GIMM: InfoMin-Max for Automated Graph Contrastive Learning

Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task. To solve this problem, we propose InfoMin-Max for automated Graph contrastive learning (GIMM), which prevents GCL from encoding redundant information and losing essential information. GIMM consists of two major modules: (1) automated graph view generator, which acquires the approximation of InfoMin's optimal views through adversarial training without requiring task-relevant information; (2) view comparison, which learns an excellent encoder by applying InfoMax to view representations. To the best of our knowledge, GIMM is the first method that combines the InfoMin and InfoMax principles in GCL. Besides, GIMM introduces randomness to augmentation, thus stabilizing the model against perturbations. Extensive experiments on unsupervised and semi-supervised learning for node and graph classification demonstrate the superiority of our GIMM over state-of-the-art GCL methods with automated and manual data augmentation

A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored

Mechanisms Of Intrinsic Epileptogenesis In Human Gelastic Seizures With Hypothalamic Hamartoma

Human hypothalamic hamartoma (HH) is a rare developmental malformation often characterized by gelastic seizures, which are refractory to medical therapy. Ictal EEG recordings from the HH have demonstrated that the epileptic source of gelastic seizures lies within the HH lesion itself. Recent advances in surgical techniques targeting HH have led to dramatic improvements in seizure control, which further supports the hypothesis that gelastic seizures originate within the HH. However, the basic cellular and molecular mechanisms of epileptogenesis in this subcortical lesion are poorly understood. Since 2003, Barrow Neurological Institute has maintained a multidisciplinary clinical program to evaluate and treat patients with HH. This program has provided a unique opportunity to investigate the basic mechanisms of epileptogenesis using surgically resected HH tissue. The first report on the electrophysiological properties of HH neurons was published in 2005. Since then, ongoing research has provided additional insights into the mechanisms by which HH generate seizure activity. In this review, we summarize this progress and propose a cellular model that suggests that GABA-mediated excitation contributes to epileptogenesis in HH lesions

Human α4β2 Nicotinic Acetylcholine Receptor As A Novel Target Of Oligomeric α-Synuclein

Cigarette smoking is associated with a decreased incidence of Parkinson disease (PD) through unknown mechanisms. Interestingly, a decrease in the numbers of α4β2 nicotinic acetylcholine receptors (α4β2-nAChRs) in PD patients suggests an α4β2-nAChR-mediated cholinergic deficit in PD. Although oligomeric forms of α-synuclein have been recognized to be toxic and involved in the pathogenesis of PD, their direct effects on nAChR-mediated cholinergic signaling remains undefined. Here, we report for the first time that oligomeric α-synuclein selectively inhibits human α4β2-nAChR-mediated currents in a dose-dependent, non-competitive and use-independent manner. We show that pre-loading cells with guanyl-5′-yl thiophosphate fails to prevent this inhibition, suggesting that the α-synuclein-induced inhibition of α4β2-nAChR function is not mediated by nAChR internalization. By using a pharmacological approach and cultures expressing transfected human nAChRs, we have shown a clear effect of oligomeric α-synuclein on α4β2-nAChRs, but not on α4β4- or α7-nAChRs, suggesting nAChR subunit selectivity of oligomeric α-synuclein-induced inhibition. In addition, by combining the size exclusion chromatography and atomic force microscopy (AFM) analyses, we find that only large (\u3e4 nm) oligomeric α-synuclein aggregates (but not monomeric, small oligomeric or fibrillar α-synuclein aggregates) exhibit the inhibitory effect on human α4β2-nAChRs. Collectively, we have provided direct evidence that α4β2-nAChR is a sensitive target to mediate oligomeric α-synuclein-induced modulation of cholinergic signaling, and our data imply that therapeutic strategies targeted toward α4β2-nAChRs may have potential for developing new treatments for PD. © 2013 Liu et al

Non-volatile memory based on PZT/FeGa thin film memtranstor

The PZT/FeGa thin film memtranstor was prepared and the modulation of the magnetoelectric coefficient by external magnetic and electric fields was studied. The magnetoelectric coefficient of the PZT/FeGa memtranstor can be reversed by flipping the direction of magnetization of FeGa or ferroelectric polarization of PZT. Notably, the sign of the magnetoelectric coefficient can be switched repeatedly by reversing ferroelectric polarization of PZT when the external magnetic field remains constant. Moreover, the binary switching behavior can still be maintained under zero DC bias magnetic field. When the polarization direction remains stable, the magnetoelectric coefficient also does not change. This means that the magnetoelectric coefficient of PZT/FeGa is non-volatile. Furthermore, the retention and endurance characteristics of the PZT/FeGa thin film memtranstor have been investigated. These findings demonstrate the potential of the PZT/FeGa thin film memtranstor for non-volatile memory applications.Comment: 10 pages, 4 figure