Implementing an eleven year through-train model to complete Primary and Secondary Education: creating a platform for accommodating the newest pedagogical practices and technologies in school

In educational transformation, Logos Academy of Hong Kong has started to create space in two aspects: to accommodate for new learning areas, and to use the most updated technologies for learning. In different Learning Stages, new learning areas like "Family Life Education", "Analytical study of Current Issues", Mind-mapping, MegaSkills and Media Education are introduced. The teachers will design different level- and age-appropriate activities and assignments that encourage the mastery of basic concepts and development of aesthetic appreciation, family life education, character formation, physique building and inquiry/research skills. Moreover, integrated tasks and projects intertwining with different study skills are mounted to enable the children to experiment creative designs and try out increasingly complex investigations. To facilitate learning and teaching, Logos Academy also creates new platforms to use the newest technologies for pre-lesson use, for lesson use, and for post-lesson use. It is reviewed that with the aid of some updated technologies, our teachers are committed to facilitate change, reflect on current practices, explore further improvements in new learning areas and to use the new technologies effectively - which will in turn enhance the effectiveness of integrated study skills, self-directed learning, team work and social interaction of the students

Implementing an eleven year through-train model to complete Primary and Secondary Education , is it possible? Why Not? : some challenges and principles

As learning and teaching is moving away from imparting student with mere "knowledge", the simple one-size-fits-all solution of a prescribed years of schooling with some rigid and prescriptive subject syllabuses designated for different year-levels in primary and secondary schools will no longer meet the developmental needs of students. Logos Academy of Hong Kong has started an "Eleven-year Through-train Program in September 2002, to re-define the different key stages in primary, junior and senior secondary levels to provide a broad and balanced curriculum which maintains seamless continuity. The eleven-year program consists of three stages, each with its particular characteristics: Foundation Stage: (FS1- FS3); Developmental Stage: (DS1 - DS5) and Mastery Stage (MS1 - MS3). We have achieved some pleasing outcome so far and we believe that this re-definition of Key Learning Stages is forward looking and keeping abreast of global trends. If this "Eleven-year Through-Train Schooling System" model is proved to be successful, it will throw some light on a new schooling structure - which will have significant implications on the government's funding and planning policie

Implementing an eleven year through-train model to complete Primary and Secondary Education: an innovative curriculum design, and optimizing the roles of subject specialists in the early learning stages

In an eleven year "through-train" model, to construct a new road map for learning, Logos Academy of Hong Kong has delineated clearly the roles of "Homeroom Teachers" and "Subject Specialists". In the Foundation Stage (The first three years in Primary Schooling), the "Homeroom Teachers" will no longer teach most of the academic subjects for their respective Homeroom classes. They will undertake mainly pastoral care functions whilst different subject specialists are deployed to teach different subject areas accordingly. Each Subject teacher will teach ALL the classes within a year-band. In some Subjects like English Studies, two or three teachers will share the teaching load according to their specialties. After putting in practice for two years, evidence has shown that with this "Subject specialist across the year band" approach, the curriculum rigor has been strengthened and children have made much more remarkable progress in specific learning areas. Moreover, it has created space and opportunities for co-teaching and joint projects. This has in turn facilitated communication, collaboration and professional development of teachers in their subject specialty. Within the same subject area, the inter-teacher difference between classes of the same year level has been diminished, and the effectiveness of teaching and learning across the whole year-band may be better monitored and evaluated. The subject specialist is also in a better position to design and organize necessary follow-up actions (including enrichment or remedial work) more efficientl

Hamiltonian 2-forms in Kahler geometry, III Extremal metrics and stability

This paper concerns the explicit construction of extremal Kaehler metrics on total spaces of projective bundles, which have been studied in many places. We present a unified approach, motivated by the theory of hamiltonian 2-forms (as introduced and studied in previous papers in the series) but this paper is largely independent of that theory. We obtain a characterization, on a large family of projective bundles, of those `admissible' Kaehler classes (i.e., the ones compatible with the bundle structure in a way we make precise) which contain an extremal Kaehler metric. In many cases, such as on geometrically ruled surfaces, every Kaehler class is admissible. In particular, our results complete the classification of extremal Kaehler metrics on geometrically ruled surfaces, answering several long-standing questions. We also find that our characterization agrees with a notion of K-stability for admissible Kaehler classes. Our examples and nonexistence results therefore provide a fertile testing ground for the rapidly developing theory of stability for projective varieties, and we discuss some of the ramifications. In particular we obtain examples of projective varieties which are destabilized by a non-algebraic degeneration.Comment: 40 pages, sequel to math.DG/0401320 and math.DG/0202280, but largely self-contained; partially replaces and extends math.DG/050151

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment