Bioavailable Iron in Equatorial Pacific Ocean Aerosol Samples

Oceanic iron (Fe) fertilization experiments performed in remote regions have established that Fe additions draw carbon into the ocean, at least over the months-long time frame of the experiments. However, the mechanisms that control Fe speciation in atmospheric aerosol particles before and after deposition into the surface ocean remain largely unknown. This is in part due to the analytical challenge of quantifying Fe at environmentally significant sub-nano molar levels. The flow injection analysis method combined with the luminol chemiluminescence analytical system allows us to explore the near-real time determination of pico-molar levels of both Fe(II) and H2O2 produced from real marine aerosol particles collected over the Equatorial Pacific Ocean, as a function of both sunlight and electron donors (EDs) such as dimethyl sulfide and organic acids. Detection limits were as low as 40 pM Fe(II) and 100 pM H2O2. Fe(II)in aerosol concentration was found to be 0.29 ± 1.48 pg m-3 in large, 19.14 ± 18.31 pg m-3 in coarse, 38.80 ± 37.87 pg m-3 in fine, and 43.61 ± 42.93 pg m-3 in ultrafine size aerosol samples. A typical analysis of photochemical reaction with addition of EDs can be performed in five minutes. Results indicate that Fe(III) is reduced in the presence of light with ED that are already present in the collected aerosols, the external additions of ED have an enhancing effect in some of the samples, and the Fe(II) concentration shows positive corrected to non-sea-salt sulfate (NSS-SO42-) and some other anions. Fe(II) is found to be 3% of total Fe in the aerosols. These results contribute to resolving current inconsistencies in chemical models on the speciation of Fe and sulfur cycles in the marine atmosphere

Hydrocarbon fuel additives and method for preparing same

A compound having the general formula R*COH[- CORJj,, wherein R is a lower alkyl hydrocarbon radical, y is 0 or 1, and x is 2 when y is 1 and 3 when y is 0, is prepared by admixing carbon monoxide, a transition metal halide, and an organomonolithium compound or an anionic equivalent thereof.https://digitalcommons.mtu.edu/patents/1100/thumbnail.jp

Explaining Inconsistencies in Implicit and Explicit Attitudes towards Domestic and Foreign Products

Purpose This article examines the inconsistency of explicit and implicit domestic country bias (DCB) across different types of products and in the context of two countries. Design/methodology/approach Two studies in two countries are conducted to examine the inconsistencies in implicit and explicit DCB. The first study collected data through mall intercept survey method in Taiwan and identified 189 valid respondents. The second study applied a mixed (within and between subjects) factorial experiment in China using 200 subjects Findings Results show that explicit and implicit attitudes are moderately related to each other. It also confirms that ethnic product typicality can explain inconsistencies in both explicit and implicit DCB. For ethnically typical products, domestic country bias is more pronounced in consumers’ explicit attitudes than in consumers’ implicit attitudes. On the contrary, for ethnically atypical goods, domestic country bias makes itself present in both explicit and implicit attitudes. Originality/value The results shed new light on domestic country bias and confirm that the bias could divaricate between explicit and implicit attitudes in the case of ethnically typical products

ηc\eta_c mixing effects on charmonium and BB meson decays

We include the $\eta_c$ meson into the $\eta$-$\eta'$-$G$ mixing formalism constructed in our previous work, where $G$ represents the pseudoscalar gluball. The mixing angles in this tetramixing matrix are constrained by theoretical and experimental implications from relevant hadronic processes. Especially, the angle between $\eta_c$ and $G$ is found to be about $11^\circ$ from the measured decay widths of the $\eta_c$ meson. The pseudoscalar glueball mass $m_G$, the pseudoscalar densities $m_{qq,ss,cc}$ and the U(1) anomaly matrix elements associated with the mixed states are solved from the anomalous Ward identities. The solution $m_G\approx 1.4$ GeV obtained from the $\eta$-$\eta'$-$G$ mixing is confirmed, while $m_{qq}$ grows to above the pion mass, and thus increases perturbative QCD predictions for the branching ratios $Br(B\to\eta'K)$. We then analyze the $\eta_c$-mixing effects on charmonium magnetic dipole transitions, and on the $B\to\eta^{(\prime)}K_S$ branching ratios and CP asymmetries, which further improve the consistency between theoretical predictions and data. A predominant observation is that the $\eta_c$ mixing enhances the perturbative QCD predictions for $Br(B\to\eta'K)$ by 18%, but does not alter those for $Br(B\to\eta K)$. The puzzle due to the large $Br(B\to\eta'K)$ data is then resolved.Comment: 12 pages, version to appear in PR

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis