Titania Nanomaterials Produced from Ti-Salt Flocculated Sludge in Water Treatment

Titania is the most widely used metal oxide for the applications of pigments, paper, solar cells and environmental purification. In order to meet the demand of a large amount of titania, our group has developed a novel process which could significantly lower the cost of waste disposal in water treatment, protect the environment and public health and yield economically valuable titania. Titanium tetrachloride (TiCl4) or titanium sulfate (Ti(SO4)2) as an alternative coagulant in water treatment has been explored for the removal of various pollutants from contaminated water or wastewater. Flocculation efficiencies of the Ti-salts were superior to those of Al- and Fe- salts with additional benefits in that a large amount of titania can be produced by calcinating the flocculated floc. The produced titania showed high photocatalytic activity for the removal of volatile organic compounds. The large amount of titania can be applied to pigments, environment and construction materials which require a lot of titania usages. This review paper presents an historical progress from fundamental to application in terms of the detailed production process, characterization, photoactivity of titania produced from Ti-salt flocculated sludge, and its various applications. © 2011 Springer Science+Business Media, LLC

Self-Supervised Relative Depth Learning for Urban Scene Understanding

As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, faraway mountains don't move much; nearby trees move a lot. This natural relationship between the appearance of objects and their motion is a rich source of information about the world. In this work, we start by training a deep network, using fully automatic supervision, to predict relative scene depth from single images. The relative depth training images are automatically derived from simple videos of cars moving through a scene, using recent motion segmentation techniques, and no human-provided labels. This proxy task of predicting relative depth from a single image induces features in the network that result in large improvements in a set of downstream tasks including semantic segmentation, joint road segmentation and car detection, and monocular (absolute) depth estimation, over a network trained from scratch. The improvement on the semantic segmentation task is greater than those produced by any other automatically supervised methods. Moreover, for monocular depth estimation, our unsupervised pre-training method even outperforms supervised pre-training with ImageNet. In addition, we demonstrate benefits from learning to predict (unsupervised) relative depth in the specific videos associated with various downstream tasks. We adapt to the specific scenes in those tasks in an unsupervised manner to improve performance. In summary, for semantic segmentation, we present state-of-the-art results among methods that do not use supervised pre-training, and we even exceed the performance of supervised ImageNet pre-trained models for monocular depth estimation, achieving results that are comparable with state-of-the-art methods

Phosphorylation of the androgen receptor is associated with reduced survival in hormonerefractory prostate cancer patients

Cell line studies demonstrate that the PI3K/Akt pathway is upregulated in hormone-refractory prostate cancer (HRPC) and can result in phosphorylation of the androgen receptor (AR). The current study therefore aims to establish if this has relevance to the development of clinical HRPC. Immunohistochemistry was employed to investigate the expression and phosphorylation status of Akt and AR in matched hormone-sensitive and -refractory prostate cancer tumours from 68 patients. In the hormone-refractory tissue, only phosphorylated AR (pAR) was associated with shorter time to death from relapse (<i>P</i>=0.003). However, when an increase in expression in the transition from hormone-sensitive to -refractory prostate cancer was investigated, an increase in expression of PI3K was associated with decreased time to biochemical relapse (<i>P</i>=0.014), and an increase in expression of pAkt<sup>473</sup> and pAR<sup>210</sup> were associated with decreased disease-specific survival (<i>P</i>=0.0019 and 0.0015, respectively). Protein expression of pAkt<sup>473</sup> and pAR<sup>210</sup> also strongly correlated (<i>P</i><0.001, c.c.=0.711) in the hormone-refractory prostate tumours. These results provide evidence using clinical specimens, that upregulation of the PI3K/Akt pathway is associated with phosphorylation of the AR during development of HRPC, suggesting that this pathway could be a potential therapeutic target

Epidemics in partially overlapped multiplex networks

Many real networks exhibit a layered structure in which links in each layer reflect the function of nodes on different environments. These multiple types of links are usually represented by a multiplex network in which each layer has a different topology. In real-world networks, however, not all nodes are present on every layer. To generate a more realistic scenario, we use a generalized multiplex network and assume that only a fraction $q$ of the nodes are shared by the layers. We develop a theoretical framework for a branching process to describe the spread of an epidemic on these partially overlapped multiplex networks. This allows us to obtain the fraction of infected individuals as a function of the effective probability that the disease will be transmitted $T$. We also theoretically determine the dependence of the epidemic threshold on the fraction $q > 0$ of shared nodes in a system composed of two layers. We find that in the limit of $q \to 0$ the threshold is dominated by the layer with the smaller isolated threshold. Although a system of two completely isolated networks is nearly indistinguishable from a system of two networks that share just a few nodes, we find that the presence of these few shared nodes causes the epidemic threshold of the isolated network with the lower propagating capacity to change discontinuously and to acquire the threshold of the other network.Comment: 13 pages, 4 figure

