The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces pfb-imaging: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances.

STIMELA2 is a new-generation framework for developing data reduction workflows. It is designed for radio astronomy data but can be adapted for other data processing applications. STIMELA2 aims at the middle ground between ease of development, human readability, and enabling robust, scalable and repeatable workflows. STIMELA2 defines a YAML-based domain specific language (DSL), which represents workflows by linear, concise and intuitive YAML-format recipes. Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format cab definitions detailing each task's schema (inputs and outputs). The STIMELA2 DSL provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. The container images are open-source and maintained through a companion package called CULT-CARGO. This enables the development of system-agnostic and repeatable workflows. STIMELA2 facilitates the deployment of scalable, distributed workflows by interfacing with the SLURM scheduler and the KUBERNETEs API. The latter allows workflows to be readily deployed in the cloud. Previous papers in this series used STIMELA2 as the underlying technology to run workflows on the AWS cloud. This paper presents an overview of STIMELA2's design, architecture and use in the radio astronomy context.

The physical configuration of new radio interferometers such as MeerKAT, SKA, ngVLA and DSA-2000 informs the development of software in two important areas. Firstly, tractably processing the sheer quantity of data produced by new instruments necessitates subdivision and processing on multiple nodes. Secondly, the sensitivity inherent in modern instruments due to improved engineering practices and greater data quantities necessitates the development of new techniques to capitalize on the enhanced sensitivity of modern interferometers. This produces a critical tension in radio astronomy software development: a fully optimized pipeline is desirable for producing science products in a tractable amount of time, but the design requirements for such a pipeline are unlikely to be understood upfront in the context of artefacts unveiled by greater instrument sensitivity. Therefore, new techniques must continuously be developed to address these artefacts and integrated into a full pipeline. As Knuth reminds us, "Premature optimization is the root of all evil". This necessitates a fundamental trade-off between a trifecta of (1) performant code (2) flexibility and (3) ease-of-development. At one end of the spectrum, rigid design requirements are unlikely to capture the full scope of the problem, while throw-away research code is unsuitable for production use. This work proposes a framework for the development of radio astronomy techniques within the above trifecta. In doing so, we favour flexibility and ease-of-development over performance, but this does not necessarily mean that the software developed within this framework is slow. Practically this translates to using data formats and software from the Open Source Community. For example, by using NuMPy arrays and/or PANDAS dataframes, a plethora of algorithms immediately become available to the scientific developer. Focusing on performance, the breakdown of Moore's Law in the 2010s and the resultant growth of both multi-core and distributed (including cloud) computing, a fundamental shift in the writing of radio astronomy algorithms and the storage of data is required: It is necessary to shard data over multiple processors and compute nodes, and to write algorithms that operate on these shards in parallel. The growth in data volumes compounds this requirement. Given the fundamental shift in compute architecture we believe this is central to the performance of any framework going forward, and is given especial emphasis in this one. This paper describes two Python libraries, DASK-MS and CODEX AFRICANuS which enable the development of distributed High-Performance radio astronomy code with DASK. DASK is a lightweight Python parallelization and distribution framework that seamlessly integrates with the PyDATA ecosystem to address radio astronomy "Big Data" challenges.

Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity to probe this class of sources, using image-plane detection techniques. We aim to systematically mine archival synthesis imaging data in order to search for medium-timescale transients and variables that are not detected by conventional long-track image synthesis techniques. We deploy a prototype blind transient and variable search pipeline named TRON. This processes calibrated visibility data, constructs high-time cadence images, performs a search for variability on multiple timescales, and extracts lightcurves for detected sources. As proof of concept, we apply it to three MeerKAT observations of globular clusters, known to host transient or variable sources. We detect a previously known eclipsing MSP suspected to be a `black widow' system, in the globular cluster Omega Centauri, with a light curve confirming the eclipsing nature of the emission. We detect a previously known `red back' eclipsing MSP in the globular cluster Terzan 5. Using observations of the globular cluster 47 Tucanae, we detect two known millisecond pulsars (MSPs), and one previously reported MSP candidate, with hints of eclipsing behaviour.

