Photochemistry of Bacteriorhodopsin with Various Oligomeric Statuses in Controlled Membrane Mimicking Environments: A Spectroscopic Study from Femtoseconds to Milliseconds.

Preparing transmembrane protein in controllable lipid bilayers is essential for unravelling the coupling of the environments and its dynamic functions. Monomerized bacteriorhodopsin (mbR) embedded in covalently circularized nanodiscs was prepared with dimyristoylphosphatidylglycerol (DMPG) lipid and circular membrane scaffold proteins of two different sizes, cE3D1 and cΔ H5, respectively. The retinal photoisomerization kinetics and thermodynamic photocycle were examined by femtosecond and nanosecond transient absorption, respectively, covering the time scale from femtoseconds to hundreds of milliseconds. The kinetics of the retinal isomerization and proton migration from the protonated Schiff base to Asp-85 were not significantly different for monomeric bR solubilized in Triton X-100 or embedded in circularized nanodiscs. This can be ascribed to the local tertiary structures at the retinal pocket vicinity being similar among monomeric bR in various membrane mimicking environments. However, the aforementioned processes are intrinsically different for trimeric bR in purple membrane (PM) and delipidated PM. The reprotonation of the deprotonated Schiff base from Asp-96 in association with the decay of intermediate M, which involved wide-ranged structural alteration, manifested a difference in terms of the oligomeric statuses, as well as a slight dependence on the size of the nanodisc. In summary, bR oligomeric statuses, rather than the environmental factors, such as membrane mimicking systems and nanodisc size, play a significant role in bR photocycle associated with short-range processes, such as the retinal isomerization and deprotonation of protonated Schiff base at the retinal pocket. On the other hand, the environmental factors, such as the types of membrane mimicking systems and the size of nanodiscs, affect those dynamic processes involving wider structural alterations during the photocycle.

Apportionment of the sources of high fine particulate matter concentration events in a developing aerotropolis in Taoyuan, Taiwan.

To investigate the characteristics and contributions of the sources of fine particulate matter with a size of up to 2.5 µm (PM2.5) during the period when pollution events could easily occur in Taoyuan aerotropolis, Taiwan, this study conducted sampling at three-day intervals from September 2014 to January 2015. Based on the mass concentration of PM2.5, the sampling days were classified into high PM2.5 concentration event days (PM2.5>35 µg m(-3)) and non-event days (PM2.5<35 µg m(-3)). In addition, the chemical species, including water-soluble inorganic ions, carbonaceous components, and metal elements, were analyzed. The sources of pollution and their contributions were estimated using the positive matrix factorization (PMF) model. Furthermore, the effect of the weather type on the measurement results was also explored based on wind field conditions. The mass fractions of Cl(-) and NO3(-) increased when a high PM2.5 concentration event occurred, and they were also higher under local emitted conditions than under long range transported conditions, indicating that secondary nitrate aerosols were the major increasing local species that caused high PM2.5 concentration events. Seven sources of pollution could be distinguished using the PMF model on the basis of the characteristics of the species. Industrial emissions, coal combustion/urban waste incineration, and local emissions from diesel/gasoline vehicles were the main sources that contributed to pollution on high PM2.5 concentration event days. In order to reduction of high PM2.5 concentration events, the control of diesel and gasoline vehicle emission is important and should be given priority.

The real-time method of assessing the contribution of individual sources on visibility degradation in Taichung.

Visibility degradation caused by air pollution has become a serious environmental problem in megacities in Northeast Asia. In general, aerosol chemical compositions are measured by a conventional method of time integrated filter sampling for off-line analysis, which cannot represent temporal and spatial variations in the real atmosphere. The in situ air composition measuring equipment, OCEC carbon aerosol analyzer and a long-path visibility transmissometer-3 were used to collect hourly measurements of the soluble ions, organic/elemental carbon, and ambient visibility, respectively. During the observation, two types of weather conditions were identified: transport and stagnant. Because PM2.5 was identified as the predominant species of light extinction, the sources of PM2.5 were determined and investigated using a positive matrix factorization (PMF) analysis. The PMF outputs characterized the six main emission sources (marine/crustal aerosols, secondary nitrate, secondary sulfate, direct vehicle exhaust, coal/incinerator combustion, and local sewage emission) and reconstructed the PM2.5 mass concentrations of each pollutant source in two weather conditions. In addition, the light extinction (bext) was reconstructed using a multivariate linear regression analysis with hourly-reconstructed PM2.5 mass concentrations to determine the contributions of each source to bext. The primary results showed that the extinction coefficient was proportional to the PM2.5 with high value in stagnant weather conditions. The secondary sulfate was the most abundant source of bext contribution during the sampling period. In addition, the bext contributions of direct vehicle exhaust and coal/incinerator combustion significantly increased in the stagnant weather condition. According to the results of hourly measurements, this work further emphasized that the sources of direct vehicle exhaust and coal/incinerator combustion in PM2.5 were the important sources of visibility degradation in the stagnant weather conditions, which suggests that the pollutants derived from direct vehicle exhaust and coal/incinerator combustion should be controlled first to improve visibility in Taichung.

Predicting post-treatment survivability of patients with breast cancer using Artificial Neural Network methods.

In the last decade, the use of data mining techniques has become widely accepted in medical applications, especially in predicting cancer patients' survival. In this study, we attempted to train an Artificial Neural Network (ANN) to predict the patients' five-year survivability. Breast cancer patients who were diagnosed and received standard treatment in one hospital during 2000 to 2003 in Taiwan were collected for train and test the ANN. There were 604 patients in this dataset excluding died not in breast cancer. Among them 140 patients died within five years after their first radiotherapy treatment. The artificial neural networks were created by STATISTICA(®) software. Five variables (age, surgery and radiotherapy type, tumor size, regional lymph nodes, distant metastasis) were selected as the input features for ANN to predict the five-year survivability of breast cancer patients. We trained 100 artificial neural networks and chose the best one to analyze. The accuracy rate is 85% and area under the receiver operating characteristic (ROC) curve is 0.79. It shows that artificial neural network is a good tool to predict the five-year survivability of breast cancer patients.