, following which PCMV-driven expression of the candidate vaccine gene occurs. In the example given here, a semirandom genome library of the bovine pathogen Mycoplasma mycoides subsp. mycoides small colony (SC) biotype was cloned into λ ZAP Express, and two strongly immunodominant clones, λ-A8 and λ-B1, were identified and subsequently tested for vaccine potential against M. mycoides subsp. mycoides SC biotype-induced mycoplasmemia. Sequencing and immunoblotting indicated that clone λ-A8 expressed an isopropyl-β-d-thiogalactopyranoside (IPTG)-inducible M. mycoides subsp. mycoides SC biotype protein with a 28-kDa apparent molecular mass, identified as a previously uncharacterized putative lipoprotein (MSC_0397). Clone λ-B1 contained several full-length genes from the M. mycoides subsp. mycoides SC biotype pyruvate dehydrogenase region, and two IPTG-independent polypeptides, of 29 kDa and 57 kDa, were identified on immunoblots. Following vaccination, significant anti-M. mycoides subsp. mycoides SC biotype responses were observed in mice vaccinated with clones λ-A8 and λ-B1. A significant stimulation index was observed following incubation of splenocytes from mice vaccinated with clone λ-A8 with whole live M. mycoides subsp. mycoides SC biotype cells, indicating cellular proliferation. After challenge, mice vaccinated with clone λ-A8 also exhibited a reduced level of mycoplasmemia compared to controls, suggesting that the MSC_0397 lipoprotein has a protective effect in the mouse model when delivered as a bacteriophage DNA vaccine. Bacteriophage-mediated immunoscreening using an appropriate vector system offers a rapid and simple technique for the identification and immediate testing of putative candidate vaccines from a variety of pathogens.

MIMO and SISO identification of radiation force terms for models of marine structures in waves

This paper presents a discussion on the use of MIMO and SISO techniques for identification of the radiation force terms in models for surface vessels. We compare and discuss two techniques recently proposed in literature for this application: time domain identification and frequency domain identification. We compare the methods in terms of estimates model order, accuracy of the fit, use of the available information, and ease of use and implementation.

Superstars as drivers of organizational identification : empirical findings from professional soccer

This paper examines the effect of superstars on external stakeholders’ organizational identification through the lens of sport. Drawing on social identity theory and the concept of organizational identification, as well as on role model theories and superstar economics, several hypotheses are developed regarding the influence of soccer stars on their fans’ degree of team identification. Using a proprietary data set that combines archival data on professional German soccer players and clubs with survey data on more than 1,400 soccer fans, this study finds evidence for a positive effect of superstar characteristics and role model perception. Moreover, it is found that players who qualify for the definition of a superstar are more important to fans of established teams than to fans of unsuccessful teams. The player's club tenure, however, seems to have no influence on fans’ team identification. It is further argued that the effect of soccer stars on their fans is comparable to that of executives on external stakeholders, and hence, the results are applied to the business domain. The results of this study contribute to existing research by extending the list of personnel-related determinants of organizational identification.

Toward a model for predicting magnetic susceptibility of bedrock regolith and soils

One of the Department of Defense's most pressing environmental problems is the efficient detection and identification of unexploded ordnance (UXO). In regions of highly magnetic soils, magnetic and electromagnetic sensors often detect anomalies that are of geologic origin, adding significantly to remediation costs. In order to develop predictive models for magnetic susceptibility, it is crucial to understand modes of formation and the spatial distribution of different iron oxides. Most rock types contain iron and their magnetic susceptibility is determined by the amount and form of iron oxides present. When rocks weather, the amount and form of the oxides change, producing concomitant changes in magnetic susceptibility. The type of iron oxide found in the weathered rock or regolith is a function of the duration and intensity of weathering, as well as the original content of iron in the parent material. The rate of weathering is controlled by rainfall and temperature; thus knowing the climate zone, the amount of iron in the lithology and the age of the surface will help predict the amount and forms of iron oxide. We have compiled analyses of the types, amounts, and magnetic properties of iron oxides from soils over a wide climate range, from semi arid grasslands, to temperate regions, and tropical forests. We find there is a predictable range of iron oxide type and magnetic susceptibility according to the climate zone, the age of the soil and the amount of iron in the unweathered regolith.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

A longitudinal investigation of coping processes during a merger: Implications for job satisfaction and organizational identification

This study tested the utility of a stress and coping model of employee adjustment to a merger. Two hundred and twenty employees completed both questionnaires (Time 1: 3 months after merger implementation; Time 2: 2 years later). Structural equation modeling analyses revealed that positive event characteristics predicted greater appraisals of self-efficacy and less stress at Time 1. Self-efficacy, in turn, predicted greater use of problem-focused coping at Time 2, whereas stress predicted a greater use of problem-focused and avoidance coping. Finally, problem-focused coping predicted higher levels of job satisfaction and identification with the merged organization (Time 2), whereas avoidance coping predicted lower identification.

Pedestrian Crashes in Ethiopia: Identification of contributing factors through modelling of exposure and road environment variables

Pedestrian safety is a critical issue in Ethiopia. Reports show that 50 to 60% of traffic fatality victims in the country are pedestrians. The primary aim of this research was to examine the possible causes of and contributing factors to crashes with pedestrians in Ethiopia, and improve pedestrian safety by recommending possible countermeasures. The secondary aim was to develop appropriate pedestrian crash models for two-way two-lane rural roads and roundabouts in the capital city of Ethiopia. This research uses quantitative methods throughout the process of the investigation. The research has applied various statistical methods. The results of this research support the idea that geometric and operational features have significant influence on pedestrian safety and crashes. Accordingly, policies and strategies are needed to safeguard pedestrians in Ethiopia.

Identification of mechanism, PECARN risk factors and injury patterns in severe paediatric cervical spine injuries in Queensland

Introduction Canadian C spine rule and NEXUS criteria have identified risk factors for cervical spine injury in adults but not for children. PECARN has developed an 8 variable model for cervical spine injury in children. We sought to identify the mechanism, prevalence of PECARN risk factors, injury patterns, and management of severe Paediatric cervical spine injuries presenting to the major children’s hospitals in Brisbane, Australia. Methods This a retrospective study of the children with cervical spine injuries who presented directly or were referred to the major children’s hospitals in Brisbane over 5 years. Results There were 38 patients with 18 male and 20 female.The mean age was 8.6 years. They were divided into two groups according to their age, (Group 1 < =8 years had 18 (47%) patients, while group 2 (9-15 years) had 20 (53%) patients. Motor vehicle related injuries were the most common (61%) in Group 1 while it was sporting injuries (50%) in group 2. All patients in group 1 had upper cervical injury (C0-C2) while subaxial injuries were most common in group 2 (66.6%). 82% of the patients had 2 or more PECARN risk factors. 18 children (47%) had normal neurological assessment at presentation, 6 (16%) had radicular symptoms, 11 (29%) could not be assessed as they had already been intubated due to the severity of the injury, 3 (8%) had incomplete cord injury. 29 (69%) patients had normal neurological assessment at final follow up and 2 children died from their injuries. Conclusion Our study confirms that younger children sustain upper cervical injuries most commonly secondary to motor vehicle accidents, while the older sustain subaxial injuries from sporting activities. The significant prevalence of the PECARN risk factors among this cohort of patients have led to them being incorporated into a protocol at these hospitals used to assess patients with suspected cervical spinal injury.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: Theoretical framework and validation

Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.

Working-correlation-structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well-known criterion of QIC for selecting a working correlation Structure. and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads LIS to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study.