PNAS Nexus

Oculocutaneous albinism (OCA) is a genetic condition associated with impaired visual acuity and increased skin cancer risk. When OCA is due to defects in melanosome ion transport, abnormally acidic conditions in the melanosome lumen inhibit tyrosinase, the critical pigment synthetic enzyme. Hence, a therapeutic approach that optimizes melanosome pH to increase pigment production presents a potential treatment for OCA and a method for decreasing skin cancer risk. Here, we report that reduction in sAC (ADCY10) activity via naturally occurring human variants in ADCY10 restores OCA pigmentation, and sAC inhibition increases melanin synthesis in both human and mouse OCA models. These findings demonstrate that targeting melanosome pH is an effective, previously untapped therapeutic strategy for OCA and elevated skin cancer risk.

Biological phase changes provoked by stress, such as vitrification or gel-sol transitions, enable many organisms, including extremotolerant tardigrades, to enter quiescent states and survive extreme environmental conditions. Protein-driven phase transitions are hypothesized to produce large-scale changes in intracellular viscosity, allowing tardigrades to survive extreme stresses such as desiccation. We report that the tardigrade Hypsibius exemplaris undergoes both large-scale and local increases in intracellular viscosity following exposure to anoxic and hyperosmotic stress. Such dramatic shifts in cellular viscosity would be expected to enhance cellular resilience to physical force. Indeed, we found that tardigrades can survive, behave normally, and reproduce after exposure to the highest simulated hypergravity (HG) achievable in an ultracentrifuge (one million times Earths gravity). In contrast, Caenorhabditis elegans, a similarly sized animal, does not survive these extreme forces owing to loss of cellular integrity. Remarkably, tardigrades frozen during exposure to extreme hypergravitational force show minimal disruption of fine cellular ultrastructure and little evidence of stratification of cellular components whose density varies by nearly a factor of two. Further, exposure to anoxia, hyperosmotic stress, and HG all result in a large increase in reactive oxygen species (ROS), which is required for survival under these extreme environments. Inhibition of NADPH oxidase (NOX) suppresses survival both to HG and hyperosmotic stress. Our findings suggest that intracellular viscosity changes in response to multiple extreme stresses may underlie the resilience of these animals to extraordinary physical stress, and that survival in or recovery from these states relies on ROS signaling via NADPH oxidase. Significance StatementTardigrades are renowned for surviving conditions that are lethal to nearly all other life forms. We reveal two mechanisms that support this resilience: intracellular viscosity changes and NADPH oxidase-mediated ROS signaling. Through direct assessment of the effects of altered cellular material properties, found that tardigrades are resilient to forces up to one million times Earths gravity, establishing them as the most hypergravity-resistant animal currently known.

Cells lying in a curved environment can respond to the surface curvature by reorienting their shape. However, whether cells respond to the mean curvature and/or the Gaussian curvature remains largely unexplored. Here, inspired by experimental observations of how ovarian theca cells (TCs) orient themselves on substrates with different curvatures, we propose a theoretical framework for active nematic layers on curved surfaces. In this model, we assume that the nematic directors of the cells respond to both the mean curvature and the Gaussian curvature of the underlying substrate surface. Our theory predicts specific cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. In addition, by incorporating curvature-induced active traction, our model successfully recapitulates the experimental observation of TC accumulation at convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Overall, our work reveals the unexpected role of cell curvature sensing in driving collective migration and pattern formation on various substrate curvature. SIGNIFICANCESubstrate surface curvature is a critical environmental cue that can influence multicellular organization and functions. Yet how cells collectively align and migrate on complex curved surfaces remains unclear. Here, we proposed a hydrodynamic theory of active nematic layers over curved surfaces for contractile theca cells (TCs), where we assume that the nematic directors of cells can respond to both the mean curvature and the Gaussian curvature of the underlying substrates. Our theory predicts distinct cell orientation patterns on hemicylindrical, hourglass- and dome-like substrates, consistent with experimental observations. Furthermore, by introducing curvature-induced active traction, our model recapitulates experimentally observed accumulation of TCs at the convex regions of hemicylindrical substrates as well as saddle-shaped regions of more complex geometries. Together, our study provides a simple theoretical framework to unify our understanding of curvature sensing across complex topology, providing insights into geometric control of tissue pattern formation.

