Serum levels of brain-derived neurotrophic factor in schizophrenia on a hypocaloric diet

Dietary factors influence BDNF in animal studies, but there is no comparable data in clinical populations. We examined the effect of a dietary intervention on BDNF serum levels in 67 DSM-IV schizophrenic outpatients (51 males and 16 females). Two groups were assessed in a cross-sectional study: one on a hypocaloric diet (HD) and the other not on a hypocaloric diet. Weight, height and BMI data were collected concurrently with 5-ml blood sampling of each subject. BDNF levels were measured with a sandwich-ELISA. The blood sample was obtained a minimum of one month after the exposure to dietary intervention. Serum BDNF levels were significantly higher in patients on the HD (p = 0.023). Additional research examining the interaction among patterns of nutritional food behavior and underlying physiopathology may result in insights upon which evidence-based decisions regarding dietary interventions can be made in people identified with major psychiatric disorders, such as schizophrenia.

Malmquist indices of total factor productivity changes in the Australian construction industry

Construction is an important industry and forms a vital part of national economics in the world. Factors affecting the productivity of the construction industry should be measured appropriately to reflect its development situation and economic performance. The Malmquist index method with a novel decomposition technique is employed to estimate the total factor productivity of the Australian construction industry during the period 1990-2007 and to analyse the factors affecting the technological change in the industry. Research results exemplified by two input variables and one output variable elaborate how construction technology, pure technical efficiency and scale economy take effect in the change of construction productivity. In addition, based on temporal and spatial comparisons, the analysis for construction productivities reveals their changes over time and across the country. Proposals and recommendations are expected to be beneficial for policy making and strategic decisions to improve the performance of the Australian construction industry.

A factor-augmented VAR approach : the effect of a rise in the US personal income tax rate on the US and Canada

In this paper a factor-augmented vector autoregressive (FAVAR) model is estimated to characterize the dynamic effects of shocks in the personal income tax rate in the United States on United States and Canadian economies. The representation and the estimate of the FAVAR model is based on Stock and Watson (2005) and the shocks are recovered applying the identification scheme proposed by Bernanke et al. (2005); this method allows impulse response functions to be generated for all the variables in the dataset and provides a description of the domestic and international transmission mechanisms of United States movements in the personal income tax rate. A distinguishing feature of our model is the disaggregation of traded goods sector where imports and exports are disaggregated into 12 and 13 industries, respectively. This provides extra information on the domestic and international transmission mechanism across the two countries. The results show that the FAVAR approach generates a reasonable characterisation of the effects of United States movements in the US personal income tax rate on the United States economy and its transmission to the Canadian economy.

Impact of positively- versus negatively-worded items on the factor structure of three psychological measures: W-BQ22, W-BQ12 & SF-36

Background: The 22-item Well-being Questionnaire (W-BQ22) (Bradley, 1994) includes Anxiety, Depression, Energy and Positive Well-being subscales. However, the constructs of anxiety and depression could not be easily distinguished from each other. Consequently, the W-BQ12 was designed to include just three subscales, Negative Well-being (including only negatively-worded anxiety and depression items), Energy (2 positively- and 2 negativelyworded items) and Positive Well-being (all positively-worded items). The SF-36 (Ware & Sherbourne, 1983), a widely used health status measure, includes a 4-item vitality subscale (2 positively- and 2 negatively-worded items) and a 5-item mental health scale (2 positively worded and 3 negatively-worded items).

Method: Factor structures of W-BQ22, W-BQ12 and SF-36 were compared using data from 789 outpatients with diabetes.

Results: W-BQ22 factor analysis showed negatively-worded anxiety and depression items loading together, while positively-worded depression items loaded with positive well-being items and separately from positively-worded anxiety items. W-BQ12 loaded as intended on 3 factors, with negatively-worded anxiety and depression items loading together on one factor: negative well-being. The four energy items (2 positively- and 2 negatively-worded) loaded together (factor 3) and the four positive well-being items loaded together (factor 1). Unforced factor analysis of the SF-36 produced 5 factors and split the mental health and vitality items into two components, which could only be defined in terms of positive and negative wording. A forced 8-factor solution produced similar results, with the mental health and vitality items being split into two components according to their positive or negative wording. A forced 2-factor solution brought mental health/vitality items together, separate from physical health items.

