Anhalt University of Applied Sciences

Suberin is a hydrophobic biopolymer that acts as an internal and external diffusion and transpiration barrier in plants. It is involved in two phases of wound healing, i.e. initial closing layer formation and subsequent wound periderm development. Transcriptomic and metabolomic analyses of wounded potato leaf tissue revealed preferential induction of cell wall modifying processes during closing layer formation, accompanied by a highly active defense response. To address the importance of suberin in this process, we generated loss of function mutants by CRISPR-Cas9 editing the suberin transporter gene StABCG1. Both wound-induced StABCG1 transcript levels and suberin formation around wounded leaf tissue were reduced in CRISPR-lines. Moreover, wound-induced tissue damage was characterized by browning of wound-adjacent areas. Transcriptome analyses of these areas revealed up-regulation of genes encoding defense proteins and enzymes of the phenylpropanoid pathway. Levels of hydroxycinnamic acid amides, acting in defense and in cell wall reinforcement, were drastically enhanced in CRISPR compared to control plants. These results suggest that the reduction in suberin formation around wounded tissue leads to a loss of barrier function, resulting in tissue browning due to enhanced exposure to oxygen.

The purpose of this research is to optimise the design of modular organic polymer shapes inspired by coral organisms through a computational workflow, with the objective of providing any existing free-form structure with a new skin by adding a new layer composed by modules robotically manufactured. The rise of digital fabrication in architecture has led to the growth of free-form design as a popular method in the industry. This is due in part to the availability of advanced software tools such as Grasshopper (GH), a graphical algorithm editor tightly integrated with Rhinoceros’s 3D modeling tools, and the advancement of robotic fabrication technologies. GH enables designers to build complex forms through visual programming, offering a highly intuitive way to generate and manipulate free-form structures with precision and efficiency. When combined with the capabilities of robotic manufacturing, this design approach allows for the production of unique, complex modules that can be assembled into larger structures. Robotic manufacturing provides a high degree of precision and flexibility, making it possible to produce components with intricate details and variable geometries that would be difficult, if not impossible, to create using traditional manufacturing methods. The integration of computational design with robotic manufacturing also presents opportunities for sustainability in architecture. By optimizing the design and production of building components, material waste can be minimized, and components can be designed for disassemble and recycling at the end of their life cycle. This study explores the integration of these methods with Large-Scale Additive Manufacturing (LSAM) by developing a methodology and later presenting a demonstrator using an existing timber structure, which we refer to as the “Timber Skeleton” (TS). The goal is to enhance the aesthetic and functional qualities of free-form structures, such as TS, while addressing the challenges of sustainability and efficiency in construction.

Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substrate spectrum, thereby expanding the range of potential applications for laccase enzymes. This study aims to investigate the molecular evolution of bacterial laccases using computational 3D‐structure prediction and molecular docking tools such as AlphaFold2, Metal3D, AutoDockVina, and Rosetta. We isolated a bacterium with laccase activities from fecal samples from a Hermann's tortoise ( Testudo hermanni ) , identified it as Klebsiella michiganensis using 16S rRNA sequencing and nanopore genome sequencing, and then identified a sequence encoding a laccase with a predicted molecular weight of approximately 27.5 kDa. Expression of the corresponding, chemically synthesized DNA fragment resulted in the isolation of an active laccase. The enzyme showed considerable thermostability, retaining 21% of its activity after boiling for 30 min. Using state‐of‐the‐art information technology and machine learning techniques, we conducted 3D‐structure prediction on this sequence, predicted its copper‐ion binding sites, and validated these predictions through site‐directed mutagenesis and expression. Subsequently, we performed computer‐aided evolution studies on this sequence and found that 90% of the results from the selected mutations exhibited improved performance. In summary, this study not only revealed a novel laccase but also demonstrated an efficient approach for advancing research on the molecular evolution of bacterial laccases using cutting‐edge machine learning, next‐generation sequencing, traditional bioinformatics approaches, and laboratory techniques, providing an effective strategy for discovering and design new bacterial laccases.

In addition to the aspects of power generation, land use, aesthetics, nature conservation, and multifunctionality considered so far, there are still overlooked issues in the relatively new topic of solar landscapes. I reveal a connection with a supposedly not equally contemporary theme: ecological succession. Understanding succession provides the background for interrelationships, and explains why, in large solar parks, the occurrence of large operational disruptors, such as trees, cannot be sustainably countered with the usual maintenance measures. Woody plants benefit from the thousands of safe sites amongst the modular panel constructions, and softwoods often avoid being cut due to their flexibility, or grow back from their stumps. Stronger and stronger over time. Instead of relying exclusively on labour-intensive and costly mowing, managers can make use of grazing animals. In this way, simply anticipating the ecological succession process and taking it into account when planning and managing a solar park can boost overall sustainability. The recommendation makes connections with social dimensions and can result in ethically produced meat.

