Tuning of plasma cell lifespan by competition explains the longevity and heterogeneity of antibody persistence.

Plasma cells that emerge after infection or vaccination exhibit heterogeneous lifespans; most survive for days to months, whereas others persist for decades, providing antigen-specific long-term protection. We developed a mathematical framework to explore the dynamics of plasma cell removal and its regulation by survival factors. Analyses of antibody persistence following hepatitis A and B and HPV vaccination revealed specific patterns of longevity and heterogeneity within and between responses, implying that this process is fine-tuned near a critical "flat" state between two dynamic regimes. This critical state reflects the tuning of rates of the underlying regulatory network and is highly sensitive to variation in parameters, which amplifies lifespan differences between cells. We propose that fine-tuning is the generic outcome of competition over shared survival signals, with a competition-based mechanism providing a unifying explanation for a wide range of experimental observations, including the dynamics of plasma cell accumulation and the effects of survival factor deletion. Our theory is testable, and we provide specific predictions.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit.

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Dynamic coupling of fast channel gating with slow ATP-turnover underpins protein transport through the Sec translocon.

The Sec translocon is a highly conserved membrane assembly for polypeptide transport across, or into, lipid bilayers. In bacteria, secretion through the core channel complex-SecYEG in the inner membrane-is powered by the cytosolic ATPase SecA. Here, we use single-molecule fluorescence to interrogate the conformational state of SecYEG throughout the ATP hydrolysis cycle of SecA. We show that the SecYEG channel fluctuations between open and closed states are much faster (~20-fold during translocation) than ATP turnover, and that the nucleotide status of SecA modulates the rates of opening and closure. The SecY variant PrlA4, which exhibits faster transport but unaffected ATPase rates, increases the dwell time in the open state, facilitating pre-protein diffusion through the pore and thereby enhancing translocation efficiency. Thus, rapid SecYEG channel dynamics are allosterically coupled to SecA via modulation of the energy landscape, and play an integral part in protein transport. Loose coupling of ATP-turnover by SecA to the dynamic properties of SecYEG is compatible with a Brownian-rachet mechanism of translocation, rather than strict nucleotide-dependent interconversion between different static states of a power stroke.

Collective chemomechanical oscillations in active hydrogels.

We report on the collective response of an assembly of chemomechanical Belousov-Zhabotinsky (BZ) hydrogel beads. We first demonstrate that a single isolated spherical BZ hydrogel bead with a radius below a critical value does not oscillate, whereas an assembly of the same BZ hydrogel beads presents chemical oscillation. A BZ chemical model with an additional flux of chemicals out of the BZ hydrogel captures the experimentally observed transition from oxidized nonoscillating to oscillating BZ hydrogels and shows this transition is due to a flux of inhibitors out of the BZ hydrogel. The model also captures the role of neighboring BZ hydrogel beads in decreasing the critical size for an assembly of BZ hydrogel beads to oscillate. We finally leverage the quorum sensing behavior of the collective to trigger their chemomechanical oscillation and discuss how this collective effect can be used to enhance the oscillatory strain of these active BZ hydrogels. These findings could help guide the eventual fabrication of a swarm of autonomous, communicating, and motile hydrogels.

Albatrosses employ orientation and routing strategies similar to yacht racers.

The way goal-oriented birds adjust their travel direction and route in response to wind significantly affects their travel costs. This is expected to be particularly pronounced in pelagic seabirds, which utilize a wind-dependent flight style called dynamic soaring. Dynamic soaring seabirds in situations without a definite goal, e.g. searching for prey, are known to preferentially fly with crosswinds or quartering-tailwinds to increase the speed and search area, and reduce travel costs. However, little is known about their reaction to wind when heading to a definite goal, such as homing. Homing tracks of wandering albatrosses (Diomedea exulans) vary from beelines to zigzags, which are similar to those of sailboats. Here, given that both albatrosses and sailboats travel slower in headwinds and tailwinds, we tested whether the time-minimizing strategies used by yacht racers can be compared to the locomotion patterns of wandering albatrosses. We predicted that when the goal is located upwind or downwind, albatrosses should deviate their travel directions from the goal on the mesoscale and increase the number of turns on the macroscale. Both hypotheses were supported by track data from albatrosses and racing yachts in the Southern Ocean confirming that albatrosses qualitatively employ the same strategy as yacht racers. Nevertheless, albatrosses did not strictly minimize their travel time, likely making their flight robust against wind fluctuations to reduce flight costs. Our study provides empirical evidence of tacking in albatrosses and demonstrates that man-made movement strategies provide a new perspective on the laws underlying wildlife movement.

