Structure and dielectric properties of cubic Bi<inf>2</inf>(Zn <inf>1/3</inf>Ta<inf>2/3</inf>)<inf>2</inf> O<inf>7</inf> thin films

Pyrochlore Bi2(Zn1/3Ta2/3)2 O7 (BZT) films were prepared by pulsed laser deposition on Pt/TiO2/SiO2/Si substrates. In contrast to bulk monoclinic BZT ceramics, the BZT films have a cubic structure mediated by an interfacial layer. The dielectric properties of the cubic BZT films [ε∼177, temperature coefficient of capacitance (TCC) ∼-170 ppm/°C] are much different from those of monoclinic BZT ceramics (ε∼61, TCC ∼+60 ppm/°C). Increasing the thickness of the BZT films returns the crystal structure to the monoclinic phase, which allows the dielectric properties of the BZT films to be tuned without changing their chemical composition. © 2009 American Institute of Physics

Preparation of titanium dioxide (TiO<inf>2</inf>) from sludge produced by titanium tetrachloride (TiCl<inf>4</inf>) flocculation of wastewater

Sludge disposal is one of the most costly and environmentally problematic challenges of modern wastewater treatment worldwide. In this study, a new process was developed, which has a significant potential for lower cost of waste disposal, protection of the environment and public health, and yield of economically useful byproducts. Titanium oxide (TiO2), which is the most widely used metal oxide, was produced from the wastewater sludge generated by the flocculation of secondary wastewater with titanium tetrachloride (TiCl4). Detailed analyses were conducted to compare TiCl 4, ferric chloride (FeCl3), and aluminum sulfate (Al 2(SO4)3) flocculation. Removal of organic matter and different molecular sizes by Ti-salt flocculation was similar to that of the most widely used Fe- and Al-salt flocculation. The mean size of Ti-, Fe-, and Al-salt flocs was 47.5, 42.5, and 16.9 μm, respectively. The decantability of the settled flocs by TiCl4 coagulant was similar to that by FeCl3 coagulant and much higher than that of Al 2(SO4)3. The photocatalyst from wastewater (PFW) produced by TiCl4 flocculation was characterized by X-ray diffraction, BET surface area, scanning electron microscopy/energy dispersive X-ray, transmission electron microscopy, photocatalytic activity, and X-ray photoelectron spectroscopy. The resulting PFW was found to be superior to commercial TiO2 (P-25) in terms of photocatalytic activity and surface area. The PFW was also found to be mainly doped with C and P atoms. The atomic percentage of the PFW was TiO1.42C0.44P 0.14. © 2007 American Chemical Society

Novel membrane bioreactor (MBR) coupled with a nonwoven fabric filter for household wastewater treatment

Conventional and modified membrane bioreactors (MBRs) are increasingly used in small-scale wastewater treatment. However, their widespread applications are hindered by their relatively high cost and operational complexity. In this study, we investigate a new concept of wastewater treatment using a nonwoven fabric filter bag (NFFB) as the membrane bioreactor. Activated sludge is charged in the nonwoven fabric filter bag and membrane filtration via the fabric is achieved under gravity flow without a suction pump. This study found that the biofilm layer formed inside the NFFB achieved 10 mg/L of suspended solids in the permeate within 20 min of initial operation. The dynamic biofilter layer showed good filterability and the specific membrane resistance consisted of 0.3-1.9 × 1012 m/kg. Due to the low F/M ratio (0.04-0.10 kg BOD5/m3/d) and the resultant low sludge yield, the reactor was operated without forming excess sludge. Although the reactor provided aerobic conditions, denitrification occurred in the biofilm layer to recover the alkalinity, thereby eliminating the need to supplement the alkalinity. This study indicates that the NFFB system provides a high potential of effective wastewater treatment with simple operation at reduced cost, and hence offer an attractive solution for widespread use in rural and sparsely populated areas. Crown Copyright © 2009