Accurate forecasting of seasonal influenza is critical for public health preparedness, and data-driven models are central to this effort. However, most approaches rely on aggregate indicators of influenza-like-illness (ILI), which can obscure heterogeneity and limit predictability at longer horizons. While subtype dynamics are well established, their role in data-driven forecasting remains incompletely understood. Here, we integrate subtype-resolved surveillance data into diverse data-driven frameworks using over a decade of U.S. surveillance records to evaluate and decompose predictive signal in influenza forecasting. Across pre- and post-COVID-19 periods, subtype-informed models consistently improve over baseline models trained on aggregate ILI alone, with the largest gains at longer horizons. Decomposition reveals a horizon-dependent reorganization of predictability: autoregressive persistence in recent aggregate incidence dominates at short horizons but declines with lead time, while predictive signal shifts toward subtype-derived structure. Within this structure, interaction-related features among co-circulating subtypes grow systematically with forecast horizon, indicating that longer-term predictability is driven increasingly by interaction structure rather than marginal subtype composition alone. Together, our results show that subtype information provides non-redundant predictive signal and extends the effective forecasting window of data-driven models. More broadly, our findings suggest that aggregation of heterogeneous subtype processes can obscure latent predictability, supporting subtype-resolved surveillance.

Snow algae darken snowpacks and accelerate melt worldwide. Although elevation strongly structures the physical conditions of mountain snowfields, its influence on snow algal traits and their effects on snowpack reflectance remains unclear. Here, we investigated snow algal composition, cellular traits, and optical properties in summer blooms across an elevational range of 1,059-3,423 m a.s.l. in the western United States, spanning two elevational gradients in the Cascade Range (CA, OR, WA) and the Rocky Mountains (UT, WY, MT). Across all samples (n = 294), snow albedo declined strongly with increasing algal cell density, indicating that total biomass, rather than pigment composition, is the dominant driver of albedo reduction. However, within Sanguina-dominated blooms (117 of 206 samples bloom samples identified across the dataset), neither relative abundance nor algal cell density varied systematically with elevation. Instead, mean cell size increased with elevation, while per-cell pigment concentrations declined, leading to higher astaxanthin:chlorophyll-a ratios driven primarily by reductions in chlorophyll-a per cell. These elevation-dependent shifts in cell size and pigment balance were consistent across both mountain ranges, indicating phenotypic acclimation to increasing environmental stress with elevation. Together, these findings link cellular-scale acclimation of a widespread snow alga to radiative processes shaping mountain snowpacks.

Background. On 8 May 2023 the Japanese Ministry of Health, Labour and Welfare reclassified COVID-19 under the Infectious Disease Control Law from a designated infectious disease (with case-by-case reporting requirements comparable to those of a Category-2 disease) to a Category-5 ("Class-5") notifiable disease, joining the same category as seasonal influenza and most other endemic respiratory infections. Under this regime, COVID-19 case counts are reported weekly from a nationwide network of sentinel medical facilities (initially approximately 5,000, reduced to approximately 3,000 following an April 2025 surveillance reform), and individual case reporting is no longer required. We aimed to characterize the spatial topology of COVID-19 epidemics under this sentinel-surveillance regime and to detect, in a data-driven manner, any structural change in epidemic dynamics over this period. Methods. We analyzed weekly per-sentinel-facility COVID-19 case counts in all 47 prefectures of Japan from 2023-W17 to 2026-W19 (159 weeks). For each week we computed the Shannon pseudo-entropy S of the prefecture-share distribution and global, local, and time-lagged Moran's I across a 92-edge contiguity-based adjacency matrix. To identify any structural change in a data-driven manner, we adopted a two-stage approach motivated by an empirical regularity established in Section 3: we first verified the wave-amplitude-invariant entropy ceiling (S_max >= 3.80 in all five pre-transition waves), then restricted change-point detection to the weeks after S(t) last attained this ceiling, applying PELT, CUSUM, and Bai-Perron sup-F within this restricted region. Seasonal structure was characterized by truncated Fourier regression with first-order autoregressive errors (Cochrane-Orcutt) over harmonic orders K = 1 to 6; between-period comparisons used moving block bootstrap as the principal inferential statistic. Results. The five epidemic waves during 2023-2025 followed a stereotyped spatial template in which S(t) traced a characteristic U-shape around each peak, with a wave-amplitude-invariant entropy ceiling reaching on average 99.4% of the theoretical maximum ln 47 (range 3.820-3.836, SD 0.006). The last week in which S(t) attained this entropy ceiling was 2025-W42. Restricting change-point detection to the 29 subsequent weeks, PELT and CUSUM localised the structural break to late 2025: PELT identified 2025-W48 (robust across penalty values >= sigma^2*ln(n) and across entropy-ceiling thresholds 3.78-3.82) and CUSUM peaked at 2025-W50 (p < 0.0001), placing the break within a two-week window centred on late November 2025. Bai-Perron sup-F peaked later at 2026-W02 (p = 0.062, with reduced power on n = 29). We adopted 2025-W48 as the principal change-point, defining 135 pre-transition weeks and 24 post-transition weeks. Two anti-phase spatial modes were identified in the pre-transition record: a summer-onset Okinawa-seeded Kyushu cascade (Mode A; annual peak epi week 26) and a winter-onset Tohoku-centred connected-cluster mode (Mode B; annual peak epi week 51), approximately 25 epi weeks out of phase. After the regime transition, this ceiling was not attained, and the spatial-persistence ratio I(tau = 8 wk)/I(0) shifted from a highly variable distribution centred near 0.27 (pre-transition, 125 weeks) to a tightly clustered distribution around 0.89 (post-transition, 24 weeks); the mean difference was 0.62 (95% bootstrap CI 0.32 to 0.90; moving block bootstrap p < 0.0001 across block lengths 1-12). The principal finding remained significant under autoregressive-augmented null models and was robust to adjacency-matrix choice, the April 2025 surveillance reform, harmonic order K = 1 to 6, and Okinawa exclusion. Conclusions. Data-driven analysis of 159 weeks of Japanese sentinel surveillance identifies a candidate spatial-persistence regime transition emerging in late November 2025, in which the spatial structure of weekly case shares persists for at least 8 weeks rather than dissipating as in pre-transition. The transition coincides with loss of the wave-amplitude-invariant entropy ceiling and with absence of the Mode A signature through the observed post-transition period. The recent uptick in Okinawa case shares (continuing through 2026-W19) leaves open whether the Mode A signature is structurally suppressed or merely deferred; observation through summer 2026 is required to distinguish a sustained shift from a transient anomaly.

Transmissible hospital-acquired infections (HAIs) arise from complex, time-varying interactions among patients, healthcare workers, and clinical environments. Although data-driven approaches like graph neural networks (GNNs) effectively model these contacts, they often function as black boxes that over-look established epidemiological principles, limiting interpretability and clinical trust. Inspired by physics-informed neural networks, we propose a epidemiology-informed GNN (EIGNN) framework for patient-level state transitions prediction in dynamic hospital settings, integrating mechanistic epidemiological models into GNNs in a principled manner. Patient-level risk factors learned from dynamic contact networks are jointly leveraged to infer latent epidemiological states, predict state transitions across multiple horizons, and estimate key epidemiological parameters, including transmission and recovery rates. We evaluate the approach on a real-world hospital-onset COVID-19 cohort and two public datasets simulating viral and bacterial HAIs. Across multiple architectures and horizons, EIGNNs achieves AUC-ROC up to 98.46% while providing interpretable, mechanistically consistent insights, offering a transparent tool for infection prevention and control.

