Periodate – an alternative oxidant for testing potential water oxidation catalysts

The redox catalyst ruthenium dioxide, prepared via the Adams technique, i.e.Ru(Adams), is used as a water oxidation catalyst using the oxidants (i) Ce(IV) in 0.5M H2SO4 and (ii) periodate in 0.5 M H2SO4, water and 0.1 M KOH. Like Ce(IV),periodate is a very strong oxidant that is able to oxidise water to oxygen and can bereadily monitored spectrophotometrically at 280 nm, compared with 430 nm for Ce(IV).More importantly, unlike Ce(IV), which is unstable towards hydrolysis above pH 1,periodate is stable in acid, water and strong alkali. A spectrophotometric study of thekinetics of periodate reduction, and concomitant oxidation of water to O2, reveals thatin the presence of a suitable redox catalyst, Ru(Adams) in this work, periodate is ableto effect the stoichiometric oxidation of water, with a turnover number > 64. In justwater, the kinetics of the latter reaction appear diffusion-controlled, due to the largethermodynamic driving force, a measure of which is the difference in redox potential,i.e. ∆E = 423 mV. As this difference is decreased, ∆E = 396 mV in acid and 290 mVin strong alkali (0.1 M KOH), the kinetics become increasingly activation-controlledand slower. These findings are discussed briefly with regard to the possible use of (i)periodate as an alternative oxidant in the rapid screening of new potential wateroxidation catalyst material powders that are stable only under near neutral and/oralkaline conditions, and (ii) Ru(Adams) as a benchmark catalyst.

PTEN mRNA detection by chromogenic, RNA in situ technologies: a reliable alternative to PTEN immunohistochemistry

Immunohistochemical staining for phosphatase and tensin homolog (PTEN) does not have either an acceptable standard protocol or concordance of scoring between pathologists. Evaluation of PTEN mRNA with a unique and verified sequence probe may offer a realistic alternative providing a robust and reproducible protocol. In this study, we have evaluated an in situ hybridization (ISH) protocol for PTEN mRNA using RNAScope technology and compared it with a standard protocol for PTEN immunohistochemistry (IHC). PTEN mRNA expression by ISH was consistently more sensitive than PTEN IHC, with 56% of samples on a mixed-tumor tissue microarray (TMA) showing high expression by ISH compared with 42% by IHC. On a prostate TMA, 49% of cases showed high expression by ISH compared with 43% by IHC. Variations in PTEN mRNA expression within malignant epithelium were quantifiable using image analysis on the prostate TMAs. Within tumors, clear overexpression of PTEN mRNA on malignant epithelium compared with benign epithelium was frequently observed and quantified. The use of SpotStudio software in the mixed-tumor TMA allowed for clear demonstration of varying levels of PTEN mRNA between tumor samples by the mRNA methodology. This was evident by the quantifiable differences between distinct oropharyngeal tumors (up to 3-fold increase in average number of spots per cell between 2 cases). mRNA detection of PTEN or other biomarkers, for which optimal or standardized immunohistochemical techniques are not available, represents a means by which heterogeneity of expression within focal regions of tumor can be explored with more confidence.

Measurement of fibre fracture toughness using an alternative specimen geometry

This paper presents an experimental and numerical study focused on the tensile fibre fracture toughness characterisation of hybrid plain weave composite laminates using non-standardized Overheight Compact Tension (OCT) specimens. The position as well as the strain field ahead of the crack tip in the specimens was determined using a digital speckle photogrammetry system. The limitation on the applicability of standard data reduction schemes for the determination of the intralaminar fibre fracture toughness of composites is presented and discussed. A methodology based on the numerical evaluation of the strain energy release rate using the J-integral method is proposed to derive new geometric correction functions for the determination of stress intensity factor for alternative composite specimen geometries. A comparison between different methods currently available to compute the intralaminar fracture toughness in composites is also presented and discussed. Good agreement between numerical and experimental results using the proposed methodology was obtained.

Seed dormancy in Arabidopsis thaliana is controlled by alternative polyadenylation of DOG1

DOG1 is a key regulator of seed dormancy in Arabidopsis and other plants. Interestingly, the C-terminus of DOG1 is either absent or not conserved in many plant species. Here, we show that in Arabidopsis DOG1 transcript is subject to alternative polyadenylation. In line with this, mutants in RNA 3' processing complex display weakened seed dormancy in parallel with defects in DOG1 proximal polyadenylation site selection, suggesting that the short DOG1 transcript, is functional. This is corroborated by the finding that the proximally polyadenylated short DOG1 mRNA is translated in vivo and complements the dog1 mutation. In summary, our findings indicate that the short DOG1 protein isoform produced from the proximally polyadenylated DOG1 mRNA is a key player in the establishment of seed dormancy in Arabidopsis and characterize a set of mutants in RNA 3' processing complex required for production of proximally polyadenylated functional DOG1 transcript.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.