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In dairy cattle research, in vitro assessment of innate immune function is commonly evaluated by flow cytometry via the quantitative analysis of circulating polymorphonuclear leukocytes (PMN) functionalities specifically focusing on the capacities for phagocytosis (PC) and oxidative burst (OB). Variations in these PMN functions, however, may not only be influenced by the health status of the animals but also by technical, non-animal related factors. Our objectives were to assess the PMN viability, PC and OB capacities from blood samples collected in tubes coated with different anticoagulants (acid citrate dextrose (ACD) and ethylenediaminetetraacetic acid (EDTA)) and stored for 0, 3, 6, 9, and 12 h at 4°C (to mimic transportation timeframe). Furthermore, we evaluated the PMN functionalities (PC and OB) in samples incubated in culture medium with glucose (7.2 mM) versus no glucose. Over five replicates, coccygeal blood samples were collected from three nulliparous Holstein heifers (5 ACD and 5 EDTA per heifer) and allocated in a refrigerated container (4°C) for 0, 3, 6, 9, and 12 h. At each time point, PMN were isolated using gradient centrifugation. Immunolabeled PMN (CH138A) were subjected to a tricolor fluorescent staining to evaluate their viability (viable, apoptotic, and necrotic PMN). Phagocytosis and OB were assessed by incubating PMN with fluorescent beads and by phorbol 12-myristate 13-acetate stimulation, respectively. The effects of anticoagulant type, storage time, and presence of glucose in the culture medium on PMN viability and function parameters were fitted in mixed linear regression models. The proportion of viable PMN at 0 h was similar for ACD and EDTA (92 ± 4.6% and 93 ± 4.6%, respectively) but it decreased to 78 ± 4.6% for ACD and 79 ± 4.6% for EDTA after 6 h of storage. The proportion of viable PMN was not different between ACD and EDTA at any time point. The proportion of PMN that engulfed beads (PC percentage) and the PC median fluorescence intensity (MFI) reached their highest value after 3 h of storage compared with the other time points. However, the anticoagulant type (ACD versus EDTA) and the presence of glucose in the culture medium did not influence these PC parameters. Oxidative burst MFI was higher in PMN incubated in glucose-supplemented culture medium versus no glucose. We demonstrated that technical factors interfere with the evaluation of PMN viability and functionality, which can potentially lead to bias in the findings of a research hypothesis. To conclude, the present study showed that the optimal timeframe for performing PMN function analyses is within 3 hours after blood sampling. Furthermore, the presence of 7.2 mM glucose in the culture medium, a common concentration in formulation of cell culture medium, increases the in vitro OB capacity, potentially masking any impairments in in vivo PMN dysfunctionality. [ABSTRACT FROM AUTHOR]

The kernel graph cut method provides good performance in image segmentation. However, its efficiency strongly depends on the data term and regularization term in the objective function. The data term maps the standard deviation of the data of each region to the transformation space. The regularization term is responsible for smoothing the boundaries. The regularization term, based on a constant coefficient, is added to the data term. Allocating a fixed coefficient for all images leads to inappropriate image segmentation. In this regard, we deal with the automatic adjustment of the regularization term coefficient in the kernel graph cut method based on image energy. This approach is useful in image segmentation with thin structures. The laboratory results were taken on three datasets: fingerprint, coronary artery, and real. The efficiency evaluation of the algorithm, compared with other methods in the field of energy-based algorithms, shows a maximum score of 88.27 in Dice, 79.67 in Jaccard, 75.67 in accuracy, 61.06 in peak-signal-to noise ratio, and a minimum score of 0.06 in mean sum of squared distance on the mentioned datasets. The proposed method also takes advantage of appropriate computing load in the methods based on a graph cut. [ABSTRACT FROM AUTHOR]

Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]