This study aims to characterize differences during fine-motor execution between late-life depression (LLD) and healthy control (HC) older adults, and to evaluate the feasibility and robustness of classification models based on cluster-derived features. We enrolled 95 individuals in 2024 at The First People's Hospital of Yunnan Province (LLD, n=58; HC, n=37). Participants wore AX6 triaxial accelerometers on both wrists (100Hz) and completed four Purdue Pegboard subtests and the Clock Drawing Test. Preprocessing included three-axis standardization, computation of signal vector magnitude (SVM), sliding-window segmentation, principal component analysis, and Gaussian mixture model clustering. On scales, HAMD-17 was significantly higher in LLD than HC, and MMSE also differed. LLD performed worse than HC on all four Purdue Pegboard subtests with significant differences, while the raw Clock Drawing score was not significant. Task-level comparisons of "pattern composition" were significant across all five tasks (Purdue subtests p=0.001 each; Clock Drawing test p=0.010). Of 50 clusterwise comparisons, 31 showed significant between-group differences with replication across tasks. Classification performance was as follows: Lasso-logistic, accuracy 0.89 (95% CI 0.81-0.95), sensitivity 0.81, specificity 0.95, AUC 0.95; XGBoost, accuracy 0.89 (95% CI 0.81-0.95), sensitivity 0.91, specificity 0.89, AUC 0.94. Overall, LLD more often exhibited intermittent, higher-peak, rhythm-unstable kinematic patterns during task execution. Wrist-worn accelerometry combined with standardized fine-motor tasks can distill interpretable, reproducible "motor pattern fingerprints," objectively delineating differences between LLD and HC in rhythmic organization and motor stability.

Phthalate mixtures and pregnancy loss: Linking MCNP exposure with placental dysfunction via epidemiologic and in vivo evidence.

Serum polychlorinated biphenyls and risk of metabolic dysfunction-associated steatotic liver disease: Cross-sectional and prospective analyses from the Dongfeng-Tongji cohort.

Small sample sizes in preclinical research limit the extraction of reliable knowledge and hinder translational progress. We propose genESOM, a generative artificial intelligence method based on emergent self‑organizing maps. genESOM is designed to augment small biomedical datasets while controlling α‑error inflation. It separates structure learning from data synthesis and integrates error propagation mitigation through dimensionality modulation, enabling safe and interpretable data augmentation. Using lipid signaling data from a preclinical multiple sclerosis study employing the experimental autoimmune encephalomyelitis (EAE) model (26 female SJL/J mice, three treatment groups, and 62 lipid mediators), we intentionally reduced the sample size from 26 to 18 animals. This reduction abolished detectable group differences by both statistical and machine learning analyses. Augmenting the reduced dataset with AI‑generated cases restored treatment‑specific segregation and recovered the original key lipid mediators. genESOM achieved consistent fidelity without introducing false positives. In contrast, Gaussian mixture and conditional GAN models failed under comparable constraints. These results demonstrate that genESOM provides a robust, error‑controlled framework for enhancing knowledge extraction from limited preclinical samples. While synthetic augmentation cannot substitute for biological replication, it can support exploratory analyses and help reduce the need for additional animal experimentation.

Distinct metabolic signatures of Alzheimer's and Parkinson's disease revealed through genetic overlap.

In this paper we present a numerical study of supersonic wave propagation in water droplets with shapes deviating from sphericity; uniformly distributed gas nuclei are assumed to be present in the droplet volume, which are subjected to growth under the influence of the passing shockwave. A homogeneous compressible mixture model is utilised together with a finite-rate relaxation approach for the liquid-gas interfacial dynamics, and validated against experimental results. The effect of varying deformation for a range of nominal droplet diameters and for Mach numbers based on the axial velocity ranging from M = 2.4 to 4.4 is reported. The results reveal that as the examined deformed droplet shapes have a shorter length in the direction of the impacting shockwave, gas phase expansion is advanced and reaches higher volume fractions. Additionally, the effect of coherent liquid-aid interfacial structures in the form of harmonic surface disturbances attributed resembling Kelvin–Helmholtz (KH) instabilities is examined. The presence of those surface disturbances lead to multiple wave reflections and wave superpositions that give rise to further amplification of pressure and stronger gas-expansion effects.

