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.

Mandatory Reporting Laws and the Identification of Severe Child Abuse and Neglect

This book provides the first comprehensive international coverage of key issues in mandatory reporting of child abuse and neglect. The book draws on a collection of the foremost scholars in the field, as well as clinicians and practice-based experts, to explore the nature, history, impact and justifiability of mandatory reporting laws, their optimal form, legal and conceptual issues, and practical issues and challenges for reporters, professional educators and governments. Key issues in non-Western nations are also explored briefly to assess the potential of socio-legal responses sex trafficking, forced child labour and child marriage. The book is of particular value to policy makers, educators and opinion leaders in government departments dealing with children, and to professionals and organisations who work with children. It is also intended to be a key authority for researchers and teachers in the fields of medicine, nursing, social work, education, law, psychology, health and allied health fields.

Identification of a putative cellulase gene in the giant freshwater prawn, Macrobrachium rosenbergii (De Man, 1879)

Nutrition plays an important role in the development of all organisms and in particular that of farmed aquatic species where costs associated with feed can often exceed 60% of total production costs. Crustacean species in addition, have the added metabolic requirement for regular moulting to allow normal growth and this requires large amounts of energy in the form of sugars (glucose). The current study explored the capacity of the giant freshwater prawn to produce endogenous cellulose-degrading enzymes capable of extracting nutrients (simple sugars) from plant sources in formulated feeds used in the prawn aquaculture industry. We identified a putative cellulase cDNA fragment in the target organism of 1576 base pairs in length of non-microbial origin that after protein modelling exhibited a TM-score of 0.916 with a described cellulase reported from another crustacean species. The functional role of cellulase enzymes is to hydrolyse cellulose to glucose and the fragment identified in GFP was highly expressed in the hepatopancreas, the site of primary food digestion and absorption in crustaceans. Hepatopancreatic tissue from Macrobrachium rosenbergii also showed active digestion of cellulose to glucose following an endoglucanase assay. Cellulase gene(s) are present in the genomes of many invertebrate taxa and play an active role in the conversion of cellulose to available energy. Identification and characterization of endogenous cellulase gene(s) in giant freshwater prawn can assist development of the culture industry because the findings confirm that potentially greater levels of low-cost plant-material could be included in artificial formulated diets in the future without necessarily compromising individual growth performance. Ultimately, this development may contribute to more efficient, cost-effective production systems for freshwater prawn culture stocks that meet the animal's basic nutritional requirements and that also support good individual growth rates.

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.

Development of a switch mode assisted linear amplifier for use as a high fidelity voltage source

Frequency Domain Spectroscopy (FDS) is one of the major techniques used for determining the condition of the cellulose based paper and pressboard components in large oil/paper insulated power transformers. This technique typically makes use of a sinusoidal voltage source swept from 0.1 mHz to 1 kHz. The excitation test voltage source used must meet certain characteristics, such as high output voltage, high fidelity, low noise and low harmonic content. The amplifier used; in the test voltage source; must be able to drive highly capacitive loads. This paper proposes that a switch-mode assisted linear amplifier (SMALA) can be used in the test voltage source to meet these criteria. A three level SMALA prototype amplifier was built to experimentally demonstrate the effectiveness of this proposal. The developed SMALA prototype shows no discernable harmonic distortion in the output voltage waveform, or the need for output filters, and is therefore seen as a preferable option to pulse width modulated digital amplifiers. The lack of harmonic distortion and high frequency switching noise in the output voltage of this SMALA prototype demonstrates its feasibility for applications in FDS, particularly on highly capacitive test objects such as transformer insulation systems.

Identification of genetic variants contributing to the migraine phenotype in different Australian populations

This project aimed to identify novel genetic risk variants associated with migraine in the Norfolk Island population. Statistical analysis and bioinformatics approaches such as polygenic modeling and gene clustering methods were carried out to explore genotypic and expression data from high-throughput techniques. This project had a particular focus on hormonal genes and other genetic variants and identified a modest effect size on the migraine phenotype.

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.

