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.

Improving peak shapes with counter gradients in two-dimensional high performance liquid chromatography

To achieve the greatest peak capacity in two-dimensional high performance liquid chromatography (2D-HPLC) a gradient should be operated in both separation dimensions. However, it is known that when an injection solvent that is stronger than the initial mobile phase composition is deleterious to peak performance, thus causing problems when cutting a portion from one gradient into another. This was overcome when coupling hydrophilic interaction with reversed phase chromatography by introducing a counter gradient that changed the solvent strength of the second dimension injection. It was found that an injection solvent composition of 20% acetonitrile in water gave acceptable results in one-dimensional simulations with an initial composition of 5% acetonitrile. When this was transferred to a 2D-HPLC separation of standards it was found that a marked improvement in peak shape was gained for the moderately retained analytes (phenol and dimethyl phthalate), some improvement for the weakly retained caffeine and very little change for the strongly retained n-propylbenzene and anthracene which already displayed good chromatographic profiles. This effect was transferred when applied to a 2D-HPLC separation of a coffee extract where the indecipherable retention profile was transformed to a successful application multidimensional chromatography with peaks occupying 71% of the separation space according to the geometric approach to factor analysis.

Interleukin-6 and tumor necrosis factor-α are not increased in patients with type 2 diabetes: Evidence that plasma interleukin-6 is related to fat mass and not insulin responsiveness

Aims/hypothesis. Our aim was to examine the possible direct relationship of interleukin-6 and TNFα with insulin sensitivity in humans. Methods. We carried out two series of euglycaemic-hyperinsulinaemic clamp experiments. In the first (CLAMP1), skeletal muscle mRNA expression and plasma concentrations of IL-6 and TNFα were examined in patients with Type 2 diabetes (n=6), subjects matched for age (n=6), and young healthy (n=11) control subjects during a 120-min supra-physiological hyperinsulinaemic (40 mU·m -2·min-1) euglycaemic clamp. In the second series of experiments (CLAMP2), patients with Type 2 diabetes (n=6) and subjects matched for age (n=7) were studied during a 240-min high-physiological hyperinsulinaemic (7 mU·m-2·min-1) euglycaemic clamp, during which arterial and venous (femoral and subclavian) blood samples were measured for IL-6 and TNFα flux. Results. In both experiments the glucose infusion rate in the patients was markedly lower than that in the other groups. In CLAMP1, basal skeletal muscle IL-6 and TNFα mRNA were the same in all groups. They were not affected by insulin and they were not related to the glucose infusion rate. In CLAMP2, neither cytokine was released from the arm or leg during insulin stimulation in either group. In both experiments plasma concentrations of these cytokines were similar in the patients and in the control subjects, although in CLAMP1 the young healthy control group had lower (p<0.05) plasma IL-6 concentrations. Using data from all subjects, a strong positive correlation (r=0.85; p<0.00001) was observed between basal plasma IL-6 and BMI. Conversely, a negative relationship (r=-0.345; p<0.05) was found between basal plasma TNFα and BMI, although this was not significant when corrected for BMI. When corrected for BMI, no relationship was observed between either basal plasma IL-6 or TNFα and GIR. Conclusions/interpretation. These data show that the increased circulating IL-6 concentrations seen in patients with Type 2 diabetes are strongly related to fat mass and not insulin responsiveness, and suggest that neither IL-6 nor TNFα are indicative of insulin resistance.

Peripheral brain-derived neurotrophic factor in schizophrenia and the role of antipsychotics: meta-analysis and implications

