Phylogeographic Structure of a Tethyan Relict Capparis spinosa

Complex geological movements more or less affected or changed floristic structures, while the alternation of glacials and interglacials is presumed to have further shaped the present discontinuous genetic pattern of temperate plants. Here we consider Capparis spinosa, a xeromorphic Tethyan relict, to discuss its divergence pattern and explore how it responded in a stepwise fashion to Pleistocene geologic and climatic changes. 267 individuals from 31 populations were sampled and 24 haplotypes were identified, based on three cpDNA fragments (trnL-trnF, rps12-rpl20, and ndhF). SAMOVA clustered the 31 populations into 5 major clades. AMOVA suggests that gene flow between them might be restricted by vicariance. Molecular clock dating indicates that intraspecific divergence began in early Pleistocene, consistent with a time of intense uplift of the Himalaya and Tianshan Mountains, and intensified in mid-Pleistocene. Species distribution modeling suggests range reduction in the high mountains during the Last Glacial Maximum (LGM) as a result of cold climates when glacier advanced, while gorges at midelevations in Tianshan appear to have served as refugia. Populations of low-altitude desert regions, on the other hand, probably experienced only marginal impacts from glaciation, according to the high levels of genetic diversity

Tris[2-eth­oxy-6-(methyl­imino­meth­yl)phenolato-κ2 N,O 1]cobalt(III) monohydrate

In the title compound, [Co(C10H12NO2)3]·H2O, the CoIII ion is coordinated by three O atoms and three N atoms from three bidentate 2-eth­oxy-6-(methyl­imino­meth­yl)phenolate ligands in a slightly distorted octa­hedral environment. The water mol­ecule connects two ligands by O—H⋯O hydrogen bonds. One terminal methyl group is disordered over two positions, with site-occupancy factors of 0.412 (15) and 0.588 (15)

Finite iterative algorithms for solving generalized coupled Sylvester systems – Part I: One-sided and generalized coupled Sylvester matrix equations over generalized reflexive solutions

AbstractThe generalized coupled Sylvester systems play a fundamental role in wide applications in several areas, such as stability theory, control theory, perturbation analysis, and some other fields of pure and applied mathematics. The iterative method is an important way to solve the generalized coupled Sylvester systems. In this two-part article, finite iterative methods are proposed for solving one-sided (or two-sided) and generalized coupled Sylvester matrix equations and the corresponding optimal approximation problem over generalized reflexive solutions (or reflexive solutions). In part I, an iterative algorithm is constructed to solve one-sided and coupled Sylvester matrix equations (AY−ZB,CY−ZD)=(E,F) over generalized reflexive matrices Y and Z. When the matrix equations are consistent, for any initial generalized reflexive matrix pair [Y1,Z1], the generalized reflexive solutions can be obtained by the iterative algorithm within finite iterative steps in the absence of round-off errors, and the least Frobenius norm generalized reflexive solution pair can be obtained by choosing a special kind of initial matrix pair. The unique optimal approximation generalized reflexive solution pair [Y^,Z^] to a given matrix pair [Y0,Z0] in Frobenius norm can be derived by finding the least-norm generalized reflexive solution pair [Y∼∗,Z∼∗] of two new corresponding generalized coupled Sylvester matrix equations (AY∼-Z∼B,CY∼-Z∼D)=(E∼,F∼), where E∼=E-AY0+Z0B,F∼=F-CY0+Z0D. Several numerical examples are given to show the effectiveness of the presented iterative algorithm

Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints

BMP9-Induced Osteogenetic Differentiation and Bone Formation of Muscle-Derived Stem Cells

Efficient osteogenetic differentiation and bone formation from muscle-derived stem cells (MDSCs) should have potential clinical applications in treating nonunion fracture healing or bone defects. Here, we investigate osteogenetic differentiation ability of MDSCs induced by bone morphogenetic protein 9 (BMP9) in vitro and bone formation ability in rabbit radius defects repairing model. Rabbit's MDSCs were extracted by type I collagenase and trypsin methods, and BMP9 was introduced into MDSCs by infection with recombinant adenovirus. Effects of BMP9-induced osteogenetic differentiation of MDSCs were identified with alkaline phosphatase (ALP) activity and expression of later marker. In stem-cell implantation assay, MDSCs have also shown valuable potential bone formation ability induced by BMP9 in rabbit radius defects repairing test. Taken together, our findings suggest that MDSCs are potentiated osteogenetic stem cells which can be induced by BMP9 to treat large segmental bone defects, nonunion fracture, and/or osteoporotic fracture