Rooftop surface temperature analysis in an urban residential environment

The urban heat island (UHI) phenomenon is a significant worldwide problem caused by rapid population growth and associated urbanization. The UHI effect exacerbates heat waves during the summer, increases energy and water consumption, and causes the high risk of heat-related morbidity and mortality. UHI mitigation efforts have increasingly relied on wisely designing the urban residential environment such as using high albedo rooftops, green rooftops, and planting trees and shrubs to provide canopy coverage and shading. Thus, strategically designed residential rooftops and their surrounding landscaping have the potential to translate into significant energy, long-term cost savings, and health benefits. Rooftop albedo, material, color, area, slope, height, aspect and nearby landscaping are factors that potentially contribute. To extract, derive, and analyze these rooftop parameters and outdoor landscaping information, high resolution optical satellite imagery, LIDAR (light detection and ranging) point clouds and thermal imagery are necessary. Using data from the City of Tempe AZ (a 2010 population of 160,000 people), we extracted residential rooftop footprints and rooftop configuration parameters from airborne LIDAR point clouds and QuickBird satellite imagery (2.4 m spatial resolution imagery). Those parameters were analyzed against surface temperature data from the MODIS/ASTER airborne simulator (MASTER). MASTER images provided fine resolution (7 m) surface temperature data for residential areas during daytime and night time. Utilizing these data, ordinary least squares (OLS) regression was used to evaluate the relationships between residential building rooftops and their surface temperature in urban environment. The results showed that daytime rooftop temperature was closely related to rooftop spectral attributes, aspect, slope, and surrounding trees. Night time temperature was only influenced by rooftop spectral attributes and slope

La pédagogie active en physique : les facteurs qui améliorent l'engagement et la collaboration des élèves

Affiche présentée dans le cadre du Colloque de l'ARC, «La relève scientifique et la recherche collégiale : pratiques inspirantes au regard des chercheuses et chercheurs, et enjeux spécifiques à la formation des étudiantes et étudiants», dans le cadre du 84e Congrès de l'Acfas, Université du Québec à Montréal, Montréal, le 10 mai 2016.La pédagogie active (PA) améliore nettement l’apprentissage des élèves. Le grand défi de la PA consiste à gérer un écosystème d’apprentissage et à mobiliser les ressources humaines, documents et outils d’apprentissage à sa disposition – un processus appelé l’« orchestration ». La présente étude compare deux enseignants travaillant en PA dans un cours de physique (38 et 32 élèves respectivement). Ces enseignants sont excellents, comme le montrent les résultats remarquables de leurs élèves à un test standardisé sur les concepts en physique. Cependant, leur démarche pédagogique n’est pas la même, en raison des différences entre leurs points de vue épistémologiques et leurs ressources respectives, chacun dans leur classe aménagée de façon unique. Pour la comparaison, les deux enseignants devaient réaliser les mêmes activités. À partir d’observations en classe et de productions des élèves, nous analysons : 1) l’orchestration différente des ressources; 2) l’effet sur les productions des élèves; 3) les conséquences de ces orchestrations sur l’apprentissage et la collaboration des élèves. Selon nos résultats : 1) l’accès à des tableaux interactifs réservés aux élèves augmente les possibilités d’orchestration de l’enseignant; 2) les ressources ont un effet sur l’ampleur du suivi et de la rétroaction (évaluation par les pairs, suivi des progrès du groupe, retour en groupe classe); et 3) l’ajout d’activités intéressantes préalables au cours favorise l’engagement des élèves en classe

Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer's disease model.

Many risk genes for the development of Alzheimer's disease (AD) are exclusively or highly expressed in myeloid cells. Microglia are dependent on colony-stimulating factor 1 receptor (CSF1R) signaling for their survival. We designed and synthesized a highly selective brain-penetrant CSF1R inhibitor (PLX5622) allowing for extended and specific microglial elimination, preceding and during pathology development. We find that in the 5xFAD mouse model of AD, plaques fail to form in the parenchymal space following microglial depletion, except in areas containing surviving microglia. Instead, Aβ deposits in cortical blood vessels reminiscent of cerebral amyloid angiopathy. Altered gene expression in the 5xFAD hippocampus is also reversed by the absence of microglia. Transcriptional analyses of the residual plaque-forming microglia show they exhibit a disease-associated microglia profile. Collectively, we describe the structure, formulation, and efficacy of PLX5622, which allows for sustained microglial depletion and identify roles of microglia in initiating plaque pathogenesis

Loss of MyoD Promotes Fate Transdifferentiation of Myoblasts Into Brown Adipocytes

