Mature simulation systems for Spiking Neural Networks (SNNs) become more relevant than ever for understanding the brain and supporting neuromorphic computing. The CARL-sim SNN platform is one of the first Open Source simulation systems that utilized CUDA GPUs to address the tremendous parallel processing demands of natural brains. It has evolved over almost a decade in numerous scientific research projects requiring efficient biologically plausible modeling at scale. With its sixth major release, CARLsim 6 respects this legacy by supporting the latest versions of operating systems, development tool chains, multi-core computers, and of course GPUs. It runs on a range of platforms; from Notebooks up to the NVIDIA DGX-A100 supercomputer, and is used in biologically plausible simulations of the hippocampus and neocortex. The latest version has added flexibility for incorporating long-term and short-term synaptic plasticity. Neuromodulation is an important property of neurobiology that can lead to rapid few shot learning, network rewiring, and neural activity modulation. Because of this, CARLsim 6 now supports four multiple neuromodulators for simulating neural excitability and synaptic plasticity.

MOTIVATION: Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers. RESULTS: AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data. AVAILABILITY AND IMPLEMENTATION: AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

MOTIVATION: The continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open. RESULTS: In this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data. AVAILABILITY: http://www.cs.toronto.edu/~pashadag. CONTACT: [email protected].

This paper details the design and development of a robotic hardware-in-the-loop simulation platform that can be used for rapid-prototyping industrial manipulators. The architecture of the proposed platform has been presented by Martin and Emami (2006). Potential benefits of such a platform include allowing concurrent development of hardware and control system components and providing a reusable platform for reconfigurable manipulators through a generic and modular structure. Some preliminary tests on the platform have also been discussed in the paper