An outline for the pharmacological effect of icariin in the nervous system.

Brain neurons, to support their neurotransmitter functions, require a several times higher supply of glucose than non-excitable cells. Pyruvate, the end product of glycolysis, through pyruvate dehydrogenase complex reaction, is a principal source of acetyl-CoA, which is a direct energy substrate in all brain cells. Several neurodegenerative conditions result in the inhibition of pyruvate dehydrogenase and decrease of acetyl-CoA synthesis in mitochondria. This attenuates metabolic flux through TCA in the mitochondria, yielding energy deficits and inhibition of diverse synthetic acetylation reactions in all neuronal sub-compartments. The acetyl-CoA concentrations in neuronal mitochondrial and cytoplasmic compartments are in the range of 10 and 7 μmol/L, respectively. They appear to be from 2 to 20 times lower than acetyl-CoA Km values for carnitine acetyltransferase, acetyl-CoA carboxylase, aspartate acetyltransferase, choline acetyltransferase, sphingosine kinase 1 acetyltransferase, acetyl-CoA hydrolase, and acetyl-CoA acetyltransferase, respectively. Therefore, alterations in acetyl-CoA levels alone may significantly change the rates of metabolic fluxes through multiple acetylation reactions in brain cells in different physiologic and pathologic conditions. Such substrate-dependent alterations in cytoplasmic, endoplasmic reticulum or nuclear acetylations may directly affect ACh synthesis, protein acetylations, and gene expression. Thereby, acetyl-CoA may regulate the functional and adaptative properties of neuronal and non-neuronal brain cells. The excitotoxicity-evoked intracellular zinc excess hits several intracellular targets, yielding the collapse of energy balance and impairment of the functional and structural integrity of postsynaptic cholinergic neurons. Acute disruption of brain energy homeostasis activates slow accumulation of amyloid-β1-42 (Aβ). Extra and intracellular oligomeric deposits of Aβ affect diverse transporting and signaling pathways in neuronal cells. It may combine with multiple neurotoxic signals, aggravating their detrimental effects on neuronal cells. This review presents evidences that changes of intraneuronal levels and compartmentation of acetyl-CoA may contribute significantly to neurotoxic pathomechanisms of different neurodegenerative brain disorders.

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The Regulatory Effects of Acetyl-CoA Distribution in the Healthy and Diseased Brain.

Icariin (ICA) is the main active ingredient of Epimedium brevicornu Maxim (Berberidaceae), which is used in the immune, reproductive, neuroendocrine systems, and anti-aging To evaluate the effect 

Icariin improves brain function decline in aging rats by enhancing neuronal autophagy through the AMPK/mTOR/ULK1 pathway.

Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks, unexplainable discrimination against disadvantaged groups, or excessive resource consumption in edge computing environments. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness. To this end, we propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

—Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisﬁed in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem deﬁnition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the ﬁrst systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.

Out-Of-Distribution Generalization on Graphs: A Survey

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions.
Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution.
To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver. 
This problem is challenging because it is non-trivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver.
To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap. The codes are publicly available.

Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum

To examine the effect of icariin (ICA) on the cognitive impairment induced by traumatic brain injury (TBI) in mice and the underlying mechanisms related to changes in hippocampal acetylation level. The modifified free-fall method was used to establish the TBI mouse model. Mice with post-TBI cognitive impairment were randomly divided into 3 groups using the randomised block method (n=7): TBI (vehicle-treated), low-dose (75 mg/kg) and high-dose (150 mg/kg) of ICA groups. An additional sham-operated group (vehicle-treated) was employed. The vehicle or ICA was administrated by gavage for 28 consecutive days. The Morris water maze (MWM) test was conducted. Acetylcholine (ACh) content, mRNA and protein levels of choline acetyltransferase (ChAT), and protein levels of acetylated H3 (Ac-H3) and Ac-H4 were detected in the hippocampus. Compared with the sham-operated group, the MWM performance, hippocampal ACh content, mRNA and protein levels of ChAT, and protein levels of Ac-H3 and Ac-H4 were signifificantly decreased in the TBI group (P<0.05). High-dose of ICA signifificantly ameliorated the TBI-induced weak MWM performance, increased hippocampal ACh content, and mRNA and protein levels of ChAT, as well as Ac-H3 protein level compared with the TBI group (P<0.05). ICA improved post-TBI cognitive impairment in mice by enhancing hippocampal acetylation, which improved hippocampal cholinergic function and ultimately improved cognition.

Icariin Improves Cognitive Impairment after Traumatic Brain Injury by Enhancing Hippocampal Acetylation.

Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To tackle the challenge, automated graph machine learning, which aims at discovering the best hyper-parameter and neural architecture configuration for different graph tasks/data without manual design, is gaining an increasing number of attentions from the research community. In this paper, we extensively discuss automated graph machine approaches, covering hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We briefly overview existing libraries designed for either graph machine learning or automated machine learning respectively, and further in depth introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Last but not least, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive discussion of approaches, libraries as well as directions for automated graph machine learning.

Automated Graph Machine Learning: Approaches, Libraries and Directions

Self-supervised learning for graph neural networks has attracted considerable attention and shows notable successes in graph representation learning. However, the formation of a real-world graph typically arises from highly complex interactions of many latent factors. The existing self-supervised learning methods for GNNs are inherently holistic and neglect the entanglement of the latent factors, resulting in suboptimal learned representations for downstream tasks and difficult to be interpreted. Learning disentangled graph representations with self-supervised learning poses great challenges and remains largely ignored by the existing literature. In this paper, we introduce Independence Promoted Disentangled Graph Contrastive Learning (IDGCL) method, which can learn disentangled graph-level representations with self-supervision. In particular, we first identify the latent factors of the input graph and derive its factorized representations. Then we propose a factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors. To further promote the independence between the representations, we employ the Hilbert-Schmidt Independence Criterion to eliminate the dependence among different representations, which is effectively integrated into the self-supervised framework as a regularizer. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method against several state-of-the-art baselines.

Zi-gang Zhang

Papers

Out-Of-Distribution Generalization on Graphs: A Survey

Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum

Icariin Improves Cognitive Impairment after Traumatic Brain Injury by Enhancing Hippocampal Acetylation.

Automated Graph Machine Learning: Approaches, Libraries and Directions

Disentangled Graph Contrastive Learning With Independence Promotion