D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Decoupled Model Schedule for Deep Learning Training

Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off between usability and performance. On one hand, DL frameworks such as PyTorch use dynamic graphs to facilitate model developers at a price of sub-optimal model training performance. On the other hand, practitioners propose various approaches to improving the training efficiency by sacrificing some of the flexibility, ranging from making the graph static for more thorough optimization (e.g., XLA) to customizing optimization towards large-scale distributed training (e.g., DeepSpeed and Megatron-LM). In this paper, we aim to address the tension between usability and training efficiency through separation of concerns. Inspired by DL compilers that decouple the platform-specific optimizations of a tensor-level operator from its arithmetic definition, this paper proposes a schedule language to decouple model execution from definition. Specifically, the schedule works on a PyTorch model and uses a set of schedule primitives to convert the model for common model training optimizations such as high-performance kernels, effective 3D parallelism, and efficient activation checkpointing. Compared to existing optimization solutions, we optimize the model as-needed through high-level primitives, and thus preserving programmability and debuggability for users to a large extent. Our evaluation results show that by scheduling the existing hand-crafted optimizations in a systematic way, we are able to improve training throughput by up to 3.35x on a single machine with 8 NVIDIA V100 GPUs, and by up to 1.32x on multiple machines with up to 64 GPUs, when compared to the out-of-the-box performance of DeepSpeed and Megatron-LM

Preparation and Characterization of Folate Targeting Magnetic Nanomedicine Loaded with Cisplatin

We used Aldehyde sodium alginate (ASA) as modifier to improve surfactivity and stability of magnetic nanoparticles, and folate acid (FA) as targeting molecule. Fe3O4 nanoparticles were prepared by chemical coprecipitation method. FA was activated and coupled with diaminopolyethylene glycol (NH2-PEG-NH2). ASA was combined with Fe3O4 nanoparticles, and FA-PEG was connected with ASA by Schiff’s base formation. Then Cl- in cisplatin was replaced by hydroxyl group in ASA, and FA- and ASA-modified cisplatin-loaded magnetic nanomedicine (CDDP-FA-ASA-MNPs) was prepared. This nanomedicine was characterized by transmission electron microscopy, dynamic lighterring scattering, phase analysis light scattering and vibrating sample magnetometer. The uptake of magnetic nanomedicine by nasopharyngeal and laryngeal carcinoma cells with folate receptor positive or negative expression were observed by Prussian blue iron stain and transmission electron microscopy. We found that CDDP-FA-ASA-MNPs have good water-solubility and stability. Mean diameter of Fe3O4 core was 8.17 ± 0.24 nm, hydrodynamic diameters was 110.90±1.70 nm, and zeta potential was -26.45±1.26 mV. Maximum saturation magnetization was 22.20 emu/g. CDDP encapsulation efficiency was 49.05±1.58% (mg/mg), and drug loading property was 14.31±0.49% (mg/mg). In vitro, CDDP-FA-ASA-MNPs were selectively taken up by HNE-1 cells and Hep-2 cells, which express folate receptor positively

Downregulation of E-Cadherin enhances proliferation of head and neck cancer through transcriptional regulation of EGFR

<p>Abstract</p> <p>Background</p> <p>Epidermal growth factor receptor (EGFR) has been reported to downregulate E-cadherin (E-cad); however, whether the downregulation of E-cad has any effect on EGFR expression has not been elucidated. Our previous studies have found an inverse correlation between EGFR and E-cad expression in tissue specimens of squamous cell carcinoma of the head and neck (SCCHN). To understand the biological mechanisms underlying this clinical observation, we knocked down E-cad expression utilizing E-cad siRNA in four SCCHN cell lines.</p> <p>Results</p> <p>It was observed that downregulation of E-cad upregulated EGFR expression compared with control siRNA-transfected cells after 72 hours. Cellular membrane localization of EGFR was also increased. Consequently, downstream signaling molecules of the EGFR signaling pathway, p-AKT, and p-ERK, were increased at 72 hours after the transfection with E-cad siRNA. Reverse transcriptase-polymerase chain reaction (RT-PCR) showed EGFR mRNA was upregulated by E-cad siRNA as early as 24 hours. In addition, RT-PCR revealed this upregulation was due to the increase of EGFR mRNA stability, but not protein stability. Sulforhodamine B (SRB) assay indicated growth of E-cad knocked down cells was enhanced up to 2-fold more than that of control siRNA-transfected cells at 72-hours post-transfection. The effect of E-cad reduction on cell proliferation was blocked by treating the E-cad siRNA-transfected cells with 1 μM of the EGFR-specific tyrosine kinase inhibitor erlotinib.</p> <p>Conclusion</p> <p>Our results suggest for the first time that reduction of E-cad results in upregulation of EGFR transcriptionally. It also suggests that loss of E-cad may induce proliferation of SCCHN by activating EGFR and its downstream signaling pathways.</p

Modulate Molecular Interaction between Hole Extraction Polymers and Lead Ions toward Hysteresis-Free and Efficient Perovskite Solar Cells

Herein three polymeric hole extraction materials (HEMs), poly(benzene‐dithiophene) (PB2T)‐O, PB2T‐S, and PB2T‐SO are presented for p–i–n perovskite solar cells (PVSCs). This study reveals that the perovskite device hysteresis and performance heavily rely on the perovskite grain boundary conditions. More specifically, they are predetermined through the molecular interaction between Lewis base atoms of HEMs and perovskites. It is revealed that only changing the side chain terminals (-OCH_3, -SCH_3, and –SOCH_3) of HEMs results in effective modulating PVSC performance and hysteresis, due to the effective tune of interaction strength between HEM and perovskite. With an in situ grown perovskite‐HEM bulk heterojunction structure, PB2T‐O with weak binding group (-OCH_3, −78.9 kcal mol^(−1) bonding energy) to lead ions allows delivering hysteresis‐free and efficient devices, which is sharp contrast to the strong binding PB2T‐SO (−119.3 kcal mol^(−1) bonding energy). Overall, this work provides new insights on PVSC hysteresis and the related curing methods via multifunctional HEM design in PVSCs

Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on distributed GNN training

Network during light-induced cotyledons opening and greening in Astragalus membranaceus

Opening and greening are main characteristics of morphogenesis of cotyledons. For revealing interrelationship between metabolism and morphogenesis, metabolic shifts were analyzed in cotyledon of A. membranaceus seedlings with different stages in light and in darkness. Light induced 69 metabolites (MA), related to cotyledon greening; additional 89 metabolites (MB), related to cotyledon opening, were identified by WGCNA. The screening of metabolites shared in both MA and MB obtained 37 specific metabolites (MC) related to both opening and greening. In this context, main changes in MC occurred during A3, the stage in which cotyledons fully opened and greened. Within MC, few sugars, including L-(-)-sorbose, mannose, glucose and its derivatives, markedly decreased, while other sugars, amino acids, and unsaturated fatty acids increased. Most isoflavones and flavonols including ononin, caycosin-7-glucosides, quercetin, genistein, and catechin increased 5.3, 5.5, 13.4, 6.4 and 1.8 times, respectively. Thus, accumulated flavonoids play an important role during this developmental stage. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group