IntroductionAlzheimer’s disease (AD) is the most common form of dementia worldwide. The biological mechanisms underlying the pathogenesis of AD aren’t completely clear. Studies have shown that the gut microbiota could be associated with AD pathogenesis; however, the pathways involved still need to be investigated.AimsTo explore the possible pathways of the involvement of gut microbiota in AD pathogenesis through metabolites and to identify new AD biomarkers.MethodsSeven-month-old APP/PS1 mice were used as AD models. The Morris water maze test was used to examine learning and memory ability. 16S rRNA gene sequencing and widely targeted metabolomics were used to identify the gut microbiota composition and fecal metabolic profile, respectively, followed by a combined analysis of microbiomics and metabolomics.ResultsImpaired learning abilities were observed in APP/PS1 mice. Statistically significant changes in the gut microbiota were detected, including a reduction in β-diversity, a higher ratio of Firmicutes/Bacteroidota, and multiple differential bacteria. Statistically significant changes in fecal metabolism were also detected, with 40 differential fecal metabolites and perturbations in the pyrimidine metabolism. Approximately 40% of the differential fecal metabolites were markedly associated with the gut microbiota, and the top two bacteria associated with the most differential metabolites were Bacillus firmus and Rikenella. Deoxycytidine, which causes changes in the pyrimidine metabolic pathway, was significantly correlated with Clostridium sp. Culture-27.ConclusionsGut microbiota may be involved in the pathological processes associated with cognitive impairment in AD by dysregulating pyrimidine metabolism. B. firmus, Rikenella, Clostridium sp. Culture-27, and deoxyuridine may be important biological markers for AD. Our findings provide new insights into the host-microbe crosstalk in AD pathology and contribute to the discovery of diagnostic markers and therapeutic targets for AD.

DEtection TRansformer (DETR) is a recently proposed method that streamlines the detection pipeline and achieves competitive results against two-stage detectors such as Faster-RCNN. The DETR models get rid of complex anchor generation and post-processing procedures thereby making the detection pipeline more intuitive. However, the numerous redundant parameters in transformers make the DETR models computation and storage intensive, which seriously hinder them to be deployed on the resources-constrained devices. In this paper, to obtain a compact end-to-end detection framework, we propose to deeply compress the transformers with low-rank tensor decomposition. The basic idea of the tensor-based compression is to represent the large-scale weight matrix in one network layer with a chain of low-order matrices. Furthermore, we propose a gated multi-head attention (GMHA) module to mitigate the accuracy drop of the tensor-compressed DETR models. In GMHA, each attention head has an independent gate to determine the passed attention value. The redundant attention information can be suppressed by adopting the normalized gates. Lastly, to obtain fully compressed DETR models, a low-bitwidth quantization technique is introduced for further reducing the model storage size. Based on the proposed methods, we can achieve significant parameter and model size reduction while maintaining high detection performance. We conduct extensive experiments on the COCO dataset to validate the effectiveness of our tensor-compressed (tensorized) DETR models. The experimental results show that we can attain 3.7 times full model compression with 482 times feed forward network (FFN) parameter reduction and only 0.6 points accuracy drop.

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The electromigration (EM)-induced reliability issues in very large scale integration (VLSI) circuits have attracted increased attention due to the continuous technology scaling. Traditional EM models often lead to overly pessimistic prediction incompatible with the shrinking design margin in future technology nodes. Motivated by the latest success of neural networks in solving differential equations in physical problems, we propose a novel mesh-free model to compute EM-induced stress evolution in VLSI circuits. The model utilizes a specifically crafted spacetime physics-informed neural network (STPINN) as the solver for EM analysis. By coupling the physics-based EM analysis with dynamic temperature incorporating Joule heating and via effect, we can observe stress evolution along multi-segment interconnect trees under constant, time-dependent and space-time-dependent temperature during the void nucleation phase. The proposed STPINN method obviates the time discretization and meshing required in conventional numerical stress evolution analysis and offers significant computational savings. Numerical comparison with competing schemes demonstrates a 2× ∼ 52× speedup with a satisfactory accuracy.