Features of the analysis of fixed assets the organization

The article examines trends and analysis tools, plant and equipment company, which is one of the most important factors of any production. The authors emphasized that the state and the effective use of fixed assets has a direct impact on the final results of the organization. During the analysis of fixed assets identified existing problems defined reserves of increase of efficiency of managing. Based on the analysis of the results management solutions are being developed that will improve the results of financial and economic activities of the organization

Features of the analysis of fixed assets the organization

The article examines trends and analysis tools, plant and equipment company, which is one of the most important factors of any production. The authors emphasized that the state and the effective use of fixed assets has a direct impact on the final results of the organization. During the analysis of fixed assets identified existing problems defined reserves of increase of efficiency of managing. Based on the analysis of the results management solutions are being developed that will improve the results of financial and economic activities of the organization

Dickkopf-3 links HSF1 and YAP/TAZ signalling to control aggressive behaviours in cancer-associated fibroblasts

Aggressive behaviours of solid tumours are highly influenced by the tumour microenvironment. Multiple signalling pathways can affect the normal function of stromal fibroblasts in tumours, but how these events are coordinated to generate tumour-promoting cancer-associated fibroblasts (CAFs) is not well understood. Here we show that stromal expression of Dickkopf-3 (DKK3) is associated with aggressive breast, colorectal and ovarian cancers. We demonstrate that DKK3 is a HSF1 effector that modulates the pro-tumorigenic behaviour of CAFs in vitro and in vivo. DKK3 orchestrates a concomitant activation of β-catenin and YAP/TAZ. Whereas β-catenin is dispensable for CAF-mediated ECM remodelling, cancer cell growth and invasion, DKK3-driven YAP/TAZ activation is required to induce tumour-promoting phenotypes. Mechanistically, DKK3 in CAFs acts via canonical Wnt signalling by interfering with the negative regulator Kremen and increasing cell-surface levels of LRP6. This work reveals an unpredicted link between HSF1, Wnt signalling and YAP/TAZ relevant for the generation of tumour-promoting CAFs

Dickkopf-3 links HSF1 and YAP/TAZ signalling to control aggressive behaviours in cancer-associated fibroblasts

Aggressive behaviours of solid tumours are highly influenced by the tumour microenvironment. Multiple signalling pathways can affect the normal function of stromal fibroblasts in tumours, but how these events are coordinated to generate tumour-promoting cancer-associated fibroblasts (CAFs) is not well understood. Here we show that stromal expression of Dickkopf-3 (DKK3) is associated with aggressive breast, colorectal and ovarian cancers. We demonstrate that DKK3 is a HSF1 effector that modulates the pro-tumorigenic behaviour of CAFs in vitro and in vivo. DKK3 orchestrates a concomitant activation of β-catenin and YAP/TAZ. Whereas β-catenin is dispensable for CAF-mediated ECM remodelling, cancer cell growth and invasion, DKK3-driven YAP/TAZ activation is required to induce tumour-promoting phenotypes. Mechanistically, DKK3 in CAFs acts via canonical Wnt signalling by interfering with the negative regulator Kremen and increasing cell-surface levels of LRP6. This work reveals an unpredicted link between HSF1, Wnt signalling and YAP/TAZ relevant for the generation of tumour-promoting CAFs

Automatic deformable registration of histological slides to {μCT} volume {3D}-Data

Localizing a histological section in the three‐dimensional dataset of a different imaging modality is a challenging 2D‐3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground‐truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset

Automatic histology registration in application to X-ray modalities

Registration of microscope images to Computed Tomography (CT) 3D volumes is a challenging task because it requires not only multi-modal similarity measure but also 2D-3D or slice-to-volume correspondence. This type of registration is usually done manually which is very time-consuming and prone to errors. Recently we have developed the first automatic approach to localize histological sections in μCT data of a jaw bone. The median distance between the automatically found slices and the ground truth was below 35 μm. Here we explore the limitations of the method by applying it to three tomography datasets acquired with grating interferometry, laboratory-based μCT and single-distance phase retrieval. Moreover, we compare the performance of three feature detectors in the proposed framework, i.e. Speeded Up Robust Features (SURF), Scale Invariant Feature Transform (SIFT) and Affine SIFT (ASIFT). Our results show that all the feature detectors performed significantly better on the grating interferometry dataset than on other modalities. The median accuracy for the vertical position was 0.06 mm. Across the feature detector types the smallest error was achieved by the SURF-based feature detector (0.29 mm). Furthermore, the SURF-based method was computationally the most efficient. Thus, we recommend to use the SURF feature detector for the proposed framework

Computational cell quantification in the human brain tissues based on hard X-ray phase-contrast tomograms

Cell visualization and counting plays a crucial role in biological and medical research including the study of neurodegenerative diseases. The neuronal cell loss is typically determined to measure the extent of the disease. Its characterization is challenging because the cell density and size already differs by more than three orders of magnitude in a healthy cerebellum. Cell visualization is commonly performed by histology and fluorescence microscopy. These techniques are limited to resolve complex microstructures in the third dimension. Phase-contrast tomography has been proven to provide sufficient contrast in the three-dimensional imaging of soft tissue down to the cell level and, therefore, offers the basis for the three-dimensional segmentation. Within this context, a human cerebellum sample was embedded in paraffin and measured in local phase-contrast mode at the beamline ID19 (ESRF, Grenoble, France) and the Diamond Manchester Imaging Branchline I13-2 (Diamond Light Source, Didcot, UK). After the application of Frangi-based filtering the data showed sufficient contrast to automatically identify the Purkinje cells and to quantify their density to 177 cells per mm3 within the volume of interest. Moreover, brain layers were segmented in a region of interest based on edge detection. Subsequently performed histological analysis validated the presence of the cells, which required a mapping from the two-dimensional histological slices to the three-dimensional tomogram. The methodology can also be applied to further tissue types and shows potential for the computational tissue analysis in health and disease.</p