Successful transplantation of guinea pig gut microbiota in mice and its effect on pneumonic plague sensitivity

Microbiota-driven variations in the inflammatory response are predicted to regulate host responses to infection. Increasing evidence indicates that the gastrointestinal and respiratory tracts have an intimate relationship with each other. Gut microbiota can influence lung immunity whereby gut-derived injurious factors can reach the lungs and systemic circulation via the intestinal lymphatics. The intestinal microbiota’s ability to resist colonization can be extended to systemic infections or to pathogens infecting distant sites such as the lungs. Unlike the situation with large mammals, the microtus Yersinia pestis 201 strain exhibits strong virulence in mice, but nearly no virulence to large mammals (such as guinea pigs). Hence, to assess whether the intestinal microbiota from guinea pigs was able to affect the sensitivity of mice to challenge infection with the Y. pestis 201 strain, we fed mice with guinea pig diets for two months, after which they were administered 0.5 ml of guinea pig fecal suspension for 30 days by oral gavage. The stools from each mouse were collected on days 0, 15, and 30, DNA was extracted from them, and 16S rRNA sequencing was performed to assess the diversity and composition of the gut microbiota. We found that the intestinal microbiota transplants from the guinea pigs were able to colonize the mouse intestines. The mice were then infected with Yersinia pestis 201 by lung invasion, but no statistical difference was found in the survival rates of the mice that were colonized with the guinea pig’s gut microbiota and the control mice. This indicates that the intestinal microbiota transplantation from the guinea pigs did not affect the sensitivity of the mice to pneumonic plague

Evaluation of femtosecond laser-induced breakdown spectroscopy system as an offline coal analyzer.

Developments in femtosecond laser induced breakdown spectroscopy (fs-LIBS) applications during the last two decades have further centered on innovative métier tie-in to the advantageous properties of femtosecond laser ablation (fs-LA) introduced into LIBS. Yet, for industrially-oriented application like coal analysis, no research has exposed to view the analytical capabilities of fs-LA in enhancing the physical processes of coal ablation and the impact into quantitative correlation of spectra and data modeling. In a huge coal market, fast and accurate analysis of coal property is eminently important for coal pricing, combustion optimization, and pollution reduction. Moreover, there is a thirst need of precision standardization for coal analyzers in use. In this letter, the analytical performance of a one-box femtosecond laser system is evaluated relative to an industrially applied coal analyzer based on five objectives/measures: spectral correlation, relative sensitivity factors, craters topology, plasma parameters, and repeatability. Despite high-threshold operation parameters of the fs system, competitive results are achieved compared to the optimized analytical conditions of the ns-coal analyzer. Studies targeting the in-field optimization of fs-LIBS systems for coal analysis can potentially provide insights into fs-plasma hydrodynamics under harsh conditions, instrumental customization, and pave the way for a competitive next-generation of coal analyzers

Evaluation of femtosecond laser-induced breakdown spectroscopy system as an offline coal analyzer.

Developments in femtosecond laser induced breakdown spectroscopy (fs-LIBS) applications during the last two decades have further centered on innovative métier tie-in to the advantageous properties of femtosecond laser ablation (fs-LA) introduced into LIBS. Yet, for industrially-oriented application like coal analysis, no research has exposed to view the analytical capabilities of fs-LA in enhancing the physical processes of coal ablation and the impact into quantitative correlation of spectra and data modeling. In a huge coal market, fast and accurate analysis of coal property is eminently important for coal pricing, combustion optimization, and pollution reduction. Moreover, there is a thirst need of precision standardization for coal analyzers in use. In this letter, the analytical performance of a one-box femtosecond laser system is evaluated relative to an industrially applied coal analyzer based on five objectives/measures: spectral correlation, relative sensitivity factors, craters topology, plasma parameters, and repeatability. Despite high-threshold operation parameters of the fs system, competitive results are achieved compared to the optimized analytical conditions of the ns-coal analyzer. Studies targeting the in-field optimization of fs-LIBS systems for coal analysis can potentially provide insights into fs-plasma hydrodynamics under harsh conditions, instrumental customization, and pave the way for a competitive next-generation of coal analyzers

