GigaScience

The integration of digital technologies for high-throughput field phenotyping is critical for accelerating crop improvement in agriculture. However, extracting traits from remote sensing data remains constrained by fragmented workflows, manual intervention, and limited interoperability among existing tools, resulting in delays that hinder timely biological insight and decision-making. To address these challenges, we present PhenoStream (Phenotyping Streaming), a scalable, end-to-end cyberinfrastructure designed to automate the full lifecycle of aerial imagery-based phenotyping, from data acquisition to plot- and genotype-level inference. The framework integrates automated data ingestion from distributed field sites, geospatial processing, and AI-enabled trait extraction within a unified, user-accessible graphical interface. Its modular and extensible architecture supports adaptable trait modeling and seamless integration of new data sources, enabling deployment across diverse crops, environments, and experimental designs. We demonstrate the system across a large multi-location field trial network of bioenergy crops, where it enables high-throughput characterization of spatiotemporal growth dynamics, genotype-by-environment (GxE) interactions, and predictive modeling of key agronomic traits. By significantly reducing processing latency and manual effort, the platform facilitates near-real-time analysis and reproducible workflows. This work establishes a generalizable and scalable pathway for operationalizing very-high-spatial resolution aerial phenotyping in agricultural research. By bridging data acquisition and analytics, the end-to-end cyberinfrastructure provides a foundation for integrating heterogeneous and unstructured data streams--including remote sensing, environmental, and management data--toward data-driven decision making in agriculture.

Network-based analyses of molecular interactions are useful for interpreting high-throughput omics data and identifying therapeutic targets. Cytoscape is the standard platform for these tasks, but users face a trade-off between accessible graphical workflows that are difficult to document and reproducible automation in Python or R that requires programming expertise. General-purpose coding assistants can generate Cytoscape Automation scripts, but remain external to Cytoscape. We present CyChat, a Cytoscape Desktop app that integrates a chat interface and a large language model (LLM) agent into the application. CyChat translates natural language into executable Cytoscape Automation workflows, runs generated Python code, and exports chat sessions with executed code as standalone Jupyter notebooks. To reduce setup barriers, CyChat includes an embedded Python runtime and supports both cloud-based and locally hosted LLMs. CyChat was evaluated across ten Cytoscape workflows using seven LLM providers, each represented by one LLM. The strongest configuration achieves a pass rate above 99%. In a qualitative evaluation based on a published network visualization, CyChat completes the task in 1.5-5 minutes, compared with 15-20 minutes for manual GUI workflows by computational biologists. CyChat is available through the Cytoscape App Store at https://apps.cytoscape.org/apps/cychat.

Motivation: A continued decrease in sequencing costs has facilitated the exponential increase in available sequencing data, with public databases like the European Nucleotide Archive (ENA) and Sequence Read Archive (SRA) reaching well in the order of petabases. This has been the incentive to develop more scalable tools for common bioinformatics tasks. One such task is the approximate searching of short sequence patterns like genes or reads in reference data sets. In recent years, a variety of indexing data structures have been proposed for searching large sequencing databases. The state-of-the-art index, the Hierarchical Interleaved Bloom Filter (HIBF) was first-in-class to index one million samples. To be useful for expanding repositories, it must be extended to support dynamic updates. Results: In this paper, we introduce a scalable and updatable sequence-search index by extending the HIBF with partial rebuilding to support efficient updates. We demonstrate the Dynamic HIBF's capacity for large-scale data by iteratively creating an index from over 100 TB of compressed reads across more than 39,000 full human RNA-Seq samples, updated in consecutive batches of 100. To benchmark against state-of-the-art tools, we evaluated incremental performance on a subset of 5,000 samples sub-sampled to 1% of their original read depth. In this comparative setting, the dynamic HIBF completed the sequential insertion of all 5,000 samples within 5 hours--24 to 65 times faster than competing methods and twice as fast as the static HIBF.

SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.

Identifying causal connections between existing drugs and mechanistic profiles of diseases is a foundational step for effective drug repurposing. Although knowledge graphs (KGs) are highly suited for consolidating biomedical databases and tracking these connections, a single biomedical KG is constrained by its ingestion pipeline and knowledge sources. While different biomedical KGs could be complementary if combined, efforts to combine them into a unified and more comprehensive KG are hindered by lack of interoperability and poor provenance. To address those issues, we present EC-KG, a Biolink Model-compatible KG for computational drug repurposing. EC-KG is an interoperable, provenance-first KG which integrates RTX-KG2, ROBOKOP, and PrimeKG at the network-level, encapsulating over 7 million nodes and 81 million edges from 95 primary data sources. EC-KG has improved coverage of core biomedical entities such as drugs, targets, and diseases relevant to drug repurposing vs source graphs, and captures complex biomedical mechanisms within its topology. We demonstrate that the network unification in EC-KG leads to emergence of novel, mechanistically relevant pathways which are disconnected in the underlying constituent networks and show its applications in method development, benchmarking and predictive drug repurposing applications. EC-KG has already been successfully used in drug repurposing research to surface Botulinum Toxin A as a candidate to treat Major Depressive Disorder, as well as to validate repurposing of Lenalidomide and Dexamethasone for a subgroup of patients with Rosai-Dorfman Disease.

