Recent ucistat items

Genetic Variation and Stroke Recovery: The STRONG Study

Tue, 12 May 2026 00:00:00 +0000

BACKGROUND: Genetic association studies can reveal biology and treatment targets but have received limited attention for stroke recovery. STRONG (Stroke, Stress, Rehabilitation, and Genetics) was a prospective, longitudinal (1-year), genetic study in adults with stroke at 28 US stroke centers. The primary aim was to examine the association that candidate genetic variants have with (1) motor/functional outcomes and (2) stress-related outcomes. METHODS: For motor/functional end points, 3 candidate gene variants (ApoE ε4, BDNF [brain-derived neurotrophic factor], and a dopamine polygenic score) were analyzed for associations with change in grip strength (3 months-baseline), function (3-month Stroke Impact Scale-Activities of Daily Living), mood (3-month Patient Health Questionnaire-8), and cognition (12-month telephone-Montreal Cognitive Assessment). For stress-related outcomes, 7 variants (serotonin transporter gene-linked promoter region, ACE [angiotensin-converting enzyme], oxytocin...

Neurodatascience: Past, Present, and Future

Thu, 7 May 2026 00:00:00 +0000

The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing...

Hippocampal ensembles represent sequential relationships among discrete nonspatial events

Thu, 7 May 2026 00:00:00 +0000

ABSTRACT The hippocampus is critical to the temporal organization of our experiences, including the ability to remember past event sequences and predict future ones. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain poorly understood. Here we recorded hippocampal ensemble activity as rats remembered a sequence of nonspatial events (5 odor presentations unfolding over several seconds), using a task with established parallels in humans. Using novel statistical methods and deep learning techniques, we then identified new forms of sequential organization in hippocampal activity linked with task performance. We discovered that sequential firing fields (“time cells”) provided temporal information within and across events in the sequence, and that distinct types of task-critical information (stimulus identity, temporal order, and trial outcome) were also sequentially differentiated within event presentations. Finally,...

Modeling Local Field Potentials with Regularized Matrix Data Clustering

Thu, 7 May 2026 00:00:00 +0000

In this paper, we propose a novel regularized mixture model for clustering matrix-valued image data. The new framework introduces a sparsity structure (e.g., low rank, spatial sparsity) and separable covariance structure motivated by scientific interpretability. We formulate the problem as a fi-nite mixture model of matrix-normal distributions with regularization terms, and then develop an Expectation-Maximization-type of algorithm for efficient computation. Simulation results and analysis on brain signals show the excellent performance of the proposed method in terms of a better prediction accuracy than the competitors and the scientific interpretability of the solution.

Unity by Diversity: Improved Representation Learning for Multimodal VAEs

Thu, 7 May 2026 00:00:00 +0000

Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality's latent representation towards a shared aggregate posterior. This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods.

A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

Thu, 7 May 2026 00:00:00 +0000

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extract knowledge from high-order neighbors, and provide meaningful and interpretable results by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher...

A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making

Thu, 7 May 2026 00:00:00 +0000

Efficient decision-making in context-dependent, sequential tasks remains a fundamental challenge in reinforcement learning (RL). Inspired by the function of the brain’s hippocampal system, we introduce Hippocampal-Augmented Memory Integration (HAMI), a biologically inspired memory-based RL framework that leverages symbolic indexing, hierarchical memory refinement, and structured episodic retrieval to enhance both learning efficiency and adaptability. We also propose Hierarchical Contextual Sequences (HiCoS), a structured RL environment grounded in neuroscience studies on episodic and sequence memory and context-driven decision-making, which serves as a controlled testbed for evaluating biologically inspired memory-based decision-making systems. Our experimental results demonstrate that HAMI achieves high decision accuracy and improved sample efficiency while maintaining low memory utilization. HAMI’s architecture exhibits significantly lower inference latency than baseline memory-based...

