Search results for: Wolf-Christian Mueller

Authors: Alec Chabwinja, Cannan Tawonezvi, Jerneja Vidmar, Constance Chingwaru, Walter Chingwaru

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Objective: To evaluate the potential of commercial fermented / probiotic products available in Zimbabwe or internationally, and strains of Lactobacillus plantarum (L. plantarum) as prophylaxis and therapy against diarrhoeal and sexually transmitted infections. Methods: The antimicrobial potential of cultures of lactobacilli enriched from 4 Zimbabwean commercial food/beverage products, namely Dairibord Lacto sour milk (DLSM), Probrand sour milk (PSM), Kefalos Vuka cheese (KVC) and Chibuku opaque beer (COB); three probiotic products obtainable in Europe and internationally; and four strains of L. plantarum obtained from Balkan traditional cheeses and Zimbabwean foods against clinical strains of Escherichia coli (E. coli) and non-clinical strains of Candida albicans and Rhodotorula spp. was assayed using the well diffusion method. Three commercial Agar diffusion assay and a competitive exclusion assay were carried out on Mueller-Hinton agar. Results: Crude cultures of putative lactobacillus strains obtained from Zimbabwean dairy products (Probrand sour milk, Kefalos Vuka vuka cheese and Chibuku opaque beer) exhibited significantly greater antimicrobial activities against clinical strains of E. coli than strains of L. plantarum isolated from Balkan cheeses (CLP1, CLP2 or CLP3) or crude microbial cultures from commercial paediatric probiotic products (BG, PJ and PL) of a culture of Lactobacillus rhamnosus LGG (p < 0.05). Furthermore, the following has high antifungal activities against the two yeasts: supernatant-free microbial pellet (SFMP) from an extract of M. azedarach leaves (27mm ± 2.5) > cell-free culture supernatants (CFCS) from Maaz Dairy sour milk and Mnandi sour milk (approximately 26mm ± 1.8) > CFCS and SFMP from Amansi hodzeko (25mm ± 1.5) > CFCS from Parinari curatellifolia fruit (24mm ± 1.5), SFMP from P. curatellifolia fruit (24mm ± 1.4) and SFMP from mahewu (20mm ± 1.5). These cultures also showed high tolerance to acidic conditions (~pH4). Conclusions: The putative lactobacilli from four commercial Zimbabwean dairy products (Probrand sour milk, Kefalos Vuka vuka cheese and Chibuku opaque beer), and three strains of L. plantarum from Balkan cheeses (CLP1, CLP2 or CLP3) exhibited high antibacterial activities, while Maaz Dairy sour-, Mnandi sour- and Amansi hodzeko milk products had high antifungal activities. Our selection of Zimbabwean probiotic products has potential for further development into probiotic products for use in the control diarrhea caused by pathogenic strains of E. coli or yeast infections. Studies to characterise the probiotic potential of the live cultures in the products are underway.

Keywords: lactic acid bacteria, Staphylococcus aureus, Streptococcus spp, yeast, inhibition, acid tolerance

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Authors: Mehdi Soula, Yosra Mani, Estelle Saras, Antoine Drapeau, Raoudha Grami, Mahjoub Aouni, Jean-Yves Madec, Marisa Haenni, Wejdene Mansour

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Multi-resistance to antibiotics in gram-negative bacilli and particularly in enterobacteriaceae, has become frequent in hospitals in Tunisia. However, data on antibiotic resistant bacteria in aquatic products are scarce. The aims of this study are to estimate the proportion of ESBL- and carbapenemase-producing Enterobacterales in seafood (clams and fish) in Tunisia and to molecularly characterize the collected isolates. Two types of seafood were sampled in unrelated markets in four different regions in Tunisia (641 pieces of farmed fish and 1075 mediterranean clams divided into 215 pools, and each pool contained 5 pieces). Once purchased, all samples were incubated in tubes containing peptone salt broth for 24 to 48h at 37°C. After incubation, overnight cultures were isolated on selective MacConkey agar plates supplemented with either imipenem or cefotaxime, identified using API20E test strips (bioMérieux, Marcy-l’Étoile, France) and confirmed by Maldi-TOF MS. Antimicrobial susceptibility was determined by the disk diffusion method on Mueller-Hinton agar plates and results were interpreted according to CA-SFM 2021. ESBL-producing Enterobacterales were detected using the Double Disc Synergy Test (DDST). Carbapenem-resistance was detected using an ertapenem disk and was respectively confirmed using the ROSCO KPC/MBL and OXA-48 Confirm Kit (ROSCO Diagnostica, Taastrup, Denmark). DNA was extracted using a NucleoSpin Microbial DNA extraction kit (Macherey-Nagel, Hoerdt, France), according to the manufacturer’s instructions. Resistance genes were determined using the CGE online tools. The replicon content and plasmid formula were identified from the WGS data using PlasmidFinder 2.0.1 and pMLST 2.0. From farmed fishes, nine ESBL-producing strains (9/641, 1.4%) were isolated, which were identified as E. coli (n=6) and K. pneumoniae (n=3). Among the 215 pools of 5 clams analyzed, 18 ESBL-producing isolates were identified, including 14 E. coli and 4 K. pneumoniae. A low isolation rate of ESBL-producing Enterobacterales was detected 1.6% (18/1075) in clam pools. In fish, the ESBL phenotype was due to the presence of the blaCTX-M-15 gene in all nine isolates, but no carbapenemase gene was identified. In clams, the predominant ESBL phenotype was blaCTX-M-1 (n=6/18). blaCPE (NDM1, OXA48) was detected only in 3 isolates ‘K. pneumoniae isolates’. Replicon typing on the strains carring the ESBL and carbapenemase gene revelead that the major type plasmid carried ESBL were IncF (42.3%) [n=11/26]. In all, our results suggest that seafood can be a reservoir of multi-drug resistant bacteria, most probably of human origin but also by the selection pressure of antibiotic. Our findings raise concerns that seafood bought for consumption may serve as potential reservoirs of AMR genes and pose serious threat to public health.

Keywords: BLSE, carbapenemase, enterobacterales, tunisian seafood

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Authors: Alaa Fezai, Anuj Sharma, Wolfgang Mueller-Hirsch, André Zimmermann

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Viscoelastic materials have been widely used in the automotive industry over the last few decades with different functionalities. Besides their main application as a simple and efficient surface damping treatment, they may ensure optimal operating conditions for on-board electronics as thermal interface or sealing layers. The dynamic behavior of viscoelastic materials is generally dependent on many environmental factors, the most important being temperature and strain rate or frequency. Prior to the reliability analysis of systems including viscoelastic layers, it is, therefore, crucial to accurately predict the dynamic and lifetime behavior of these materials. This includes the identification of the dynamic material parameters under critical temperature and frequency conditions along with a precise damage localization and identification methodology. The goal of this work is twofold. The first part aims at applying an inverse viscoelastic material-characterization approach for a wide frequency range and under different temperature conditions. For this sake, dynamic measurements are carried on a single lap joint specimen using an electrodynamic shaker and an environmental chamber. The specimen consists of aluminum beams assembled to adapter plates through a viscoelastic adhesive layer. The experimental setup is reproduced in finite element (FE) simulations, and frequency response functions (FRF) are calculated. The parameters of both the generalized Maxwell model and the fractional derivatives model are identified through an optimization algorithm minimizing the difference between the simulated and the measured FRFs. The second goal of the current work is to guarantee an on-line detection of the damage, i.e., delamination in the viscoelastic bonding of the described specimen during frequency monitored end-of-life testing. For this purpose, an inverse technique, which determines the damage location and size based on the modal frequency shift and on the change of the mode shapes, is presented. This includes a preliminary FE model-based study correlating the delamination location and size to the change in the modal parameters and a subsequent experimental validation achieved through dynamic measurements of specimen with different, pre-generated crack scenarios and comparing it to the virgin specimen. The main advantage of the inverse characterization approach presented in the first part resides in the ability of adequately identifying the material damping and stiffness behavior of soft viscoelastic materials over a wide frequency range and under critical temperature conditions. Classic forward characterization techniques such as dynamic mechanical analysis are usually linked to limitations under critical temperature and frequency conditions due to the material behavior of soft viscoelastic materials. Furthermore, the inverse damage detection described in the second part guarantees an accurate prediction of not only the damage size but also its location using a simple test setup and outlines; therefore, the significance of inverse numerical-experimental approaches in predicting the dynamic behavior of soft bonding layers applied in automotive electronics.

Keywords: damage detection, dynamic characterization, inverse approaches, vibration testing, viscoelastic layers

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Authors: Sarah Mueller-Saegebrecht, André Brendler

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Business model innovation (BMI) as an intentional change of an existing business model (BM) or the design of a new BM is essential to a firm's development in dynamic markets. The relevance of BMI is also evident in the ongoing COVID-19 pandemic, in which start-ups, in particular, are affected by limited access to resources. However, first studies also show that they react faster to the pandemic than established firms. A strategy to successfully handle such threatening dynamic changes represents BMI. Entrepreneurship literature shows how and when firms should utilize BMI in times of crisis and which barriers one can expect during the BMI process. Nevertheless, research merging BMI barriers and crises is still underexplored. Specifically, further knowledge about antecedents and the effect of moderators on the BMI process is necessary for advancing BMI research. The addressed research gap of this study is two-folded: First, foundations to the subject on how different crises impact BM change intention exist, yet their analysis lacks the inclusion of barriers. Especially, entrepreneurship literature lacks knowledge about the individual perception of BMI barriers, which is essential to predict managerial reactions. Moreover, internal BMI barriers have been the focal point of current research, while external BMI barriers remain virtually understudied. Second, to date, BMI research is based on qualitative methodologies. Thus, a lack of quantitative work can specify and confirm these qualitative findings. By focusing on the crisis context, this study contributes to BMI literature by offering a first quantitative attempt to embed BMI barriers into a structural equation model. It measures managers' perception of BMI development and implementation barriers in the BMI process, asking the following research question: How does a manager's perception of BMI barriers influence BMI development and implementation in times of crisis? Two distinct research streams in economic literature explain how individuals react when perceiving a threat. "Prospect Theory" claims that managers demonstrate risk-seeking tendencies when facing a potential loss, and opposing "Threat-Rigidity Theory" suggests that managers demonstrate risk-averse behavior when facing a potential loss. This study quantitively tests which theory can best predict managers' BM reaction to a perceived crisis. Out of three in-depth interviews in the German chemical industry, 60 past BMIs were identified. The participating start-up managers gave insights into their start-up's strategic and operational functioning. After, each interviewee described crises that had already affected their BM. The participants explained how they conducted BMI to overcome these crises, which development and implementation barriers they faced, and how severe they perceived them, assessed on a 5-point Likert scale. In contrast to current research, results reveal that a higher perceived threat level of a crisis harms BM experimentation. Managers seem to conduct less BMI in times of crisis, whereby BMI development barriers dampen this relation. The structural equation model unveils a mediating role of BMI implementation barriers on the link between the intention to change a BM and the concrete BMI implementation. In conclusion, this study confirms the threat-rigidity theory.

