Cervical cancer remains a major global health burden. Despite standard-of-care therapies, 30–50% of locally advanced-stage patients develop treatment resistance, leading to recurrence and mortality. While tumour-intrinsic mechanisms (e.g., DNA methylation, cancer-associated fibroblasts) explain only partial resistance heterogeneity, emerging evidence identifies the microbiome as a critical modulator of therapeutic efficacy. This review synthesizes recent advances demonstrating that vaginal microbial dysbiosis, characterized by Lactobacillus iners enrichment and L. crispatus depletion, drives resistance through lactate-mediated metabolic rewiring, immune checkpoint stabilization and drug metabolism alteration. Longitudinal studies reveal dynamic microbiome trajectories during therapy, with geographic variations (notably HIV co-infection in sub-Saharan Africa) further modulating treatment responses. We critically evaluate microbiome-based interventions, from probiotics to engineered bacteria, including synthetic biology-driven precision microbiome therapies, and establishing standardized multi-centre trial protocols. Bridging mechanistic insights with clinical application represents a paradigm shift towards microbiome-informed cervical cancer management.

African swine fever (ASF) is a highly contagious animal disease caused by African swine fever virus (ASFV). It is listed by the World Organization for Animal Health (WOAH) as an animal disease subject to statutory reporting. ASFV, a large, enveloped double-stranded DNA virus with high genomic complexity, exhibits a case fatality rate of up to 100%, posing a significant threat to the global pig industry and food safety. To date, the absence of a safe commercial ASFV vaccine primarily stems from challenges in identifying immunogenic viral antigens, insufficient characterization of ASFV pathogenesis, and limited understanding of the virus’s immune evasion mechanisms. Here, we review the pathogenic characteristics (morphological structure, clinical symptoms, and epidemiological characteristics), molecular biological characteristics, and infection mechanism of ASFV, as well as the immune response mechanism, vaccine research, and the latest information on ASFV in other areas. This review will be in favour of understanding the current state of knowledge of ASF and developing effective vaccines to control this disease.

Grasslands are one of the major ecosystem types in drylands. Encroachment of shrubs into grasslands affects the functioning of drylands by altering community structure, with impacts exacerbated under greater intensity of encroachment. Yet, we have a limited understanding of how ecosystem structure responds to the degree of shrub encroachment. Here, we describe a field-based study designed to examine changes in ecosystem structure beneath shrub patches (patch condition) and between patches (spatial distribution pattern of patches) along a gradient in encroachment in a semiarid grassland in Inner Mongolia, China. We found that greater encroachment was associated with wider and taller shrubs with more branches. As shrub encroachment intensified, the area beneath shrubs had more litter and was less exposed to grazing. The landscape was characterized by more discontinuous patches of vegetation and more bare ground as encroachment intensified. Either the patch condition or the patch spatial pattern was shaped mainly by the magnitude of shrub encroachment rather than by the structure of individual shrubs (e.g., height and canopy width). Our study highlights the idiosyncratic response of ecosystem structure (patch condition and patch spatial pattern) to intensifying encroachment, reinforcing the importance of considering the degree of shrub encroachment when managing encroached grasslands.

The present work investigates the thermochemical non-equilibrium effect in the DLR combustor using a two-temperature model combined with vibration-chemistry coupling model. Two operating conditions with inflow Mach 2 and 6 are selected for study. The simulation results illustrate that translational-vibrational non-equilibrium is related to energy transfer behaviour and the translational-vibrational relaxation time. When kinetic energy and chemical energy are converted into internal energy, there is a significant difference in the degree of conversion to translational and vibrational energy. If the translational-vibrational relaxation time is larger than the flow time, such as the relaxation time of the mainstream aftershock wave is 0.25 s for the condition with inflow Mach 2, and the flow time is 3 × 10−5 s, non-equilibrium will occur. Significant differences exist between the flow fields with Mach 2 and 6. A clear boundary layer separation occurs at Mach 6. Combustion occurs at the shear layer, which is in translational-vibrational equilibrium, and there are varying degrees of non-equilibrium in other locations. The dissociation of N2 and production of NO primarily occur on the strut walls and the upper/lower walls of the combustor. The mass fraction of NO is higher than the value at Mach 2. The combustion performance is influenced by the thermochemical non-equilibrium effect. At the condition of Mach 2, it increases the combustion efficiency by 10% near the injector and 0.27% at outlet relatively. Non-equilibrium inhibits the initial upstream combustion while slightly promoting downstream combustion under inflow Mach 6 condition.