Optimal Transport based Cross-Domain Integration for Heterogeneous Data

Thu, 7 May 2026 00:00:00 +0000

Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this article, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in the conditional mean dynamics of target responses. The key advantage of the proposed method is its ability to enhance discriminative power by reducing heterogeneity unrelated...

Evaluating the potential of acupuncture for Alzheimer’s disease treatment: A meta-analysis and systematic review of mouse model studies

Wed, 8 Apr 2026 00:00:00 +0000

Acupuncture is an ancient practice that was developed within the framework of traditional Chinese medicine. While acupuncture has been recently proposed as a therapy for Alzheimer’s disease (AD), acupuncture effects are not well understood in terms of neural mechanisms. Here, we review and examine the studies that used AD mouse models and analyze the experiments where researchers administered electroacupuncture (EA) to AD mice to assess the potential therapeutic impact of acupuncture on disease pathology and cognitive function in controlled laboratory settings. We analyzed 29 relevant PubMed articles published between January 2014 and July 2025. Our results reveal that EA significantly reduces both amyloid-beta (Aβ) and phosphorylated tau (p-tau) levels and neuroinflammatory biomarkers, including molecular signatures for activated microglia and astrocytes in the brain. EA also enhances cognitive functions. While no study directly compared acupoint strategies, the indirect comparisons...

How much we express love predicts how much we feel loved in daily life

Wed, 25 Mar 2026 00:00:00 +0000

Feeling and expressing love in daily life are interconnected and perhaps mutually influential experiences. In this study we examined the reciprocal dynamics of feeling and expressing love and its relation to well-being using an ecological momentary assessment design. Over a four-week period, we asked participants (N = 52; 67% Female; 80% White) to report their levels of feeling loved and expressing love six times a day. Using a continuous-time process model, we explored individual differences in inertia (i.e., persistence of a process over time) and cross-influences of felt and expressed love over time. We found that increases in expressing love led to increased feelings of being loved over time; however, increases in felt love did not lead to increases in expressing love. Notably, participants who experienced more persistent feelings of love (that is, greater inertia over time) indicated higher levels of flourishing. These results suggest new avenues for psychological well-being...

An Expert Guide to Planning Experimental Tasks For Evidence-Accumulation Modeling

Wed, 25 Mar 2026 00:00:00 +0000

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the...

Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment

Wed, 25 Mar 2026 00:00:00 +0000

BACKGROUND: Recent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes. OBJECTIVE: We explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling. METHODS: We analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was...

Partially Observable Predictor Models for Identifying Cognitive Markers

Wed, 25 Mar 2026 00:00:00 +0000

Repeated assessments of cognitive performance yield rich data from which we can extract markers of cognitive performance. Computational cognitive process models are often fit to repeated cognitive assessments to quantify individual differences in terms of substantively meaningful cognitive markers and link them to other person-level variables. Most studies stop at this point and do not test whether these cognitive markers have utility for predicting some meaningful outcomes. Here, we demonstrate a partially observable predictor modeling approach that can fill this gap. Using this approach, we can simultaneously extract cognitive markers from repeated assessment data and use these together with demographic covariates for predictive modeling of a clinically interesting outcome in a Bayesian multilevel modeling framework. We describe this approach by constructing a predictive process model in which features of learning are combined with demographic variables to predict mild cognitive...

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

Wed, 25 Mar 2026 00:00:00 +0000

The EZ-diffusion model is a simplification of the popular drift diffusion model of choice response times that allows researchers to calculate diffusion model parameters directly from data with no need for expensive computations. The EZ-diffusion model is based on a system of equations in which the diffusion model’s drift rate, boundary separation, and nondecision time parameters are jointly used to predict three summary statistics (the accuracy rate and the mean and variance of the correct response times). These equations can then be inverted to obtain estimators for the three parameters from these summary statistics. Here, we describe a probabilistic formulation of the EZ-diffusion model that can serve as a hyper-efficient proxy model to the drift diffusion model. The new formulation is based on sampling distributions of summary statistics and consists only of normal and binomial distributions. It can easily be implemented in any probabilistic programming language. We demonstrate...

