The surface behaviour and catalytic properties of Nd2-XSrXCoO4±Λ mixed oxides

The mixed oxides, Nd2-XSrXCoO4±λ (0.4 ≤ x ≤ 1.2), (λ = non-stochiometric oxygen) with the K2NiF4 structure were prepared by the polyglycol gel method and used as catalysts for NO reduction. The samples were investigated by IR, TPD, TPR, and XRD methods and iodometry and the effects of the coefficient x on the structure and catalytic activity of the samples were studied. The results show that the Nd2-XSrXCoO4±λ mixed oxides have the K2NiF4 structure; other phases are found when x 1.2. The amount of Co3+ and the lattice oxygen in Nd2-XSrXCoO4±λ increase with increasing x. The catalytic activity of Nd2-XSrXCoO4±λ for NO reduction is closely correlated with the concentration of oxygen vacancies and the amount of Co3+.KEY WORDS: A2BO4, Co-containing mixed oxide, NO reduction, Rare-earthBull. Chem. Soc. Ethiop. 2006, 20(2), 201-206

Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization

The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing. Many state-of-the-art language models are first trained on a large text corpus and then fine-tuned on downstream tasks. Despite its recent success and wide adoption, fine-tuning a pre-trained language model often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR). Specifically, we propose to inject the standard Gaussian noise or In-manifold noise and regularize hidden representations of the fine-tuned model. We first provide theoretical analyses to support the efficacy of our method. We then demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART. While these previous works only verify the effectiveness of their methods on relatively simple text classification tasks, we also verify the effectiveness of our method on question answering tasks, where the target problem is much more difficult and more training examples are available. Furthermore, extensive experimental results indicate that the proposed algorithm can not only enhance the in-domain performance of the language models but also improve the domain generalization performance on out-of-domain data.Comment: Accepted by TNNL

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

Phosphocreatine Preconditioning Attenuates Apoptosis in Ischemia-Reperfusion Injury of Rat Brain

Phosphocreatine (PCr) is an endogenous compound containing high-energy phosphate bonds. It has been confirmed that PCr is effective in preventing and treating cardiac and renal ischemia-reperfusion injury. In this study, rat cerebral ischemia-reperfusion injury models were constructed. Apoptotic cells in the cortex region were measured by TUNEL method. Malondialdehyde (MDA) content was detected by chromatometry, and calmodulin (CaM) activity was detected by ELISA. Compared with sham-operated group (sham group), TUNEL-positive cells, MDA, and level of CaM activity increased in ischemia-reperfusion group (I/R group) and PCr preconditioning group (PCr group); compared with I/R group, TUNEL-positive cells, MDA content, and level of CaM activity decreased in PCr group. This study indicated that PCr can decrease the morphological damage and the neuron apoptosis of the ischemia-reperfusion injury brain through attenuating abnormalities of calcium balance and production of oxygen free radicals

DNA polymeraseη protein expression predicts treatment response and survival of metastatic gastric adenocarcinoma patients treated with oxaliplatin-based chemotherapy

<p>Abstract</p> <p>Background</p> <p>DNA polymerase η (pol η) is capable of bypassing DNA adducts produced by cisplatin or oxaliplatin and is associated with cellular tolerance to platinum. Previous studies showed that defective pol η resulted in enhanced cisplatin or oxaliplatin sensitivity in some cell lines. The purpose of the present study was to investigate the role of pol η protein expression in metastatic gastric adenocarcinoma.</p> <p>Methods</p> <p>Four gastric adenocarcinoma cell lines were chosen to explore the relationship between pol η protein expression and oxaliplatin sensitivity by western blotting and MTT assay. Eighty metastatic gastric adenocarcinoma patients treated with FOLFOX or XELOX regimen as first-line chemotherapy were analyzed, corresponding pretreatment formalin-fixed paraffin-embedded tumor tissues were used to detect pol η protein expression by immunohistochemistry. Relationship between pol η protein expression and clinical features and outcome of these patients was analyzed.</p> <p>Results</p> <p>A positive linear relationship between pol η protein expression and 48 h IC50 values of oxaliplatin in four gastric cancer cell lines was observed. Positivity of pol η protein expression was strongly associated with poor treatment response, as well as shorter survival at both univariate (8 versus 14 months; P < 0.001) and multivariate (hazard ratio, 4.555; 95% confidence interval, 2.461-8.429; P < 0.001) analysis in eighty metastatic gastric adenocarcinoma patients.</p> <p>Conclusions</p> <p>Our study indicates that polη is a predictive factor of treatment response and survival of metastatic gastric adenocarcinoma patients treated with FOLFOX or XELOX as first-line chemotherapy. Therefore confirming the value of polη in studies with prospective design is mandatory.</p

1,4-Dibromo-2,5-dimeth­oxy­benzene

The asymmetric unit of the title compound, C8H8Br2O2, contains one half-mol­ecule, the complete mol­ecule being generated by inversion symmetry

Modular co-evolution of metabolic networks

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear. In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do. The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.Comment: 26 pages, 7 figure