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Perfecting the particular microstructure of a fresh machinable bioactive glass-ceramic.
Periodic rhythms are ubiquitous phenomena that illuminate the underlying mechanism of cyclic activities in biological systems, which can be represented by cyclic attractors of the related biological network. Disorders of periodic rhythms are detrimental to the natural behaviours of living organisms. Previous studies have shown that the state transition from one to another attractor can be accomplished by regulating external signals. However, most of these studies until now have mainly focused on point attractors while ignoring cyclic ones. The aim of this study is to investigate an approach for reconciling abnormal periodic rhythms, such as diminished circadian amplitude and phase delay, to the regular rhythms of complex biological networks. For this purpose, we formulate and solve a mixed-integer nonlinear dynamic optimization problem simultaneously to identify regulation variables and to determine optimal control strategies for state transition and adjustment of periodic rhythms. Numerical experiments are implemented in three examples including a chaotic system, a mammalian circadian rhythm system and a gastric cancer gene regulatory network. The results show that regulating a small number of biochemical molecules in the network is sufficient to successfully drive the system to the target cyclic attractor by implementing an optimal control strategy. © 2020 The Authors.It is well-established that sustained exercise training can enhance brain plasticity and boost cognitive performance in mammals, but this phenomenon has not received much attention in fish. The aim of this study was to determine whether sustained swimming exercise can enhance brain plasticity in juvenile Atlantic salmon. Brain plasticity was assessed by both mapping the whole telencephalon transcriptome and conducting telencephalic region-specific microdissections on target genes. We found that 1772 transcripts were differentially expressed between the exercise and control groups. Gene ontology (GO) analysis identified 195 and 272 GO categories with a significant overrepresentation of up- or downregulated transcripts, respectively. A multitude of these GO categories was associated with neuronal excitability, neuronal signalling, cell proliferation and neurite outgrowth (i.e. cognition-related neuronal markers). Additionally, we found an increase in proliferating cell nuclear antigen (pcna) after both three and eight weeks of exercise in the equivalent to the hippocampus in fish. Furthermore, the expression of the neural plasticity markers synaptotagmin (syt) and brain-derived neurotrophic factor (bdnf) were also increased due to exercise in the equivalent to the lateral septum in fish. In conclusion, this is the first time that swimming exercise has been directly linked to increased telencephalic neurogenesis and neural plasticity in a teleost, and our results pave the way for future studies on exercise-induced neuroplasticity in fish. © 2020 The Authors.In this study, we propose a growth pathway of scorodite in an atmospheric scorodite synthesis. Scorodite is a non-direct product, which is derived from the transformation of its precursor. Different precursor speciation leads to different crystallinity and morphology of synthesized scorodite. At 10 and 20 g l-1 initial arsenic concentration, the precursor of scorodite is identified as ferrihydrite. At 10 g l-1 initial arsenic concentration, low arsenic concentration is unfavourable to the complex between arsenate and ferrihydrite, inhibiting the transformation of ferrihydrite into scorodite. The synthesized scorodite is 1-3 µm in size. At 20 g l-1 initial arsenic concentration, higher arsenic concentration favours the complex between arsenate and ferrihydrite. The transformation process is accessible. Large scorodite in the particle size of 5-20 µm with excellent crystallinity is obtained. However, the increasing initial arsenic concentration is not always a positive force for the growth of scorodite. When initial arsenic concentration increases to 30 g l-1, Fe(O,OH)6 octahedron preferentially connects to As(O,OH)4 tetrahedron to form Fe H 2 As O 4 2 + or FeHAs O 4 + ion. Fe-As complex ions accumulate in solution. Once the supersaturation exceeds the critical value, the Fe-As complex ions deprotonate and form poorly crystalline ferric arsenate. Even poorly crystalline ferric arsenate can also transform to crystalline scorodite, its transformation process is much slower than ferrihydrite. Therefore, incomplete developed scorodite with poor crystallinity is obtained. NF-κΒ activator 1 © 2020 The Authors.The gastrointestinal tract (GIT) consists of connected structures that vary in function and physiology, and different GIT sections potentially provide different habitats for microorganisms. Birds possess unique GIT structures, including the oesophagus, proventriculus, gizzard, small intestine, caeca and large intestine. To understand birds as hosts of microbial ecosystems, we characterized the microbial communities in six sections of the GIT of two shorebird species, the Dunlin and Semipalmated Sandpiper, identified potential host species effects on the GIT microbiome and used microbial source tracking to determine microbial origin throughout the GIT. The upper three GIT sections had higher alpha diversity and genus richness compared to the lower sections, and microbial communities in the upper GIT showed no clustering. The proventriculus and gizzard microbiomes primarily originated from upstream sections, while the majority of the large intestine microbiome originated from the caeca. The heterogeneity of the GIT sections shown in our study urges caution in equating data from faeces or a single GIT component to the entire GIT microbiome but confirms that ecologically similar species may share many attributes in GIT microbiomes. © 2020 The Authors.To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. back propagation (BP), radial basis function (RBF) and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.
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