Organic materials with high C/N ratio: more beneficial to soil improvement and soil health.

In order to figure out the effect of organic fertilizers with different carbon-nitrogen (C/N) ratios on the soil improvement and the healthy cultivation, the pot experiment method was used to study effects on the physical and chemical properties and the bacterial community structure of sandy loam soil using five treatments of chemical fertilizer application with the C/N ratios of 15 (CN15), 20 (CN20), 25 (CN25), 30 (CN30) and the control (CK) respectively. Results show that the organic materials with different C/N ratios significantly improve the soil porosity and water content, which all show a linear change rule with the C/N ratio. It can also significantly increase the soil total carbon, total nitrogen, soil C/N ratio, soil microbial biomass carbon, microbial biomass nitrogen and microbial biomass C/N ratio. Among them, CN30 significantly increases the soil total carbon and C/N ratio, which are 5.34-24.13% and 8.87-30.15% respectively compared with other treatments. It can be also found that the dominant flora (at the phylum level) of each treatment are Actinobacteria, Proteobacteria and Chlorobi. The CN30 treatment presents the most obvious improvement in the diversity and richness of the soil bacterial community and is more conducive to the growth and reproduction of Proteobacteria and Firmicutes. The correlation analysis shows that Ctotal/Ntotal and Cmic/Nmic are the most important environmental factors affecting the soil physical and chemical properties and their correlation with the bacterial communities. The higher C/N ratio of organic materials results in a more significant improvement of the soil physical and chemical properties. This study provides a new theoretical basis for the soil health cultivation technology.

Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks.

This article considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative prediction unit (CoPU), which aggregates the predictions from multiple collaborative predictors according to a collaborative graph. Each collaborative predictor is trained to predict the agent status by integrating the impact of another agent. The edge weights of the collaborative graph reflect the importance of each predictor. The collaborative graph is adjusted online by multiplicative update, which can be motivated by minimizing an explicit objective. With this objective, we also conduct regret analysis to indicate that, along with training, our CoPU achieves similar performance with the best individual collaborative predictor in hindsight. This theoretical interpretability distinguishes our method from many other graph networks. To progressively refine predictions, multiple CoPUs are stacked to form a collaborative graph neural network. Extensive experiments are conducted on three tasks: online simulated trajectory prediction, online human motion prediction, and online traffic speed prediction, and our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4%, and 21.0% on average, respectively; in addition, the proposed CoGNNs have lower average time costs in one online training/testing iteration than most previous methods.

Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction.

3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. In many previous works: 1) they studied two tasks separately, neglecting internal correlations; and 2) they did not capture sufficient relations inside the body. To address these issues, we propose a symbiotic model to handle two tasks jointly; and we propose two scales of graphs to explicitly capture relations among body-joints and body-parts. Together, we propose symbiotic graph neural networks, which contain a backbone, an action-recognition head, and a motion-prediction head. Two heads are trained jointly and enhance each other. For the backbone, we propose multi-branch multiscale graph convolution networks to extract spatial and temporal features. The multiscale graph convolution networks are based on joint-scale and part-scale graphs. The joint-scale graphs contain actional graphs, capturing action-based relations, and structural graphs, capturing physical constraints. The part-scale graphs integrate body-joints to form specific parts, representing high-level relations. Moreover, dual bone-based graphs and networks are proposed to learn complementary features. We conduct extensive experiments for skeleton-based action recognition and motion prediction with four datasets, NTU-RGB+D, Kinetics, Human3.6M, and CMU Mocap. Experiments show that our symbiotic graph neural networks achieve better performances on both tasks compared to the state-of-the-art methods.

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction.

We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales. Different from many previous hierarchical structures, our multiscale spatio-temporal graph is built in a data-adaptive fashion, which captures nonphysical, yet motion-based relations. The key module of MST-GNN is a multiscale spatio-temporal graph computational unit (MST-GCU) based on the trainable graph structure. MST-GCU embeds underlying features at individual scales and then fuses features across scales to obtain a comprehensive representation. The overall architecture of MST-GNN follows an encoder-decoder framework, where the encoder consists of a sequence of MST-GCUs to learn the spatial and temporal features of motions, and the decoder uses a graph-based attention gate recurrent unit (GA-GRU) to generate future poses. Extensive experiments are conducted to show that the proposed MST-GNN outperforms state-of-the-art methods in both short and long-term motion prediction on the datasets of Human 3.6M, CMU Mocap and 3DPW, where MST-GNN outperforms previous works by 5.33% and 3.67% of mean angle errors in average for short-term and long-term prediction on Human 3.6M, and by 11.84% and 4.71% of mean angle errors for short-term and long-term prediction on CMU Mocap, and by 1.13% of mean angle errors on 3DPW in average, respectively. We further investigate the learned multiscale graphs for interpretability.

