A Talk-Listen-Ack Beaconing Strategy for Neighbor Discovery Protocols in Wireless Sensor Networks

Neighbor discovery is a fundamental function for sensor networking. Sensor nodes discover each other by sending and receiving beacons. Although many time-slotted neighbor discovery protocols (NDPs) have been proposed, the theoretical discovery latency is measured by the number of time slots rather than the unit of time. Generally, the actual discovery latency of a NDP is proportional to its theoretical discovery latency and slot length, and inversely proportional to the discovery probability. Therefore, it is desired to increase discovery probability while reducing slot length. This task, however, is challenging because the slot length and the discovery probability are two conflicting factors, and they mainly depend on the beaconing strategy used. In this paper, we propose a new beaconing strategy, called talk-listen-ack beaconing (TLA). We analyze the discovery probability of TLA by using a fine-grained slot model. Further, we also analyze the discovery probability of TLA that uses random backoff mechanism to avoid persistent collisions. Simulation and experimental results show that, compared with the 2-Beacon approach that has been widely used in time-slotted NDPs, TLA can achieve a high discovery probability even in a short time slot. TLA is a generic beaconing strategy that can be applied to different slotted NDPs to reduce their discovery latency.

Predictors of human-infective RNA virus discovery in the United States, China and Africa, an ecological study

Background: The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions—the United States, China and Africa—differ from one another and from those at the global level. Methods: Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010–2019 was mapped using the fitted models and historical predictors. Results: The numbers of human-infective virus species discovered in the United States, China and Africa up to 2019 were 95, 80 and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was best predicted by land use and socio-economic variables, followed by climatic variables and biodiversity, though the relative importance of these predictors varied by region. Map of virus discovery probability in 2010–2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high risk areas as predicted by our model. Conclusions: The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010–2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high risk areas.

Accelerated Cuckoo Search With Extended Diversification and Intensification

Metaheuristics have been great to solve NP-hard class problems in the deterministic time, but due to so many parameter settings, they lack in generality (i.e., not easy to implement on all types of problems) and also lack in global search. But the cuckoo search (CS) algorithm has only one parameter as input and also has a good reachable probability to global solution due to Levy flight. But this algorithm lacks self-adaptive parameters and extended strategies. In this paper, a deep study and improvement of cuckoo search performance has been done by introducing self-adaptive step size, extended alien egg discovery replacement (on each dimension with the use of good neighbor study), and adaptive discovery probability, and it has been named accelerated cuckoo search (ACS). Then this ACS has been utilized as an example in the load balancing problem in cloud with minimum makespan time as an objective parameter to evaluate the performance of ACS over CS. Furthermore, to validate ACS superiority over CS in all problems, these have been successfully compared on a few benchmark functions.

A Method for Battery Health Estimation Based on Charging Time Segment

The problem of low accuracy and low convenience in the existing state of health (SOH) estimation method for vehicle lithium-ion batteries has become one of the important problems in the electric vehicle field. This paper proposes an improved cuckoo search particle filter (ICS-PF) algorithm based on a charging time segment from equal voltage data to estimate battery health status. Appropriate voltage ranges of charging time segments are selected according to the battery charging law, and in the meantime, the charging time segments are collected as a health indicator to establish the corresponding relationship with battery capacity attenuation value. An improved cuckoo search particle filter algorithm based on the traditional particle filter (PF) and cuckoo search (CS) algorithm is proposed by enhancing the search step size and discovery probability to estimate the capacity attenuation. The estimation result shows that this method is superior to the traditional particle filter and cuckoo search particle filter (CS-PF) method, as the maximum estimation error is less than 2%.

PAI-1 Polymorphisms Have Significant Associations With Cancer Risk, Especially Feminine Cancer

Background: The plasminogen activator inhibitor-1 (PAI-1) was found in many types of tumor cells, which involved in tumorigenesis. Some studies investigated the associations between PAI-1 polymorphisms and various cancers, but the results were inconsistent. So this study did a meta-analysis to assess the strength of relationship between PAI-1 and cancer. Methods: Articles that meet the requirements were searched from PubMed, EMBASE, MEDLINE, Scopus, CNKI, Wanfang and SinoMed electronic databases before June 17th 2021. Stata version 11.2 was performed to merge the odds ratios (ORs) values and calculate 95% confidence intervals (CIs). Stratified analyses were assessed on the basis of types of cancer, ethnicity and source of the control group. Heterogeneity and sensitivity analysis were tested, and publication bias was also estimated. A meta-regression analysis was applied to explore sources of heterogeneity. The false-positive report probabilities (FPRP) and the Bayesian False Discovery Probability (BFDP) test were used to assess the credibility of statistically significant associations. Results: Ultimately, in this study, 33 eligible reports were included with 9550 cases and 10431 controls for the rs1799889 polymorphism, 5 reports with 2705 cases and 3168 controls for the rs2227631 polymorphism, and 4 reports with 2799 cases and 4011 controls for the rs2227667 polymorphism. The ORs and 95% CIs showed a statistically significant relationship between rs1799889 4G>5G polymorphism and cancer risk, especially in feminine cancer. The term refers to cancers that occur in the female reproductive system, such as ovarian, breast, endometrial and cervical cancer. Moreover, there was no association observed for the PAI-1 promoter A>G polymorphism (rs2227631 and rs2227667). In further subgroup analyses of 4G>5G polymorphism (rs1799889), an increased susceptibility to cancer was observed in Caucasians group and some types of cancer groups. Conclusions: This article comes to a conclusion that the rs1799889 polymorphism might help to increase the risk of cancer; moreover, the susceptibility to feminine cancer is more evident.

