Simulation of Plasmonic Waveguides Based on Long-Range Surface Plasmon Polaritons

The demand for faster and smaller computing devices is growing larger and larger. In the recent decade, research has proven that plasmonic devices have exciting characteristics and performance for next generation on‑chip structures. However, most of these devices contain noble metals and are not CMOS compatible. This work numerically investigates the performance of plasmonic waveguide designs made of TiN, a CMOS compatible material with optical properties similar to gold. Through our work, we demonstrate that TiN nanophotonic devices can be useful for inter-chip connections. A series of simulations using COMSOL Multiphysics were performed to test the performance of these structures. 2D simulations were completed to gain insights into the relationship between the mode size, propagation length trade-off and how additional parameters such as cladding material, a slight mismatch in refractive index of super and substrate, and the thickness of the metal inside the waveguide, affect performance. We found that waveguides using materials of higher refractive will have better mode confinement, albeit with larger losses. If the same material is used, a slight change of refractive index typically in the range of ±0.01, causes the mode to expand to the side of lower index. Additional 3D simulations for waveguide bends, power splitters, and couplers are still in progress. The data of bend loss, power distribution, and mode shapes will be collected upon completion of the 3-D models. With the simulation data, our group will fabricate these waveguides accordingly and attempt further lab experiments to explore how these structures behave

Vertical distributions of soil microbial biomass carbon: a global dataset

Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables were converted to the standard international units or commonly used ones and the values were transformed correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC by using the equation (SOC = SOM × 0.58). This dataset can be used in predicting global SOC changes along soil profiles by using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls

An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method

The Probiotic Effectiveness in Preventing Experimental Colitis Is Correlated With Host Gut Microbiota

Current evidence to support extensive use of probiotics in inflammatory bowel disease is limited and factors that contribute to the inconsistent effectiveness of clinical probiotic therapy are not completely known. Here, we used Bifidobacterium longum JDM 301 as a model probiotic to study potential factors that may influence the effect of probiotics in experimental colitis. We found that the effect of B. longum JDM 301 in tempering experimental colitis varied across individual mice even with the same genetic background. The probiotic efficacy was highly correlated with the host gut microbial community features. Consumption of a diet rich in fat could exacerbate mucosal injury-induced colitis but could not change the host responsiveness to B. longum JDM 301 treatment, suggesting of potential mechanistic differences between regulating colitis pathogenesis, and modulating probiotic efficacies by the gut microbiota. Together, our results suggest that personalized microbiome features may modify the probiotic therapeutic effect and support the idea of personalized probiotic medicine in inflammatory bowel disease