The evolution of Nyctereutes (Carnivora: Canidae) in the Nihewan Basin,Hebei, northern China

The occurrence of Nyctereutes during the Plio-Pleistocene has long been reported in northern China, with the highest abundance in the Nihewan Basin. However, due to site dispersal, the coexistence of different taxa, and lack of a precise stratigraphic constraint, the evolutionary process of this genus remains enigmatic. In this study, we re-examined the available Nyctereutes materials recovered from the Nihewan Basin housed in IVPP and Tianjin Natural History Museum, in addition to a newly recovered specimen from our latest excavation. Furthermore, we compared these materials with Nyctereutes fossils recovered from the Pleistocene Zhoukoudian sites near Beijing and the extant species N. procyonoides. Our analysis of the upper molar morphometry reveals the variations in size and dietary characteristics within different species of Nyctereutes during the late Plio-Pleistocene. The examination of molars indicates an increase in the size of Nyctereutes sinensis compared to early Pliocene N. tingi as well as changes in the molar teeth morphology. Subsequently, changes in diet or environmental factors possibly caused the decrease of body size in the late Pleistocene. We also estimate an age constraint for the fossils of N. sinensis from the Xiashagou section by relocating Licent's localities and referring of updated magnetostratigraphic data.     

Structural Differences between Aβ(1-40) Intermediate Oligomers and Fibrils Elucidated by Proteolytic Fragmentation and Hydrogen/Deuterium Exchange

The aggregation of amyloid-β protein (Aβ) in vivo is a critical pathological event in Alzheimer's disease. Although more and more evidence shows that the intermediate oligomers are the primary neurotoxic species in Alzheimer's disease, the particular structural features responsible for the toxicity of these intermediates are poorly understood. We measured the peptide level solvent accessibility of multiple Aβ(1-40) aggregated states using hydrogen exchange detected by mass spectrometry. A gradual reduction in solvent accessibility, spreading from the C-terminal region to the N-terminal region was observed with ever more aggregated states of Aβ peptide. The observed hydrogen exchange protection begins with reporter peptides 20-34 and 35-40 in low molecular weight oligomers found in fresh samples and culminates with increasing solvent protection of reporter peptide 1-16 in long time aged fibrillar species. The more solvent exposed structure of intermediate oligomers in the N-termini relative to well-developed fibrils provides a novel explanation for the structure-dependent neurotoxicity of soluble oligomers reported previously.     

LncRNA Bmp1 promotes the healing of intestinal mucosal lesions via the miR-128-3p/PHF6/PI3K/AKT pathway

Intestinal mucosal injuries are directly or indirectly related to many common acute and chronic diseases. Long non-coding RNAs (lncRNAs) are expressed in many diseases, including intestinal mucosal injury. However, the relationship between lncRNAs and intestinal mucosal injury has not been determined. Here, we investigated the functions and mechanisms of action of lncRNA Bmp1 on damaged intestinal mucosa. We found that Bmp1 was increased in damaged intestinal mucosal tissue and Bmp1 overexpression was able to alleviate intestinal mucosal injury. Bmp1 overexpression was found to influence cell proliferation, colony formation, and migration in IEC-6 or HIEC-6 cells. Moreover, miR-128-3p was downregulated after Bmp1 overexpression, and upregulation of miR-128-3p reversed the effects of Bmp1 overexpression in IEC-6 cells. Phf6 was observed to be a target of miR-128-3p. Furthermore, PHF6 overexpression affected IEC-6 cells by activating PI3K/AKT signaling which was mediated by the miR-128-3p/PHF6 axis. In conclusion, Bmp1 was found to promote the expression of PHF6 through the sponge miR-128-3p, activating the PI3K/AKT signaling pathway to promote cell migration and proliferation.Subject terms: Cell growth, Cell migration     

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.