Axonal growth machinery is largely shared across neurons, but axons exhibit strikingly different fates depending on developmental and environmental context: sustained elongation in peripheral neurons under permissive conditions, length homeostasis in cortical neurons after target innervation, and pathological collapse into dystrophic endbulbs in injured adult central nervous system (CNS) axons. These divergent outcomes have resisted explanation by length-sensing and biomechanical models that treat long-range transport and local force generation as separate problems. We unify these phenomena through a mechanochemical model that couples bidirectional motor transport and intracellular signaling to growth-cone balance of axonal tension, actomyosin contraction, and adhesion within a single feedback loop: The lengthening axon uses its own growing transport delay to establish self-limiting mechanical feedback, as the accumulating delay reshapes signaling waveforms to progressively elevate myosin contractility against actin-driven protrusion. Two physical parameters, the transport delay and the signaling feedback strength, organize axon fate into a unified state-space landscape spanning sustained growth, length homeostasis, and pathological collapse. Reduced feedback strength weakens this self-limiting mechanical feedback by simultaneously enhancing protrusion and suppressing contractility, providing a mechanistic interpretation of axon lengthening under partial reduction of retrograde transport. Bistability in the inhibitory regime explains why mature CNS tracts persist where nascent sprouts collapse, recasting regenerative failure as a state-dependent gate rather than a capacity limit. The framework predicts that partial rather than complete inhibition of retrograde transport could shift collapsed axons across the bifurcation into a growth-permissive regime, identifying a class of interventions complementary to existing capacity-restoration strategies for nerve injury.
The accumulation of microswimmers near walls has been studied for decades. While hydrodynamics and single-cell kinematics are well-explored, the role of cell-cell interactions remains understudied. However, in realistic, crowded environments such as the human gut, these interactions can be just as crucial. Here, we demonstrate that they actively weaken bacterial surface accumulation. Analysis of entrapped cell tracks reveals that crowding enhances orientational fluctuations, which increase the effective rotational diffusion and facilitate wall escape. A minimal model shows that this collective effect is equivalent to raising the system’s effective temperature with density.
Natural selection enriches populations for genotypes that are well adapted to their environments, accumulating information about the selective conditions in the process. Therefore, selection can be viewed as a learning process through which populations acquire information about the environment. Motoo Kimura derived a relationship between the amount of information gained by selection and a measure of the inefficiency of the selective process, known as substitution load [M. Kimura, Genet. Res. 2, 127–140 (1961)]. Due to the gradual nature of selection and the ongoing mismatch of types to the environment, a lineage that is still acquiring information has lower mean fitness than a lineage that already has it. While this has been an influential insight, we find that Kimura’s model is inconsistent with selection experiments involving Escherichia coli . To address limitations in this model and develop a more robust measure for the efficiency of selection, we turn to computational learning theory. We show that the learning-theoretic concept of regret quantifies the inefficiency of selection as a learning process in terms of relative lineage fitness. We establish bounds on the efficiency of information gain in general settings, including frequency-dependent selection and arbitrary environmental change. We experimentally validate these bounds and demonstrate that our extended theory recovers Kimura’s fundamental insight. Finally, we show that selection is an asymptotically optimal algorithm for the basic problem faced by evolving populations and that it acquires information more efficiently than comparable processes in complex environments.
Clay minerals, ubiquitous across diverse geological systems, exert fundamental controls on critical geochemical processes, particularly through their adsorptive interactions with economically vital elements such as heavy rare earth elements (HREEs). The adsorption of HREEs onto clay surfaces is not only the key process that enriches these critical resources but also a potential archive for dating significant weathering events. However, the atomic-scale mechanisms governing the preferential adsorption and fractionation of rare earth elements (REEs) at mineral–water interfaces have remained poorly constrained. Here, employing in situ atomic-scale imaging via high-resolution atomic force microscopy, we systematically investigate the adsorption behaviors of light and heavy REEs (La, Y, Yb) onto key crystallographic planes—Si-basal, Al-basal, and edge terminations—of representative clay minerals (kaolinite, illite, mica) under environmentally relevant conditions (pH 4.8 to 6.4). We directly visualize and quantitatively reveal that Si-basal planes selectively enrich HREEs with pH-enhanced adsorption capacity, whereas Al-basal planes exhibit negligible REE adsorption below pH 5.8 but preferential HREE adsorption at pH 6.4. Integrating synchrotron X-ray absorption spectroscopy, we identify the adsorption mechanism of REE on Si-basal and Al-basal/edge planes as outer-sphere complexation and inner-sphere binding, respectively. Furthermore, we quantify the contributions of distinct planes via surface complexation modeling, revealing Si-dominated to Al- and edge-controlled adsorption with elevating pH. These atomic-scale findings not only resolve long-standing debates about clay mineral-mediated REE fractionation but also redefine fundamental paradigms of surface reactivity at silicate mineral–water interfaces, offering insights into the cycling and metallogeny of other critical metals in aqueous and sedimentary environments.
Bioregionalization, the partitioning of the globe into areas of biotic similarity, is one of nature’s most striking patterns, and highly congruent boundaries have been identified across plants, tetrapods, ants, and butterflies. However, these taxa also share a unimodal latitudinal diversity gradient (LDG), raising the question of whether bioregionalization and the LDG are mechanistically coupled or instead arise from largely independent processes acting on different timescales. Bees (Hymenoptera: Anthophila) provide a natural test, their diversity peaking at mid-latitudes rather than in the tropics. Using range estimates for 3,370 species and a phylogenetic beta-diversity framework, we recover 21 bee bioregions nested within at least six major realms (Australasia, Indomalaya-Oceania, Nearctic, Neotropics, Palearctic, and Sub-Sahara) that closely align with those of other taxa despite bees’ atypical diversity gradient. We propose that bioregionalization and species richness are decoupled because they arise from distinct processes: the former reflects historical dispersal and evolutionary history, the latter local diversification, extinction, and environmental filtering. Our analysis provides a global biogeographic framework for future bee research and highlights the processes shaping the world‘s major biogeographic boundaries.