Intensity-Modulated Polymer Optical Fiber-Based Refractive Index Sensor: A Review.

The simple and highly sensitive measurement of the refractive index (RI) of liquids is critical for designing the optical instruments and important in biochemical sensing applications. Intensity modulation-based polymer optical fiber (POF) RI sensors have a lot of advantages including low cost, easy fabrication and operation, good flexibility, and working in the visible wavelength. In this review, recent developments of the intensity modulation POF-based RI sensors are summarized. The materials of the POF and the working principle of intensity modulation are introduced briefly. Moreover, the RI sensing performance of POF sensors with different structures including tapered, bent, and side-polished structures, among others, are presented in detail. Finally, the sensing performance for different structures of POF-based RI sensors are compared and discussed.

Simultaneous determination of 20 bioactive components in Chuanxiong Rhizoma from different production origins in Sichuan province by ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry combined with multivariate statistical analysis.

Chuanxiong Rhizoma is a commonly used in traditional Chinese medicine. Chuanxiong Rhizoma is widely distributed in Sichuan province, China, including the cities of Dujiangyan, Pengzhou, Meishan, Qionglai, and Shifang. However, reports on the comparisons of quality of Chuanxiong Rhizoma of different production origins are limited. Therefore, an ultra-HPLC with triple quadrupole MS method was developed for the determination of 20 bioactive components (12 aromatic acids and eight phthalides) in 36 samples from different production origins and further assessed its quality. The contents of these 20 constituents of samples were analyzed by hierarchical cluster analysis and orthogonal partial least squares discrimination analysis; the result indicated that Chuanxiong Rhizoma of different production origins had some differences. Thirteen constituents of quality difference markers were acquired by variable importance for the project. Furthermore, the sum of the contents of these quality difference markers was different from various production origins of Chuanxiong Rhizoma. Meanwhile, Z-ligustilide and senkyunolide A as main constituents of quality difference markers, the rate of various production origins of Chuanxiong Rhizoma was different. This study provides a foundation for the quality assessment of Chuanxiong Rhizoma.

[Study on the Relationship between susceptibility of stomach neoplasm cancer and polymorphism of inducible nitric oxide synthase gene].

OBJECTIVE: To study the relationship between polymorphism of inducible Nitric Oxide Synthase (iNOS) gene and the susceptibility of intestinal type stomach cancer and stomach cardia cancer in Chinese people. METHODS: A community-based case-control study was designed. Ninety-three intestinal type of stomach cancer and 50 stomach cardia cancer patients with endoscopy and pathology diagnosis were identified as cases. Two hundred and forty-six controls served as controls. RESULTS: C-->T polymorphism was found in exon 16 of iNOS gene, which changed the coding amino acid from serine to leucine, and formed a recognition site identified by Tsp 509 I restriction enzyme (we called it C-->T polymorphism). The T allele gene frequency in the control group was 13.21%. No statistically significant difference was found between C-->T polymorphism alone and the increased susceptibility to intestinal stomach cancer or stomach cardia cancer. A significant type 2 multiplicative interaction was found in increasing both the risk of intestinal stomach cancer and stomach cardia cancer when both C-->T polymorphism and tobacco smoking exposure existed. An additive interaction model, which showed statistically significant difference, was found to increase only the risk of stomach cardia cancer when CagA antibody shared negative but C-->T polymorphism occurred. CONCLUSION: C-->T polymorphism of iNOS gene was considered as one of the possible susceptible genes, which specifically increased the risk of tobacco-related but CagA negative types of intestinal stomach cancer and stomach cardia cancer.

[The distributive features of three kinds of metabolic genes polymorphisms in population of Han nationality in south area of China].

OBJECTIVE: To find out the distributive features of some metabolic genes polymorphisms in Han population of south area of China. METHODS: Study population was obtained from the controls of a community based case-control study, which included 290 blood relatives (inner control) and 404 non-blood relatives (outer control). RESULTS: Frequencies of CYP1A1, GSTM1 and GSTT1 polymorphisms had no significant difference among confounding factors, such as sex, living areas, stomach cancer family history and history of tobacco smoking etc. Some controls showed significant difference in age group and alcohol drinking which would be adjusted in analysis of the relationship between polymorphisms and cancers. CYP1A1 Ile/Val and Val/Val genotypes were 33.43% and 5.62% respectively, which were similar to other results from Chinese and Japanese, but higher than those from Caucasians in American, Europe and African-Americans. GSTM1 null allele frequency was 53.48% in our population, which showed difference even among Chinese in different areas. GSTT1 null allele frequency was 45.78%, which was significantly higher than that in Caucasians and African-American. CONCLUSION: The frequencies of CYP1A1 Ile/Val, Val/Val and GSTT1 null in Han population in south area of China are significantly higher than those in other races, while the ethnic difference of frequency of GSTM1 null is less.