It has been postulated that schizophrenia (SZ) is related to a lower expression of brain-derived neurotrophic factor (BDNF). In the past few years, an increasing number of divergent clinical studies assessing BDNF in serum and plasma have been published. It is now possible to verify the relationship between BDNF levels and severity of symptoms in SZ as well as the effects of antipsychotic drugs on BDNF using meta-analysis. The aims of this study were to verify if peripheral BDNF is decreased in SZ, whether its levels are correlated with positive and negative symptomatology and if BDNF levels change after antipsychotic treatment. This report consists of two distinct meta-analyses of peripheral BDNF in SZ including a total of 41 studies and more than 7000 participants: (1) peripheral BDNF levels in serum and plasma were moderately reduced in SZ compared with controls. Notably, this decrease was accentuated with the disease duration. However, the extent of peripheral BDNF level decrease did not correlate with the severity of positive and negative symptoms. (2) In plasma, but not serum, peripheral BDNF levels are consistently increased after antipsychotic treatment irrespective of the patient's response to medication. In conclusion, there is compelling evidence that there are decreased levels of peripheral BDNF in SZ, in parallel to previously described reduced cerebral BDNF expression. It remains unclear whether these systemic changes are causally related to the development of SZ or if they are merely a pathologic epiphenomenon.Molecular Psychiatry advance online publication, 30 September 2014; doi:10.1038/mp.2014.117.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

Robust multi-factor authentication for fragile communications

In large-scale systems, user authentication usually needs the assistance from a remote central authentication server via networks. The authentication service however could be slow or unavailable due to natural disasters or various cyber attacks on communication channels. This has raised serious concerns in systems which need robust authentication in emergency situations. The contribution of this paper is two-fold. In a slow connection situation, we present a secure generic multi-factor authentication protocol to speed up the whole authentication process. Compared with another generic protocol in the literature, the new proposal provides the same function with significant improvements in computation and communication. Another authentication mechanism, which we name stand-alone authentication, can authenticate users when the connection to the central server is down. We investigate several issues in stand-alone authentication and show how to add it on multi-factor authentication protocols in an efficient and generic way.

Type D personality and the Five-Factor Model : a facet-level analysis

Objective: This study assessed the degree to which NEO-PI-R facets contribute to understanding of Type D personality over and above the Big 5. Method: Healthy participants (n = 268) completed the DS14 and the NEO-PI-R. In addition to analyzing the Type D subscales of social inhibition and negative affectivity, we computed a continuous measure of Type D scored as the sum of the two subscales. Results: Facets provided moderate incremental prediction of Type D subscales. The facets of assertiveness, self- consciousness, and positive emotion provided incremental prediction of negative affectivity, and warmth, activity, and gregariousness provided incremental prediction of social inhibition. Facets provided minimal incremental prediction of continuous Type D. The Big 5 explained substantially more variance in continuous Type D (71%) than negative affectivity (59%) or social inhibition (61%). Conclusions: Overall, the facet-level analysis provided additional insights into the nature of Type D. The incremental prediction of the Type D subscales by facets supported the idea that Type D scales are narrower constructs than the Big 5. However, the strong prediction of continuous Type D by the Big 5 and the lack of incremental prediction by facets points to the overall Type D construct being well represented by the Big 5.

Coiled polymeric growth factor gradients for multi-luminal neural chemotaxis

In the injured adult nervous system, re-establishment of growth-promoting molecular gradients is known to entice and guide nerve repair. However, incorporation of three-dimensional chemotactic gradients in nerve repair scaffolds, particularly in those with multi-luminal architectures, remains extremely challenging. We developed a method that establishes highly tunable three-dimensional molecular gradients in collagen-filled multi-luminal nerve guides by anchoring growth-factor releasing coiled polymeric fibers onto the walls of collagen-filled hydrogel microchannels. Differential pitch in the coiling of neurotrophin-eluting fibers generated sustained three-dimensional chemotactic gradients that appropriately induced the differentiation of Pheochromocytoma (PC12) cells into neural-like cells along an increasing concentration of nerve growth factor (NGF). Computer modeling estimated the stability of the molecular gradient within the luminal collagen, which we confirmed by observing the significant effects of neurotrophin gradients on axonal growth from dorsal root ganglia (DRG). Neurons growing in microchannels exposed to a NGF gradient showed a 60% increase in axonal length compared to those treated with a linear growth factor concentration. In addition, a two-fold increment in the linearity of axonal growth within the microchannels was observed and confirmed by a significant reduction in the turning angle ratios of individual axons. These data demonstrate the ability of growth factor-loaded polymeric coiled fibers to establish three-dimensional chemotactic gradients to promote and direct nerve regeneration in the nervous system and provides a unique platform for molecularly guided tissue repair.