Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis (MA). Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model (BGLMM) can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this article, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios against the BGLMM and conventional two-stage MA that excludes DZS. Through extensive simulation studies and real-world MA case studies, we demonstrate that ZIBGLMM outperforms the BGLMM and conventional two-stage MA that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

Machine learning (ML) models have been developed to identify randomised controlled trials (RCTs) to accelerate systematic reviews (SRs). However, their use has been limited due to concerns about their performance and practical benefits. We developed a high-recall ensemble learning model using Cochrane RCT data to enhance the identification of RCTs for rapid title and abstract screening in SRs and evaluated the model externally with our annotated RCT datasets. Additionally, we assessed the practical impact in terms of labour time savings and recall improvement under two scenarios: ML-assisted double screening (where ML and one reviewer screened all citations in parallel) and ML-assisted stepwise screening (where ML flagged all potential RCTs, and at least two reviewers subsequently filtered the flagged citations). Our model achieved twice the precision compared to the existing SVM model while maintaining a recall of 0.99 in both internal and external tests. In a practical evaluation with ML-assisted double screening, our model led to significant labour time savings (average 45.4%) and improved recall (average 0.998 compared to 0.919 for a single reviewer). In ML-assisted stepwise screening, the model performed similarly to standard manual screening but with average labour time savings of 74.4%. In conclusion, compared with existing methods, the proposed model can reduce workload while maintaining comparable recall when identifying RCTs during the title and abstract screening stages, thereby accelerating SRs. We propose practical recommendations to effectively apply ML-assisted manual screening when conducting SRs, depending on reviewer availability (ML-assisted double screening) or time constraints (ML-assisted stepwise screening).

The study aims were to present in vitro susceptibilities of clinical isolates from Gram-negative bacteria bloodstream infections (GNBSI) collected in China. GNBSI isolates were collected from 18 tertiary hospitals in 7 regions of China from 2018 to 2020. Minimum inhibitory concentrations were assessed using a Trek Diagnostic System. Susceptibility was determined using CLSI broth microdilution, and breakpoints were interpreted using CLSI M100 (2021). A total of 1,815 GNBSI strains were collected, with E. coli (42.4%) and Klebsiella pneumoniae (28.6%) being the most prevalent species, followed by P. aeruginosa (6.7%). Susceptibility analyses revealed low susceptibilities (<40%) of ESBL-producing E. coli and K. pneumonia to third-/fourth-generation cephalosporins, monobactamases, and fluoroquinolones. High susceptibilities to colistin (95.0%) and amikacin (81.3%) were found for K. pneumoniae, while Acinetobacter baumannii exhibited a high susceptibility (99.2%) to colistin but a low susceptibility to other antimicrobials (<27.5%). Isolates from ICUs displayed lower drug susceptibility rates of K. pneumoniae and A. baumannii than isolates from non-ICUs (all P < 0.05). Carbapenem-resistant and ESBL-producing K. pneumoniae detection was different across regions (both P < 0.05). E. coli and K. pneumoniae were major contributors to GNBSI, while A. baumannii exhibited severe drug resistance in isolates obtained from ICU departments.

Obesity, a global health issue, is associated with numerous diseases and has been shown to affect male reproductive health by inducing endocrine hormonal changes, chronic inflammation, oxidative stress and epigenetic alterations in reproductive cells. This study investigates the impact of obesity on testicular gene expression across mice, monkeys and humans, identifying 730 conserved testis-specific genes. High-fat diet-induced obesity upregulates GNG5, INHA, MSH5, SLC30A8 and SLC7A4 in testes, suggesting their potential as regulatory targets in testicular damage associated with obesity. Single-cell analysis reveals species-conserved expression patterns of SLC7A4 in Sertoli cells and SLC30A8 in SPG cells. It also confirmed that SLC30A8 and SLC7A4 were significantly upregulated in the testes of spontaneously obese mice. The findings highlight the potential of these genes as regulatory targets in obesity-related testicular dysfunction, providing insights into male reproductive health impairments caused by obesity.

In this paper, we develop an analytical model to investigate the generation of instability waves triggered by the upstream acoustic forcing near the nozzle lip of a supersonic jet. This represents an important stage, i.e. the jet receptivity, of the screech feedback loop. The upstream acoustic forcing, resulting from the shock-instability interaction (SII), reaches the nozzle lip and excites new shear-layer instability waves. To obtain the newly excited instability wave, we first determine the scattered sound field due to the upstream forcing using the Wiener–Hopf technique, with the kernel function factored using asymptotic expansions and overlapping approximations. Subsequently, the unsteady Kutta condition is imposed at the nozzle lip, enabling the derivation of the dispersion relation for the newly excited instability wave. A linear transfer function between the upstream forcing and the newly excited instability wave is obtained. We calculate the amplitude and phase delay in this receptivity process and examine their variations against the frequency. The analytically obtained phase delay enables us to evaluate the phase condition for jet screech and predict the screech frequency accordingly. The results show improved agreement with the experimental data compared with classical models. It is hoped that this model may help in developing a full screech model.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

The rise of online voicing and campaigns empowered by digital technologies and online social media is rejuvenating retail investor activism that has been mostly ignored in the traditional offline setting. This article argues that online activism that is initiated by retail investors will affect managerial attention intensity and attention priority on environmental issues, thus promoting green innovation. Using a Chinese-listed companies database with 13,795 firm-year observations over the period from 2011 to 2018, our results confirm that online environmental activism induces corporate green innovation. Online activism is more effective when the retail investor base holds larger shares in total and presents questions with a more intensely negative tone. Additionally, the above-mentioned moderating effects are stronger in digital firms. Our study offers insights into the online patterns of shareholder activism in the digital era and highlights the role of minority voicing in promoting corporate sustainable transformation.