Catch bond kinetics are instrumental to cohesion of fire ant rafts under load.

Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants (S. invicta). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35[Formula: see text] strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that-even upon reinstatement of initial densities-ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants' status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series.

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Hydrogel biomaterials that stiffen and soften on demand reveal that skeletal muscle stem cells harbor a mechanical memory.

Muscle stem cells (MuSCs) are specialized cells that reside in adult skeletal muscle poised to repair muscle tissue. The ability of MuSCs to regenerate damaged tissues declines markedly with aging and in diseases such as Duchenne muscular dystrophy, but the underlying causes of MuSC dysfunction remain poorly understood. Both aging and disease result in dramatic increases in the stiffness of the muscle tissue microenvironment from fibrosis. MuSCs are known to lose their regenerative potential if cultured on stiff plastic substrates. We sought to determine whether MuSCs harbor a memory of their past microenvironment and if it can be overcome. We tested MuSCs in situ using dynamic hydrogel biomaterials that soften or stiffen on demand in response to light and found that freshly isolated MuSCs develop a persistent memory of substrate stiffness characterized by loss of proliferative progenitors within the first three days of culture on stiff substrates. MuSCs cultured on soft hydrogels had altered cytoskeletal organization and activity of Rho and Rac guanosine triphosphate hydrolase (GTPase) and Yes-associated protein mechanotransduction pathways compared to those on stiff hydrogels. Pharmacologic inhibition identified RhoA activation as responsible for the mechanical memory phenotype, and single-cell RNA sequencing revealed a molecular signature of the mechanical memory. These studies highlight that microenvironmental stiffness regulates MuSC fate and leads to MuSC dysfunction that is not readily reversed by changing stiffness. Our results suggest that stiffness can be circumvented by targeting downstream signaling pathways to overcome stem cell dysfunction in aged and disease states with aberrant fibrotic tissue mechanics.

Decoupling particle-impact dissipation mechanisms in 3D architected materials.

Ultralight architected materials enabled by advanced manufacturing processes have achieved density-normalized strength and stiffness properties that are inaccessible to bulk materials. However, the majority of this work has focused on static loading and elastic-wave propagation. Fundamental understanding of the mechanical behavior of architected materials under large-deformation dynamic conditions remains limited, due to the complexity of mechanical responses and shortcomings of characterization methods. Here, we present a microscale suspended-plate impact testing framework for three-dimensional micro-architected materials, where supersonic microparticles to velocities of up to 850 m/s are accelerated against a substrate-decoupled architected material to quantify its energy dissipation characteristics. Using ultra-high-speed imaging, we perform in situ quantification of the impact energetics on two types of architected materials as well as their constituent nonarchitected monolithic polymer, indicating a 47% or greater increase in mass-normalized energy dissipation under a given impact condition through use of architecture. Post-mortem characterization, supported by a series of quasi-static experiments and high-fidelity simulations, shed light on two coupled mechanisms of energy dissipation: material compaction and particle-induced fracture. Together, experiments and simulations indicate that architecture-specific resistance to compaction and fracture can explain a difference in dynamic impact response across architectures. We complement our experimental and numerical efforts with dimensional analysis which provides a predictive framework for kinetic-energy absorption as a function of material parameters and impact conditions. We envision that enhanced understanding of energy dissipation mechanisms in architected materials will serve to define design considerations toward the creation of lightweight impact-mitigating materials for protective applications.

Data-driven classification of ligand unbinding pathways.

Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics (MD) simulations, often in quantitative agreement with experiments. However, only a qualitative picture of the ligand binding/unbinding paths can be obtained through a conventional analysis of the MD trajectories. Besides, the higher degree of manual effort involved in analyzing pathways limits its applicability in large-scale drug discovery. Here, we address this limitation by introducing an automated approach for analyzing molecular transition paths with a particular focus on protein-ligand dissociation. Our method is based on the dynamic time-warping algorithm, originally designed for speech recognition. We accurately classified molecular trajectories using a very generic descriptor set of contacts or distances. Our approach outperforms manual classification by distinguishing between parallel dissociation channels, within the pathways identified by visual inspection. Most notably, we could compute exit-path-specific ligand-dissociation kinetics. The unbinding timescale along the fastest path agrees with the experimental residence time, providing a physical interpretation to our entirely data-driven protocol. In combination with appropriate enhanced sampling algorithms, this technique can be used for the initial exploration of ligand-dissociation pathways as well as for calculating path-specific thermodynamic and kinetic properties.

