The influence of ambient temperature on birth outcomes in Brisbane, Australia

Lately, there has been increasing interest in the association between temperature and adverse birth outcomes including preterm birth (PTB) and stillbirth. PTB is a major predictor of many diseases later in life, and stillbirth is a devastating event for parents and families. The aim of this study was to assess the seasonal pattern of adverse birth outcomes, and to examine possible associations of maternal exposure to temperature with PTB and stillbirth. We also aimed to identify if there were any periods of the pregnancy where exposure to temperature was particularly harmful. A retrospective cohort study design was used and we retrieved individual birth records from the Queensland Health Perinatal Data Collection Unit for all singleton births (excluding twins and triplets) delivered in Brisbane between 1 July 2005 and 30 June 2009. We obtained weather data (including hourly relative humidity, minimum and maximum temperature) and air-pollution data (including PM10, SO2 and O3) from the Queensland Department of Environment and Resource Management. We used survival analyses with the time-dependent variables of temperature, humidity and air pollution, and the competing risks of stillbirth and live birth. To assess the monthly pattern of the birth outcomes, we fitted month of pregnancy as a time-dependent variable. We examined the seasonal pattern of the birth outcomes and the relationship between exposure to high or low temperatures and birth outcomes over the four lag weeks before birth. We further stratified by categorisation of PTB: extreme PTB (< 28 weeks of gestation), PTB (28–36 weeks of gestation), and term birth (≥ 37 weeks of gestation). Lastly, we examined the effect of temperature variation in each week of the pregnancy on birth outcomes. There was a bimodal seasonal pattern in gestation length. After adjusting for temperature, the seasonal pattern changed from bimodal, to only one peak in winter. The risk of stillbirth was statistically significant lower in March compared with January. After adjusting for temperature, the March trough was still statistically significant and there was a peak in risk (not statistically significant) in winter. There was an acute effect of temperature on gestational age and stillbirth with a shortened gestation for increasing temperature from 15 °C to 25 °C over the last four weeks before birth. For stillbirth, we found an increasing risk with increasing temperatures from 12 °C to approximately 20 °C, and no change in risk at temperatures above 20 °C. Certain periods of the pregnancy were more vulnerable to temperature variation. The risk of PTB (28–36 weeks of gestation) increased as temperatures increased above 21 °C. For stillbirth, the fetus was most vulnerable at less than 28 weeks of gestation, but there were also effects in 28–36 weeks of gestation. For fetuses of more than 37 weeks of gestation, increasing temperatures did not increase the risk of stillbirth. We did not find any adverse affects of cold temperature on birth outcomes in this cohort. My findings contribute to knowledge of the relationship between temperature and birth outcomes. In the context of climate change, this is particularly important. The results may have implications for public health policy and planning, as they indicate that pregnant women would decrease their risk of adverse birth outcomes by avoiding exposure to high temperatures and seeking cool environments during hot days.

Apoptosis-related genes confer resistance to Fusarium wilt in transgenic ‘Lady Finger’ bananas

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most devastating diseases of banana (Musa spp.). Apart from resistant cultivars, there are no effective control measures for the disease. We investigated whether the transgenic expression of apoptosis-inhibition related genes in banana could be used to confer disease resistance. Embryogenic cell suspensions of the banana cultivar, ‘Lady Finger’, were stably transformed with animal genes that negatively regulate apoptosis, namely Bcl-xL, Ced-9 and Bcl-2 3’ UTR, and independently transformed plant lines were regenerated for testing. Following a 12 week exposure to Foc race 1 in small-plant glasshouse bioassays, seven transgenic lines (2 x Bcl-xL, 3 x Ced-9 and 2 x Bcl-2 3’ UTR) showed significantly less internal and external disease symptoms than the wild-type susceptible ‘Lady Finger’ banana plants used as positive controls. Of these, one Bcl-2 3’ UTR line showed resistance that was equivalent to that of wild-type Cavendish bananas that were included as resistant negative controls. Further, the resistance of this line continued for 23 weeks post-inoculation at which time the experiment was terminated. Using TUNEL assays, Foc race 1 was shown to induce apoptosis-like features in the roots of wild-type ‘Lady Finger’ plants consistent with a necrotrophic phase in the lifecycle of this pathogen. This was further supported by the observed reduction of these effects in the roots of the resistant Bcl-2 3’ UTR transgenic line. This is the first report on the generation of transgenic banana plants with resistance to Fusarium wilt.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Fast and effective focused retrieval