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

Background: Reproducible taxonomic collapsing and geological-timescale annotation of time-calibrated phylogenetic trees in R often require coordination among several packages and repeated code for label parsing, clade validation, plotting, and export. Workflow-managed analyses additionally benefit from non-interactive configuration, predictable diagnostics, and machine-readable exit status. Results: We present Rclade, an R package that consolidates the multi-package coordination required for taxonomic collapsing into a streamlined, single-function interface. Rclade provides (1) custom ggproto objects (GeomPolygonStraight/GeomSegmentStraight) that bypass coord_munch() interpolation to achieve straight-edge rendering of collapsed triangles in circular layouts; (2) automatic detection and parsing of four taxonomic-label formats (GTDB, Silva, NCBI, embedded) plus user-supplied custom regex, with explicit input-validation contracts and parsing-accuracy evaluation on real and derived test sets; and (3) workflow embeddability through YAML configuration, library-mode APIs, and standard Unix exit codes. Benchmarks on synthetic and real datasets (200-10,000 synthetic tips and real reference trees up to 10,122 tips; 5 replicates at every scale under a unified fully rendered measurement protocol) show that the full-pipeline overhead is modest for interactive use (median {approx}0.87 s in-session rendering and {approx}8.4 s process-level wall-clock at 10,000 tips). Conclusions: Rclade is a convenience layer over the ggtree/deeptime ecosystem that reduces boilerplate while adding targeted technical improvements for circular-layout rendering and format heterogeneity management.

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations. However, a limitation to date has been the performance of existing segmentation tools for measuring various brain phenotypes in clinical scans. In particular, many tools underperform in infant scans due to morphological and physical changes such as rapid myelination. Here, we introduce ClinSeg: a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast. We leverage existing registration and synthetic data generation tools to construct a training corpus for a 3d U-Net spanning anatomical and contrast diversity, including scans with morphological abnormalities from a pediatric hospital. Validated against manual segmentations, ClinSeg outperforms existing models in infancy while matching them in childhood and adolescence. Finally, ClinSeg enables the construction of reference brain growth trajectories in 11,699 individuals from 0-21 years of age, leading to the detection of more nuanced age-related findings in clinical groups.

High-phenolic plant genera present a major technical limitation in genomic research. Standard extraction approaches that perform reliably across diverse flora often perform poorly when applied to recalcitrant taxa, producing low DNA yield and integrity incompatible with sequencing requirements. The genus Anigozanthos (Kangaroo paws) from the family Haemodoraceae exemplifies this problem. We identified key physicochemical factors governing extraction failure in this genus and resolved them through targeted modifications to lysis chemistry and contaminant management. The resulting protocol achieved a near threefold improvement in DNA purity, substantially reducing contaminant carry over and consistently yielded high-integrity, long DNA fragments (DIN > 7) across a diverse sample set spanning cultivated and wild material across four diverse genera of Haemodoraceae. We also tested a straightforward purity assessment framework that can be implemented in any standard molecular laboratory, enabling rapid pre-submission quality assessment without the need for specialised equipment. Together these advances open a practical path to genomic characterisation of Anigozanthos that establishes a transferable model for genomic research across Australia ' s chemically complex native flora.

