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Development of antibodies against mycotoxins and their application in immunochemical methods
No full textFood safety is a central and topical issue in our society and is governed by food law.
Mycotoxins are secondary metabolic products formed by molds. These contaminants can enter food and thus the food chain through infestation, posing a serious health risk to humans and animals. For this reason, the European Commission has issued Regulation (EU) 2023/915, which sets maximum levels for certain mycotoxins in food. Currently, around 25 % of foodstuffs are contaminated with mycotoxins above the legally prescribed limits. Regular checks are essential to prevent such exceedances.
The analytical methods currently available, mainly based on chromatographic techniques such as LC-MS/MS, are considered inadequate for on-site use – i.e., at processing and production facilities in the food industry – because they are technically complex and labor-intensive. One possible improvement is the use of immunoassays, which are widely accepted and employed in medical diagnostics. However, a basic prerequisite for developing such assays is the availability of specific antibodies.
Our focus in this project is the development of high-affinity, highly selective monoclonal antibodies against mycotoxins for which either no antibodies, only polyclonal antibodies, or antibodies with insufficient specificity are currently available. The goal is to generate antibodies targeting patulin, Alternaria toxins, and ergot alkaloids for use in rapid tests and on-site analytical systems. A key aspect is the group selectivity of the antibodies with regard to the various ergot alkaloids. Depending on the mycotoxin, heterologous or homologous haptens are used to immunize mice. To ensure monoclonality, we apply limiting dilution and antigen-specific fluorescence-activated cell sorting (FACS) during the selection process.
The antibodies developed will be used to expand SAFIA Technologies GmbH’s existing mycotoxin test-kit, enabling reliable detection of mycotoxins in food and thereby contributing to improved food safety standards.
We will showcase our approach along with the results achieved so fa
D-CNN and VQ-VAE Autoencoders for Compression and Denoising of Industrial X-Ray Computed Tomography Images
No full textThe ever-growing volume of data in imaging sciences stemming from advancements in imaging technologies, necessitates efficient and reliable storage solutions for such large datasets. This study investigates the compression of industrial X-ray computed tomography (XCT) data using deep learning autoencoders and examines how these compression algorithms affect the quality of the recovered data. Two network architectures with different compression rates were used, a deep convolution neural network (D-CNN) and a vector quantized variational autoencoder (VQ-VAE). The XCT data used was from a sandstone sample with a complex internal pore network as a good test case for the importance of feature preservation. The quality of the decoded images obtained from the two different deep learning architectures with different compression rates were quantified and compared to the original input data. In addition, to improve image decoding quality metrics, we introduced a metric sensitive to edge preservation, which is crucial for three-dimensional data analysis. We showed that different architectures and compression rates are required depending on the specific characteristics needed to be preserved for later analysis. The findings presented here can aid scientists in determining the requirements and strategies needed for appropriate data storage and analysis
Adsorptive performance of single-walled carbon nanotubes for divalent manganese sorption characterized by X-ray absorption spectroscopy
No full textThe adsorptive performance of divalent manganese onto single-walled carbon nanotubes (SWCNTs) is investigated by X-ray absorption spectroscopy (XAS). The study is focused on the one hand, on the use of SWCNT as adsorbent to remove divalent manganese II) pollutant controlling batch parameters such as pH, adsorbent dose and contact time; and on the other hand, on the characterization of manganese adsorbed by SWCNT (Mn-SWCNT) adsorbent to probe the chemical composition, oxidation state, and local structural environment of Mn absorber. Freundlich adsorption isotherm well fitted the experimental data and suggested the maximum adsorption capacity at pH 2. Ion exchange was proposed as the main adsorption mechanism for removing manganese using SWCNT. XAS results revealed the change in the oxidation state of manganese. The effect of pH, adsorbent dose, and contact time is shown. XAS also showed that Mn-SWCNT material is principally composed of MnCl2, Mn2O3, MnO2, Mn3O4, and MnO in decreasing order with MnCl2 and Mn2O3 as major compounds
Data-driven sparse coding for onboard condition monitoring of railway tracks