Genetic Variation and Autism: A Field Synopsis and Systematic Meta-Analysis

This study aimed to verify noteworthy findings between genetic risk factors and autism spectrum disorder (ASD) by employing the false positive report probability (FPRP) and the Bayesian false-discovery probability (BFDP). PubMed and the Genome-Wide Association Studies (GWAS) catalog were searched from inception to 1 August, 2019. We included meta-analyses on genetic factors of ASD of any study design. Overall, twenty-seven meta-analyses articles from literature searches, and four manually added articles from the GWAS catalog were re-analyzed. This showed that five of 31 comparisons for meta-analyses of observational studies, 40 out of 203 comparisons for the GWAS meta-analyses, and 18 out of 20 comparisons for the GWAS catalog, respectively, had noteworthy estimations under both Bayesian approaches. In this study, we found noteworthy genetic comparisons highly related to an increased risk of ASD. Multiple genetic comparisons were shown to be associated with ASD risk; however, genuine associations should be carefully verified and understood.

Association between the combined effects of GSTM1 present/null and CYP1A1 MspI polymorphisms with lung cancer risk: an updated meta-analysis

Background: Many studies have been performed to explore the combined effects of glutathione-S-transferase M1 (GSTM1) present/null and cytochrome P4501A1 (CYP1A1) MspI polymorphisms with lung cancer (LC) risk, but the results are contradictory. Two previous meta-analyses have been reported on the issue in 2011 and 2014. However, several new articles since then have been published. In addition, their meta-analyses did not valuate the credibility of significantly positive results. Objectives: We performed an updated meta-analysis to solve the controversy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Methods: False-positive report probability (FPRP), Bayesian false discovery probability (BFDP), and the Venice criteria were used to verify the credibility of meta-analyses. Results: Twenty-three publications including 5734 LC cases and 7066 controls met the inclusion criteria in the present study. A significantly increased risk of LC was found in overall analysis, Asians and Indians. However, all positive results were considered as ‘less-credible’ when we used the Venice criteria, FPRP, and BFDP test to assess the credibility of the positive results. Conclusion: These positive findings should be interpreted with caution and results indicate that significant associations may be less-credible, there are no significantly increased LC risk between the combined effects of GSTM1 present/null and CYP1A1 MspI polymorphisms.

Systematic meta-analyses, field synopsis and global assessment of the evidence of genetic association studies in colorectal cancer

ObjectiveTo provide an understanding of the role of common genetic variations in colorectal cancer (CRC) risk, we report an updated field synopsis and comprehensive assessment of evidence to catalogue all genetic markers for CRC (CRCgene2).DesignWe included 869 publications after parallel literature review and extracted data for 1063 polymorphisms in 303 different genes. Meta-analyses were performed for 308 single nucleotide polymorphisms (SNPs) in 158 different genes with at least three independent studies available for analysis. Scottish, Canadian and Spanish data from genome-wide association studies (GWASs) were incorporated for the meta-analyses of 132 SNPs. To assess and classify the credibility of the associations, we applied the Venice criteria and Bayesian False-Discovery Probability (BFDP). Genetic associations classified as ‘positive’ and ‘less-credible positive’ were further validated in three large GWAS consortia conducted in populations of European origin.ResultsWe initially identified 18 independent variants at 16 loci that were classified as ‘positive’ polymorphisms for their highly credible associations with CRC risk and 59 variants at 49 loci that were classified as ‘less-credible positive’ SNPs; 72.2% of the ‘positive’ SNPs were successfully replicated in three large GWASs and the ones that were not replicated were downgraded to ‘less-credible’ positive (reducing the ‘positive’ variants to 14 at 11 loci). For the remaining 231 variants, which were previously reported, our meta-analyses found no evidence to support their associations with CRC risk.ConclusionThe CRCgene2 database provides an updated list of genetic variants related to CRC risk by using harmonised methods to assess their credibility.