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Results for “Robust optimisation”

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PubMed2026

Robust optimisation for photon radiotherapy: A scoping review of models, paradigms, and reporting.

BACKGROUND AND PURPOSE: Robust optimisation offers an alternative to conventional margin-based photon radiotherapy planning by explicitly modelling uncertainty, but practice is variable and not standardised. MATERIALS AND METHODS: A scoping review was conducted to map robust optimisation for photon external beam radiotherapy. Electronic searches of Scopus, PubMed and Google Scholar (2000-2025, English language) identified planning studies that incorporated modelled uncertainties into the optimisation process and reported at least one robustness-related outcome. Data were charted on clinical context, uncertainty models, optimisation paradigms, robustness metrics and evidence for clinical implementation. RESULTS: Seventy-one studies were included. Most investigated prostate, breast or lung cancer and used intensity-modulated radiotherapy or volumetric-modulated arc therapy in commercial or research treatment planning systems. Scenario-based worst-case (minimax) optimisation was the dominant paradigm in clinically oriented work, while chance-constrained, conditional value at-risk, distributionally robust and adaptive formulations were confined to small methodological series. Uncertainty modelling focused mainly on rigid set-up error; fewer studies incorporated respiratory motion, inter-fraction anatomical change, dose-calculation uncertainty or biological variation. Robustness was evaluated with diverse scenario-based dose-volume metrics, probabilistic coverage measures, composite robustness indices and, less often, biological endpoints. Direct clinical implementation reports were scarce. CONCLUSION: Robust photon planning is technically feasible and generally maintains or improves target coverage and organ sparing compared with margin-based planning. However, heterogeneity in uncertainty models, optimisation configuration and robustness reporting limits comparison and synthesis. Pragmatic minimum standards are proposed to support future consensus and wider clinical adoption.

Humans↗

PubMed2025

Optimising parent selection in plant breeding: comparing metaheuristic algorithms for genotype building.

Stacking desirable haplotypes across the genome to develop superior genotypes has been implemented in several crop species. A major challenge in Optimal Haplotype Selection is identifying a set of parents that collectively contain all desirable haplotypes, a complex combinatorial problem with countless possibilities. In this study, we evaluated the performance of metaheuristic search algorithms (MSAs)-genetic algorithm (GA), differential evolution (DE), particle swarm optimisation (PSO), and simulated annealing (SA) for optimising parent selection under two genotype building (GB) objectives: Optimal Haplotype Selection (OHS) and Optimal Population Value (OPV). Using a diverse wheat population of 583 lines genotyped for 29,972 SNPs, forming 7645 haplotype blocks and phenotyped for stripe rust scores, we assessed each algorithm's performance across fitness optimisation, convergence speed, and computational efficiency. GA consistently achieved high fitness and rapid convergence, while DE showed robustness but required longer runtime and careful tuning. PSO performed well under the OHS criterion but was less effective for OPV. SA, although computationally lighter, was less consistent in finding optimal solutions. Simulation over 100 breeding cycles showed that OHS outperformed both OPV and GEBV-based selection in long-term genetic gain and diversity retention. OHS maintained heterozygosity and additive variance, which are key for sustainable improvement, while GEBV selection led to early allele fixation. Our findings underscore the potential of GB strategies that prioritise the collective performance of parent sets rather than individual ranking to enhance selection outcomes in genomic-assisted breeding programmes.

Plant Breeding↗

PubMed2024

Development of a clinical metagenomics workflow for the diagnosis of wound infections.

BACKGROUND: Wound infections are a common complication of injuries negatively impacting the patient's recovery, causing tissue damage, delaying wound healing, and possibly leading to the spread of the infection beyond the wound site. The current gold-standard diagnostic methods based on microbiological testing are not optimal for use in austere medical treatment facilities due to the need for large equipment and the turnaround time. Clinical metagenomics (CMg) has the potential to provide an alternative to current diagnostic tests enabling rapid, untargeted identification of the causative pathogen and the provision of additional clinically relevant information using equipment with a reduced logistical and operative burden. METHODS: This study presents the development and demonstration of a CMg workflow for wound swab samples. This workflow was applied to samples prospectively collected from patients with a suspected wound infection and the results were compared to routine microbiology and real-time quantitative polymerase chain reaction (qPCR). RESULTS: Wound swab samples were prepared for nanopore-based DNA sequencing in approximately 4 h and achieved sensitivity and specificity values of 83.82% and 66.64% respectively, when compared to routine microbiology testing and species-specific qPCR. CMg also enabled the provision of additional information including the identification of fungal species, anaerobic bacteria, antimicrobial resistance (AMR) genes and microbial species diversity. CONCLUSIONS: This study demonstrates that CMg has the potential to provide an alternative diagnostic method for wound infections suitable for use in austere medical treatment facilities. Future optimisation should focus on increased method automation and an improved understanding of the interpretation of CMg outputs, including robust reporting thresholds to confirm the presence of pathogen species and AMR gene identifications.

Humans↗

PubMed2026

Controlling GRF4-GIF1 expression for efficient, genotype-independent transformation across wheat cultivars.

Wheat is a staple crop critical for global food security, and its continuous genetic improvement is essential to meet the demands of a growing population. Efficient, genotype-independent transformation is a major bottleneck in wheat functional genomics and gene editing. The growth regulating factor (GRF)-GRF-interacting factor (GIF) fusion technology enhances regeneration efficiency and broadens the range of transformable cultivars, but constitutive expression can reduce fertility and spikelet number. Here, we present an optimised Agrobacterium-mediated wheat transformation protocol incorporating GRF4-GIF1, tested across multiple tetraploid and hexaploid cultivars. Transformation efficiency was improved through adjustments in selection pressure, zeatin concentration, and promoter choice, with GRF4-GIF1 consistently enabling successful transformation across genotypes. Tissue-specific promoters and heat-inducible excision strategies effectively minimised pleiotropic effects, such as reduced fertility, while maintaining high transformation rates. This refined system provides a robust and versatile platform for gene function studies and gene editing, advancing genotype-independent wheat transformation and supporting breeding efforts to improve crop productivity, resilience, and nutritional value.

Triticum↗

PubMed2026

Machine learning for population-level risk prediction of future cholangiocarcinoma.

BACKGROUND: The poor prognosis of cholangiocarcinoma (CCA) is largely driven by rapid, asymptomatic disease progression, which usually results in a late diagnosis in the absence of established screening strategies. An early, cost-effective, and universally applicable risk assessment strategy would therefore be valuable. METHODS: We developed machine learning (ML) models on prospective, multimodal data from 487,495 UK Biobank (UKB) participants, of whom 649 developed CCA during follow-up. Data from England (80%) were utilised for ML development via five-fold cross-validation, and then all models were tested on withheld data from Scotland, Wales, and Newcastle (20%). Iterative ablation studies reduced inputs from >150 features across demographic data, lifestyle, health records, blood parameters, genomics, and metabolomics to models built on five and ten routinely available clinical parameters. These were externally validated in the Penn Medicine Biobank (PMBB; n = 2638; 28 CCA), All of Us Research Program (AOU; n = 330,433; 362 CCA), Japan Medical Data Centre Claims Database (JMDC; n = 8,425,522; 723 CCA) and TriNetX (n = 728,886; 1592 CCA). FINDINGS: We show that ML models integrating biliary-disease associated health records and Gamma glutamyltransferase can stratify risk of future CCA. Evaluation on the UKB test set as well as three independent cohorts revealed robust performance and generalisability across ethnicities. We achieved AUROCs of 0.71 [95% CI: 0.703-0.711], 0.77 [95% CI: 0.764-0.778 ], 0.796 [95% CI: 0.795-0.798] and 0.8 [95% CI: 0.794-0.805] for UKB, PMBB, AOU, and JMDC respectively, with respective AUPRCs of 0.014 [95% CI: 0.009-0.018], 0.042 [95% CI: 0.037-0.048], 0.038 [95% CI: 0.033-0.042] and 0.001 [95% CI: 0.001-0.001]. In AOU, application of the Youden J-optimised threshold yielded a number needed to screen of 79. Separate models for intra- and extrahepatic CCA did not improve performance. In line with the pathophysiology, performance declined for longer intervals between assessment and event. A group-level analysis in the TriNetX cohort revealed hazard ratios of up to 82.5 [95% CI: 26.4-257.96]. We provide extensive interpretability results and release all source codes used to develop the presented models. INTERPRETATION: We provide a comprehensive framework for early CCA risk stratification in the general population, identifying key predictors, and demonstrating the potential of data-driven models in personalised screening for hepatobiliary cancer. FUNDING: German Cancer Aid (grant #70115730), Junior Principal Investigator Fellowship programme of RWTH Aachen Excellence strategy.

Humans↗

PubMed2026

Combining Annotation Software to Identify Orthologous Genes (CASIO) Provides a New Dataset of Orthologous Genes for Swallowtail Butterflies.

With the massive increase in genomic resources, it is becoming increasingly popular to analyse thousands of loci across many species. However, many of the available genomes are not annotated, which hinders an efficient search for orthologous protein-coding genes. Here, we aim to develop a semi-automated pipeline and compare four genomic annotation methods (BRAKER2, BUSCO, Miniprot and Scipio). Our results highlight the importance of integrating multiple annotation tools to optimise ortholog detection and improve genomic studies. Each annotation method showed different strengths. BRAKER2 annotated a substantial number of genes. BUSCO, despite limitations inherent to its reference database, identified a higher number of orthologs. Miniprot exhibited notable flexibility in accommodating diverse protein datasets, whereas Scipio successfully recovered a considerable set of genes that were not detected by the other tools. The combination of these tools allowed for more comprehensive ortholog detection. Taking advantage of this pipeline, we developed a comprehensive dataset of orthologous genes for swallowtail butterflies (Lepidoptera: Papilionidae), called Papilionidae_odb, which will facilitate future studies, especially for a non-model group with abundant genomic data and few transcriptomic resources. We tested Papilionidae_odb by inferring a robust phylogenetic framework for Leptocircini using 142 complete genomes, which improved branch support for some phylogenetic relationships, although challenges remained in resolving relationships within certain species groups, likely due to rapid radiations. Our results highlight the complementary nature of the annotation methods and suggest that combining these tools can yield more accurate results in genomic research. This approach was implemented in a Snakemake workflow called CASIO (Combining Annotation Software to Identify Orthologous genes) and can easily be applied to other non-model groups to improve genomic datasets in diverse taxa where transcriptomic resources are still limited.

