Breakdown of hardly degradable carbohydrates (lignocellulose) in a two-stage anaerobic digestion plant is favored in the main fermenter.

The yield and productivity of biogas plants depend on the degradation performance of their microbiomes. The spatial separation of the anaerobic digestion (AD) process into a separate hydrolysis and a main fermenter should improve cultivation conditions of the microorganisms involved in the degradation of complex substrates like lignocellulosic biomass (LCB) and, thus, the performance of anaerobic digesters. However, relatively little is known about such two-stage processes. Here, we investigated the process performance of a two-stage agricultural AD over one year, focusing on chemical and technical process parameters and metagenome-centric metaproteomics. Technical and chemical parameters indicated stable operation of the main fermenter but varying conditions for the open hydrolysis fermenter. Matching this, the microbiome in the hydrolysis fermenter has a higher dynamic than in the main fermenter. Metaproteomics-based microbiome analysis revealed a partial separation between early and common steps in carbohydrate degradation and primary fermentation in the hydrolysis fermenter but complex carbohydrate degradation, secondary fermentation, and methanogenesis in the main fermenter. Detailed metagenomics and metaproteomics characterization of the single metagenome-assembled genomes showed that the species focus on specific substrate niches and do not utilize their full genetic potential to degrade, for example, LCB. Overall, it seems that a separation of AD in a hydrolysis and a main fermenter does not improve the cleavage of complex substrates but significantly improves the overall process performance. In contrast, the remaining methanogenic activity in the hydrolysis fermenter may cause methane losses.

The importance of graph databases and graph learning for clinical applications.

The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. Graphical abstract.

Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets.

BACKGROUND: Graph databases enable efficient storage of heterogeneous, highly-interlinked data, such as clinical data. Subsequently, researchers can extract relevant features from these datasets and apply machine learning for diagnosis, biomarker discovery, or understanding pathogenesis. METHODS: To facilitate machine learning and save time for extracting data from the graph database, we developed and optimized Decision Tree Plug-in (DTP) containing 24 procedures to generate and evaluate decision trees directly in the graph database Neo4j on homogeneous and unconnected nodes. RESULTS: Creation of the decision tree for three clinical datasets directly in the graph database from the nodes required between 0.059 and 0.099 s, while calculating the decision tree with the same algorithm in Java from CSV files took 0.085-0.112 s. Furthermore, our approach was faster than the standard decision tree implementations in R (0.62 s) and equal to Python (0.08 s), also using CSV files as input for small datasets. In addition, we have explored the strengths of DTP by evaluating a large dataset (approx. 250,000 instances) to predict patients with diabetes and compared the performance against algorithms generated by state-of-the-art packages in R and Python. By doing so, we have been able to show competitive results on the performance of Neo4j, in terms of quality of predictions as well as time efficiency. Furthermore, we could show that high body-mass index and high blood pressure are the main risk factors for diabetes. CONCLUSION: Overall, our work shows that integrating machine learning into graph databases saves time for additional processes as well as external memory, and could be applied to a variety of use cases, including clinical applications. This provides user with the advantages of high scalability, visualization and complex querying.

Sub-10-fs observation of bound exciton formation in organic optoelectronic devices.

Fundamental mechanisms underlying exciton formation in organic semiconductors are complex and elusive as it occurs on ultrashort sub-100-fs timescales. Some fundamental aspects of this process, such as the evolution of exciton binding energy, have not been resolved in time experimentally. Here, we apply a combination of sub-10-fs Pump-Push-Photocurrent, Pump-Push-Photoluminescence, and Pump-Probe spectroscopies to polyfluorene devices to track the ultrafast formation of excitons. While Pump-Probe is sensitive to the total concentration of excited states, Pump-Push-Photocurrent and Pump-Push-Photoluminescence are sensitive to bound states only, providing access to exciton binding dynamics. We find that excitons created by near-absorption-edge photons are intrinsically bound states, or become such within 10 fs after excitation. Meanwhile, excitons with a modest >0.3 eV excess energy can dissociate spontaneously within 50 fs before acquiring bound character. These conclusions are supported by excited-state molecular dynamics simulations and a global kinetic model which quantitatively reproduce experimental data.

MPA_Pathway_Tool: User-Friendly, Automatic Assignment of Microbial Community Data on Metabolic Pathways.

Taxonomic and functional characterization of microbial communities from diverse environments such as the human gut or biogas plants by multi-omics methods plays an ever more important role. Researchers assign all identified genes, transcripts, or proteins to biological pathways to better understand the function of single species and microbial communities. However, due to the versality of microbial metabolism and a still-increasing number of newly biological pathways, linkage to standard pathway maps such as the KEGG central carbon metabolism is often problematic. We successfully implemented and validated a new user-friendly, stand-alone web application, the MPA_Pathway_Tool. It consists of two parts, called 'Pathway-Creator' and 'Pathway-Calculator'. The 'Pathway-Creator' enables an easy set-up of user-defined pathways with specific taxonomic constraints. The 'Pathway-Calculator' automatically maps microbial community data from multiple measurements on selected pathways and visualizes the results. The MPA_Pathway_Tool is implemented in Java and ReactJS.

Ultrafast isomerization initiated by X-ray core ionization.

Rapid proton migration is a key process in hydrocarbon photochemistry. Charge migration and subsequent proton motion can mitigate radiation damage when heavier atoms absorb X-rays. If rapid enough, this can improve the fidelity of diffract-before-destroy measurements of biomolecular structure at X-ray-free electron lasers. Here we study X-ray-initiated isomerization of acetylene, a model for proton dynamics in hydrocarbons. Our time-resolved measurements capture the transient motion of protons following X-ray ionization of carbon K-shell electrons. We Coulomb-explode the molecule with a second precisely delayed X-ray pulse and then record all the fragment momenta. These snapshots at different delays are combined into a 'molecular movie' of the evolving molecule, which shows substantial proton redistribution within the first 12 fs. We conclude that significant proton motion occurs on a timescale comparable to the Auger relaxation that refills the K-shell vacancy.

Carrier-envelope phase stability of hollow fibers used for high-energy few-cycle pulse generation.

We investigated the carrier-envelope phase (CEP) stability of hollow-fiber compression for high-energy few-cycle pulse generation. Saturation of the output pulse energy is observed at 0.6 mJ for a 260 µm inner-diameter, 1 m long fiber, statically filled with neon. The pressure is adjusted to achieve output spectra supporting sub-4-fs pulses. The maximum output pulse energy can be increased to 0.8 mJ by either differential pumping (DP) or circularly polarized input pulses. We observe the onset of an ionization-induced CEP instability, which saturates beyond input pulse energies of 1.25 mJ. There is no significant difference in the CEP stability with DP compared to static-fill.