Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page

GCN-Based Linkage Prediction for Face Clustering on Imbalanced Datasets: An Empirical Study

In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations. The problem of imbalanced linkage labels is similar to that in image classification task, but the latter is a particular problem in GCN-based clustering via linkage prediction. Significantly biased graph representations in training can cause catastrophic overfitting of a GCN model. To tackle these problems, we evaluate the feasibility of those existing methods for imbalanced image classification problem on graphs with extensive experiments, and present a new method to alleviate the imbalanced labels and also augment graph representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy, followed with insightful analyses and discussions. The code and a series of imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion are available on https://github.com/espectre/GCNs_on_imbalanced_datasets.Comment: 7 page

Sequence Dependent Repair of 1,N6-Ethenoadenine by DNA Repair Enzymes ALKBH2, ALKBH3, and AlkB

Mutation patterns of DNA adducts, such as mutational spectra and signatures, are useful tools for diagnostic and prognostic purposes. Mutational spectra of carcinogens derive from three sources: adduct formation, replication bypass, and repair. Here, we consider the repair aspect of 1,N6-ethenoadenine (εA) by the 2-oxoglutarate/Fe(II)-dependent AlkB family enzymes. Specifically, we investigated εA repair across 16 possible sequence contexts (5′/3′ flanking base to εA varied as G/A/T/C). The results revealed that repair efficiency is altered according to sequence, enzyme, and strand context (ss- versus ds-DNA). The methods can be used to study other aspects of mutational spectra or other pathways of repair

A new pathogenic isolate of Kocuria kristinae identified for the first time in the marine fish Larimichthys crocea

In recent years, new emerging pathogenic microorganisms have frequently appeared in animals, including marine fish, possibly due to climate change, anthropogenic activities, and even cross-species transmission of pathogenic microorganisms among animals or between animals and humans, which poses a serious issue for preventive medicine. In this study, a bacterium was clearly characterized among 64 isolates from the gills of diseased large yellow croaker Larimichthys crocea that were raised in marine aquaculture. This strain was identified as K. kristinae by biochemical tests with a VITEK 2.0 analysis system and 16S rRNA sequencing and named K. kristinae_LC. The potential genes that might encode virulence-factors were widely screened through sequence analysis of the whole genome of K. kristinae_LC. Many genes involved in the two-component system and drug-resistance were also annotated. In addition, 104 unique genes in K. kristinae_LC were identified by pan genome analysis with the genomes of this strain from five different origins (woodpecker, medical resource, environment, and marine sponge reef) and the analysis results demonstrated that their predicted functions might be associated with adaptation to living conditions such as higher salinity, complex marine biomes, and low temperature. A significant difference in genomic organization was found among the K. kristinae strains that might be related to their hosts living in different environments. The animal regression test for this new bacterial isolate was carried out using L. crocea, and the results showed that this bacterium could cause the death of L. crocea and that the fish mortality was dose-dependent within 5 days post infection, indicating the pathogenicity of K. kristinae_LC to marine fish. Since K. kristinae has been reported as a pathogen for humans and bovines, in our study, we revealed a new isolate of K. kristinae_LC from marine fish for the first time, suggesting the potentiality of cross-species transmission among animals or from marine animals to humans, from which we would gain insight to help in future public prevention strategies for new emerging pathogens

Probing the Effect of Bulky Lesion-Induced Replication Fork Conformational Heterogeneity Using 4-Aminobiphenyl-Modified DNA

Bulky organic carcinogens are activated in vivo and subsequently react with nucleobases of cellular DNA to produce adducts. Some of these DNA adducts exist in multiple conformations that are slowly interconverted to one another. Different conformations have been implicated in different mutagenic and repair outcomes. However, studies on the conformation-specific inhibition of replication, which is more relevant to cell survival, are scarce, presumably due to the structural dynamics of DNA lesions at the replication fork. It is difficult to capture the exact nature of replication inhibition by existing end-point assays, which usually detect either the ensemble of consequences of all the conformers or the culmination of all cellular behaviors, such as mutagenicity or survival rate. We previously reported very unusual sequence-dependent conformational heterogeneities involving FABP-modified DNA under different sequence contexts (TG1*G2T [67%B:33%S] and TG1G2*T [100%B], G*, N-(2′-deoxyguanosin-8-yl)-4′-fluoro-4-aminobiphenyl) (Cai et al. Nucleic Acids Research, 46, 6356–6370 (2018)). In the present study, we attempted to correlate the in vitro inhibition of polymerase activity to different conformations from a single FABP-modified DNA lesion. We utilized a combination of surface plasmon resonance (SPR) and HPLC-based steady-state kinetics to reveal the differences in terms of binding affinity and inhibition with polymerase between these two conformers (67%B:33%S and 100%B)

Analysis of monitoring results of food microbial pathogenic factors in Zhoushan City from 2017 to 2019

Objective To understand the contamination status of microorganisms and pathogenic factors in 19 kinds of food in Zhoushan City, so as to provide basic data for food safety risk monitoring and early warning. Methods A total of 1 246 food samples were collected from 2017 to 2019 according to the requirements of the national risk monitor manual of food contamination and harmful factors from 2017 to 2019. The samples were tested for food microbial pathogenic factors. Results A total of 243 pathogenic factors were detected and the total detection rate was 19.50% (243/1 246). The detection rate of Salmonella in raw meat was 41.67% (30/72), the detection rate of Vibrio parahaemolyticus in bivalve shellfish was 31.58% (48/152), the detection rate of Anisakid in fresh marine fish was 27.00% (27/100), and the detection rate of Staphylococcus aureus in cold-made pastry was 16.25% (13/80). The detection rate of microorganism and its pathogenic factors in bulk food was higher than that in pre-packaged food. The difference was statistically significant (χ2=92.333, P<0.05). In different sampling locations, the highest detection rate of pathogenic factors was found in farmers’ markets (32.54%, 150/461), followed by online stores (25.44%, 29/114) and small restaurants (23.88%, 16/67). Conclusion From 2017 to 2019, 19 types of food on sale in Zhoushan City were contaminated by microorganisms and pathogenic factors at varying degrees. The contamination of microorganisms and pathogenic factors in take-out meals, raw animal meat and bivalve shellfish products was relatively serious. It is suggested to strengthen hygiene supervision on these types of food to prevent the occurrence of foodborne diseases