Game Representation Research Articles

Brewing Happiness Design Aspects of Coffee Representation in Cozy Games

This paper examines the way coffee is represented and used in cozy games. As a genre, cozy games are defined by the concepts of safety, abundance, and softness, which together establish a sense of coziness. Coffee, commonly associated with casual and friendly social situations, has a potential to be a strong contributor to coziness within cozy games. To explore this, the authors identified 27 cozy games containing coffee-related elements, and then performed a qualitative analysis on their content. The analysis found it was comparatively rare for coffee themes to strongly impact the overall game experience; however, there was a significant tendency for coffee to amplify the game’s overall sense of coziness.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Forms of Representation of Military Culture in Computer Games

The purpose of this article is to identify the forms of representation of military culture in computer games. In the course of the research, six types of interpretations of military culture were presented on the basis of a systematic approach, which made it possible to formulate a more general, broad concept that served as a basis for the subsequent discovery of its aspects and representations in computer games developed since the 2000s. The representation of aspects of military culture in games is reflected in four forms of representation: through game theme, individual game elements and stories, game genre, and game mechanics. The first one takes into account that the military theme is revealed in computer games in several forms of relation to reality: military-historical games; games thematically related to the modern army and modern military conflicts; games modelling military conflicts of the near future; fantasy games representing the army or armed forces of the distant future; fantasy games. The second demonstrates that mechanical elements and plot moves related to military culture can be present in games of almost all genres. The third defines genres related to military culture, the division of which is along the lines of personalized participation in military actions/simulation of a certain command and operation level, and each of them has its own aspects. The distinctive feature of the above forms is a limited representation of military culture, subjective focus on individual aspects. The fourth form of representation assumes a positive forecast of the development of computer games about military culture. It is expected that a simulator of everyday activities of servicemen will appear, which would make it possible to take a new look at military games and reflect the requirements of Russian legislation in the field of defense. This will help, among other things, to shape the directions of the state cultural policy for the development and preservation of the spiritual and moral foundations and traditional values of Russian culture embodied in modern forms and genres of art.

Use of 3D chaos game representation to quantify DNA sequence similarity with applications for hierarchical clustering

A 3D chaos game is shown to be a useful way for encoding DNA sequences. Since matching subsequences in DNA converge in space in 3D chaos game encoding, a DNA sequence’s 3D chaos game representation can be used to compare DNA sequences without prior alignment and without truncating or padding any of the sequences. Two proposed methods inspired by shape-similarity comparison techniques show that this form of encoding can perform as well as alignment-based techniques for building phylogenetic trees. The first method uses the volume overlap of intersecting spheres and the second uses shape signatures by summarizing the coordinates, oriented angles, and oriented distances of the 3D chaos game trajectory. The methods are tested using: (1) the first exon of the beta-globin gene for 11 species, (2) mitochondrial DNA from four groups of primates, and (3) a set of synthetic DNA sequences. Simulations show that the proposed methods produce distances that reflect the number of mutation events; additionally, on average, distances resulting from deletion mutations are comparable to those produced by substitution mutations.

Sequence-Based Analysis of Tyrosinase-Producing Bacteria for Rapid Identification, Differentiation, and Comparison

The present research provides a comprehensive protocol for systematically creating digital data on tyrosinase-producing bacteria isolated from soil. Three tyrosinase-producing bacteria were isolated and identified using the 16S rRNA sequencing method. 16S rRNA sequences were used to generate the Quick Response (QR) codes, Chaos Game Representation (CGR), Chaos Game Representation (FCGR) of Fre¬quency, and Principal Component Analysis (PCA). These QR codes, CGR, FCGR, and PCA, are unique for each strain and allow distinction between those cells isolated from the natural environment. The QR codes, neighbor-joining plot, CGR, FCGR, and PCA of tyrosinase producers were created by us in-house to ena¬ble us to compare similar bacterial taxa. The generated digital data acts as an additional confirmatory tool to evaluate and compare any tyrosinase-producing bacteria. This helps to support existing identification, differentiation, and evaluation and reduces time and cost for the comparison of sequence patterns.