Identification of major loci controlling clinical manifestations of ankylosing spondylitis

Objective. To identify genomic regions linked with determinants of age at symptom onset, disease activity, and functional impairment in ankylosing spondylitis (AS). Methods. A whole genome linkage scan was performed in 188 affected sibling pair families with 454 affected individuals. Traits assessed were age at symptom onset, disease activity assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), and functional impairment assessed by the Bath Ankylosing Spondylitis Functional Index (BASFI). Parametric and nonparametric quantitative linkage analysis was performed using parameters defined in a previous segregation study. Results. Heritabilities of the traits studied in this data set were as follows: BASDAI 0.49 (P = 0.0001, 95% confidence interval [95% CI] 0.23-0.75), BASFI 0.76 (P = 10-7, 95% CI 0.49-1.0), and age at symptom onset 0.33 (P = 0.005, 95% CI 0.04-0.62). No linkage was observed between the major histocompatibility complex (MHC) and any of the traits studied (logarithm of odds [LOD] score &lt;1.0). "Significant" linkage (LOD score 4.0) was observed between a region on chromosome 18p and the BASDAI. Age at symptom onset showed "suggestive" linkage to chromosome 11p (LOD score 3.3). Maximum linkage with the BASFI was seen at chromosome 2q (LOD score 2.9). Conclusion. In contrast to the genetic determinants of susceptibility to AS, clinical manifestations of the disease measured by the BASDAI, BASFI, and age at symptom onset are largely determined by a small number of genes not encoded within the MHC.

A polymerase chain reaction-based method for isolating clones from a complimentary DNA library in sheep

The sheep (Ovis aries) is favored by many musculoskeletal tissue engineering groups as a large animal model because of its docile temperament and ease of husbandry. The size and weight of sheep are comparable to humans, which allows for the use of implants and fixation devices used in human clinical practice. The construction of a complimentary DNA (cDNA) library can capture the expression of genes in both a tissue- and time-specific manner. cDNA libraries have been a consistent source of gene discovery ever since the technology became commonplace more than three decades ago. Here, we describe the construction of a cDNA library using cells derived from sheep bones based on the pBluescript cDNA kit. Thirty clones were picked at random and sequenced. This led to the identification of a novel gene, C12orf29, which our initial experiments indicate is involved in skeletal biology. We also describe a polymerase chain reaction-based cDNA clone isolation method that allows the isolation of genes of interest from a cDNA library pool. The techniques outlined here can be applied in-house by smaller tissue engineering groups to generate tools for biomolecular research for large preclinical animal studies and highlights the power of standard cDNA library protocols to uncover novel genes.

Identification of bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei reveals distinct temperature-driven patterns

Change in temperature is often a major environmental factor in triggering waterborne disease outbreaks. Previous research has revealed temporal and spatial patterns of bacterial population in several aquatic ecosystems. To date, very little information is available on aquaculture environment. Here, we assessed environmental temperature effects on bacterial community composition in freshwater aquaculture system farming of Litopenaeus vannamei (FASFL). Water samples were collected over a one-year period, and aquatic bacteria were characterized by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and 16S rDNA pyrosequencing. Resulting DGGE fingerprints revealed a specific and dynamic bacterial population structure with considerable variation over the seasonal change, suggesting that environmental temperature was a key driver of bacterial population in the FASFL. Pyrosequencing data further demonstrated substantial difference in bacterial community composition between the water at higher (WHT) and at lower (WLT) temperatures in the FASFL. Actinobacteria, Proteobacteria and Bacteroidetes were the highest abundant phyla in the FASFL, however, a large number of unclassified bacteria contributed the most to the observed variation in phylogenetic diversity. The WHT harbored remarkably higher diversity and richness in bacterial composition at genus and species levels when compared to the WLT. Some potential pathogenenic species were identified in both WHT and WLT, providing data in support of aquatic animal health management in the aquaculture industry.