Prediction Interval Transfer Learning for Linear Regression Using an Empirical Bayes Approach

Wed, 11 Mar 2026 00:00:00 +0000

ABSTRACT Current literature on transfer learning has been focused on improving the predictive performance corresponding to a small dataset by transferring information to it from a larger but possibly biassed dataset. However, the transfer learning methods currently available do not allow the computation of prediction intervals, and hence, one has to rely on using either the small dataset alone or combining it with the possibly biassed dataset to obtain prediction intervals using traditional linear regression methods. In this article, we propose an E mpirical B ayes approach for P rediction I nterval T ransfer L earning (EB‐PITL), to compute prediction intervals for transfer learning in linear regression tasks. We have proved that the Gibbs sampler associated with EB‐PITL is geometrically ergodic, so EB‐PITL can also quantify the Monte Carlo uncertainty associated with its predicted value. The efficiency of EB‐PITL against currently available methods is demonstrated using simulation...

Accuracy of Continuous Noninvasive Hemoglobin Monitoring

Thu, 26 Feb 2026 00:00:00 +0000

BACKGROUND: Noninvasive hemoglobin (Hb) monitoring devices are available in the clinical setting, but their accuracy and precision against central laboratory Hb measurements have not been evaluated in a systematic review and meta-analysis. METHODS: We conducted a comprehensive search of the literature (2005 to August 2013) with PubMed, Web of Science and the Cochrane Library, reviewed references of retrieved articles, and contacted manufactures to identify studies assessing the accuracy of noninvasive Hb monitoring against central laboratory Hb measurements. Two independent reviewers assessed the quality of studies using recommendations for reporting guidelines and quality criteria for method comparison studies. Pooled mean difference and standard deviation (SD) (95% limits of agreement) across studies were calculated using the random-effects model. Heterogeneity was assessed using the I statistic. RESULTS: A total of 32 studies (4425 subjects, median sample size of 44, ranged...

Estimating causal effects for binary outcomes using per-decision inverse probability weighting

Tue, 11 Nov 2025 00:00:00 +0000

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We...

Distal causal excursion effects: modeling long-term effects of time-varying treatments in micro-randomized trials

Tue, 11 Nov 2025 00:00:00 +0000

Micro-randomized trials (MRTs) play a crucial role in optimizing digital interventions. In an MRT, each participant is sequentially randomized among treatment options hundreds of times. While the interventions tested in MRTs target short-term behavioral responses (proximal outcomes), their ultimate goal is to drive long-term behavior change (distal outcomes). However, existing causal inference methods, such as the causal excursion effect, are limited to proximal outcomes, making it challenging to quantify the long-term impact of interventions. To address this gap, we introduce the distal causal excursion effect (DCEE), a novel estimand that quantifies the long-term effect of time-varying treatments. The DCEE contrasts distal outcomes under two excursion policies while marginalizing over most treatment assignments, enabling a parsimonious and interpretable causal model even with a large number of decision points. We propose two estimators for the DCEE-one with cross-fitting and...

Ambulatory assessment to predict problem anger in trauma-affected adults: Study protocol

Tue, 11 Nov 2025 00:00:00 +0000

BACKGROUND: Problem anger is common after experiencing a traumatic event. Current evidence-driven treatment options are limited, and problem anger negatively affects an individual's capacity to engage with traditional psychological treatments. Smartphone interventions hold significant potential in mental health because of their ability to deliver low-intensity, precision support for individuals at the time and place they need it most. While wearable technology has the capacity to augment smartphone-delivered interventions, there is a dearth of evidence relating to several key areas, including feasibility of compliance in mental health populations; validity of in vivo anger assessment; ability to predict future mood states; and delivery of timely and appropriate interventions. METHODS: This protocol describes a cohort study that leverages 10 days of ambulatory assessment in the form of ecological momentary assessment and a wearable. Approximately 100 adults with problem anger will...

