Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks

The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and spiking neural networks are unable to adequately auto-regulate the limited resources in the network, which leads to performance drop along with energy consumption rise as the increase of tasks. In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks. The proposed model demonstrates consistent superiority in performance, energy consumption, and memory capacity on diverse continual learning tasks ranging from child-like simple to complex tasks, as well as on generalized CIFAR100 and ImageNet datasets. In particular, the SOR-SNN model excels at learning more complex tasks as well as more tasks, and is able to integrate the past learned knowledge with the information from the current task, showing the backward transfer ability to facilitate the old tasks. Meanwhile, the proposed model exhibits self-repairing ability to irreversible damage and for pruned networks, could automatically allocate new pathway from the retained network to recover memory for forgotten knowledge

Pre‐symptomatic transmission of novel coronavirus in community settings

We used contact tracing to document how COVID‐19 was transmitted across 5 generations involving 10 cases, starting with an individual who became ill on January 27. We calculated the incubation period of the cases as the interval between infection and development of symptoms. The median incubation period was 6.0 days (interquartile range, 3.5‐9.5 days). The last two generations were infected in public places, 3 and 4 days prior to the onset of illness in their infectors. Both had certain underlying conditions and comorbidity. Further identification of how individuals transmit prior to being symptomatic will have important consequences.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/2/irv12773.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/1/irv12773_am.pd

Diversifying search in bee algorithms for numerical optimisation

© Springer Nature Switzerland AG 2018. Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks

The superhydrophobicity of polymer surfaces: Recent developments

Superhydrophobicity is the extreme water repellence of highly textured surfaces. The field of superhydrophobicity research has reached a stage where huge numbers of candidate treatments have been proposed and jumps have been made in theoretically describing them. There now seems to be a move to more practical concerns and to considering the demands of individual applications instead of more general cases. With these developments, polymeric surfaces with their huge variety of properties have come to the fore and are fast becoming the material of choice for designing, developing, and producing superhydrophobic surfaces. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 49: 1203–1217, 201

DMS triggers apoptosis associated with the inhibition of SPHK1/NF-κB activation and increase in intracellular Ca2+ concentration in human cancer cells

N,N-Dimethyl-D-erythro-sphingosine (DMS) is known to induce cell apoptosis by specifically inhibiting sphingosine kinase 1 (SPHK1) and modulating the activity of cellular ceramide levels. The present study investigated the effects and the mechanism(s) of action of DMS in human lung cancer cells. We found that DMS dose-dependently suppressed cell proliferation and induced cell apoptosis in the human lung cancer cell line, A549. Mechanistically, treatment with DMS suppressed the activation of SPHK1 and nuclear factor-B (NF-B) p65, but increased intracellular [Ca2+]i in A549 cells. This study demonstrates that DMS triggers the apoptosis of human lung cancer cells through the modulation of SPHK1, NF-B and calcium signaling. These molecules may represent targets for anticancer drug design

Loess plateau storage of northeastern Tibetan plateau-derived Yellow River sediment

Marine accumulations of terrigenous sediment are widely assumed to accurately record climatic- and tectonic-controlled mountain denudation and play an important role in understanding late Cenozoic mountain uplift and global cooling. Underpinning this is the assumption that the majority of sediment eroded from hinterland orogenic belts is transported to and ultimately stored in marine basins with little lag between erosion and deposition. Here we use a detailed and multi-technique sedimentary provenance dataset from the Yellow River to show that substantial amounts of sediment eroded from Northeast Tibet and carried by the river’s upper reach are stored in the Chinese Loess Plateau and the western Mu Us desert. This finding revises our understanding of the origin of the Chinese Loess Plateau and provides a potential solution for mismatches between late Cenozoic terrestrial sedimentation and marine geochemistry records, as well as between global CO2 and erosion records

Current trends in drug metabolism and pharmacokinetics.

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice