Gut Microbiota–Medication Interaction in Rheumatic Diseases

Besides its contribution to the development of rheumatic diseases, the gut microbiota interact with anti-rheumatic drugs. The intestinal microbiota can directly metabolize many drugs and indirectly change drug metabolism through a complex multi-dimensional interaction with the host, thus affecting individual response to drug therapy and adverse effects. The focus of the current review is to address recent advances and important progress in our understanding of how the gut microbiota interact with anti-rheumatic drugs and provide perspectives on promoting precision treatment, drug discovery, and better therapy for rheumatic diseases.

Stereotypical Processing of Emotional Faces: Perceptual and Decisional Components

People tend to associate anger with male faces and happiness or surprise with female faces. This angry-men-happy-women bias has been ascribed to either top-down (e.g., well-learned stereotypes) or bottom-up (e.g., shared morphological cues) processes. The dissociation between these two theoretical alternatives has proved challenging. The current effort addresses this challenge by harnessing two complementary metatheoretical approaches to dimensional interaction: Garner's logic of inferring informational structure and General Recognition Theory—a multidimensional extension of signal detection theory. Conjoint application of these two rigorous methodologies afforded us to: (a) uncover the internal representations that generate the angry-men-happy-women phenomenon, (b) disentangle varieties of perceptual (bottom-up) and decisional (top-down) sources of interaction, and (c) relate operational and theoretical meanings of dimensional independence. The results show that the dimensional interaction between emotion and gender is generated by varieties of perceptual and decisional biases. These outcomes document the involvement of both bottom-up (e.g., shared morphological structures) and top-down (stereotypes) factors in social perception.

Residual Triplet Attention Network for Single-Image Super-Resolution

Single-image super-resolution (SISR) techniques have been developed rapidly with the remarkable progress of convolutional neural networks (CNNs). The previous CNNs-based SISR techniques mainly focus on the network design while ignoring the interactions and interdependencies between different dimensions of the features in the middle layers, consequently hindering the powerful learning ability of CNNs. In order to address this problem effectively, a residual triplet attention network (RTAN) for efficient interactions of the feature information is proposed. Specifically, we develop an innovative multiple-nested residual group (MNRG) structure to improve the learning ability for extracting the high-frequency information and train a deeper and more stable network. Furthermore, we present a novel lightweight residual triplet attention module (RTAM) to obtain the cross-dimensional attention weights of the features. The RTAM combines two cross-dimensional interaction blocks (CDIBs) and one spatial attention block (SAB) base on the residual module. Therefore, the RTAM is not only capable of capturing the cross-dimensional interactions and interdependencies of the features, but also utilizing the spatial information of the features. The simulation results and analysis show the superiority of the proposed RTAN over the state-of-the-art SISR networks in terms of both evaluation metrics and visual results.

Multimodal Tucker Decomposition for Gated RBM Inference

Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional distribution of one image (the output) given another image (the input). This allows the hidden units of a gated RBM to model the transformations between two successive images. Inference in the model consists in extracting the transformations given a pair of images. However, a fully connected multiplicative network creates cubically many parameters, forming a three-dimensional interaction tensor that requires a lot of memory and computations for inference and training. In this paper, we parameterize the bilinear interactions in the gated RBM through a multimodal tensor-based Tucker decomposition. Tucker decomposition decomposes a tensor into a set of matrices and one (usually smaller) core tensor. The parameterization through Tucker decomposition helps reduce the number of model parameters, reduces the computational costs of the learning process and effectively strengthens the structured feature learning. When trained on affine transformations of still images, we show how a completely unsupervised network learns explicit encodings of image transformations.

HISTORICAL ASPECTS OF FORMATION AND DEVELOPMENT OF CORPORATE SOCIAL RESPONSIBILITY IN UKRAINE AND THE WORLD

The article analyzes the main stages of development of corporate social responsibility in the world. Stages of formation of social responsibility in Ukraine from Kievan Rus are highlighted, where social responsibility was manifested as patronage and charity, which in our opinion is a manifestation of the national mentality of the population before the establishment of the Center for Corporate Social Responsibility. as well as to properly compile non-financial reports which contain information about social and environmental programs of companies. The relevance of the chosen topic is justified by the fact that today corporate social responsibility is considered not only from a theoretical perspective but also from a clear effective practical. The introduction of corporate social responsibility in enterprises is the basis for effective business development. In order to meet the high demands of society, the company must address important social issues in the long run with clearly defined goals, objectives and results to be achieved. The most important competitive advantage of enterprises is CSR, which will improve the reputation of enterprises, which will lead to increased financial performance. The article proposes to consider corporate social responsibility as a three - dimensional interaction of business - state - society. However, it is business that must assume the role of a leading link in the mechanism of coordination in the economy and society of individual and group interests. Every corporation must be responsible for the results of its activities, to employees, partners, shareholders, stakeholders and society as a whole. Examining the history of corporate social responsibility, we can conclude that it has slowly evolved over time and has become part of the daily vocabulary only in the last decade. Today, much attention is paid worldwide to the development of corporate social responsibility, because it is not only economic prosperity but also preparedness for crises that occur around the world. Ukrainian business is ready to implement the concepts of corporate social responsibility, which should be regulated by state legislation.

Research on the Application Strategy of Mixed Teaching Mode of Visual Communication Design Specialty in Colleges and Universities Based on Multidimensional Interaction

With the change of teaching methods and the continuous advancement of educational reform, blended teaching has become a new trend in higher education. The application of network information technology in classroom has changed the traditional learning environment, teaching structure and learning methods, and improved teaching efficiency. In the information age, the talent training of visual communication design major must keep pace with the times, aiming at training innovative talents and practical talents. According to this goal, this paper puts forward a mixed teaching mode based on multi-dimensional interaction and massive open online course platform to make up for the shortcomings in current teaching. It has a certain guiding effect on the practical teaching of visual communication design specialty.

Direct Annihilation Position Classification Based on Deep Learning Using Paired Cherenkov Detectors: A Monte Carlo Study

To apply deep learning to estimate the three-dimensional interaction position of a Cherenkov detector, an experimental measurement of the true depth of interaction is needed. This requires significant time and effort. Therefore, in this study, we propose a direct annihilation position classification method based on deep learning using paired Cherenkov detectors. The proposed method does not explicitly estimate the interaction position or time-of-flight information and instead directly estimates the annihilation position from the raw data of photon information measured by paired Cherenkov detectors. We validated the feasibility of the proposed method using Monte Carlo simulation data of point sources. A total of 125 point sources were arranged three-dimensionally with 5 mm intervals, and two Cherenkov detectors were placed face-to-face, 50 mm apart. The Cherenkov detector consisted of a monolithic PbF2 crystal with a size of 40 × 40 × 10 mm3 and a photodetector with a single photon time resolution (SPTR) of 0 to 100 picosecond (ps) and readout pitch of 0 to 10 mm. The proposed method obtained a classification accuracy of 80% and spatial resolution with a root mean square error of less than 1.5 mm when the SPTR was 10 ps and the readout pitch was 3 mm.