Monitoring the edges of product networks using distances

Foucaud {\it et al.} recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. Let $G$ be a graph with vertex set $V(G)$, $M$ a subset of $V(G)$, and $e$ be an edge in $E(G)$, and let $P(M, e)$ be the set of pairs $(x,y)$ such that $d_G(x, y)\neq d_{G-e}(x, y)$ where $x\in M$ and $y\in V(G)$. $M$ is called a \emph{distance-edge-monitoring set} if every edge $e$ of $G$ is monitored by some vertex of $M$, that is, the set $P(M, e)$ is nonempty. The {\em distance-edge-monitoring number} of $G$, denoted by $\operatorname{dem}(G)$, is defined as the smallest size of distance-edge-monitoring sets of $G$. For two graphs $G,H$ of order $m,n$, respectively, in this paper we prove that $\max\{m\operatorname{dem}(H),n\operatorname{dem}(G)\} \leq\operatorname{dem}(G\,\Box \,H) \leq m\operatorname{dem}(H)+n\operatorname{dem}(G) -\operatorname{dem}(G)\operatorname{dem}(H)$, where $\Box$ is the Cartesian product operation. Moreover, we characterize the graphs attaining the upper and lower bounds and show their applications on some known networks. We also obtain the distance-edge-monitoring numbers of join, corona, cluster, and some specific networks.Comment: 19 page

Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.Comment: 11 pages, 5 figure

miR‐155 promotes macrophage pyroptosis induced by Porphyromonas gingivalis through regulating the NLRP3 inflammasome

ObjectiveThe aim of this study is to detect pyroptosis in macrophages stimulated with Porphyromonas gingivalis and elucidate the mechanism by which P. gingivalis induces pyroptosis in macrophages.MethodsThe immortalized human monocyte cell line U937 was stimulated with P. gingivalis W83. Flow cytometry was carried out to detect pyroptosis in macrophages. The expression of miR‐155 was detected by real‐time PCR and inhibited using RNAi. Suppressor of cytokine signaling (SOCS) 1, cleaved GSDMD, caspase (CAS)‐1, caspase‐11, apoptosis‐associated speck‐like protein (ASC), and NOD‐like receptor protein 3 (NLRP3) were detected by Western blotting, and IL‐1β and IL‐18 were detected by ELISA.ResultsThe rate of pyroptosis in macrophages and the expression of miR‐155 increased upon stimulation with P. gingivalis and pyroptosis rate decreased when miR‐155 was silenced. GSDMD‐NT, CAS‐11, CAS‐1, ASC, NLRP3, IL‐1β, and IL‐18 levels increased, but SOCS1 decreased in U937 cells after stimulated with P. gingivalis. These changes were weakened in P. gingivalis‐stimulated U937 macrophages transfected with lentiviruses carrying miR‐155 shRNA compared to those transfected with non‐targeting control sequence. However, there was no significant difference in ASC expression between P. gingivalis‐stimulated shCont and shMiR‐155 cells.ConclusionsPorphyromonas gingivalis promotes pyroptosis in macrophages during early infection. miR‐155 is involved in this process through regulating the NLRP3 inflammasome.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/1/odi13198_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152887/2/odi13198.pd

A Sialidase‐Deficient Porphyromonas gingivalis Mutant Strain Induces Less Interleukin‐1β and Tumor Necrosis Factor‐α in Epi4 Cells Than W83 Strain Through Regulation of c‐Jun N‐Terminal Kinase Pathway

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142178/1/jpere129.pd

Detection and exposure assessment of pesticide residues in leek in He’nan Province

ObjectiveTo evaluate the health risk of pesticide exposure from leek， the pesticide residue in leek from Henan market was investigated.MethodsThe residues of 16 pesticides in leek sold on Henan market in 2020 were detected and analyzed. According to health guidance values such as food consumption data of the World Health Organization， acute reference dose formulated by Joint Meeting on Pesticide Residues and adaptable daily intake in “National food safety standard-Maximum residue limits for pesticides in food”， the acute and chronic exposure risks of pesticide residues in leek were evaluated by point assessment method， and the cumulative exposure was evaluated by hazard index method.ResultsThere were many types of pesticide residues in leek samples and 93.81% （424/452） of the samples were positive. 7 of the 14 pesticides exceeded their MRLs， and the violation rate of all samples was 16.15%. The detection of multiple pesticides was relatively serious， and 56.42% of the samples contained more than two pesticide residues. In the acute exposure assessment， the acute risks of carbofuran， procymidone and phorate exceeded the acceptable level. In the chronic exposure assessment， the chronic risk of omethoate exceeded the acceptable level. And insecticide pesticides had cumulative poisoning risk.ConclusionThe situation of pesticide residues in leek in Henan province was relatively prominent. To ensure the safety of agricultural products， it was recommended that the routine monitoring and use of pesticide， especially high-risk pesticides such as omethoate， carbofuran， procymidone and phorate should be strengthened

Pruning random resistive memory for optimizing analogue AI

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, respectively. This is accompanied by 82.1%, 51.2%, and 99.8% improvement in energy efficiency thanks to analogue in-memory computing. By embracing the intrinsic stochasticity and in-memory computing, this work may solve the biggest obstacle of analogue computing systems and thus unleash their immense potential for next-generation AI hardware

Associations of psychological wellbeing with COVID-19 hospitalization and mortality in adults aged 50 years or older from 25 European countries and Israel

BackgroundLower psychological wellbeing is associated with poor outcomes in a variety of diseases and healthy populations. However, no study has investigated whether psychological wellbeing is associated with the outcomes of COVID-19. This study aimed to determine whether individuals with lower psychological wellbeing are more at risk for poor outcomes of COVID-19.MethodsData were from the Survey of Health, Aging, and Retirement in Europe (SHARE) in 2017 and SHARE's two COVID-19 surveys in June–September 2020 and June–August 2021. Psychological wellbeing was measured using the CASP-12 scale in 2017. The associations of the CASP-12 score with COVID-19 hospitalization and mortality were assessed using logistic models adjusted for age, sex, body mass index, smoking, physical activity, household income, education level, and chronic conditions. Sensitivity analyses were performed by imputing missing data or excluding cases whose diagnosis of COVID-19 was solely based on symptoms. A confirmatory analysis was conducted using data from the English Longitudinal Study of Aging (ELSA). Data analysis took place in October 2022.ResultsIn total, 3,886 individuals of 50 years of age or older with COVID-19 were included from 25 European countries and Israel, with 580 hospitalized (14.9%) and 100 deaths (2.6%). Compared with individuals in tertile 3 (highest) of the CASP-12 score, the adjusted odds ratios (ORs) of COVID-19 hospitalization were 1.81 (95% CI, 1.41–2.31) for those in tertile 1 (lowest) and 1.37 (95% CI, 1.07–1.75) for those in tertile 2. As for COVID-19 mortality, the adjusted ORs were 2.05 (95% CI, 1.12–3.77) for tertile 1 and 1.78 (95% CI, 0.98–3.23) for tertile 2, compared with tertile 3. The results were relatively robust to missing data or the exclusion of cases solely based on symptoms. This inverse association of the CASP-12 score with COVID-19 hospitalization risk was also observed in ELSA.ConclusionThis study shows that lower psychological wellbeing is independently associated with increased risks of COVID-19 hospitalization and mortality in European adults aged 50 years or older. Further study is needed to validate these associations in recent and future waves of the COVID-19 pandemic and other populations