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"Qian, Hong"
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Internal Energy, Fundamental Thermodynamic Relation, and Gibbs’ Ensemble Theory as Emergent Laws of Statistical Counting
Statistical counting ad infinitum is the holographic observable to a statistical dynamics with finite states under independent and identically distributed N sampling. Entropy provides the infinitesimal probability for an observed empirical frequency ν^ with respect to a probability prior p, when ν^≠p as N→∞. Following Callen’s postulate and through Legendre–Fenchel transform, without help from mechanics, we show that an internal energy u emerges; it provides a linear representation of real-valued observables with full or partial information. Gibbs’ fundamental thermodynamic relation and theory of ensembles follow mathematically. u is to ν^ what chemical potential μ is to particle number N in Gibbs’ chemical thermodynamics, what β=T−1 is to internal energy U in classical thermodynamics, and what ω is to t in Fourier analysis.
Journal Article
Environmental Determinants of Woody Plant Diversity at a Regional Scale in China
Understanding what drives the geographic variation of species richness across the globe is a fundamental goal of ecology and biogeography. Environmental variables have been considered as drivers of global diversity patterns but there is no consensus among ecologists on what environmental variables are primary drivers of the geographic variation of species richness. Here, I examine the relationship of woody plant species richness at a regional scale in China with sixteen environmental variables representing energy availability, water availability, energy-water balance, seasonality, and habitat heterogeneity. I found that temperature seasonality is the best predictor of woody species richness in China. Other important environmental variables include annual precipitation, mean temperature of the coldest month, and potential evapotranspiration. The best model explains 85% of the variation in woody plant species richness at the regional scale in China.
Journal Article
A Drift-Aware Clustering and Recovery Strategy for Surface-Deployed Wireless Sensor Networks in Ocean Environments
Wireless sensor networks (WSNs) are deployed in terrestrial environments. However, on the sea surface, sensor nodes can drift due to ocean currents and wind; thus, network topologies continuously evolve, and the communication between nodes is frequently disrupted. These unstable connections significantly degrade data transmission stability and overall network performance. These problems are particularly significant in maritime regions where the sea state changes rapidly, thus imposing stringent technical requirements on the design of long-range, reliable, low-latency, and persistent sensing systems. This study proposes a wireless sensor network architecture for sea surface drifting nodes, which is termed Drift-Aware Routing and Clustering with Recovery (DARCR). The proposed system consists of three major components: (1) an enhanced dynamic drift model that more accurately predicts node movement for realistic ocean conditions; (2) a cluster-based framework that prevents disconnection and minimizes delay, which improves cluster stability and adaptability to dynamic environments through refined clustering and route setup mechanisms; and (3) a self-recovery routing strategy for re-establishing communication after disconnection. The proposed method is evaluated using ocean current data from the Copernicus Ocean Data Center simulating a 60-h drifting scenario around the central Taiwan Strait. The experimental results show that the average hourly disconnection rate is maintained at 6.2%, with a variance of 0.31%, and the transmission of newly sensed data is completed within 3 to 5 s, with a maximum delay of approximately 10 s. These findings demonstrate the feasibility of maintaining communication stability and low-latency data transmission for sea surface WSNs that operate in highly dynamic marine conditions.
Journal Article
Effects of climate and environmental heterogeneity on the phylogenetic structure of regional angiosperm floras worldwide
The tendency of species to retain ancestral ecological distributions (phylogenetic niche conservatism) is thought to influence which species from a species pool can persist in a particular environment. Thus, investigating the relationships between measures of phylogenetic structure and environmental variables at a global scale can help understand the variation in species richness and phylogenetic structure in biological assemblages across the world. Here, we analyze a comprehensive data set including 341,846 species in 391 angiosperm floras worldwide to explore the relationships between measures of phylogenetic structure and environmental variables for angiosperms in regional floras across the world and for each of individual continental (biogeographic) regions. We find that the global phylogenetic structure of angiosperms shows clear and meaningful relationships with environmental factors. Current climatic variables have the highest predictive power, especially on phylogenetic metrics reflecting recent evolutionary relationships that are also related to current environmental heterogeneity, presumably because this favors plant speciation in various ways. We also find evidence that past climatic conditions, and particularly refugial conditions, play an important role in determining the phylogenetic structure of regional floras. The relationships between environmental conditions and phylogenetic metrics differ between continents, reflecting the different evolutionary histories of their floras.
