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

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

Genome-Wide Association Study in BRCA1 Mutation Carriers Identifies Novel Loci Associated with Breast and Ovarian Cancer Risk

BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7×10-8, HR = 1.14, 95% CI: 1.09-1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4×10-8, HR = 1.27, 95% CI: 1.17-1.38) and 4q32.3 (rs4691139, P = 3.4×10-8, HR = 1.20, 95% CI: 1.17-1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific associat

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (, , ) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14