The dynamics of sexually transmitted infections involve interconnected transmission networks, including men who have sex with men and heterosexual populations. Understanding the extent of bridging between these networks can inform surveillance, guide interventions, and aid in the interpretation of their impact, but methods for quantifying bridging have been lacking. Here, we addressed whether pathogen genomics tools, successfully used to reconstruct transmission in other contexts, could accurately infer sexual network bridging. Based on simulations of gonorrhea spread, we evaluated phylodynamic bridging metrics inferred by ancestral state reconstruction under a range of sampling schemes, from comprehensive to sparse. These metrics differentiated sexual network structures even with biased sampling schemes, but accuracy depended on the sampling scheme and density: phylodynamic bridging estimates using sequences from all detected infections for one network configuration were on average 6.9% above the true value, whereas estimates from 5% of infections in symptomatic men with many partners were on average >1000% above the true value. These results suggest routine overestimation of bridging from unadjusted inferences from genomics data and provide context for interpreting existing genomic surveillance data and targeted studies.

Despite concerns about the spread and pandemic potential of H5N1, there is no commercial H5N1 vaccine. Seasonal influenza vaccines offer some cross-protection against H5N1, but to date there has been no consideration of whether protection differs between the sexes. We investigated immune responses and protection in adult male and female C57BL/6 mice following vaccination with either inactivated H1N1 or H5N1 (LAIV backbone) virus vaccines. Vaccination induced strong homologous antibody responses, with females generating greater total IgG than males against both H1N1 and H5N1 vaccine, which was primarily mediated by greater IgG responses to neuraminidase (NA) than hemagglutinin (HA) protein. IgG cross-recognition of H1N1 also was greater among H5N1 vaccinated females and was primarily caused by greater IgG responses to N1. IgG2b and IgG2c were the primary isotypes generated in response to these vaccines, with females having greater IgG2b responses and greater binding to Fc{gamma}RIV for avian and human NA than males in response to both homologous and heterologous vaccination. Antibody-dependent complement deposition was measured as an FcR-mediated non-neutralizing response against HA and NA and was robust in both sexes. Vaccinated females had greater neutralizing antibody titers than males against the homologous vaccine virus, with limited cross-neutralizing antibodies detected in either sexes. Neuraminidase inhibition titers were greater in vaccinated females than males against the heterologous virus following H1N1 vaccination and against both the vaccine and heterologous viruses following H5N1 vaccination. When H1N1 and H5N1 vaccinated mice were challenged with a lethal dose of A/Texas/37/2024 H5N1, all H5N1 vaccinated mice were protected, regardless of sex. Among H1N1 vaccinated mice, while both sexes were protected against disease, H1N1 vaccinated females cleared virus faster than their male counterparts. These findings highlight that female-biased NA-specific antibodies result in greater cross-protection and should be considered in studies of influenza vaccines. HighlightsO_LIFemales mount stronger IgG responses than males to both H1N1 and H5N1 vaccines C_LIO_LISex differences in vaccine responses are driven by immunity to neuraminidase (NA) C_LIO_LINA inhibition titers are greater in females, supporting broader cross-protection C_LIO_LIH5N1 vaccination confers full protection in both sexes against lethal H5N1 challenge C_LIO_LIH1N1-vaccinated females clear H5N1virus faster than males after lethal challenge C_LI