Autoimmune antibodies and systemic inflammatory markers are prevalent and associated with cognition in individuals aged 90+

Tue, 11 Nov 2025 00:00:00 +0000

BackgroundWhile recent studies have found associations between markers of autoimmunity/inflammation and cognitive performance in individuals aged 60-90, these findings remain unexplored in individuals aged 90 and above.ObjectiveTo examine the prevalence of autoimmune antibodies and raised inflammatory markers and their associations with cognition in participants aged 90 + .MethodsWe included participants with serological testing from The 90+ Study, a community-based longitudinal study in southern California. For measures of autoimmunity, we evaluated antinuclear, antineutrophil cytoplasmic (ANCA), rheumatoid factor, double stranded DNA, antithyroglobulin, and thyroid peroxidase antibodies. For inflammatory markers, we examined interleukin-6 (IL-6) and erythrocyte sedimentation rate (ESR). To examine the relationship between autoimmune antibodies and inflammatory markers with cognitive performance, we ran linear mixed effects models.ResultsAmong 201 participants (mean age 94.8...

Predicting high anger intensity using ecological momentary assessment and wearable-derived physiological data in a trauma-affected sample

Tue, 11 Nov 2025 00:00:00 +0000

Background: Digital technologies offer tremendous potential to predict dysregulated mood and behavior within an individual's environment, and in doing so can support the development of new digital health interventions. However, no prediction models have been built in trauma-exposed populations that leverage real-world data.Objective: This project aimed to determine if wearable-derived physiological data can predict anger intensity in trauma-exposed adults.Method: Heart rate variability (i.e. a commercial wearable stress score) was combined with ecological momentary assessment (EMA) data collected over 10 days (n = 84). Five summary measures from stress scores collected 10 min prior to each EMA were selected using factor analysis of 24 candidates.Results: A high area under the receiver operating curve (AUC) was found for a logistic mixed effects model including these measures as predictors, ranging 0.761 (95% CI:0.569-0.921) to 0.899 (95% CI:0.784-0.980)...

A Framework for Variational Inference and Data Assimilation of Soil Biogeochemical Models Using Normalizing Flows

Thu, 2 Oct 2025 00:00:00 +0000

Soil biogeochemical models (SBMs) represent soil variables and their responses to global change. Data assimilation approaches help determine whether SBMs accurately represent soil processes consistent with soil pool and flux measurements. Bayesian inference is commonly used in data assimilation procedures that estimate posterior parameter distributions with Markov chain Monte Carlo (MCMC) methods. The ability to account for data and parameter uncertainty is a strength of MCMC inference, but the computational inefficiency of MCMC methods remains a barrier to their wider application, especially with large data sets. Given the limitations of MCMC approaches, we developed an alternative variational inference framework that uses a method called normalizing flows from the field of machine learning. Normalizing flows rely on deep learning to map probability distributions and approximate SBMs that have been discretized into state space models. As a test of our method, we fit approximated...

Single-cell spatial transcriptomics reveals distinct patterns of dysregulation in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer’s risk gene mutation

Thu, 11 Sep 2025 00:00:00 +0000

The R47H missense mutation of the TREM2 gene is a known risk factor for development of Alzheimer’s Disease. In this study, we analyze the impact of the Trem2R47H mutation on specific cell types in multiple cortical and subcortical brain regions in the context of wild-type and 5xFAD mouse background. We profile 19 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H; 5xFAD genotypes using MERFISH spatial transcriptomics, a technique that enables subcellular profiling of spatial gene expression. Spatial transcriptomics and neuropathology data are analyzed using our custom pipeline to identify plaque and Trem2R47H-induced transcriptomic dysregulation. We initially analyze cell type-specific transcriptomic alterations induced by plaque proximity. Next, we analyze spatial distributions of disease associated microglia and astrocytes, and how they vary between 5xFAD and Trem2R47H; 5xFAD mouse models. Finally, we analyze the impact of the Trem2R47H mutation on...

