Browse Articles

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

Chelator‐Free Radiometal Labeling Inside Engineered Affibodies

ABSTRACT Affibodies are remarkably stable three‐helix bundle proteins that can be engineered to selectively bind target proteins. When combined with radioactive metals, they serve as imaging agents or cancer therapeutics, depending on the metal used. Traditionally, this involves bifunctional linkers that attach large chelators to the affibody via reactive groups. Here, we present an alternative approach that eliminates the need for such linkers by burying the metal within the core of the affibody, surrounded by its three helices. A simple engineered triple cysteine motif, with one cysteine in each helix, stably binds Bi(III), Pb(II), In(III), and Ga(III), which are commonly used in imaging and radiotherapy. Quantitative metal uptake is instantaneous at room temperature and physiological pH, and all metal‐affibody complexes remain fully intact for one week at 4 °C. All retain their metal cargo when challenged with cellular concentrations of glutathione, while only the bismuth‐affibody complex withstands a challenge with 100 equivalents of strong chelators, even over two weeks. We demonstrate that, a bismuth‑loaded affibody retains binding affinity to the HER2 receptor comparable to the wildtype affibody, while selectively binding and retaining  213 Bi, a promising radioisotope for targeted alpha therapy, thereby enabling targeting of HER2‑overexpressing SKBR3 cancer cells.

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

Lithium–Nitrogen Battery: Promise and Development Roadmap

ABSTRACT Lithium–nitrogen (Li–N 2 ) batteries offer a unique electrochemical paradigm that combines intrinsically safe energy storage with sustainable N 2 conversion to value‐added chemicals. About ten years after the first ground‐breaking report, the practical development of Li–N 2 batteries is still restricted by poor reversibility and limited cycling stability. Here, we first introduce this highly innovative and promising Li–N 2 battery technology and then specify the key challenges ahead for the poor reversibility of Li–N 2 batteries from reaction mechanisms, characterization technologies, and the battery configurations. We further highlight a flow field‐assisted “flow‐type” cell configuration that presents exciting future opportunities to realize reversible Li–N 2 batteries. Besides, we discuss that the diversity in terms of electrolytes, electrode materials, and separators is key to establishing long‐term Li–N 2 batteries. The possible mechanistic pathways of N 2 reduction and lithiation over heterogeneous electrocatalysts are also discussed. We recommend a rigorous experimental protocol for evaluating Li–N 2 batteries to ensure reproducibility and reliable performance comparison across studies. Overall, this perspective aims to inspire future generations of researchers to advance both fundamental understanding and practical breakthroughs, thereby engineering a paradigm shift in Li–N 2 chemistry research.

Jerry Alfred Fereiro

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

Anionic T‐Shaped Platinum(0) Pincer Complexes and Platinum(I) Intermediates in Radical Reactions With Alkyl Halides

ABSTRACT Two anionic T‐shaped d 10 platinum(0) complexes supported by a sterically demanding carbazole‐based PNP ligand were obtained by reduction of a platinum(II) chloride precursor and characterized as contact ion aggregates 2‐Na and 2‐Mg with the Na + and [MgCl(thf) 2 ] + counterions, respectively. Reactions with organyl bromides produced the homocoupled hydrocarbons and the platinum(II) bromide and alkyl species, depending on the stability of the generated radicals. In situ EPR spectroscopic studies allowed the detection of the transient T‐shaped 15‐electron platinum(I) metalloradical (g iso  = 2.219 and a hyperfine coupling (HFC) interaction with a 195 Pt atom A iso of 2124 MHz) involved in the sequential single‐electron transfer steps.

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