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Abstract Background Legionellosis remains a public health problem. The most common diagnostic method to detect Legionella pneumophila (L. pneumophila) is culture. Polymerase chain reaction (PCR) is a fast and accurate method for this detection in environmental samples. Methods Four databases were searched for studies that evaluated the detection efficiency of PCR in L. pneumophila. The quality evaluation was conducted using Review Manager 5.3. We used Meta-DiSc 1.4 software and the Stata 15.0 software to create forest plots, a meta-regression, a bivariate boxplot and a Deeks’ funnel plot. Results A total of 18 four-fold tables from 16 studies were analysed. The overall pooled sensitivity and specificity of PCR was 94% and 72%, respectively. The positive likelihood ratio (RLR) and negative likelihood ratio (NLR) was 2.73 and 0.12, respectively. The result of the diagnostic odds ratio (DOR) was 22.85 and the area under the curve (AUC) was 0.7884. Conclusion Establishing a laboratory diagnostic tool for L. pneumophila detection is important for epidemiological studies. In this work, PCR demonstrated a promising diagnostic accuracy for L. pneumophila.

Background There are no clear conclusions as to whether inflammatory proteins and plasma metabolites influence erectile dysfunction (ED). Aim In this research, we used Mendelian randomization (MR) analysis to discover a causal relationship between inflammatory proteins, plasma metabolites, and ED. Methods Raw data with ED, inflammatory proteins, and plasma metabolites were obtained from the MRC IEU OpenGWAS and FinnGen database. After a series of screenings, the remaining single nucleotide polymorphisms were selected as instrumental variables or MR analysis to assess the relationship between genetically predicted inflammatory proteins or plasma metabolites and the pathogenesis of ED. Outcomes The relationship between inflammatory factors and ED was fully analyzed and elaborated. Results In the inverse variance–weighted method, there exists a significant causal relationship between 4 types of genetically predicted inflammatory proteins and 50 types of plasma metabolites with the incidence of ED. The primary discovery is that 3 inflammatory proteins, fibroblast growth factor 5, interleukin-22 receptor subunit alpha-1, and protein S100-A12, can impact the risk of ED through plasma metabolites. Clinical Implications ED metabolites and inflammatory proteins are also closely associated with cardiovascular diseases, warranting further exploration. Strengths and Limitations Our analysis is based on a European population, limiting its generalizability, the genome-wide association study dataset for ED has a relatively small number of cases, and we hope for larger genome-wide association study datasets for future validation. Conclusion This study has identified that inflammatory proteins can influence ED through plasma metabolites. [ABSTRACT FROM AUTHOR]

As a novel signal control method, signal Cooperative Control with traffic Supply and Demand (CCSD) is superior to the traditional control methods and could satisfy the control requirements under all traffic conditions. However, the optimization solution of CCSD cannot meet the real-time control requirement for its exhaustive search. To overcome the problem, in this paper, the optimization problem of CCSD is reconstructed by the time-varying traffic supply and demand, and the problem is reduced as a problem of space search. Then, the krill herd (KH) algorithm is introduced and employed to realize the fast solution of CCSD (named KH-CCSD). During the process of optimization, the search space representation and fitness function for the KH algorithm are constructed to satisfy the solution of CCSD. The optimal signal timing plan is obtained by an iterated search of krill swarm in a multi-dimensional time-varying space cooperatively constrained by traffic supplies and demands. The convergence and effectiveness of KH-CCSD are validated by comparing experiments, in which the convergence of KH-CCSD is tested by different initializations and KH-CCSD is compared with the Webster method and capacity-aware back-pressure (CABP) control under unsaturated, saturated, and oversaturated conditions. The experiments results show that KH-CCSD performs a fast convergence and KH-CCSD is superior to the Webster method and CABP. As a result, KH-CCSD could satisfy the application of CCSD under all traffic conditions.

Traffic signal control is widely used at intersection to improve its operation efficiency. However, the existing signal control systems cannot satisfy the control requirements under unsaturated, saturated, and oversaturated conditions, which will induce queue spillover, even network deadlock. A signal Cooperative Control method with traffic Supply and Demand (CCSD) on a single intersection is put forward to maximize the efficiency and avoid queue spillover by the cooperation between traffic supply and demand. A general CCSD control framework is constructed by the control relationship description and discrete-time statespace equations. Furthermore, the uniform matrix description of CCSD is put forward under the framework to fast solve the problem by matrix calculation. An artificial intelligence planning model on CCSD is established by an objective function compromising between throughput and fairness to satisfy the control requirements under dynamic unknown traffic environment. CCSD is compared with the Webster method and capacity-aware back-pressure (CABP) control in the experiments by both the simulation data and investigation data under unsaturated, saturated, and oversaturated conditions. The results show that CCSD is superior to CABP control and the Webster method in the throughput, the number of stops, and stop time, and can avoid the queue spillover. Accordingly, CCSD can be used to improve the efficiency and avoid queue spillover at intersection under all traffic conditions.