The Role of Water and Adsorbed Hydroxyls on Ethanol Electrochemistry on Pd: New Mechanism, Active Centers and Energetics for Direct Ethanol Fuel Cell Running in Alkaline Medium

First principles calculations with molecular dynamics are
utilized to simulate a simplified electrical double layer formed in the
active electric potential region during the electrocatalytic oxidation of
ethanol on Pd electrodes running in an alkaline electrolyte. Our
simulations provide an atomic level insight into how ethanol oxidation
occurs in fuel cells: New mechanisms in the presence of the simplified
electrical double layer are found to be different from the traditional
ones; through concerted-like dehydrogenation paths, both acetaldehyde
and acetate are produced in such a way as to avoid a variety of
intermediates, which is consistent with the experimental data obtained
from in situ FTIR spectroscopy. Our work shows that adsorbed OH on
the Pd electrode rather than Pd atoms is the active center for the
reactions; the dissociation of the C−H bond is facilitated by the
adsorption of an OH− anion on the surface, resulting in the formation
of water. Our calculations demonstrate that water dissociation rather than H desorption is the main channel through which
electrical current is generated on the Pd electrode. The effects of the inner Helmholtz layer and the outer Helmholtz layer are
decoupled, with only the inner Helmholtz layer being found to have a significant impact on the mechanistics of the reaction. Our
results provide atomic level insight into the significance of the simplified electrical double layer in electrocatalysis, which may be
of general importance.

Contribution of water and cooked rice to an estimation of the dietary intake of inorganic arsenic in a rural village of West Bengal, India

Arsenic contamination of rice plants by arsenic-polluted irrigation groundwater could result in high arsenic concentrations in cooked rice. The main objective of the study was to estimate the total and inorganic arsenic intakes in a rural population of West Bengal, India, through both drinking water and cooked rice. Simulated cooking of rice with different levels of arsenic species in the cooking water was carried out. The presence of arsenic in the cooking water was provided by four arsenic species (arsenite, arsenate, methylarsonate or dimethylarsinate) and at three total arsenic concentrations (50, 250 or 500 mu g l(-1)). The results show that the arsenic concentration in cooked rice is always higher than that in raw rice and range from 227 to 1642 mu g kg(-1). The cooking process did not change the arsenic speciation in rice. Cooked rice contributed a mean of 41% to the daily intake of inorganic arsenic. The daily inorganic arsenic intakes for water plus rice were 229, 1024 and 2000 mu g day(-1) for initial arsenic concentrations in the cooking water of 50, 250 and 500 g arsenic l(-1), respectively, compared with the tolerable daily intake which is 150 mu g day(-1).

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

Spatial and temporal regulation of the pesticide dieldrin within industrial catchments

The river catchments of south Yorkshire support a very high density of wool processing industries. Dieldrin was once used as a moth proofing agent, as a sheep dip, and as a pesticide to protect wool fleeces during storage and transport, all of which caused pollution of these catchments due to textile processing. Weekly sampling of four of these rivers revealed two classes of dieldrin contamination: the Aire and Calder (the rivers which support very high concentrations of wool processing industries) had higher concentrations (averaging ~3 ng/l) than the Don and Trent (~1 ng/l). The average flux of dieldrin from these rivers into the Humber estuary was 9.8 g/day, with the Aire (of which the Calder is a tributary) and the Trent contributing almost equally, with a smaller contribution from the Don. The Trent has the highest average flow, explaining its large contribution to dieldrin flux. Less detailed sampling of rivers from the north Humber catchment which drain predominantly rural areas had dieldrin concentrations similar to the heavily industrialized southern catchment rivers. This suggests that dieldrin from agronomic and domestic usage may be more persistent than the pollution caused by textile processing industries. Evidence is presented to suggest that the principle dieldrin sources to the Humber catchments are sewage treatment plants, and that the dieldrin sources are in rapid equilibrium with the water column. (C) 2000 Elsevier Science B.V.

Identification of Small Signal Oscillation Mode Parameters from Simulated and Actual PMU Ringdown Data

This paper presents the practical use of Prony Analysis to identify small signal oscillation mode parameters from simulated and actual phasor measurement unit (PMU) ringdown data. A well-known two-area four-machine power system was considered as a study case while the latest PMU ringdown data were collected from a double circuit 275 kV main interconnector on the Irish power system. The eigenvalue analysis and power spectral density were also conducted for the purpose of comparison. The capability of Prony Analysis to identify the mode parameters from three different types of simulated PMU ringdown data has been shown successfully. Furthermore, the results indicate that the Irish power system has dominant frequency modes at different frequencies. However, each mode has good system damping.