We introduce an amortized Bayesian framework for spatial boundary detection that generalizes posterior inference across areal graphs with varying numbers of regions and diverse adjacency structures. The underlying model couples a Poisson count likelihood with a covariate-driven rule to interrupt smoothing across dissimilar neighboring areas, utilizing a directed acyclic graph autoregressive (DAGAR) prior to capture residual spatial dependence. To approximate the target posterior distribution, a neural engine is trained on simulated maps: a permutation-invariant summary network encodes graph-aware representations of the observed counts, offsets, covariates, and adjacency matrices, while a conditional normalizing flow generates the approximate posterior draws. Simulation studies demonstrate accurate parameter recovery, near-nominal interval coverage, well-calibrated posterior predictive behavior, and informative posterior boundary probabilities. Benchmarking against Markov chain Monte Carlo (MCMC) confirms close agreement regarding primary boundary evidence, and an ablation study validates the inclusion of model-guided graph summaries. Finally, applications to Glasgow respiratory disease and California lung cancer data demonstrate that a single trained neural engine can be seamlessly deployed across real-world maps with distinct graph structures, yielding boundary conclusions consistent with established localized smoothing analyses.

Alzheimer's disease is characterized by the progressive accumulation of amyloid-$β$ and tau followed years later by cognitive impairment. Despite this established motif, substantial subject-level variability exists in the age of pathological progression and the onset of cognitive symptoms. To understand the source of this variation, subjects must be aligned across heterogeneous disease timelines via frameworks that jointly model disease progression and time to cognitive impairment with reference to landmark positivity thresholds. Existing neurodegenerative disease progression models rely on restrictive parametric forms, fail to anchor disease timelines to positivity thresholds, and decouple biomarker trajectories from cognitive survival endpoints. To address these limitations, we introduce the Bayesian Threshold-Aligned Joint Disease Progression Model (B-TAJ DPM). This generative, semi-parametric framework models multivariate disease progression trajectories over latent disease timelines anchored at landmark positivity thresholds. Crucially, the framework integrates a survival model to link pathological progression to cognitive impairment. Posterior inference and posterior predictions for unseen subjects are carried out in open-source software. Simulation studies demonstrate excellent estimation accuracy and interval coverage. When applied to Alzheimer's Disease Neuroimaging Initiative data, B-TAJ DPM characterizes non-linear progression patterns, quantifies subject-level variation in positivity age, and reveals links between age of tau positivity and acceleration of cognitive impairment.

Prior to the version 1.3.1 update on CRAN in December 2025, gsynth, a popular R package for estimating Interactive Fixed Effects (IFE) models, could drastically and systematically underestimate standard errors. This underestimation would occur when two estimation options (inference = "parametric", and EM = TRUE) were used together, in which case the package would apply a parametric bootstrap procedure to Gobillon and Magnac (2016)'s IFE-EM estimator. The package ceased supporting this combination in December 2025, and the latest documentation now describes the parametric bootstrap as not suitable for use with the IFE-EM estimator due to a theoretical incompatibility. Our focus is an implementation error we identified in the pre-1.3.1 versions of gsynth: the parametric bootstrap used when EM = TRUE did not match the algorithm proposed in Xu (2017), using in-sample residuals instead of out-of-sample errors. We show that this implementation error alone can cause underestimation by orders of magnitude. We conduct an empirical Monte Carlo study using randomly assigned placebo treatments on a series of state-level panel datasets, and show that gsynth could yield high false positive rates in realistic settings. We identify three papers published in the American Political Science Review that are affected by this behavior. Reanalyzing the relevant sections of these papers, we show that (i) correcting the implementation error renders most findings insignificant, and (ii) using Xu (2017)'s Generalized Synthetic Control method in place of IFE-EM renders every finding insignificant.

