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Trait and state predictors of the intensity of emotions experienced in everyday dreams: a multilevel approach
Math and verbal fluency across adulthood provide insights into aging and individual differences
Explainable clustering and ranking of student satisfaction in online learning: a hybrid FAHP-TOPSIS and SHAP-LIME approach
Machine learning prediction of postoperative recurrence in bladder cancer using clinical and laboratory indicators
A benchmarking framework for comparative evaluation of low-complexity region detection tools in the human proteome
Abstract Low-complexity regions (LCRs) are compositionally biased segments of proteins that play critical roles in molecular recognition, structural flexibility, and phase separation. Yet, their accurate detection remains challenging due to methodological variability among computational tools. In this study, we conducted a comprehensive benchmarking of eight widely used LCR detection methods (under multiple parameter settings) across the Homo sapiens proteome. A modular computational framework was developed to systematically compare LCR characteristics, including residue-centric analyses such as length distribution and coverage percentage. Protein-centric analyses included compositional bias, amino acid composition, and Shannon entropy. Consensus analyses revealed that regions detected by multiple tools were typically longer, more repetitive, and compositionally purer, suggesting stronger structural or functional relevance. Jaccard similarity matrices revealed distinct clustering patterns among algorithms based on shared detection principles. Additionally, entropy and purity analyses highlighted fundamental differences in sequence complexity captured by each tool. Together, these results provide a unified, reproducible framework for evaluating LCR detection performance and offer practical guidelines for reliable annotation of low-complexity regions in proteome-scale studies.
The Rhisotope project: using radiation for conservation
Developing giant grass biochar-Fe₃O₄-g-C₃N₄ composite for the adsorption of methylene blue from aqueous solutions
Geomechanical performance of a novel L-shaped wellbore design for hot dry rock geothermal reservoirs: insights from fully coupled thermo-hydro-mechanical modeling
Health-related quality of life in patients with psoriasis in Asian countries: a systematic review and meta-analysis of EQ-5D utility scores
Diagnostic yield and genetic landscape of rare pediatric diseases in Vietnam identified by exome sequencing
OHAM analysis of quadratic radiative heat flux and chemical reactions in hybrid nanofluid flow over variable thickness stretching surface
Proteomics to reveal relationship between menstrual cycle and subjective conditions
Suicide literacy and its association with attitude towards seeking professional psychological help among Iranian nursing students
Differential transcriptomic and cytotoxic responses to polyvinylpyrrolidone-stabilized silver nanoparticles in breast cancer cells and human dermal fibroblasts
Biosynthesis of zinc oxide nanoparticles and evaluation of anticancer activity against MCF-7 breast cancer cell line via phyto-synthesised nanoparticles
Early-warning industrial fault detection based on physics-guided residual learning and calibrated CRNNs
An optimisation framework for resource allocation in palliative and end-of-life care
Abstract End-of-life care for frail and elderly patients is frequently characterised by high healthcare utilisation, fragmented service delivery, and limited coordination, resulting in variable quality and excess cost. This study presents a proof-of-concept framework, tested using synthetic data to illustrate potential applications in strategic planning. Few planning approaches integrate patient-level pathways into operational models that balance efficiency with patient-centred outcomes. Optimisation models were developed to support strategic resource planning for frail, elderly, and palliative patients in the final year of life. Two formulations were explored: one minimising overall cost and another aligning demand with available capacity. Patients were stratified into ten representative categories and assigned to structured pathways with varying resource intensities across hospital beds, palliative beds, community nursing, and virtual wards. A synthetic dataset representing plausible twelve-month service trajectories was used to assess model performance. Both models produced feasible allocations that satisfied expected demand within capacity limits. Most patient groups were consistently assigned to dominant pathways, while some shifted depending on the optimisation objective, illustrating trade-offs between cost efficiency and balanced utilisation. Demand intensified in the final months of life but remained manageable under planning assumptions. The modelling framework demonstrates the feasibility of applying optimisation to anticipatory planning, enabling comparison of service configurations and supporting more coordinated, efficient, and patient-centred end-of-life care.