Search Results (47)

Serious games are an important tool for education and training that offers interactive and powerful experience. However, a significant challenge lays with adapting a game to meet the specific needs of each player in real-time. The present paper introduces a framework to guide the development of serious games using a phased approach. The framework introduces a level of standardization for the game elements, scenarios and data descriptions, mainly to support portability, interpretability and comprehension. This standardization is achieved through semantic annotation and it is utilized by digital twins to support self-adaptation. The proposed approach describes the game environment using ontologies and specific semantic structures, while it collects and semantically tags data during players’ interactions, including performance metrics, decision-making patterns and levels of engagement. This information is then used by a digital twin for automatically adjusting the game experience using a set of rules defined by a group of domain experts. The framework thus follows a hybrid approach, combing expert knowledge with automated adaptation actions being performed to ensure meaningful educational content delivery and flexible, real-time personalization. Real-time adaptation includes modifying the game’s level of difficulty, controlling the learning ability support and maintaining a suitable level of challenge for each player based on progress. The framework is demonstrated and evaluated using two real-word examples, the first targeting at supporting the education of children with syndromes that affect their learning abilities in close collaboration with speech therapists and the second being involved with training engineers in a poultry meat factory. Preliminary, small-scale experimentation indicated that this framework promotes personalized and dynamic user experience, with improved engagement through the adjustment of gaming elements in real-time to match each player’s unique profile, actions and achievements. Using a specially prepared questionnaire the framework was evaluated by domain experts that suggested high levels of usability and game adaptation. Comparison with similar approaches via a set of properties and features indicated the superiority of the proposed framework. Full article

With the rapid development of sports analytics and artificial intelligence, accurate human pose estimation in badminton is becoming increasingly important. However, challenges such as the lack of domain-specific datasets and the complexity of athletes’ movements continue to hinder progress in this area. To address these issues, we propose an enhanced pose estimation framework tailored to badminton players, built upon an improved YOLOv8-Pose architecture. In particular, we introduce an efficient local attention (ELA) mechanism that effectively captures fine-grained spatial dependencies and contextual information, thereby significantly improving the keypoint localization accuracy and overall pose estimation performance. To support this study, we construct a dedicated badminton pose dataset comprising 4000 manually annotated samples, captured using a Microsoft Kinect v2 camera. The raw data undergo careful processing and refinement through a combination of depth-assisted annotation and visual inspection to ensure high-quality ground truth keypoints. Furthermore, we conduct an in-depth comparative analysis of multiple attention modules and their integration strategies within the network, offering generalizable insights to enhance pose estimation models in other sports domains. The experimental results show that the proposed ELA-enhanced YOLOv8-Pose model consistently achieves superior accuracy across multiple evaluation metrics, including the mean squared error (MSE), object keypoint similarity (OKS), and percentage of correct keypoints (PCK), highlighting its effectiveness and potential for broader applications in sports vision tasks. Full article

The heavy metal-associated (HMA) domain-harboring proteins constitute critical players involved in the transport of various metal ions in plants, and are associated with development and stress responses. Herein, a total of 243 TaHMA genes were identified in the bread wheat genome, each of which had a characteristic molecular profile and a distinct chromosomal localization. The TaHMA proteins were distributed in five clades in phylogeny, which differed with respect to the distribution of the key HMA domain. Sub-cellular localization was variable for the TaHMA proteins. Gene structure analysis yielded similar results when compared with the orthologous counterparts. Cis-regulatory element analysis produced a range of promoter elements, suggesting their diverse biological roles. Gene duplication analysis revealed a crucial role played by tandem and segmental duplication events in the expansion of TaHMA genes. Synteny analysis highlighted the evolutionary relatedness of TaHMA genes with those derived from Arabidopsis and rice. Expression analysis provided crucial information about the role of TaHMAs in mediating vital responses in the plant body, including the development of tissues and the regulation of various abiotic stress conditions. Overall, the study provides significant cues and evidence to functionally annotate and characterize the differentially expressed TaHMAs in order to validate their role. Full article