Brown adipose tissue (BAT) represents a promising agent to ameliorate obesity and other metabolic disorders. How- ever, the abundance of BAT decreases with age and BAT paucity is a common feature of obese subjects. As brown adipocytes and myoblasts share a common Myf5 lineage origin, elucidating the molecular mechanisms underlying the fate choices of brown adipocytes versus myoblasts may lead to novel approaches to expand BAT mass. Here we identify MyoD as a key negative regulator of brown adipocyte development. CRISPR/CAS9-mediated deletion of MyoD in C2C12 myoblasts facilitates their adipogenic transdifferentiation. MyoD knockout downregulates miR- 133 and upregulates the miR-133 target Igf1r, leading to amplification of PI3K–Akt signaling. Accordingly, inhibition of PI3K or Akt abolishes the adipogenic gene expression of MyoD null myoblasts. Strikingly, loss of MyoD converts satellite cell-derived primary myoblasts to brown adipocytes through upregulation of Prdm16, a target of miR-133 and key determinant of brown adipocyte fate. Conversely, forced expression of MyoD in brown preadipocytes blocks brown adipogenesis and upregulates the expression of myogenic genes. Importantly, miR-133a knockout signifi- cantly blunts the inhibitory effect of MyoD on brown adipogenesis. Our results establish MyoD as a negative regu- lator of brown adipocyte development by upregulating miR-133 to suppress Akt signaling and Prdm16

Hereditary predisposition to ovarian cancer, looking beyond BRCA1/BRCA2

AbstractObjectiveGenetic predisposition to ovarian cancer is well documented. With the advent of next generation sequencing, hereditary panel testing provides an efficient method for evaluating multiple genes simultaneously. Therefore, we sought to investigate the contribution of 19 genes identified in the literature as increasing the risk of hereditary breast and ovarian cancer (HBOC) in a BRCA1 and BRCA2 negative population of patients with a personal history of breast and/or ovarian cancer by means of a hereditary cancer panel.MethodsSubjects were referred for multi-gene panel testing between February 2012 and March 2014. Clinical data was ascertained from requisition forms. The incidence of pathogenic mutations (including likely pathogenic), and variant of unknown significance were then calculated for each gene and/or patient cohort.ResultsIn this cohort of 911 subjects, panel testing identified 67 mutations. With 7.4% of subjects harboring a mutation on this multi-gene panel, the diagnostic yield was increased, compared to testing for BRCA1 and BRCA2 mutations alone. In the ovarian cancer probands, the most frequently mutated genes were BRIP1 (n=8; 1.72%) and MSH6 (n=6; 1.29%). In the breast cancer probands, mutations were most commonly observed in CHEK2 (n=9; 2.54%), ATM (n=3; 0.85%), and TP53 (n=3; 0.85%).ConclusionsAlthough further studies are needed to clarify the exact management of patients with a mutation in each gene, this study highlights information that can be captured with panel testing and provides support for incorporation of panel testing into clinical practice

Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies

Quantifying and assessing changes in biological diversity are central aspects of many ecological studies, yet accurate methods of estimating biological diversity from sampling data have been elusive. Hill numbers, or the effective number of species, are increasingly used to characterize the taxonomic, phylogenetic, or functional diversity of an assemblage. However, empirical estimates of Hill numbers, including species richness, tend to be an increasing function of sampling effort and, thus, tend to increase with sample completeness. Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data. Here we extended previous rarefaction and extrapolation models for species richness (Hill number qD, where q = 0) to measures of taxon diversity incorporating relative abundance (i.e., for any Hill number qD, q \u3e 0) and present a unified approach for both individual-based (abundance) data and samplebased (incidence) data. Using this unified sampling framework, we derive both theoretical formulas and analytic estimators for seamless rarefaction and extrapolation based on Hill numbers. Detailed examples are provided for the first three Hill numbers: q = 0 (species richness), q = 1 (the exponential of Shannon\u27s entropy index), and q = 2 (the inverse of Simpson\u27s concentration index). We developed a bootstrap method for constructing confidence intervals around Hill numbers, facilitating the comparison of multiple assemblages of both rarefied and extrapolated samples. The proposed estimators are accurate for both rarefaction and short-range extrapolation. For long-range extrapolation, the performance of the estimators depends on both the value of q and on the extrapolation range. We tested our methods on simulated data generated from species abundance models and on data from large species inventories. We also illustrate the formulas and estimators using empirical data sets from biodiversity surveys of temperate forest spiders and tropical ants. © 2014 by the Ecological Society of America

Immune deconvolution and temporal mapping identifies stromal targets and developmental intervals for abrogating murine low-grade optic glioma formation

BACKGROUND: Brain tumor formation and progression are dictated by cooperative interactions between neoplastic and non-neoplastic cells. This stromal dependence is nicely illustrated by tumors arising in the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome, where children develop low-grade optic pathway gliomas (OPGs). Using several authenticated METHODS: A combination of bulk and single-cell RNA mouse optic nerve sequencing, immunohistochemistry, T cell assays, and pharmacologic and antibody-mediated inhibition methods were used in these experiments. RESULTS: We show that T cells and microglia are the main non-neoplastic immune cell populations in both murine and human LGGs. Moreover, we demonstrate that CD8 CONCLUSIONS: Collectively, these findings provide proof-of-concept demonstrations that targeting stromal support during early gliomagenesis durably blocks murine LGG formation