Advanced materials for stable Li-S and Li-organic batteries

Lithium ion batteries (LIBs) are playing an increasingly important role in our everyday life. LIBs are powering consumer electronics (e.g., cameras, smartphones, laptops), electric vehicles and large-scale industrial facilities. Also, LIBs are important energy storage systems for renewable energies like solar and wind. With respect to conventional LIBs, typically, the cathode material is LiCoO2 and the anode material is graphite. However, the upper limit of the conventional LIBs cannot meet the long-term needs of the rapidly developing society, for instance, extended-range of electric vehicles. In this regard, next-generation battery types are highly needed to build up a more sustainable society. Li-S batteries, with high theoretical capacity of 1675 mA h g–1 and high theoretical energy density of 2600 W h kg–1, is a promising candidate for next-generation high-energy batteries. Also, the low cost and abundance of sulphur is an attracting advantage for Li-S batteries. In order to achieve the high capacity and high energy density of Li-S batteries, two severe problems should be overcome, that is, the poor electrical conductivity, as well as the dissolution and shuttling of the intermediate products of lithium polysulphides

Exercise training results in lower amyloid plaque load and greater cognitive function in an intensity dependent manner in the Tg2576 mouse model of Alzheimer\u27s disease

Three months of exercise training (ET) decreases soluble A

Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis

Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer from a lack of transparency, that is, it is challenging to interpret the model predictions. Prior works focused on specific factor explanations for a GNN model. In this work, we have designed and implemented Illuminati, a comprehensive and accurate explanation framework for cybersecurity applications using GNN models. Given a graph and a pre-trained GNN model, Illuminati is able to identify the important nodes, edges, and attributes that are contributing to the prediction while requiring no prior knowledge of GNN models. We evaluate Illuminati in two cybersecurity applications, i.e., code vulnerability detection and smart contract vulnerability detection. The experiments show that Illuminati achieves more accurate explanation results than state-of-the-art methods, specifically, 87.6% of subgraphs identified by Illuminati are able to retain their original prediction, an improvement of 10.3% over others at 77.3%. Furthermore, the explanation of Illuminati can be easily understood by the domain experts, suggesting the significant usefulness for the development of cybersecurity applications.Comment: EuroS&P 202

Anti-human TREM2 induces microglia proliferation and reduces pathology in an Alzheimer\u27s disease model

TREM2 is a receptor for lipids expressed in microglia. The R47H variant of human TREM2 impairs ligand binding and increases Alzheimer\u27s disease (AD) risk. In mouse models of amyloid β (Aβ) accumulation, defective TREM2 function affects microglial response to Aβ plaques, exacerbating tissue damage, whereas TREM2 overexpression attenuates pathology. Thus, AD may benefit from TREM2 activation. Here, we examined the impact of an anti-human TREM2 agonistic mAb, AL002c, in a mouse AD model expressing either the common variant (CV) or the R47H variant of TREM2. Single-cell RNA-seq of microglia after acute systemic administration of AL002c showed induction of proliferation in both CV- and R47H-transgenic mice. Prolonged administration of AL002c reduced filamentous plaques and neurite dystrophy, impacted behavior, and tempered microglial inflammatory response. We further showed that a variant of AL002c is safe and well tolerated in a first-in-human phase I clinical trial and engages TREM2 based on cerebrospinal fluid biomarkers. We conclude that AL002 is a promising candidate for AD therapy

Behavioral Consequences of NMDA Antagonist-Induced Neuroapoptosis in the Infant Mouse Brain

Background: Exposure to NMDA glutamate antagonists during the brain growth spurt period causes widespread neuroapoptosis in the rodent brain. This period in rodents occurs during the first two weeks after birth, and corresponds to the third trimester of pregnancy and several years after birth in humans. The developing human brain may be exposed to NMDA antagonists through drug-abusing mothers or through anesthesia. Methodology/Principal Findings: We evaluated the long-term neurobehavioral effects of mice exposed to a single dose of the NMDA antagonist, phencyclidine (PCP), or saline, on postnatal day 2 (P2) or P7, or on both P2 and P7. PCP treatment on P2 + P7 caused more severe cognitive impairments than either single treatment. Histological examination of acute neuroapoptosis resulting from exposure to PCP indicated that the regional pattern of degeneration induced by PCP in P2 pups was different from that in P7 pups. The extent of damage when evaluated quantitatively on P7 was greater for pups previously treated on P2 compared to pups treated only on P7. Conclusions: These findings signify that PCP induces different patterns of neuroapoptosis depending on the developmental age at the time of exposure, and that exposure at two separate developmental ages causes more severe neuropathological and neurobehavioral consequences than a single treatment

Tango: rethinking quantization for graph neural network training on GPUs

Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in critical graph-related tasks. While quantization is widely used to accelerate GNN computation, quantized training faces unprecedented challenges. Current quantized GNN training systems often have longer training times than their full-precision counterparts for two reasons: (i) addressing the accuracy challenge leads to excessive overhead, and (ii) the optimization potential exposed by quantization is not adequately leveraged. This paper introduces Tango which re-thinks quantization challenges and opportunities for graph neural network training on GPUs with three contributions: Firstly, we introduce efficient rules to maintain accuracy during quantized GNN training. Secondly, we design and implement quantization-aware primitives and inter-primitive optimizations that can speed up GNN training. Finally, we integrate Tango with the popular Deep Graph Library (DGL) system and demonstrate its superior performance over state-of-the-art approaches on various GNN models and datasets

Health disparities in aging: Improving dementia care for Black women

In the United States, 80% of surveyed Black patients report experiencing barriers to healthcare for Alzheimer\u27s disease and related dementias (ADRD), delaying the time-sensitive treatment of a progressive neurodegenerative disease. According to the National Institute on Aging, Black study participants are 35% less likely to be given a diagnosis of ADRD than white participants, despite being twice as likely to suffer from ADRD than their white counterparts. Prior analysis of prevalence for sex, race, and ethnicity by the Centers for Disease Control indicated the highest incidence of ADRD in Black women. Older (≥65 years) Black women are at a disproportionately high risk for ADRD and yet these patients experience distinct inequities in obtaining clinical diagnosis and treatment for their condition. To that end, this perspective article will review a current understanding of biological and epidemiological factors that underlie the increased risk for ADRD in Black women. We will discuss the specific barriers Black women face in obtaining access to ADRD care, including healthcare prejudice, socioeconomic status, and other societal factors. This perspective also aims to evaluate the performance of intervention programs targeted toward this patient population and offer possible solutions to promote health equity