Geostatistical spatial prediction for environmental processes is typically undertaken using Gaussian process models via Kriging, while machine learning (ML) algorithms are state-of-the-art for non-spatial prediction. An exciting recent fusion of these ideas imbibes traditional ML algorithms with the capacity to deal with spatial autocorrelation, leading to improved predictive performance. A range of approaches have been proposed, including fusion with Gaussian processes, observation-driven correlation structures, spatial basis functions and local geographical fitting. However, there has been no numerical comparison of their relative predictive performances, which is needed to advise environmental scientists on the optimal approach to use. This paper fills this knowledge gap, and focuses on random forests as the ML algorithm because they are more computationally and conceptually straightforward to implement than deep learning algorithms. The results from two studies are presented, the first being a controlled simulation experiment investigating whether any single approach is consistently superior across different spatial autocorrelation types. The second study focuses on the prediction of air pollution concentrations within a tuberculosis prevalence study in Blantyre, Malawi. The results show that whilst no single approach is universally superior, utilising spatial basis functions appears to perform consistently well across both the simulation and real data studies.

Understanding urban mobility patterns is crucial for designing efficient and sustainable transportation systems. Motivated by an application to the municipality of Padova and its surroundings, we propose a novel statistical framework for the analysis and clustering of mobility trajectories derived from telephonic data. We introduce a compositional representation of individual movements that integrates the uncertain device location with information on the surrounding road network, encoding at each time point the proportions of different road types compatible with the observed position. This formulation naturally accounts for measurement uncertainty and yields trajectories evolving in the simplex. To model these data, we develop a state-space framework for compositional time series that captures both the telephonic measurement error and the temporal dynamics of the latent mobility process. Building on this representation, we propose a model-based clustering approach based on mixtures of state-space models to identify groups of trajectories with similar evolution. This allows us to aggregate individual movements into interpretable mobility patterns at the population level. The results of the case study demonstrate the ability of the approach to uncover meaningful mobility behaviors, providing insights that are potentially relevant to policy makers.

Understanding how individual metro usage evolves over multi-year horizons is essential for transit planning and passenger retention. However, existing approaches typically characterize mobility patterns as static clusters or short-term variability, leaving the lifecycle dynamics of transit participation underexplored. This study proposes a state-based lifecycle modeling framework that integrates Hidden Semi-Markov Models (HSMM) with discrete-time survival analysis to characterize the evolution of individual metro mobility. The HSMM infers latent mobility states with explicit duration distributions and a transition matrix governing regime changes, while the survival component models exit and re-entry events via state-dependent hazard functions conditioned on mobility-state trajectories and behavioral history. Applied to four years of smart card data from the Shanghai metro system (2021-2024), the framework enables the identification of interpretable mobility states, the characterization of transition dynamics, and the quantification of state-dependent exit and re-entry processes. The analysis reveals five robust mobility states with a directional transition hierarchy centered on an occasional-usage gateway state, and fundamentally different temporal mechanisms governing disengagement and return: exit hazard is state-dependent but duration-independent, whereas re-entry hazard decays sharply with inactivity length. These findings provide a methodological foundation for lifecycle-oriented mobility analysis and practical guidance for transit operators to identify at-risk users and time retention interventions.

The influence of environmental exposures, such as air pollution, on human health has become increasingly recognized. A growing body of evidence suggests that the microbiome may mediate these effects, explaining the relationship between the environment and host biology. However, the impact of environmental exposures on the microbiome is not yet fully understood, and statistical modeling in this context is challenged by complex dependency structures. In particular, microbiome data exhibit spatial dependencies across sampling regions as well as ecological correlations among microbial taxa, which, if ignored, can substantially reduce detection power, leading to missed true signals. We introduce a novel spatial mixed modeling framework for microbiome data that accounts for both region-level spatial dependency and taxon-level ecological dependency using conditional autoregressive priors. Through simulations, we demonstrate that this framework outperforms existing methods that ignore such dependencies, by achieving high detection power in feature selection while maintaining low false positive rates and reduced mean squared error in estimation. Applied to two real studies-data from Food and Microbiome Longitudinal Investigation study and lung microbiome dataset-with fine particulate matter (PM_2.5) exposures, our model identified genera, which are known to be involved in pollution-related health outcomes, as well as novel taxa that may mediate host responses to air pollution. This novel approach offers a powerful and flexible tool for uncovering biologically meaningful associations in complex environmental data.

