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Catalytic Asymmetric Cationic Geranyl Cyclizations

ATR-SEIRAS Studies of Butadiene Electrocarboxylation in Aprotic Media

Heteroatom Doping Restructures Interfacial H ₂ O to Resolve Mechanistic Contradictions in CO ₂ Electroreduction

Sensing Synthon Architectures with Hyperfine-Resolved Rotational Spectroscopy

Revealing and Controlling Atomic-Scale Stacking Disorder in SAPO-34 for Enhanced MTO Performance

Conformation-Guided Remote C–H Functionalization of Aromatic Compounds

Realization of Giant Negative Thermal Expansion via a Significantly Decreased Local Moment in a Kagome Ferromagnet with Spin Reorientation

Robust Hexagonal 2D COF Single Crystals for the Construction of Porous Moiré Superstructures

Smart Charge Buffer-Modulated Multitime Scale Chemistry for Photocatalysis

Comparative Metabolomics Reveals the Production of Sulfated Metabolites by Human Gut Bacteria

Why some trees never come back after rainforests are cleared

Huge study finds first genetic clues for borderline personality disorder

Measuring the optimistic bias of cross-validation in radiomics

Abstract In radiomics, independent external data are often unavailable and cross-validation (CV) schemes are widely used to obtain performance estimates. Simple k-fold CV is preferred over nested CV due to its lower computational cost, although nested CV is known to provide more accurate estimates. In a recent study, it was concluded that for practical reasons, there is no significant difference between the two schemes. However, that study was conducted only on low-dimensional datasets, which do not reflect typical radiomic data characteristics. While previous studies suggest that an optimistic bias, in which the true performance of the model is overestimated, might occur in high-dimensional datasets when only simple k-fold CV is used, the extent of this bias in radiomic datasets is currently unknown. This study evaluated whether nested CV is necessary in radiomics by comparing simple k-fold CV, holdout CV, and nested CV across 32 public radiomic datasets. In total, 30,720 models were evaluated to ensure a robust and comprehensive comparison. Each validation scheme was tested by repeatedly splitting the data using a 50:50 train-test ratio. The training data was used for model development and internal validation, whereas the test data was reserved for performance evaluation. Model performance was measured using the area under the receiver operating characteristic curve (AUC), F1-score, and Matthews correlation coefficient (MCC). Cross-validation estimates were then compared to the estimates on the test set to assess potential optimistic bias. In addition, the experiments were repeated on 110 low-dimensional datasets from the UCI repository, involving 105,600 models to compare findings between high- and low-dimensional data. The results revealed that in radiomic data, simple k-fold CV exhibited significant optimistic bias, with a bias of up to 0.128 in AUC, 0.130 in F1-score, and 0.264 in MCC, whereas holdout CV remained relatively unbiased. Nested CV showed low, but variable bias depending on the model, with a simple ensemble achieving the best performance. Larger sample sizes reduced both optimistic bias and variability, particularly in simple k-fold CV, but no significant relationship was observed with dataset dimensionality or feature count. In contrast, simple k-fold CV was unbiased on the UCI datasets (bias < 0.014 in AUC, < 0.016 in F1, and < 0.014 in MCC), and no clear differences were found between the three CV schemes. Therefore, in radiomics, there is a substantial practical difference between simple k-fold and nested CV. This finding further underscores that results from low-dimensional datasets do not necessarily generalize to radiomics.

Daily briefing: How to nail an industry interview

Deep learning for multimodal physiological signal based assessment of sleep disordered breathing

Investigations on the initiation mechanism of rainfall-induced loess landslides driven by sinkhole-assisted seepage: a case study in Qinghai, China

Reinforcement learning-driven imbalanced classification enhanced by a GAN model augmentation for lung cancer detection

Smuggling charges against NIH virologists trigger political uproar

Nuclear decay networks reveal stability patterns in superheavy nuclei

Abstract Predicting the stability of superheavy nuclei remains a major challenge owing to limited experimental accessibility and substantial uncertainties among existing theoretical nuclear models. In this work, we introduce a network science–based framework in which nuclear decay chains, constructed from evaluated nuclear data, are represented as directed weighted networks, which allows for the analysis of topological properties alongside conventional nuclear descriptors. We find a statistically significant negative association between betweenness centrality and a structural stability score, indicating that nuclei occupying more topologically peripheral positions within the decay network tend to be relatively more stable. This behavior highlights known regions of enhanced relative stability within the analyzed transuranic domain and provides a qualitative network-based trend consistent with the expected movement toward the long-discussed superheavy stability region. Rather than identifying isolated topological clusters, the framework maps topological peripherality to localized regions of high stability determined by nuclear shell effects. Sensitivity analyses with respect to stability-score weighting schemes confirm the robustness of these trends. Rather than replacing existing nuclear models, the proposed approach provides a complementary, system-level perspective that captures collective decay patterns beyond single-nucleus descriptions. These results suggest that simple network measures can serve as useful qualitative indicators of relative nuclear stability and may help prioritize regions for further theoretical and experimental investigation.

Experiments and discussions on the shear strength of root-soil complexes after phytoremediation of ionic rare earth tailings soils