Calibration of radio interferometer data ought to be a solved problem; it has been an integral part of data reduction for some time. However, as larger, more sensitive radio interferometers are conceived and built, the calibration problem grows in both size and difficulty. The increasing size can be attributed to the fact that the data volume scales quadratically with the number of antennas in an array. Additionally, new instruments may have up to two orders of magnitude more channels than their predecessors. Simultaneously, increasing sensitivity is making calibration more challenging: low-level RFI and calibration artefacts (in the resulting images) which would previously have been subsumed by the noise may now limit dynamic range and, ultimately, the derived science. It is against this backdrop that we introduce QuartiCal: a new Python package implementing radio interferometric calibration routines. QuartiCal improves upon its predecessor, CubiCal, in terms of both flexibility and performance. Whilst the same mathematical framework - complex optimization using Wirtinger derivatives - is in use, the approach has been refined to support arbitrary length chains of parameterized gain terms. QuartiCal utilizes Dask, a library for parallel computing in Python, to express calibration as an embarrassingly parallel task graph. These task graphs can (with some constraints) be mapped onto a number of different hardware configurations, allowing QuartiCal to scale from running locally on consumer hardware to a distributed, cloud-based cluster. QuartiCal's qualitative behaviour is demonstrated using MeerKAT observations of PSR J2009-2026. These qualitative results are followed by an analysis of QuartiCal's performance in terms of wall time and memory footprint for a number of calibration scenarios and hardware configurations.

Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity to probe this class of sources, using image-plane detection techniques. The previous letter in this series describes our project and associated TRON pipeline designed to mine archival MeerKAT data for transient and variable sources. In this letter, we report on a new transient, a radio flare, associated with Gaia DR3 6865945581361480448, a G type star, whose parallax places it at a distance of 1334 pc. Its duration and high degree of circular polarization suggests electron cyclotron maser instability as the mechanism, consistent with an RS CVn variable.

ABSTRACT Protein tyrosine sulfation (sY) is a post-translational modification (PTM) catalysed by Golgi-resident Tyrosyl Protein SulfoTransferases (TPSTs). Information on protein tyrosine sulfation is currently limited to ∼50 human proteins with only a handful having verified sites of sulfation. The contribution of this chemical moiety for the regulation of biological processes, both inside and outside the cell, remains poorly defined, in large part due to analytical limitations. Mass spectrometry-based proteomics is the method of choice for PTM analysis, but has yet to be applied for the systematic investigation and large-scale analysis of biomolecular sulfation (constituting the ‘sulfome’), primarily due to issues associated with discrimination of sY-from phosphotyrosine (pY)-containing peptides. In this study, we developed a mass spectrometry (MS)-based workflow centred on the characterization of sY-peptides, incorporating optimised Zr 4+ -IMAC and TiO 2 enrichment strategies. Extensive characterization of a panel of sY- and pY-peptides using an array of MS fragmentation regimes (CID, HCD, EThcC, ETciD, UVPD) highlights differences in the ability to generate site-determining product ions, which can be exploited to differentiate sulfated peptides from nominally isobaric phosphopeptides based on precursor ion neutral loss at low collision energy. Application of our analytical workflow to a HEK-293 cell extracellular secretome facilitated identification of 21 new sulfotyrosine-containing proteins, several of which we validate enzymatically using in vitro sulfation assays. This study demonstrates the applicability of our strategy for confident, high-throughput, ‘sulfomics’ studies, and reveals new sY interplay between enzymes relevant to both protein and glycan sulfation.

Protein tyrosine sulfation (sY) is a post-translational modification (PTM) catalyzed by Golgi-resident tyrosyl protein sulfo transferases (TPSTs). Information on sY in humans is currently limited to ∼50 proteins, with only a handful having verified sites of sulfation. As such, the contribution of sulfation to the regulation of biological processes remains poorly defined. Mass spectrometry (MS)-based proteomics is the method of choice for PTM analysis but has yet to be applied for systematic investigation of the "sulfome", primarily due to issues associated with discrimination of sY-containing from phosphotyrosine (pY)-containing peptides. In this study, we developed an MS-based workflow for sY-peptide characterization, incorporating optimized Zr4+ immobilized metal-ion affinity chromatography (IMAC) and TiO2 enrichment strategies. Extensive characterization of a panel of sY- and pY-peptides using an array of fragmentation regimes (CID, HCD, EThcD, ETciD, UVPD) highlighted differences in the generation of site-determining product ions and allowed us to develop a strategy for differentiating sulfated peptides from nominally isobaric phosphopeptides based on low collision energy-induced neutral loss. Application of our "sulfomics" workflow to a HEK-293 cell extracellular secretome facilitated identification of 21 new sulfotyrosine-containing proteins, several of which we validate enzymatically, and reveals new interplay between enzymes relevant to both protein and glycan sulfation.

