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Comprehensive phenotypic analysis of multiple gene deletions of α-glucan synthase and Crh-transglycosylase gene families in Aspergillus niger highlighting the versatility of the fungal cell wall
Get PDFMultiple paralogs are found in the fungal genomes for several genes that encode proteins involved in cell wall biosynthesis. The genome of A. niger contains five genes encoding putative α-1,3-glucan synthases (AgsA-E) and seven genes encoding putative glucan-chitin crosslinking enzymes (CrhA-G). Here, we systematically studied the effects of the deletion of single (agsA or agsE), double (agsA and agsE), or all five ags genes (agsA-E) present in A. niger. Morphological and biochemical analysis of ags mutants emphasizes the important role of agsE in cell wall integrity, while deletion of other ags genes had minimal impact. Loss of agsE compromised cell wall integrity and altered pellet morphology in liquid cultures.
Previous studies have indicated that deletion of all crh genes in A. niger did not result in cell wall integrity-related phenotypes. To determine whether the ags and crh gene families have redundant functions, both gene families were deleted using iterative CRISPR/Cas9 mediated genome editing. The 12-fold deletion mutant was viable and did not exhibit growth defects under non-stressing growth conditions. A synergistic effect on cell wall integrity was observed in this 12-fold deletion mutant, particularly when exposed to cell wall-perturbing compounds. The cell wall composition, extractability of glucans by alkali, and scanning electron microscopy analysis showed no differences between the parental strain and mutants lacking ags genes, crh genes, or both. These observations underscore the ability of fungal cells to adapt and secure cell wall integrity, even when two entire cell wall protein-encoding gene families are missing
Nutrient+CtrlIVF - Ionenselektive Regelung zur ressourceneffizienten und pflanzenbedarfsgerechten Nährstoffversorgung in rezirkulierenden, hydroponischen Indoor Vertical Farms (IVF)
Get PDFDas Projekt Nutrient+CtrlIVF der Hochschule Osnabrück verfolgt das Ziel, eine bedarfsgenaue und ressourcenschonende Nährstoffversorgung in rezirkulierenden, hydroponischen Indoor Vertical Farms (IVF) zu realisieren. Im Mittelpunkt steht die Entwicklung eines innovativen Steuerungssystems, das mithilfe ionensensitiver Feldeffekttransistoren (ISFET) die Konzentrationen einzelner Pflanzennährstoffe wie Nitrat, Ammonium und Kalium in Echtzeit überwacht und reguliert. Dadurch wird die bisher übliche Steuerung über den unspezifischen EC-Wert deutlich präzisiert. Das System ermöglicht eine adaptive, pflanzenartspezifische Nährstoffversorgung und trägt zur Optimierung von Ertrag, Qualität und Ressourceneffizienz bei. Pilotversuche mit Wasserlinsen und Süßkartoffeln belegen die Praxistauglichkeit und das Potenzial für den Einsatz in nachhaltigen Agrarsystemen der Zukunft
A Novel Method for GPTP-Based Time Synchronization of 5G UEs Considering Asymmetric Uplink/Downlink Latency
No full textFor Precision Time Protocol based Time Sensitive Networking (TSN) time synchronization it is assumed that the latency in uplink and downlink direction is the same, which is true for fixed TSN-Ethernet networks. However, with TSN-5G integration (where the 5G system acts as a TSN bridge), an equal uplink/downlink delay cannot be assumed for the 5G wireless links, thus drastically decreasing the synchronization accuracy. In the 3GPP standardization, Device Side and Network Side Time Translators (TT) were introduced to resolve this issue. In this paper, a novel method is presented to increase the synchronization accuracy for 5G User Equipment (UE) without requiring the TT. Our method works by time-stamping the radio resource request and grant messages within the UE and utilizes the measured delay in the PTP synchronization process. Through simulations with OMNeT++ we investigated our approach for various traffic scenarios and showed that TSN time synchronization over a 5G network with high accuracy is possible even without TT
A Novel Method to Improve gPTP Based Time Synchronization Accuracy for Asymmetric 5G Links
No full textThis paper addresses the issue of Time Synchronization for Time Sensitive Networks (TSN) that include wireless 5G links. Precision Time Protocol (PTP) based time synchronization assumes symmetric Ethernet links over which the devices are synchronized. While the assumption of symmetric links is true for Ethernet networks, it might be violated for 5G networks because of the uplink-downlink latency asymmetry. To address latency asymmetry, PTP provides a constant user configurable asymmetry parameter which can be set before starting the PTP synchronization process. However, the latency asymmetry in 5G networks is not constant due to the radio resource allocation requests and grants required for uplink transmissions. In this paper we introduce a novel concept based on time-stamping of the resource requests and grants in order to increase the PTP synchronization accuracy in TSN over 5G networks. For the proposed method, Time Translators, as introduced by the IEEE for 5G-TSN integration, are not required. Additionally the presented method also works for 5G devices and networks which do not support SIB9 messages
Effect of reducing zinc and copper in piglet feed on performance and excretion of trace minerals
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Evaluation of Automated Machinery Functional Safety Risk Assessment Using LLMs
No full textAccurate classification of required Performance Level (PLr ) for hazard scenarios in machinery functional safety risk assessment demands expert-level interpretation of technical standards (e.g. ISO 12100 and ISO 13849-1) and functional safety domain knowledge. To address this, a prompt-layered architecture is proposed, introducing a structured, multi-stage prompting design that operationalizes the ISO 13849-1 risk graph through deterministic Natural Language Processing and Natural Language Understanding (NLP–NLU) logic. Building on this architecture, this study develops and evaluates a novel, structured, multi-stage, standards-aligned Large Language Model (LLM) prompting framework engineered to explicitly map the sequential, conditional decision logic of the ISO 13849-1 risk graph. It represents a novel application of deterministic NLP–NLU prompting to non-reasoning LLMs, implementing a standards-aligned functional safety workflow that systematically evaluates rule-constrained and retrievalaugmented prompts for reproducible PLr classification. The evaluation pipeline systematically tests LLMs for PLr determination across modular prompts (zero-shot, rule-based, retrieval-augmented, and hybrid), leveraging a 7,800-scenario dataset and three complementary test regimes namely, baseline, robustness, and cross-architecture comparison, to characterize model accuracy and stability. Results show that explicit ISO rule-based prompting lifts PLr accuracy from less than 50% (zero-shot) to about 99% with mid-scale, instruction-tuned models. Retrieval augmentation adds little beyond rules and can increase latency, while hybrid prompts do not consistently improve stability. These findings provide clear deployment guidance: use rule-based prompting with mid-scale instruction-tuned LLMs for reliable, low-latency industrial safety assessments, and consider on-premise or domain-adapted open-source models to maintain data sovereignty and regulatory compliance