Building an efficient and an effective search engine is a very challenging task. In this paper, we present the efficiency and effectiveness of our search engine at the INEX 2009 Efficiency and Ad Hoc Tracks. We have developed a simple and effective pruning method for fast query evaluation, and used a two-step process for Ad Hoc retrieval. The overall results from both tracks show that our search engine performs very competitively in terms of both efficiency and effectiveness.

Effective hybrid recommendation combining users-searches correlations using tensors

Most recommendation methods employ item-item similarity measures or use ratings data to generate recommendations. These methods use traditional two dimensional models to find inter relationships between alike users and products. This paper proposes a novel recommendation method using the multi-dimensional model, tensor, to group similar users based on common search behaviour, and then finding associations within such groups for making effective inter group recommendations. Web log data is multi-dimensional data. Unlike vector based methods, tensors have the ability to highly correlate and find latent relationships between such similar instances, consisting of users and searches. Non redundant rules from such associations of user-searches are then used for making recommendations to the users.

Individual user behaviour modelling for effective web recommendation

With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.

Identifying interests of web users for effective recommendations

Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

Effective adsorption of sodium dodecylsulfate (SDS) by hydrocalumite (CaAl-LDH-Cl) induced by self-dissolution and re-precipitation mechanism

Hydrocalumite (CaAl-LDH-Cl) were synthesized through a rehydration method involving a freshly prepared tricalcium aluminate (C3A) with CaCl2 solution. To understand the intercalation behaviour of sodium dodecylsulfate (SDS) with CaAl-LDH-Cl, X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma-atomic emission spectrometer (ICP) and elemental analysis have been undertaken. The sorption isotherms with SDS reveal that the maximum sorption amount of SDS by CaAl-LDH-Cl could reach 3.67 mmol•g-1. The results revealed that CaAl-LDH-Cl holds a self-dissolution property, about 20-30% of which is dissolved. And the dissolved Ca2+, Al3+ ions are combined with SDS to form CaAl-SDS or Ca-SDS precipitation. It has been highlighted that the composition of resulting products is strongly dependent upon the SDS concentration. With increasing SDS concentrations, the main resulting product changes from CaAl-SDS to Ca-SDS, and the value of interlayer spacing increased to 3.27 nm.

The effect of various pH, ionic strength and temperature on papain hydrolysis of salivary film

Stimulated human whole saliva (WS) was used to study the dynamics of papain hydrolysis at defined pH, ionic strength and temperature with the view of reducing an acquired pellicle. A quartz crystal microbalance with dissipation (QCM-D) was used to monitor the changes in frequency due to enzyme hydrolysis of WS films and the hydrolytic parameters were calculated using an empirical model. The morphological and conformational changes of the salivary films before and after enzymatic hydrolysis were characterized by atomic force microscopy (AFM) imaging and grazing angle infrared spectroscopy (GA-FTIR) spectra, respectively. The characteristics of papain hydrolysis of WS films were pH-, ionic strength- and temperature-dependent. The WS films were partially removed by the action of enzyme, resulting thinner and smoother surfaces. The IR data suggested that hydrolysis-induced deformation did not occur onto the remnants salivary films. The processes of papain hydrolysis of WS films can be controlled by properly regulating pH, ionic strength and temperature.

