Detailed analysis of the plasma extracellular vesicle proteome after separation from lipoproteins

The isolation of extracellular vesicles (EVs) from blood is of great importance to understand the biological role of circulating EVs and to develop EVs as biomarkers of disease. Due to the concurrent presence of lipoprotein particles, however, blood is one of the most difficult body fluids to isolate EVs from. The aim of this study was to develop a robust method to isolate and characterise EVs from blood with minimal contamination by plasma proteins and lipoprotein particles. Plasma and serum were collected from healthy subjects, and EVs were isolated by size-exclusion chromatography (SEC), with most particles being present in fractions 8–12, while the bulk of the plasma proteins was present in fractions 11–28. Vesicle markers peaked in fractions 7–11; however, the same fractions also contained lipoprotein particles. The purity of EVs was improved by combining a density cushion with SEC to further separate lipoprotein particles from the vesicles, which reduced the contamination of lipoprotein particles by 100-fold. Using this novel isolation procedure, a total of 1187 proteins were identified in plasma EVs by mass spectrometry, of which several proteins are known as EV-associated proteins but have hitherto not been identified in the previous proteomic studies of plasma EVs. This study shows that SEC alone is unable to completely separate plasma EVs from lipoprotein particles. However, combining SEC with a density cushion significantly improved the separation of EVs from lipoproteins and allowed for a detailed analysis of the proteome of plasma EVs, thus making blood a viable source for EV biomarker discovery.     

A Systemic Framework for Business Model Design and Development -Part A: Theorizing Perspective

Developing innovative business models is one of the complicated issues. Owing to the multiplicity of stakeholders with diverse worldviews about value creation and dynamic markets, the development of innovative business models has turned out to be a complex issue. Therefore, this research seeks to: (1) examine the complicated nature of the development of business model problem and, (2) provide a holistic and systemic framework for the development of business models. The proposed framework employs the Soft Systems Methodology (SSM). This framework is composed of 18 phases, which ranges from the phase of finding out the problem of developing a business model to the phase of its implementation. This methodology is characterized by taking into account the considerations of rational positioning views, evolutionary views, and cognitive views in designing and developing business models.     

Liquid-phase aerobic oxidation of benzyl alcohol catalyzed by mechanochemically synthesized manganese oxide

Benzyl alcohol oxidation by molecular oxygen using solid manganese oxide as a catalyst and n-heptane as the solvent was studied in the liquid phase. The catalyst was synthesized by a simple mechanochemical process at room temperature and was characterized by different physical methods. The catalyst was highly active and 100% selective for benzyl alcohol conversion to benzaldehyde. The solid catalyst can be recovered from the reaction mixture by simple filtration and can be re-used. We found n-heptane to be an excellent solvent for the oxidation reaction, and that the catalyst did not leach into solution.     

Singular spectrum analysis: methodology and application to economics data

This paper describes the methodology of singular spectrum analysis (SSA) and demonstrate that it is a powerful method of time series analysis and forecasting, particulary for economic time series. The authors consider the application of SSA to the analysis and forecasting of the Iranian national accounts data as provided by the Central Bank of the Islamic Republic of Iran. This research was in part supported by a grant (No. 88/121230) from Institute for Trade Studies and Research (ITSR), Tehran, Iran.     

Deposition behavior and tribological properties of diamond-like carbon coatings on stainless steels via chemical vapor deposition

A systematic investigation was carried out to observe the deposition rate of a diamond-like carbon (DLC) coating on two stainless steel substrates by chemical vapor deposition (CVD). The objective of this research is to study the deposition behavior of the DLC coating and its tribological properties in different combinations of methane (CH₄) and nitrogen, which were used as precursor gases. The results reveal that the deposition rate increases with increasing CH₄ content up to 50vol%. The hardness of the DLC-deposited layer also increases while the friction coefficient decreases with increasing CH₄ gas content up to 50% in the precursor gas mixture.     

Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function.     

CFH haplotypes without the Y402H coding variant show strong association with susceptibility to age-related macular degeneration

In developed countries, age-related macular degeneration is a common cause of blindness in the elderly. A common polymorphism, encoding the sequence variation Y402H in complement factor H (CFH), has been strongly associated with disease susceptibility. Here, we examined 84 polymorphisms in and around CFH in 726 affected individuals (including 544 unrelated individuals) and 268 unrelated controls. In this sample, 20 of these polymorphisms showed stronger association with disease susceptibility than the Y402H variant. Further, no single polymorphism could account for the contribution of the CFH locus to disease susceptibility. Instead, multiple polymorphisms defined a set of four common haplotypes (of which two were associated with disease susceptibility and two seemed to be protective) and multiple rare haplotypes (associated with increased susceptibility in aggregate). Our results suggest that there are multiple disease susceptibility alleles in the region and that noncoding CFH variants play a role in disease susceptibility.