On non-abelian extensions of 3-Lie algebras

In this paper, we study non-abelian extensions of 3-Lie algebras through Maurer-Cartan elements. We show that there is a one-to-one correspondence between isomorphism classes of non-abelian extensions of 3-Lie algebras and equivalence classes of Maurer-Cartan elements in a DGLA. The structure of the Leibniz algebra on the space of fundamental objects is also analyzed.Comment: 17 page

Using Crop Phenology to Assess Changes in Cultivated Land after the Anfal Genocide in Iraqi Kurdistan

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The Anfal genocide campaign, carried out by the Iraqi government against the Kurdish population in 1988, has been reported to have severe consequences for agriculture and food security by causing large scale land abandonment. This study uses Landsat satellite data to detect agricultural changes that can be attributed to the Anfal genocide. Cultivated land were distinguished from other land cover types by focusing on crop phenology. Initial results show a strong decrease in cultivated land in the years after the genocide, especially in the areas that were targeted by the genocide campaign

A Novel Method for Detecting p53 Autoantibodies in Sera of Patients with NSCLC

Background and objective Serum autoantibody detection is useful means for the early diagnosis and prognosis of cancer. So our objective was to synthesize peptide array to analyse p53 autoantibodies in the sera of patients with non small cell lung cancer (NSCLC). Methods Cellulose-bound overlapping peptides (12 mers) derived from p53 wild type protein were synthesized using SOPTs synthesis technique by an AutoSpot robot –ASP SL (Intavis, Germany). The membrane was incubated with 1/400 dilutions of p53 monoclonal antibody (Sc-53394) to establish a new approach to detect p53 antibody, and the epitopes of the p53 monoclonal antibody is already known. We analysed the p53 autoantibodies from the sera of NSCLC and controls by peptide array and ELISA. Results We synthesized on cellulose membranes twelve-amino-acid overlapping peptides which included all of the sequences of the polypeptide chain of p53. The p53 autoantibody was positive in seven cases of thirty patients’ sera with NSCLC and was negative in sera of the controls, with the same result of ELISA. Conclusion The peptide array could be applied not only to detect the autoantibodies in the sera of patients with lung cancer, but also to map the epitopes of the autoantibodies which might be useful for the early diagnosis and prognosis of cancer

Codon Optimization for Alpha 1-Antitrypsin Disease

Alpha 1-antitrypsin deficiency is a genetic disorder caused by defective production of alpha 1-antitrypsin (AAT). Gene therapy approaches have been conducted in patients with AAT deficiency with successful AAT expression, but not to the therapeutic levels required to reduce the risk of emphysema. Codon optimization, a somewhat new and evolving technique, is used by many scientists to maximize protein expression in living organisms by altering translational and transcriptional efficiency as well as protein refolding. The purpose of this study was to develop single stranded and double stranded AAT gene constructs, test their protein expression in vitro, and compare with those levels expressed by the AAT construct that is currently in clinical trials. Three constructs were to be developed, yet only one construct was successfully cloned. This clone, optimized ds-CB-AAT, illustrated increased AAT protein expression as the transfection time increased. However, protein levels were appreciably lower in the optimized construct compared to the single stranded (long intron) AAT construct that is currently being administered in clinical trials. The data did not suggest that the optimized AAT construct does in fact express more AAT protein in vitro as expected. In order to achieve data that can be reproduced, the 2 remaining constructs need to be cloned and all of the isolated plasmid DNA should be prepared on the same scale to minimize any additional confounding variables

Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation

Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training. A consistency loss that makes full use of unlabeled data is then proposed to constrain the model on not only semantic prediction but also the BEV feature. Furthermore, we propose a novel and effective data augmentation method named conjoint rotation which reasonably augments the dataset while maintaining the geometric relationship between the front-view images and the BEV semantic segmentation. Extensive experiments on the nuScenes and Argoverse datasets show that our semi-supervised framework can effectively improve prediction accuracy. To the best of our knowledge, this is the first work that explores improving visual BEV semantic segmentation performance using unlabeled data. The code is available at https://github.com/Junyu-Z/Semi-BEVsegComment: Accepted by ICRA202

COVID-19 Crisis: Exploring Community of Inquiry in Online Learning for Sub-Degree Students

The COVID-19 pandemic has brought a tremendous impact on the pedagogy and learning experience of students in sub-degree education sector of Hong Kong. Online learning has become the “sole” solution to deal with student learning challenges during this chaotic period. In this study, we explore online learning for sub-degree students by using a community of inquiry (CoI). As such, confirmatory factor analysis (CFA) was conducted on survey data gathered from 287 sub-degree students from the business and engineering disciplines. Results indicated that the network speed for online education determines the perceived cognitive presence, social presence, and teaching presence of students, whereas gender and academic disciplines of students are not moderating factors that create a significant difference in perceived cognitive presence, social presence, and teaching presence of students. Our study findings for creating and sustaining a purposeful online learning community are highlighted