Developing a fully applicable machine learning (ML) based sex classification model using linear cranial dimensions.

Recent advances in artificial intelligence (AI) and machine learning (ML) applications have elevated accomplishments in various scientific fields, primarily those that benefit the economy and society. Contemporary threats, such as armed conflicts, natural and man-made disasters, and illegal immigration, often require fast and innovative but reliable identification aids, in which forensic anthropology has a significant role. However, forensic anthropology has not yet exploited new scientific advances but instead relies on traditionally used methods.

Guided growth with aligned neurites in adult spiral ganglion neurons cultured in vitro on silicon micro-pillar substrates.

Objective: Assessment of the relationship between the topographical organization of silicon micro-pillar surfaces (MPS) on guidance and neural alignment of adult spiral ganglion neurons (SGN) and use of the otosurgical approach as an alternative for the extraction and isolation of SGNs from adult guinea pigs.

Approach: SGNs from adult guinea pigs were isolated using conventional and otosurgical approach for in vitro cell culturing on MPS of various micro-pillar widths (1-5.6 µm) and spacing (0.

Sex estimation of the sternum by automatic image processing of multi-slice computed tomography images in a Croatian population sample: a retrospective study.

Aim: To determine the sexual dimorphism of the sternum with standard measurements in a contemporary Croatian population sample using multi-slice computed tomography (MSCT) and to compare the data obtained by an automatic with those obtained by a manual approach.

Methods: Five sternal measurements were obtained from MSCT images of 73 men and 55 women and three sternal indices were calculated. Custom image analysis software was developed for automatic segmentation and calculation of sternal measurements.