The successful colonization of invasive plants (IPs) may be facilitated by their nutrient release during decomposition, which alters soil physicochemical properties, enzyme activities, microbial metabolic processes and the diversity of soil microorganisms. This study aimed to examine the effects of co-decomposition of four Asteraceae IPs (Conyza canadensis, Conyza sumatrensis, Erigeron annuus and Solidago canadensis) along a gradient of invasion and a native plant (Pterocypsela laciniata) on decomposition rate, soil physicochemical properties, soil enzyme activities and the diversity of soil bacterial communities (SBCs). Leaves of C. canadensis with heavy invasion and S. canadensis with light and heavy invasion decomposed more slowly than P. laciniata. Leaves of C. canadensis with full invasion decomposed more rapidly than P. laciniata. Pterocypsela laciniata and C. sumatrensis had synergistic effects on each other’s decomposition, whereas P. laciniata and S. canadensis displayed an antagonistic effect. Decomposition of the four IPs increased soil microbial carbon content but reduced soil fluorescein diacetate (FDA) hydrolase activity compared to P. laciniata. Thus, invasion degree and species identity of IPs modulate the effects of the four IPs on the decomposition rate, mixed-effect intensity of co-decomposition, soil microbial carbon content, soil FDA hydrolase activity and SBC structure.

Entangled vortex filaments are essential to turbulence, serving as coherent structures that govern nonlinear fluid dynamics and support the reconstruction of fluid fields to reveal statistical properties. This study introduces a quantum implicit representation of vortex filaments in turbulence, employing a levelset method that models the filaments as the intersection of the real and imaginary zero iso-surfaces of a complex scalar field. Describing the fluid field via the scalar field offers distinct advantages in capturing complex structures, topological properties and fluid dynamics, while opening new avenues for innovative solutions through quantum computing platforms. The representation is reformulated into an eigenvalue problem for Hermitian matrices, enabling the conversion of velocity fields into complex scalar fields that embed the vortex filaments. The resulting optimisation is addressed using a variational quantum eigensolver, with Pauli operator truncation and deep learning techniques applied to improve efficiency and reduce noise. The proposed quantum framework achieves a near-linear time complexity and a exponential storage reduction while maintaining a balance of accuracy, robustness and versatility, presenting a promising tool for turbulence analysis, vortex dynamics research, and machine learning dataset generation.

The extracellular matrices, such as the haemolymph, in insects are at the centre of most physiological processes and are protected from oxidative stress by the extracellular antioxidant enzymes. In this study, we identified two secreted superoxide dismutase genes (PxSOD3 and PxSOD5) and investigated the oxidative stress induced by chlorpyrifos (CPF) in the aquatic insect Protohermes xanthodes (Megaloptera: Corydalidae). PxSOD3 and PxSOD5 contain the signal peptides at the N-terminus. Structure analysis revealed that PxSOD3 and PxSOD5 contain the conserved CuZn-SOD domain, which is mainly composed of β-sheets and has conserved copper and zinc binding sites. Both PxSOD3 and PxSOD5 are predicted to be soluble proteins located in the extracellular space. After exposure to different concentrations of sublethal CPF, MDA content in P. xanthodes larvae were increased in a dose-dependent manner; SOD and CAT activities were also higher in CPF-treated groups than that in the no CPF control, indicating that sublethal CPF induces oxidative stress in P. xanthodes larvae. Furthermore, PxSOD3 and PxSOD5 expression levels and haemolymph SOD activity in the larvae were downregulated by sublethal CPF at different concentrations. Our results suggest that the PxSOD3 and PxSOD5 are putative extracellular antioxidant enzymes that may play a role in maintaining the oxidative balance in the extracellular space. Sublethal CPF may induce oxidative stress in the extracellular space of P. xanthodes by reducing the gene expression and catalytic activity of extracellular SODs.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial membrane, leading to cartilage destruction and bone erosion. Due to the complex pathogenesis of RA and the limitations of current therapies, increasing research attention has been directed towards novel strategies targeting fibroblast-like synoviocytes (FLS), which are key cellular components of the hyperplastic pannus. Recent studies have highlighted the pivotal role of FLS in the initiation and progression of RA, driven by their tumour-like transformation and the secretion of pro-inflammatory mediators, including cytokines, chemokines and matrix metalloproteinases. The aggressive phenotype of RA-FLS is marked by excessive proliferation, resistance to apoptosis, and enhanced migratory and invasive capacities. Consequently, FLS-targeted therapies represent a promising avenue for the development of next-generation RA treatments. The efficacy of such strategies – particularly those aimed at modulating FLS signalling pathways – has been demonstrated in both preclinical and clinical settings, underscoring their therapeutic potential. This review provides an updated overview of the pathogenic mechanisms and functional roles of FLS in RA, with a focus on critical signalling pathways under investigation, including Janus kinase/signal transducer and activator of transcription (JAK/STAT), mitogen-activated protein kinase (MAPK), nuclear factor kappa B (NF-κB), Notch and interleukin-1 receptor-associated kinase 4 (IRAK4). In addition, we discuss the emerging understanding of FLS-subset-specific contributions to immunometabolism and explore how computational biology is shaping novel targeted therapeutic strategies. A deeper understanding of the molecular and functional heterogeneity of FLS may pave the way for more effective and precise therapeutic interventions in RA.