Motivation: Interactive tools that let non-programmers explore an analyzed single-cell dataset, its embeddings, gene expression, cell metadata, and marker genes, have become standard laboratory infrastructure. Every actively maintained tool in this space (ShinyCell, ScRDAVis, sCIRCLE, scViewer) is built on R Shiny and requires a Seurat object as input. Laboratories whose primary analysis pipeline is Python/scanpy, the dominant framework for single-cell RNA-seq, spatial, and multi-omic analysis, therefore have no lightweight, language-native option that pairs a shareable web-based viewer with a scriptable Python API: sharing a scanpy result means either exporting to Seurat first or handing over a notebook that only a programmer can run. Results: We present scPyviewer, a web-based viewer that ingests AnnData objects directly and reproduces the core interaction patterns of the incumbent R Shiny tools without leaving the Python stack. In a feature-parity audit against three actively maintained R Shiny incumbents, scPyviewer matches or exceeds every baseline capability (7/7); among these, it uniquely offers native AnnData ingestion with no Seurat conversion, and cross-dataset comparison over shared genes and matched cell-type composition. Benchmarked head-to-head against the R/Seurat rendering substrate the incumbents are built on, identical operations, identical data, across three datasets spanning 22,315 to roughly 313,000 cells, scPyviewer renders every core view faster at every scale tested (up to 3.6x on a single view) and at a fraction of the memory (5.2x lower on the smallest dataset). At the largest scale tested, the gap becomes categorical rather than incremental: scPyviewer completes every view on a 313,000-cell dataset while the Seurat substrate exhausts an 8 GB memory budget and fails outright. Beyond the interactive app, scPyviewer installs via pip or conda and exposes a public Python API that returns Matplotlib figures and pandas tables for scripted, publication-ready output. Availability and implementation: scPyviewer is implemented in Python 3.11 (scanpy 1.11.5, anndata 0.12.19, streamlit 1.59.2, plotly 6.9.0) and distributed with a one-command reproduction interface that installs pinned dependencies, regenerates the benchmark and all figures, and launches the interactive app. Source code is available at https://github.com/xuan13hao/scPyviewer.git.

The openEHR standard provides an open, vendor-neutral architecture for clinical data repositories (CDRs), yet its real-world deployment has not been systematically documented. We conducted a dual-perspective survey combining a vendor survey of openEHR CDR providers with a community survey of openEHR practitioners. Eleven vendor organisations reported deployments across 22 countries and over 100 institutions and health regions. A complementary community survey (n=29, 17 countries) provided context on regulatory environments, adoption drivers, and barriers. Combined, the surveys cover 28 countries, 26 of them with a reported openEHR CDR deployment. Three findings emerge: openEHR has achieved national-scale presence through two distinct channels. Through vendor-market convergence, openEHR-based systems cover the majority of regional health authorities without a national mandate, including 19 of 21 Swedish regions, 3 of 4 Norwegian health regions, and 16 of 21 Finnish wellbeing services counties. Through national health record adoption, governments have built or procured national systems on openEHR as their technical foundation, including Ireland, Malta, Greece, Jamaica and Slovenia. Across Europe, this constitutes an openEHR-based interoperability infrastructure already in place across multiple EU member states. We identified no country in which openEHR is named in binding national regulation, creating structural fragility and an unrealised opportunity for alignment with the European Health Data Space (EHDS). Second, 61% of deployments serve primary use only, and 12% support both primary and secondary use. Third, lack of openEHR-specific knowledge is the most consistent adoption barrier across all geographies and deployment tiers. Adoption is driven by practitioner need and innovation, not by regulatory mandate.

MicroRNAs (miRNAs) are key post-transcriptional regulators of development, stress response, and secondary cell wall formation in woody plants, yet annotations for Eucalyptus grandis, the world's most widely planted hardwood, remain fragmented across studies using incompatible discovery pipelines and filtering criteria. Here we present the Eucalyptus MicroRNA Archive (EMA), a curated, locus-resolved database integrating three independent small RNA sequencing datasets spanning vegetative tissue, somatic embryogenesis, and mechanically induced tension wood formation. Applying annotation criteria aligned with current plant miRNA standards, EMA catalogs 99 curated miRNAs (31 previously described, 68 novel) organized into 34 family-level groupings under a three-tier confidence system, known-reference-supported, multi-study replicated, or single-study, that preserves study-of-origin and sample-level evidence for every entry. Cross-study comparison showed that only 9 of 99 entries (9.1%) were independently supported by all three datasets, supporting an evidence-tiered rather than binary annotation scheme. Target prediction against the E. grandis transcriptome yielded 1,773 miRNA-target interactions spanning 764 loci, integrated into a combined miRNA-target and protein-protein interaction network. This network resolved into functionally coherent, mutually isolated clusters, including an miR482-associated NBS-LRR/TIR disease-resistance hub with a substantial translational-repression component, alongside modules enriched for ribosome biogenesis and translation, DNA replication, and nitrogen and carbohydrate metabolism. EMA is publicly accessible through an interactive web dashboard, with all curated data, source code, and analysis scripts openly available, providing a reproducible, extensible framework for E. grandis miRNA research and a template for similarly structured resources in other non-model woody species.

Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (~100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19-74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep-learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross-walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini-Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings - a sexually dimorphic axis (PC1), a slender-versus-stout body-habitus contrast (PC2), and a free-rib-size axis at ribs 11-12 (PC3). The sexes were nearly fully separated along PC1 (Cohen's d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

Amplicon sequencing of the 16S rRNA gene is the most widely used approach for profiling bacterial communities, but its taxonomic resolution is typically limited to the genus level. Many species carry multiple divergent 16S rRNA alleles that overlap across species boundaries, an ambiguity that even full-length, long-read sequencing cannot fully resolve. Shotgun metagenomics achieves species-level resolution but remains costly, particularly when only a single genus is of interest. Amplicon sequencing of rapidly evolving, protein-coding housekeeping genes offers a cost-effective alternative, yet no tool exists to identify suitable primer sets for a given target taxon. Here we present AmPair, a Snakemake pipeline that, given a target genus and one or more candidate housekeeping genes, designs and ranks primer pairs binding conserved regions while flanking a variable region capable of species-level discrimination, and validates them in silico across all available genomes. Using the genus Bacillus and the housekeeping gene tuf as a case study, the primer set recommended by AmPair amplified 99% of 2,392 genomes; only 0.04% carried multiple alleles and none showed inter-species allele overlap, compared with 91.41% and 69.49%, respectively, for the standard 16S rRNA V1-V9 region. Applied to a Bacillus community profiled by Nanopore sequencing, the same primers resolved closely related species. AmPair thus offers a generalizable and accessible route to species-level community profiling.

Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependencies, expected outputs, and scientific limitations. Natural-language requests are translated into structured tasks, validated against this registry, executed through scientific tools, and recorded in a provenance-aware Living Lab Notebook. The system is designed to detect invalid inputs, failed tool calls, missing dependencies, and remote timeouts, and to route them to repair, retry, or escalation. The implemented and tested scope comprises RDKit-based molecular property and rule screening, protein structure analysis, docking-pose inspection, 3D visualization, and structured reporting. We demonstrate the workflow on a PubChem-retrieved osimertinib structure and a supplied 6LU7 docking artifact: the former yields deterministic descriptors (molecular weight 499.619 Da, cLogP 4.5098, TPSA 87.55 A^2), while the latter contains 2,387 protein ATOM records, 309 residues, and nine pose records. These examples are workflow demonstrations, not efficacy or affinity studies. Beyond retrospective prediction, the manuscript specifies a prior-minimized constructive mode in which a desired function is compiled into explicit physical, chemical, and systems constraints, candidate mechanisms are simulated, and observations are reintroduced for calibration and falsification; this is a proposed extension rather than a result of the present case studies. We describe an evaluation protocol that compares BloClaw with a standard single-agent workflow and fixed-script execution using task completion, scientific correctness, recovery success, provenance completeness, reproducibility, human review time, latency, and cost. This manuscript reports the system design, verified capability boundary, deterministic software artifacts, and a reproducible evaluation protocol; it does not claim benchmark improvements before those experiments are run. BloClaw is an execution and accountability layer for AI-assisted research, complementing expert review and experimental validation rather than replacing them.

Three-dimensional (3D) mural cell morphology is heterogeneous and coupled to vessel geometry, however, measurements from two-dimensional (2D) maximum intensity projections (MIP) obscure overlapping processes and cell-vessel contacts. Accordingly, we developed Mural-VISTA, a semi-automated Python workflow for mural cell-vessel interaction and single-cell topo-morphology analysis of reconstructed surface meshes. This workflow integrates mesh pretreatment, interactive centerline extraction, hierarchical segmentation of cell soma, main axis and secondary processes (branches), and extraction of 36 multiscale (cell process segment level, process level, and whole cell level) topo-morphological and vessel-referenced metrics. Mural-VISTA identified morphological changes in pericytes and vascular smooth muscle cells (vSMCs) with altered RhoA activity. Constitutive active RhoA (RhoA CA) over-expression reduced branch complexity and increased process alignment in both cell types, while increased whole-cell and branch solidity only in vSMCs. Dominant negative RhoA (RhoA DN) over-expression increased branch abundance and reduced branch solidity in pericytes but not vSMCs, suggesting cell-type specific effect of reduced RhoA activity. In conclusion, Mural-VISTA enables quantitative 3D profiling of mural cell architecture and its spatial relationship with the vessel.

