15064 research outputs found
Sort by
Neuronal heterogeneity of normalization strength in a circuit model
No full textNeurons in higher-order visual areas integrate information through a canonical computation called normalization. The strength of normalization is highly heterogeneous across neurons, and this heterogeneity correlates with attention-mediated modulations in neural responses. However, the circuit mechanism underlying the heterogeneous normalization strength is unclear. In this work, we study normalization in a spiking neuron network model of visual cortex. Our model reveals that the heterogeneity of normalization strength is highly correlated with the inhibitory current each neuron receives. The correlation between inhibition and other synaptic inputs explains the experimentally observed dependence of spike count correlations on normalization strength. Further, we find that neurons with stronger normalization encode information more efficiently, and that networks with more heterogeneity in normalization encode visual stimuli with higher information and capacity. Together, our model provides a mechanistic explanation of heterogeneous normalization strengths in the visual cortex and sheds light on the computational benefits of neuronal heterogeneity.</p
Tunable molecular interactions near an atomic Feshbach resonance: Stability and collapse of a molecular Bose-Einstein condensate
No full textUnderstanding and controlling interactions of ultracold molecules is a cornerstone of quantum chemistry. While the laboratory creation of degenerate molecular gases comprised of bosonic atoms has unlocked powerful platforms for quantum simulation, progress is limited by the absence of a robust theoretical framework for characterizing intermolecular interactions. This is in stark contrast to the situation for Fermi gases. In this Letter, we present such a framework providing universal expressions for these molecular scattering lengths as functions of experimentally measurable quantities. Our discoveries are crucial for understanding molecular condensate formation. Calculations of the compressibility reveal that a sign change in such molecular scattering lengths is directly correlated with the instability of these condensates. These results offer fresh insight with broad applications for atomic, molecular, and condensed matter physics, as well as quantum chemistry.</p
Characterizing Gut Microbiome Composition, Activity, and Host Response in Global Populations
No full textThe human gut microbiome plays a central role in health, influencing metabolism, immune function, and susceptibility to chronic disease. However, most microbiome studies have focused on high-income populations, limiting our understanding of microbiome–host interactions across diverse global populations. Diet, lifestyle, and environmental exposures vary widely across populations, and these factors are thought to shape both microbial composition and functional activity, but few studies have directly measured active microbial functions outside of industrialized settings. To address these gaps, I reviewed computational analyses of global microbiome datasets, conducted experimental co-culture studies with human colonic epithelial cells, and profiled metatranscriptomic activity of fecal samples from industrialized and non-industrialized populations. Co-culture experiments revealed that urban-associated microbiomes preferentially activate innate immune pathways, and high-diversity microbiomes elicit stronger host transcriptional responses. Moreover, specific taxa, including Bifidobacterium adolescentis and Bacteroides dorei, correlate with lifestyle factors such as diet, and drive expression of specific host genes. Metatranscriptomic analyses demonstrate that microbial genomic abundance is not always predictive of transcriptional activity, and that transcriptionally active microbes differ across industrialized and non-industrialized populations. Together, these studies illuminate how lifestyle and industrialization shape gut microbial activity and host physiology across populations
The Geography of Immunity: From System-Level Interactions to Cellular Niches in the Intestine
No full textThis dissertation establishes geography as a unifying principle for understanding intestinal homeostasis, arguing that position fundamentally dictates biological outcome. This work comprises several investigations that use a computationally driven discovery approach and the integration of complex, regionally sampled transcriptomic datasets to reveal a multi-scale model of intestinal immunity. At the systems level, peripheral neurons and epithelial cells form an integrated surveillance infrastructure that coordinates rapid barrier protection. Regionally, the transcription factor GATA4 functions as a master zoning regulator that partitions the small intestine into specialized functional districts. Within these districts, secretory IgA provides molecular border control that enforces spatial segregation between host and virome. At the tissue structure level, organized lymphoid tissues (i.e., Peyer's patches) exhibit region-specific transcriptional profiles. Inside Peyer's patches, specialized microfold gatekeeper cells undergo microbial-induced reprogramming, demonstrating that even specialized immune niches are susceptible to geographic tuning. These findings underscore the idea that, rather than just using molecular signatures to distinguish between friend and foe, the immune system maintains homeostasis partly by enforcing where in the body various microorganisms are allowed. When geographic organization fails at any scale, the cascade of consequences can result in bacteria colonizing inappropriate locations, molecular boundaries collapsing, and cellular positioning becoming disrupted. This framework suggests the necessity of tissue-regionalized medicine---therapeutic interventions that selectively target specific niches within the body. Beyond advancing intestinal immunology, this work establishes a conceptual architecture and methodological toolkit for spatially informed discovery that is applicable to diverse biological systems
