Quantitative Monitoring of Mycelial Growth of Aspergillus fumigatus in Liquid Culture by Optical Density

Filamentous fungi generally form hyphal pellets in liquid culture. This property prevents filamentous fungi to apply the methods used for unicellular organisms such as yeast and bacteria.

Dynamical Component Exchange in a Model Phase Separating System: an NMR-based Approach

Biomolecular phase separation plays a key role in spatial organization of cellular activities. Dynamic formation and rapid component exchange between phase separated cellular bodies and their environment are crucial for their function. Here, we employ a well-established phase separating model system, namely, triethylamine (TEA)-water mixture, and develop an NMR approach to detect the exchange of scaffolding TEA molecules between separate phases and determine the underlying exchange rate. We further demonstrate how the advantageous NMR properties of fluorine nuclei provide access to otherwise inaccessible exchange processes of a client molecule. The developed NMR-based approach allows quantitative monitoring of the effect of regulatory factors on component exchange and facilitates “exchange”-based screening and optimization of small molecules against druggable biomolecular targets located inside condensed phases.

Quantitative Neuromuscular Monitoring and Postoperative Outcomes: A Narrative Review

Over the past five decades, quantitative neuromuscular monitoring devices have been used to examine the incidence of postoperative residual neuromuscular block in international clinical practices, and to determine their role in reducing the risk of residual neuromuscular block and associated adverse clinical outcomes. Several clinical trials and a recent meta-analysis have documented that the intraoperative application of quantitative monitoring significantly reduces the risk of residual neuromuscular blockade in the operating room and postanesthesia care unit. In addition, emerging data show that quantitative monitoring minimizes the risk of adverse clinical events, such as unplanned postoperative reintubations, hypoxemia, and postoperative episodes of airway obstruction associated with incomplete neuromuscular recovery, and may improve postoperative respiratory outcomes. Several international anesthesia societies have recommended that quantitative monitoring be performed whenever a neuromuscular blocking agent is administered. Therefore, a comprehensive review of the literature was performed to determine the potential benefits of quantitative monitoring in the perioperative setting.

Polyclonal Aptamers for Specific Fluorescence Labeling and Quantification of the Health Relevant Human Gut Bacterium Parabacteroides distasonis

Single-stranded DNA aptamers as affinity molecules for the rapid, reliable detection of intestinal bacteria are of particular interest to equip health systems with novel robust and cheap diagnostic tools for monitoring the success of supplementation strategies with selected probiotic gut bacteria in the fight against major widespread threats, such as obesity and neurodegenerative diseases. The human gut bacterium Parabacteroides distasonis (P. distasonis) is positively associated with diseases such as obesity, non-alcoholic fatty liver disease and multiple sclerosis with reduced cell counts in these diseases and is thus a promising potential probiotic bacterium for future microbial supplementation. In this paper we report on the evolution of a specific polyclonal aptamer library by the fluorescence based FluCell-SELEX directed against whole cells of P. distasonis that specifically and efficiently binds and labels P. distasonis. The aptamer library showed high binding affinity and was suited to quantitatively discriminate P. distasonis from other prominent gut bacteria also in mixtures. We believe that this library against a promising probiotic bacterium as a prototype may open new routes towards the development of novel biosensors for the easy and efficient quantitative monitoring of microbial abundance in human microbiomes in general.

Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features

Background Fractional vegetation cover (FVC) is an important basic parameter for the quantitative monitoring of the alpine grassland ecosystem on the Qinghai-Tibetan Plateau. Based on unmanned aerial vehicle (UAV) acquisition of measured data and matching it with satellite remote sensing images at the pixel scale, the proper selection of driving data and inversion algorithms can be determined and is crucial for generating high-precision alpine grassland FVC products. Methods This study presents estimations of alpine grassland FVC using optimized algorithms and multi-dimensional features. The multi-dimensional feature set (using original spectral bands, 22 vegetation indices, and topographical factors) was constructed from many sources of information, then the optimal feature subset was determined based on different feature selection algorithms as the driving data for optimized machine learning algorithms. Finally, the inversion accuracy, sensitivity to sample size, and computational efficiency of the four machine learning algorithms were evaluated. Results (1) The random forest (RF) algorithm (R2: 0.861, RMSE: 9.5%) performed the best for FVC inversion among the four machine learning algorithms driven by the four typical vegetation indices. (2) Compared with the four typical vegetation indices, using multi-dimensional feature sets as driving data obviously improved the FVC inversion accuracy of the four machine learning algorithms (R2 of the RF algorithm increased to 0.890). (3) Among the three variable selection algorithms (Boruta, sequential forward selection [SFS], and permutation importance-recursive feature elimination [PI-RFE]), the constructed PI-RFE feature selection algorithm had the best dimensionality reduction effect on the multi-dimensional feature set. (4) The hyper-parameter optimization of the machine learning algorithms and feature selection of the multi-dimensional feature set further improved FVC inversion accuracy (R2: 0.917 and RMSE: 7.9% in the optimized RF algorithm). Conclusion This study provides a highly precise, optimized algorithm with an optimal multi-dimensional feature set for FVC inversion, which is vital for the quantitative monitoring of the ecological environment of alpine grassland.

Quantitative Monitoring of Selected Groups of Parasites in Domestic Ruminants: A Comparative Review

Parasites have had a significant impact on domestic ruminant health and production for a long time, but the emerging threat of drug resistance urgently requires an improved approach to parasite monitoring and control activities. The study reviewed the international literature to analyze the different proposals for the sampling approach and the quantitative estimation of parasite burdens in groups of animals. Moreover, the use of thresholds to decide when and which animal to treat was also investigated. The findings of the study highlighted the presence of a wide-ranging literature on quantitative monitoring for gastrointestinal nematodes (GIN), while more limited data were found for coccidia, and no specific indications were reported for tapeworms. Concerning liver flukes, bronchopulmonary nematodes (BPN) and permanent ectoparasites (lice and mange mites), the diagnostic process is usually aimed at the detection of the parasite rather than at the burden estimation. The main research gaps that need further investigation were also highlighted. For some groups of parasites (e.g., GIN and coccidia) the quantitative approach requires an improved standardization, while its usefulness needs to be confirmed for others (e.g., BPN and lice). The development of practical guidelines for monitoring is also encouraged.

Insights into the Development of Phototrophic Biofilms in a Bioreactor by a Combination of X-ray Microtomography and Optical Coherence Tomography

As productive biofilms are increasingly gaining interest in research, the quantitative monitoring of biofilm formation on- or offline for the process remains a challenge. Optical coherence tomography (OCT) is a fast and often used method for scanning biofilms, but it has difficulty scanning through more dense optical materials. X-ray microtomography (μCT) can measure biofilms in most geometries but is very time-consuming. By combining both methods for the first time, the weaknesses of both methods could be compensated. The phototrophic cyanobacterium Tolypothrix distorta was cultured in a moving bed photobioreactor inside a biocarrier with a semi-enclosed geometry. An automated workflow was developed to process µCT scans of the biocarriers. This allowed quantification of biomass volume and biofilm-coverage on the biocarrier, both globally and spatially resolved. At the beginning of the cultivation, a growth limitation was detected in the outer region of the carrier, presumably due to shear stress. In the later phase, light limitations could be found inside the biocarrier. µCT data and biofilm thicknesses measured by OCT displayed good correlation. The latter could therefore be used to rapidly measure the biofilm formation in a process. The methods presented here can help gain a deeper understanding of biofilms inside a process and detect any limitations.