Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues.

Advanced 3D imaging techniques and image segmentation and classification methods can profoundly transform biomedical research by offering deep insights into the cytoarchitecture of the human brain in relation to pathological conditions. Here, we propose a comprehensive pipeline for performing 3D imaging and automated quantitative cellular phenotyping on Formalin-Fixed Paraffin-Embedded (FFPE) human brain specimens, a valuable yet underutilized resource. We exploited the versatility of our method by applying it to different human specimens from both adult and pediatric, normal and abnormal brain regions.

Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.

Cultured meat in the European Union: Legislative context and food safety issues.

The current food system, which is responsible for about one third of all global gas emissions, is considered one of the main causes of resource depletion. For this reason, scientific research is investigating new alternatives capable of feeding an ever-growing population that is set to reach 9-11 billion by 2050. Among these, cell-based meat, also called cultured meat, is one possible solution.

Precision Agriculture Applied to Harvesting Operations through the Exploitation of Numerical Simulation.

When it comes to harvesting operations, precision agriculture needs to consider both combine harvester technology and the precise execution of the process to eliminate harvest losses and minimize out-of-work time. This work aims to propose a complete control framework defined by a two-layer-based algorithm and a simulation environment suitable for quantitative harvest loss, time, and consumption analyses. In detail, the path-planning layer shows suitable harvesting techniques considering field boundaries and irregularities, while the path-tracking layer presents a vision-guided Stanley Lateral Controller.