Background/Objectives: Breast cancer continues to be a major global health challenge, driving the urgent need for innovative therapeutic strategies. This study evaluates the anticancer and immunomodulatory potential of skimmianine in breast cancer through a comprehensive approach, integrating biochemical, histopathological, immunohistochemical, and bioinformatics analyses. Methods: Thirty-six female Wistar albino rats were divided into three groups: control, 7,12-dimethylbenz[a]anthracene (DMBA)-induced breast cancer, and DMBA + skimmianine (n = 12/group). Breast cancer was induced with a single oral dose of 50 mg/kg DMBA in sesame oil. After 16 weeks, skimmianine (40 mg/kg) was administered intraperitoneally for four weeks. Serum CA15-3 levels were measured via enzyme-linked immunosorbent assay (ELISA). Histopathological assessment was performed using hematoxylin and eosin (H&E) staining, and proliferating cell nuclear antigen (PCNA) and tumor necrosis factor-alpha (TNF-α) were evaluated immunohistochemically. Pathway and hub gene analyses were performed using Cytoscape, functional annotation with Enrichr, and immune analyses via the Tumor and Immune System Interaction Database (TISIDB) and Sangerbox. Results: The tumor burden in the animals increased after DMBA induction compared to the control groups (0.00 ± 0.00% vs. 89.00 ± 6.60%, respectively, p < 0.001), while skimmianine treatment significantly reduced the tumor burden in the animals (49.00 ± 9.40%, vs. DMBA group, p = 0.191). Histopathological analysis showed DMBA-induced structural disorganization and malignant clustering, whereas skimmianine preserved ductal structures and mitigated the damage. Compared to the control group, DMBA administration markedly elevated serum CA15-3 levels (0.23 ± 0.06 ng/mL vs. 8.57 ± 1.01 ng/mL, respectively), along with PCNA (13.0 ± 3.0% vs. 25.0 ± 4.0%, respectively) and TNF-α (8.4 ± 1.7% vs. 34.0 ± 5.3%, respectively) expression, indicating active tumor progression. Skimmianine treatment significantly reduced CA15-3 (3.72 ± 0.58 ng/mL), PCNA (20.0 ± 4.1%), and TNF-α (25.0 ± 3.9%) levels (p < 0.001). In silico analyses indicated skimmianine’s effects on PCNA influence cell cycle pathways, while TNF-α suppression impacts toll-like receptor (TLR) signaling (adjusted p < 0.05). PCNA- and TNF-α-related anticancer effects were especially notable in basal molecular and C2 immune subtypes (p < 0.05). Related hub proteins may regulate immune dynamics by reducing immunosuppression and tumor-promoting inflammation (p < 0.05). Conclusions: Skimmianine shows promise as a breast cancer therapy by simultaneously targeting tumor growth and immune regulation, with PCNA and TNF-α identified as potential key players. Full article

In the era of digital advancement, the integration of Deep Learning (DL) algorithms is revolutionizing performance monitoring in football. Due to restrictions on monitoring devices during games to prevent unfair advantages, coaches are tasked to analyze players’ movements and performance visually. As a result, Computer Vision (CV) technology has emerged as a vital non-contact tool for performance analysis, offering numerous opportunities to enhance the clarity, accuracy, and intelligence of sports event observations. However, existing CV studies in football face critical challenges, including low-resolution imagery of distant players and balls, severe occlusion in crowded scenes, motion blur during rapid movements, and the lack of large-scale annotated datasets tailored for dynamic football scenarios. This review paper fills this gap by comprehensively analyzing advancements in CV, particularly in four key areas: player/ball detection and tracking, motion prediction, tactical analysis, and event detection in football. By exploring these areas, this review offers valuable insights for future research on using CV technology to improve sports performance. Future directions should prioritize super-resolution techniques to enhance video quality and improve small-object detection performance, collaborative efforts to build diverse and richly annotated datasets, and the integration of contextual game information (e.g., score differentials and time remaining) to improve predictive models. The in-depth analysis of current State-Of-The-Art (SOTA) CV techniques provides researchers with a detailed reference to further develop robust and intelligent CV systems in football. Full article

Mammalian fertilization is a complex and highly regulated process that has garnered significant attention, particularly with advancements in assisted reproductive technologies such as in vitro fertilization (IVF). The fusion of egg and sperm involves a sequence of molecular and cellular events, including capacitation, the acrosome reaction, adhesion, and membrane fusion. Critical genetic factors, such as IZUMO1, JUNO (also known as FOLR4), CD9, and several others, have been identified as essential mediators in sperm–egg recognition and membrane fusion. Additionally, glycoproteins such as ZP3 within the zona pellucida are crucial for sperm binding and triggering the acrosome reaction. Recent gene-editing technologies, such as CRISPR/Cas9 and conditional knockout models, have facilitated the functional annotation of genes such as SPAM1 and ADAM family members, further elucidating their roles in capacitation and adhesion. Furthermore, the integration of CRISPR-Cas9 with omics technologies, including transcriptomics, proteomics, and lipidomics, has unlocked new avenues for identifying previously unknown genetic players and pathways involved in fertilization. For instance, transcriptomics can uncover gene expression profiles during gamete maturation, while proteomics identifies key protein interactions critical for processes such as capacitation and the acrosome reaction. Lipidomics adds another dimension by revealing how membrane composition influences gamete fusion. Together, these tools enable the discovery of novel genes, pathways, and molecular mechanisms involved in fertility, providing insights that were previously unattainable. These approaches not only deepen our molecular understanding of fertility mechanisms but also hold promise for refining diagnostic tools and therapeutic interventions for infertility. This review summarizes the current molecular insights into genes orchestrating fertilization and highlights cutting-edge methodologies that propel the field toward novel discoveries. By integrating these findings, this review aims to provide valuable knowledge for clinicians, researchers, and technologists in the field of reproductive biology and assisted reproductive technologies. Full article

