CONCLUSIONS: Adult migraine is associated with segmental thinning of the corpus callosum, particularly in the genu and splenium. The relationships with age at onset and disease duration support the concept of cumulative, long-term interhemispheric remodeling in migraine, rathe...

Horse skulls recovered from the Yenikapı Metro and Marmaray excavations in Istanbul, representing the Early to Late Byzantine periods, were investigated to assess cranial size, proportions, and morph....

Genomic and morphometric studies published in 2020 proposed splitting the gentoo penguin (Pygoscelis papua) into four species instead of one. Two carry brand-new scientific names: Pygoscelis poncetii,

Encarsia parasitoids suppress Aleurodicus dispersus through species-specific efficiency influenced by seasonal dynamics and functional traits. Field evaluation across cassava and eggplant demonstrated that Encarsia guadeloupae achieved the highest pest reduction and lowest cumulative population (AUPPC), with enhanced suppression following repeated releases. Seasonal parasitism peaked during March–April, aligning with improved field performance. Morphometric differences, particularly larger body size in E. guadeloupae, may contribute to superior host exploitation. Integrating field efficacy, seasonal dynamics, and morphometric traits provides a framework for optimizing parasitoid-based biological control strategies.

NDT provide 1) Neurohistological service of the brain and spinal cord), 2) Neural staining kits (Apoptosis, Timms, Golgi-Cox, Timms stains & GolgiChrome), 3) Digital microscopic imaging and morphometric analysis, and 4) GLP and non-GLP studies.

A comprehensive statistical and visualization toolkit tailored for fisheries science, stock assessment workflows, and aquatic ecology. It provides streamlined wrappers for univariate and factorial ANOVA, ANCOVA, MANOVA, generalized linear models (GLMs) for count data, non-linear morphometric regressions, custom correlation heatmaps, and integrated Mantel test network linkages.

A comprehensive statistical and visualization toolkit tailored for fisheries science, stock assessment workflows, and aquatic ecology. It provides streamlined wrappers for univariate and factorial ANOVA, ANCOVA, MANOVA, generalized linear models (GLMs) for count data, non-linear morphometric regressions, custom correlation heatmaps, and integrated Mantel test network linkages.

BACKGROUND AND PURPOSE: Freezing of gait (FOG) presents a significant challenge in the management of Parkinson disease (PD). Our study explored the potential to predict PD-FOG using an unbiased machine learning (ML) approach that leverages conventional T1-weighted MRI and clinical measures. MATERIALS AND METHODS: Thirty-seven participants (16 with PD-FOG, 21 PD without freezing of gate [PD-nFOG]) underwent standard isotropic 1 mm³ T1-weighted MRI. Brain morphometric measures, including subcortical volume, cortical volume, mean curvature, area, local gyrification index, and thickness, were extracted using FreeSurfer7. Participants were divided into discovery (13 PD-FOG, 17 PD-nFOG) and independent testing (3 PD-FOG, 4 PD-nFOG) data sets. We tested the predictive ability of each FreeSurfer-derived measure, each clinical measure, and every combination of those measures using 3 ML models: random forest (RF), support vector machine (SVM)-linear, and SVM-nonlinear. Feature reduction was performed using the least absolute shrinkage and selection operator before model development. RESULTS: The SVM-linear model outperformed SVM-nonlinear and RF when tested on the independent data set (area under the curve [AUC] = 0.71, precision: 75%, sensitivity: 75%, specificity: 66.67%). FreeSurfer-derived cortical area (CA) from 27 regions predicted PD-FOG, involving several cortical and subcortical regions. None of the other measures, either in combination or isolation, predicted PD-FOG. The identified features were significantly correlated with clinical and physical therapy measures of PD-FOG using univariate and multivariate statistics, bolstering confidence in the selected feature set. CONCLUSIONS: Our results demonstrate that FreeSurfer-derived CA measures from 27 key regions across the frontal, temporal, parietal, and occipital lobes can moderately predict FOG in PD (AUC = 0.71) using a linear SVM model. While preliminary, our work outlines an MRI-based analytical approach that may inform future external validation efforts and contribute to understanding the potential role of cortical morphology in PD-FOG risk. However, given the limited sample size and constrained independent testing cohort, these findings should be interpreted as exploratory and warrant replication in larger, multicenter studies. AUC : area under the curve CA : cortical area CMC : cortical mean curvature CT : cortical thickness CVOL : cortical volume DDX : disease duration FOG : freezing of gait FOG-Q : Freezing of Gait Questionnaire H&Y : Hoehn and Yahr LASSO : least absolute shrinkage and selection operator LEDD : levodopa equivalent daily dose LGI : local gyrification index MDS-UPDRS-III : Movement Disorder Society-Unified Parkinson Disease Rating Scale-Part III ML : machine learning MoCA : Montreal Cognitive Assessment PD : Parkinson disease PD-FOG : Parkinson disease with freezing of gait PD-nFOG : Parkinson disease without freezing of gait PT : physical therapy RF : random forest SV : segmented volumes SVM : support vector machine TUG : Timed Up and Go TUG-COG : Timed Up and Go-cognitive YOE : years of education

Herein, we first mathematically explored the design space of 3D non-stochastic meta-biomaterials, enabling the systematic generation of a wide range of possible designs (Fig. 1, Table 1, Supplementary Fig. 1 and Supplementary Table 1). Subsequently, we discuss the numerical modeling of the generated meta-biomaterials and their experimental validation, with the aim of achieving decoupled mechanical properties in meta-biomaterials. Optimizing voxel resolution for the homogenization of the meta-biomaterials The mechanical behavior of meta-biomaterials is highly sensitive to the quality and resolution of voxelization, particularly when sharp geometrical features or overlapping struts are present. This sensitivity is especially pronounced in unit cells from Categories I and III, where extreme angles (e.g., \(\theta \cong {\theta }_{\min }\) in Category I and \(\theta \cong {\theta }_{\max }\) in Category III) lead to strut intersections. Capturing such geometric details with high fidelity requires higher voxel resolutions, but this significantly increases computational runtime. In some cases, accurate representation may necessitate voxel counts exceeding \(n\ge 200\). Therefore, identifying an optimal voxel resolution that balances accuracy and computational efficiency is essential. To determine a resolution suitable for the entire design space, we selected three representative unit cells: one critical case from Category I, an intermediate case from Category...