Subgroup analysis is routinely used in randomized controlled trials to examine whether treatment effects are homogeneous across patient subgroups or differ because of treatment-effect heterogeneity. In this paper, we investigate the properties of the odds ratio and the relative response in subgroup analyses with binary outcomes, extending previous work with new theoretical insights and methodological developments. We establish several new theorems that characterize how the odds ratio for the overall population changes in both magnitude and direction when two subgroups are combined. These results further confirm that the odds ratio is inappropriate as an efficacy measure in this subgroup setting, whereas the relative response is appropriate. We also present the formal relationship between the odds ratio and the relative response, and clarify their differences in terms of the logic-respecting property, that is, whether the overall efficacy lies between the subgroup efficacies, and the dilution property, that is, whether mixing subgroups moves the overall odds ratio toward 1. Although the odds ratio is generally not logic-respecting, it may behave approximately like a logic-respecting efficacy measure under certain conditions. To illustrate our findings, we present an illustrative example based on clinical trial data and discuss its implications for subgroup analysis in randomized controlled trials.

Understanding how extreme price movements propagate across financial and energy markets is critical for risk management and regulatory design in the EU Emissions Trading System (EU ETS). We apply Hüsler-Reiss graphical models of extremes to a system of 20 daily variables centred on EU allowances futures across Phases 3 and 4 of the EU ETS (2013--2025), with a Gaussian graphical model as the average-dependence baseline. The tail networks are structurally distinct from the average dependence network: substantially denser, organized around different central nodes, and governed by within-sector homophily that binds sector boundaries more tightly than at the average-dependence level. EU allowances futures are peripheral in the standard graphical model but achieve the highest centrality in the tail networks, while equity indices and major FX pairs follow the opposite trajectory. Exponential random graph models confirm equity and FX peripherality in tail networks across all sample periods and identify triadic closure during market downturns as a Phase~3 phenomenon that vanishes in Phase~4. The phase transition restructures the tail network without thinning it: average dependence contracts sharply while tail dependence persists, and crash contagion shifts from clustered to diffuse propagation. These findings have direct implications for hedge construction by compliance entities, stress-test calibration by regulators, and the design of systemic-risk monitoring tools for EU ETS markets.

Explaining precipitation behavior at daily scale is important for fine scale understanding of the mechanisms driving precipitation. However, this effort is challenging because of the frequent incidence of zeros. The challenge is amplified by the acknowledged incidence of two types of zeros -- absence of precipitation as a dry event and absence of measured precipitation due to detection limits. In this work, we propose a multilevel spatio-temporal model which allows us to distinguish and explain the two types of zeros, as well as to model positive precipitation above the detection limit. The methodology combines a point mass at zero with probability modeled through a probit regression, a Gamma regression for latent positive precipitation amounts, and an observation mechanism subject to threshold-induced censoring. To capture spatial dependencies, Gaussian processes are employed in each regression model. Working within a Bayesian framework, we can obtain a rich range of inference with exact uncertainty. In particular, we provide model-based inference tools to compare and quantify differences between the true precipitation process and its observed counterpart across relevant characteristics. We apply our model to the analysis of daily spring observations at 70 sites over 15 years from the Ebro River Basin in northeastern Spain. Our findings indicate that the threshold strongly affects the occurrence of observed precipitation, especially in humid regions. While its impact on total accumulated amounts is small, it can exert a relevant effect on upper quantiles.

We develop an anytime-valid framework for optimal policy identification from logged contextual bandit data. In many applied settings, the analyst wants to select the optimal policy from a candidate policy class $Π$, but data are generated by an externally determined logging policy that they do not control. The analyst may also wish to monitor evidence continuously, stopping once the optimal policy is clear rather than committing to a fixed sample size in advance. This paper addresses these challenges by constructing a time-indexed set $S_t$ that retains the true optimal policy set uniformly over time with high probability. The resulting procedure allows the analyst to monitor policy values, eliminate clearly suboptimal policies, and stop at data-dependent times without invalidating inference. When the optimal policy is unique, we define a stopping time for its identification and derive a sample-complexity bound scaling as $O\!\left(\frac{\log |Π|+\log\log(1/Δ_{\min})}{Δ_{\min}^2}\right)$, where $Δ_{\min}$ is the gap between the best and second-best policy values. Simulations demonstrate that the anytime-valid approach can yield substantial sample savings relative to fixed-$N$ designs. An application to a large adaptive experiment on reducing misinformation online illustrates how the method provides a dynamic view as evidence on the optimal policy accumulates.

