A collision of technology and art learning

This research thesis investigates the integration of technological tools into the curriculum and pedagogy of art education. By focusing explicitly on art education outside of school, it examines how technological tools expand the art learning possibilities for children and youth, enrich their art learning experiences in balance and connection with traditional arts media as well as strengthen engagement for those digital natives. Meanwhile, this research aims to contribute to a current understanding of technology integration into art curriculum and pedagogy for children and youth through literature reviews and research projects using a variety of methodologies, and then to determine what art learning might look like for children and youth where integrated with technological tools. This thesis will be the beginning point for me and extends my curiosity about what innovative art education will look like in the future for the next generation when advanced technology becomes available

The role of LuxS in metabolism and signalling in Helicobacter pylori

In many bacteria, LuxS functions as a quorum sensing (QS) molecule synthase. Its product, auto-inducer-2 (AI-2) plays a role in regulation of various bacterial activities in concert with cell population density. LuxS also has a second more central metabolic function in the activated methyl cycle (AMC) which generates the Sadenosylmethionine (SAM) required by methyltransferases and recycles the product via methionine. Helicobacter pylori lacks an enzyme catalysing homocysteine to methionine conversion, rendering the AMC incomplete and thus making any metabolic role of LuxSHp uncertain. Consequently, the mechanism underlying phenotypic changes upon luxS inactivation is not always clear. The aims of this project were to define the metabolic role of LuxS in H. pylori; to assess whether LuxSHp effects on bacterial motility were through metabolic effects or via production of the signalling molecule AI-2; and to explore the mechanism underlying motility phenotypic changes upon luxSHp inactivation. luxSHp is located next to genes annotated as cysKHp and metBHp, which are involved in cysteine and methionine metabolism in other bacteria. This study showed that isogenic mutants in luxSHp, cysKHp and metBHp could not grow without added cysteine (whereas wild-type could), suggesting roles in cysteine synthesis. Together with data from metabolite analyses, it confirmed that cysK-metB-luxS encode the capacity to generate cysteine from products of the incomplete AMC of H. pylori in a process of reverse transsulphuration. Consequently, the misnamed genes cysKHp and metBHp were recommended to be renamed mccAHp (methionine-to-cysteine-conversion gene A) and mccBHp, respectively. Data presented in this thesis also showed that disruption of luxS in H. pylori renders it non-motile, whereas disruption of mccA or mccB does not, implying that the loss of phenotype is not due to disruption of cysteine provision. The motility defect of the ΔluxSHp mutant could be genetically complemented with luxSHp and also by addition of in vitro synthesised AI-2, but not by addition of cysteine. Microscopy and immunoblotting further revealed that the motility defect of the ΔluxSHp mutant likely resulted from a reduction in the number and length of flagella due to loss of AI-2. This is supported by data obtained from quantitative RT-PCR (qRT-PCR). In conclusion, this study looked into the metabolic capacity of a three-gene cluster in H. pylori, including luxS. It showed that LuxSHp has a previously undescribed metabolic function in a cysteine provision pathway through a process of reverse transsulphuration. It also defined the precise steps in this pathway, and re-defined the roles of and renamed the two previously misnamed genes in the luxSHp cluster. It then addressed the controversial topic of the role of LuxS in bacteria: apart from being a central metabolic enzyme, is it a QS signalling molecule synthase? This study distinguished between the mechanisms underlying the alteration in motility of H. pylori ΔluxS mutants, and clarified whether this originated from a disruption of cysteine metabolism or signalling. Results showed that LuxS and its product, AI-2, influence motility via regulating flagellar gene transcription, suggesting the existence of an additional role for LuxS in H. pylori as a signalling molecule synthase

Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks

Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficient with discrete signal processing, but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established DNN, rarely automatically design Neural Architecture Search (NAS) for SNNs. The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture. In this paper, we propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously considering micro-, meso- and macro-scale brain topologies as the evolutionary search space. MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs). This training-free fitness function could greatly reduce computational consumption and NAS's time, and its task-independent property enables the searched SNNs to exhibit excellent transferbility and scalability. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also illustrates the significant performance improvement and consistent bio-interpretability deriving from the topological evolution at different scales and the RDMs fitness function

Chewing Gum for Intestinal Function Recovery after Colorectal Cancer Surgery: A Systematic Review and Meta-Analysis

Background. This meta-analysis was performed to assess the efficacy and safety of chewing gum in intestinal function recovery after colorectal cancer surgery. Methods. A systematic search was conducted in PubMed, Embase, Science Direct, and Cochrane library for relevant randomized controlled trials (RCTs) published until April 2017. Summary risk ratios or weighted mean differences with 95% confidence intervals were used for continuous and dichotomous outcomes, respectively. Results. 17 RCTs with a total number of 1845 patients were included. Gum chewing following colorectal cancer surgery significantly reduced the time to first passage of flatus (WMD −0.55; 95% CI −0.94 to −0.16; P=0.006), first bowel movement (WMD −0.60; 95% CI −0.87 to −0.33; P<0.0001), start feeding (WMD −1.32; 95% CI −2.18 to −0.46; P=0.003), and the length of postoperative hospital stay (WMD −0.88; 95% CI −1.59 to −0.17; P=0.01), but no obvious differences were found in postoperative nausea, vomiting, abdominal distention, pneumonia, and mortality, which were consistent with the findings of intention to treat analysis. Conclusions. Chewing gum could accelerate the recovery of intestinal function after colorectal cancer surgery. However, it confers no advantage in postoperative clinical complications. Further large-scale and high-quality RCTs should be conducted to confirm these results

Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks

The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training of synaptic weights, leading to static connections that limit adaptability in dynamic environments. Instead, our approach delves into the heart of synaptic behavior, prioritizing the learning of plasticity rules themselves. This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt. Our PDLF does not merely adapt existing concepts of functional and Presynaptic-Dependent Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artificial intelligence systems, such as working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light on the intricate relationships between various forms of plasticity and cognitive functions, thereby contributing to a deeper understanding of the brain's learning mechanisms. Integrating this groundbreaking plasticity-centric approach in SNNs marks a significant advancement in the fusion of neuroscience and artificial intelligence. It paves the way for developing AI systems that not only learn but also adapt in an ever-changing world, much like the human brain

(meso-5,7,7,12,14,14-Hexamethyl-1,4,8,11-tetra­aza­cyclo­tetra­deca-4,11-diene)nickel(II) dibromide dihydrate

The asymmetric unit of the title compound, [Ni(C16H32N4)]Br2·2H2O, consists of one half [Ni(C16H32N4)]2+ cation, one Br− anion and one water mol­ecule of crystallization. The NiII ion lies on an inversion centre in a square-planar environment formed by the four macrocyclic ligand N atoms. In the crystal structure, the cations, anions and water mol­ecules are linked via inter­molecular N—H⋯Br and O—H⋯Br hydrogen bonds, forming discrete chains with set-graph motif D(2)D 2 2(7)D 2 1(3)D 3 2(8). The water mol­ecules and Br− ions are linked with set-graph motif R 4 2(8)

RbSn2(PO4)3, a NASICON-type phosphate

The title compound, rubidium ditin(IV) tris­(phosphate), RbSn2(PO4)3, belongs to the NASICON-type family of phosphates and crystallizes in the space group R . The structure is composed of PO4 tetra­hedra (1 symmetry) and two slightly distorted SnO6 octa­hedra, both with 3. symmetry, which are inter­linked through corner-sharing O atoms to form a 3 ∞[Sn2(PO4)3]− framework. The Rb+ cations are located on threefold inversion axes in the voids of this framework and exhibit a coordination number of 12. The crystal studied was twinned by merohedry with a component ratio of 0.503:0.497