Conversion of Land Use from Upland to Paddy Field Changes Soil Bacterial Community Structure in Mollisols of Northeast China.

Mollisols are extremely important soil resource for crop and forage production. In northeast China, it is a major land use management practice from dry land crops to irrigated rice. However, there is few data regarding soil quality and microbial composition in Mollisols during land use transition. Here, we analyzed the upper 30 cm of soil from land with more than 30 years of paddy use and from adjacent areas with upland crops. Our results showed that land use and soil depth had a significant effect on soil properties and enzyme activities. Soil moisture (SM) and soil organic carbon (SOC) contents were substantially higher in paddy fields than in upland crop lands, while nitrogen-related enzyme activities were lower. Following the land use change, bacterial diversity was increased and bacterial community composition changed. Taxonomic analyses showed that Proteobacteria, Chloroflexi, Firmicutes, and Bacteroidetes were the dominant phyla present. At family level, Gemmatimonadaceae decreased with land use change, while Syntrophorhabdaceae and Syntrophacea that play a part in methane cycling and nitrifying bacteria such as Nitrospiraceae increased, indicating that the structure and composition of the bacterial community might be a promising indicator of Mollisol health. Redundancy analysis indicated that land use type had a stronger effect on the soil bacterial community composition than soil depth. Additionally, bacterial community composition was closely associated with soil parameters such as soil moisture, pH, SOC, NO3--N, and NH4+-N. Overall, land use change affects the physical and chemical properties of the soil, resulting in changes in the composition of the soil bacterial community and flora. These changes could provide a view of the bacterial community assembly and functional shifts following land use change.

Soil bacterial communities of paddy are dependent on root compartment niches but independent of growth stages from Mollisols of Northeast China.

Introduction: Deep insights into adhering soil of root zones (rhizosphere and rhizoplane) microbial community could provide a better understanding of the plant-microbe relationship. To better understand the dynamics of these microbial assemblies over the plant life cycle in rhizodeposition along rice roots. Methods: Here, we investigated bacterial distribution in bulk, rhizosphere, and rhizoplane soils at tillering, heading, and mature stage, from rice (Oryza sativa) fields of the Northeast China. Results and Discussion: Our results revealed that soil bacterial α-diversity and community composition were significantly affected by root compartment niches but not by temporal change. Compared to rhizoplane soils in the same period, bulk in the heading and rhizosphere in the mature had the largest increase in Shannon's index, with 11.02 and 14.49% increases, respectively. Proteobacteria, Chloroflexi, Bacteroidetes, and Acidobacteria are predominant across all soil samples, bulk soil had more phyla increased across the growing season than that of root related-compartments. Deterministic mechanisms had a stronger impact on the bacterial community in the compartments connected to the roots, with the relative importance of the bulk soil, rhizoplane and rhizosphere at 83, 100, and 56%, respectively. Because of ecological niche drivers, the bacterial networks in bulk soils exhibit more complex networks than rhizosphere and rhizoplane soils, reflected by more nodes, edges, and connections. More module hub and connector were observed in bulk (6) and rhizoplane (5) networks than in rhizosphere (2). We also detected shifts from bulk to rhizoplane soils in some functional guilds of bacteria, which changed from sulfur and nitrogen utilization to more carbon and iron cycling processes. Taken together, our results suggest distinct bacterial network structure and distribution patterns among rhizosphere, rhizoplane, and bulk soils, which could possibly result in potential functional differentiation. And the potential functional differentiation may be influenced by plant root secretions, which still needs to be further explored.

Parkinson's Disease Action Tremor Detection with Supervised-Leaning Models.

People with Parkinson's Disease (PD) have multiple symptoms, such as freezing of gait (FoG), hand tremors, speech difficulties, and balance issues, in different stages of the disease. Among these symptoms, hand tremors are present across all stages of the disease. PD hand tremors have critical consequences and negatively impact the quality of PD patients' everyday lives. Researchers have proposed a variety of wearable devices to mitigate PD tremors. However, these devices require accurate tremor detection technology to work effectively while the tremor occurs. This paper introduces a PD action tremor detection method to recognize PD tremors from regular activities. We used a dataset from 30 PD patients wearing accelerometers and gyroscope sensors on their wrists. We selected time-domain and frequency-domain hand-crafted features. Also, we compared our hand-crafted features with existing CNN data-driven features, and our features have more specific boundaries in 2-D feature visualization using the t-SNE tool. We fed our features into multiple supervised machine learning models, including Logistic Regression (LR), K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs), for detecting PD action tremors. These models were evaluated with 30 PD patients' data. The performance of all models using our features has more than 90% of F1 scores in five-fold cross-validations and 88% F1 scores in the leave-one-out evaluation. Specifically, Support Vector Machines (SVMs) perform the best in five-fold cross-validation with over 92% F1 scores. SVMs also show the best performance in the leave-one-out evaluation with over 90% F1 scores.

Use of mulberry-soybean intercropping in salt-alkali soil impacts the diversity of the soil bacterial community.

Diverse intercropping system has been used to control disease and improve productivity in the field. In this research, the bacterial communities in salt-alkali soils of monoculture and intercropping mulberry and soybean were studied using 454-pyrosequencing of the 16S rDNA gene. The dominant taxonomic groups were Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Bacteroidetes, Planctomycetes and Gemmatimonadetes and these were present across all samples. However, the diversity and composition of bacterial communities varied between monoculture and intercropping samples. The estimated bacterial diversity (H') was higher with intercropping soybean than in monoculture soybean, whereas H' showed an opposite pattern in monoculture and intercropping mulberry. Populations of Actinobacteria, Acidobacteria, and Proteobacteria were variable, depending on growth of plants as monoculture or intercropped. Most of Actinobacteria and Chloroflexi were found in intercropping samples, while Acidobacteria and Proteobacteria were present at a higher percentage in monoculture samples. The plant diversity of aboveground and microbial diversity of belowground was linked and soil pH seemed to influence the bacterial community. Finally, the specific plant species was the major factor that determined the bacterial community in the salt-alkali soils.