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.

Slope stability charts for two-layered purely cohesive soils based on finite-element limit analysis methods

Stability charts for soil slopes, first produced in the first half of the twentieth century, continue to be used extensively as design tools, and draw the attention of many investigators. This paper uses finite-element upper and lower bound limit analysis to assess the short-term stability of slopes in which the slopematerial and subgrade foundation material have two distinctly different undrained strengths. The stability charts are proposed, and the exact theoretical solutions are bracketed to within 4.2% or better. In addition, results from the limit-equilibrium method (LEM) have been used for comparison. Differences of up to 20% were found between the numerical limit analysis and LEM solutions. It also shown that the LEM sometimes leads to errors, although it is widely used in practice for slope stability assessments.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).

Hydraulic economy and logographic writing in ancient China: factor of topography

Chinese civilisation is characterised by two distinctive features – a strong centralised administration and the system of logographic writing. This paper investigates nature’s powerful impacts on human development, in terms of organisation and communication in ancient China. It argues that ancient China’s topography played a crucial role in the formation of ancient Chinese civilisation and its hydraulic economy pushed the state stronger than society, hence a strong centralised government emerged.

Energy productivity and total-factor productivity in the Australian construction industry

There is urgent need to consider energy consumption when measuring total-factor productivity in the construction industry. This paper adopts the Malmquist index method to investigate the factors affecting the energy productivity of the Australian construction industry and compares them with those decomposed from the total-factor productivity. An input-oriented distance function and a contemporaneous benchmark technology are employed to develop the data envelopment analysis models. The Malmquist productivity index is decomposed into the technological change, pure technical efficiency change and activity effect to gain comprehensive insights into changes of construction productivity in the Australian states and territories over the past two decades. Research results show that both energy productivity and total-factor productivity improved in Australia, particularly related to technological development. The pure technical efficiency and activity indices changed slightly over time or across regions. This study demonstrates that there exists a linkage between energy productivity and total-factor productivity through their technological and technical efficiency changes. The Australian construction industry could enhance these two productivities by introducing advanced technologies and implementing them efficiently.