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We study the evolution of the friendship index in complex social networks over time. The elements of the networks are the users, and the connections correspond to the interactions between them. The friendship index of a node is defined as the ratio of the average degree of its neighbors to the degree of the node itself. Obviously, in the process of network growth, the value of the friendship index for any node in the network may change due to the fact that new edges can be attached to this node or its neighbors. In this paper, we study the dynamics of the friendship index of a single node over time for growth networks generated on the basis of the preferential attachment mechanism. We find both the asymptotics of their expected values and the variances over time. In addition, we analyze the behavior of the friendship index for five real networks. [ABSTRACT FROM AUTHOR]

The umbilical cord is a material that enhances regeneration and is devoid of age-related changes in the extracellular matrix (ECM). The aim of this work was to develop a biodegradable scaffold from a decellularized human umbilical cord (UC-scaffold) to heal full-thickness wounds. Decellularization was performed with 0.05% sodium dodecyl sulfate solution. The UC-scaffold was studied using morphological analysis methods. The composition of the UC-scaffold was studied using immunoblotting and Fourier transform infrared spectroscopy. The adhesion and proliferation of mesenchymal stromal cells were investigated using the LIVE/DEAD assay. The local reaction was determined by subcutaneous implantation in mice (n = 60). A model of a full-thickness skin wound in mice (n = 64) was used to assess the biological activity of the UC-scaffold. The proposed decellularization method showed its effectiveness in the umbilical cord, as it removed cells and retained a porous structure, type I and type IV collagen, TGF-β3, VEGF, and fibronectin in the ECM. The biodegradation of the UC-scaffold in the presence of collagenase, its stability during incubation in hyaluronidase solution, and its ability to swell by 1617 ± 120% were demonstrated. Subcutaneous scaffold implantation in mice showed gradual resorption of the product in vivo without the formation of a dense connective tissue capsule. Epithelialization of the wound occurred completely in contrast to the controls. All of these data suggest a potential for the use of the UC-scaffold. [ABSTRACT FROM AUTHOR]

Articular cartilage damage still remains a major problem in orthopedical surgery. The development of tissue engineering techniques such as autologous chondrocyte implantation is a promising way to improve clinical outcomes. On the other hand, the clinical application of autologous chondrocytes has considerable limitations. Mesenchymal stromal cells (MSCs) from various tissues have been shown to possess chondrogenic differentiation potential, although to different degrees. In the present study, we assessed the alterations in chondrogenesis-related gene transcription rates and extracellular matrix deposition levels before and after the chondrogenic differentiation of MSCs in a 3D spheroid culture. MSCs were obtained from three different tissues: umbilical cord Wharton's jelly (WJMSC—Wharton's jelly mesenchymal stromal cells), adipose tissue (ATMSC—adipose tissue mesenchymal stromal cells), and the dental pulp of deciduous teeth (SHEDs—stem cells from human exfoliated deciduous teeth). Monolayer MSC cultures served as baseline controls. Newly formed 3D spheroids composed of MSCs previously grown in 2D cultures were precultured for 2 days in growth medium, and then, chondrogenic differentiation was induced by maintaining them in the TGF-β1-containing medium for 21 days. Among the MSC types studied, WJMSCs showed the most similarities with primary chondrocytes in terms of the upregulation of cartilage-specific gene expression. Interestingly, such upregulation occurred to some extent in all 3D spheroids, even prior to the addition of TGF-β1. These results confirm that the potential of Wharton's jelly is on par with adipose tissue as a valuable cell source for cartilage engineering applications as well as for the treatment of osteoarthritis. The 3D spheroid environment on its own acts as a trigger for the chondrogenic differentiation of MSCs. [ABSTRACT FROM AUTHOR]

We studied the evolution of complex social networks over time. The elements of the networks are users, and the connections correspond to the interactions between them. At a particular moment in time, each node of a complex network has such characteristics as its degree, as well as the total degree of its neighbors. Obviously, in the process of network growth, these characteristics are constantly changing due to the fact that new edges are attached to this node or its neighbors. In this paper, we study the dynamics of these characteristics over time for networks generated on the basis of a nonlinear preferential attachment mechanism, and we find both the asymptotics of their expected values and the characteristics of their spread around the mean. In addition, we analyze the behavior of these local characteristics for three real social networks. The applicability of the findings to actual problems in the study of social media in the digital humanities is discussed. [ABSTRACT FROM AUTHOR]

Mesenchymal stromal cells (MSCs) have shown a high potential for cartilage repair. Collagen-based scaffolds are used to deliver and retain cells at the site of cartilage damage. The aim of the work was a comparative analysis of the capacity of the MSCs from human adipose tissue to differentiate into chondrocytes in vitro and to stimulate the regeneration of articular cartilage in an experimental model of rabbit knee osteoarthrosis when cultured on microheterogenic collagen-based hydrogel (MCH) and the microparticles of decellularized porcine articular cartilage (DPC). The morphology of samples was evaluated using scanning electron microscopy and histological staining methods. On the surface of the DPC, the cells were distributed more uniformly than on the MCH surface. On day 28, the cells cultured on the DPC produced glycosaminoglycans more intensely compared to the MCH with the synthesis of collagen type II. However, in the experimental model of osteoarthrosis, the stimulation of the cartilage regeneration was more effective when the MSCs were administered to the MCH carrier. The present study demonstrates the way to regulate the action of the MSCs in the area of cartilage regeneration: the MCH is more conducive to stimulating cartilage repair by the MSCs, while the DPC is an inducer for a formation of a cartilage-like tissue by the MSCs in vitro. [ABSTRACT FROM AUTHOR]

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models. [ABSTRACT FROM AUTHOR]