Conclusion: The previously unrecognised influence of positive and negative wording on factor structure is clearly shown here to be of importance in conceptualising and designing measures of psychological well-being to be used with people with diabetes and may be of relevance for other populations.

Meehl's crud factor, subjective wellbeing and religiosity : a critique

Researchers in the area of subjective wellbeing are now building on a body of research that spans more than three decades and includes many thousands of articles. It is time to move from exploratory studies to those based on accumulated psychometric understanding and theory. Two aspects of the article by Abdel-Khalek (2011) warrant attention from this perspective: the choice of scale and the issue of shared variance.

Comparisons between two empirical yield criteria for rock masses

In general, rock masses are inhomogeneous, discontinuous media composed of rock material and naturally occurring discontinuities such as joints, fractures and bedding planes. Because of these features, the strength of rock masses is notoriously difficult to assess. Nonetheless, many criteria have been proposed for estimating rock mass strength. Based on the finite element upper and lower bound limit analysis methods, this study examined two empirical yield criteria for rock masses, the Hoek-Brown failure criterion (2002) and the Douglas criterion (2002). The comparisons showed that very different results may be obtained using the same input parameters. Therefore, it is interesting to discuss the source of these differences.

A comparison of two methods in acquiring stimulus-response curves with transcranial magnetic stimulation

Background
The stimulus–response (S–R) curve is a well accepted constituent in transcranial magnetic stimulation (TMS) studies. However, it has been suggested that parameters of the S–R curve differ when stimuli are provided in a “ramped” (measured steps from low to high intensity), or “random” fashion.

Hypothesis
We hypothesized that there would be no difference in the parameters of the S–R curve between either methodologies.

Methods
Using a randomised cross-over design, 10 healthy participants (29.6 ± 6.4 yrs, 3 f) completed “ramped” or “random” curves in biceps brachii (BB) and first dorsal interosseous (FDI) muscles of both limbs. Curves were compared using mixed-factor ANOVA and correlated between limbs and methodologies.

Results
No differences (P > 0.05) and high correlations (range 0.71–0.97; P < 0.001) were observed in BB and FDI data between curves.

Conclusions
This study demonstrated that either methodology provides similar parameters of the S–R curve in healthy participants.

Channel estimation of dual-hop MIMO relay system via parallel factor analysis

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

Underdetermined blind source separation by parallel factor analysis in time-frequency domain

This paper presents a new time-frequency approach to the underdetermined blind source separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active sources at an auto-term time-frequency point, this approach can directly separate the sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the sources, our approach yields better source separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the source separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage blind source separation methods that use the time-frequency representation as well.

Selectivity in separation using π electron-rich stationary phases for the comprehensive two-dimensional analysis of cafe espresso

The multidimensional high-performance liquid chromatography separations of the complex sample matrix found in café espresso coffee were completed on the propyl phenyl and butyl phenyl columns that contain 3 and 4 carbon atoms in the spacer chain, respectively. Phenyl type stationary phases are able to undergo unique π–π interactions with aromatic compounds. Previous works have found that there are differences in retention characteristics between these chain lengths and this was explored further here. It was found that when analysing the separations by quadrants, using a geometric approach to factor analysis and by measuring the normalised mean radius, subtle differences in the separations were observed and the butyl phenyl phase was more selective for the high to medium polarity species. However, there was very little difference in separation behaviour for the hydrophobic components within the coffee sample. Overall, the analysis of the entire separation showed very little difference.

A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources

Joint analysis of multiple data sources is becoming increasingly popular in transfer learning, multi-task learning and cross-domain data mining. One promising approach to model the data jointly is through learning the shared and individual factor subspaces. However, performance of this approach depends on the subspace dimensionalities and the level of sharing needs to be specified a priori. To this end, we propose a nonparametric joint factor analysis framework for modeling multiple related data sources. Our model utilizes the hierarchical beta process as a nonparametric prior to automatically infer the number of shared and individual factors. For posterior inference, we provide a Gibbs sampling scheme using auxiliary variables. The effectiveness of the proposed framework is validated through its application on two real world problems - transfer learning in text and image retrieval.

