Appealing to motivation to change attitudes, intentions, and behavior: A systematic review and meta-analysis of 702 experimental tests of the effects of motivational message matching on persuasion.

Message matching refers to the design and distribution of persuasive messages such that message features (e.g., the themes emphasized) align with characteristics of the target audience (e.g., their personalities). Motivational message matching is a form of this technique that seeks to enhance persuasion by matching specifically to differences in motivational characteristics (e.g., salient goals, needs, values). Despite the widespread use of motivational matching, there is little understanding of how and when to use it. We conducted a preregistered (PROSPERO CRD42019116688; https://osf.io/rpjdg) systematic review and three-level meta-analysis of 702 experimental studies on motivational matching (synthesizing 5,251 effect sizes from N = 206,482). Studies were inclusive of publications until December 2018 and primarily identified using APA PsycINFO, MEDLINE, and Scopus. We evaluate moderation using metaregressions and provide bias assessments (sensitivity analyses, funnel plots). Motivational matching increases persuasion by an average of r = .20 (95% CI [.18, .22]) as assessed by differences in attitudes, intentions, self-reported behavior, and observed behavior, relative to comparison conditions. This effect is larger than previously observed for other message matching approaches (e.g., message tailoring, message framing), which usually average r < .10. Although motivational matching can effectively improve persuasion, its effects are also marked by meaningful heterogeneity. Notably, motivational matching effects are largest when matching to contextual factors (than to individual differences), when compared to messages that conflict with people’s motivations, and when target characteristics are manipulated rather than assessed. Through this review, we develop and evaluate theoretical propositions that inform the optimization of motivational matching

100% RAG: Architectural Education | Theory vs. Practice, Volume 2, Number 4

100% RAG: Architectural Education | Theory vs. Practice, Syracuse School of Architecture, Student Newspaper, Volume 2, Number 4. Student newsletter from student contributors of Syracuse School of Architecture in 1977

Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe

This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 124, 321-333.DOI :10.1016/j.rse.2012.05.024.The remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument. © 2012 Elsevier Inc. All rights reserved.This work was funded by the Generalitat Valenciana (project PRO-METEO/2009/086, and contract of Eduardo Caselles) and the Spanish Ministerio de Ciencia e Innovacion (projects CGL2007-64666/CLI, CGL2010-17577/CLI and CGL2007-29819-E, co-financed with FEDER funds). AATSR data were provided by European Space Agency (ESA) under Cat-1 project 3466. We also thank ESA and the ESA GLOBCOVER Project, led by MEDIAS-France, for the GLOBCOVER classification data. The comments and suggestions of three anonymous reviewers that improved the paper are also acknowledged.Caselles, E.; Valor, E.; Abad Cerdá, FJ.; Caselles, V. (2012). Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment. 124:321-333. https://doi.org/10.1016/j.rse.2012.05.024S32133312

Differential Expression of Iron Acquisition Genes by Brucella melitensis and Brucella canis during Macrophage Infection

Brucella spp. cause chronic zoonotic disease often affecting individuals and animals in impoverished economic or public health conditions; however, these bacteria do not have obvious virulence factors. Restriction of iron availability to pathogens is an effective strategy of host defense. For brucellae, virulence depends on the ability to survive and replicate within the host cell where iron is an essential nutrient for the growth and survival of both mammalian and bacterial cells. Iron is a particularly scarce nutrient for bacteria with an intracellular lifestyle. Brucella melitensis and Brucella canis share ∼99% of their genomes but differ in intracellular lifestyles. To identify differences, gene transcription of these two pathogens was examined during infection of murine macrophages and compared to broth grown bacteria. Transcriptome analysis of B. melitensis and B. canis revealed differences of genes involved in iron transport. Gene transcription of the TonB, enterobactin, and ferric anguibactin transport systems was increased in B. canis but not B. melitensis during infection of macrophages. The data suggest differences in iron requirements that may contribute to differences observed in the lifestyles of these closely related pathogens. The initial importance of iron for B. canis but not for B. melitensis helps elucidate differing intracellular survival strategies for two closely related bacteria and provides insight for controlling these pathogens

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere