Urinary alkylresorcinol metabolites, biomarkers of whole grain wheat and rye intake, are beneficially associated with liver fat and other fat measures.

Aims: Epidemiological studies that use dietary biomarkers to investigate the association between whole grain intake and the risk of obesity are sparse. We assessed the association between urinary alkylresorcinol metabolites including 3-(3,5-dihydroxyphenyl)-1-propanoic acid (DHPPA) and 3,5-dihydroxybenzoic acid (DHBA), biomarkers of whole grain wheat and rye intake, and body fat measures. Methods: We measured urinary excretion of DHPPA and DHBA, body weight, height, and circumferences of the waist and hip at the baseline and again after 1-year in a representative sample of 306 community-dwelling adults in Huoshan, China. We also measured liver fat accumulation [indicated by the controlled attenuation parameter (CAP)] and other body composition after 1 year. Multivariate-adjusted linear models and linear mixed-effects models were used to analyze single measurement and repeated measurements, respectively. Results: Each 1 µg g-1 creatinine increase in urinary DHPPA levels was associated with 0.21%, 0.23%, 3.64%, and 4.80% decrease in body weight, body mass index (BMI), body fat mass (BFM) and visceral fat level (VFL), respectively (all P < 0.05). Higher DHBA levels were inversely associated with CAP (percentage difference per 1 µg g-1 creatinine increment: -1.98%, P < 0.05). Higher total urinary alkylresorcinol metabolite (DHPPA + DHBA) levels were associated with lower body weight, BMI, BFM, VFL, and CAP, with the percentage differences per 1 µg g-1 creatinine increment of -0.27%, -0.27%, -3.79%, -5.12%, and -2.24%, respectively (all P < 0.05). Conclusions: Our findings suggest that the intake of whole grain wheat and rye, reflected by urinary DHPPA and DHBA, is favorably associated with liver fat and other fat measures.

Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

Characterization of circular RNAs in dorsal root ganglia after central and peripheral axon injuries.

In central nervous system, axons fail to regenerate after injury while in peripheral nervous system, axons retain certain regenerative ability. Dorsal root ganglion (DRG) neuron has an ascending central axon branch and a descending peripheral axon branch stemming from one single axon and serves as a suitable model for the comparison of growth competence following central and peripheral axon injuries. Molecular alterations underpin different injury responses of DRG branches have been investigated from many aspects, such as coding gene expression, chromatin accessibility, and histone acetylation. However, changes of circular RNAs are poorly characterized. In the present study, we comprehensively investigate circular RNA expressions in DRGs after rat central and peripheral axon injuries using sequencing analysis and identify a total of 33 differentially expressed circular RNAs after central branch injury as well as 55 differentially expressed circular RNAs after peripheral branch injury. Functional enrichment of host genes of differentially expressed circular RNAs demonstrate the participation of Hippo signaling pathway and Notch signaling pathway after both central and peripheral axon injuries. Circular RNA changes after central axon injury are also linked with apoptosis and cellular junction while changes after peripheral axon injury are associated with metabolism and PTEN-related pathways. Altogether, the present study offers a systematic evaluation of alterations of circular RNAs in rat DRGs following injuries to the central and peripheral axon branches and contributes to the deciphering of essential biological activities and mechanisms behind successful nerve regeneration.

Impact of Artificial Intelligence on Regional Green Development under China's Environmental Decentralization System-Based on Spatial Durbin Model and Threshold Effect.

Artificial intelligence (AI) is the core technology of digital economy, which leads the transition to a sustainable economic growth approach under the Chinese-style environmentally decentralized system. In this paper, we first measured the green total factor productivity (GTFP) of 30 Chinese provinces from 2011 to 2020 using the super-efficiency slacks-based measure (SBM) model, analyzed the mechanism of the effect of AI on GTFP under the environmental decentralization regime, and secondly, empirically investigated the spatial evolution characteristics and the constraining effect of the impact of AI on GTFP using the spatial Durbin model (SDM) and the threshold regression model. The findings reveal: a U shape of the correlation of AI with GTFP; environmental decentralization acts as a positive moderator linking AI and GTFP; the Moran index demonstrates the spatial correlation of GTFP; under the constraint of technological innovation and regional absorptive capacity as threshold variables, the effect of AI over GTFP is U-shaped. This paper provides a useful reference for China to accelerate the formation of a digital-driven green economy development model.

Study on ecological adaptability construction characteristics of residential buildings in Kangba area, Tibet, China.

