Developing Resilient Agency in Learning: The Internal Structure of Learning Power

Understanding students’ learning dispositions has been a focus for research in education for many years. A range of alternative approaches to conceptualising and measuring this broad construct have been developed. Traditional psychometric measures aim to produce scales that satisfy the requirements for research, however such measures have an additional use - to provide formative feedback to the learner. In this paper we re-analyse 15 years of data derived from the Effective Lifelong Learning Inventory. We explore patterns and relationships within its practical measures and generate a more robust, parsimonious measurement model, strengthening its research attributes and its practical value. We show how the constructs included in the model link to relevant research and how it serves to integrate a number of ideas which have hitherto been treated as separate. The new model suggests a view of learning that is an embodied and relational process through which we regulate the flow of energy and information over time in order to achieve a particular purpose. Learning dispositions reflect the ways in which we develop resilient agency in learning by regulating this flow of energy and information in order to engage with challenge, risk and uncertainty and to adapt and change positively

A new treatment for neurogenic inflammation caused by EV71 with CR2-targeted complement inhibitor

BACKGROUND: Enterovirus 71 (EV71), one of the most important neurotropic EVs, has caused death and long-term neurological sequelae in hundreds of thousands of young children in the Asia-Pacific region in the past decade. The neurological diseases are attributed to infection by EV71 inducing an extensive peripheral and central nervous system (CNS) inflammatory response with abnormal cytokine production and lymphocyte depletion induced by EV71 infection. In the absence of specific antiviral agents or vaccines, an effective immunosuppressive strategy would be valuable to alleviate the severity of the local inflammation induced by EV71 infection. PRESENTATION OF THE HYPOTHESIS: The complement system plays a pivotal role in the inflammatory response. Inappropriate or excessive activation of the complement system results in a severe inflammatory reaction or numerous pathological injuries. Previous studies have revealed that EV71 infection can induce complement activation and an inflammatory response of the CNS. CR2-targeted complement inhibition has been proved to be a potential therapeutic strategy for many diseases, such as influenza virus-induced lung tissue injury, postischemic cerebral injury and spinal cord injury. In this paper, a mouse model is proposed to test whether a recombinant fusion protein consisting of CR2 and a region of Crry (CR2-Crry) is able to specifically inhibit the local complement activation induced by EV71 infection, and to observe whether this treatment strategy can alleviate or even cure the neurogenic inflammation. TESTING THE HYPOTHESIS: CR2-Crry is expressed in CHO cells, and its biological activity is determined by complement inhibition assays. 7-day-old ICR mice are inoculated intracranially with EV71 to duplicate the neurological symptoms. The mice are then divided into two groups, in one of which the mice are treated with CR2-Crry targeted complement inhibitor, and in the other with phosphate-buffered saline. A group of mice deficient in complement C3, the breakdown products of which bind to CR2, are also infected with EV71 virus. The potential bioavailability and efficacy of the targeted complement inhibitor are evaluated by histology, immunofluorescence staining and radiolabeling. IMPLICATIONS OF THE HYPOTHESIS: CR2-Crry-mediated targeting complement inhibition will alleviate the local inflammation and provide an effective treatment for the severe neurological diseases associated with EV71 infection

Bushfield School ELLI Data Analysis Report

This is the report of a two-year action-research programme at Bushfield School which had two main purposes: firstly, to build on the School’s success in developing children’s capacity to learn; secondly, to track and measure the impact of its interventions for this purpos

Construction Waste Landfill Dataset of Two Districts in Beijing, China from GF-2 satellite images

CWLD constructs and forms a construction waste landfill dataset in Changping and Daxing districts of Beijing using Gaofen-2 remote sensing satellite as the data source. The dataset contains samples of the original image area and provides mask labeled images in the semantic segmentation domain.Each pixel inside a construction waste landfill is categorized in detail according to the image background area, the open space area, the engineering facility area and the waste dumping area. It contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The dataset consists of three folders: Original Dataset, Construction Waste Landfill Dataset, and Deep Learning Datasets. Original Dataset. Remote sensing images and labelled images are stored separately according to Changping and Daxing districts in the Original Dataset folder. The Construction Waste Landfill Dataset folder contains the raw data enriched with data enhancement techniques. Deep Learning Datasets. The input layer of the neural network model usually needs to have a fixed input size, so it is necessary to preprocess the data before training by adjusting the input data to 512×512px, which is divided into the training set and the validation set in accordance with 8:2. Visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on https://github.com/huangleinxidimejd/CWLD_Model

Text classification of micro-blog's “tree hole” based on convolutional neural network

Rapid recognition of depression is an important step in the research of depression. With the development of social networking platform, more and more depressive patients regard micro-blog as one of the ways of self-expression. And this information provides support of data for the recognition of depression. In this study, the data crawled from micro-blog's “tree hole”[1] is used as experimental corpus. Combined with the features of micro-blog text with depression, a double-input convolutional neural network structure (D-CNN) is proposed. This method takes both the external features and the semantic features of text as input. By comparing the accuracy of classification with Support Vector Machine (SVM) and convolutional neural network (CNN) algorithm, it is finally shown that the D-CNN can further improve the accuracy of text classify

Making semantic annotation on patient data of depression

Patient data, more exactly, electronic medical records (EMR), usually contain a lot of free texts. Those unstructured medical data cannot be easily understood by computers. In addition, EMR data have a strong privacy, which hinders the sharing and use of medical data and makes it impossible to conduct more in-depth medical research. This paper presents a method of the realization of semantic EMR by making semantic annotations on free texts in medical records. We will show how to use Natural Language Processing (NLP) tools to create semantic annotation with wellknown biomedical terminologies/ontologies such as the Unified Medical Language System (UMLS). Moreover, we will describe how to make the semantic annotations on a set of virtual patient data for depression, which are generated by using the Advanced Patient Data Generator (APDG), a knowledge-based patient data generator. In short, our goal is to use semantic technology to improve the sharing and utilization of medical data and the interoperability among systems