Small sample sizes pose significant challenges in regression analysis, often leading to violations of classical assumptions such as normality, homoscedasticity, and independence of residuals. These violations compromise parameter estimation accuracy, reduce statistical power, and limit the generalizability of findings. This study introduces the Gaussian Process-based Modified Extreme Value Theorem (GP-MEVT) method, a novel hybrid data augmentation approach that combines Gaussian Process with Extreme Value Theory to address these limitations. The GP-MEVT method generates augmented observations that extend the predictor space beyond the observed range while preserving the underlying linear structure and introducing controlled variability based on residual variation, through comprehensive simulation studies across three variance scenarios (sigma = 2, 5, 8) and sample sizes (n = 10, 15, 20). Here, we demonstrate that GP-MEVT achieves a higher rate of assumption satisfaction, substantially outperforming standard bootstrap and bootstrap with noise methods. The proposed method also exhibits reasonable parameter estimation accuracy, with intercept and slope estimates consistently closer to true parameter values, and maintains competitive or superior model fitting performance as measured by root mean square error. Application to a real-world dataset confirms these advantages, with GP-MEVT achieving a 67.1% assumption satisfaction rate compared to 17.3% and 21.2% for bootstrap alternatives. These findings establish GP-MEVT as a robust and reliable framework for fitting linear regression models to small datasets, offering practitioners a principled approach to statistical inference when sample size limitations are unavoidable.

In recent years, there has been much progress toward the development of methods for converting three- and five-number summary statistics (i.e. minimum, maximum, median, and quartiles) to means and standard deviations (SDs). This is commonly done in the meta-analysis setting, where some studies report means and SDs, while other report quantile summaries. However, we show that three-number summaries, which are the most common, do not contain enough information to reliably estimate SDs. We show that very poor estimates can result, which may invalidate any inference and provide details of a sensitivity analysis that can allow researchers to have greater confidence in their results, or highlight potential sources of bias. We further explore whether nominating additional information can provide enough information regarding the unknown data shape to improve SD estimations, and in doing so introduce a new estimator using the scaled Beta distribution. Simulations and a real data example are used to highlight the advantages and disadvantages of this approach. A Web application is also provided to help researchers perform sensitivity analyses.

We study causal effect estimation with compositional treatments, where the exposure lies on a simplex and the estimand is defined over compositions rather than scalar or binary values. By considering a projection of the average potential outcome onto the treatment space, a kernel-based covariate functional balancing approach is adopted for weight construction. The weights are obtained by directly minimizing a worst-case balancing error over a reproducing kernel Hilbert space (RKHS) defined on the joint space of treatments and covariates, instead of being estimated under a treatment assignment model. Building on these weights, an augmented weighted estimator (AWE) is proposed, where the outcome function is estimated via kernel ridge regression and combined with a marginal augmentation over the covariate distribution. Despite the complex structure of the resulting objective, a finite-dimensional convex optimization problem is formulated via a representer theorem and a low-rank approximation. The proposed estimator achieves $\sqrt{n}$-consistency without requiring consistent estimation or smoothness of the weights. An asymptotic normality result is established around a sample-specific target. Empirical performance is demonstrated through simulation studies and a real data application.

Mediation analysis is widely used to disentangle causal pathways, yet in many real-world studies the mediator M and outcome Y are never jointly observed. This incompleteness breaks the standard identification strategy for natural direct and indirect effects. We introduce a novel data fusion framework that restores the identification by combining two incomplete data sources, one measuring $M$ and the other measuring Y. Our approach leverages shared instrumental variables (IVs) to circumvent the need to observe (M,Y) jointly, remains valid under unmeasured confounding via a no-interaction condition, and accommodates covariate and exposure shifts across data sources under a latent alignment condition. We establish two identification strategies, one for settings with a known set of valid IVs, and another for settings where valid IVs must be learned. We further develop semiparametric, influence-function-based estimators with multiple robustness properties, and propose an estimator that attains the semiparametric efficiency bound under appropriate conditions. We apply our framework to quantify the extent to which the effect of SNP rs610932 on dementia risk is mediated through immune-related gene-expression pathways.

Hidden Markov models are widely used to infer latent state sequences from sequential data, but Viterbi decoding reports only one most likely complete path. When decoded states carry scientific meaning, this single maximizer can conceal pathwise uncertainty created by multiple near-optimal trajectories. Conditional on a fitted HMM, we introduce the tropical Viterbi tube: the set of hidden trajectories whose complete-data log-score lies within a tolerance of the Viterbi optimum. State, transition, and change-status projections show which local features remain compatible with globally near-optimal complete paths, giving a pathwise uncertainty layer for HMMs in sequence analysis, ecology, finance, biomedical monitoring, and related domains. The tube is a posterior superlevel set on complete hidden-path space, with tolerance interpreted as a log posterior-odds loss relative to a Viterbi path. Calibrating the tolerance to a target posterior mass gives an HPD-threshold credible region for the complete latent path and conservative simultaneous projected bands. We prove monotonicity, step-function behavior, and deterministic stability guarantees, and compute projected tubes exactly by max-plus forward-backward recursions in O(TK^2) time for dense transitions. Posterior tube mass and HPD calibration are separate pathwise calculations approximated by FFBS. In a public bat-tracking application, robust foraging tube segments are enriched for feeding buzzes, whereas robust commuting segments are depleted: at eta = 0.005, enrichment is 2.25 with 95% bootstrap interval (1.73, 2.85) for robust foraging and 0.27 with interval (0.16, 0.44) for robust commuting.

This paper studies how long-run earthquake risk shapes national identity, separating a distributive margin (national membership as a rule for allocating scarce resources) from an expressive margin (pride, willingness to fight, and affective attachment). Linking World Values Survey respondents (1981-2022; 63 countries, 494 subnational regions) to subnational seismic-risk geography, I find that people living closer to high-risk zones express stronger national in-group orientation: more pride, more willingness to fight, and more priority for nationals when jobs are scarce. Family attachment and out-group hostility do not rise, while religiosity increases in parallel. The expressive margin is conditional: the pride response is pronounced where state-religion alignment and a cohesive religious field lend the symbolic infrastructure to cast disaster as a shared national ordeal, and indistinguishable from zero where they do not. A complementary design exploiting earthquakes between adjacent survey waves finds no average short-run response, yet the response it does detect concentrates among older, place-attached residents who cannot leave -- consistent with attitudes tracking a chronic, inescapable risk rather than single events. Together, the results point to a demand-side origin of national attachment: where a covariate shock would overwhelm local and family insurance, people turn to larger communities of protection and meaning -- the nation and religion -- a logic I formalize in a simple social-interaction model.

Despite near-universal electrification in many countries, electricity supply shortages continue to shape household energy use. This paper examines how households adapt to chronic grid failure in high-electrification, high-dependence contexts, using Lebanon as a case study. Drawing on original survey data from 1,000 households, we analyze both supply-side coping mechanisms such as diesel generators and solar photovoltaic (PV)-battery systems, and demand-side adaptations, including load shifting and demand suppression. The results reveal a landscape of household responses, where socioeconomic status plays a central role in determining access to backup solutions and the extent of met demand. While diesel generators remain widespread, a transition toward PV-battery systems is observed, especially among financially capable households. However, decentralized self-generation is associated with inefficiencies, including substantial levels of curtailed solar generation. On the demand side, households exhibit reductions in electricity use, leading to distinct consumption profiles depending on the type of backup system employed. These findings highlight the importance of distinguishing between met and unmet demand when assessing energy needs under unreliable supply. The paper contributes to the literature by providing a quantitative characterization of the interaction between self-generation and demand adaptation in a supply-constrained high-electrification context. It also offers empirical demand profiles that incorporate suppressed consumption, addressing a key gap in electricity system planning. From a policy perspective, the results underscore the need to account for unmet demand, address inequities in access to coping technologies, and reduce inefficiencies in decentralized systems.

