Single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) is a powerful method for characterizing micro- and nanoparticulate materials. The technique primarily relies on the linear relationship between the integrated intensities of individual events (peak areas) and the analyte mass, though transit times (peak widths) have also been used for quantitative purposes. This work (1) evaluates the potential of using peak heights as analytical signals in SP-ICP-MS, (2) introduces a new method for determining peak heights, and (3) explores scenarios in which peak height offers added value over the commonly used SP-ICP-MS signals. A new method was proposed to estimate peak height values in SP-ICP-MS accurately. The cumulative intensity across consecutive dwell times was modeled using a third-degree polynomial, from which the adjusted peak height was derived. This approach reduces the uncertainty associated with using raw maximum intensity values, yielding NP distributions comparable to those obtained via integrated intensities. The effect of dwell time on peak height was also evaluated. An optimal range (50 μs-200 μs) was identified, where a linear relationship was observed between the peak height and the square of the NP diameter. Within this range, peak height showed the lowest bias when characterizing smaller NPs, indicating the potential to improve the limit of quantification (LoQ). Additionally, peak heights proved helpful in determining the limit of detection (LoD) and setting appropriate threshold values for data processing, thereby helping to flag incorrect resultsand addressing a challenge in SP-ICP-MS analysis. This is the first study to evaluate peak height as an analytical signal in SP-ICP-MS. The results highlight its advantages in specific applications, such as sizing NPs near the LoD, and in supporting the more reliable use of other signals, such as peak areas, by helping to identify incorrect threshold selection that could lead to biased distributions. Finally, monitoring peak heights allows for a more realistic and assumption-free determination of the LoD.

All-atom, force field-based molecular dynamics simulations are essential tools in computational chemistry, enabling the prediction and analysis of biomolecular systems with atomic-level resolution. However, as system sizes and simulation time scales increase, so does the associated computational cost. To extend simulated time using the same resources, a common strategy is to constrain the fastest degrees of freedom, such as bond lengths, allowing for larger integration time steps without compromising accuracy. The de facto state-of-the-art algorithms for this purpose─SHAKE, LINCS, and P-LINCS─are integrated into most molecular dynamics packages and widely adopted across the field. Despite their impact, these methods exhibit limitations: all converge slowly when high numerical accuracy is required, and the LINCS and P-LINCS algorithms cannot handle general angular constraints, limiting further increases in time step. In this article, we introduce ILVES, a family of parallel algorithms that converge so rapidly that it is now practical to solve bond length and associated angular constraint equations as accurately as the hardware will allow. We have integrated ILVES into Gromacs, and our analysis demonstrates that it is superior to the state-of-the-art when constraining bond lengths. Due to its better convergence properties, we also show that if the time step is increased up to 3.5 fs by enforcing angular constraints, ILVES enables a 1.65× increase in simulated time using the same computational resources and wall-clock time, an outcome unattainable with current methods. This advance can significantly reduce the computational cost of most all-atom molecular dynamics simulations while improving their accuracy and extending access to larger systems and longer time scales.

Motivation: Advances in DNA sequencing have outpaced advances in computation, making sequence alignment a major bottleneck in genome data analyses. Classical dynamic programming (DP) algorithms are particularly memory-intensive, especially when computing gap-affine and dual gap-affine alignments. Existing strategies to reduce memory consumption often sacrifice speed or alignment accuracy. Results: We present Singletrack, an efficient algorithm for backtrace gap-affine and dual gap-affine alignments that requires storing a single DP matrix while preserving optimal alignment results. Compared to classical DP algorithms, Singletrack removes the need to store additional matrices (i.e. 2 for gap-affine and 4 for dual gap-affine), significantly reducing memory consumption and, in turn, reducing pressure on the memory hierarchy and improving overall performance. Most importantly, Singletrack is a general backtrace method compatible with state-of-the-art DP-based algorithms and heuristics, such as the Suzuki-Kasahara (SK) and the Wavefront Alignment (WFA) algorithms. We demonstrate that Singletrack reduces memory consumption for both SK and WFA algorithms, lowering SK usage by 2× and 4× and WFA usage by 3× and 5× for gap-affine and dual gap-affine alignments, respectively. Moreover, replacing KSW2’s memory-reduction technique with Singletrack accelerates its SK implementation by up to 1.4× at the cost of doubling memory consumption, while Singletrack increases the performance of the WFA implementation in WFA2-lib by 1.2–2.1×. Compared to the efficient linear-memory BiWFA algorithm, the Singletrack-accelerated version of WFA trades a practical increase in memory usage for up to 5.2× higher performance. Availability and implementation: The Singletrack implementations presented in this work are available on Zenodo (DOI: 10.5281/zenodo.18770585) and GitHub (https://github.com/LorienLV/singletrack).

Motivation: Advances in DNA sequencing have outpaced advances in computation, making sequence alignment a major bottleneck in genome data analyses. Classical dynamic programming (DP) algorithms are particularly memory-intensive, especially when computing gap-affine and dual gap-affine alignments. Existing strategies to reduce memory consumption often sacrifice either speed or alignment accuracy. Results: We present Singletrack, an efficient algorithm for backtrace gap-affine and dual gap-affine alignments that requires only storing a single DP matrix. Compared to classical DP algorithms, Singletrack removes the need to store additional matrices (i.e., 2 for gap-affine and 4 for dual gap-affine), significantly reducing memory consumption and, in turn, reducing pressure on the memory hierarchy and improving overall performance. Most importantly, Singletrack is a general backtrace method compatible with state-of-the-art DP-based algorithms and heuristics, such as the Suzuki-Kasahara (SK) and the Wavefront Alignment (WFA) algorithms. Our results demonstrate that Singletrack accelerates the SK implementation of KSW2, used within Minimap2, by up to 1.4×. Similarly, Singletrack enhances the performance of the WFA implementation in WFA2-lib by 1.2–2.1× while reducing memory usage by 3× for gap-affine and 5× for dual gap-affine. Compared to the efficient linear-memory BiWFA algorithm, the Singletrack-accelerated version of WFA trades a practical increase in memory usage for up to 5.2× higher performance. Availability: All the implementations of the Singletrack algorithm presented in this work are available at https://github.com/LorienLV/singletrack .