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Diabetes seriously endangers public health and brings a heavy economic burden to the country. Inflammation is one of the main inducing factors of type-2 diabetes (T2D) and may cause some complications of diabetes, such as diabetic encephalopathy and peripheral neuropathy. In-depth research and development of drugs to cure diabetes and complications are of great significance. Pueraria lobate is a medicinal herb used in several countries to treat many diseases. Here, two new polysaccharides (PLB-1-1 and PLB-1-2) were isolated and purified from the root of Pueraria lobata with molecular weights of 9.1 × 103 Da and 3.8 × 103 Da, respectively. The structure was evaluated by monosaccharide composition, GC-MS and NMR spectroscopy. It was determined that PLB-1-1 comprised →4)-α-D-Glcp-(1→, α-D-Glcp-(1→, →6)-β-D-Galp-(1→, →3)-α-L-Araf-(1→, →3,6)-β-D-Manp-(1→ and →4,6)-β-D-Manp-(1→, and PLB-1-2 consisted of →4)-α-D-Glcp-(1→, β-D-Glcp-(1→, →4,6)-β-D-Glcp-(1→, →3,6)-β-D-Manp-(1→ and α-L-Fucp-(1→. Furthermore, both PLB-1-1 and PLB-1-2 showed anti-inflammatory and inhibitory activities of α-glucosidase and α-amylase in vitro. Therefore, the new polysaccharides, i.e., PLB-1-1 and PLB-1-2, may be considered candidates for the treatment of diabetes and its related complications.

Arsenic can be specifically enriched by rice, and the health hazards caused by high arsenic rice are gradually attracting attention. This study aimed to explore the potential of microbial detoxification via gut microbiome in the treatment of sub-chronic arsenic poisoning. We first exposed mice to high-dose arsenic feed (30 mg/kg, rice arsenic composition) for 60 days to promote arsenic-induced microbes in situ in the gastrointestinal tract, then transplanted their fecal microbiota (FMT) into another batch of healthy recipient mice, and dynamically monitored the microbial colonization by 16S rRNA sequencing and ITS sequencing. The results showed that in situ arsenic-induced fecal microbiome can stably colonized and interact with indigenous microbes in the recipient mice in two weeks, and established a more stable network of gut microbiome. Then, the recipient mice continued to receive high-dose arsenic exposure for 52 days. After above sub-chronic arsenic exposure, compared with the non-FMT group, fecal arsenic excretion, liver and plasma arsenic accumulation were significantly lower (P 0.05), and that in kidney, hair, and thighbone present no significant differences. Metabolomics of feces- plasma-brain axis were also disturbed, some up-regulated metabolites in feces, plasma, and cerebral cortex may play positive roles for the host. Therefore, microbial detoxification has potential in the treatment of sub-chronic arsenic poisoning. However, gut flora is an extremely complex community with different microorganisms have different arsenic metabolizing abilities, and various microbial metabolites. Coupled with the matrix effects, these factors will have various effects on the efflux and accumulation of arsenic. The definite effects (detoxification or non-detoxification) could be not assured based on the current study, and more systematic and rigorous studies are needed in the future.

Diabetes seriously endangers public health and brings a heavy economic burden to the country. Inflammation is one of the main inducing factors of type-2 diabetes (T2D) and may cause some complications of diabetes, such as diabetic encephalopathy and peripheral neuropathy. In-depth research and development of drugs to cure diabetes and complications are of great significance.

In situ-based studies on microbiome-host interactions after arsenic exposure are few. In this study, the variations in arsenics, microbiota, and host genes along murine intestinal tracts were determined after arsenic exposure for two months. There was a gradual increase in the concentration of total As (CtAs) in feces from ileum to colon, whereas CtAs in the corresponding tissues were relatively stable. Differences in arsenic levels between feces and tissues were significantly different. The proportion of arsenite (iAsⅢ) in feces gradually decreased, however, it gradually increased in tissues. After arsenic exposure, the diversity and abundance of microbial community and networks in each segment were significantly dysregulated. Notably, 328, 579 and 90 differently expressed genes were detected in ileum, cecum, and colon, respectively. In addition, microbiome and transcriptome analyses showed a significant correlation between the abundance of Faecalibaculum and expressions of Plb1, Hspa1b, Areg and Duoxa2 genes. This implies that they may be involved in arsenic biotransformation. In vitro experiments using Biofidobactrium and Lactobacillus showed that probiotics have arsenic transformation abilities. Therefore, gut microbiome may modulate arsenic accumulation, excretion and detoxification along the digestive tract. Moreover, the abundance and diversity of gut microbiome may be related to the changes in host health.

Grain boundary energy anisotropy plays an important role in many science and engineering problems. This paper presents a new numerical method for determining the grain boundary energy distribution (GBED) from the microstructures of polycrystalline materials. The method assumes that triple junctions are in local equilibrium, that this equilibrium is described by the Herring equation, and can be expressed using the Hoffman-Cahn formalism of the capillarity vector. The conventional method discretizes the five-parameter space and then fits the energy for each discrete bin. The new method minimizes the difference between similar boundaries in the dataset while obeying the equilibrium equation at triple junctions. This non-parametric approach shows smaller error than the conventional method, and it can also determine the grain boundary energies from datasets that have small numbers of triple junctions if their configurations are clustered in the five-parameter space, which is not possible using the conventional method. In addition, the proposed non-parametric approach performs better when the number of triple junctions increases, which is not always true for the conventional method.

Many markets include a product and a platform product, where the product can only achieve its intended functions and performance in conjunction with or under the operation of its platform, such as a video game can only run on its game console. The growth of the user population of these products or services is a kind of collective behaviour propagation phenomenon. Here, questions come: how can we describe the collective behaviour propagation as a function of time? How the endogenous and exogenous social effects influence the collective behaviour propagation and how to quantify these two effects? In order to answer all these questions, an ordinary differential equation model is proposed to describe the growth of the user population of this class of markets. Firstly, a networked community is constructed, where users and prospective users are considered as nodes, and their relationship provides the method of building edges. Then, two fundamental influences of decision-making can be realized based on the network. A useful application of the model can be conceived and illustrated by one new database containing weekly sales of 25,237 video games released in the home and handle consoles and personal computer in USA, UK, Germany, France and Japan from 1989 to 2018. Results show that historical sales profile of a video game follows the growth equation, and the numerical procedure for finding the model parameters allows the market size, and the relative effectiveness of customer service and promotional efforts to be estimated according to the available historical data.

Long-term consumption of arsenic-contaminated rice has become a public health issue that urgently needs to be addressed. In this study, mice were exposed to arsenic in rice (low dose, 0.91 mg/kg; medium dose, 9.1 mg/kg) for 30 days and 60 days, respectively, and the effects on pathological structures of spleen and skin, as well as the structure of the fecal microbiome were examined. The findings revealed dose/time cumulative effects on pathological changes, with even a low dose exposure for 30 days causing destruction of splenic follicular structure and thickening of dermal keratinized and epidermal layers. The Firmicutes/Bacteroidetes ratio in the community and the positive/negative ratio in network links were higher in arsenic groups, suggesting that arsenic resulted in a less healthy and unstable microbiome for the host. Thus lifetime consumption of arsenic in rice may have potential health effects on humans and must be carefully assessed to safeguard human health. Furthermore, in arsenic groups, arsenic-resistant bacteria or arsenic hazards remediation bacteria changed to be the dominant bacteria and acted as the core bacteria in the network modules. Some microbial arsenic transforming genes (arsC, arsR, arsA, ACR3, and aoxB) differed, indicating that the gut microbiome changed to withstand arsenic stress. Furthermore, Faecalibaculum, Lachnospiraceae_NK4A136_group, Angelakisella, Ruminiclostridium, and Desulfovibrionaceae are positively associated with arsenic dosage and may be useful in the early detection of arsenicals.

The distribution of grain boundary curvatures as a function of five independent crystallographic parameters is measured in an austenitic and a ferritic steel. Both local curvatures and integral mean curvatures are measured from three dimensional electron backscattered diffraction data. The method is first validated on ideal shapes. When applied to real microstructures, it is found that the grain boundary mean curvature varies with the boundary crystallography and is more sensitive to the grain boundary plane orientation than to the disorientation. The grain boundaries with the smallest curvatures also have low grain boundary energy and large relative areas. The results also show that the curvature is influenced by the grain size and by the number of nearest neighbors. For austenite, when the number of faces on a grain is equal to the average number of faces of its neighbors, it has zero integral mean curvature.

A distribution of grain boundary curvature as a function of five independent crystallographic parameters is proposed and tested on some simple geometries. The results show that the grain boundary mean curvature distribution (GBHD) is able to capture the curvature of a digitized microstructure though noise may arise from several sources.The GBHD is measured in two sets of three-dimensional electron backscattered diffraction (EBSD) data collected in an austenitic and a ferritic steel. It is found that the grain boundary mean curvature varies with the boundary crystallography and is more sensitive to the grain boundary plane orientation than to the disorientation. The grain boundaries with the smallest curvatures also have low grain boundary energy and large relative areas while the curvature and energy of more general grain boundaries are, on average, inversely correlated.The GBHD is also computed from a set of electron backscattered diffraction (EBSD) data collected from SrTiO3 annealed at 1470 ℃. Unlike the steels, the average grain boundary curvature is found to be directly correlated with the grain boundary energy, suggesting that the microstructure of SrTiO3 at 1470 ℃ may contain many singular grain boundaries.The integral mean curvature of grain faces (𝑀s) is analyzed for the grains in the steel samples and in SrTiO3. Similar results are obtained in the three datasets. For a given grain, its 𝑀s is closely related to its topological characteristics. Grains with a small number of faces have positive 𝑀s and grains with many faces have negative 𝑀𝑠. The grains with zero 𝑀s are those whose number of neighbors equal the average number of faces of their nearest neighbors.Various geometric, topological, and mean-field features are hypothesized to capture the evolution of grain faces in a high purity Ni sample. The dataset was collected by the Suter group at Carnegie Mellon University using the high energy diffraction microscopy (HEDM) technique. It consists of two orientation maps of a given volume, one for the pre-anneal state and one for the after-anneal state between which the sample was annealed at 800 ℃ for 25 minutes. By fitting the various features with a few machine learning models, we show that curvature does affect the evolution of grain faces but the effect is not deterministic.A face-averaged approximation is proposed to study the evolution of grain boundary properties. The effectiveness of this face-averaged assumption is validated by a comparison between the true GBHD and the face-averaged GBHD. The results show that the grain boundary area and curvature change vary with the grain boundary inclination systematically.

Professional programmatic accreditation of engineering education (PPAEE) provides an internationally accepted mechanism for quality assurance in engineering education. Since it targets at cultivating applied talents, independent colleges can be seen as the best practitioners of PPAEE. In fact, the cultivation of applied talents must meet the practical enterprise requirements, and the superiority of enterprises in resources and techniques should be utilized through co-education for zero market convergence by enhancing students’ practical ability and enriching their professional knowledge. To promote the progress of PPAEE in the School of Electrical and Computer Engineering of Nanfang College of Sun Yat-Sen University, plenty of explorations and practices have been made, especially in the aspect of school-enterprise cooperation, where a set of flexible school-enterprise cooperation mechanisms have been designed and have been proved to be very effective in related practical courses