During the COVID-19 pandemic, limited testing capacity and reporting delays complicated epidemic surveillance and decision-making in Mexico. We calibrated \textit{covidestim}, a Bayesian nowcasting model, to estimate the total SARS-CoV-2 infections from reported cases and deaths using Mexican surveillance data. Disease-progression distribution priors were calibrated using Mexico City records and validated through comparisons with national seroprevalence surveys, hospitalization data, and annual reported severe-case rates across all states. Using the reconstructed estimates of active infections, we implemented an event-based risk framework that quantifies the probability of encountering at least one infectious individual in gatherings of different sizes. This probability was subsequently translated into a four-level epidemiological traffic-light indicator and computed at both state and municipality levels. The resulting estimates revealed substantial spatial heterogeneity that is obscured by state-level aggregation, particularly in states with marked differences between urban and rural municipalities. To evaluate consistency with public-health indicators, we compared the proposed risk classification with the official Mexican epidemiological traffic-light system, considering interpretable gathering sizes relevant to public-health decision making. Weekly reports derived from this framework were delivered to policymakers in the State of Queretaro in Mexico, as an anticipation tool for school reopening and public-space management. This demonstrates that this Bayesian reconstruction of infections combined with event-based risk metrics can provide an interpretable and generalizable municipality-level complement to routine surveillance systems, particularly in regions with limited testing capacity and heterogeneous local transmission dynamics.

Organic carbon (OC) burial in lakes is an important component of the global carbon cycle, but the source organisms of preserved OC remain poorly resolved. Here we develop the genC pipeline, which combines sedimentary ancient DNA concentrations, read-based taxonomic assignments, and group-specific priors for DNA and cellular carbon content to derive OCDNA-projected, a semi-quantitative proxy for the magnitude and taxonomic composition of preserved sedimentary OC. We apply genC to six high-latitude lake records spanning the last 30,000 years. OCDNA-projected broadly agrees with independent proxies for total organic carbon and aquatic contribution, supporting its reliability. Our results indicate that environmental conditions, especially warming, rather than preservation alone, are the main drivers of preserved OC variation. Terrestrial sources, mainly woody plants, dominate lake sediment OC. Eukaryotic algae as well as aquatic and terrestrial bacteria become more important during the warmer Holocene. These results establish sedaDNA as a taxonomically resolved tool for reconstructing long-term changes in preserved lake-sediment OC.

Currently, global conservation efforts for wildlife focus on a limited number of cell types and species. Although protocols from domestic and non-threatened related species have been applied to endangered species, cryopreservation techniques are species-specific and are constrained by a lack of understanding of reproductive biology in these species. Based on a review of 126 original studies from 27 countries, encompassing 160 species, we assess the current state of cryopreservation in wildlife, including gametes, embryos, somatic cells, and various tissues. Furthermore, we focused on the most homogeneous and frequently studied cell type in wildlife cryobanking: mammals sperm.. A meta-analysis of 27 studies was conducted to examine species-specific and protocol-dependent factors that affect post-thaw sperm quality. Our findings provide quantitative estimates of cryopreservation for various cell types and tissues in wildlife taxa. Furthermore, they serve as a crucial research roadmap, identifying major challenges in cryopreservation and proposing solutions.

BackgroundHigh-risk human papillomavirus (HPV) infection is necessary for cervical carcinogenesis, but HPV detection alone does not distinguish transient infection from lesions at greatest risk of progression. We evaluated whether HPV burden, vaginal microbiota structure, and host-context variables jointly characterize cervical intraepithelial neoplasia grade 3 (CIN3) in a Black/African American and White analytic cohort from the Vaginal Microbiome Health Project (VaMHP), integrating L1-based HPV typing, 16S rRNA vaginal microbiota profiling, and linked clinical metadata. ResultsAmong 1181 participants, 75 had CIN3. CIN3 was associated with HPV positivity (55/75, 73.3% vs 431/1106, 39.0%; odds ratio [OR] 4.31, 95% CI 2.55-7.29; Fisher exact p = 7.9 x 10^-9) and with multiple HPV infection among HPV-positive participants (35/55, 63.6% vs 176/431, 40.8%; OR 2.54, 95% CI 1.42-4.54; p = 0.0022). HPV communities in CIN3-positive samples showed higher Shannon diversity, greater observed strain richness, higher evenness, and significant beta-diversity separation. In vaginal microbiota analyses, alpha diversity did not differ by CIN3 status, but community composition did, and Lactobacillus crispatus was the only taxon depleted in CIN3 after multiple-testing correction. Race, age, and metronidazole exposure were central nodes in the host-factor network. In predictive modeling, a full integrated model combining metadata, HPV, and vaginal microbiota features (auROC = 0.745) outperformed both HPV + vaginal microbiota (auROC = 0.670) and HPV-only (auROC = 0.440) models. ConclusionsCIN3 in this cohort was associated with coordinated shifts in virologic burden, vaginal community structure, and host social-clinical context. The results support a structure-function interpretation in which loss of Lactobacillus crispatus-dominant states and enrichment of dysbiosis-associated communities define a host-microbiome context that is more permissive to HPV persistence and precancer. These findings move beyond descriptive omics by showing that microbiome and host-context features add nonredundant discriminatory signal beyond HPV-only models.

