Increasing project demands, implementation times, quality and sustainability meet the challenges of a shortage of skilled workers, low productivity and a low level of digitization in the construction industry. 5G technology represents a promising approach as a key technology to increase the performance of the industry. In order to be able to use the 5G technology effectively in practice, approaches and recommendations for action are needed for future users to make the introduction of the technology as efficient as possible. For this reason, this paper critically examines how the topic of the application of 5G technology in the construction industry is currently being investigated in the literature. Therefore, a novel AI-assisted systematic literature review methodology (ASCAPE), based on the rigor of conventional approaches, is introduced to raise the efficiency and quality of this process. The main result of this work shows that the current literature is focusing on isolated technology-heavy investigations. The readiness level regarding the Level of Development (LOD) of specific 5G technology use cases is at an unspecified stage. In addition to that, advice for the roll-out of the technology is provided roughly. In contrast to that, a macroscopic investigation of a theory-based approach from the field of innovation research has not yet been carried out. This research could bridge the gap between research and practice. In a critical analysis, this paper provides insights into research gaps in the current literature as a foundation for an approach to support the 5G roll-out at the company level.

Higher organisms are able to respond to continuously changing external conditions by transducing cellular signals into specific regulatory programs, which control gene expression states of thousands of different genes. One of the central problems in understanding gene regulation is to decipher how combinations of transcription factors control sets of co expressed genes under specific experimental conditions. Existing methods in this field mainly focus on sequence aspects and pattern recognition, e.g., by detecting cis-regulatory modules (CRMs) based on gene expression profiling data. We propose a novel approach by combining experimental data with a priori knowledge of respective experimental conditions. These various sources of evidence are likewise considered using multi-objective evolutionary optimization. In this work, we present three objective functions that are especially designed for stimulus-response experiments and can be used to integrate a priori knowledge into the detection of gene regulatory modules. This method was tested and evaluated on whole-genome microarray measurements of drug-response in human hepatocytes.

MOTIVATION: Statins are the most widely used cholesterol-lowering drugs. The primary target of statins is HMG-CoA reductase, a key enzyme in cholesterol synthesis. However, statins elicit pleitropic responses including beneficial as well as adverse effects in the liver or other organs. Today, the regulatory mechanisms that cause these pleiotropic effects are not sufficiently understood. RESULTS: In this work, genome-wide RNA expression changes in primary human hepatocytes of six individuals were measured at up to six time points upon atorvastatin treatment. A computational analysis workflow was applied to reconstruct regulatory mechanisms based on these drug-response data and available knowledge about transcription factor (TF) binding specificities and protein-drug interactions. Several previously unknown TFs were predicted to be involved in atorvastatin-responsive gene expression. The novel relationships of nuclear receptors NR2C2 and PPARA on CYP3A4 were successfully validated in wet-lab experiments. AVAILABILITY: Microarray data are available at the Gene Expression Omnibus (GEO) database at www.ncbi.nlm.nih.gov/geo/, under accession number GSE29868. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The XML-based Systems Biology Markup Language (SBML) has emerged as a standard for storage, communication and interchange of models in systems biology. As a machine-readable format XML is difficult for humans to read and understand. Many tools are available that visualize the reaction pathways stored in SBML files, but many components, e.g. unit declarations, complex kinetic equations or links to MIRIAM resources, are often not made visible in these diagrams. For a broader understanding of the models, support in scientific writing and error detection, a human-readable report of the complete model is needed. We present SBML2L(A)T(E)X, a Java-based stand-alone program to fill this gap. A convenient web service allows users to directly convert SBML to various formats, including DVI, L(A)T(E)X and PDF, and provides many settings for customization. AVAILABILITY: Source code, documentation and a web service are freely available at (http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX).

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic programming algorithms exist for this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. In this paper, we present a new approach to bio-sequence database scanning using computer graphics hardware to gain high performance at low cost. To derive an efficient mapping onto this type of architecture, we have reformulated the Smith-Waterman dynamic programming algorithm in terms of computer graphics primitives. Our OpenGL implementation achieves a speedup of approximately sixteen on a high-end graphics card over available straightforward and optimized CPU Smith-Waterman implementations