Prediction markets are usually evaluated after their contracts exist, by asking how well prices forecast outcomes. We study the prior institutional margin of market formation, asking which uncertainties become tradable contracts at all. Using an audited dataset of 6,047 Africa-topic and Latin America-topic contracts listed on Polymarket and Kalshi, we construct a coded measure of settlement legibility, the degree to which an uncertainty can be worded, sourced, and credibly resolved by third parties, and validate it on 451 units under a frozen codebook, where independent double scoring reaches ordinal reliabilities of 0.92 and 0.96 on the primary dimensions and blind human benchmarks reach 0.97 and 0.92. Using this measure, we find that formation is selective in ways that public importance does not explain, with African inventory concentrated overwhelmingly in football while salient civic events produce little or no inventory, and Latin American inventory deeper but dominated by Venezuela, where attention to prospective United States military action sustains the largest civic cluster in the data. Legibility orders the inventory steeply, with sports and elections near the top of the scale and conflict at the bottom. In a formation test against an externally assembled frame of 131 civic events, legibility predicts listing in the expected direction but falls short of pre-specified acceptance criteria, while among listed contracts the relation between legibility and trading value is negative, as a model of selective listing implies and as we predicted before estimation. Prediction-market inventories therefore measure what platforms can settle as much as what traders believe, and reading them as maps of public interest conflates the two.

We introduce Martingale Doppelgänger-Eval, a public shadow-market benchmark for auditing whether vision-language models (VLMs) use candlestick evidence rather than extrapolate past trends. The central difficulty is identification: on real market histories, chart evidence and trend are strongly coupled, so an observational score cannot determine whether a fluent technical-analysis narrative is grounded in local visual evidence. We prove this limitation formally: no evaluation functional computed from observational chart--label data can distinguish a grounded responder from a trend-shortcut responder under strong coupling, whereas matched evidence interventions separate the same responders at an exponential rate and trend--label swaps provide an independent shortcut stress test. The benchmark therefore evaluates frozen VLMs on rendered OHLCV charts under four controlled mechanisms: a martingale-null market, injected-alpha counterfactual pairs, trend-confounder swaps, and regime shifts. A structural behavioral model identifies null-market bias, trend sensitivity, evidence sensitivity, prompt/renderer fragility, and evidence faithfulness; the accompanying statistical toolkit provides minimum detectable effects, block-aware sequential testing for metered APIs, and an overlap-weighted artifact check. Across frozen commercial and open VLMs, the identified regression assigns large positive coefficients to past trend but evidence coefficients that are zero or opposite to the rule-implied sign. Matched-pair analyses show that models either ignore injected candlestick semantics or move opposite to the rule-implied direction conditional on responding. The benchmark isolates a failure mode that standard observational chart benchmarks cannot detect and gives a reusable audit template for time-series imagery with controllable label mechanisms.

Private Property is one of the central institutions of civilized society. We first consider its social, legal, and economic aspects. We then follow the Lockean tradition by focusing on a specific procedural definition: Homesteading is the acquisitive act of first using an object that is initially unowned. The ontology of concrete objects and the nature of their uses determine how objects may be acquired. In this article, we address the open-texture problem in the definition of property, then provide the necessary conditions for an object to be property in the Lockean Scheme.

Climate policy in global network industries is implemented across fragmented jurisdictions, yet firms respond through integrated operational networks. We develop a two-stage game-theoretic framework to analyze how firm-level responses interact with alternative governance structures. Regulators first choose emissions charges. Firms subsequently compete through pricing, service capacity and capital deployment decisions. The analytical results demonstrate that uniform global regulation maximizes welfare in symmetric markets. However, in sufficiently asymmetric markets, a uniform global charge is dominated by decentralized regimes. Multiple regulatory instruments better accommodate region-specific market externalities. We apply this framework to a calibrated case study of North American, Western European and transatlantic aviation markets. The numerical results establish that a globally coordinated regulator setting region-specific charges achieves the highest aggregate welfare. These aggregate gains nonetheless mask substantial distributional disparities across jurisdictions. Effective climate governance in network industries therefore requires more than determining an efficient emissions charge. Policy instruments ought to accommodate regional heterogeneity and transfer mechanisms will be necessary to ensure efficient, politically stable cooperation.

Which regional exposure conclusions are identified when public data do not observe buyer-seller links across states? We study this question by treating the missing intermediate-input spatial kernel as an unknown coupling constrained by regional activity margins, support restrictions, and auxiliary shipment moments. For linear exposure statistics, the sharp identified set is computed by transportation linear programs. Applying the method to U.S. state-sector data, we find that shipment data are inconsistent with the spatial diffuseness implied by proportional regionalization in key goods sectors. However, they do not identify a unique regional production network or a precise ranking of state exposure to local shocks. Bilateral shipment restrictions tighten the bounds, but much of the remaining uncertainty comes from large service and mixed sectors that are weakly covered by goods-movement data. The results show which exposure conclusions are supported by public data and which are imposed by maintained regionalization assumptions.

