LAESI: Leaf Area Estimation with Synthetic Imagery

We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resource for leaf morphology analysis primarily aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine learning models for leaf surface area prediction and semantic segmentation, using real images for validation. Our validation shows that these models can be trained to predict leaf surface area with a relative error not greater than an average human annotator. LAESI also provides an efficient framework based on 3D procedural models and generative AI for the large-scale, controllable generation of data with potential further applications in agriculture and biology. We evaluate the inclusion of generative AI in our procedural data generation pipeline and show how data filtering based on annotation consistency results in datasets which allow training the highest performing vision models.Comment: 10 pages, 12 figures, 1 tabl

Evaluation of chronic HCV infection in transplanted livers using a modified histological activity index

The majority of histopathological classifications of primary chronic viral hepatitis and recurrence of HCV infection in liver transplants is based on the histological activity index (HAI) introduced by Knodell et al in 1981; however, correlation between HAI and clinical/laboratory data is poor. Therefore, the aim of this study was to present a modification of HAI (mHAI) adapted to distinct features of graft infection, and to evaluate its usefulness in the description of disease activity

Screening for the Most Suitable Reference Genes for Gene Expression Studies in Equine Milk Somatic Cells

<div><p>Apart from the well-known role of somatic cell count as a parameter reflecting the inflammatory status of the mammary gland, the composition of cells isolated from milk is considered as a valuable material for gene expression studies in mammals. Due to its unique composition, in recent years an increasing interest in mare's milk consumption has been observed. Thus, investigating the genetic background of horse’s milk variability presents and interesting study model. Relying on 39 milk samples collected from mares representing three breeds (Polish Primitive Horse, Polish Cold-blooded Horse, Polish Warmblood Horse) we aimed to investigate the utility of equine milk somatic cells as a source of mRNA and to screen the best reference genes for RT-qPCR using geNorm and NormFinder algorithms. The results showed that despite relatively low somatic cell counts in mare's milk, the amount and the quality of the extracted RNA are sufficient for gene expression studies. The analysis of the utility of 7 potential reference genes for RT-qPCR experiments for the normalization of equine milk somatic cells revealed some differences between the outcomes of the applied algorithms, although in both cases the <i>KRT8</i> and <i>TOP2B</i> genes were pointed as the most stable. Analysis by geNorm showed that the combination of 4 reference genes (<i>ACTB</i>, <i>GAPDH</i>, <i>TOP2B</i> and <i>KRT8</i>) is required for apropriate RT-qPCR experiments normalization, whereas NormFinder algorithm pointed the combination of <i>KRT8</i> and <i>RPS9</i> genes as the most suitable. The trial study of the relative transcript abundance of the beta-casein gene with the use of various types and numbers of internal control genes confirmed once again that the selection of proper reference gene combinations is crucial for the final results of each real-time PCR experiment.</p></div

Results obtained by the geNorm algorithm.

<p>Pairwise variation of relative transcription levels for studied genes (combinations which do not exceed the treshold of V = 0.150 are useful in present RT-qPCR study normalization).</p

The effect of selection of different types and numbers of reference genes on beta-casein <i>(CSN2)</i> gene relative transcription level in equine milk somatic cells.

<p>Reference gene combinations: A − <i>KRT8 + TOP2B + ACTB + GAPDH</i> (the best combination according to geNORM); B − <i>KRT8 + TOP2B +ACTB</i>; C − <i>KRT8 + TOP2B</i>, D − <i>TOP2B + RPS9</i> (the best combination according to NormFinder), E − <i>KRT8</i> (alone—the most stable reference gene according to both algorithms).</p

Primer sequences and cycling details for RT-qPCR analyses.

<p>* all primers are located in the CDS of investigated genes.</p><p>Primer sequences and cycling details for RT-qPCR analyses.</p