Variable topological space robots are essential for providing adaptability and flexibility, enabling the robot to adjust its morphology to perform a range of tasks in the unstructured environment of space. However, impact is a common consequence of topology transformation in space robotics, which may lead to irreversible damage, such as the shedding of solid lubrication on joints. Nevertheless, determining the precise force-time relationships of such impacts poses significant challenges, especially when accounting for various connection mechanisms. In this work, a docking strategy that optimizes the manipulator’s joint angle configuration to minimize the impulse when the topology changes is proposed. First, an estimation technique is developed to quantify the impulse generated by topology transformation, employing spatial operator algebra and generalized momentum balance equations. Based on this model, the impulse minimization is modelled as a bilevel optimization problem, which decomposes a complex multipolar problem into two simpler subproblems. Although this optimization model may compromise computational efficiency, it increases the probability of achieving an optimal solution. To address this, a bilevel solution strategy based on a heuristic algorithm is proposed. In this framework, the lower level uses particle swarm optimization to determine the global optimum, while the upper level adopts simulated annealing to enhance computational speed. Finally, simulations are conducted to validate the proposed approach. Results demonstrate that the proposed method substantially reduces impulse.

We present a high-power mid-infrared single-frequency pulsed fiber laser (SFPFL) with a tunable wavelength range from 2712.3 to 2793.2 nm. The single-frequency operation is achieved through a compound cavity design that incorporates a germanium etalon and a diffraction grating, resulting in an exceptionally narrow seed linewidth of approximately 780 kHz. Employing a master oscillator power amplifier configuration, we attain a maximum average output power of 2.6 W at 2789.4 nm, with a pulse repetition rate of 173 kHz, a pulse energy of 15 μJ and a narrow linewidth of approximately 850 kHz. This achievement underscores the potential of the mid-infrared SFPFL system for applications requiring high coherence and high power, such as high-resolution molecular spectroscopy, precision chemical identification and nonlinear frequency conversion.

Random effects meta-analysis model is an important tool for integrating results from multiple independent studies. However, the standard model is based on the assumption of normal distributions for both random effects and within-study errors, making it susceptible to outlying studies. Although robust modeling using the t distribution is an appealing idea, the existing work, that explores the use of the t distribution only for random effects, involves complicated numerical integration and numerical optimization. In this article, a novel robust meta-analysis model using the t distribution is proposed (tMeta). The novelty is that the marginal distribution of the effect size in tMeta follows the t distribution, enabling that tMeta can simultaneously accommodate and detect outlying studies in a simple and adaptive manner. A simple and fast EM-type algorithm is developed for maximum likelihood estimation. Due to the mathematical tractability of the t distribution, tMeta frees from numerical integration and allows for efficient optimization. Experiments on real data demonstrate that tMeta is compared favorably with related competitors in situations involving mild outliers. Moreover, in the presence of gross outliers, while related competitors may fail, tMeta continues to perform consistently and robustly.

CEOs who develop strong clan values as a result of exposure to clan culture in early life wish to bring honor to their clan, motivating them to engage in increased CSR activities. We propose that the influence of CEOs' clan values on CSR is subject to contextual boundaries. Specifically, we predict that the positive relationship between CEOs' clan values and CSR results primarily in an improved level of institutional CSR and varies with CEOs' personal attributes such as overseas experience and hometown identity. An analysis of a longitudinal sample of Chinese publicly listed firms for 2010–2019 provides strong support for our predictions. The implications for upper echelons theory and CSR research are discussed.

Precise and efficient grasping detection is vital for robotic arms to execute stable grasping tasks in industrial and household applications. However, existing methods fail to consider refining different scale features and detecting critical regions, resulting in coarse grasping rectangles. To address these issues, we propose a real-time coarse and fine granularity residual attention (CFRA) grasping detection network. First, to enable the network to detect different sizes of objects, we extract and fuse the coarse and fine granularity features. Then, we refine these fused features by introducing a feature refinement module, which enables the network to distinguish between object and background features effectively. Finally, we introduce a residual attention module that handles different shapes of objects adaptively, achieving refined grasping detection. We complete training and testing on both Cornell and Jacquard datasets, achieving detection accuracy of 98.7% and 94.2%, respectively. Moreover, the grasping success rate on the real-world UR3e robot achieves 98%. These results demonstrate the effectiveness and superiority of CFRA.