We present a novel multiscale algorithm for directly recovering the atomic model structure of a protein from single-particle cryo-EM data. Our algorithm is able to estimate protein structures to state-of-the-art accuracy for high-noise and low-contrast data. It is also robust to misspecifications in the TEM image formation model. These desirable properties are primarily due to the use of an explicit representation of the protein backbone in terms of bonds, torsion angles and bond angles, which supplies rich prior information to the structure recovery process. We apply our method on three protein cryo-EM datasets, generated using an electron microscope digital twin, and show that using a multiscale approach yields an improvement of the root-mean-square deviation (RMSD) and template modelling (TM) scores with respect to the ground truth. Furthermore, there is evidence that larger-scale structures are being prioritised with the multiscale algorithm, which reduces the possibility of convergence to bad local minima.

High-throughput chromatin accessibility assays such as ATAC-seq generate thousands of candidate regulatory elements (peaks), yet no standardized tool exists for assembling the diverse quantitative features needed to prioritize peaks for functional validation. Here we present PyPeakRankR, an open-source Python package that extracts peak-level features, namely BigWig signal summaries, GC content, PhyloP conservation scores, distribution moments (kurtosis, skewness, bimodality), and cell-type specificity rankings, into a single reproducible peak by feature matrix stored as a tab-separated values (TSV) file. PyPeakRankR separates deterministic feature extraction from downstream ranking, enabling transparent benchmarking of prioritization strategies on the same upstream data. The package provides both a command-line interface and a matching Python API, supports cross-assembly scoring via liftOver, and runs in minutes on thousands of peaks. PyPeakRankR was validated in the Brain Initiative Cell Census Network (BICCN) community challenge, where its predecessor PeakRankR ranked among the top 3 of 16 methods for cell-type specific enhancer prediction. In a recent basal ganglia study, PyPeakRankR was used within the Cross-species Enhancer Ranking Pipeline (CERP) to identify enhancer-AAV tools achieving greater than 70% on-target specificity across cell types. PyPeakRankR is freely available under the MIT license at https://github.com/AllenInstitute/PeakRankR/tree/python-package.

Electron-microscopy reconstruction now yields complete synaptic wiring diagrams, or connectomes, of entire small brains, including the larval Drosophila, the first insect brain reconstructed in full. How far a wiring diagram alone fixes a circuit's activity, as opposed to the finer physiological detail it does not record, is debated. We run a complete connectome as a fixed, rate-based dynamical operator in which no single-neuron parameter is fitted, so that, at one fixed dynamical regime, the model's behavior reflects the wiring and its connection strengths rather than tuned single-neuron physiology, and compare it against a hierarchy of randomized networks that each preserve a coarser description of the wiring. The model's overall dynamical regime, how strongly and how richly it responds, is mostly statistical: networks keeping only the connectome's coarse wiring statistics reproduce it. The wiring beyond these statistics instead sets where activity travels and which circuits shape it. Sparse input is confined to a compact olfactory pathway that randomized networks flood, and the mushroom body, the insect learning center, takes an outsized role in the leading adjoint-side modes, the directions that weigh which neurons shape the recurrent dynamics. Coarse statistics set the regime; the precise pattern of connections sets the geometry, a separation that clarifies which connectome-based claims rest on wiring alone.