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.

Alzheimers disease (AD) progression involves systemic network transitions. To capture these using ROSMAP bulk RNA-seq (n=624), we focused on the geometry of the covariance structure, performing a Riemannian (Log-Euclidean) analysis of stage-wise covariance matrices as points on the manifold of symmetric positive-definite (SPD) matrices. On the SPD manifold the three stages were non-collinear: geodesic distances were non-uniform and MCI was displaced from the NCI-AD chord, while the von Neumann entropy of the covariance structure dipped at MCI (S = 2.760, 2.639, 2.647 for normal cognitive intact NCI, mild cognitive impairment MCI, AD) and the path-curvature profile reached a minimum there -- together identifying MCI as a saddle/bifurcation state. The differential covariance spectrum (CAD - CNCI) separated AD-amplified ("structural collapse") from AD-suppressed ("protective loss") modes. Ultimately, second-order statistics analyzed through Riemannian geometry, rather than Euclidean summaries, reveal AD progression structure invisible to mean-level analysis.

Hearts change their wall thickness (concentric growth) and chamber size (eccentric growth) as they adapt to circulatory demands and the intrinsic function of their contractile cells. Factors associated with wall thickening include variants of sarcomeric proteins that enhance contractility, mitochondrial dysfunction, and hypertension. Chambers can dilate due to many factors including sarcomeric variants that depress contractility and aortic and / or mitral valve insufficiency. Despite intensive study, the mechanisms that regulate cardiac growth remain unclear. It is also uncertain whether inherited variants induce growth via the same mechanisms as more common clinical pathologies, such as hypertension. Here we show that computer simulations of a beating left ventricle reproduce both variant and non-variant-related growth patterns when myocytes grow concentrically to regulate intracellular ATP concentration and eccentrically to maintain titin-based intracellular stress. The simulations support the hypothesis that cardiac growth reflects homeostatic feedback through three interacting systems whereby myocytes add or remove mitochondria and sarcomeres (1) in parallel to match ATP generation to myocardial energy demand, and (2) in series to regulate passive tension, while (3) the autonomic nervous system regulates cardiac power, and thus myocardial ATPase, via baroreflex control. The new framework provides a mechanistic basis for the patterns of eccentric and concentric growth induced by a wide range of clinically-relevant conditions and could facilitate in silico testing of potential therapies for cardiac disease. Significance statementHearts grow in response to both physiological and pathological stimuli. The patterns of concentric (wall thickening / thinning) and eccentric (chamber dilation / constriction) induced by different challenges are well recognized but the underlying mechanisms remain unclear. This work presents simulations of a beating left ventricle where (1) concentric growth is regulated by myocytes attempting to stabilize the intracellular ATP concentration and (2) eccentric growth is regulated by titin-mediated stress. The calculations reproduce the growth associated with inherited variants of sarcomeric proteins, mitochondrial dysfunction, hypertension, and both mitral and aortic valve insufficiency. The new ability to predict cardiac growth and its potential modification by treatments, including myotropes, brings the field closer to in silico optimization of therapy for cardiovascular disease.