Ancestry of pink disease (Infantile Acrodynia) identified as a risk factor for autism spectrum disorders

Pink disease (infantile acrodynia) was especially prevalent in the first half of the 20th century. Primarily attributed to exposure to mercury (Hg) commonly found in teething powders, the condition was developed by approximately 1 in 500 exposed children. The differential risk factor was identified as an idiosyncratic sensitivity to Hg. Autism spectrum disorders (ASD) have also been postulated to be produced by Hg. Analogous to the pink disease experience, Hg exposure is widespread yet only a fraction of exposed children develop an ASD, suggesting sensitivity to Hg may also be present in children with an ASD. The objective of this study was to test the hypothesis that individuals with a known hypersensitivity to Hg (pink disease survivors) may be more likely to have descendants with an ASD. Five hundred and twenty-two participants who had previously been diagnosed with pink disease completed a survey on the health outcomes of their descendants. The prevalence rates of ASD and a variety of other clinical conditions diagnosed in childhood (attention deficit hyperactivity disorder, epilepsy, Fragile X syndrome, and Down syndrome) were compared to well-established general population prevalence rates. The results showed the prevalence rate of ASD among the grandchildren of pink disease survivors (1 in 25) to be significantly higher than the comparable general population prevalence rate (1 in 160). The results support the hypothesis that Hg sensitivity may be a heritable/genetic risk factor for ASD.

Confirmatory factor analysis of the Beck Depression inventory in obese individuals seeking surgery

Background : The Beck Depression Inventory (BDI) is frequently employed as measure of depression in studies of obesity. The aim of the study was to assess the factorial structure of the BDI in obese patients prior to bariatric surgery.

Methods : Confirmatory factor analysis was conducted on the current published factor analyses of the BDI. Three published models were initially analysed with two additional modified models subsequently included. A sample of 285 patients presenting for Lap-Band® surgery was used.

Results : The published bariatric model by Munoz et al. was not an adequate fit to the data. The general model by Shafer et al. was a good fit to the data but had substantial limitations. The weight loss item did not significantly load on any factor in either model. A modified Shafer model and a proposed model were tested, and both were found to be a good fit to the data with minimal differences between the two. A proposed model, in which two items, weight loss and appetite, were omitted, was suggested to be the better model with good reliability.

Conclusions : The previously published factor analysis in bariatric candidates by Munoz et al. was a poor fit to the data, and use of this factor structure should be seriously reconsidered within the obese population. The hypothesised model was the best fit to the data. The findings of the study suggest that the existing published models are not adequate for investigating depression in obese patients seeking surgery.

Computational simulation of the influence of inert particles on incomplete combustion of methane at a low air factor

It is well known that the gas–solid system plays a significant role in many industrial processes. It is a complex physical and chemical process, generally consisting of heat transfer, mass transfer, species diffusion, and chemical reactions. In this paper, the reaction of methane with air at a low air factor and the gas flow in a fluidized bed with 0.1 mm solid particles are computationally simulated to enable the study of the effect of the inert particles on the species diffusion and the chemical reactions. The reaction of methane and air is modeled by a two-step reaction mechanism that produces a continuous fluid phase composed of six gases (CH₄, CO, O₂, CO₂, H₂O, and N₂) and discrete solid particles in the reactor. The simulation results are compared with experiment and show that the finite rate model and the eddy dissipation model can well describe the reactions of gases in high-density gas–solid systems. The distribution of each gas and the particle behaviors are analyzed for incomplete combustion at different concentrations of loaded solid particles. The inert particles change the reactions by enhancing both the chemical kinetics and the species diffusion dynamics.

A cluster randomised controlled trial of vascular risk factor management in general practice

Letter to the editor: Harris and colleagues report failure to obtain reduction in several important risk factor-based intermediate outcomes for vascular disease from their lifestyle intervention in the Health Improvement and Prevention Study (HIPS).1 Using intention-to-treat analysis, if only 117 of 384 participants completed at least two of six group sessions, a positive result could not be expected. We know that interventions for prevention of cardiovascular disease (CVD) and diabetes can be run successfully in Australian primary care, which raises questions about the design of Harris et al’s intervention.