The BK channel-NS1619 agonist complex reveals molecular insights into allosteric activation gating
No full textBK channels play essential roles in a wealth of physiological functions, including regulating smooth muscle tone and neurotransmitter release. Its dysfunction, often caused by loss-of-function mutations, can lead to severe phenotypes, including ataxia and sensory impairment. Despite the therapeutic potential of BK channel agonists, the molecular mechanisms by which they stabilize the pore’s open conformation remain unclear. Using cryoelectron microscopy and molecular dynamic simulations, we identified that NS1619, a synthetic benzimidazolone agonist, first described as a BK opener, binds within a pocket formed by the S6/RCK1 linker and the S4 transmembrane segment. Our simulations suggest that agonist binding promotes a twisting motion in the S6 segment, enabling critical interactions with residues K330, K331, and F223. These findings provide a molecular model for the mechanism of NS1619 and suggest that its binding site can accommodate other agonists, highlighting a promising target for therapeutic development.</p
The Development of Canonical Proportion as a Function of Community, Multilingualism, and Target Language’s Syllable Complexity
No full textThis study investigates the development of canonical proportion (CP), an indicator of speech development, across diverse language and environmental contexts. Using the Speech Maturity Dataset (SMD) comprising 366 children, aged 0;2–6;4, across 10 different languages and cultures, we explore the influence of multilingual exposure, language syllable complexity, and community type (industrialised, non-industrialised) on CP. We find that monolingual children display higher CP measures than their multilingual peers. In addition, CP is higher for children learning languages with simple syllable complexity than those with more complex syllables. We also find no significant differences in the CP trajectory of children from industrialised versus non-industrialised communities. Integrating these findings in the broader literature, we highlight the importance of diversifying participant samples to capture the complex relationship between language exposure, social environment, and language development
Evolutionary pathways in epistatic mechanical networks
No full textAn elastic spring network is an example of evolvable matter. It can be pruned to couple separated pairs of nodes so that when a strain is applied to one of them, the other responds either in-phase or out-of-phase. This produces two pruned networks, with incompatible functions, that are nearly identical but differ from each other by a set of “mutations” each of which removes or adds a single bond in the network. We generate ensembles of network pairs that differ by a fixed number, M, of discrete mutations and evaluate all M! mutational paths between the in- and out-of-phase behaviors up to M 14. With a threshold response for the network to be considered sufficiently fit for either function, so that nonfunctional networks are disallowed, only some mutational pathways are viable. We find that there is a surprisingly high critical response threshold above which no evolutionarily viable path exists between the two networks. The few remaining pathways at this critical value dictate much of the behavior along the evolutionary trajectory. The effect of multiple mutations is epistatic, that is, the impact of a mutation is not invariant but depends on what other mutations have already occurred. In most cases, the mutations break up into two distinct classes based on epistasis. The analysis clarifies how the number of mutations and the position of a mutation along the pathway affect the evolutionary outcome
Examining the role of IgA in a persistent model of Staphylococcus aureus colonization
No full text Staphylococcus aureus is a human-adapted pathogen that replicates by asymptomatically colonizing its host. Nasal colonization occurs in the first weeks of life and persists in about 30% of the population. Using the mouse-adapted strain WU1 to model persistent colonization, we reported earlier that inoculation of bacteria lacking Staphylococcal protein A (SpA/Δ spa ) or neutralization of SpA through vaccination result in the slow decolonization of animals. Secretory (S)IgA is considered a first line of defense against pathogens at mucosal surfaces. Here, we use Ighasec -/- mutant mice to evaluate the contribution of SIgA towards decolonization. We observe that WU1 burdens are reduced in colonized Ighasec -/- mice compared to C57BL/6J animals. Both C57BL/6J and Ighasec -/- mice eliminate Δ spa bacteria, yet elimination occurs more rapidly in animals lacking IgA. SpA captures Fab-V H 3-type antibodies, including IgA, on the bacterial cell surface. We propose that this activity