Correction to: A two-player game representation for a class of infinite horizon control problems under state constraints

Correction to: A two-player game representation for a class of infinite horizon control problems under state constraints

Genome analysis through image processing with deep learning models.

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

Revealing miRNAs patterns by employing matrix representations and energy analysis

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression. Despite their relatively short length (about 21 nucleotides), they can regulate thousands of transcripts within a cell. Due to their low complementarity to targets, studying their activity and binding region preferences (3′UTR, 5′UTR, or CDS) is challenging. In this paper, we analyzed a set of human miRNAs to uncover their general patterns. We began with a sequence logo to verify conservation at specific positions. To discover long-range correlations, we employed chaos game representation (CGR) and genomatrix, methods that enable both graphical and analytical analysis of sequence sets and are well-established in bioinformatics. Our results showed that miRNAs exhibit strongly non-random and characteristic patterns. To incorporate physicochemical properties into the analysis, we applied the electron-ion interaction potential (EIIP) parameter. An important part of our study was to validate the division of miRNAs into two parts—seed and puzzle. The seed region is responsible for target binding, while the puzzle region likely interacts with the RISC complex. We estimated duplex binding energy within the 3′UTR, 5′UTR, and CDS regions using the miRanda tool. Based on the median energy distribution, we divided the miRNAs into two subsets, reflecting different patterns in chaos game representation. Interestingly, one subset displayed significant similarity to conserved and highly confidential miRNAs. Our results confirm the low complementarity of miRNA/mRNA interactions and support the functional division of miRNA structure. Additionally, we present findings related to the localization of transcript target sites, which form the basis for further analyses.

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model's accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE's efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

Exploring geometry of genome space via Grassmann manifolds

It is important to understand the geometry of genome space in biology. After transforming genome sequences into frequency matrices of the chaos game representation (FCGR), we regard a genome sequence as a point in a suitable Grassmann manifold by analyzing the column space of the corresponding FCGR. To assess the sequence similarity, we employ the generalized Grassmannian distance, an intrinsic geometric distance that differs from the traditional Euclidean distance used in the classical k-mer frequency-based methods. With this method, we constructed phylogenetic trees for various genome datasets, including influenza A virus hemagglutinin gene, Orthocoronavirinae genome, and SARS-CoV-2 complete genome sequences. Our comparative analysis with multiple sequence alignment and alignment-free methods for large-scale sequences revealed that our method, which employs the subspace distance between the column spaces of different FCGRs (FCGR-SD), outperformed its competitors in terms of both speed and accuracy. In addition, we used low-dimensional visualization of the SARS-CoV-2 genome sequences and spike protein nucleotide sequences with our methods, resulting in some intriguing findings. We not only propose a novel and efficient algorithm for comparing genome sequences but also demonstrate that genome data have some intrinsic manifold structures, providing a new geometric perspective for molecular biology studies.

Women's representation in surf video games: A stereotyped, objectified and romanticized portrayal

This paper examines the relationship between the video game industry and sports. These two disciplines traditionally contribute to the perpetuation of stereotypes that create a reality around women based on inappropriate assumptions, which are generally interpreted from a masculinized perspective. Taking surfing as an example, a sport associated with a markedly masculine environment, this paper aims to investigate how women surfers are portrayed in order to show how the representations of women's identity as surfers are represented in video games. A mixed method is proposed which aims to gather information from two distinct perspectives: that of the participant and the communicative aspect. The analyzed video game highlights a masculinized, sexualized and romanticized stereotype of women. It is emphasized that a specific style of women surfers is characterized by an objectified character stripped of any discernable sports-related attributes, thereby establishing a potential relationship between the representation of women in the video game and its influence on women's identity as sports subjects. The gender gap between men and women extends beyond the field of sports, causing a substantial impact on both industries.

On leveraging self-supervised learning for accurate HCV genotyping

Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.

CapsEnhancer: An Effective Computational Framework for Identifying Enhancers Based on Chaos Game Representation and Capsule Network.

Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification of enhancers holds paramount importance in the field of biology. However, traditional experimental methods for enhancer identification demand substantial human and material resources. Consequently, there is a growing interest in employing computational methods for enhancer prediction. In this study, we propose a two-stage framework based on deep learning, termed CapsEnhancer, for the identification of enhancers and their strengths. CapsEnhancer utilizes chaos game representation to encode DNA sequences into unique images and employs a capsule network to extract local and global features from sequence "images". Experimental results demonstrate that CapsEnhancer achieves state-of-the-art performance in both stages. In the first and second stages, the accuracy surpasses the previous best methods by 8 and 3.5%, reaching accuracies of 94.5 and 95%, respectively. Notably, this study represents the pioneering application of computer vision methods to enhancer identification tasks. Our work not only contributes novel insights to enhancer identification but also provides a fresh perspective for other biological sequence analysis tasks.

РЕПРЕЗЕНТАЦІЇ ІГРОВОГО МОДЕЛЮВАННЯ ЖИТТЄДІЯЛЬНОСТІ ЛЮДИНИ В УМОВАХ НЕВИЗНАЧЕНОСТІ

Purpose. Features of modernity that correspond to the features of a world dominated by volatility, uncertainty, complexity and ambiguity, threats of using such features as means of geopolitical influence, conditions of a full-scale russian-ukrainian war, the necessity restoration for a safe environment throughout the territory of Ukraine requires effective proposals from domestic psychology to ensure human life in the given conditions. A well-founded system-forming position for this increasingly appears as a game as a universal socio-cultural phenomenon and an integral factor of human self-realization, and game modeling as a way and method of human life activity in uncertainty. The purpose of the article is a theoretical study of the psychological regularities of game representation in the aspects of modeling human life, substantiation of psychological content, functional essence, classification of representations as an attributive factor of game modeling of human life in uncertainty conditions. Methods. The research uses methods of analysis and systematization for psychological and interdisciplinary approaches in the study of representation and modeling of human life activities, theoretical reconstruction and classification of the psychological content and functional essence for the game modeling representation of human life activities in current living conditions. Results. Based on the results of the research, the categorical and conceptual content and functional essence for game modeling representations of human life activity in uncertainty conditions were specified. The attribution of representation in the game modeling aspects is updated and substantiated. The definition of "game modeling representations of human life activity in uncertainty conditions" and the corresponding classification are proposed. Scientific ideas about representation, game and game modeling are deepened. It was established that the game modeling representations of human life activity in uncertainty conditions is a universal, pragmatic, attributive phenomenon; non-specific mental formation; the way of life of a person and a social group, which occurs simultaneously in two complementary dimensions: a) the intrasubjective space of a person as an interiorizational mental representation and b) in the external activity dimension as a deed, activity representation of exteriorization. Conclusions. The results of the study allow us to draw conclusions about the relevance of continuing psychological explorations for game modeling representations of human life activity in uncertainty conditions. The clarifying regularities tasks of influence for representations on psychological well-being and productivity of individual and group activities are considered promising.

Gender perspectives on educational contributions to the study of video-gaming: A baseline feminist genealogy

As video games have evolved, they have emerged as useful tools in areas such as education, tackling global issues through their storylines and mechanics. However, never in the history of video games, not even today, has there been parity in the proportion of women directly employed in the development of video games. This paper is part of a broader research project undertaken in the context of a doctoral thesis within the framework of the R+D+I project ‘Building global citizenship with young people: researching transformative practices with participatory and inclusive methodologies’. It provides a historical perspective on video games and their place in culture and society, and attempts to outline a brief genealogy of the contributions of pioneering women in the video game industry. Recognising the work of women and addressing gender representation in video games is now a matter of great importance. Current trends in the video game industry reflect the impact of the pandemic on video game consumption habits, its expected continued growth in the future and its relationship with the development of virtual and augmented reality. Video games are therefore likely to remain an important part of culture and society in the future, with an ever-increasing role in education. It is therefore imperative to showcase the achievements of women in the video game industry, to highlight inequalities and to provide girls with genuine role models.