Torsional force microscopy of van der Waals moirés and atomic lattices.

In a stack of atomically thin van der Waals layers, introducing interlayer twist creates a moiré superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system's electronic properties. Setting a precise and uniform twist angle for a stack remains difficult; hence, determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moiré, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that torsional force microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of van der Waals stacks on multiple length scales: the moirés formed between bi-layers of graphene and between graphene and hexagonal boron nitride (hBN) and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an Atomic Force Microscope (AFM) cantilever is monitored as it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the sample surface. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moiré superlattices and crystallographic orientation of van der Waals flakes to support predictable moiré heterostructure fabrication.

Predicting resilience of migratory birds to environmental change.

The pace and scale of environmental change represent major challenges to many organisms. Animals that move long distances, such as migratory birds, are especially vulnerable to change since they need chains of intact habitat along their migratory routes. Estimating the resilience of such species to environmental changes assists in targeting conservation efforts. We developed a migration modeling framework to predict past (1960s), present (2010s), and future (2060s) optimal migration strategies across five shorebird species (Scolopacidae) within the East Asian-Australasian Flyway, which has seen major habitat deterioration and loss over the last century, and compared these predictions to empirical tracks from the present. Our model captured the migration strategies of the five species and identified the changes in migrations needed to respond to habitat deterioration and climate change. Notably, the larger species, with single or few major stopover sites, need to establish new migration routes and strategies, while smaller species can buffer habitat loss by redistributing their stopover areas to novel or less-used sites. Comparing model predictions with empirical tracks also indicates that larger species with the stronger need for adaptations continue to migrate closer to the optimal routes of the past, before habitat deterioration accelerated. Our study not only quantifies the vulnerability of species in the face of global change but also explicitly reveals the extent of adaptations required to sustain their migrations. This modeling framework provides a tool for conservation planning that can accommodate the future needs of migratory species.

Atomic observation and structural evolution of covalent organic framework rotamers.

Dynamic 3D covalent organic frameworks (COFs) have shown concerted structural transformation and adaptive gas adsorption due to the conformational diversity of organic linkers. However, the isolation and observation of COF rotamers constitute undergoing challenges due to their comparable free energy and subtle rotational energy barrier. Here, we report the atomic-level observation and structural evolution of COF rotamers by cryo-3D electron diffraction and synchrotron powder X-ray diffraction. Specifically, we optimize the crystallinity and morphology of COF-320 to manifest its coherent dynamic responses upon adaptive inclusion of guest molecules. We observe a significant crystal expansion of 29 vol% upon hydration and a giant swelling with volume change up to 78 vol% upon solvation. We record the structural evolution from a non-porous contracted phase to two narrow-pore intermediate phases and the fully opened expanded phase using n-butane as a stabilizing probe at ambient conditions. We uncover the rotational freedom of biphenylene giving rise to significant conformational changes on the diimine motifs from synclinal to syn-periplanar and anticlinal rotamers. We illustrate the 10-fold increment of pore volumes and 100% enhancement of methane uptake capacity of COF-320 at 100 bar and 298 K. The present findings shed light on the design of smarter organic porous materials to maximize host-guest interaction and boost gas uptake capacity through progressive structural transformation.

Two distinct networks for encoding goals and forms of action: An effective connectivity study.

Goal-directed actions are characterized by two main features: the content (i.e., the action goal) and the form, called vitality forms (VF) (i.e., how actions are executed). It is well established that both the action content and the capacity to understand the content of another's action are mediated by a network formed by a set of parietal and frontal brain areas. In contrast, the neural bases of action forms (e.g., gentle or rude actions) have not been characterized. However, there are now studies showing that the observation and execution of actions endowed with VF activate, in addition to the parieto-frontal network, the dorso-central insula (DCI). In the present study, we established-using dynamic causal modeling (DCM)-the direction of information flow during observation and execution of actions endowed with gentle and rude VF in the human brain. Based on previous fMRI studies, the selected nodes for the DCM comprised the posterior superior temporal sulcus (pSTS), the inferior parietal lobule (IPL), the premotor cortex (PM), and the DCI. Bayesian model comparison showed that, during action observation, two streams arose from pSTS: one toward IPL, concerning the action goal, and one toward DCI, concerning the action vitality forms. During action execution, two streams arose from PM: one toward IPL, concerning the action goal and one toward DCI concerning action vitality forms. This last finding opens an interesting question concerning the possibility to elicit VF in two distinct ways: cognitively (from PM to DCI) and affectively (from DCI to PM).

