As a product of phosphorous recovery from anaerobic digestion (AD) of waste activated sludge (WAS), vivianite has received increasing attention. However, key factors controlling vivianite formation have not yet been fully addressed. Thus, this study was initiated to ascertain key factors controlling vivianite formation. A simulation of chemical equilibriums indicates that interfering ions such as metallic ions and inorganic compounds may affect vivianite formation, especially at a PO₄^3-concentration lower than 3 mM. The experiments demonstrated that the rate of ferric bio-reduction conducted by dissimilatory metal-reducing bacteria (DMRB) and the competition of methane-producing bacteria (MPB) with DMRB for VFAs (acetate) were not the key factors controlling vivianite formation, and that ferric bio-reduction of DMRB can proceed when a sufficient amount of Fe³⁺ exists in WAS. The determined affinity constants (K_s) of both DMRB and MPB on acetate revealed that the K_HAc constant (4.2 mmol/g VSS) of DMRB was almost 4 times lower than that of MPB (15.67 mmol/g VSS) and thus MPB could not seriously compete for VFAs (acetate) with DMRB. As a result, vivianite formation was controlled mainly by the amount of Fe³⁺ in WAS. In practice, a Fe/P molar ratio of 2:1 should be enough for vivianite formation in AD of WAS. Otherwise, exogenously dosing Fe³⁺ or Fe²⁺ into AD must be applied in AD.

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The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between these batches. Typically, these batch effects are resolved by matching similar cells across the different batches. Current approaches, however, do not take into account that we can constrain this matching further as cells can also be matched on their cell type identity. We use an auto-encoder to embed two batches in the same space such that cells are matched. To accomplish this, we use a loss function that preserves: (1) cell-cell distances within each of the two batches, as well as (2) cell-cell distances between two batches when the cells are of the same cell-type. The cell-type guidance is unsupervised, i.e., a cell-type is defined as a cluster in the original batch. We evaluated the performance of our cluster-guided batch alignment (CBA) using pancreas and mouse cell atlas datasets, against six state-of-the-art single cell alignment methods: Seurat v3, BBKNN, Scanorama, Harmony, LIGER, and BERMUDA. Compared to other approaches, CBA preserves the cluster separation in the original datasets while still being able to align the two datasets. We confirm that this separation is biologically meaningful by identifying relevant differential expression of genes for these preserved clusters.@en