Introduction Idiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets. Methods We analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets. Results A total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified. Discussion This study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF.

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km² grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include, among others, experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

One Health seeks to integrate and balance the health of humans, animals, and environmental systems. These three spheres are intricately interconnected through microbiomes, which are universally present and exchange microbes and genes, influencing not only human and animal health but also key environmental, agricultural, and biotechnological processes. Preventing the emergence of pathogens as well as monitoring and controlling the composition of microbiomes through microbial effectors including virulence factors, toxins, antibiotics, non-ribosomal peptides, and viruses holds transformative potential. However, the mechanisms by which these microbial effectors shape microbiomes and their broader functional consequences in relation to host and ecosystem health remain poorly understood to date. Metaproteomics offers a novel methodological framework as it provides insights into microbial dynamics by quantifying microbial biomass composition, metabolic functions and detecting effectors like viruses, antimicrobial resistance proteins, and non-ribosomal peptides. Here, we document the potential of metaproteomics for elucidating microbial effectors and their impact on microbiomes, and discuss their potential for modulating microbiomes to foster desired functions.

This paper introduces a novel method for monitoring steel structures for fatigue cracks. The method combines measurements from strain gauges (SG) with pre‐conducted structural simulations to quantitatively and precisely determine the position and length of cracks in critical areas. Experimental results validate the reliability and effectiveness of this approach, demonstrating its ability to enable early crack detection. A key advantage of this method is its simplicity: it requires only three strain gauge bridges (one reference SG and two for crack detection). This makes the approach both cost‐efficient and flexible. It is particularly suited for localized monitoring tasks and offers significant benefits over other, often more complex, methods.

Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell.

The formation of amyloid-β (Aβ) aggregates in brain is a neuropathological hallmark of Alzheimer’s disease (AD). However, there is mounting evidence that Aβ also plays a pathogenic role in other types of dementia and that specific post-translational Aβ modifications contribute to its pathogenic profile. The objective of this study was to test the hypothesis that distinct types of dementia are characterized by specific patterns of post-translationally modified Aβ variants. We conducted a comparative analysis and quantified Aβ as well as Aβ with pyroglutamate (pGlu3-Aβ and pGlu11-Aβ), N-truncation (Aβ(4-X)), isoaspartate racemization (isoAsp7-Aβ and isoAsp27-Aβ), phosphorylation (pSer8-Aβ and pSer26-Aβ) or nitration (3NTyr10-Aβ) modification in post mortem human brain tissue from non-demented control subjects in comparison to tissue classified as pre-symptomatic AD (Pre-AD), AD, dementia with Lewy bodies and vascular dementia. Aβ modification-specific immunohistochemical labelings of brain sections from the posterior superior temporal gyrus were examined by machine learning-based segmentation protocols and immunoassay analyses in brain tissue after sequential Aβ extraction were carried out. Our findings revealed that AD cases displayed the highest concentrations of all Aβ variants followed by dementia with Lewy bodies, Pre-AD, vascular dementia and non-demented controls. With both analytical methods, we identified the isoAsp7-Aβ variant as a highly abundant Aβ form in all clinical conditions, followed by Aβ(4-X), pGlu3-Aβ, pGlu11-Aβ and pSer8-Aβ. These Aβ variants were detected in distinct plaque types of compact, coarse-grained, cored and diffuse morphologies and, with varying frequencies, in cerebral blood vessels. The 3NTyr10-Aβ, pSer26-Aβ and isoAsp27-Aβ variants were not found to be present in Aβ plaques but were detected intraneuronally. There was a strong positive correlation between isoAsp7-Aβ and Thal phase and a moderate negative correlation between isoAsp7-Aβ and performance on the Mini Mental State Examination. Furthermore, the abundance of all Aβ variants was highest in APOE 3/4 carriers. In aggregation assays, the isoAsp7-Aβ, pGlu3-Aβ and pGlu11-Aβ variants showed instant fibril formation without lag phase, whereas Aβ(4-X), pSer26-Aβ and isoAsp27-Aβ did not form fibrils. We conclude that targeting Aβ post-translational modifications, and in particular the highly abundant isoAsp7-Aβ variant, might be considered for diagnostic and therapeutic approaches in different types of dementia. Hence, our findings might have implications for current antibody-based therapies of AD. Supplementary Information The online version contains supplementary material available at 10.1007/s00401-024-02824-9.

