Convergent Patterns Research Articles

Altered Functional Connectivity in Working Memory Network After Acute Sleep Deprivation

Acute sleep deprivation (SD) has a detrimental effect on working memory (WM). However, prior functional magnetic resonance imaging (fMRI) studies have failed to reach consistent results on brain functions underlying WM decline after acute SD. Thus, we aimed to identify convergent patterns of abnormal brain functions due to WM decline after acute SD. A coordinate-based activation likelihood estimation (ALE) meta-analysis of task-state fMRI studies testing the effects of acute SD on WM was performed to construct WM network. Then 26 healthy subjects with regular sleep performed the n-back task and underwent resting-state fMRI scanning before and after 24 h of SD. The functional connectivity (FC) among these brain regions and correlations with WM performance were calculated. The ALE results displayed that SD subjects performing WM-related tasks had consistent hypoactivation in the occipital lobe, left middle occipital gyrus, parietal lobe, precuneus, inferior parietal lobule, right sub-gyral, right cuneus, right limbic lobe, and right posterior cingulate. Consistent hyperactivation was showed in the left cerebrum, including the lingual gyrus, posterior lobe, cuneus, temporal lobe, and fusiform gyrus. These identified brain regions as the seeds to construct WM network. The increased FC between the left declive and right sub-gyral, left cuneus and left lingual gyrus, and left cuneus and right post cingulate were found. Furthermore, the impaired WM performance negatively correlated with increased FC. Taken together, our findings highlight that the altered FC in WM network may be the underlying mechanisms of WM decline after acute SD.

Arthroscopy Technique: Repair of Musculotendinous Junction Rotator Cuff Tears in the Shoulder Using a Dynamic Convergence Suture Bridge Technique

Musculotendinous junction (MTJ) rotator cuff tears in the shoulder are rare injuries in which the tendon fails medial to its tuberosity attachment. There is difficulty in striking a balance between restoring the length–tension relationship of the tendon while avoiding high suture tension at the repair site. In view of the rare incidences of these tears, there is a paucity of literature on their repair techniques. We seek to share our surgical technique in addressing type A MTJ tears—where the medial muscular tear margin is short but remains adequate for suture bridge repair, whereas the lateral tendon remains on the footprint. We used mattress sutures from the medial row of anchors, threaded through the lateral tendon stump, then passed medially to engage the medial stump, before being fixed to a lateral row in a knotless fashion. Pulling on this pair of sutures will thus bring into closer apposition of both medial and lateral tear margins in a dynamic convergence pattern. Our surgical technique is a safe and effective method of repairing type A MTJ tear that confers improved biological and biomechanical advantage via the formation of a dynamic convergence suture bridging technique in addition to a double-row repair construct.

Transforming Text Generation in NLP: Deep Learning with GPT Models and 2023 Twitter Corpus Using Transformer Architecture

This paper presents the design, implementation, and evaluation of a Transformer-based Generative Pre-trained Transformer (GPT) model tailored for character-level text generation. Leveraging the robust architecture of the Transformer, the model has been trained on a corpus sourced from social media text data, with the aim of exploring the intricacies of language patterns within a condensed and informal text setting. Key aspects of the model include a multi-head self-attention mechanism with a custom head configuration, positional embeddings, and layer normalization to promote stability in learning. It operates with a defined set of hyperparameters: a batch size of 32, a block size of 128, 200 iterations, a learning rate of 3e-4, and employs 4 attention heads across 4 layers with an embedding dimension of 384. The model has been optimized using the AdamW optimizer and includes regularization through dropout to prevent overfitting.Through a series of training iterations, the model demonstrates a converging behavior in loss metrics, indicating effective learning, and showcases the capacity to generate coherent text sequences post-training. Training and validation losses have been reported, revealing the nuances in model performance and generalization capabilities. The generated text samples postulate the model's potential in capturing the contextual flow of the dataset. This study further plots the loss curves, visually representing the training dynamics and convergence patterns. The final model, encapsulated within a PyTorch framework, presents a step forward in the realm of neural text generation, contributing to the ongoing advancements in language modeling and its applications in understanding and generating human-like text.