No full textContinuous monitoring of the rail condition plays an important role in railway maintenance and the planning of noise- and vibration-reducing measures. Rail monitoring can be carried out efficiently using vibro-acoustic measurements with onboard sensors. However, this approach generates large amounts of acoustic and vibration data, which makes real-time transmission, processing and storage a challenge. This paper presents a sparse coding framework applied in the time–frequency domain that aims to overcome these challenges by significantly reducing the amount of data while preserving important information for rail defect detection and diagnosis. The Short-Time Fourier Transform is used as a preprocessing step to transform raw signals into a time–frequency representation, capturing the non-stationary characteristics of the signals. The spectrum at each time window is then represented by a sparse linear combination of basis spectra, which form a dictionary. Online sparse dictionary learning is used to create a data-driven, adaptive representation tailored to the frequency characteristics of vibro-acoustic signals related to rail defects. Experimental data acquired with a microphone and an accelerometer mounted on the wheelset of a tram are used to evaluate the framework. The experimental results show that the framework is able to achieve high compression rates and reduce noise. A reduction in data size of 98% was obtained without loss of relevant information. The proposed approach offers significant advantages for modern railway condition monitoring systems. It is scalable for large amounts of data, energy efficient and suitable for real-time implementation. By reducing data bottlenecks, it enables efficient track monitoring with on-board sensors. This work thus contributes to the development of intelligent and cost-effective solutions for infrastructure management
Social immune response reflects infection progression in a soldierless termite
No full textSocial interactions represent a double-edged sword. On one hand, sociality can facilitate sanitary collective behaviours; on the other hand, it creates opportunities for pathogen transmission. In termites, sanitary behaviours can entail a rescuing strategy at early stages of infection, followed by the elimination at later stages. We explored whether the neotropical soldierless species Anoplotermes pacificus employs a progressive approach towards infected nestmates, with different behavioural displays depending on the infection stage. We infected A. pacificus workers with the fungus Metarhizium anisopliae and incubated them for 2, 12, 15 and 20 h, corresponding to infection progression and, therefore, severity. Infected termites were placed with naïve nestmates and their behaviours were recorded for 3 h. Fungus-infected termites triggered up to fourfold higher levels of sanitary behaviour than in non-infected termites. Antennation behaviour decreased during the observation period, whereas sanitary behaviour, which we defined as directed behaviour towards the focal termite involving mouthparts, increased in frequency as incubation duration increased. Sanitary behaviour therefore appears to be a strategy for colony disinfection, which varies in intensity according to infection status, ultimately resulting in the immobilisation of infected individuals at later stages of infection. Alarm responses were also up to three times more frequent in treatment than in control groups and did not vary with incubation duration. A. pacificus workers therefore identify, communicate and respond to pathogen-treated individuals in a progressive manner, indicating that collective responses in this species are also significantly shaped by the stage of infection. By progressively modulating their social immune responses, termites may be able to optimize resource allocation within the colony by balancing the risk of individual infection versus protection of the group
Heat capacity estimation of complex materials for energy technologies
No full textThe control of heat in energy materials is one of the greatest current engineering challenges. Accurate estimations of heat capacity are key in creating and using materials safely and efficiently. Current models for heat capacity are often limited due to crude estimations of the phonon density of states, which is a key component of the thermodynamic definition of heat capacity. Utilization of a more detailed phonon density of states, which can easily be obtained from machine-learned algorithms, combined with dilation and electronic contributions, yields heat capacity estimations that are 29% better than the widely utilized Debye model and are comparable to state-of-the-art quantum mechanical calculations. The framework and necessary tools for heat capacity estimations demonstrated herein can be built into more detailed models and analyses, such as high-throughput characterization, transport models, or other thermodynamic assessments. Consequently, the so-called vibrational + dilation + electronic (VDE) model of heat capacity developed in this work better enables the informed design of next-generation devices
Application of the substructure method to assess the fire resistance of thermally restrained columns
No full textUsually, the fire resistance of load-bearing structural elements is determined by single members testing. A mechanical load is applied to the member in a force-controlled manner and is maintained constant throughout the fire test. After applying the mechanical load, the thermal exposure starts according to the ISO 834 fire curve. In this conventional test method, no interaction between the tested member and the entire building structure is considered. In buildings, the surrounding structure can restrain the thermal expansion of a member in case of fire. This may have both positive and negative effects on the fire resistance of this structural element.