Contrafreeloading (CFL) refers to animals’ tendency to prefer obtaining food through effort rather than accessing food that is freely available. Researchers have proposed various hypotheses to explain this intriguing phenomenon, but few studies have provided a comprehensive analysis of the factors influencing this behaviour. In this study, we observed the choice of alternative food containers in budgerigars (Melopsittacus undulatus) to investigate their CFL tendencies and the effects of pre-training, food deprivation, and effort required on the CFL tasks. The results showed that budgerigars did not exhibit significant difference in their first choices or the time interacting with less challenging versus more challenging food containers. Moreover, when evaluating each budgerigar’s CFL level, only half of them were identified as strong contrafreeloaders. Thus, we suggest that budgerigars exhibit an intermediate CFL level that lies somewhere between a strong tendency and the absence of such behaviour. Furthermore, we also found that food-deprived budgerigars tended to select less challenging food containers, and pre-trained budgerigars were more likely to choose highly challenging food containers than moderately challenging food containers, which means that the requirement of only a reasonable effort (access to food from moderately challenging food containers in this study) and the experience of pre-training act to enhance their CFL levels, whereas the requirement of greater effort and the experience of food deprivation act to decrease their CFL levels. Studying animal CFL can help understand why animals choose to expend effort to obtain food rather than accessing it for free, and it also has implications for setting feeding environments to enhance the animal welfare of captive and domesticated animals.

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

The widespread distribution of wolf poison (Stellera chamaejasme L.), spanning from southern Russia to southwestern China and the western Himalayas, contributes to its prevalence as an invasive species in grassland ecosystems. Its extensive range, coupled with its ability to thrive in harsh environments, enables it to rapidly colonize grasslands. Once established, it rapidly spreads and dominates large areas. This process inevitably leads to grassland degradation over time, thereby exerting significant impacts on both ecology and economy. In China, grasslands (26.45 million hectares, 27.5% of land area) face severe degradation, with over 90% impacted by overgrazing and climate change. S. chamaejasme infestations exceed 1.4 million hectares in Qinghai, 546,700 in Gansu, and 133,000 in Inner Mongolia, causing annual forage losses of 137.5 million kg and economic damages of 15–20 million yuan in Gansu alone. These impacts threaten ecosystem stability and pastoral livelihoods. Therefore, research on the spread mechanisms of invasive plants is crucial. In this comprehensive description, we investigated the effects of S. chamaejasme on plant communities and herbivore interactions. Our research showed how this species successfully invades grasslands and establishes itself as a dominant species. S. chamaejasme enhances Its expansion alters soil physicochemical properties, reduces nutrient cycling, and increases pathogenic fungi abundance while enhancing microbial diversity, creating self-favoring soil conditions. With high genetic diversity, robust reproductive capacity, and potent allelopathic effects, it suppresses neighboring vegetation and escapes herbivory due to toxicity, accelerating invasion. These interrelated traits facilitate the rapid invasion and spread of S. chamaejasme on grasslands, ultimately leading to its dominance. This trend poses a significant threat to the health and stability of the grassland ecosystem. Future research should delve into the ecological adaptability and allelopathic mechanisms of S. chamaejasme, aiming to develop effective management strategies for controlling its spread and promoting grassland recovery and biodiversity conservation.

The hydrodynamic behaviours of finite-size microorganisms in turbulent channel flows are investigated using a direct-forcing fictitious domain method. The classical ‘squirmer’ model, characterized by self-propulsion through tangential surface waves at its boundaries, is employed to mimic the swimming microorganisms. We adopt various simulation parameters, including a friction Reynolds number Reτ = 180, two squirmer volume fractions 𝜑0 = 12.7 % and 2.54 % and a blocking ratio (squirmer radius/half-channel width) κ = 0.125. Results show that pushers (propelled from the rear) induce a more pronounced decrease in the velocity profile than neutral squirmers and pullers (propelled from the front). This hindrance and the induced particle inner stress τpI positively correlate with the quantity of squirmers accumulated in the near-wall region. Notably, the increase in τpI primarily occurs at the expense of diminishing the fluid Reynolds stress τfR. Compared with passive spheres, a low volume fraction (𝜑0 = 2.54 %) of pullers results in a slightly enhanced velocity profile across the channel. In the near-wall region, the swimming direction of the squirmers shows no significant tendency with respect to the flow direction. In the bulk-flow region, pushers and neutral squirmers tend to align their axes more along the flow direction, whereas pullers exhibit a slight preference for alignment with the normal direction.