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Multifractal Properties of Human Chromosome Sequences

The intricacy and fractal properties of human DNA sequences are examined in this work. The core of this study is to discern whether complete DNA sequences present distinct complexity and fractal attributes compared with sequences containing exclusively exon regions. In this regard, the entire base pair sequences of DNA are extracted from the NCBI (National Center for Biotechnology Information) database. In order to create a time series representation for the base pair sequence {G,C,T,A}, we use the Chaos Game Representation (CGR) approach and a mapping rule f, which enables us to apply the metric known as the Complexity–Entropy Plane (CEP) and multifractal detrended fluctuation analysis (MF-DFA). To carry out our investigation, we divided human DNA into two groups: the first is composed of the 24 chromosomes, which comprises all the base pairs that form the DNA sequence, and another group that also includes the 24 chromosomes, but the DNA sequences rely only on the exons’ presence. The results show that both sets provide fractal patterns in their structure, as obtained by the CGR approach. Complete DNA sequences show a sharper visual fractal pattern than sequences composed only of exons. Moreover, the sequences occupy distinct areas of the complexity–entropy plane, and the complete DNA sequences lead to greater statistical complexity and lower entropy than the exon sequences. Also, we observed that different fractal parameters between chromosomes indicate diversity in genomic sequences. All these results occur in different scales for all chromosomes.

The Development of Video Game Representations of the Middle East

This article critically explores the historical representation of the Middle East in video games until 2002 and following the 9/11 event. Using thematic analysis, we identify four main themes and discuss how this representation shifts with time, offering a brief overview of the broader context. I argue that there is a clear association between political developments and this western representation, for we can clearly observe changes from the romantic, exotic, and orientalist views of the region and Arab and Muslim figure shown within the realms of the Arabian Nights theme to a more militant figure and dangerous place due to featuring armed conflict in the Middle East such as the first Gulf War. Though not included in the discussion, 9/11 introduced a more negative and Islamophobic representation that is associated with Islamist terrorism, and it marks the latest development in these themes.

Lagrangian, game theoretic, and PDE methods for averaging G-equations in turbulent combustion: existence and beyond

G-equations are popular level set Hamilton–Jacobi nonlinear partial differential equations (PDEs) of first or second order arising in turbulent combustion. Characterizing the effective burning velocity (also known as the turbulent burning velocity) is a fundamental problem there. We review relevant studies of the G-equation models with a focus on both the existence of effective burning velocity (homogenization), and its dependence on physical and geometric parameters (flow intensity and curvature effect) through representative examples. The corresponding physical background is also presented to provide motivations for mathematical problems of interest. The lack of coercivity of Hamiltonian is a hallmark of G-equations. When either the curvature of the level set or the strain effect of fluid flows is accounted for, the Hamiltonian becomes highly nonconvex and nonlinear. In the absence of coercivity and convexity, the PDE (Eulerian) approach suffers from insufficient compactness to establish averaging (homogenization). We review and illustrate a suite of Lagrangian tools, most notably min-max (max-min) game representations of curvature and strain G-equations, working in tandem with analysis of streamline structures of fluid flows and PDEs. We discuss open problems for future development in this emerging area of dynamic game analysis for averaging noncoercive, nonconvex, and nonlinear PDEs such as geometric (curvature-dependent) PDEs with advection.

Multifractal analysis of maize and soybean DNA

This paper investigates the complexity of DNA sequences in maize and soybean using the multifractal detrended fluctuation analysis (MF-DFA) method, chaos game representation (CGR), and the complexity-entropy plane approach. The study aims to understand the patterns and structures of these DNA sequences, which can provide insights into their genetic makeup and improve crop yield and quality. The results show that maize and soybean DNA sequences exhibit fractal properties, indicating a complex and self-organizing structure. We observe the persistence trend between sequences of base pairs, which indicates long-range correlations between base pairs. We also identified the stochastic nature of the DNA sequences of both species.