Hierarchical ZSM‐5 catalysts: The effect of different intracrystalline pore dimensions on catalyst deactivation behaviour in the MTO reaction

We present the effect of different combinations of intracrystalline pore systems in hierarchical ZSM‐5 zeolites on their performance as MTO catalysts. We prepared ZSM‐5 zeolites with additional intracrystalline mesoporous, intracrystalline macropores and a novel ZSM‐5 type zeolite with intracrystalline meso and macropores. The catalytic results showed that both used catalysts with mesopores and macropores exhibited three times longer catalyst lifetime compared to a conventional catalyst. However, TGA analysis of the deactivated catalysts showed much larger coke content in the mesoporous catalyst than in the macroporous catalyst. Consequently, macropores predominantly led to reduced coke formation rate while additional mesopores predominantly enhanced the resistance against deactivation by coke. Combining both intracrystalline meso and macropores in one catalyst lead to a tenfold increase in catalyst lifetime. Besides the effect on the catalyst lifetime there was also a strong effect of the additional pore systems on the selectivity of the catalysts. The catalysts containing mesopores showed reduced selectivity to short chain olefins and increased selectivity to larger hydrocarbons in comparison to the catalysts without a mesopores system

Synthesis and Characterisation of Hierarchically Structured Titanium Silicalite‐1 Zeolites with Large Intracrystalline Macropores

The successful synthesis of hierarchically structured titanium silicalite‐1 (TS‐1) with large intracrystalline macropores by steam‐assisted crystallisation of mesoporous silica particles is reported. The macropore topology was imaged in 3D by using electron tomography and synchrotron radiation‐based ptychographic X‐ray computed tomography, revealing interconnected macropores within the crystals accounting for about 30 % of the particle volume. The study of the macropore formation mechanism revealed that the mesoporous silica particles act as a sacrificial macropore template during the synthesis. Silicon‐to‐titanium ratio of the macroporous TS‐1 samples was successfully tuned from 100 to 44. The hierarchically structured TS‐1 exhibited high activity in the liquid phase epoxidation of 2‐octene with hydrogen peroxide. The hierarchically structured TS‐1 surpassed a conventional nano‐sized TS‐1 sample in terms of alkene conversion and showed comparable selectivity to the epoxide. The flexible synthesis route described here can be used to prepare hierarchical zeolites with improved mass transport properties for other selective oxidation reactions

Ethics and Nanopharmacy: Value Sensitive Design of New Drugs

Although applications are being developed and have reached the market, nanopharmacy to date is generally still conceived as an emerging technology. Its concept is ill-defined. Nanopharmacy can also be construed as a converging technology, which combines features of multiple technologies, ranging from nanotechnology to medicine and ICT. It is still debated whether its features give rise to new ethical issues or that issues associated with nanopharma are merely an extension of existing issues in the underlying fields. We argue here that, regardless of the alleged newness of the ethical issues involved, developments occasioned by technological advances affect the roles played by stakeholders in the field of nanopharmacy to such an extent that this calls for a different approach to responsible innovation in this field. Specific features associated with nanopharmacy itself and features introduced to the associated converging technologies- bring about a shift in the roles of stakeholders that call for a different approach to responsibility. We suggest that Value Sensitive Design is a suitable framework to involve stakeholders in addressing moral issues responsibly at an early stage of development of new nanopharmaceuticals

A machine learning pipeline for quantitative phenotype prediction from genotype data

<p>Abstract</p> <p>Background</p> <p>Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.</p> <p>Methods</p> <p>The core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected.</p> <p>Results</p> <p>With respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms.</p> <p>Conclusions</p> <p>The L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.</p

Spry1 Is Expressed in Hemangioblasts and Negatively Regulates Primitive Hematopoiesis and Endothelial Cell Function

Development of the hematopoietic and endothelial lineages derives from a common mesodermal precursor, the Flk1(+) hemangioblast. However, the signaling pathways that regulate the development of hematopoietic and endothelial cells from this common progenitor cell remains incompletely understood. Using mouse models with a conditional Spry1 transgene, and a Spry1 knockout mouse, we investigated the role of Spry1 in the development of the endothelial and hematopoietic lineages during development.Quantitative RT-PCR analysis demonstrates that Spry1, Spry2, and Spry4 are expressed in Flk1(+) hemangioblasts in vivo, and decline significantly in c-Kit(+) and CD41(+) hematopoietic progenitors, while expression is maintained in developing endothelial cells. Tie2-Cre-mediated over-expression of Spry1 results in embryonic lethality. At E9.5 Spry1;Tie2-Cre embryos show near normal endothelial cell development and vessel patterning but have reduced hematopoiesis. FACS analysis shows a reduction of primitive hematopoietic progenitors and erythroblastic cells in Spry1;Tie2-Cre embryos compared to controls. Colony forming assays confirm the hematopoietic defects in Spry1;Tie2-Cre transgenic embryos. Immunostaining shows a significant reduction of CD41 or CD71 and dpERK co-stained cells in Spry1;Tie2-Cre embryos compared to controls, whereas the number of VEC(+) and dpERK co-stained cells is comparable. Compared to controls, Spry1;Tie2-Cre embryos also show a decrease in proliferation and an increase in apoptosis. Furthermore, loss of Spry1 results in an increase of CD41(+) and CD71(+) cells at E9.5 compared with controls.These data indicate that primitive hematopoietic cells derive from Tie2-expressing hemangioblasts and that Spry1 over expression inhibits primitive hematopoietic progenitor and erythroblastic cell development and expansion while having no obvious effect on endothelial cell development

Sprouty Proteins Inhibit Receptor-mediated Activation of Phosphatidylinositol-specific Phospholipase C

PLCγ03B3 binds Spry1 and Spry2. Overexpression of Spry decreased PLCγ03B3 activity and IP3 and DAG production, whereas Spry-deficient cells yielded more IP3. Spry overexpression inhibited T-cell receptor signaling and Spry1 null T-cells hyperproliferated with TCR ligation. Through action of PLCγ03B3, Spry may influence signaling through multiple receptors

The Impact of Multifunctional Genes on "Guilt by Association" Analysis

Many previous studies have shown that by using variants of “guilt-by-association”, gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the “associations” in the data (e.g., protein interaction partners) of a gene are necessary in establishing “guilt”. In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies

Suppression of Sproutys Has a Therapeutic Effect for a Mouse Model of Ischemia by Enhancing Angiogenesis

Sprouty proteins (Sproutys) inhibit receptor tyrosine kinase signaling and control various aspects of branching morphogenesis. In this study, we examined the physiological function of Sproutys in angiogenesis, using gene targeting and short-hairpin RNA (shRNA) knockdown strategies. Sprouty2 and Sprouty4 double knockout (KO) (DKO) mice were embryonic-lethal around E12.5 due to cardiovascular defects. The number of peripheral blood vessels, but not that of lymphatic vessels, was increased in Sprouty4 KO mice compared with wild-type (WT) mice. Sprouty4 KO mice were more resistant to hind limb ischemia and soft tissue ischemia than WT mice were, because Sprouty4 deficiency causes accelerated neovascularization. Moreover, suppression of Sprouty2 and Sprouty4 expression in vivo by shRNA targeting accelerated angiogenesis and has a therapeutic effect in a mouse model of hind limb ischemia. These data suggest that Sproutys are physiologically important negative regulators of angiogenesis in vivo and novel therapeutic targets for treating peripheral ischemic diseases

Sprouty2 and Spred1-2 Proteins Inhibit the Activation of the ERK Pathway Elicited by Cyclopentenone Prostanoids

Sprouty and Spred proteins have been widely implicated in the negative regulation of the fibroblast growth factor receptor-extracellular regulated kinase (ERK) pathway. In considering the functional role of these proteins, we explored their effects on ERK activation induced by cyclopentenone prostanoids, which bind to and activate Ras proteins. We therefore found that ectopic overexpression in HeLa cells of human Sprouty2, or human Spred1 or 2, inhibits ERK1/2 and Elk-1 activation triggered by the cyclopentenone prostanoids PGA1 and 15d-PGJ2. Furthermore, we found that in HT cells that do not express Sprouty2 due to hypermethylation of its gene-promoter, PGA1-provoked ERK activation was more intense and sustained compared to other hematopoietic cell lines with unaltered Sprouty2 expression. Cyclopentenone prostanoids did not induce Sprouty2 tyrosine phosphorylation, in agreement with its incapability to activate tyrosine-kinase receptors. However, Sprouty2 Y55F, which acts as a defective mutant upon tyrosine-kinase receptor stimulation, did not inhibit cyclopentenone prostanoids-elicited ERK pathway activation. In addition, Sprouty2 did not affect the Ras-GTP levels promoted by cyclopentenone prostanoids. These results unveil both common and differential features in the activation of Ras-dependent pathways by cyclopentenone prostanoids and growth factors. Moreover, they provide the first evidence that Sprouty and Spred proteins are negative regulators of the ERK/Elk-1 pathway activation induced not only by growth-factors, but also by reactive lipidic mediators