Located in the southwest of the Qinghai-Tibet Plateau, Tibet is characterized by high cold, high radiation, and large differences in temperature between day and night. Tibetan residential buildings are famous for adapting to the harsh ecological environment and maintaining durability. Based on the residential buildings in Tibet, this paper extracts the technical process and color decoration culture in the construction process in order to adapt to the harsh natural environment. This paper first analyzed the four ecological construction modes of Tibetan residential buildings, analyzed the interior layout characteristics and cultural customs connotation, and introduced the architectural decoration characteristics and decorative color painting. The results show that the ramming type of adobe mainly includes the selection of building foundation, wall laying, floor and roof construction and so on, and its insulation effect is better. The rubble masonry type mainly adopts irregular gneiss, supplemented by clay, which has strong compressive capacity. Logs dry type using log masonry, heat preservation. and shock resistance is better. Concrete-infilled wall frame is composed of horizontal and vertical load-bearing system, which has stronger seismic performance. Tibetan residential buildings generally have two or three floors. The first floor is the enclosure and sundry room, the second floor is the rest place, and the third floor is the Sutra hall and sun terrace. The overall outdoor color of Kangba Tibetan buildings is mainly red and black, while the indoor color is mainly blue and red, with wood carvings and furniture. The layout of Tibetan villages can be divided into centripetal layout and scattered layout. Tibetan residential buildings provide a new sustainable development direction for the current global urbanization process at the expense of the ecological environment. It can alleviate the crisis of global resource shortage, climate warming, and biodiversity degradation.

Intermittent evolution routes to the periodic or the chaotic orbits in Rulkov map.

This paper concerns the intermittent evolution routes to the asymptotic regimes in the Rulkov map. That is, the windows with transient approximate periodic and transient chaotic behaviors occur alternatively before the system reaches the periodic or the chaotic orbits. Meanwhile, the evolution routes to chaotic orbits can be classified into different types according to the windows before reaching asymptotic chaotic states. In addition, the initial values can be regarded as a key factor affecting the asymptotic behaviors and the evolution routes. The effects of the initial values are given by parameter planes, bifurcation diagrams, and waveforms. In order to investigate whether the intermittent evolution routes can be learned by machine learning, some experiments are given to understanding the differences between the trajectories of the Rulkov map generated by the numerical simulations and predicted by the neural networks. These results show that there is about 60% accuracy rate of successfully predicting both the evolution routes and the asymptotic period-3 orbits using a three-layer feedforward neural network, while the bifurcation diagrams can be reconstructed using reservoir computing except a few parameter conditions.

Synchronization of Rulkov neuron networks coupled by excitatory and inhibitory chemical synapses.

This paper takes into account a neuron network model in which the excitatory and the inhibitory Rulkov neurons interact each other through excitatory and inhibitory chemical coupling, respectively. Firstly, for two or more identical or non-identical Rulkov neurons, the existence conditions of the synchronization manifold of the fixed points are investigated, which have received less attention over the past decades. Secondly, the master stability equation of the arbitrarily connected neuron network under the existence conditions of the synchronization manifold is discussed. Thirdly, taking three identical Rulkov neurons as an example, some new results are presented: (1) topological structures that can make the synchronization manifold exist are given, (2) the stability of synchronization when different parameters change is discussed, and (3) the roles of the control parameters, the ratio, as well as the size of the coupling strength and sigmoid function are analyzed. Finally, for the chemical coupling between two non-identical neurons, the transversal system is given and the effect of two coupling strengths on synchronization is analyzed.

Hybrid discrete-time neural networks.

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

A mechanism for elliptic-like bursting and synchronization of bursts in a map-based neuron network.

A system consisting of two Rulkov map-based neurons coupled through reciprocal electrical synapses as a simple phenomenological example is discussed. When the electrical coupling is excitatory, the square-wave bursting can be well predicted by the bifurcation analysis of a comparatively simple low-dimensional subsystem embedded in the invariant manifold. While, when the synapses are inhibitory due to the artificial electrical coupling, a fast-slow analysis is carried out by treating the two slow variables as two different bifurcation parameters. The main result of this paper is to present a mechanism for the occurrence of a kind of special elliptic bursting. The mechanism for this kind of elliptic-like bursting is due to the interaction between two chaotic oscillations with different amplitudes. Moreover, the generation of antiphase synchronization of networks lies in the different switching orders between two pairs of different chaotic oscillations corresponding to the first neuron and the second neuron, respectively.

Bursting regimes in map-based neuron models coupled through fast threshold modulation.

A system consisting of two map-based neurons coupled through reciprocal excitatory or inhibitory chemical synapses is discussed. After a brief explanation of the basic mechanism behind generation and synchronization of bursts, parameter space is explored to determine less obvious but biologically meaningful regimes and effects. Among them, we show how excitatory synapses without any delays may induce antiphase synchronization; that a synapse may change its character from excitatory to inhibitory and vice versa by changing its conductance, without any change in reversal potential; and that small variations in the synaptic threshold may result in drastic changes in the synchronization of spikes within bursts. Finally we show how the synchronization effects found in the two-neuron system carry over to larger networks.