The deflection of fatigued neck.

The human neck is a unique mechanical structure, highly flexible but fatigue prone. The rising prevalence of neck pain and chronic injuries has been attributed to increasing exposure to fatigue loading in activities such as prolonged sedentary work and overuse of electronic devices. However, a causal relationship between fatigue and musculoskeletal mechanical changes remains elusive. This work aimed to establish this relationship through a unique experiment design, inspired by a cantilever beam mechanical model of the neck, and an orchestrated deployment of advanced motion-force measurement technologies including dynamic stereo-radiographic imaging. As a group of 24 subjects performed sustained-till-exhaustion neck exertions in varied positions-neutral, extended, and flexed, their cervical spine musculoskeletal responses were measured. Data verified the occurrence of fatigue and revealed fatigue-induced neck deflection which increased cervical lordosis or kyphosis by 4-5° to 11°, depending on the neck position. This finding and its interpretations render a renewed understanding of muscle fatigue from a more unified motor control perspective as well as profound implications on neck pain and injury prevention.

In situ fabrication of atomically adjacent dual-vacancy sites for nearly 100% selective CH4 production.

The photocatalytic CO2-to-CH4 conversion involves multiple consecutive proton-electron coupling transfer processes. Achieving high CH4 selectivity with satisfactory conversion efficiency remains challenging since the inefficient proton and electron delivery path results in sluggish proton-electron transfer kinetics. Herein, we propose the fabrication of atomically adjacent anion-cation vacancy as paired redox active sites that could maximally promote the proton- and electron-donating efficiency to simultaneously enhance the oxidation and reduction half-reactions, achieving higher photocatalytic CO2 reduction activity and CH4 selectivity. Taking TiO2 as a photocatalyst prototype, the operando electron paramagnetic resonance spectra, quasi in situ X-ray photoelectron spectroscopy measurements, and high-angle annular dark-field-scanning transmission electron microscopy image analysis prove that the VTi on TiO2 as initial sites can induce electron redistribution and facilitate the escape of the adjacent oxygen atom, thereby triggering the dynamic creation of atomically adjacent dual-vacancy sites during photocatalytic reactions. The dual-vacancy sites not only promote the proton- and electron-donating efficiency for CO2 activation and protonation but also modulate the coordination modes of surface-bound intermediate species, thus converting the endoergic protonation step to an exoergic reaction process and steering the CO2 reduction pathway toward CH4 production. As a result, these in situ created dual active sites enable nearly 100% CH4 selectivity and evolution rate of 19.4 µmol g-1 h-1, about 80 times higher than that of pristine TiO2. Thus, these insights into vacancy dynamics and structure-function relationship are valuable to atomic understanding and catalyst design for achieving highly selective catalysis.

Dynamic defects boost in-situ H2O2 piezocatalysis for water cleanup.

Creating efficient catalysts for simultaneous H2O2 generation and pollutant degradation is vital. Piezocatalytic H2O2 synthesis offers a promising alternative to traditional methods but faces challenges like sacrificial reagents, harsh conditions, and low activity. In this study, we introduce a cobalt-loaded ZnO (CZO) piezocatalyst that efficiently generates H2O2 from H2O and O2 under ultrasonic (US) treatment in ambient aqueous conditions. The catalyst demonstrates exceptional performance with ~50.9% TOC removal of phenol and in situ generation of 1.3 mM H2O2, significantly outperforming pure ZnO. Notably, the CZO piezocatalyst maintains its H2O2 generation capability even after multiple cycles, showing continuous improvement (from 1.3 mM to 1.8 mM). This is attributed to the piezoelectric electrons promoting the generation of dynamic defects under US conditions, which in turn promotes the adsorption and activation of oxygen, thereby facilitating efficient H2O2 production, as confirmed by EPR spectrometry, XPS analysis, and DFT calculations. Moreover, the CZO piezocatalysts maintain outstanding performance in pollutant degradation and H2O2 production even after long periods of inactivity, and the deactivated catalyst due to metal ion dissolution could be rejuvenated by pH adjustment, offering a sustainable solution for wastewater purification.

Inference of gene regulatory networks based on directed graph convolutional networks.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.