Ultra-High Sensitivity Ultrasonic Sensor with an Extrinsic All-Polymer Cavity

An ultra-high sensitivity ultrasonic sensor with an extrinsic all-polymer cavity is presented. The probe is constructed with a polymer ferrule and a polymer-based reflection diaphragm. A specially designed polymer cover is used to seal the cavity sensor head and apply pretension to the sensing diaphragm. It can be manufactured by a commercial 3D printer with good reproducibility. Due to its all-polymer structure and high coherence depth, the sensitivity of our proposed sensor is improved significantly compared with that of the other sensor structures. Its sensitivity is 189 times as great as that of the commercial standard ultrasonic sensor at the ultrasonic frequency of 50 KHz, and it has a good response to ultrasonic within the frequency range of 18.5 KHz–200 KHz

On the Spectral Identification and Wavelength Dependence of Rare-Earth Ore Emission by Laser-Induced Breakdown Spectroscopy

The increasing demand for rare earth elements (REE) requires faster analysis techniques for their rapid exploration. Laser-induced breakdown spectroscopy (LIBS) has on-site and real time analysis capability. However, interference and the weaker emission of minor REEs are key challenges for the complex REE emission spectra. Using simulations and experimental results, we presented essential principles for improved line identification in the transient spectra of complicated samples, such as those of REE ores (e.g., monazite). Knowledge of plasma conditions, spectral collection setup, and capability of the spectral system are key parameters to consider for the identification of an emission line in such spectra. Furthermore, emission intensity dependence on laser wavelength was analyzed for major and minor REEs using IR (1064 nm), visible (532 nm) and UV (266 nm) irradiation. A higher plasma temperature was found with the IR laser, while stronger material ablation was observed by UV irradiation. Higher particle density by UV laser ablation was the key factor in the higher signal intensity of the minor elements, and this laser can improve the emission signals for LIBS use as an REE analyzer

Recent Advances of Preparation and Application of Two-Dimension van der Waals Heterostructure

With paramount electrical, optical, catalytic, and other physical and chemical properties, van der Waals heterostructures (vdWHs) have captured increasing attention. vdWHs are two-dimension (2D) heterostructures formed via van der Waals (vdW) force, paving the way for fabricating, understanding, and applications of 2D materials. vdWHs materials of large lattice constant difference can be fabricated together, forming a series of unique 2D materials that cannot form heterostructures earlier. Additionally, vdWHs provide a new platform to study the interlayer interactions between materials, unraveling new physics in the system. Notably, vdWHs embody short-range bonds weaker than covalent and ionic bonds, almost only interactions between nearest particles are considered. Owing to a clear interface, vdW interaction between two different components, devices made by vdWHs can bring amazing physicochemical properties, such as unconventional superconductivity, super capacitance in intercalation 2D structure, etc. Recently, impressive progress has been achieved in the controlled preparation of vdWHs and various applications, which will be summarized in this review. The preparation methods comprise mechanical exfoliation, liquid phase stripping, physical vapor deposition, chemical vapor deposition, and metalorganic chemical vapor deposition. The applications sections will focus on photoelectric devices, logic devices, flexible devices, and piezotronics. Finally, some perspectives in the future on the controlled preparation of vdWHs with desired properties for advanced applications will be discussed

Incorporating domain knowledge into machine learning for laser-induced breakdown spectroscopy quantification

During the last decade, various machine learning methods have been applied to improve the accuracy of quantitative analysis in laser-induced breakdown spectroscopy (LIBS) by modelling the complex relationship between spectral intensity and analyte concentration. However, machine learning methods tend to have high model complexity and are difficult to interpret their predictions. Moreover, their decision-making mechanisms rarely consider the physical principles behind quantitative analysis, resulting in a reduction of LIBS quantification accuracy and a question of trust in the quantification results. This work investigates the feasibility of incorporating domain knowledge into machine learning to improve LIBS quantification performance. A new regression method based on dominant factor and kernel extreme learning machine is proposed, namely DF-K-ELM. It uses knowledge-based spectral lines, related to analyte compositions, to construct a linear physical principle based model and adopts K-ELM to account for the residuals of the linear model. DF-K-ELM intuitively explains how knowledge-based spectral lines influence prediction results and improves model interpretability without reducing model complexity. The proposed method, DF-K-ELM, is tested on 10 regression tasks based on 3 LIBS datasets and compared with 6 baseline methods. It achieves the best and second best performance on 4 and 2 tasks, respectively. Moreover, compared to traditional machine learning methods, dominant factor based methods yield higher accuracy in most cases. Such results demonstrate that incorporating domain knowledge into machine learning is a viable approach to improve the performance of LIBS quantification.</p