2,3-Dihydroxyisovalerate production by Klebsiella pneumoniae

2,3-Dihydroxyisovalerate is an intermediate of valine and leucine biosynthesis pathway; however, no natural microorganism has been found yet that can accumulate this compound. Klebsiella pneumoniae is a useful bacterium that can be used as a workhorse for the production of a range of industrially desirable chemicals. Dihydroxy acid dehydratase, encoded by the ilvD gene, catalyzes the reaction of 2-ketoisovalerate formation from 2,3-dihydroxyisovalerate. In this study, an ilvD disrupted strain was constructed which resulted in the inability to synthesize 2-ketoisovalerate, yet accumulate 2,3-dihydroxyisovalerate in its culture broth. 2,3-Butanediol is the main metabolite of K. pneumoniae and its synthesis pathway and the branched-chain amino acid synthesis pathway share the same step of the α-acetolactate synthesis. By knocking out the budA gene, carbon flow into the branched-chain amino acid synthesis pathway was upregulated, which resulted in a distinct increase in 2,3-dihydroxyisovalerate levels. Lactic acid was identified as a by-product of the process and by blocking the lactic acid synthesis pathway, a further increase in 2,3-dihydroxyisovalerate levels was obtained. The culture parameters of 2,3-dihydroxyisovalerate fermentation were optimized, which include acidic pH and medium level oxygen supplementation to favor 2,3-dihydroxyisovalerate synthesis. At optimal conditions (pH 6.5, 400 rpm), 36.5 g/L of 2,3-dihydroxyisovalerate was produced in fed-batch fermentation over 45 h, with a conversion ratio of 0.49 mol/mol glucose. Thus, a biological route of 2,3-dihydroxyisovalerate production with high conversion ratio and final titer was developed, providing a basis for an industrial process.Key Points• A biological route of 2,3-dihydroxyisovalerate production was setup.• Disruption of budA causes 2,3-dihydroxuisovalerate accumulation in K. pneumoniae.• Disruption of ilvD prevents 2,3-dihydroxyisovalerate reuse by the cell.• 36.5 g/L of 2,3-dihydroxyisovalerate was obtained in fed-batch fermentation

A survey of parametric modelling methods for designing the head of a high-speed train

With the continuous increase of the running speed, the head shape of the high-speed train (HST) turns out to be a critical factor for further speed boost. In order to cut down the time used in the HST head design and improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the HST head to obtain an optimal head shape so that the aerodynamic effect acting on the head of HSTs can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: 2D, 3D, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability

Visualization and quantitation of the expression of microRNAs and their target genes in neuroblastoma single cells using imaging cytometry

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are regulatory molecules that play an important role in many physiological processes, including cell growth, differentiation, and apoptosis. In addition to modulating normal cellular functions, it has also been reported that miRNAs are involved in the development of many pathologies, including cardiovascular diseases, cancer, inflammation, and neurodegeneration. Methods for the sensitive detection and measurement of specific miRNAs and their cellular targets are essential for both basic research endeavours, as well as diagnostic efforts aimed at understanding the role of miRNAs in disease processes.</p> <p>Findings</p> <p>In this study, we describe a novel, imaging cytometry-based protocol that allows for simultaneous visualisation and quantification of miRNAs and their putative targets. We validated this methodology in a neuronal cell line by examining the relationship of the miRNA miR-124 and its known target, cyclin dependent kinase 6 (CDK6). We found that ectopic overexpression of miR-124 resulted in the downregulation of CDK6, decreased cellular proliferation, and induced cellular morphological changes.</p> <p>Conclusions</p> <p>This method is suitable for analysing the expression and cellular localisation of miRNAs and target proteins in small cell subsets within a heterogeneous cell suspension. We believe that our cytometry-based methodology will be easily adaptable to miRNA studies in many areas of biomedical research including neuroscience, stem cell biology, immunology, and oncology.</p

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Identification of chemokine receptors as potential modulators of endocrine resistance in oestrogen receptor–positive breast cancers

Introduction Endocrine therapies target oestrogenic stimulation of breast cancer (BC) growth, but resistance remains problematic. Our aims in this study were (1) to identify genes most strongly associated with resistance to endocrine therapy by intersecting global gene transcription data from patients treated presurgically with the aromatase inhibitor anastrazole with those from MCF7 cells adapted to long-term oestrogen deprivation (LTED) (2) to assess the clinical value of selected genes in public clinical data sets and (3) to determine the impact of targeting these genes with novel agents. Methods Gene expression and Ki67 data were available from 69 postmenopausal women with oestrogen receptor–positive (ER+) early BC, at baseline and 2 weeks after anastrazole treatment, and from cell lines adapted to LTED. The functional consequences of target genes on proliferation, ER-mediated transcription and downstream cell signalling were assessed. Results By intersecting genes predictive of a poor change in Ki67 with those upregulated in LTED cells, we identified 32 genes strongly correlated with poor antiproliferative response that were associated with inflammation and/or immunity. In a panel of LTED cell lines, C-X-C chemokine receptor type 7 (CXCR7) and CXCR4 were upregulated compared to their wild types (wt), and CXCR7, but not CXCR4, was associated with reduced relapse-free survival in patients with ER+ BC. The CXCR4 small interfering RNA variant (siCXCR4) had no specific effect on the proliferation of wt-SUM44, wt-MCF7 and their LTED derivatives. In contrast, siCXCR7, as well as CCX733, a CXCR7 antagonist, specifically suppressed the proliferation of MCF7-LTED cells. siCXCR7 suppressed proteins associated with G1/S transition and inhibited ER transactivation in MCF7-LTED, but not wt-MCF7, by impeding association between ER and proline-, glutamic acid– and leucine-rich protein 1, an ER coactivator. Conclusions These data highlight CXCR7 as a potential therapeutic target warranting clinical investigation in endocrine-resistant BC