Absolute Absorption Cross-Section Measurement of a Submonolayer film on a silica microresonator

Conventional absorption spectroscopy is not nearly sensitive enough for quantitative overtone measurements on submonolayer coatings. While cavity-enhanced absorption detection methods using microresonators have the potential to provide quantitative absorption cross sections of even weakly absorbing submonolayer films, this potential has not yet been fully realized. To determine the absorption cross section of a submonolayer film of ethylene diamine (EDA) on a silica microsphere resonator, we use phase-shift cavity ringdown spectroscopy simultaneously on near-IR radiation that is Rayleigh backscattered from the microsphere and transmitted through the coupling fiber taper. We then independently determine both the coupling coefficient and the optical loss within the resonator. Together with a coincident measurement of the wavelength frequency shift, an absolute overtone absorption cross section of adsorbed EDA, at submonolayer coverage, was obtained and was compared to the bulk value. The smallest quantifiable absorption cross section is σmin 2.7 × 10−12 cm2. This absorption cross section is comparable to the extinction coefficients of, e.g., single gold nanoparticles or aerosol particles. We therefore propose that the present method is also a viable route to absolute extinction measurements of single particles.

Differential cross section measurements for elastic scattering of electrons from Ar2+ and Xe2+

A crossed-beams energy-loss spectrometer has been used to investigate angular distributions for electron scattering from Ar2+ and Xe2+ ions, at a collision energy of 16 eV. For Ar2+ the measurements are compared with the predictions of a partial waves calculation based on a semi-empirical potential, where it is shown that the interference term governs the position of the observed minimum in the angular distribution.

A meta-analysis of the association between Caesarean section and childhood asthma

Background: Children born by Caesarean section have modified intestinal bacterial colonization and consequently may have an increased risk of developing asthma under the hygiene hypothesis. The results of previous studies that have investigated the association between Caesarean section and asthma have been conflicting.

Objective: To review published literature and perform a meta-analysis summarizing the evidence in support of an association between children born by Caesarean section and asthma.

Methods: MEDLINE, Web Science, Google Scholar and PubMed were searched to identify relevant studies. Odds ratio (OR) and 95% confidence interval (CI) were calculated for each study from the reported prevalence of asthma in children born by Caesarean section and in control children. Meta-analysis was then used to derive a combined OR and test for heterogeneity in the findings between studies.

Results: Twenty-three studies were identified. The overall meta-analysis revealed an increase in the risk of asthma in children delivered by Caesarean section (OR=1.22, 95% CI 1.14, 1.29). However, in this analysis, there was evidence of heterogeneity (I2=46%) that was statistically significant (P<0.001). Restricting the analysis to childhood studies, this heterogeneity was markedly decreased (I2=32%) and no longer attained statistical significance (P=0.08). In these studies, there was also evidence of an increase (P<0.001) in the risk of asthma after Caesarean section (OR=1.20, 95% CI 1.14, 12.6).

Conclusion: In this meta-analysis, we found a 20% increase in the subsequent risk of asthma in children who had been delivered by Caesarean section.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.