Does club convergence matter in health outcomes? Evidence from Indian states

BackgroundPopulation health is vital to a nation’s overall well-being and development. To achieve sustainable human development, a reduction in health inequalities and an increase in interstate convergence in health indicators is necessary. Evaluation of the convergence patterns can aid the government in monitoring the health progress across the Indian states. This study investigates the progressive changes in the convergence and divergence patterns in health status across major states of India from 1990 to 2018.MethodsSigma plots (σ), kernel density plots, and log t-test methods are used to test the convergence, divergence, and club convergence patterns in the health indicators at the state level.ResultsThe result of the sigma convergence suggests that life expectancy at birth has converged across all states. After 2006, however, the infant mortality rate, neonatal mortality rate, and total fertility rate experienced a divergence pattern. The study’s findings indicate that life expectancy at birth converges in the same direction across all states, falling into the same club (Club One). However, considerable cross-state variations and evidence of clubs’ convergence and divergence are observed in the domains of infant mortality rate, neonatal death rate, and total fertility rate. As suggested by the kernel density estimates, life expectancy at birth stratifies, polarizes, and becomes unimodal over time, although with a single stable state. A bimodal distribution was found for infant, neonatal, and total fertility rates.ConclusionsTherefore, healthcare strategies must consider each club’s transition path while focusing on divergence states to reduce health variations and improve health outcomes for each group of individuals.

Time-specific convergence and divergence in individual differences in behavior: Theory, protocols and analyzes.

Over the years, theoreticians and empiricists working in a wide range of disciplines, including physiology, ethology, psychology, and behavioral ecology, have suggested a variety of reasons why individual differences in behavior might change over time, such that different individuals become more similar (convergence) or less similar (divergence) to one another. Virtually none of these investigators have suggested that convergence or divergence will continue forever, instead proposing that these patterns will be restricted to particular periods over the course of a longer study. However, to date, few empiricists have documented time-specific convergence or divergence, in part because the experimental designs and statistical methods suitable for describing these patterns are not widely known. Here, we begin by reviewing an array of influential hypotheses that predict convergence or divergence in individual differences over timescales ranging from minutes to years, and that suggest how and why such patterns are likely to change over time (e.g., divergence followed by maintenance). Then, we describe experimental designs and statistical methods that can be used to determine if (and when) individual differences converged, diverged, or were maintained at the same level at specific periods during a longitudinal study. Finally, we describe why the concepts described herein help explain the discrepancy between what theoreticians and empiricists mean when they describe the "emergence" of individual differences or personality, how they might be used to study situations in which convergence and divergence patterns alternate over time, and how they might be used to study time-specific changes in other attributes of behavior, including individual differences in intraindividual variability (predictability), or genotypic differences in behavior.

Evolution, Inheritance, and Strata Formation of the W Chromosome in Duck (Anas platyrhynchos)

Abstract The nonrecombining female-limited W chromosome is predicted to experience unique evolutionary processes. Difficulties in assembling W chromosome sequences have hindered the identification of duck W-linked sequences and their evolutionary footprint. To address this, we conducted three initial contig-level genome assemblies and developed a rigorous pipeline by which to successfully expand the W-linked data set, including 11 known genes and 24 newly identified genes. Our results indicate that the W chromosome expression may not be subject to female-specific selection; a significant convergent pattern of upregulation associated with increased female-specific selection was not detected. The genetic stability of the W chromosome is also reflected in the strong evolutionary correlation between it and the mitochondria; the complete consistency of the cladogram topology constructed from their gene sequences proves the shared maternal coevolution. By detecting the evolutionary trajectories of W-linked sequences, we have found that recombination suppression started in four distinct strata, of which three were conserved across Neognathae. Taken together, our results have revealed a unique evolutionary pattern and an independent stratum evolutionary pattern for sex chromosomes.

Optimizing K-Means Clustering: A Comparative Study of Optimization Algorithms For Convergence And Efficiency

The K-Means clustering algorithm is a widely used technique for grouping data into clusters, with applications spanning various domains. This study presents a comparative investigation into the optimization of K-Means clustering through the evaluation of different optimization algorithms. The primary focus is on enhancing the convergence speed and computational efficiency of the K-Means algorithm, with implications for diverse real-world scenarios. The research systematically examines a range of optimization techniques, including gradient descent, stochastic gradient descent, and metaheuristic algorithms such as genetic algorithms and simulated annealing. A comprehensive analysis of convergence speed, clustering quality, and computational efficiency is conducted across these algorithms. By assessing their performance on diverse datasets, the study aims to provide insights into the trade-offs between different optimization strategies and their implications for practical clustering tasks. The results reveal distinct convergence patterns, highlighting the advantages and limitations of each optimization algorithm. Gradient-based approaches demonstrate rapid convergence but susceptibility to local optima, while stochastic gradient descent and metaheuristic algorithms exhibit a balance between exploration and exploitation. The findings shed light on the interplay between optimization techniques, convergence speed, and clustering quality, offering valuable guidance for practitioners seeking to optimize K-Means clustering according to specific dataset characteristics and computational requirements. This comparative study contributes to the broader understanding of optimizing K-Means clustering algorithms and aids researchers and practitioners in selecting suitable optimization strategies for efficient and effective data clustering in real-world applications.