Hydrogen production affected by Pt concentration on TiO <inf>2</inf> produced from the incineration of dye wastewater flocculated sludge using titanium tetrachloride

TiO 2 from the incineration of dye wastewater flocculated sludge using TiCl 4 coagulant was produced. Optimal catalyst amount and Pt-loading on TiO 2 were studied for the production of H 2 by photocatalytic reforming of methanol (6% vol.). On the other hand, BTSE (biologically treated sewage effluent) was flocculated using TiCl4 and produced sludge was incinerated to generate TiO 2 . TiO 2 was loaded with optimum Pt and added to the supernatant in a photocatalytic reactor to test the efficiency of using remaining organics as a “sacrificial reagent” for photocatalytic hydrogen production. Dissolved organic carbon (DOC) and molecular weight distribution (MWD) were measured for nanofiltration (NF) and TiCl 4 flocculation followed by photocatalysis. TiO 2 (from the incineration of BTSE flocculated sludge using TiCl4) was produced and loaded with 0.5% Pt. Results showed that the optimum concentration of TiO 2 (from dye wastewater) for H 2 production was 0.3 g/L, while the optimum amount of Pt was 0.5%. DOC and MWD removal was similar for the flocculation of BTSE followed by photocatalytic reaction and the NF process. Remaining organic compounds after flocculation could not be used as sacrificial reagent to induce H 2 production. Further investigations on studying the UV intensity and/or identifying organic/inorganic scavengers to inhibit H 2 production are underway. © 2010, Taylor & Francis Group, LLC

Association analysis of polymorphism in KIAA1717, HUMMLC2B, DECR1 and FTO genes with meat quality traits of the Berkshire breed

Single nucleotide polymorphisms (SNPs) in KIAA1717, HUMMLC2B, DECR1, and FTO genes have been found to be associated with some pork meat quality traits. In this study, we discovered that, in addition to meat quality traits reported previously, SNPs in these genes also are significantly associated with other meat quality traits in the Berkshire breed. A total of 323 Berkshire pigs bred under the same conditions were used for meat quality evaluation and polymerase chain reaction-amplified genes with restriction endonucleases (PCR-RFLP) genotyping analyses. The association analysis of RFLP genotyping with meat quality traits revealed that the SNPs in these 4 genes have novel associations with multiple meat quality traits (p &lt; 0.01 or p &lt; 0.05); a SNP in KIAA1717 was associated with meat color (CIE L), backfat thickness, drip loss, water-holding capacity, and pH24hr; a SNP in HUMMLC2B was associated with chemical composition (collagen), drip loss, shear force, and pH24hr; a SNP in DECR1 was associated with meat color (CIE a and b) and backfat thickness; and a SNP in FTO was associated with meat color (CIE L, a and b), protein content, drip loss, and water-holding capacity. Taken collectively, our results suggest that these 4 SNPs may be used for marker-assisted selection as a genetic marker for meat quality traits in Berkshire pigs.Key words: Berkshire, genetic markers, meat quality, SN

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Regression Analysis of PEM Fuel Cell Transient Response

To develop operating strategies in polymer electrolyte membrane (PEM) fuel cell-powered applications, precise computationally efficient models of the fuel cell stack voltage are required. Models are needed for all operating conditions, including transients. In this work, transient evolutions of voltage, in response to load changes, are modeled with a sum of three exponential decay functions. Amplitude factors are correlated to steady-state operating data (temperature, humidity, average current, resistance, and voltage). The obtained time constants reflect known processes of the membrane heat/water transport. These model parameters can form the basis for the prediction of voltage overshoot/undershoot used in computational-based control systems, used in real-time simulation. Furthermore, the results provide an empirical basis for the estimation of the magnitude of temporary voltage loss to be expected with sudden load changes, as well as a systematic method for the analysis of experimental data. Its applicability is currently limited to thin membranes with low to moderate humidity gases, and with adequately high reactant-gas stoichiometry

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration