Volume 2

  • No. 5 May 2022

    Evaluation of brain connectomes at scale

    Identifying structural brain connectivity, also known as the connectome, is imperative for elucidating how neurons and neural networks process information. Existing algorithms for pruning big connectome data, however, have limited speed and memory performance for connectome-wide association studies. In this issue, Sreenivasan et al. propose a GPU-based implementation for connectome pruning called ReAl-LiFE and demonstrate its computational efficiency and utility by applying it to a wide range of datasets.

    See Sreenivasan et al. and Zuo

  • No. 4 April 2022

    High information density for DNA data storage

    DNA is a promising medium for data storage. Yet, designing a transcoding algorithm that can achieve high information density (meaning, high number of bytes per gram of DNA) while providing robust error tolerance is still a challenge. In this issue, Ping et al. introduce a codec that achieves an in vivo physical information density that is close to the theoretical maximum, while being robust to various types of errors.

    See Ping et al. and Manish K. Gupta

  • No. 3 March 2022

    Modeling antibody binding on antigen patterns

    Antibodies are seen binding to and walking on arrays of antigens, which are represented as red spheres and arranged in gradients of decreasing separation distance. Stochastic modeling suggests that antibodies exhibit directed migration in the direction of the gradient that leads toward the most stable binding. Bind stability is determined by the strain on each antibody induced by different antigen separation distances.

    See Hoffecker et al.

  • No. 2 February 2022

    Interpretable visualizations for large-scale networks

    Many complex systems can be represented as networks of interacting components. However, it is still difficult to visually investigate and interpret complex network structures. Hütter et al. introduce a computational method for creating landscapes and network maps for visualization, which helps to explore the characteristics of large-scale networks and identify patterns in large datasets. In the cover image, the dots and lines in the visualization represent proteins and their interactions in the human cell.

    See Hütter et al.

  • No. 1 January 2022

    Time–frequency analysis of signals

    Analyzing and processing signals, such as sound, images, and scientific measurements, is important for allowing the interpretation of the information they carry. Time–frequency analysis is a common technique for studying signals, but a high-resolution analysis often comes with high computational costs. In this issue, Arts and van den Broek present an open-source framework that enables real-time, accurate, and noise-resilient time–frequency analysis of signals, demonstrating its applicability on real-world applications, such as on brain signals obtained from electroencephalography.

    See Arts and van den Broek