Peer effect, political competition and eco-efficiency: evidence from city-level data in China

ABSTRACT This study examines the impacts of political competition on eco-efficiency. We first develop a theoretical model in which local government officials compete against each other to maximise their own political score. We find that after an initial stage of decline, eco-efficiency eventually turns upwards, once environmental performance becomes a meaningful component of local government officials’ annual assessment. Eco-efficiency also exhibits a pattern of convergence. Lastly, the level of political competition is found to be negatively correlated with eco-efficiency. For the empirical analysis, we use a data envelopment analysis (DEA) model to compute the eco-efficiency level for 191 Chinese cities from 2003 to 2015. Our empirical evidence presents a ‘U’-shape pattern in the trend of eco-efficiency and identifies two peer effects that work in opposite directions: the incentivising effect arising from higher performing neighbours, and the disincentivising effect when a city outperforms its competitors. Both peer effects lead to convergence in eco-efficiency, and our spatial econometric modeling analysis suggests that the net peer effect is significantly positive. We also find evidence of political competition reducing eco-efficiency, as predicted in the theoretical model. Our findings are robust to alternative measures of eco-efficiency.

Investor behavior in the currency option market during the COVID-19 pandemic

This study investigates the COVID-19 pandemic's impact on investor behavior in the currency options market, emphasizing its relationship with underlying exchange rates. Using a sample of daily data from select futures continuous calls from September 22, 2016, to December 31, 2021, we introduce a novel variable, “market imperfections,” to quantify the gap between observed and theoretical currency option prices based on the Garman and Kohlhagen model. Through the application of a Markov switching model, we identify pandemic-related changes in investor behavior, characterized by patterns of divergence and convergence. Our research distinguishes between two key behavioral types in the market: fundamentalists and chartists. This study enriches the literature by clarifying how crises, specifically the COVID-19 period, influence investor dynamics and affect market responses. Overall, we provide critical insights into the factors shaping behavior during challenging periods.

Illuminating Plant Community Assembly on Karst Mountain Road Slopes through Plant Traits and Environmental Filters

Understanding how assembly processes shape local plant assemblages from the potential species pool is crucial for biodiversity conservation and revegetation. Mountainous regions are global biodiversity hotspots with high levels of diversity, concentration, and vulnerability. Road construction in these areas poses ecological challenges, including habitat loss and reduced biodiversity. Feature-based ecology highlights non-biological filtering as a key driver of habitat-specific community formation. Analyzing trait structures and their association with the environment can reveal community assembly processes under specific environmental conditions. However, quantifying species-environment-traits interactions during community assembly on roadside slopes is still underexplored. In our study, 76 naturally recovered roadside slopes, 656 self-established plant communities and 113 plant species across ten functional traits, along with their environmental associations, in the karst mountain region of southwestern China, were examined. Our findings show that there are still abundant native plants with colonization potential settled on steep roadside slopes in karst mountain areas. Diffusion constraints stemming from distance to the core species pool, elevation, and differences in adjacent vegetation types emerged as key factors causing variations in species composition of self-established communities. The slope environment exerts strong selective pressures leading to a convergence pattern in traits related to dispersal and colonization while showing a divergence pattern in traits linked to competitive strategies and regeneration. These findings identify critical functional traits and environmental factors shaping roadside plant communities and illustrate the predictability of environmental filtering and fundamental community assembly. Overall, our study sheds light on the intricate interactions among assembly processes, functional traits, and environmental factors driving local plant assemblages in mountainous regions, providing insights for effective diversity conservation and revegetation strategies.

Regional variation in the organization and connectivity of the first synapse in the primate night vision pathway

Sensitivity of primate daylight vision varies across the visual field. This is attributed to regional variations in cone photoreceptor density and synaptic connectivity of the underlying circuitry. In contrast, we have limited understanding of how synapse organization of the primate night vision pathway changes across space. Using serial electron microscopy, we reconstructed the first synapse of the night vision pathway between rod photoreceptors and second-order neurons, at multiple locations from the central part of the primate retina, fovea, to the periphery. We find that most facets of the rod synapse connectivity vary across retinal regions. However, rod synaptic divergence and convergence patterns do not change in the same manner across locations. Moreover, patterns of rod synapse organization are tightly correlated with photoreceptor density. Such regional heterogeneities revise the connectivity diagram of the primate rod synapse which will shape synapse function and sensitivity of the night vision pathway across visual space.

