Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

A Putative Calcium-Permeable Cyclic Nucleotide-Gated Channel, CNGC18, Regulates Polarized Pollen Tube Growth

A tip-focused Ca^2＋ gradient is tightly coupled to polarized pollen tube growth, and tip-localized influxes of extracellular Ca^2＋ are required for this process. However the molecular identity and regulation of the potential Ca^2＋ channels remains elusive. The present study has implicated CNGC18 （cyclic nucleotide-gated channel 18） in polarized pollen tube growth, because its overexpression induced wider and shorter pollen tubes. Moreover, CNGC18 overexpression induced depolarization of pollen tube growth was suppressed by lower extracellular calcium （[Ca^2＋]ex）. CNGC18-yellow fluorescence protein （YFP） was preferentially localized to the apparent post-Golgi vesicles and the plasma membrane （PM） in the apex of pollen tubes. The PM localization was affected by tip-localized ROP1 signaling. Expression of wild type ROP1 or an active form of ROP1 enhanced CNGC18-YFP localization to the apical region of the PM, whereas expression of RopGAP1 （a ROP1 deactivator） blocked the PM localization. These results support a role for PM-Iocalized CNGC18 in the regulation of polarized pollen tube growth through its potential function in the modulation of calcium influxes.     

Extracellular vesicles in bone and tooth: A state-of-art paradigm in skeletal regeneration

Bone and tooth, fundamental parts of the craniofacial skeleton, are anatomically and developmentally interconnected structures. Notably, pathological processes in these tissues underwent together and progressed in multilevels. Extracellular vesicles (EVs) are cell-released small organelles and transfer proteins and genetic information into cells and tissues. Although EVs have been identified in bone and tooth, particularly EVs have been identified in the bone formation and resorption, the concrete roles of EVs in bone and tooth development and diseases remain elusive. As such, we review the recent progress of EVs in bone and tooth to highlight the novel findings of EVs in cellular communication, tissue homeostasis, and interventions. This will enhance our comprehension on the skeletal biology and shed new light on the modulation of skeletal disorders and the potential of genetic treatment.     

A neural network study on the dynamic identification of a fermentation system

The objective of this paper is to propose neural networks for the study of dynamic identification and prediction of a fermentation system which produces mainly 2,3-butanediol (2,3-BDL). The metabolic products of the fermentation, acetic acid, acetoin, ethanol, and 2,3-BDL were measured on-line via a mass spectrometer modified by the insertion of a dimethylvinylsilicone membrane probe. The measured data at different sampling times were included as the input and output nodes, at different learning batches, of the network. A fermentation system is usually nonlinear and dynamic in nature. Measured fermentation data obtained from the complex metabolic pathways are often difficult to be entirely included in a static process model, therefore, a dynamic model was suggested instead. In this work, neural networks were provided by a dynamic learning and prediction process that moved along the time sequence batchwise. In other words, a scheme of two-dimensional moving window (number of input nodes by the number of training data) was proposed for reading in new data while forgetting part of the old data. Proper size of the network including proper number of input/output nodes were determined by trained with the real-time fermentation data. Different number of hidden nodes under the consideration of both learning performance and computation efficiency were tested. The data size for each learning batch was determined. The performance of the learning factors such as the learning coefficient η and the momentum term coefficient α were also discussed. The effect of different dynamic learning intervals, with different starting points and the same ending point, both on the learning and prediction performance were studied. On the other hand, the effect of different dynamic learning intervals, with the same starting point and different ending points, was also investigated. The size of data sampling interval was also discussed. The performance from four different types of transfer functions, x/(1+|x|), sgn(x)·x ²/(1+x ²), 2/(1+e ^{? x} )?1, and 1/(1+e ^{? x} ) was compared. A scaling factor b was added to the transfer function and the effect of this factor on the learning was also evaluated. The prediction results from the time-delayed neural networks were also studied.