Machine learning-based automated fungal cell counting under a complicated background with ilastik and ImageJ

TRAIP suppresses bladder cancer progression by catalyzing K48-linked polyubiquitination of MYC

TRAF-interacting protein (TRAIP), an E3 ligase containing a RING domain, has emerged as a significant contributor to maintaining genome integrity and is closely associated with cancer. Our study reveals that TRAIP shows reduced expression in bladder cancer (BLCA), which correlates with an unfavorable prognosis. In vitro and in vivo, TRAIP inhibits proliferation and migration of BLCA cells. MYC has been identified as a novel target for TRAIP, wherein direct interaction promotes K48-linked polyubiquitination at neighboring K428 and K430 residues, ultimately resulting in proteasome-dependent degradation and downregulation of MYC transcriptional activity. This mechanism effectively impedes the progression of BLCA. Restoring MYC expression reverses suppressed proliferation and migration of BLCA cells induced by TRAIP. Moreover, our results suggest that MYC may bind to the transcriptional start region of TRAIP, thereby exerting regulatory control over TRAIP transcription. Consequently, this interaction establishes a negative feedback loop that regulates MYC expression, preventing excessive levels. Taken together, this study reveals a mechanism that TRAIP inhibits proliferation and migration of BLCA by promoting ubiquitin-mediated degradation of MYC.

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images

Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review

By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.