Subjective tinnitus - the perception of sound in the absence of an external acoustic stimulus - remains one of the most debated phenomena in auditory neuroscience. In 2016, the stochastic resonance (SR) model was introduced as an alternative account of tinnitus-related neuronal hyperactivity, proposing that internally generated neural noise is adaptively upregulated to restore information transmission after hearing loss. Rather than interpreting increased spontaneous activity as maladaptive, the model reframed it as a functional mechanism that enhances signal detection near sensory thresholds, with tinnitus emerging as a side effect of adaptive sensory optimization. Over the past decade, this framework has evolved from a phenomenological hypothesis into a broader neurocomputational theory linking information theory, adaptive signal detection, multichannel auditory processing, and cross-modal plasticity. Computational modeling, large-scale clinical analyses, and animal experiments have provided converging support for key predictions, including improved detectability under specific noise conditions and frequency-specific phantom percepts. The framework has also inspired a therapeutic approach based on spectrally matched near-threshold noise stimulation and has recently been integrated into a unified account of auditory phantom perception that combines stochastic resonance, central gain, homeostatic plasticity, and predictive coding. This review provides a chronological overview of the development of the stochastic resonance model, summarizes major theoretical and empirical advances, and outlines future directions for mechanistic validation and clinical translation. By redefining tinnitus as a consequence of adaptive sensory computation, the model shifts the conceptual focus from pathological dysfunction toward principles of information optimization in neural systems.

Skeletal muscle undergo remarkable changes during aging including anatomical, ultrastructural, and moreover biochemical. The aging associated reduction of muscle mass, termed as sarcopenia, is a major factor in geriatric functional decline and frailty, contributing to the lowering of self-confidence. In an adult skeletal muscle fibers, sarcoplasmic reticulum (SR) and mitochondria exhibit most intricate and precise distribution along with the sarcolemmal (forming T-tubule), which is critical for muscle function. In healthy young muscle tissue, the close physical proximity of SR and mitochondrial membranes shows contacts called mitochondria-associated membranes (MAMs). Recent literature highlights the role of MAMs network in smooth functioning of muscle by regulating localization of Ca2+-signaling, lipid transport, and other signalling molecules like reactive oxygen species. Several tethering mechanisms are proposed to stabilize the MAMs network, the classical ones being the mitofusins (MFN1 and MFN2). Emerging consensus suggest that MAMs in the skeletal muscle facilitate accuracy of excitation-metabolic coupling ensuring spatial energy supply. However, upon aging the precision of SR and mitochondria co-localization as well as crosstalk seems to be affected. In this review, we have critically examined the current literature about MAMs network structure and function during health and diseases mainly from an aging perspective. We have further evaluated the role of exercise, nutritional, nutraceutical and pharmacological approaches in lowering MAMs loss in an effort to retard aging progression. Retention of skeletal muscle health and performance is a major factor in achieving the goal of healthy aging.

Spinal cord stimulation with implantable leads is a valuable therapy used to treat a variety of chronic pain conditions. However, lead migration is a common complication causing loss of efficacy. Previous reports have characterized lead migration using radiographs, but methods are not consistent and lack rigorous validation. The purpose of this study was to develop a technique to perform radiographic measurements of the position of epidural spinal cord leads within the lumbosacral spinal canal and establish its accuracy, repeatability, and reproducibility. Computed tomography scans were acquired from three clinical trial participants implanted percutaneously with two eight-contact cylindrical leads; from these, electrode positions were established using three-dimensional measurements, and digitally reconstructed radiographs were created. Two operators applied a digitization and measurement protocol for each lead. Bland-Altman plots were created to determine smallest detectable change, and a gage repeatability and reproducibility analysis was performed. Smallest detectable change was found to be less than the distance between adjacent electrodes and variability introduced by repeatability and reproducibility was less than 10% of the total study variability. We conclude that the method developed to measure lead electrode position has sufficient accuracy and acceptable repeatability and reproducibility.

How should recommender systems be designed when recommendations shape access to scarce, short-lived opportunities? We study this question in a production setting: Timee, Japan's largest platform for spot work, where workers favorite job templates and receive notifications when firms post shifts from those templates. Maximizing predicted favoriting can generate misdirected concentration: recommendations accumulate on popular templates that create few viable job openings, while templates with unmet labor demand receive too little exposure. We design exposure-control mechanisms for favorite-list management, reallocating template exposure based on posting activity and unfilled capacity. The proposed recommender, thresholded eligibility control (TEC), is fully parallelizable and suitable for large-scale digital platforms. In simulations calibrated to Timee data, TEC raises the per-round job-finding rate from 57.6\% to 70.0\%. A prefecture-level randomized field experiment increases realized matches and exposure per active template, reduces the share of low-exposure templates, and improves impression-level favoriting and downstream matching.