This study selected 27 soil samples from four representative horizontally distributed onshore oilfields in China to explore the diversity of soil microbial communities and their carbon fixation capacity, with a focus on the potential interaction between pollution and carbon fixation under oil pollution stress. The analysis of the soil physicochemical properties and microbial community structures from these oilfield samples confirmed a clear biogeographic isolation effect, indicating spatial heterogeneity in the microbial communities. Additionally, the key factors influencing microbial community composition differed across regions. The dominant bacterial phyla of soil microorganisms under soil pollution stress were Proteobacteria, Actinobacteriota, Chloroflexi, Acidobacteriota, Firmicutes, Bacteroidota, and Gemmatimonadota. A correlation network analysis identified Immundisolibacter, Acinetobacter, Blastococcus, Truepera, and Kocuria as key players in the microbial network, with most showing positive correlations. The results of the KEGG database functional annotation showed that degradation and carbon fixation metabolic pathways coexist in soil samples and maintain a balanced relative abundance. These metabolic pathways highlight the functional diversity of microorganisms. Among them, prokaryotic and eukaryotic carbon fixation pathways, along with benzoate degradation pathways, are predominant. These findings establish a theoretical basis for further exploration of the synergistic mechanisms underlying pollution reduction and carbon sequestration by microorganisms in petroleum-contaminated soils. Full article

Background: Patients with inflammatory bowel disease (IBD) have an increased risk of developing immune-mediated diseases. However, the genetic basis of IBD is complex, and an integrated approach should be used to elucidate the complex genetic relationship between IBD and immune-mediated diseases. Methods: The genetic relationship between IBD and 16 immune-mediated diseases was examined using linkage disequilibrium score regression. GWAS data were synthesized from two IBD databases using the METAL, and multi-trait analysis of genome-wide association studies was performed to enhance statistical robustness and identify novel genetic associations. Independent risk loci were meticulously examined using conditional and joint genome-wide multi-trait analysis, multi-marker analysis of genomic annotation, and functional mapping and annotation of significant genetic loci, integrating the information of quantitative trait loci and different methodologies to identify risk-related genes and proteins. Results: The results revealed four immune-mediated diseases (AS, psoriasis, iridocyclitis, and PsA) with a significant relationship with IBD. The multi-trait analysis revealed 909 gene loci of statistical significance. Of these loci, 28 genetic variants were closely related to IBD, and 7 single-nucleotide polymorphisms represented novel independent risk loci. In addition, 14 genes and 514 proteins were found to be associated with susceptibility to immune-mediated diseases. Notably, IL1RL1 emerged as a key player, present within pleiotropic genes across multiple protein databases, highlighting its potential as a therapeutic target. Conclusions: This study suggests that the common polygenic determinants between IBD and immune-mediated diseases are widely distributed across the genome. The findings not only support a shared genetic relationship between IBD and immune-mediated diseases but also provide novel therapeutic targets for these diseases. Full article

The process of manually annotating sports footage is a demanding one. In American football alone, coaches spend thousands of hours reviewing and analyzing videos each season. We aim to automate this process by developing a system that generates comprehensive statistical reports from full-length football game videos. Having previously demonstrated the proof of concept for our system, here, we present optimizations to our preprocessing techniques along with an inventive method for multi-person event detection in sports videos. Employing a long short-term memory (LSTM)-based architecture to detect the snap in American football, we achieve an outstanding LSI (Levenshtein similarity index) of 0.9445, suggesting a normalized difference of less than 0.06 between predictions and ground truth labels. We also illustrate the utility of snap detection as a means of identifying the offensive players’ assuming of formation. Our results exhibit not only the success of our unique approach and underlying optimizations but also the potential for continued robustness as we pursue the development of our remaining system components. Full article

