Enrichment of phosphate-accumulating organisms (PAOs) in a microfluidic model biofilm system by mimicking a typical aerobic granular sludge feast/famine regime.

Wastewater treatment using aerobic granular sludge has gained increasing interest due to its advantages compared to conventional activated sludge. The technology allows simultaneous removal of organic carbon, nitrogen, and phosphorus in a single reactor system and is independent of space-intensive settling tanks. However, due to the microscale, an analysis of processes and microbial population along the radius of granules is challenging. Here, we introduce a model system for aerobic granular sludge on a small scale by using a machine-assisted microfluidic cultivation platform. With an implemented logic module that controls solenoid valves, we realized alternating oxic hunger and anoxic feeding phases for the biofilms growing within. Sampling during ongoing anoxic cultivation directly from the cultivation channel was achieved with a robotic sampling device. Analysis of the biofilms was conducted using optical coherence tomography, fluorescence in situ hybridization, and amplicon sequencing. Using this setup, it was possible to significantly enrich the percentage of polyphosphate-accumulating organisms (PAO) belonging to the family Rhodocyclaceae in the community compared to the starting inoculum. With the aid of this miniature model system, it is now possible to investigate the influence of a multitude of process parameters in a highly parallel way to understand and efficiently optimize aerobic granular sludge-based wastewater treatment systems.Key points• Development of a microfluidic model to study EBPR.• Feast-famine regime enriches polyphosphate-accumulating organisms (PAOs).• Microfluidics replace sequencing batch reactors for aerobic granular sludge research.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.