Several years ago, the Institute for Sustainability and Innovation in Structural Engineering (ISISE) at the University of Coimbra in Portugal and the Bundesanstalt für Materialforschung und prüfung (BAM) in Germany carried out fire tests on circular and square steel-reinforced concrete columns with restrained thermal expansion. BAM´s column test furnace allows the specimen to be subjected to thermal exposure and mechanical loading simultaneously. In addition, this device has a substructure test module, which can also provide restrained test conditions. In an ongoing research project at BAM and Technische Universität Braunschweig, the effect of restrained test conditions on the behaviour of steel-reinforced columns under fire exposure is further investigated
Modulating the crystalline forms of silver–sulfadiazine complexes by mechanochemistry
No full textMechanochemical synthesis of pharmaceutical compounds has gained significant attention due to its potential to overcome traditional synthetic challenges while offering the possibility of improving the physicochemical properties of drugs. This study delves into the mechanochemical synthesis of silver sulfadiazine (AgSD) coordination compounds, obtained under different mechanochemical stress and processing conditions. The aim of this work was to investigate the influence of mechanochemical conditions on the selectivity in the preparation of AgSD coordination compounds. Through a series of experiments, we demonstrate the successful synthesis of two different AgSD coordination networks, using high-energy ball milling. By strategically manipulating the starting materials and milling parameters — including milling time, milling frequency, type of mechanical stress (as determined by different milling devices), and the presence of co-milling agents — we were able to control the product outcome. As a result, we achieved two different forms of silver-sulfadiazine metal frameworks, one of which was not previously disclosed. The crystal structure of the new form, obtained from high resolution PXRD synchrotron data, was compared with the previously known structure of a silver sulfadiazine compound. The in-depth antimicrobial activity systematic study of these AgSD forms on the generic systems showed increased antibacterial activity when compared to sulfadiazine. This research sheds light on the mechanochemical synthesis of silver sulfadiazine complexes. The obtained knowledge may guide the development of novel synthetic strategies for other drug molecules, leading to improved drug performance, stability, and therapeutic outcomes
High‑throughput investigation of grain boundary segregation landscape in the Fe–Ni–Cr system
No full textUnderstanding phase stability in multicomponent alloy systems, particularly at internal interfaces, remains a major challenge in materials science. Grain boundary (co-)segregation is a critical factor influencing interfacial stability, often leading to microstructural degradation and safety concerns. In this study, we investigate segregation behavior in the face-centered cubic (FCC) Fe–Ni–Cr alloy system, a foundational system for many steels, superalloys, and high-entropy alloys. CALPHAD-integrated density-based phase-field model is extended to compute the segregation of Fe, Ni, and Cr at grain boundaries as a function of the bulk composition, with the relative GB density serving as a key parameter representing grain boundary character. A high-throughput computational screening is performed across the stable compositional space at 723 K, 1023 K, and 1323 K. The results reveal a rich and temperature-sensitive segregation landscape, with element-specific enrichment and depletion patterns that vary with alloy composition. Notably, opposite segregation trends between Ni and Cr, and frequent co-segregation of Fe and Ni, are observed at lower temperatures. The developed framework captures the coupled effects of temperature, chemical interactions, grain boundary structure, and enthalpy-entropy compensation on segregation and GB phase stability. The origin and implications of these phenomena are discussed in terms of the underlying thermodynamic driving forces
Quantitative Analysis of Gadolinium Deposits in Liver Tissue of Patients After Single or Multiple Gadolinium-based Contrast Agent Application
No full textGadolinium-based contrast agents (GBCAs) are widely used in magnetic resonance imaging. Concerns exist regarding gadolinium deposition and its potential histopathologic tissue alterations, especially after repeated administrations of linear, less stable GBCAs. This study aimed to quantify gadolinium mass fractions in liver specimens of subjects exposed to GBCAs in correlation with histopathologic features.
In this study, mass fractions of gadolinium in human liver specimens from 25 subjects who underwent liver tumor resection surgery and had received GBCA (1 to 9 times over 4 years), were quantitatively analyzed using inductively coupled plasma–mass spectrometry (ICP-MS). Histomorphology was assessed based on the nonalcoholic fatty liver disease activity score (NAS).
Our results suggest that after intravenous administration of GBCA, a small fraction of gadolinium is retained in the liver over a time period of at least several weeks. A relationship was observed between Gadolinium retention and the number of GBCA administrations, but not with the cumulative dose and the degree of fatty liver disease