The individual (poly)phenols of red wines cultivated in two different Western Balkan wine-growing regions were determined using the HPLC method, while the ABTS and DPPH tests were employed to investigate antioxidant activity. The reduction potential of antioxidants was determined by FRAP assay. Five distinct classes of phenolic compounds, including phenolic acids, flavan-3-ols, flavonols, stilbenes, and anthocyanins, were identified. The analyzed wines showed very good antioxidant properties. All of the studied wines exhibited a very strong correlation between their antioxidant potential and the concentration of significant antioxidants. Phenolic components that were the most represented in the investigated samples were selected for the theoretical investigation of the antioxidant effect. For this purpose, epicatechin gallate and sinapic acid were used. Their concentrations in the tested samples ranged up to 132.76 mg/mL and 125.66 mg/mL. Theoretical aspects of reactions of the mentioned compounds towards DPPH and ABTS radicals were examined.

One Health seeks to integrate and balance the health of humans, animals, and environmental systems. These three spheres are intricately interconnected through microbiomes, which are universally present and exchange microbes and genes, influencing not only human and animal health but also key environmental, agricultural, and biotechnological processes. Preventing the emergence of pathogens as well as monitoring and controlling the composition of microbiomes through microbial effectors including virulence factors, toxins, antibiotics, non-ribosomal peptides, and viruses holds transformative potential. However, the mechanisms by which these microbial effectors shape microbiomes and their broader functional consequences in relation to host and ecosystem health remain poorly understood to date. Metaproteomics offers a novel methodological framework as it provides insights into microbial dynamics by quantifying microbial biomass composition, metabolic functions and detecting effectors like viruses, antimicrobial resistance proteins, and non-ribosomal peptides. Here, we document the potential of metaproteomics for elucidating microbial effectors and their impact on microbiomes, and discuss their potential for modulating microbiomes to foster desired functions.

In Europe, various conservation programs adopted to maintain or restore biodiversity have experienced differing levels of success. However, a synthesis about major factors for success of biodiversity-related conservation programs across ecosystems and national boundaries, such as incentives, subsidies, enforcement, participation, or spatial context, is missing. Using a balanced scorecard survey among experts, we analyzed and compared factors contributing to success or failure of three different conservation programs: two government programs (Natura 2000 and the ecological measures of the Water Framework Directive) and one conservation program of a non-governmental organization (NGO; Rewilding Europe), all focusing on habitat and species conservation. The experts perceived the NGO program as more successful in achieving biodiversity-related aims than governmental conservation legislation. Among the factors perceived to influence the success of biodiversity conservation, several stood out: Biodiversity-damaging subsidies, external economic interests competing with conservation goals or policies conflicting with biodiversity conservation were recognized as major factors for the lack of conservation success. Outreach to raise societal interest and awareness as well as stakeholder involvement were perceived as closely related to the success of programs. Our expert survey demonstrated that external factors from economy and policy often hinder success of conservation programs, while societal and environmental factors rather contribute to it. This study implies that conservation programs should be designed to be as inclusive as possible and provides a basis for developing a standardized methodology that explicitly considers indirect drivers from areas such as economy, policy and society.

Poly(ethylene furanoate) (PEF) is considered the greener alternative to poly(ethylene terephthalate) (PET) and other plastics, as it can be produced 100% biobased from renewable resources based on the building blocks 2,5-furandicarboxylic acid (FDCA) and ethylene glycol (EG). So far, most of the literature has dealt with the synthesis and detailed characterization of this synthetic polymer, but very few articles deal with enzymatic depolymerization, which is increasingly favored due to environmental reasons. This study therefore aimed to perform hydrolysis of Nano-PEF using 12 different esterases, which have been shown to depolymerize PET very efficiently. All enzymes were compared in terms of their hydrolysis efficiency, showing very different hydrolysis rates and different product profiles over time. A wide variety of hydrolysis products were identified using ESI-TOF including FDCA, (mono(2-hydroxyethyl)-furanoate) (MHEF), (bis(2-hydroxyethyl)-furanoate) (BHEF), dimers, and trimers. Among the tested enzymes, LCCICCG was the most efficient one performing best at pH 8–9 and elevated temperatures (>70 °C). Finally, all hydrolysis intermediates were hydrolyzed to the final building block FDCA (>99% with almost complete depolymerization of Nano PEF), and higher Nano-PEF-concentrations (up to about 1.4 mg mL⁻¹) were depolymerized equally efficient.