In cortical networks, how to simultaneously achieve both reliable inter-areal signal propagation and areal-specific local dynamics remains largely unclear. In general, strong connections between areas enhance signal propagation, but blend timescales of area-specific neuronal activity, whereas weak connections have the opposite effect. Here we identify a novel dynamical regime termed "interference-free propagation" (IFP) that reconciles the two contrasting demands in the cortex. In the IFP regime, mean signals from upstream areas can propagate reliably, but fluctuations indicative of upstream areas timescales are filtered out. This result provides new insights into the operational regime of the cortex, leading to the coexistence of reliable signal propagation and the distinct property of local temporal integration of information in the cortical network.

2Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light-dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses. 1 HIGHLIGHTSO_LI24-hour live-in operant system allows autonomous training in cognitive tasks across months C_LIO_LI24-hour measurements reveal that rat performance fluctuates with time of day C_LIO_LINovel DDM-HMM framework identifies reaction time and accuracy shifts across multiple timescales C_LIO_LIDDM-HMM captures serial dependence in decisions that classic models ignore C_LI

Understanding HIV transmission in densely populated urban settings is essential to mitigate ongoing epidemic spread. We present a comprehensive analysis of recent HIV transmission dynamics in Greater Mexico City, one of the worlds largest metropolitan areas comprising Mexico City and neighbouring municipalities of the State of Mexico. Drawing from over 7,000 complete pol gene sequences representing around 50% of new cases reported between 2019 and 2022 within the study region, we reconstructed the transmission network based on pairwise genetic distance. We identified ten large transmission clusters exhibiting sustained growth up to the most recent sampling period. We further analysed paired genetic and high- resolution human mobility data using an integrated phylogeographic approach. We observed a heterogeneous pattern of viral spread across the region, supported by an extensive mixing at a wider geographic scale. Across Greater Mexico City, displaying a high population density, HIV transmission is minimally spatially constrained, a pattern likely fuelled by intense human mobility. Thus, population movement weakens isolation by distance in large urban areas even for a chronic infection that is sexually and vertically transmitted. We demonstrate the value of integrating large-scale genetic, epidemiological, and mobility data to resolve contemporary HIV transmission dynamics in densely populated urban settings

PurposeBacterial vaginosis (BV) is associated with disruption of the vaginal microbiome and extracellular matrix (ECM) remodeling, yet the contribution of host proteases to this process remains unclear. This study investigated whether expression and activity of cathepsins K, L, S, and V differ by BV diagnosis and community state type (CST). We hypothesized that BV and BV associated CSTs would exhibit increased expression and activity of collagen and elastin-degrading cathepsins. MethodsVaginal fluid samples were collected and classified by BV diagnosis and CST. Cathepsin expression was evaluated by Western blotting to distinguish inactive and active enzyme forms. Proteolytic activity was assessed using multiplex cathepsin zymography. Statistical analyses compared cathepsin expression and activity across diagnoses and CSTs. Principal component analysis and linear regression were performed to assess associations between cathepsin activity, microbial diversity, and CST. ResultsProcathepsin K expression was significantly increased in BV-positive and CST IV samples, while total cathepsin L expression was significantly elevated in samples with Nugent-intermediate scores. Cathepsins S and V showed variation in inactive and active forms in Nugent-intermediate and CST III samples. In contrast, total cathepsin activity, including cathepsins K and V, did not significantly differ across BV diagnoses or CSTs. Overall, cathepsin activity varied between individuals rather than by clinical classification. ConclusionsCathepsin expression and maturation state differ by microbiome composition, suggesting that the vaginal microbiome may regulate post-translational processing of cathepsins. As a result, cathepsin activity appears to be regulated at the individual level rather than strictly by BV diagnosis or CST. These findings link vaginal microbiome composition to ECM remodeling and potential adverse reproductive outcomes.