Observations of the neutral atomic hydrogen (H i) in the nuclear starburst galaxy NGC 4945 with MeerKAT are presented. We find a large amount of halo gas, previously missed by H i observations, accounting for 6.8 per cent of the total H i mass. This is most likely gas blown into the halo by star formation. Our maps go down to a 3 sigma column density level of 5 x 10(18) cm(-2). We model the H i distribution using tilted-ring fitting techniques and find a warp on the galaxy's approaching and receding sides. The H i in the northern side of the galaxy appears to be suppressed. This may be the result of ionization by the starburst activity in the galaxy, as suggested by a previous study. The origin of the warp is unclear but could be due to past interactions or ram pressure stripping. Broad, asymmetric H i absorption lines extending throughout the H i emission velocity channels are present towards the nuclear region of NGC 4945. Such broad lines suggest the existence of a nuclear ring moving at a high circular velocity. This is supported by the clear rotation patterns in the H i absorption velocity field. The asymmetry of the absorption spectra can be caused by outflows or inflows of gas in the nuclear region of NGC 4945. The continuum map shows small extensions on both sides of the galaxy's major axis that might be signs of outflows resulting from the starburst activity.

We present Tricolour, a package for Radio Frequency Interference mitigation of wideband finely channelized MeerKAT correlation data. The MeerKAT passband is heavily affected by interference from satellite, mobile, aircraft and terrestrial transponders. Coupled with typical data rates in excess of 100 GiB/hr at 208kHz channelization resolution, mitigation poses a significant processing challenge. Our flagger is highly configurable, parallel and optimized, employing Dask and Numba technologies to implement the widely used SumThreshold and MAD interference detection algorithms. We find that typical 208kHz channelized datasets can be processed at rates in excess of 400 GiB/hr for a typical L-band flagging strategy on a modern dual-socket Intel Xeon server.

MeerKAT's large number of antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900-1670 MHz) observations of 115 galaxy clusters, observed for $\sim$6-10 hours each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at $\sim$8" resolution, and enhanced spectral and polarisation image cubes at $\sim$8" and 15" resolutions. Typical sensitivities for the full-resolution MGCLS image products are $\sim$3-5 {\mu}Jy/beam. The basic cubes are full-field and span 4 deg^2. The enhanced products consist of the inner 1.44 deg^2 field of view, corrected for the primary beam. The survey is fully sensitive to structures up to $\sim$10' scales and the wide bandwidth allows spectral and Faraday rotation mapping. HI mapping at 209 kHz resolution can be done at $0<z<0.09$ and $0.19<z<0.48$. In this paper, we provide an overview of the survey and DR1 products, including caveats for usage. We present some initial results from the survey, both for their intrinsic scientific value and to highlight the capabilities for further exploration with these data. These include a primary beam-corrected compact source catalogue of $\sim$626,000 sources for the full survey, and an optical/infrared cross-matched catalogue for compact sources in Abell 209 and Abell S295. We examine dust unbiased star-formation rates as a function of clustercentric radius in Abell 209 and present a catalogue of 99 diffuse cluster sources (56 are new), some of which have no suitable characterisation. We also highlight some of the radio galaxies which challenge current paradigms and present first results from HI studies of four targets.

We present MeqSilhouette v2.0 (MeqSv2), a fully polarimetric, time-and frequency-resolved synthetic data generation software for simulating millimetre (mm) wavelength very long baseline interferometry (VLBI) observations with heterogeneous arrays. Synthetic data are a critical component in understanding real observations, testing calibration and imaging algorithms, and predicting performance metrics of existing or proposed sites. MeqSv2 applies physics-based instrumental and atmospheric signal corruptions constrained by empirically-derived site and station parameters to the data. The new version is capable of applying instrumental polarization effects and various other spectrally-resolved effects using the Radio Interferometry Measurement Equation (RIME) formalism and produces synthetic data compatible with calibration pipelines designed to process real data. We demonstrate the various corruption capabilities of MeqSv2 using different arrays, with a focus on the effect of complex bandpass gains on closure quantities for the EHT at 230 GHz. We validate the frequency-dependent polarization leakage implementation by performing polarization self-calibration of synthetic EHT data using PolSolve. We also note the potential applications for cm-wavelength VLBI array analysis and design and future directions.