Background and Objectives: Preterm infants are commonly transfused, yet direct cardiorespiratory effects of red blood cell (RBC) transfusions remain poorly understood. We explored the feasibility of using multicentre electronic health data (EHD) to study such cardiorespiratory responses. Methods: Highly granular routine EHD were collected from preterm infants born <32 weeks gestational age at three European centres. Heart rate, oxygen saturation, and respiratory rate were evaluated 12 hours before and after the RBC transfusion. Results: A total of 321 transfusions in 164 infants were analysed. Overall, there was no significant change in the rate of bradycardia and apnoea following transfusion. Cardiorespiratory parameters varied substantially between infants; e.g. 20% of transfusions were associated with an unexpected, significant increase in heart rate. Respiratory rate and oxygen saturation exhibited similarly heterogenous patterns following transfusion. In sub-group analysis, the proportion of transfusions with increased heart rate was significantly higher within the first two weeks than later (32% vs 13%, p=0.0019). Conclusions: Multicentre EHD extraction allows to identify otherwise masked short-term effects of RBC transfusions on cardiorespiratory parameters, possibly indicating cardiac or pulmonary overload. Such effects may vary with adaptation to anaemia. Analysing EHD may ultimately enable personalized transfusion practice.

Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largely restricted to Pseudomonas, leaving a key part of tailocin diversity uncharacterized. In this work, we present PhageTAILor (https://github.com/hjcho-bio/PhageTAILor), an integrative and fully automated pipeline that detects and classifies prophages and 3 PTE classes from bacterial genomes. PhageTAILor combines a 6-detector homology-based candidate search (geNomad, tail-gene, PHROGs-tail, SecReT6, eCIStem, and a divergence-tolerant tail-HMM detector) with a LightGBM classifier comprising 1 multiclass and 3 binary heads, trained on 6,501 bacterial genomes carrying 13,082 prophages and PTEs. A phylogeny-free feature matrix used in our model keeps predictions reproducible between model construction and user inference. PhageTAILor performs strongly at the genome level and generalizes beyond its Pseudomonas-rich training set. On a 76-strain cross-clade benchmark, PhageTAILor detected tailocins at F1 = 0.955. Furthermore, it identified 12 of 13 experimentally validated tailocins spanning five genera versus 2 of 13 for a Pseudomonas-restricted tool TattleTail. PhageTAILor also demonstrated sensitivity equivalent to viral detection tool geNomad while avoiding its higher false-positive rate. Applied to 7,925 plant- and soil-associated bacterial isolates, PhageTAILor showed that prophages in the phyllosphere and tailocins in plant-associated bacteria, whereas eCIS are enriched in soil. PhageTAILor is distributed as an open-source, modular pipeline with a command-line interface.

We describe LRSPAT (low-rank spatial toolkit), a fast and memory-efficient framework for approximating measures of spatial association for high-dimensional data. While LRSPAT can be applied to any multivariate spatial dataset, development was motivated by the computational challenge of identifying spatially variable genes in high-resolution spatial transcriptomics (ST) data generated by technologies such as 10x Visium HD, Xenium and Atera. LRSPAT leverages a truncated SVD of the expression data and a thresholded spatial weights matrix to perform reduced-rank reconstruction of spatial statistics in the quadratic form family, including global and local versions of Moran's I, Geary's C, and Getis-Ord G. A regularization approach is leveraged to account for the inflated null distribution of spatial statistics computed on latent variables. By performing key operations on the low-dimensional embeddings, LRSPAT is orders of magnitude faster than standard implementations with significantly lower memory requirements. Because the low-rank approach denoises and desparsifies ST data, LRSPAT is also more accurate than standard techniques at identifying genes with true spatial expression patterns. The dramatic improvements in execution time and memory consumption enable the genome-wide analysis of spatially variable genes (SVGs) and exploration of the full range of hyperparameters including spatial scale, distance metric, and embedding rank. This preprint outlines the background and mathematical details of the approach with limited preliminary results and a short conclusion.

Agentic workflows can coordinate modelling, but balancing predictive performance, measurement burden and reproducibility is unclear. We developed DXA Agent, an agentic workflow for dual-energy X-ray absorptiometry (DXA) outcomes integrating planning, feature-model refinement, tools, provenance and hypothesis-generating interpretation. Models were independently developed and tested in UK Biobank (5,318 participants) and the National Health and Nutrition Examination Survey (NHANES; 3,777 participants), using cost-efficient and no-limit strategies. Across 20 UK Biobank and three NHANES bone mineral density sites, cost-efficient models achieved lower RMSE and higher R2 than the best conventional comparator, with median relative RMSE reductions of 10.9% and 9.9%, respectively. Classification was task dependent: UK Biobank osteoporosis averaged AUROC 0.839 and PR-AUC 0.182, whereas NHANES performance was comparable with conventional models. Higher-burden features did not consistently improve prediction. These retrospective, cohort-internal findings position DXA Agent as an inspectable, measurement-burden-aware research workflow requiring independent prospective validation.