Animals↗

PubMed2024

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.

OBJECTIVES: To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. METHODS: Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. RESULTS: A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an 'intracellular protein score' (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. CONCLUSIONS: We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Humans↗

PubMed2026

DNA Extraction Optimisation for Minute Land Snails of Vertigo Müller, 1773 (Gastropoda: Vertiginidae): A Comparative Evaluation of Six Methods, Including a Non-Destructive Shell-Preserving Protocol.

No systematic comparison of DNA extraction strategies exists for minute Vertiginidae (shell height < 3 mm), a group posing a dual analytical challenge: extremely low tissue input and co-purified PCR-inhibitory mucus. For legally protected species, an additional requirement to preserve the shell voucher further constrains available protocols. Using Vertigo antivertigo as the model species, we compared six approaches applied to specimens preserved in 96% ethanol (n = 10 per method): two HotSHOT alkaline-lysis protocols (destructive and non-destructive shell-preserving variants), a modified CTAB protocol supplemented with PVP-40 and DTT, and three commercial silica-column kits (GeneJET Genomic, DNeasy Blood & Tissue, QIAamp DNA Micro). DNA yields were quantified by QuantiFluor fluorometry, and PCR performance was subsequently assessed across four loci (COI barcode, COI mini-barcode, ITS1, ITS2). DNeasy Blood & Tissue produced the highest fluorometric concentrations; QIAamp DNA Micro and CTAB + PVP-40 gave intermediate values. The shell-preserving HotSHOT variant yielded lower concentrations but improved A260/230 ratios. BSA and trehalose supplementation increased PCR success in inhibition-prone HotSHOT extracts from 70% to 100%. ITS1 Sanger sequencing of three Vertigo species listed in Annex II of the EU Habitats Directive, all extracted with the shell-preserving protocol, confirmed species-level identification (99.8%-100% BLASTn identity; mean Phred Q > 51). The shell-preserving non-destructive HotSHOT protocol yields sequenceable DNA from protected Vertiginidae while retaining the morphological voucher, making it the preferred option for conservation-genetic monitoring. The practical decision framework documented here-integrating voucher preservation, amplification robustness and per-sample cost-has broad applicability to other minute terrestrial gastropods processed in large-scale biodiversity surveys.

Habitats Directive↗

PubMed2026

Characterisation of metabolic burden in Pseudomonas putida reveals precursor limitation in heterologous lycopene production.

BACKGROUND: The introduction of heterologous pathways into microbial hosts often imposes a metabolic burden on the cell, arising from three major physiological constraint layers: competition for gene expression resources, limited precursor availability and flux distribution, and insufficient energy and redox supply. Although Pseudomonas putida KT2440 is considered a robust and metabolically versatile production host, it remains unclear which of these constraint layers primarily limits heterologous terpenoid production in this organism. Here, lycopene biosynthesis was used as a model system to systematically dissect these three potential sources of metabolic burden. RESULTS: A capacity-monitoring system revealed no clear reduction in transcriptional or translational capacity across the tested strains and cultivation conditions, indicating that general gene expression capacity was not the primary limiting factor. Instead, lycopene production depended strongly on promoter architecture and plasmid backbone, showing that regulatory design shaped pathway performance. Enhancing precursor supply by introducing a heterologous mevalonate (MVA) pathway substantially increased product titres, identifying precursor availability from the native MEP pathway as the dominant bottleneck. This conclusion was independently supported by exogenous mevalonate supplementation, which further increased lycopene accumulation but also revealed saturation at higher concentrations, suggesting that downstream pathway balance or enzyme capacity became limiting once precursor supply was relieved. Under controlled bioreactor conditions, lycopene titres increased from approximately 1 mg/L to nearly 25 mg/L, indicating that process conditions further modulate production performance, suggesting an additional contribution of process-dependent energy and redox constraints. CONCLUSION: Metabolic burden during heterologous lycopene production in P. putida is governed primarily by precursor availability rather than by limitations in general gene expression capacity. Regulatory properties of the vector system strongly influence pathway performance, while controlled cultivation conditions can further improve production by alleviating additional process-dependent constraints. Together, these findings provide a systematic framework for distinguishing constraint layers and guiding the optimisation of heterologous terpenoid production systems.

Lycopene↗