Arbuscular mycorrhizal fungi counteract the Janzen-Connell effect of soil pathogens

Soilborne pathogens can contribute to diversity maintenance in tree communities through the Janzen-Connell effect, whereby the pathogenic reduction of seedling performance attenuates with distance from conspecifics. By contrast, arbuscular mycorrhizal fungi (AMF) have been reported to promote seedling performance; however, it is unknown whether this is also distance dependent. Here, we investigate the distance dependence of seedling performance in the presence of both pathogens and AMF. In a subtropical forest in south China, we conducted a four-year field census of four species with relatively large phylogenetic distances and found no distance-dependent mortality for newly germinated seedlings. By experimentally separating the effects of AMF and pathogens on seedling performance of six subtropical tree species in a shade house, we found that soil pathogens significantly inhibited seedling survival and growth while AMF largely promoted seedling growth, and these effects were host specific and declined with increasing conspecific distance. Together, our field and experimental results suggest that AMF can neutralize the negative effect of pathogens and that the Janzen-Connell effect may play a less prominent role in explaining diversity of nondominant tree species than previously thought

Effects of Intranasal Oxytocin on Pup Deprivation-Evoked Aberrant Maternal Behavior and Hypogalactia in Rat Dams and the Underlying Mechanisms

Oxytocin (OT), a hypothalamic neuropeptide, applied through nasal approach (IAO), could improve maternal health during lactation that is disrupted by mother–baby separation; however, the regulation of IAO effects on maternal behaviors and lactation as well as the underlying mechanisms remain unclear. Using lactating rats, we observed effects of intermittent pup deprivation (PD) with and without IAO on maternal behaviors and lactation as well as the activity of OT neurons in the supraoptic nucleus (SON) and the activity of hypothalamic pituitary-adrenal axis, key factors determining the milk-letdown reflex during lactation and maternal behaviors. The results showed that PD reduced maternal behaviors and lactation efficiency of rat dams as indicated by significantly longer latency to retrieve their pups and low litter’s body weight gains during the observation, respectively. In addition, PD caused early involution of the mammary glands. IAO partially improved these changes in rat dams, which was not as significant as IAO effects on control dams. In the SON, PD decreased c-Fos and increased glial fibrillary acidic protein (GFAP) filaments significantly; IAO made PD-evoked c-Fos reduction insignificant while reduced GFAP filament significantly in PD dams. IAO tended to increase the levels of phosphorylated extracellular signal-regulated kinases (pERK) 1/2 in PD dams. Moreover, PD+IAO significantly increased plasma levels of dam adrenocorticotropic hormone and corticosterone but not OT levels. Lastly, PD+IAO tended to increase the level of corticotropin-releasing hormone in the SON. These results indicate that PD disrupts maternal behaviors and lactation by suppressing the activity of hypothalamic OT-secreting system through expansion of astrocytic processes, which are partially reversed by IAO through removing astrocytic inhibition of OT neuronal activity. However, the improving effect of IAO on the maternal health could be compromised by simultaneous activation of hypothalamic pituitary-adrenocortical axis

Heterogeneous and Competitive Multiagent Networks: Couple-Group Consensus with Communication or Input Time Delays

This paper discusses the couple-group consensus problems for a class of heterogeneous multiagent networks including the following two cases: with communication and input time delays, respectively. Different from the related cooperative networks, two novel delayed group consensus protocols are designed based on the competitive relationship between the agents. Furthermore, we absolutely relax the in-degree balance and other restrictive preconditions which existed in the relevant works. Some sufficient algebraic criteria for the achievement of couple-group consensus and the upper bound of the input time delays are technically obtained via the frequency domain method and matrix theory, respectively. The results show that the achievement of the couple-group consensus depends on the second-order agents’ in-degree and the control parameters of the systems, whereas it is independent of the communication time delays. Meanwhile, the upper bound of the input time delay is determined by the control parameters and the in-degree of the first-order agents. Finally, the validity of the proposed results is verified by several simulated examples

Quaternion-based attitude synchronisation for multiple rigid bodies in the presence of actuator saturation

