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ObjectiveHyperuricaemia and gout are common metabolic disorders. However, the causal relationships between blood metabolites and serum urate levels, as well as gout, remain unclear. A systematic evaluation of the causal connections between blood metabolites, hyperuricemia, and gout could enhance early screening and prevention of hyperuricemia and gout in clinical settings, providing novel insights and approaches for clinical treatment.MethodsIn this study, we employed a bidirectional two-sample Mendelian randomization analysis utilizing data from a genome-wide association study involving 7,286 participants, encompassing 486 blood metabolites. Serum urate and gout data were sourced from the Chronic Kidney Disease Genetics consortium, including 288,649 participants for serum urate and 9,819 African American and 753,994 European individuals for gout. Initially, LDSC methodology was applied to identify blood metabolites with a genetic relationship to serum urate and gout. Subsequently, inverse-variance weighting was employed as the primary analysis method, with a series of sensitivity and pleiotropy analyses conducted to assess the robustness of the results.ResultsFollowing LDSC, 133 blood metabolites exhibited a potential genetic relationship with serum urate and gout. In the primary Mendelian randomization analysis using inverse-variance weighting, 19 blood metabolites were recognized as potentially influencing serum urate levels and gout. Subsequently, the IVW p-values of potential metabolites were corrected using the false discovery rate method. We find leucine (IVW P FDR = 0.00004), N-acetylornithine (IVW P FDR = 0.0295), N1-methyl-3-pyridone-4-carboxamide (IVW P FDR = 0.0295), and succinyl carnitine (IVW P FDR = 0.00004) were identified as significant risk factors for elevated serum urate levels. Additionally, 1-oleoylglycerol (IVW P FDR = 0.0007) may lead to a substantial increase in the risk of gout. Succinyl carnitine exhibited acceptable weak heterogeneity, and the results for other blood metabolites remained robust after sensitivity, heterogeneity, and pleiotropy testing. We conducted an enrichment analysis on potential blood metabolites, followed by a metabolic pathway analysis revealing four pathways associated with serum urate levels.ConclusionThe identified causal relationships between these metabolites and serum urate and gout offer a novel perspective, providing new mechanistic insights into serum urate levels and gout.

The panel vector autoregression (PVAR) model preserves the advantages of the vector autoregression model while expanding its time series to the spatial direction, which can effectively solve the problem of individual heterogeneity using panel data. It is derived from econometrics but has been applied interdisciplinarily because of its advantages in metrology. Given its increasingly important role in econometrics and interdisciplinary applications, a systematic review based on the bibliometric tool was conducted by screening 292 articles related to PVAR from the Web of Science. First, a descriptive analysis of the related articles was conducted to identify the current research status of PVAR. It reveals that macroeconomic effects, economic growth and environmental protection, and model adaptation are the primary topics in PVAR-related research. Then, the study classifies PVAR models into three categories and summarizes the four estimation methods within the knowledge domain. Having clarity on the different categories and estimation methods enhances the practical utility of the PVAR model. Finally, to gain insight into the knowledge evolution of PVAR, this study discusses how research hotspots in the field have evolved over time. This analysis provides a historical perspective and allows researchers to anticipate future trends and emerging areas of interest within PVAR. Based on these findings, this study identifies three research opportunities that can guide future investigations in the field of PVAR. This study aims to foster extension applications of the model in econometric research and highlight its potential for interdisciplinary applications.

ObjectiveObservational studies have suggested an increased risk of cardiovascular disease in individuals with ankylosing spondylitis. However, these studies are prone to confounding factors and reverse causality. To address these limitations, we conducted a Mendelian randomization study to assess the causal relationship between AS and CVD.MethodsThe study population comprises 9,069 individuals with ankylosing spondylitis and 509,093 individuals with either of six common cardiovascular diseases and a related indicator. Causal analysis using summary effect estimates and inverse variance weighting were employed as the main methods.ResultsThe CAUSE analysis showed no evidence of a causal relationship between AS and CVD. The odds ratios for total CVD, heart failure, myocardial infarction, valvular heart disease, ischemic heart disease, and venous thromboembolism, Arterial stiffness index, were as follows: OR, 1.01; 95% confidence interval, 0.96–1.05; P = 0.91; OR, 1.03; 95% CI, 0.99–1.08; P = 0.50; OR, 0.94; 95% CI, 0.86–1.03; P = 0.53; OR, 0.99; 95% CI, 0.94–1.04; P = 0.99; OR, 0.98; 95% CI, 0.91–1.04; P = 0.94; OR, 0.98; 95% CI, 0.91–1.04; P = 0.99; β, −0.0019; 95% CI, 0.97–1.01; P = 0.99. The IVW and weighted median methods also yielded consistent results, and no heterogeneity or pleiotropy was found. Likewise, a reverse Mendelian randomization analysis did not uncover a heritable causal relationship between AS and CVD.ConclusionThis Mendelian randomization study does not support a causal relationship between AS and CVD. Further research is needed to confirm this association.

Abstract Background The use of cardiac implantable electronic devices has grown substantially over the past two decades, lead-related vascular issues are commonly encountered in clinical practice. Superior vena cava (SVC) syndrome due to pacemaker leads is an uncommon complication. Anticoagulation remains the mainstay of therapy to restore some degree of patency and relieve swelling. However, there are limited clinical trials on direct oral anticoagulants (DOACs). Case presentation We report a case of an 80-year-old man who developed SVC syndrome after transvenous pacemaker implantation with symptoms of obstruction that were significantly relieved after four months of DOACs. His symptoms had completely resolved nine months later. Conclusions DOACs are effective in the treatment of SVC syndrome after pacemaker implantation, representing an important new approach. It is a very good choice for patients who do not want to undergo interventional therapy.

