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The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
Members of the species Bacillus pumilus get more and more in focus of the biotechnological industry as potential new production strains. Based on exoproteome analysis, B. pumilus strain Jo2, possessing a high secretion capability, was chosen for an omics-based investigation. The proteome and metabolome of B. pumilus cells growing either in minimal or complex medium was analyzed. In total, 1542 proteins were identified in growing B. pumilus cells, among them 1182 cytosolic proteins, 297 membrane and lipoproteins and 63 secreted proteins. This accounts for about 43% of the 3616 proteins encoded in the B. pumilus Jo2 genome sequence. By using GC–MS, IP-LC/MS and H NMR methods numerous metabolites were analyzed and assigned to reconstructed metabolic pathways. In the genome sequence a functional secretion system including the components of the Sec- and Tat-secretion machinery was found. Analysis of the exoproteome revealed secretion of about 70 proteins with predicted secretion signals. In addition, selected production-relevant genome features such as restriction modification systems and NRPS clusters of B. pumilus Jo2 are discussed.
The invention pertains to a CellDrum electrode arrangement for measuring mechanical stress, comprising a mechanical holder (1 ) and a non-conductive membrane (4), whereby the membrane (4) is at least partially fixed at its circumference to the mechanical holder (1), keeping it in place when the membrane (4) may bend due to forces acting on the membrane (4), the mechanical holder (1) and the membrane (4) forming a container, whereby the membrane (1) within the container comprises an cell- membrane compound layer or biological material (3) adhered to the deformable membrane 4 which in response to stimulation by an agent may exert mechanical stress to the membrane (4) such that the membrane bending stage changes whereby the container may be filled with an electrolyte, whereby an electric contact (2) is arranged allowing to contact said electrolyte when filled into to the container, whereby within a predefined geometry to the fixing of the membrane (4) an electrode (7) is arranged, whereby the electrode (7) is electrically insulated with respect to the electric contact (2) as well as said electrolyte, whereby mechanical stress due to an agent may be measured as a change in capacitance.