We observed differences in lhc classification in Chromista. We proposed a classification of the lhcf family with two groups specific to haptophytes, one specific to diatoms, and one specific to seaweeds. Identification and characterization of the Fucoxanthin and Chlorophyll a/c-binding Protein (FCP) of the haptophyte microalgae Tisochrysis lutea were performed by similarity analysis. The FCP family contains 52 lhc genes in T. lutea. FCP pigment binding site candidates were characterized on Lhcf protein monomers of T. lutea, which possesses at least nine chlorophylls and five fucoxanthin molecules, on average, per monomer. The expression of T. lutea lhc genes was assessed during turbidostat and chemostat experiments, one with constant light (CL) and changing nitrogen phases, the second with a 12 h:12 h sinusoidal photoperiod and changing nitrogen phases. RNA-seq analysis revealed a dynamic decrease in the expression of lhc genes with nitrogen depletion. We observed that T. lutea lhcx2 was only expressed at night, suggesting that its role is to protect \cells from return of light after prolonged darkness exposure.

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

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