Plant–soil feedbacks contribute to vegetation dynamics by species-specific interactions between plants and soil biota. Variation in plant–soil feedbacks can be predicted by root traits, successional position, and plant nativeness. However, it is unknown whether closely related plant species develop more similar plant–soil feedbacks than more distantly related species. Where previous comparisons included plant species from distant phylogenetic positions, we studied plant–soil feedbacks of congeneric species. Using eight intra-continentally range-expanding and native Geranium species, we tested relations between phylogenetic distances, chemical and structural root traits, root microbiomes, and plant–soil feedbacks. We show that root chemistry and specific root length better predict bacterial and fungal community composition than phylogenetic distance. Negative plant–soil feedback strength correlates with root-feeding nematode numbers, whereas microbiome dissimilarity, nativeness, or phylogeny does not predict plant–soil feedbacks. We conclude that root microbiome variation among congeners is best explained by root traits, and that root-feeding nematode abundances predict plant–soil feedbacks.

In this paper, we propose to improve emotion recognition by combining acoustic information and conversation transcripts. On the one hand, a LSTM network was used to detect emotion from acoustic features like f0, shimmer, jitter, MFCC, etc. On the other hand, a multi-resolution CNN was used to detect emotion from word sequences. This CNN consists of several parallel convolutions with different kernel sizes to exploit contextual information at different levels. A temporal pooling layer aggregates the hidden representations of different words into a unique sequence level embedding, from which we computed the emotion posteriors. We optimized a weighted sum of classification and verification losses. The verification loss tries to bring embeddings from same emotions closer while separating embeddings from different emotions. We also compared our CNN with state-of-the-art text-based hand-crafted features (e-vector). We evaluated our approach on the USC-IEMOCAP dataset as well as the dataset consisting of US English telephone speech. In the former, we used human-annotated transcripts while in the latter, we used ASR transcripts. The results showed fusing audio and transcript information improved unweighted accuracy by relative 24% for IEMOCAP and relative 3.4% for the telephone data compared to a single acoustic system.

Pyridoxine-dependent epilepsy (PDE-ALDH7A1), also known as antiquitin deficiency, is an inborn error of lysine metabolism that presents with refractory epilepsy in newborns. Bi-allelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important co-factor pyridoxal-5'phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but despite this treatment, intellectual disability may occur. Early diagnosis and treatment, preferably based on newborn screening, potentially optimize long-term clinical outcome. However, the currently known diagnostic PDE-ALDH7A1 biomarkers are incompatible with newborn screening procedures. Combining the innovative analytical methods untargeted metabolomics and infrared ion spectroscopy, we were able to discover a novel biomarker for PDE-ALDH7A1, 2S,6S-and 2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP), and confirmed 6oxopiperidine-2-carboxylic acid (6-oxoPIP) as biomarker. We demonstrated the applicability of 2-OPP as a PDE-ALDH7A1 biomarker in newborn screening. Additionally, we showed that 2-OPP accumulates in brain tissue of patients and aldh7a1 knock-out mice, and induced epilepsy-like behavior in a zebrafish model system. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients.

Established laser-based ionization experiments require the surface of a sample to be as flat as possible to guarantee optimal laser focus. A laser ablation electrospray ionization (LAESI) source was custom-built to accommodate the topography of non-flat sample surfaces. Employing a confocal distance sensor, a height profile of the surface in question was recorded prior to the actual ionization experiment. The robustness of the system was evaluated by the metabolic profiling of radish (Raphanus sativus) leaves, chosen due to their pronounced surface features and known content of specialized metabolites. After the ionization experiments, light microscopy imaging was performed to evaluate ablation crater size and position. Reproducible ablation mark diameters of 69 7 mm in average have been achieved. Mass spectrometric imaging capability has been proven on R. sativus leaf samples as well