This paper concentrates on the quaternion-based attitude synchronisation problems of networked rigid bodies under fixed and undirected communication topology without relative angular measurements in the presence of actuator saturation. We first consider the leaderless attitude synchronisation problem with zero final angular velocity. In this case, we not only discuss the performance under the acyclic communication topology with the proposed bounded control algorithm, but also analyse that if there exist cycles in the topology, the proposed bounded algorithm guarantees that all equilibrium points are unstable except that the attitudes of networked rigid bodies achieve synchronisation. We also expand the result to the case of attitude tracking synchronisation with a static leader in the presence of actuator saturation. Next, the tracking synchronisation problem with the desired time-varying attitude is addressed in the presence of actuator saturation. Numerical examples are provided to validate the effectiveness of the proposed bounded schemes and illustrate the performances of multiple rigid bodies.This work was partially supported by National Natural Science Foundation of China[grant number 61375072] and Nature Science Foundation of Zhejiang Province [grant number LQ16F030005]

A Method of L1-Norm Principal Component Analysis for Functional Data

Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracted attention. Functional data principal component analysis (FPCA), as an unsupervised machine learning method, plays a vital role in the analysis of functional data. FPCA is the primary step for functional data exploration, and the reliability of FPCA plays an important role in subsequent analysis. However, classical L2-norm functional data principal component analysis (L2-norm FPCA) is sensitive to outliers. Inspired by the multivariate data L1-norm principal component analysis methods, we propose an L1-norm functional data principal component analysis method (L1-norm FPCA). Because the proposed method utilizes L1-norm, the L1-norm FPCs are less sensitive to the outliers than L2-norm FPCs which are the characteristic functions of symmetric covariance operator. A corresponding algorithm for solving the L1-norm maximized optimization model is extended to functional data based on the idea of the multivariate data L1-norm principal component analysis method. Numerical experiments show that L1-norm FPCA proposed in this paper has a better robustness than L2-norm FPCA, and the reconstruction ability of the L1-norm principal component analysis to the original uncontaminated functional data is as good as that of the L2-norm principal component analysis

Microstructure and in vitro Bioactivity of Silicon-Substituted Hydroxyapatite

Silicon-substituted hydroxyapatite has shown superior biological performance compared to its stoichio-metric counterpart both in vitro and in vivo. In the present study, single-phase silicon-substituted hydroxyapatite was successfully synthesized by the precipitation method. Chemical composition, crystalline phase, microstructure, and morphology of the materials were characterized by XRF, XRD, FT-IR, solid-state NMR and SEM. The results showed that hydroxyapatite kept its original structure with silicon up to a level of 0.9 wt%. The precipitation method was proved to be an efficient way to synthesize single-phase silicon-substituted hydroxyapatite. Solid-state NMR combined with other techniques gave direct evidence for the isomorphous substitution of PO43- by SiO44- in the hydroxyapatite structure. Silicon-substituted hydroxyapatite showed better bioactivity than stoichiometric hydroxyapatite in the in vitro bioactivity experiment. The higher the silicon content in the hydroxyapatite structure, the better the in vitro bioactivity. The enhanced bioactivity of silicon-substituted hydroxyapatite over pure hydroxyapatite has been attributed to the effect of silicate ions in accelerating dissolution

Appendix C. Detailed procedures for root staining with trypan blue.

Detailed procedures for root staining with trypan blue

A data-driven model for damage evolution of bridge stay cable

Chloride ion concentration is crucial for the reliability of stay cables, particularly when their protective covers are compromised. Previous studies have focused on the corrosion of single wires or cables over time, proposing estimation models that primarily consider corrosion duration while neglecting other significant factors. Addressing this gap, this paper establishes a corrosion damage model for stay cables considering time, temperature, humidity and inclination angle based on the diffusion characteristics of chloride ions. Firstly, We conducted accelerated corrosion experiments on stay cables in different environments to assess the characteristics of the spatial distribution of chloride ion concentrations within the cables and the mass loss characteristics of the wires. Secondly, a three-dimensional diffusion model for chloride ions in stay cables is proposed based on Fick's second law. A pivotal innovation of our research is the application of diverse machine learning techniques to explore how the diffusion coefficient of chloride ions is influenced by factors like time and environmental temperature, leading to the formulation of a sophisticated chloride ion diffusion model. Finally, by amalgamating this diffusion model with the established corrosion law for steel reinforcement, we devised a comprehensive corrosion damage evolution model for the steel wires of stay cables. The result demonstrates the anisotropic diffusion of chloride ions and its substantial correlation with the corrosion damage observed in steel wires. Notably, the PSO-BPNN method emerged as a highly accurate predictor for the diffusion coefficient of chloride ions. The model established in this paper considers a variety of influencing factors and accurately predicts the corrosion damage characteristics of stay cables, which makes an important contribution to improving the reliability of stay cables