Fungal Enzymes and Yeasts for Conversion of Plant Biomass to Bioenergy and High-Value Products

Fungi and fungal enzymes play important roles in the new bioeconomy. Enzymes from filamentous fungi can unlock the potential of recalcitrant lignocellulose structures of plant cell walls as a new resource, and fungi such as yeast can produce bioethanol from the sugars released after enzyme treatment. Such processes reflect inherent characteristics of the fungal way of life, namely, that fungi as heterotrophic organisms must break down complex carbon structures of organic materials to satisfy their need for carbon and nitrogen for growth and reproduction. This chapter describes major steps in the conversion of plant biomass to value-added products. These products provide a basis for substituting fossil-derived fuels, chemicals, and materials, as well as unlocking the biomass potential of the agricultural harvest to yield more food and feed. This article focuses on the mycological basis for the fungal contribution to biorefinery processes, which are instrumental for improved resource efficiency and central to the new bioeconomy. Which types of processes, inherent to fungal physiology and activities in nature, are exploited in the new industrial processes? Which families of the fungal kingdom and which types of fungal habitats and ecological specializations are hot spots for fungal biomass conversion? How can the best fungal enzymes be found and optimized for industrial use? How can they be produced most efficiently-in fungal expression hosts? How have industrial biotechnology and biomass conversion research contributed to mycology and environmental research? Future perspectives and approaches are listed, highlighting the importance of fungi in development of the bioeconomy

Comparison of synthetic dataset generation methods for medical intervention rooms using medical clothing detection as an example

Abstract Purpose The availability of real data from areas with high privacy requirements, such as the medical intervention space is low and the acquisition complex in terms of data protection. To enable research for assistance systems in the medical intervention room, new methods for data generation for these areas must be researched. Therefore, this work presents a way to create a synthetic dataset for the medical context, using medical clothing object detection as an example. The goal is to close the reality gap between the synthetic and real data. Methods Methods of 3D-scanned clothing and designed clothing are compared in a Domain-Randomization and Structured-Domain-Randomization scenario using two different rendering engines. Additionally, a Mixed-Reality dataset in front of a greenscreen and a target domain dataset were used while the latter is used to evaluate the different datasets. The experiments conducted are to show whether scanned clothing or designed clothing produce better results in Domain Randomization and Structured Domain Randomization. Likewise, a baseline will be generated using the mixed reality data. In a further experiment it is investigated whether the combination of real, synthetic and mixed reality image data improves the accuracy compared to real data only. Results Our experiments show, that Structured-Domain-Randomization of designed clothing together with Mixed-Reality data provide a baseline achieving 72.0% mAP on the test dataset of the clinical target domain. When additionally using 15% (99 images) of available target domain train data, the gap towards 100% (660 images) target domain train data could be nearly closed 80.05% mAP (81.95% mAP). Finally, we show that when additionally using 100% target domain train data the accuracy could be increased to 83.35% mAP. Conclusion In conclusion, it can be stated that the presented modeling of health professionals is a promising methodology to address the challenge of missing datasets from medical intervention rooms. We will further investigate it on various tasks, like assistance systems, in the medical domain

Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

Changes in gait patterns provide important information about individuals’ health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson’s disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living

Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

Changes in gait patterns provide important information about individuals’ health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson’s disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living

Use of a sequential high throughput screening assay to identify novel inhibitors of the eukaryotic SRP-Sec61 targeting/translocation pathway.

The SRP-Sec61 targeting/translocation pathway of eukaryotic cells targets nascent protein chains to the membrane of the endoplasmic reticulum. Using this machinery, secretory proteins are translocated across this membrane whereas membrane proteins are integrated into the lipid bilayer. One of the key players of the pathway is the protein-conducting Sec61 (translocon) complex of the endoplasmic reticulum. The Sec61 complex has no enzymatic activity, is expressed only intracellularly and is difficult to purify and to reconstitute. Screening for small molecule inhibitors impairing its functions is thus notoriously difficult. Such inhibitors may not only be valuable tools for cell biology, they may also represent novel anti-tumor drugs. Here we have developed a two-step, sequential screening assay for inhibitors of the whole SRP-Sec61 targeting/translocation pathway which might include molecules affecting Sec61 complex functions. The resulting hit compounds were analyzed using a whole cell biosynthesis assay and a cell free transcription/translation/translocation assay. Using this methodology, we identified novel compounds inhibiting this pathway. Following structure-based back screening, one of these substances was analyzed in more detail and we could show that it indeed impairs translocation at the level of the Sec61 complex. A slightly modified methodology may be used in the future to screen for substances affecting SecYEG, the bacterial ortholog of the Sec61 complex in order to derive novel antibiotic drugs

MYCN recruits the nuclear exosome complex to RNA polymerase II to prevent transcription-replication conflicts

The MYCN oncoprotein drives the development of numerous neuroendocrine and pediatric tumors. Here we show that MYCN interacts with the nuclear RNA exosome, a 3'-5' exoribonuclease complex, and recruits the exosome to its target genes. In the absence of the exosome, MYCN-directed elongation by RNA polymerase II (RNAPII) is slow and non-productive on a large group of cell-cycle-regulated genes. During the S phase of MYCN-driven tumor cells, the exosome is required to prevent the accumulation of stalled replication forks and of double-strand breaks close to the transcription start sites. Upon depletion of the exosome, activation of ATM causes recruitment of BRCA1, which stabilizes nuclear mRNA decapping complexes, leading to MYCN-dependent transcription termination. Disruption of mRNA decapping in turn activates ATR, indicating transcription-replication conflicts. We propose that exosome recruitment by MYCN maintains productive transcription elongation during S phase and prevents transcription-replication conflicts to maintain the rapid proliferation of neuroendocrine tumor cells

Comparison of nine different commercially available molecular assays for detection of SARS-CoV-2 RNA

To face the COVID-19 pandemic, the need for fast and reliable diagnostic assays for the detection of SARS-CoV-2 is immense. We describe our laboratory experiences evaluating nine commercially available real-time RT-PCR assays. We found that assays differed considerably in performance and validation before routine use is mandatory

A MYC–GCN2–eIF2α negative feedback loop limits protein synthesis to prevent MYC-dependent apoptosis in colorectal cancer

Tumours depend on altered rates of protein synthesis for growth and survival, which suggests that mechanisms controlling mRNA translation may be exploitable for therapy. Here, we show that loss of APC, which occurs almost universally in colorectal tumours, strongly enhances the dependence on the translation initiation factor eIF2B5. Depletion of eIF2B5 induces an integrated stress response and enhances translation of MYC via an internal ribosomal entry site. This perturbs cellular amino acid and nucleotide pools, strains energy resources and causes MYC-dependent apoptosis. eIF2B5 limits MYC expression and prevents apoptosis in APC-deficient murine and patient-derived organoids and in APC-deficient murine intestinal epithelia in vivo. Conversely, the high MYC levels present in APC-deficient cells induce phosphorylation of eIF2α via the kinases GCN2 and PKR. Pharmacological inhibition of GCN2 phenocopies eIF2B5 depletion and has therapeutic efficacy in tumour organoids, which demonstrates that a negative MYC–eIF2α feedback loop constitutes a targetable vulnerability of colorectal tumours