The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air

Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.—one of the most abundant fungal taxa in the area—were developed. Aerobiological sampling was conducted over 2004–2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors

Exploring perceptions of advertising ethics: an informant-derived approach

Whilst considerable research exists on determining consumer responses to pre-determined statements within numerous ad ethics contexts, our understanding of consumer thoughts regarding ad ethics in general remains lacking. The purpose of our study therefore is to provide a first illustration of an emic and informant-based derivation of perceived ad ethics. The authors use multi-dimensional scaling as an approach enabling the emic, or locally derived deconstruction of perceived ad ethics. Given recent calls to develop our understanding of ad ethics in different cultural contexts, and in particular within the Middle East and North Africa (MENA) region, we use Lebanon—the most ethically charged advertising environment within MENA—as an illustrative context for our study. Results confirm the multi-faceted and pluralistic nature of ad ethics as comprising a number of dimensional themes already salient in the existing literature but in addition, we also find evidence for a bipolar relationship between individual themes. The specific pattern of inductively derived relationships is culturally bound. Implications of the findings are discussed, followed by limitations of the study and recommendations for further research

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

A qualitative study of changes in expectations over time among patients with chronic low back pain seeking four CAM therapies

BACKGROUND: The relationship between patient expectations about a treatment and the treatment outcomes, particularly for Complementary and Alternative Medicine (CAM) therapies, is not well understood. Using qualitative data from a larger study to develop a valid expectancy questionnaire for use with participants starting new CAM therapies, we examined how participants' expectations of treatment changed over the course of a therapy. METHODS: We conducted semi-structured qualitative interviews with 64 participants initiating one of four CAM therapies (yoga, chiropractic, acupuncture, massage) for chronic low back pain. Participants just starting treatment were interviewed up to three times over a period of 3 months. Interviews were transcribed verbatim and analyzed using a qualitative mixed methods approach incorporating immersion/crystallization and matrix analysis for a decontexualization and recontextualization approach to understand changes in thematic emphasis over time. RESULTS: Pre-treatment expectations consisted of conjecture about whether or not the CAM therapy could relieve pain and improve participation in meaningful activities. Expectations tended to shift over the course of treatment to be more inclusive of broader lifestyle factors, the need for long-term pain management strategies and attention to long-term quality of life and wellness. Although a shift toward greater acceptance of chronic pain and the need for strategies to keep pain from flaring was observed across participants regardless of therapy, participants varied in their assessments of whether increased awareness of the need for ongoing self-care and maintenance strategies was considered a "positive outcome". Regardless of how participants evaluated the outcome of treatment, participants from all four therapies reported increased awareness, acceptance of the chronic nature of pain, and attention to the need to take responsibility for their own health. CONCLUSIONS: The shift in treatment expectations to greater acceptance of pain and the need for continued self-care suggests that future research should explore how CAM practitioners can capitalize on these shifts to encourage feelings of empowerment rather than disappointment surrounding realizations of the need for continued engagement with self-care.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]