Accurate breast cancer subtyping guides treatment selection, yet
histopathology captures morphology without molecular state, while genomic
profiling captures molecular signatures without spatial context. Existing
fusion methods rely on concatenation, or on attention applied only after each
modality is encoded independently. This work identifies a scale-dependent
asymmetry in the direction of cross-modal conditioning: the direction that
performs best under limited samples is not the one that holds at scale, and
the reversal is traced to the capacity of the modulation pathway rather than
to the fusion principle. The comparison is carried out within a
hypernetwork-guided framework in which an auxiliary network maps one
modality to conditioning parameters that modulate the other's feature
representation, shaping features at the parametric level rather than the
decision stage; modulation is patient-specific rather than patch-specific.
Both directions are instantiated --- gene-to-image (HyperG2I) and
image-to-gene (HyperI2G) --- and trained under a label-aware MixUp strategy
that interpolates within-class samples across both modalities, preserving the
hard binary labels clinical decisions require. The framework is evaluated on
two paired TCGA-BRCA cohorts --- one limited-sample, one independently
assembled at scale --- under a single protocol spanning two whole-slide
representations, multiple visual backbones, and both conditioning directions.
On the limited-sample cohort, gene-to-image conditioning at its optimal
augmentation setting exceeds early fusion and both unimodal baselines, giving
the highest recall on the aggressive Basal/HER2 class of any configuration
evaluated, and an ablation favours intra-class over inter-class mixing. At
scale this ordering does not hold: image-to-gene conditioning sustains its
performance whereas gene-to-image does not, recovering only partially under
the full tissue bag and isolating the capacity of the modulation pathway as
the binding constraint. Direction and capacity of cross-modal conditioning,
rather than fusion depth alone, therefore govern how such frameworks scale.

Urban water systems are increasingly challenged by climate extremes, aging infrastructure, and rising flood risks. Conventional water management practices remain fragmented across data, operations, and assets, limiting coordinated decision-making and scalable engineering deployment. Digital twins (DT) show great promise to overcome this fragmentation for resilient and efficient management. This review proposes an engineering-practice-oriented framework of digital twins for smart water management (DTSW). Utility demands are first structured through a scenario-oriented decomposition into points of interest (POIs), thereby linking practical engineering problems to digital variables. The review further summarizes a probabilistic graphical model-based scheme as the algorithmic backbone for POI implementation, and examines the key enabling technologies across organized data foundations, models, and real-time control. Particular attention is given to AI-empowered DTSW techniques, including soft sensing and data cleansing, hybrid modeling, and uncertainty-aware model deployment. Future development is discussed from the perspectives of proactive optimization, human-digital collaboration, and scalable engineering deployment. This review thus provides a structured framework for guiding the practical design and deployment of DT in urban water systems, facilitating coordinated, scalable and resilient water management.

Recognition of large f-block ions underlies advances in targeted radionuclide therapy and rare-earth element separations. Like the lanthanide-binding protein lanmodulin, the 18-membered macrocycle macropa displays reverse-size selectivity characterized by its preference for binding large metal ions. Its diaza-18-crown-6 scaffold enables efficient complexation of therapeutically relevant radiometals, such as 225Ac, 223Ra, and 213Bi, while accommodating diagnostic partners, including 203Pb and radiolanthanides. In parallel, systematic differences in stability constants across the lanthanide series enable size-based discrimination in separation chemistry. Macropa and its derivatives have been deployed in different strategies to recover and purify rare-earth elements and minor actinides. This manuscript describes recent studies on modifications of macropa, including cavity expansion, alteration of donor atoms, backbone rigidification, chelator-embedded 18F incorporation, and acyclic variants to demonstrate that effective selectivity arises from balancing preorganization, donor strength, and conformational adaptability. These studies establish macropa-based scaffolds as synthetic systems that bridge radiopharmaceutical coordination chemistry and rare-earth element separations.