Motivation: Nanopores are highly sensitive sensors that have achieved commercial success in DNA/RNA sequencing, with potential applications in protein sequencing and biomarker identification. Solid-state nanopores, in particular, face challenges such as instability and low signal-to-noise ratios, which lead scientists to adopt data-driven methods for nanopore signal analysis, although data acquisition remains restrictive.

Results: We address this data scarcity by augmenting the training samples with traces that emulate Brownian motion effects, based on dynamic models in the literature. We apply this method to a publicly available dataset of a classification task containing nanopore reads of DNA with encoded barcodes. A neural network named QuipuNet was previously published for this dataset, and we demonstrate that our augmentation method produces a noticeable increase in QuipuNet’s accuracy. Furthermore, we introduce a novel neural network named YupanaNet, which achieves greater accuracy (95.8%) than QuipuNet (94.6%) on the same dataset. YupanaNet benefits from both the enhanced generalization provided by Brownian motion data augmentation and the incorporation of novel architectures, including skip connections and a soft attention mask.

Availability and implementation: The source code and data are available at: https://github.com/JavierKipen/browDataAug.

Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the UE vary slowly compared to the small-scale fading. We characterize the beam coherence time, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.

Tunnel junctions have been suggested as high-throughput electronic single molecule sensors in liquids with several seminal experiments conducted using break junctions with reconfigurable gaps. For practical single molecule sensing applications, arrays of on-chip integrated fixed-gap tunnel junctions that can be built into compact systems are preferable. Fabricating nanogaps by electromigration is one of the most promising approaches to realize on-chip integrated tunnel junction sensors. However, the electrical behavior of fixed-gap tunnel junctions immersed in liquid media has not been systematically studied to date, and the formation of electromigrated nanogap tunnel junctions in liquid media has not yet been demonstrated. In this work, we perform a comparative study of the formation and electrical behavior of arrays of gold nanogap tunnel junctions made by feedback-controlled electromigration immersed in various liquid and gaseous media (deionized water, mesitylene, ethanol, nitrogen, and air). We demonstrate that tunnel junctions can be obtained from microfabricated gold nanoconstrictions inside liquid media. Electromigration of junctions in air produces the highest yield (61–67%), electromigration in deionized water and mesitylene results in a lower yield than in air (44–48%), whereas electromigration in ethanol fails to produce viable tunnel junctions due to interfering electrochemical processes. We map out the stability of the conductance characteristics of the resulting tunnel junctions and identify medium-specific operational conditions that have an impact on the yield of forming stable junctions. Furthermore, we highlight the unique challenges associated with working with arrays of large numbers of tunnel junctions in batches. Our findings will inform future efforts to build single molecule sensors using on-chip integrated tunnel junctions.

Tunnel junctions have long been used to immobilize and study the electronic transport properties of single molecules. The sensitivity of tunneling currents to entities in the tunneling gap has generated interest in developing electronic biosensors with single molecule resolution. Tunnel junctions can, for example, be used for sensing bound or unbound DNA, RNA, amino acids, and proteins in liquids. However, manufacturing technologies for on-chip integrated arrays of tunnel junction sensors are still in their infancy, and scalable measurement strategies that allow the measurement of large numbers of tunneling junctions are required to facilitate progress. Here, we describe an experimental setup to perform scalable, high-bandwidth (>10 kHz) measurements of low currents (pA–nA) in arrays of on-chip integrated tunnel junctions immersed in various liquid media. Leveraging a commercially available compact 100 kHz bandwidth low-current measurement instrument, we developed a custom two-terminal probe on which the amplifier is directly mounted to decrease parasitic probe capacitances to sub-pF levels. We also integrated a motorized three-axis stage, which could be powered down using software control, inside the Faraday cage of the setup. This enabled automated data acquisition on arrays of tunnel junctions without worsening the noise floor despite being inside the Faraday cage. A deliberately positioned air gap in the fluidic path ensured liquid perfusion to the chip from outside the Faraday cage without coupling in additional noise. We demonstrate the performance of our setup using rapid current switching observed in electromigrated gold tunnel junctions immersed in deionized water.

Inferring a state sequence from a sequence of measurements is a fundamental problem in bioinformatics and natural language processing. The Viterbi and the Beam Search (BS) algorithms are popular inference methods, but they have limitations when applied to Hierarchical Hidden Markov Models (HHMMs), when the primary interest lies in the outer state sequence. The Viterbi algorithm can not infer outer states without inner states, while the BS algorithm requires marginalization over prohibitively large state spaces. We propose two new algorithms to overcome these limitations: the greedy marginalized BS algorithm and the local focus BS algorithm. We show that they approximate the most likely outer state sequence with higher performance than the Viterbi algorithm, and we evaluate the performance of these algorithms on an explicit duration HMM with simulated and real nanopore base calling data.

Hidden Markov models (HMMs) are frequently used in areas such as speech recognition and bioinformatics. However, implementing HMM algorithms correctly and efficiently is time-consuming and error-prone. Specifically, using model-specific knowledge to improve performance, such as sparsity in the transition probability matrix, ties the implementation to a particular model, making it harder to modify. Previous work has introduced high-level frameworks for defining HMMs, thus lifting the burden of efficiently implementing HMM algorithms from the user. However, existing tools are ill-suited for sparse HMMs with many states. This paper introduces Trellis, a domain-specific language for succinctly defining sparse HMMs that use GPU acceleration to achieve high performance. We show that Trellis outperforms previous work and is on par with a hand-written CUDA kernel implementation for a particular sparse HMM.

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future.

Detection of bio-molecules through quantum tunneling currents could lead to the next-generation DNA sequencing methods. In order to analyze the stability of these sensitive devices, it is necessary to characterize their conductance switching statistics. This characterization can be realized by denoising the tunneling current signal and clustering the outcomes. The first step can be done with the CUSUM algorithm, which detects abrupt changes and has been used in similar devices. We found heavy-tailed non-Gaussian noise in the measurement setup of the experimental devices. This paper suggests an approximation in the likelihood ratio step of the CUSUM algorithm that is more robust than the simple Gaussian noise assumption and, at the same time, is computationally more efficient than computing the fitted true likelihoods.

When the optional CDIO Standard for Sustainable Development was introduced in 2020, the CDIO community was encouraged “to document the work and share their experiences, in particular reflecting on the usefulness of the new standards for future refinement and development”. This paper is a response to that call, providing insights in how this optional Standard has been used for evaluating and enhancing the status of sustainability in the Civil Engineering and Urban Management program and Electrical Engineering program at the KTH Royal Institute of Technology. Details are shared on how sustainability is integrated in the programs, and opportunities and barriers for enhancing the status of sustainability in the two programs, and in engineering education in general, are discussed. The paper concludes that the CDIO Standard for Sustainable Development provides a framework and terminology for dialogue and collaboration, within as well as between programs, that can be used for driving change, from an add-on approach, through integration approaches, towards transformative approaches to sustainability in engineering education.

This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the precoding matrix is selected in a baseband unit (BBU) and then sent over a digital fronthaul to the transmitting antenna array. The fronthaul has a limited bit resolution with a known quantization behavior. We formulate a new sum rate maximization problem where the precoding matrix elements must comply with the quantizer. We solve this non-convex mixed-integer problem to local optimality by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. The precoding optimization subproblem becomes an integer least-squares problem, which we solve with a new algorithm using a sphere decoding (SD) approach. We show numerically that the proposed precoding technique vastly outperforms the baseline of optimizing an infinite-resolution precoder and then quantizing it. We also develop a heuristic quantization-aware precoding that outperforms the baseline while having comparable complexity.