Multivalency-Driven Formation of Te-Based Monolayer Materials: A Combined First-Principles and Experimental study

published_or_final_versio

Smoking, cessation and expenditure in low income Chinese: cross sectional survey

BACKGROUND: This study was carried-out to explore smoking behaviour and smoking expenditure among low income workers in Eastern China to inform tobacco control policy. METHODS: A self-completion questionnaire was administered to 1958 urban workers, 1909 rural workers and 3248 migrant workers in Zhejiang Province, Eastern China in 2004. RESULTS: Overall 54% of the men and 1.8% of all women were current smokers (at least 1 cigarette per day). Smoking was least common in migrant men (51%), compared with 58% of urban workers and 64% rural inhabitants (P < 0.0001). Forty-nine percent of rural males smoke more than 10 cigarettes/day, and 22% over 20/day. The prevalence of smoking increased with age. Overall 9% of the males had successfully quit smoking. Reasons for quitting were to prevent future illness (58%), current illness (31%), family pressures (20%) and financial considerations (20%). Thirteen percent of current smokers had ever tried to quit (cessation for at least one week) while 22% intended to quit, with migrants most likely to intend to quit. Almost all (96%) were aware that smoking was harmful to health, though only 25% were aware of the dangers of passive smoking. A mean of 11% of personal monthly income is spent on smoking rising to a mean of 15.4% in rural smokers. This expenditure was found to have major opportunity costs, including in terms of healthcare access. CONCLUSION: The prevalence of smoking and successful quitting suggest that smoking prevalence in low income groups in Eastern China may have peaked. Tobacco control should focus on support for quitters, on workplace/public place smoking restrictions and should develop specific programmes in rural areas. Health education messages should emphasise the opportunity costs of smoking and the dangers of passive smoking

Quantum-Dot Light-Emitting Diodes with Nitrogen-Doped Carbon Nanodot Hole Transport and Electronic Energy Transfer Layer

Electroluminescence efficiency is crucial for the application of quantum-dot light-emitting diodes (QD-LEDs) in practical devices. We demonstrate that nitrogen-doped carbon nanodot (N-CD) interlayer improves electrical and luminescent properties of QD-LEDs. The N-CDs were prepared by solution-based bottom up synthesis and were inserted as a hole transport layer (HTL) between other multilayer HTL heterojunction and the red-QD layer. The QD-LEDs with N-CD interlayer represented superior electrical rectification and electroluminescent efficiency than those without the N-CD interlayer. The insertion of N-CD layer was found to provoke the Forster resonance energy transfer (FRET) from N-CD to QD layer, as confirmed by time-integrated and - resolved photoluminescence spectroscopy. Moreover, hole-only devices (HODs) with N-CD interlayer presented high hole transport capability, and ultraviolet photoelectron spectroscopy also revealed that the N-CD interlayer reduced the highest hole barrier height. Thus, more balanced carrier injection with sufficient hole carrier transport feasibly lead to the superior electrical and electroluminescent properties of the QD-LEDs with N-CD interlayer. We further studied effect of N-CD interlayer thickness on electrical and luminescent performances for high-brightness QD-LEDs. The ability of the N-CD interlayer to improve both the electrical and luminescent characteristics of the QD-LEDs would be readily exploited as an emerging photoactive material for high-efficiency optoelectronic devices.ope

Avicin D: A Protein Reactive Plant Isoprenoid Dephosphorylates Stat 3 by Regulating Both Kinase and Phosphatase Activities

Avicins, a class of electrophilic triterpenoids with pro-apoptotic, anti-inflammatory and antioxidant properties, have been shown to induce redox-dependant post-translational modification of cysteine residues to regulate protein function. Based on (a) the cross-talk that occurs between redox and phosphorylation processes, and (b) the role of Stat3 in the process of apoptosis and carcinogenesis, we chose to study the effects of avicins on the processes of phosphorylation/dephosphorylation in Stat3. Avicins dephosphorylate Stat3 in a variety of human tumor cell lines, leading to a decrease in the transcriptional activity of Stat3. The expression of Stat3-regulated proteins such as c-myc, cyclin D1, Bcl2, survivin and VEGF were reduced in response to avicin treatment. Underlying avicin-induced dephosphorylation of Stat3 was dephosphorylation of JAKs, as well as activation of protein phosphatase-1. Downregulation of both Stat3 activity and expression of Stat 3-controlled pro-survival proteins, contributes to the induction of apoptosis in avicin treated tumor cells. Based on the role of Stat3 in inflammation and wounding, and the in vivo inhibition of VEGF by avicins in a mouse skin carcinogenesis model, it is likely that avicin-induced inhibition of Stat3 activity results in the suppression of the pro-inflammatory and pro-oxidant stromal environment of tumors. Activation of PP-1, which also acts as a cellular economizer, combined with the redox regulation by avicins, can aid in redirecting metabolism from growth promoting anabolic to energy sparing pathways

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p