Recently, the analysis of cellular images, particularly the assessment of neurite activity, has gained increasing significance in the study of neurodegenerative diseases, including Alzheimer’s disease. This study introduces an automated analysis approach that focuses on neurite activity through the application of cellular segmentation techniques to bright-field images of neurons. This study proposes a method for treating individual cell instances as graphs consisting of nodes and edges. Furthermore, this study suggests a quantitative assessment for precisely identified neurites through the definition of several evaluation metrics. This approach enables the fast and objective automated analysis of bright-field images focused on neurons. In a variety of experiments, the precision of our proposed method was verified through a comparative analysis by comparing the results to manual analysis data using ImageJ for measuring the neurite length of rat adrenal pheochromocytoma PC12 cells. The findings revealed that the average discrepancy in the length of neurites is only 4.387 μm, highlighting the high level of accuracy in our method’s ability to detect neurites, which is almost on par with manual analysis. This observation holds significance in analytical applications pertinent to Alzheimer’s disease.

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (g_max) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.

When analyzing microscopic time-lapse observations, frame alignment is an essential task to visually understand the morphological and translation dynamics of cells and tissue. While in traditional single-sample microscopy, the region of interest (RoI) is fixed, multi-sample microscopy often uses a single microscope that scans multiple samples over a long period of time by laterally relocating the sample stage. Hence, the relocation of the optics induces a statistical RoI offset and can introduce jitter as well as drift, which results in a misaligned RoI for each sample’s time-lapse observation (stage drift). We introduce a robust approach to automatically align all frames within a time-lapse observation and compensate for frame drift. In this study, we present a sub-pixel precise alignment approach based on recurrent all-pairs field transforms (RAFT); a deep network architecture for optical flow. We show that the RAFT model pre-trained on the Sintel dataset performed with near perfect precision for registration tasks on a set of ten contextually unrelated time-lapse observations containing 250 frames each. Our approach is robust for elastically undistorted and translation displaced (x,y) microscopic time-lapse observations and was tested on multiple samples with varying cell density, obtained using different devices. The approach only performed well for registration and not for tracking of the individual image components like cells and contaminants. We provide an open-source command-line application that corrects for stage drift and jitter.

We present a new flare star, which was discovered during our survey on a selected field at the edge of the Pleiades cluster. The field was observed in the period 2007–2010 with three different CCD-cameras at the University Observatory Jena with telescopes from 25 to 90 cm. The flare duration is almost one hour with an amplitude in the R-band of about 1.08 mag. The location of the flare star in a color-magnitude diagram is consistent with age and distance of the Pleiades. In the optical PSF of the flare star there are two 2MASS objects (unresolved in most images in the optical Jena PSF), so it is not yet known which one of them is responsible for this flare. The BVRIJHK colors yield spectral types of M1 and M2 with extinction being A_v = 0.231 ± 0.024 mag and A_v = 0.266 ± 0.020 for those two stars, consistent with the Pleiades cluster (© 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)