Using a dataset that included 341,846 species in 391 angiosperm floras worldwide, this study finds that the global phylogenetic structure of angiosperms shows clear and meaningful relationships with environmental factors and that current climatic variables have the highest predictive power for phylogenetic metrics reflecting recent evolutionary relationships.
Journal Article
c-kit+ Cardiac Stem Cells Alleviate Post-Myocardial Infarction Left Ventricular Dysfunction Despite Poor Engraftment and Negligible Retention in the Recipient Heart
Although transplantation of c-kit+ cardiac stem cells (CSCs) has been shown to alleviate left ventricular (LV) dysfunction induced by myocardial infarction (MI), the number of exogenous CSCs remaining in the recipient heart following transplantation and their mechanism of action remain unclear. We have previously developed a highly sensitive and accurate method to quantify the absolute number of male murine CSCs in female recipient organs after transplantation. In the present study, we used this method to monitor the number of donor CSCs in the recipient heart after intracoronary infusion. Female mice underwent a 60-min coronary occlusion followed by reperfusion; 2 days later, 100,000 c-kit+/lin- syngeneic male mouse CSCs were infused intracoronarily. Only 12.7% of the male CSCs present in the heart immediately (5 min) after infusion were still present in the heart at 24 h, and their number declined rapidly thereafter. By 35 days after infusion, only ∼ 1,000 male CSCs were found in the heart. Significant numbers of male CSCs were found in the lungs and kidneys, but only in the first 24 h. The number of CSCs in the lungs increased between 5 min and 24 h after infusion, indicating recirculation of CSCs initially retained in other organs. Despite the low retention and rapid disappearance of CSCs from the recipient heart, intracoronary delivery of CSCs significantly improved LV function at 35 days (Millar catheter). These results suggest that direct differentiation of CSCs alone cannot account for the beneficial effects of CSCs on LV function; therefore, paracrine effects must be the major mechanism. The demonstration that functional improvement is dissociated from survival of transplanted cells has major implications for our understanding of cell therapy. In addition, this new quantitative method of stem cell measurement will be useful in testing approaches of enhancing CSC engraftment and survival after transplantation.
Journal Article
Multimodal therapy strategies based on hydrogels for the repair of spinal cord injury
Spinal cord injury (SCI) is a serious traumatic disease of the central nervous system, which can give rise to the loss of motor and sensory function. Due to its complex pathological mechanism, the treatment of this disease still faces a huge challenge. Hydrogels with good biocompatibility and biodegradability can well imitate the extracellular matrix in the microenvironment of spinal cord. Hydrogels have been regarded as promising SCI repair material in recent years and continuous studies have confirmed that hydrogel-based therapy can effectively eliminate inflammation and promote spinal cord repair and regeneration to improve SCI. In this review, hydrogel-based multimodal therapeutic strategies to repair SCI are provided, and a combination of hydrogel scaffolds and other therapeutic modalities are discussed, with particular emphasis on the repair mechanism of SCI.
Journal Article
Phylogenetic diversity anomaly in angiosperms between eastern Asia and eastern North America
Although eastern Asia (EAS) and eastern North America (ENA) have similar climates, plant species richness in EAS greatly exceeds that in ENA. The degree to which this diversity difference reflects the ages of the floras or their rates of evolutionary diversification has not been quantified. Measures of species diversity that do not incorporate the ages of lineages disregard the evolutionary distinctiveness of species. In contrast, phylogenetic diversity integrates both the number of species and their history of evolutionary diversification. Here we compared species diversity and phylogenetic diversity in a large number of flowering plant (angiosperm) floras distributed across EAS and ENA, two regions with similar contemporary environments and broadly shared floristic history. After accounting for climate and sample area, we found both species diversity and phylogenetic diversity to be significantly higher in EAS than in ENA. When we controlled the number of species statistically, we found that phylogenetic diversity remained substantially higher in EAS than in ENA, although it tended to converge at high latitude. This pattern held independently for herbs, shrubs, and trees. The anomaly in species and phylogenetic diversity likely resulted from differences in regional processes, related in part to high climatic and topographic heterogeneity, and a strong monsoon climate, in EAS. The broad connection between tropical and temperate floras in southern Asia also might have played a role in creating the phylogenetic diversity anomaly.
Journal Article
Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics
Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96–98% even when different enzyme–substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model’s interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.
Journal Article