Mixed convection laminar flow along a horizontal plate subject to streamwise sinusoidal surface temperature

Mixed convection of a two-dimensional laminar incompressible flow along a horizontal flat plate with streamwise sinusoidal surface temperature has been numerically investigated for different values of Rayleigh number and Reynolds number for constant values of Prandtl number, amplitude and frequency of periodic temperature. The numerical scheme is based on the finite element method adapted to rectangular non-uniform mesh elements by a non-linear parametric solution algorithm. The fluid considered in this study is air. The results are obtained for the Rayleigh number and Reynolds number ranging from 102 to 104 and 1 to 100, respectively, with constant physical properties for the fluid medium considered. Velocity and temperature profiles, streamlines, isotherms, and average Nusselt numbers are presented to observe the effect of the investigating parameters on fluid flow and heat transfer characteristics. The present results show that the convective phenomena are greatly influenced by the variation of Rayleigh numbers and Reynolds number.

Low temperature CO sensitive nanostructured WO3 thin films doped with Fe

Nanostructured tungsten oxide thin film based gas sensors have been developed by thermal evaporation method to detect CO at low operating temperatures. The influence of Fe-doping and annealing heat treatment on microstructural and gas sensing properties of these films have been investigated. Fe was incorporated in WO3 film by co-evaporation and annealing was performed at 400oC for 2 hours in air. AFM analysis revealed a grain size of about 10-15 nm in all the films. GIXRD analysis showed that as-deposited films are amorphous and annealing at 400oC improved the crystallinity. Raman and XRD analysis indicated that Fe is incorporated in the WO3 matrix as a substitutional impurity, resulting in shorter O-W-O bonds and lattice cell parameters. Doping with Fe contributed significantly towards CO sensing performance of WO3 thin films. A good response to various concentrations (10-1000 ppm) of CO has been achieved with 400oC annealed Fe-doped WO3 film at a low operating temperature of 150oC.

Effective shear modulus approach for two dimensional solids and plate bending problems by meshless point collocation method

For the analysis of material nonlinearity, an effective shear modulus approach based on the strain control method is proposed in this paper by using point collocation method. Hencky’s total deformation theory is used to evaluate the effective shear modulus, Young’s modulus and Poisson’s ratio, which are treated as spatial field variables. These effective properties are obtained by the strain controlled projection method in an iterative manner. To evaluate the second order derivatives of shape function at the field point, the radial basis function (RBF) in the local support domain is used. Several numerical examples are presented to demonstrate the efficiency and accuracy of the proposed method and comparisons have been made with analytical solutions and the finite element method (ABAQUS).

Responsiveness of the Effective Consumer Scale (EC-17)

Objective. The Effective Consumer Scale (EC-17) comprises 17 items measuring the main skills and behaviors people need to effectively manage their healthcare. We tested the responsiveness of the EC-17. Methods. Participants, in 2 waves of a 6-week Arthritis Self-Management Program (ASMP) from Arthritis Ireland, received a questionnaire at the first and last week of the weekly ASMP. The questionnaire included the EC-17 and 10 other measures for arthritis. Deficits, mean change, and standard deviations were calculated at baseline and Week 6. The EC-17 scores were compared to the Arthritis Self-Efficacy (ASE) and Patient Activation Measure (PAM) scales. Results were presented at OMERACT 9. Results. There is some overlap between the EC-17 and the ASE and PAM; however, most items of greatest deficit in the EC-17 are not covered by those scales. In 327 participants representing both intervention waves (2006 and 2007), the EC-17 was more efficient than the ASE but less efficient than the PAM for detecting improvements after the ASMP, and was moderately correlated with the PAM. Conclusion. The EC-17 appears to measure different skills and attributes than the ASE and PAM. Discussions with participants at OMERACT 9 agreed that it is worthwhile to measure the skills and attributes of an effective consumer, and supported the development of an intervention (such as proposed online decision aids) that would include education in the categories in the EC-17.