From images to detection: Machine learning for blood pattern classification

Thu, 14 Aug 2025 00:00:00 +0000

Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distributions. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot backward spatter bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, our model exhibits competitive accuracy and efficiency on the tested dataset, suggesting its potential in similar scenarios.

A Bayesian Time-Varying Psychophysiological Interaction Model

Thu, 3 Jul 2025 00:00:00 +0000

A Bayesian Time-Varying Psychophysiological Interaction Model

Beyond the Surface: Mapping DDE's Metabolic Footprint on Adolescent Obesity.

Wed, 2 Jul 2025 00:00:00 +0000

BACKGROUND: Bariatric surgery is an intervention for severe obesity, leading to significant weight loss and metabolic improvements. However, the release of lipophilic chemicals accumulated in adipose tissue during weight loss presents a unique clinical challenge and research opportunity. Dichlorodiphenyldichloroethylene (DDE) is a persistent organic pollutant increasingly recognized as obesogen, while the biological mechanisms through which DDE influences body mass index (BMI) and waist circumference remain underexplored. OBJECTIVES: We aimed to identify metabolic signatures mediating the association between DDE exposure and weight loss by plasma and adipose tissue metabolomics. METHODS: We conducted a longitudinal study involving 60 adolescents with severe obesity undergoing bariatric surgery. We quantified p,p'-DDE concentrations in visceral adipose tissue collected during surgery and analyzed metabolic profiles from both adipose tissues collected at surgery and plasma...

Who benefits from mobile health interventions? A dynamical systems analysis of psychological well‐being in early adults

Fri, 20 Jun 2025 00:00:00 +0000

Research shows that skills for improving Psychological Well-Being (PWB) may be learned through PWB interventions; however, the dynamic mechanisms underlying this learning process are not well understood. Using an Ecological Momentary Intervention (EMI) design, we conducted an 8-week Randomized Controlled Trial (N = 160; aged 18-22 years), implemented in a mobile Health (mHealth) platform to characterize these dynamical mechanisms. College-attending early adults were randomized to three groups: an active control group (N = 55); an intervention group (N = 51) with positive practices intervention; and a second intervention group (N = 54) with positive practices and meditation intervention. The mHealth implementation allowed us to introduce the interventions in participants' daily lives while also assessing their PWB (in terms of positive emotions and relationship quality) several times a day. We used a Bayesian process model to analyze changes in PWB in terms of the underlying dynamical...

Geospatial Access to Extracorporeal Membrane Oxygenation in the United States

Fri, 11 Apr 2025 00:00:00 +0000

OBJECTIVES: To conduct a Geospatial Information System analysis of extracorporeal membrane oxygenation (ECMO) centers in the United States utilizing data from the U.S. Census Bureau to better understand access to ECMO care and identify potential disparities. DESIGN: A cross-sectional descriptive and statistical analysis of geospatial access to ECMO-capable centers in the United States, accounting for demographic variables. SETTING: The unit of analysis were U.S. Census block groups and demographic variables of interest obtained from the American Community Survey. PATIENTS: Patients accounted for in the U.S. Census data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sixty-seven percent of the U.S. population had direct access to ECMO-capable centers. Disparities were present, with Puerto Rico, Wyoming, North Dakota, and Alaska having no access. Poverty, increased age, and lower population density consistently correlated with limited access. We identified significant racial...

