Formation of soliton trains in Bose-Einstein condensates as a nonlinear Fresnel diffraction of matter waves

The problem of generation of atomic soliton trains in elongated Bose-Einstein condensates is considered in framework of Whitham theory of modulations of nonlinear waves. Complete analytical solution is presented for the case when the initial density distribution has sharp enough boundaries. In this case the process of soliton train formation can be viewed as a nonlinear Fresnel diffraction of matter waves. Theoretical predictions are compared with results of numerical simulations of one- and three-dimensional Gross-Pitaevskii equation and with experimental data on formation of Bose-Einstein bright solitons in cigar-shaped traps.Comment: 8 pages, 3 figure

s-Process Nucleosynthesis in Advanced Burning Phases of Massive Stars

We present a detailed study of s-process nucleosynthesis in massive stars of solar-like initial composition and masses 15, 20,25, and 30 Msun. We update our previous results of s-process nucleosynthesis during the core He-burning of these stars and then focus on an analysis of the s-process under the physical conditions encountered during the shell-carbon burning. We show that the recent compilation of the Ne22(alpha,n)Mg25 rate leads to a remarkable reduction of the efficiency of the s-process during core He-burning. In particular, this rate leads to the lowest overproduction factor of Kr80 found to date during core He-burning in massive stars. The s-process yields resulting from shell carbon burning turn out to be very sensitive to the structural evolution of the carbon shell. This structure is influenced by the mass fraction of C12 attained at the end of core helium burning, which in turn is mainly determined by the C12(alpha,gamma)O16 reaction. The still present uncertainty in the rate for this reaction implies that the s-process in massive stars is also subject to this uncertainty. We identify some isotopes like Zn70 and Rb87 as the signatures of the s-process during shell carbon burning in massive stars. In determining the relative contribution of our s-only stellar yields to the solar abundances, we find it is important to take into account the neutron exposure of shell carbon burning. When we analyze our yields with a Salpeter Initial Mass Function, we find that massive stars contribute at least 40% to s-only nuclei with mass A 90, massive stars contribute on average ~7%, except for Gd152, Os187, and Hg198 which are ~14%, \~13%, and ~11%, respectively.Comment: 52 pages, 16 figures, accepted for publication in Ap

Secreted Glycoside Hydrolase Proteins as Effectors and Invasion Patterns of Plant-Associated Fungi and Oomycetes.

During host colonization, plant-associated microbes, including fungi and oomycetes, deliver a collection of glycoside hydrolases (GHs) to their cell surfaces and surrounding extracellular environments. The number and type of GHs secreted by each organism is typically associated with their lifestyle or mode of nutrient acquisition. Secreted GHs of plant-associated fungi and oomycetes serve a number of different functions, with many of them acting as virulence factors (effectors) to promote microbial host colonization. Specific functions involve, for example, nutrient acquisition, the detoxification of antimicrobial compounds, the manipulation of plant microbiota, and the suppression or prevention of plant immune responses. In contrast, secreted GHs of plant-associated fungi and oomycetes can also activate the plant immune system, either by acting as microbe-associated molecular patterns (MAMPs), or through the release of damage-associated molecular patterns (DAMPs) as a consequence of their enzymatic activity. In this review, we highlight the critical roles that secreted GHs from plant-associated fungi and oomycetes play in plant-microbe interactions, provide an overview of existing knowledge gaps and summarize future directions.Published onlin

Iron Implantation in Presolar Supernova Grains

We consider the potential of measured iron isotopic ratios within presolar grains from supernovae (as discovered in meteorites) for identifying the gas from which the grains condensed. We show that although iron isotopic ratios vary dramatically with radial coordinate in the initial supernova, it seems likely that the concentration of iron that thermally condenses in SiC grains within the supernova interior may be smaller than the concentration that will later be implanted by high-speed grain-gas collisions following the penetration of the reverse shock into the supernova flow. In that case, the Fe isotopic composition is much altered. We propose that the 58Fe richness that is very evident in the three SiC grains analyzed to date is the result of ion implantation during the grain’s rapid radial motion through the shocked and decelerated overlying supernova gas that is 58Fe-rich. We point to other likely applications of this same idea and speculate that only the dominant isotopes of the SiC grains, namely 28Si and 12C, can be safely assumed to be initial thermal condensate. We conclude that a violent period of implantation plus sputtering has overprinted the initial thermal condensate. If correct, this points to a new technique for sampling the velocity mixing within young supernova remnants

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data

Knowledge and practice related to compliance with mass drug administration during the Egyptian national filariasis elimination program

Lymphatic filariasis (LF) has been targeted for global elimination by 2020. The primary tool for the program is mass drug administration (MDA) with antifilarial medications to reduce the source of microfilariae required for mosquito transmission of the parasite. This strategy requires high MDA compliance rates. Egypt initiated a national filariasis elimination program in 2000 that targeted approximately 2.7 million persons in 181 disease-endemic localities. This study assessed factors associated with MDA compliance in year three of the Egyptian LF elimination program. 2,859 subjects were interviewed in six villages. The surveyed compliance rate for MDA in these villages was 85.3% (95% confidence interval = 83.9–86.5%). Compliance with MDA was positively associated with LF knowledge scores, male sex, and older age. Adverse events reported by 18.4% of participants were mild and more common in females. This study has provided new information on factors associated with MDA compliance during Egypt's successful LF elimination program

Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data

Radiotherapy outcomes are determined by complex interactions between physical and biological factors, reflecting both treatment conditions and underlying genetics. Recent advances in radiotherapy and biotechnology provide new opportunities and challenges for predicting radiation-induced toxicities, particularly radiation pneumonitis (RP), in lung cancer patients. In this work, we utilize datamining methods based on machine learning to build a predictive model of lung injury by retrospective analysis of treatment planning archives. In addition, biomarkers for this model are extracted from a prospective clinical trial that collects blood serum samples at multiple time points. We utilize a 3-way proteomics methodology to screen for differentially expressed proteins that are related to RP. Our preliminary results demonstrate that kernel methods can capture nonlinear dose-volume interactions, but fail to address missing biological factors. Our proteomics strategy yielded promising protein candidates, but their role in RP as well as their interactions with dose-volume metrics remain to be determined

Core Interprofessional Education (IPE) health competencies: The process of adaptation and implementation for a local environment

IPE: Interprofessional Healthcare Education (IPE) competencies provide the criteria against which to measure the capacity and capability of fully collaborative healthcare teams to learn and work together. Significant work already exists in the determination of IPE competencies across all disciplines. Although there is still a lack of agreement on a single set of shared core competencies, successive competency iterations enhance its development. IPE competencies need to take into account local and cultural contexts as recommended by WHO, (2010). Here we present a collaborative process that builds on existing competency development, assessing additional academic IPE needs. Core competencies: After the development of a set of shared core IPE competencies a two-day workshop was delivered to healthcare students from four professions. The results and feedback from students showed the value of the competencies. We discuss the evolving process through two major stages: (1) development of a model determining four ..