Structural and electronic properties of SrZrO3 and Sr(Ti,Zr) O3 alloys

Using hybrid density functional calculations, we study the electronic and structural properties of SrZrO3 and ordered Sr(Ti,Zr)O3 alloys. Calculations were performed for the ground-state orthorhombic (Pnma) and high-temperature cubic (Pm3m) phases of SrZrO3. The variation of the lattice parameters and band gaps with Ti addition was studied using ordered SrTixZr1-xO3 structures with x=0, 0.25, 0.5, 0.75, and 1. As Ti is added to SrZrO3, the lattice parameter is reduced and closely follows Vegard's law. On the other hand, the band gap shows a large bowing and is highly sensitive to the Ti distribution. For x=0.5, we find that arranging the Ti and Zr atoms into a 1×1SrZrO3/SrTiO3 superlattice along the [001] direction leads to interesting properties, including a highly dispersive single band at the conduction-band minimum (CBM), which is absent in both parent compounds, and a band gap close to that of pure SrTiO3. These features are explained by the splitting of the lowest three conduction-band states due to the reduced symmetry of the superlattice, lowering the band originating from the in-plane Ti 3dxy orbitals. The lifting of the t2g orbital degeneracy around the CBM suppresses scattering due to electron-phonon interactions. Our results demonstrate how short-period SrZrO3/SrTiO3 superlattices could be exploited to engineer the band structure and improve carrier mobility compared to bulk SrTiO3

Monitoring Student Cues: Tracking Student Behaviour in Order to Improve Instruction in Higher Education

In this paper, we focus on monitoring, a particular aspect of reflection related to teaching. We define monitoring as a feedback mechanism which entails attending to and evaluating a multitude of cues in the envi- ronment in order to evaluate progress towards a goal. We direct our attention to monitoring because it is a way in which a teacher is able to gain understanding of how effective his/her teaching actions are. Thus, knowing what cues to evaluate (and being able to do so) is a critical skill in reflection. Further, we focus exclusively in this paper on the concur- rent monitoring of cues related to students since we believe that attention to student cues while teaching provides teachers with a window into their students' learning experiences. We call this particular type of reflection, reflection-in-action. As well as depicting multiple examples of monitoring drawn from our research, we explore the contribution of this work to the literature in higher education and to faculty development activities, particularly, to the growing literature on teacher thinking.Cet article est principalement consacré au «monitorage», c'est-à-dire à un aspect particulier de la réflexion portant sur l'enseignement. Nous entendons par monitorage un mécanisme de rétroaction qui consiste à observer et à évaluer une multitude de signaux dans un environnement donné afin de mesurer les progrès accomplis par rapport à un objectif. Nous nous intéressons au monitorage, car ce moyen permet au professeur de mesurer l'efficacité de ses interventions. Pour mener à bien cette réflexion, il est donc essentiel de pouvoir déterminer quels signaux il faut évaluer (et d'être en mesure de les évaluer). Cet article porte en outre exclusivement sur le monitorage simultané des signaux émis par les étudiants, car nous pensons que l'observation de ces signaux fournit au professeur un aperçu des apprentissages que font les étudiants. Nous appelons "réflexion sur le vif' ce type de réflexion. En plus d'offrir de nombreux exemples de monitorage tirés de nos recherches, nous étudions sous tous ses aspects la contribution qu'elles apportent aux études consacrées à l'enseignement supérieur et au perfectionnement des professeurs et particulièrement aux études de plus en plus nombreuses qui portent sur la pensée des professeurs

Biochemical and clinical response after umbilical cord blood transplant in a boy with early childhood-onset beta-mannosidosis.

BACKGROUND: Deficiency in the enzyme β-mannosidase was described over three decades ago. Although rare in occurrence, the presentation of childhood-onset β-mannosidase deficiency consists of hypotonia in the newborn period followed by global development delay, behavior problems, and intellectual disability. No effective pharmacologic treatments have been available. METHODS: We report 2-year outcomes following the first umbilical cord blood transplant in a 4-year-old boy with early childhood-onset disease. RESULTS: We show restoration of leukocyte β-mannosidase activity which remained normal at 2 years posttransplant, and a simultaneous increase in plasma β-mannosidase activity and dramatic decrease in urine-free oligosaccharides were also observed. MRI of the brain remained stable. Neurocognitive evaluation revealed test point gains, although the magnitude of improvement was less than expected for age, causing lower IQ scores that represent a wider developmental gap between the patient and unaffected peers. CONCLUSION: Our findings suggest that hematopoietic cell transplant can correct the biochemical defect in β-mannosidosis, although preservation of the neurocognitive trajectory may be a challenge

Overview of the Langley subsonic research effort on SCR configuration

Recent advances achieved in the subsonic aerodynamics of low aspect ratio, highly swept wing designs are summarized. The most significant of these advances was the development of leading edge deflection concepts which effectively reduce leading edge flow separation. The improved flow attachment results in substantial improvements in low speed performance, significant delay of longitudinal pitch up, increased trailing edge flap effectiveness, and increased lateral control capability. Various additional theoretical and/or experimental studies are considered which, in conjunction with the leading edge deflection studies, form the basis for future subsonic research effort

Segmentation and modelling of hela nuclear envelope

This paper describes an algorithm to segment the 3D nuclear envelope of HeLa cancer cells from electron microscopy images and model the volumetric shape of the nuclear envelope against an ellipsoid. The algorithm was trained on a single cell and then tested in six separate cells. To assess the algorithm, Jaccard similarity index and Hausdorff distance against a manually-delineated gold standard were calculated on two cells. The mean Jaccard value and Hausdorff distance that the segmentation achieved for central slices were 98% and 4 pixels for the first cell and 94% and 13 pixels for the second cell and outperformed segmentation with active contours. The modelling projects a 3D to a 2D surface that summarises the complexity of the shape in an intuitive result. Measurements extracted from the modelled surface may be useful to correlate shape with biological characteristics. The algorithm is unsupervised, fully automatic, fast and processes one image in less than 10 seconds. Code and data are freely available at https://github.com/reyesaldasoro/Hela-Cell-Segmentation and http://dx.doi.org/10.6019/EMPIAR-10094

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%)