Metabolic Signatures in Adipose Tissue Linking Lipophilic Persistent Organic Pollutant Mixtures to Blood Pressure Five Years After Bariatric Surgery Among Adolescents

Wed, 2 Apr 2025 00:00:00 +0000

Persistent organic pollutants (POPs) are lipophilic environmental contaminants accumulated in the adipose tissue. Weight loss interventions, such as bariatric surgery, can mobilize POPs from adipose tissue into the bloodstream. We hypothesized that this mobilization could contribute to increases in blood pressure among 57 adolescents with severe obesity undergoing bariatric surgery. POPs and metabolic features were measured from visceral adipose tissue collected during surgery using gas and liquid chromatography, coupled with high-resolution mass spectrometry. Blood pressure was assessed at baseline, 6 months, and 5 years post-surgery. We used quantile g-computation to estimate associations of POP mixtures with blood pressure changes. With one quartile increase in POP mixtures, systolic blood pressure (SBP) increased by 6.4% five years after bariatric surgery compared to baseline SBP [95% confidence interval (CI): 0.4%, 12.4%]. The meet-in-the-middle approach identified overlapping...

Exposure to per- and polyfluoroalkyl substances and alterations in plasma microRNA profiles in children

Fri, 21 Mar 2025 00:00:00 +0000

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals that persist in the environment and can accumulate in humans, leading to adverse health effects. MicroRNAs (miRNAs) are emerging biomarkers that can advance the understanding of the mechanisms of PFAS effects on human health. However, little is known about the associations between PFAS exposures and miRNA alterations in humans. OBJECTIVE: To investigate associations between PFAS concentrations and miRNA levels in children. METHODS: Data from two distinct cohorts were utilized: 176 participants (average age 17.1 years; 75.6% female) from the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) cohort in the United States, and 64 participants (average age 6.5 years, 39.1% female) from the Rhea study, a mother-child cohort in Greece. PFAS concentrations and miRNA levels were assessed in plasma samples from both studies. Associations between individual PFAS and plasma miRNA levels were examined...

Computational Phenotyping of Cognitive Decline With Retest Learning

Wed, 12 Mar 2025 00:00:00 +0000

OBJECTIVES: Cognitive change is a complex phenomenon encompassing both retest-related performance gains and potential cognitive decline. Disentangling these dynamics is necessary for effective tracking of subtle cognitive change and risk factors for Alzheimer's Disease and Related Dementias (ADRD). METHOD: We applied a computational cognitive model of learning and forgetting to data from Einstein Aging Study (EAS; n = 316). EAS participants completed multiple bursts of ultra-brief, high-frequency cognitive assessments on smartphones. Analyzing response time data from a measure of visual short-term working memory, the Color Shapes task, and from a measure of processing speed, the Symbol Search task, we extracted several key cognitive markers: short-term intraindividual variability in performance, within-burst retest learning and asymptotic (peak) performance, across-burst change in asymptote and forgetting of retest gains. RESULTS: Asymptotic performance was related to both mild...

Quantitative Measurement of Cyber Resilience: Modeling and Experimentation

Wed, 12 Mar 2025 00:00:00 +0000

Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system's functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This article describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case - a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., "bonware") strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious mathematical models to aid in quantifying systems' resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data and show...

Serological inflammation markers and their association with cognition in the oldest old; The 90+ Study

Thu, 20 Feb 2025 00:00:00 +0000

AbstractBackground

Recent studies in the younger old have identified associations between cognitive performance and markers of inflammation, including interleukin‐6 (IL‐6) and erythrocyte sedimentation rate (ESR). These associations remain unexplored in the oldest old (age 90+), an age group most vulnerable to dementia. In addition, no studies have examined if amyloid burden impacts these associations. This study aims to: (1) examine the associations between inflammatory markers (IL‐6, ESR) and cognitive performance in individuals age 90+ and (2) to examine if amyloid burden impacts these associations.

Method

Participants with at least one plasma measure of inflammation and amyloid burden measured by positron emission tomography (PET) were selected from The 90+ Study (n = 112 for IL‐6, n = 123 for ESR). Cognitive measures included (1) global cognitive score defined as the average of the standardized scores of Mini‐Mental State Examination (MMSE) and Modified MMSE (3MS),...

Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account