promotes colonization. Yet, we also find that antibody responses to the pathogen are altered when SpA and IgA are missing. Colonized C57BL/6J mice display a low serum IgG2c/IgG1 ratio towards staphylococcal antigens. This ratio is increased in animals colonized with Δ spa and is further enhanced in Ighasec -/- mice. We attribute the former to the loss of immune evasion activity in absence of SpA, and the latter to a host compensatory mechanism upon exposure to S. aureus . Importantly, the increased IgG2c/IgG1 ratio correlates with decolonization and enhanced killing of S. aureus . Similarly, we observe that decolonization induced by SpA-vaccination is accelerated in Ighasec -/- mice which display higher anti-SpA IgG2c titers as compared to C57BL/6J animals. Together, these findings suggest that S. aureus exploits SIgA in a SpA-dependent manner for colonization and in absence of IgA, serum opsonizing antibodies may promote bacterial clearance at mucosal surfaces. </p
Modeling Sequence-Defined Charged Biopolymers: RNA Folding, Polyampholyte Necklaces, and Coacervation
No full textCharged biopolymers—including RNA, intrinsically disordered proteins (IDPs), and synthetic polyampholytes—exhibit diverse structural, dynamical, and phase behaviors that are highly sensitive to their primary sequence. These macromolecules are central to cellular function, increasingly used as programmable materials, and important therapeutic targets. A fundamental challenge is to understand how sequence-defined interactions shape structures, folding landscapes, thermodynamics, and phase behavior. This thesis develops physical modeling frameworks, including coarse-grained molecular dynamics, polymer scaling theory, and random phase approximation (RPA), to investigate sequence-defined charged biopolymers from single-molecule structure and dynamics to phase behavior. Across all systems studied, behavior is governed by the balance between enthalpic interactions (base pairing, stacking, and Coulomb interactions) and entropic penalties associated with chain flexibility and backbone geometry. The first part introduces CRANBERRY, a coarse-grained RNA model that explicitly incorporates sugar puckering and noncanonical base pairing. Using a contrastive-divergence parameterization with targeted refinement of disordered ensembles, CRANBERRY achieves realistic folding cooperativity and thermodynamics, accurately captures native fluctuations and stacking free energies, and can reversibly fold challenging tetraloop motifs \textit{de novo}. The second part examines statistically neutral polyampholytes across solvent conditions. Scaling theory and molecular dynamics simulations yield a single-chain conformational phase diagram that includes globules, extended chains, and a rich family of necklace structures. Two hierarchical necklace-in-necklace regimes emerge from the interplay between short-range attractions and Coulombic interactions encoded by blocky sequences. The final part develops an analytical RPA theory for symmetric non-neutral polyampholyte coacervates, where the ensemble-averaged net charge fraction promotes cooperative electrostatic attractions. Closed-form expressions for the correlation free energy, coacervate density, and critical salt concentration clarify how charge imbalance drives crossovers between polyampholyte-like and polyelectrolyte-like regimes and enhances salt resistance. Together, these results show how sequence-defined nonbonded interactions generate the structural and thermodynamic richness of charged biopolymers and provide insight into RNA folding, polyampholyte conformations, phase separation, and the rational design of charged polymer materials
Complex multiannual cycles of <i>Mycoplasma pneumoniae</i>: Persistence and the role of stochasticity
No full textThe epidemiological dynamics of Mycoplasma pneumoniae is characterized by poorly understood complex multiannual cycles. The origins of these cycles have long been debated, and multiple explanations of varying complexity have been suggested. Using Bayesian methods, we fit a dynamical model to half a century of M. pneumoniae surveillance data from Denmark (1958 to 1995, 2010 to 2025) and uncover a parsimonious explanation for the persistent cycles, based on the theory of quasicycles. The period of the multiannual cycle (approx. 5 y in Denmark) is explained by susceptible replenishment due, primarily, to loss of immunity. While an excellent fit to shorter time series (a few decades), the deterministic model eventually settles into an annual cycle, unable to reproduce the persistent cycles. We find that environmental stochasticity (e.g., varying contact rates) stabilizes the multiannual cycles and so does demographic noise, at least in smaller or incompletely mixing populations. The temporary disappearance of cycles during 1979 to 1985 is explained as a consequence of stochastic mode-hopping. The circulation of M. pneumoniae was recently disrupted by COVID-19 nonpharmaceutical interventions (NPIs), providing a natural experiment on the effects of large perturbations. Consequently, the effects of NPIs are included in the model and medium-term predictions are explored. Our findings highlight the intrinsic sensitivity of M. pneumoniae dynamics to perturbations and interventions, underscoring the limitations for long-term prediction. More generally, our findings provide further evidence for the role of stochasticity as a driver of complex cycles across endemic and recurring pathogens