Ecological efficiency (EE) provides much reference for formulating appropriate regional economic, social and environmental policies to promote sustainable development. Interactive subsystems of economy, society and environment within EE system have been considered in this paper. By innovatively integrating the merits of two advanced economic research methods (global super efficiency network data envelopment analysis (GSE-NDEA) and panel vector autoregression (PVAR) and updating the EE evaluation indicator system by following the new features of sustainable development in the recent China, this paper comprehensively evaluates EE by drawing evidence from 3 regions in China during the period of 2011–2020, and further reveals how the three subsystems within EE system interact to achieve EE enhancement. The findings show EE and its three subsystems’ trend, the major constrains of EE development, the regional discrepancies in EE progress, and the interactions among the subsystems of economy-society-environment within the EE system in different regions of China. The policy implications are proposed accordingly. First published online 15 November 2022

The high-quality development of regional economic system is inseparable from the collective efforts of multiple economic sectors. Increasing attention has been paid to the environmental performance evaluation of different administrative levels or economic sectors, but integrated research is scarce. Taking the three industries (the primary, secondary and tertiary industries) into account, this paper proposes a data envelopment analysis (DEA) model with parallel network structure to assess the environmental performance of 30 provinces in China from integrative perspective of efficiency and productivity. Then, the Tobit model is adopted to investigate the effects of external factors on the environmental performance. The results show that environmental efficiency of Chinese economy is only 0.4436 during 2010–2019 and the performance of the secondary industry is the highest, followed by the tertiary and the primary industries. Moreover, the environmental efficiency of eastern region is far higher than that of the central or western regions. Technological progress is the main driver of environmental productivity improvement for China’s economic system. Most of the external factors such as energy structure and technology innovation, have different effects on the environmental performance of different regions. Finally, several targeted policy implications are suggested for improving the environmental performance of China’s economic system.

Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which means that the centralized server can access and compromise user privacy. (2) Expensive resources required: conventional collaborative filtering techniques require a server with powerful computing capacity and large storage space, so that the server can train and maintain the model. (3) Considering only one form of user feedback: most existing works aim to model user preferences based on explicit feedback (e.g., ratings) or implicit feedback (e.g., purchase history, viewing history) due to their heterogeneous representation; however, these two forms of feedback are abundant in most collaborative filtering applications, can both affect the model, and very few works studied the simultaneous use thereof. To solve the above problems, in this study we focus on implementing decentralized probabilistic matrix factorization for privacy-preserving recommendations. First, we explore the existing collaborative filtering algorithms and propose a probabilistic matrix co-factorization model. By integrating explicit and implicit feedback into a shared probabilistic model, the model can cope with the heterogeneity between these two forms of feedback. Further, we devise a decentralized learning method that allows users to keep their private data on the end devices. A novel decomposing strategy is proposed for users to exchange only non-private information, in which stochastic gradient descent is used for updating the models. Complexity analysis proves that our method is highly efficient with linear computation and communication complexity. Experiments conducted on two real-world datasets FilmTrust and Epinions show that our model gains a guarantee of convergence as the RMSE decreases quickly within 100 rounds of iterations. Compared with the state-of-the-art models, our model achieves lower model loss in rating prediction task and higher precision in item recommendation task.

The dock-less shared bike systems provide a convenient transportation mode for users to find, ride, or return a bike anywhere via GPS-based smartphone apps, with the bike position turmoil arises as side effects. To solve this problem, the geofence technology has been explored and then equipped in the ride-sharing service. However, the inadequate utilization and unreasonable distribution of the geographical resource impact the effectiveness of the geofence sites seriously. In this paper, we propose a collaborative geofence site selection (CGSS), which first pick up the hotspots based on a density-based and collaboration-inspired method, and then allocate the geofence sites in the top-ranking hotspots. The CGSS aims to optimize the distribution location and the occupied area for each geofence in the city, so as to maximize the satisfactory degree of customers for both coverage ratio and capacity, with the total supplied geographical area for building geofence sites. The experimental results show that the CGSS method distributes geofence sites with a highly satisfactory degree and utilization rate.

Periodical broadcasts of individual vehicle's safety information such as velocity and acceleration are critical in vehicular ad hoc networks (VANETs). However, current contention-based media access control (MAC) protocols cannot guarantee high performance in high density VANETs. In this paper, we propose a MAC protocol based on s -disjunct code to achieve reliable and real-time broadcasts via assigning a channel to each vehicle as the vehicle enters the network. Our protocol is adaptive as vehicles are required to adjust their communication ranges according to the network density in order to avert the interference among them. We conduct an extensive simulation study, and our results indicate that the proposed scheme can successfully achieve the requirements of the dedicated short range communication (DSRC) standard, that is, to broadcast safety information 10 times per second. Even at a high vehicle density, our scheme still guarantees that the success rates of both transmission and reception are greater than 95%, and the collision rates of access collision and merging collision are lower than 10 collisions/period.

Minimizing the communication overhead is always a hot topic in wireless sensor networks. In a multirate query system, data sources disseminate the data streams to users at the frequency they request. However, sending data in different frequencies to individual users is very costly. We address this problem by broadcasting a single consolidated data stream, aiming at reducing the amount of transmitted data. Taking into account the data correlation, we can reconstruct the data streams at lower frequencies from the consolidated stream at a higher frequency. In this paper, we propose an energy-efficient framework to process multirate queries and investigate the path-sharing routing tree construction method together with the rate conversion mechanism. We evaluate both the accuracy and energy efficiency by simulation. Simulation results indicate that with a reasonable level of tolerance, the performance gain is significant. As far as we know, this is the first energy-efficient solution for multirate query in wireless sensor networks.