Forced displacement exposes refugees to a wide range of potentially traumatic experiences, increasing their vulnerability to mental health problems. However, longitudinal research indicates substantial heterogeneity in refugees' mental health trajectories. This systematic review aimed to synthesize existing evidence on these trajectories using growth mixture modeling and to identify predictors of differential outcomes. A systematic search of peer-reviewed articles was conducted in PsycINFO, Medline, PTSDPubs, and Web of Science. Studies investigating refugees' mental health trajectories longitudinally using growth mixture modeling were included. Two independent reviewers systematically screened studies for eligibility and assessed methodological quality and risk of bias. A total of 1271 publications were initially identified, of which seven met the inclusion criteria, resulting in a total of 13 distinct trajectory analyses. All studies identified chronic trajectories, followed by resilient and recovery patterns, with delayed trajectories emerging as the least frequent pattern. Among those trajectories identified, the prevalence rates within studies were highest for the resilient trajectory (57.4 %), followed by recovery (29.4 %), chronic (14.4 %), and delayed (14.3 %). Across studies, older age was associated with increased risk for unfavorable trajectories, whereas greater social support was linked to more favorable trajectories. Findings regarding gender differences were mixed. This review suggests that refugees may follow similar mental health trajectories as other trauma-exposed populations, though notable differences in their distribution exist. Person-centered approaches such as growth mixture modeling offer valuable insights but remain underutilized. Longitudinal, theory-driven research is needed to inform tailored psychological interventions in displaced populations.

Cross-scene adversarial learning with Gaussian mixture model for hyperspectral anomaly detection

The heterogeneity of depression is well documented. Consistent with the Life Course Theory, longitudinal analysis establishing trajectories may demonstrate the stability and variation of depression. Data from four waves of the US National Longitudinal Study of Adolescent to Adult Health (Add Health) were used to investigate depression trajectories. Nine items from the Center for Epidemiological Studies (CESD) measured depression of 9421 participants. Trajectories were established using growth mixture modelling (GMM) and multinomial regression was employed to explore the influence of gender and ethnicity. Five distinct and unique trajectories were established as minimal (66.8%), moderate (19.1%), Emerging Adulthood (EA) trough (7.6%), persistently high (3.3%) and EA peak (3.1%). Being female and non-white increased the probability of being in the elevated trajectories during adolescence. Three stable and two fluctuating trajectories demonstrate both the predictability and heterogeneity of depression across three distinct developmental stages.

A Bayesian compressive sensing – Stochastic harmonic function based random field simulation method incorporating with Gaussian mixture model to consider fusion of multi-groups of spatial data

Choroid plexus alterations correlate with cognitive impairment in multiple system atrophy.

Alterations of cerebrospinal fluid dynamics in post-stroke depression: A longitudinal multimodal imaging study.

Optimization of bioactive compound extraction from lychee peel and seed flours using a simplex-centroid mixture model

Hypothalamic-pituitary-adrenal axis attenuation following child sexual abuse distinguishes posttraumatic stress trajectories from late adolescence to midlife.

CFD simulations for predicting vertical upflow liquid nitrogen transfer line chilldown process using a 3D two-phase flow mixture model

Machine learning and remote sensing-based hierarchical framework for assessing non-optically observable riverine water quality parameters

Plasma organophosphate ester exposure-associated microRNA expression profiles and their functional analysis in Chinese healthy adults.

Use of Gaussian mixture model for standardized precipitation index in monsoon region

Innovative Algorithm for Keratoconus Intelligent Grading Using Variational Encoding Bayesian Gaussian Mixture Model

Magnetic levitation-based density profiling for ex vivo differentiation of oral squamous cell carcinoma, oral epithelial dysplasia, and benign oral lesions.