Different types of DNA damage can initiate phosphorylation-mediated signalling cascades that result in stimulus specific pro- or anti-apoptotic cellular responses. Amongst its many roles, the NF-κB transcription factor RelA is central to these DNA damage response pathways. However, we still lack understanding of the co-ordinated signalling mechanisms that permit different DNA damaging agents to induce distinct cellular outcomes through RelA. Here, we use label-free quantitative phosphoproteomics to examine the temporal effects of exposure of U2OS cells to either etoposide (ETO) or hydroxyurea (HU) by monitoring the phosphorylation status of RelA and its protein binding partners. Although few stimulus-specific differences were identified in the constituents of phosphorylated RelA interactome after exposure to these DNA damaging agents, we observed subtle, but significant, changes in their phosphorylation states, as a function of both type and duration of treatment. The DNA double strand break (DSB)-inducing ETO invoked more rapid, sustained responses than HU, with regulated targets primarily involved in transcription, cell division and canonical DSB repair. Kinase substrate prediction of ETO-regulated phosphosites suggest abrogation of CDK and ERK1 signalling, in addition to the known induction of ATM/ATR. In contrast, HU-induced replicative stress mediated temporally dynamic regulation, with phosphorylated RelA binding partners having roles in rRNA/mRNA processing and translational initiation, many of which contained a 14-3-3ε binding motif, and were putative substrates of the dual specificity kinase CLK1. Our data thus point to differential regulation of key cellular processes and the involvement of distinct signalling pathways in modulating DNA damage-specific functions of RelA.

Xova is a software package that implements baseline-dependent time and channel averaging on Measurement Set data. The uv-samples along a baseline track are aggregated into a bin until a specified decorrelation tolerance is exceeded. The degree of decorrelation in the bin correspondingly determines the amount of channel and timeslot averaging that is suitable for samples in the bin. This necessarily implies that the number of channels and timeslots varies per bin and the output data loses the rectilinear input shape of the input data.

The celebrated CLEAN algorithm has been the cornerstone of deconvolution algorithms in radio interferometry almost since its conception in the 1970s. For all its faults, CLEAN is remarkably fast, robust to calibration artefacts and in its ability to model point sources. We demonstrate how the same assumptions that afford CLEAN its speed can be used to accelerate more sophisticated deconvolution algorithms.

Because of their rarity, limited awareness among non-specialists, and significant overlaps in their clinical presentation, childhood autoimmune/inflammatory conditions represent a diagnostic and therapeutic challenge. Juvenile idiopathic arthritis (JIA), with its 7 sub-forms, is the most common paediatric "rheumatic" disease. Juvenile-onset systemic lupus erythematosus (jSLE) is a severe autoimmune/inflammatory disease that can affect any organ system and shares clinical features with JIA. To overcome issues around diagnostic approaches in the context of clinical overlap, we aimed at the definition of disease sub-form specific cytokine and chemokine profiles. Serum samples from patients with JIA (n = 77) and jSLE (n = 48), as well as healthy controls (n = 30), were collected. Samples were analysed using the Meso Scale Discovery (MSD) U-PLEX Biomarker Group 1 (hu) panel. Distinct serum protein signatures associate with JIA vs jSLE disease groups. Proteins with high discriminatory ability include IL-23, MIP-1 beta, MCP-1, M-CSF and MDC. Furthermore, serum IL-18, MIF, MIP-5 and YKL40 discriminate between systemic JIA and other JIA subtypes. Thus, simultaneous quantification of serum proteins in a panel format may provide an avenue for the diagnosis and monitoring of childhood autoimmune/ inflammatory conditions.

Interferometric coherency measurements scale quadratically with the number of stations in the interferometer. This, combined with high spectro temporal resolution of the data necessitates the use of modern computing strategies such as MapReduce, and cluster computing frameworks to reduce data in tractable amounts of time. Frameworks such as Spark and Dask [1] lean towards a streaming, chunked, functional programming style with minimal shared state. Individual tasks processing chunks of data are flexibly scheduled on multiple cores and nodes. To process the quantities of data produced by contemporary radio telescopes such as MeerKAT, and future telescopes such as the SKA, radio astronomy codes must adapt to these paradigms. In what follows, we describe two Python libraries, dask-ms and codex-africanus, which enable the development of distributed High-Performance Radio Astronomy code.

Abstract Motivation A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning. Results We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse-engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. The networks highlighted that although on the gene-level the models differed, they contained genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes and Inflammatory/Innate Response. Availability Data and code are available on the Gene Expression Omnibus and github. Supplementary information Supplementary data are available at Bioinformatics online.

MeerKATHI is the current development name for a radio-interferometric data reduction pipeline, assembled by an international collaboration. We create a publicly available end-to-end continuum- and line imaging pipeline for MeerKAT and other radio telescopes. We implement advanced techniques that are suitable for producing high-dynamic-range continuum images and spectroscopic data cubes. Using containerization, our pipeline is platform-independent. Furthermore, we are applying a standardized approach for using a number of different of advanced software suites, partly developed within our group. We aim to use distributed computing approaches throughout our pipeline to enable the user to reduce larger data sets like those provided by radio telescopes such as MeerKAT. The pipeline also delivers a set of imaging quality metrics that give the user the opportunity to efficiently assess the data quality.