The koala immunological toolkit: Sequence identification and comparison of key markers of the koala (Phascolarctos cinereus) immune response

The koala (Phascolarctos cinereus) is an Australian marsupial that continues to experience significant population declines. Infectious diseases caused by pathogens such as Chlamydia are proposed to have a major role. Very few species-specific immunological reagents are available, severely hindering our ability to respond to the threat of infectious diseases in the koala. In this study, we utilise data from the sequencing of the koala transcriptome to identify key immunological markers of the koala adaptive immune response and cytokines known to be important in the host response to chlamydial infection in other species. This report describes the identification and preliminary sequence analysis of (1) T lymphocyte glycoprotein markers (CD4, CD8); (2) IL-4, a marker for the Th2 response; (3) cytokines such as IL-6, IL-12 and IL-1β, that have been shown to have a role in chlamydial clearance and pathology in other hosts; and (4) the sequences for the koala immunoglobulins, IgA, IgG, IgE and IgM. These sequences will enable the development of a range of immunological reagents for understanding the koala’s innate and adaptive immune responses, while also providing a resource that will enable continued investigations into the origin and evolution of the marsupial immune system.

A quantitative metric to identify critical elements within seafood supply networks

A theoretical basis is required for comparing key features and critical elements in wild fisheries and aquaculture supply chains under a changing climate. Here we develop a new quantitative metric that is analogous to indices used to analyse food-webs and identify key species. The Supply Chain Index (SCI) identifies critical elements as those elements with large throughput rates, as well as greater connectivity. The sum of the scores for a supply chain provides a single metric that roughly captures both the resilience and connectedness of a supply chain. Standardised scores can facilitate cross-comparisons both under current conditions as well as under a changing climate. Identification of key elements along the supply chain may assist in informing adaptation strategies to reduce anticipated future risks posed by climate change. The SCI also provides information on the relative stability of different supply chains based on whether there is a fairly even spread in the individual scores of the top few key elements, compared with a more critical dependence on a few key individual supply chain elements. We use as a case study the Australian southern rock lobster Jasus edwardsii fishery, which is challenged by a number of climate change drivers such as impacts on recruitment and growth due to changes in large-scale and local oceanographic features. The SCI identifies airports, processors and Chinese consumers as the key elements in the lobster supply chain that merit attention to enhance stability and potentially enable growth. We also apply the index to an additional four real-world Australian commercial fishery and two aquaculture industry supply chains to highlight the utility of a systematic method for describing supply chains. Overall, our simple methodological approach to empirically-based supply chain research provides an objective method for comparing the resilience of supply chains and highlighting components that may be critical.

Bayesian predictive modeling and comparison of oil samples

Statistical comparison of oil samples is an integral part of oil spill identification, which deals with the process of linking an oil spill with its source of origin. In current practice, a frequentist hypothesis test is often used to evaluate evidence in support of a match between a spill and a source sample. As frequentist tests are only able to evaluate evidence against a hypothesis but not in support of it, we argue that this leads to unsound statistical reasoning. Moreover, currently only verbal conclusions on a very coarse scale can be made about the match between two samples, whereas a finer quantitative assessment would often be preferred. To address these issues, we propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from some basic assumptions on modeling assays in analytical chemistry, and to further facilitate and improve numerical evaluations, we develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches

VIP Barcoding: Composition vector-based software for rapid species identification based on DNA barcoding

Species identification based on short sequences of DNA markers, that is, DNA barcoding, has emerged as an integral part of modern taxonomy. However, software for the analysis of large and multilocus barcoding data sets is scarce. The Basic Local Alignment Search Tool (BLAST) is currently the fastest tool capable of handling large databases (e.g. >5000 sequences), but its accuracy is a concern and has been criticized for its local optimization. However, current more accurate software requires sequence alignment or complex calculations, which are time-consuming when dealing with large data sets during data preprocessing or during the search stage. Therefore, it is imperative to develop a practical program for both accurate and scalable species identification for DNA barcoding. In this context, we present VIP Barcoding: a user-friendly software in graphical user interface for rapid DNA barcoding. It adopts a hybrid, two-stage algorithm. First, an alignment-free composition vector (CV) method is utilized to reduce searching space by screening a reference database. The alignment-based K2P distance nearest-neighbour method is then employed to analyse the smaller data set generated in the first stage. In comparison with other software, we demonstrate that VIP Barcoding has (i) higher accuracy than Blastn and several alignment-free methods and (ii) higher scalability than alignment-based distance methods and character-based methods. These results suggest that this platform is able to deal with both large-scale and multilocus barcoding data with accuracy and can contribute to DNA barcoding for modern taxonomy. VIP Barcoding is free and available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.

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.