Keywords: barrier perception, business model innovation, business model innovation barriers, crises, prospect theory, start-ups, structural equation model, threat-rigidity theory

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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Authors: Viviana Manzulli, Luigina Serrecchia, Adelia Donatiello, Valeria Rondinone, Sabine Zange, Alina Tscherne, Antonio Parisi, Antonio Fasanella

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Anthrax is a zoonotic disease that affects a wide range of animal species (primarily ruminant herbivores), and can be transmitted to humans through consumption or handling of contaminated animal products. The etiological agent B.anthracis is able to survive in unfavorable environmental conditions by forming endospore which remain viable in the soil for many decades. Furthermore, B.anthracis is considered as one of the most feared agents to be potentially misused as a biological weapon and the importance of the disease and its treatment in humans has been underscored before the bioterrorism events in the United States in 2001. Due to the often fatal outcome of human cases, antimicrobial susceptibility testing plays especially in the management of anthrax infections an important role. In Italy, animal anthrax is endemic (predominantly found in the southern regions and on islands) and is characterized by sporadic outbreaks occurring mainly during summer. Between 2012 and 2017 single human cases of cutaneous anthrax occurred. In this study, 90 diverse strains of B.anthracis, isolated in Italy from 2001 to 2017, were screened to their susceptibility to sixteen clinically relevant antimicrobial agents by using the broth microdilution method. B.anthracis strains selected for this study belong to the strain collection stored at the Anthrax Reference Institute of Italy located inside the Istituto Zooprofilattico Sperimentale of Puglia and Basilicata. The strains were isolated at different time points and places from various matrices (human, animal and environmental). All strains are a representative of over fifty distinct MLVA 31 genotypes. The following antibiotics were used for testing: gentamicin, ceftriaxone, streptomycin, penicillin G, clindamycin, chloramphenicol, vancomycin, linezolid, cefotaxime, tetracycline, erythromycin, rifampin, amoxicillin, ciprofloxacin, doxycycline and trimethoprim. A standard concentration of each antibiotic was prepared in a specific diluent, which were then twofold serial diluted. Therefore, each wells contained: bacterial suspension of 1–5x104 CFU/mL in Mueller-Hinton Broth (MHB), the antibiotic to be tested at known concentration and resazurin, an indicator of cell growth. After incubation overnight at 37°C, the wells were screened for color changes caused by the resazurin: a change from purple to pink/colorless indicated cell growth. The lowest concentration of antibiotic that prevented growth represented the minimal inhibitory concentration (MIC). This study suggests that B.anthracis remains susceptible in vitro to many antibiotics, in addition to doxycycline (MICs ≤ 0,03 µg/ml), ciprofloxacin (MICs ≤ 0,03 µg/ml) and penicillin G (MICs ≤ 0,06 µg/ml), recommend by CDC for the treatment of human cases and for prophylactic use after exposure to the spores. In fact, the good activity of gentamicin (MICs ≤ 0,25 µg/ml), streptomycin (MICs ≤ 1 µg/ml), clindamycin (MICs ≤ 0,125 µg/ml), chloramphenicol(MICs ≤ 4 µg/ml), vancomycin (MICs ≤ 2 µg/ml), linezolid (MICs ≤ 2 µg/ml), tetracycline (MICs ≤ 0,125 µg/ml), erythromycin (MICs ≤ 0,25 µg/ml), rifampin (MICs ≤ 0,25 µg/ml), amoxicillin (MICs ≤ 0,06 µg/ml), towards all tested B.anthracis strains demonstrates an appropriate alternative choice for prophylaxis and/or treatment. All tested B.anthracis strains showed intermediate susceptibility to the cephalosporins (MICs ≥ 16 µg/ml) and resistance to trimethoprim (MICs ≥ 128 µg/ml).

Keywords: Bacillus anthracis, antibiotic susceptibility, treatment, minimum inhibitory concentration

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