Background: It is widely accepted that the 3D chromatin organization in human cell nuclei is not random and recent investigations point towards an interactive relation of epigenetic functioning and chromatin (re-)organization. Although chromatin organization seems to be the result of self-organization of the entirety of all molecules available in the cell nucleus, a general question remains open as to what extent chromatin organization might additionally be predetermined by the DNA sequence and, if so, if there are characteristic differences that distinguish typical regions involved in dysfunction-related aberrations from normal ones, since typical DNA breakpoint regions involved in disease-related chromosome aberrations are not randomly distributed along the DNA sequence. Methods: Highly conserved k-mer patterns in intronic and intergenic regions have been reported in eukaryotic genomes. In this article, we search and analyze regions deviating from average spectra (ReDFAS) of k-mer word frequencies in the human genome. This includes all assembled regions, e.g., telomeric, centromeric, genic as well as intergenic regions. Results: A positive correlation between k-mer spectra and 3D contact frequencies, obtained exemplarily from given Hi-C datasets, has been found indicating a relation of ReDFAS to chromatin organization and interactions. We also searched and found correlations of known functional annotations, e.g., genes correlating with ReDFAS. Selected regions known to contain typical breakpoints on chromosomes 9 and 5 that are involved in cancer-related chromosomal aberrations appear to be enriched in ReDFAS. Since transposable elements like ALUs are often assigned as major players in 3D genome organization, we also studied their impact on our examples but could not find a correlation between ALU regions and breakpoints comparable to ReDFAS. Conclusions: Our findings might show that ReDFAS are associated with instable regions of the genome and regions with many chromatin contacts which is in line with current research indicating that chromatin loop anchor points lead to genomic instability. Full article

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods. Full article

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm’s performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties. Full article

Salt stress poses a significant threat to crop yields worldwide. Melatonin (MT), an endogenous hormone synthesized in plants, has emerged as a crucial player in plant responses to various abiotic stresses, including drought, salinity, heat, and cold. However, the precise molecular mechanisms underlying MT-mediated abiotic stress responses remain incompletely understood. To elucidate the key genes and pathways involved in MT-mediated alleviation of salt stress, we conducted physiological, biochemical, and transcriptomic analyses on alfalfa seedlings. Our results demonstrated that alfalfa seedlings treated with melatonin exhibited higher germination rates, longer bud lengths, and greater fresh weights compared to those subjected to salt stress alone. Furthermore, the levels of malondialdehyde (MDA) and superoxide anion (O²⁻) were reduced, while the activities and contents of antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and glutathione (GSH) increased in response to melatonin treatment. Transcriptome analysis revealed 2181 differentially expressed genes (DEGs) in the salt-treated group, with 780 upregulated and 1401 downregulated genes. In contrast, the MT-treated group exhibited 4422 DEGs, including 1438 upregulated and 2984 downregulated genes. Functional annotation and pathway enrichment analysis indicated that DEGs were primarily involved in the biosynthesis of flavonoids, isoflavones, plant hormones, glutathione (GSH), soluble sugars, and other substances, as well as in ABC transporter and MAPK signaling pathways. Notably, the MT-treated group showed greater enrichment of DEGs in these pathways, suggesting that MT mitigates salt stress by modulating the expression of genes related to phytohormones and antioxidant capacity. Overall, our findings provide valuable insights into the molecular mechanisms underlying MT-mediated salt tolerance in alfalfa, with important implications for breeding salt-tolerant alfalfa and other crops. Full article

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly. Full article

Camellia oleifera (C. oleifera), one of the world’s four major edible woody oil crops, has been widely planted in southern China’s subtropical region for the extremely high nutritional and health benefits of its seed oil. Timing and synchronization of fruit dehiscence are critical factors influencing the oil output and quality, as well as the efficiency and cost of harvesting C. oleifera, yet they extremely lack attention. To gain an understanding of the molecular basis underlying the dehiscence of C. oleifera fruit, we sampled pericarp–replum tissues containing dehiscence zones from fruits at different developmental stages and performed time-series transcriptomic sequencing and analysis for the first time. Statistical and GO enrichment analysis of differentially expressed genes revealed that drastic transcriptional changes occurred over the last short sampling interval (4 days, 18th–22nd October), which directed functional classifications link to cell wall and cell wall macromolecule activity. WGCNA further showed that factors controlling cell wall modification, including endo-1,3;1,4-beta-D-glucanase, WAT1-like protein 37, LRR receptor-like serine/threonine-protein kinase, and cellulose synthase A catalytic subunit, were identified as core members of the co-expression network of the last stage highly related modules. Furthermore, in these modules, we also noted genes that were annotated as coding for polygalacturonase and pectinesterase, two pectinases that were expected to be major players in cell separation during dehiscence. qRT-PCR further confirmed the expression profiles of these cell wall modification relating factors, which possessed a special high transcriptional abundance at the final stage. These results suggested the cell wall associated cell separation, one of the essential processes downstream of fruit dehiscence, happened in dehiscing fruit of C. oleifera during ripening. Hydrolases acting on cell wall components are good candidates for signal mediating dehiscence of C. oleifera fruit. In conclusion, our analysis provided insights into the cell wall macromolecule-mediated fruit dehiscence during ripening in C. oleifera. Full article