PICA-PICA: Exploring a Customisable Smart STEAM Educational Approach via a Smooth Combination of Programming, Engineering and Art

The STEAM approach in education has been gaining increasing popularity over the last decade. This is due to its potential in enhancing students' learning, when teaching arts and scientific disciplines together. This paper introduces the PICA-PICA concept, where we aim to develop a smart customisable environment, combining, in a unique way, teaching programming in conjunction with the engineering of artworks. The PICA-PICA concept was implemented, used and tested in real-life, by upper primary school students in Japan, during a 4-day workshop. Initial results illustrated the quality of the solution proposed by PICA-PICA. We noted that the integration was perceived as smooth, and not contrived: all participants understood how to use the PICA-PICA environment to engineer programmable art objects. Furthermore, the PICA-PICA approach led to high motivation: children did not get bored and were fully engaged. Finally, the quality of their work as a learning outcome was high: by including a programming segment with the other expressive activities in the artwork, the children were able to design the electronics in a more concentrated and meaningful way than their curriculum-structured learning. This study also presents an innovative implementation of the STEAM approach using Micro:bits technology to create exciting artwork whilst using household recyclable items, which also teaches about sustainability. The involvement of parents and their interest in learning is another unique aspect of this study

High speed self-testing quantum random number generation without detection loophole

Quantum mechanics provides means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a self-testing manner that is independent of implementation devices. Here, we present an experimental demonstration of self-testing quantum random number generation based on an detection-loophole free Bell test with entangled photons. In the randomness analysis, without the assumption of independent identical distribution, we consider the worst case scenario that the adversary launches the most powerful attacks against quantum adversary. After considering statistical fluctuations and applying an 80 Gb $\times$ 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than $10^{-5}$. Such self-testing random number generators mark a critical step towards realistic applications in cryptography and fundamental physics tests.Comment: 34 pages, 10 figure

The Changes in China's Forests: An Analysis Using the Forest Identity

Changes in forest carbon stocks are a determinant of the regional carbon budget. In the past several decades, China has experienced a pronounced increase in forest area and density. However, few comprehensive analyses have been conducted. In this study, we employed the Forest Identity concept to evaluate the changing status of China's forests over the past three decades, using national forest inventory data of five periods (1977–1981, 1984–1988, 1989–1993, 1994–1998, and 1999–2003). The results showed that forest area and growing stock density increased by 0.51% and 0.44% annually over the past three decades, while the conversion ratio of forest biomass to growing stock declined by 0.10% annually. These developments resulted in a net annual increase of 0.85% in forest carbon sequestration, which is equivalent to a net biomass carbon uptake of 43.8 Tg per year (1 Tg = 1012 g). This increase can be attributed to the national reforestation/afforestation programs, environmentally enhanced forest growth and economic development as indicated by the average gross domestic product

An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype

AbstractBackgroundThe genetics of depression has been explored in genome-wide association studies that focused on either major depressive disorder or depressive symptoms with mostly negative findings. A broad depression phenotype including both phenotypes has not been tested previously using a genome-wide association approach. We aimed to identify genetic polymorphisms significantly associated with a broad phenotype from depressive symptoms to major depressive disorder.MethodsWe analyzed two prior studies of 70,017 participants of European ancestry from general and clinical populations in the discovery stage. We performed a replication meta-analysis of 28,328 participants. Single nucleotide polymorphism (SNP)-based heritability and genetic correlations were calculated using linkage disequilibrium score regression. Discovery and replication analyses were performed using a p-value-based meta-analysis. Lifetime major depressive disorder and depressive symptom scores were used as the outcome measures.ResultsThe SNP-based heritability of major depressive disorder was 0.21 (SE = 0.02), the SNP-based heritability of depressive symptoms was 0.04 (SE = 0.01), and their genetic correlation was 1.001 (SE = 0.2). We found one genome-wide significant locus related to the broad depression phenotype (rs9825823, chromosome 3: 61,082,153, p = 8.2 × 10–9) located in an intron of the FHIT gene. We replicated this SNP in independent samples (p = .02) and the overall meta-analysis of the discovery and replication cohorts (1.0 × 10–9).ConclusionsThis large study identified a new locus for depression. Our results support a continuum between depressive symptoms and major depressive disorder. A phenotypically more inclusive approach may help to achieve the large sample sizes needed to detect susceptibility loci for depression

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide association study identifies 74 loci associated with educational attainment

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases