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High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

A team of international scientists benchmark current compression methods for high-throughput sequencing data. High-throughput sequencing (HTS) data are commonly stored as raw sequencing reads in FASTQ format or as reads mapped to a reference, in SAM format, both with large memory footprints. Worldwide growth of HTS data has prompted the development of compression methods that aim to significantly reduce HTS data size. Here we report on a benchmarking study of available compression methods on a comprehensive set of HTS data using an automated framework.

Background Chromosome organization plays an important role in biological processes such as replication, regulation, and transcription. One way to study the relationship between chromosome structure and its biological functions is through Hi-C studies, a genome-wide method for capturing chromosome conformation. Such studies generate vast amounts of data. The problem is exacerbated by the fact that chromosome organization is dynamic, requiring snapshots at different points in time, further increasing the amount of data to be stored. We present a novel approach called the High-Efficiency Contact Matrix Compressor (HiCMC) for efficient compression of Hi-C data. Results By modeling the underlying structures found in the contact matrix, such as compartments and domains, HiCMC outperforms the state-of-the-art method CMC by approximately 8% and the other state-of-the-art methods cooler, LZMA, and bzip2 by over 50% across multiple cell lines and contact matrix resolutions. In addition, HiCMC integrates domain-specific information into the compressed bitstreams that it generates, and this information can be used to speed up downstream analyses. Conclusion HiCMC is a novel compression approach that utilizes intrinsic properties of contact matrix, such as compartments and domains. It allows for a better compression in comparison to the state-of-the-art methods. HiCMC is available at https://github.com/sXperfect/hicmc .

Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. Automatic extraction of guitar effects and their parameter settings, with the aim to copy a target guitar sound, has been previously investigated, where artificial neural networks first determine the effect class of a reference signal and subsequently the parameter settings. These approaches require a corresponding guitar effect implementation to be available. In general, for very close sound matching, additional research regarding effect implementations is necessary. In this work, we present a different approach to circumvent these issues. We propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modeling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the phaser, softclipping and slapback delay effect. Listening tests with eight subjects indicate excellent quality of the blind copies, i.e., little to no difference to the reference guitar effect.

This work investigates the application of machine learning for the analysis of video journalism to get insights into media bias in the German video journalism landscape. For this purpose, a custom dataset made up of subtitles from video data of major German news outlets ranging across the political spectrum was created. Media bias was assessed utilizing mention and sentiment analysis with respect to the major political parties in Germany. Sentiment analysis, performed using german-news-sentiment-bert, revealed significant differences in the reporting sentiment between media outlets. The German public broadcast outlet ARD was found to report with neutral sentiment less frequently than the mean, instead using negative sentiment significantly more often, especially while mentioning parties on the political edges. Mention analysis revealed that politicians get mentioned more often when in governing coalitions and, furthermore, it revealed a slight association between the assumed political ideology of media outlets and how frequently they report on political parties with a similar ideology, i.e., right-leaning outlets mention left-leaning parties and politicians less frequently and vice versa.

The growing significance of wind energy in supplying renewable electricity underlines the increasing importance of wind turbine efficiency. Hybrid towers, integrating steel and pre-stressed concrete in a stacked structure, address traditional limitations in nacelle height but face new vulnerabilities, exemplified by a collapse in September 2021. This highlights the crucial need for continuous monitoring, particularly of the tower structure’s tendons. This study introduces acoustic emission monitoring as a novel approach for the early detection of wire breaks within the highly stressed tendons of hybrid towers. The investigations described focus on evaluating the suitability of this method for the specific use case and developing a generalized monitoring approach. Accordingly, background noise in an operating wind turbine tower was recorded and analyzed over a year-long operational period. Correlation analyses of these data unveiled intricate relationships between operational parameters and noise levels, with wind speed, rotor speed, and blade pitch angle exerting influence. Laboratory experiments were conducted on a full-scale specimen, and wire breaks were artificially provoked to characterize the damage signal and assess its attenuation in relevant structural components. The experimental results were integrated into a stochastic model to determine feasible sensor distances, aiming for a 90% probability of detection at a 95% confidence level. Low attenuation along the tendon was identified, enabling reliable detection over significant distances. Nevertheless, practical considerations suggest a focus on tendon anchorages, with the potential for grouped monitoring in specific areas to optimize sensor deployment. The study proposes a sensor network configuration to enhance the safety and reliability of wind turbine structures.

Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres, or to create more variety within musical compositions. The sound not only is determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for the classification and parameter extraction of guitar effects from audio samples containing guitar, bass, keyboard, and drums. The CNN was compared to baseline methods previously proposed, like support vector machines and shallow neural networks together with predesigned features. On two datasets, the CNN achieved classification accuracies 1-5% above the baseline accuracy, achieving up to 97.4% accuracy. With parameter values between 0.0 and 1.0, mean absolute parameter extraction errors of below 0.016 for the distortion, below 0.052 for the tremolo, and below 0.038 for the slapback delay effect were achieved, matching or surpassing the presumed human expert error of 0.05. The CNN approach was found to generalize to further effects, achieving mean absolute parameter extraction errors below 0.05 for the chorus, phaser, reverb, and overdrive effect. For sequentially applied combinations of distortion, tremolo, and slapback delay, the mean extraction error slightly increased from the performance for the single effects to the range of 0.05 to 0.1. The CNN was found to be moderately robust to noise and pitch changes of the background instrumentation suggesting that the CNN extracted meaningful features.

Red fleshed apples (Malus × domestica Borkh.) differ in colour intensity between cultivars, seasons and sites. The objective of this study was to develop a procedure for predicting anthocyanin content from digital images of flesh discs. Flesh cylinders of uniform colour were excised, scanned and their colours determined in the R, G and B and the L*a*b* colour spaces. Anthocyanin content was also quantified chemically. A calibration line was constructed to predict anthocyanin content of flesh discs of varying colour from a scan or a photograph in the studio or outdoors. Anthocyanin concentration was linearly related to the logarithms of G, B and L*. From these relationships, the anthocyanin content of a flesh disc was predicted, pixel by pixel. Colour corrections were applied using a reference colour chart included in all images. The Finlayson algorithm was most effective for correcting the G parameter obtained by a flatbed scanner. For variable imaging methods (scanning or photography), the Vandermonde algorithm for correcting the L* parameter and the Finlayson algorithm for correcting the G parameter were most effective in predicting anthocyanin content. The procedure allows accurate prediction of anthocyanin content of red fleshed apples from simple colour scans or photographs.

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder. Genie, an open-source encoder compliant with ISO/IEC’s MPEG-G standard, delivers state-of-the-art genomic data compression ratios and guarantees long-term data sustainability and decodability through interoperability.

Background In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/ .