Single-cell transcriptome and metagenome profiling reveals the genetic basis of rumen functions and convergent developmental patterns in ruminants.

The rumen undergoes developmental changes during maturation. To characterize this understudied dynamic process, we profiled single-cell transcriptomes of about 308,000 cells from the rumen tissues of sheep and goats at 17 time points. We built comprehensive transcriptome and metagenome atlases from early embryonic to rumination stages, and recapitulated histomorphometric and transcriptional features of the rumen, revealing key transitional signatures associated with the development of ruminal cells, microbiota, and core transcriptional regulatory networks. In addition, we identified and validated potential cross-talk between host cells and microbiomes and revealed their roles in modulating the spatiotemporal expression of key genes in ruminal cells. Cross-species analyses revealed convergent developmental patterns of cellular heterogeneity, gene expression, and cell-cell and microbiome-cell interactions. Finally, we uncovered how the interactions can act upon the symbiotic rumen system to modify the processes of fermentation, fiber digestion, and immune defense. These results significantly enhance understanding of the genetic basis of the unique roles of rumen.

Replicated radiations in the South American marsh pitcher plants (Heliamphora) lead to convergent carnivorous trap morphologies.

The evolution of carnivorous pitcher traps across multiple angiosperm lineages represents a classic example of morphological convergence. Nevertheless, no comparative study to-date has examined pitcher evolution from a quantitative morphometric perspective. In the present study, we used comparative morphometric approaches to quantify the shape space occupied by Heliamphora pitchers and to trace evolutionary trajectories through this space to examine patterns of divergence and convergence within the genus. We also investigated pitcher development, and, how the packing of pitchers is affected by crowding, a common condition in their natural environments. Our results showed that Heliamphora pitchers have diverged along three main axes in morphospace: (1) pitcher curvature; (2) nectar spoon elaboration; and (3) pitcher stoutness. Both curvature and stoutness are correlated with pitcher size, suggesting structural constraints in pitcher morphological evolution. Among the four traits (curvature, spoon elaboration, stoutness, and size), all but curvature lacked phylogenetic signal and showed marked convergence across the phylogeny. We also observed tighter packing of pitchers in crowded conditions, and this effect was most pronounced in curved, slender pitchers. Overall, our study demonstrates that diversification and convergent evolution of carnivory-related traits extends to finer evolutionary timescales, reinforcing the notion that ecological specialization may not necessarily be an evolutionary dead end.

Convergence and Disparities in Higher Education Fiscal Expenditures in China: A Regional Perspective

This research investigates the disparities and convergence in higher education fiscal expenditures across different regions in China. The study utilises Gini coefficient analysis and σ-convergence/β-convergence tests to quantify the extent of disparities and explore convergence trends over a twelve-year investigation period (2007–2018). The results shed light on the imbalances in resource allocation and provide valuable insights into the efforts required to achieve a more equitable distribution of fiscal resources for higher education. The findings reveal significant disparities in higher education fiscal expenditures between the Eastern, Central, Western, and Northeastern regions, with the Eastern region exhibiting the largest gap compared to others. Remarkably, the disparity between the Eastern and Central regions is even greater than that between the Eastern and Western regions, emphasising the need for targeted interventions to address regional imbalances. Over the study period, the gap between the Eastern and Central regions remained consistently higher than other regional disparities. Moreover, the research shows a general trend towards narrowing regional fiscal expenditure disparities, with the most pronounced convergence observed between the Central and Northeastern regions. The Western region exhibits slightly larger disparities than the Central and Northeastern regions, possibly attributed to greater fiscal policy support and lower student enrollments. Nevertheless, the fiscal expenditure gap between the Western and Central regions has shown a trend towards reduction. The study also explores absolute and conditional β-convergence, revealing notable convergence patterns in the Eastern and Central regions. However, the Western and Northeastern regions exhibit varying degrees of convergence, indicating the necessity for region-specific convergence mechanisms. To achieve a balanced allocation of financial resources for higher education across regions, the study recommends targeted fiscal policies, additional funding, and improved transparency and accountability. Policymakers should focus on enhancing convergence mechanisms to ensure a more equitable distribution of resources and foster the sustainable development of higher education throughout the country. While this research provides valuable insights, it is essential to consider other potential factors influencing fiscal expenditure disparities, such as policy orientation, economic disparities, and demographic structures, for a more comprehensive understanding. Future research may benefit from qualitative investigations to further explore the complexities of higher education fiscal expenditure imbalances and identify effective policy interventions.

Fractured Zone Detection Using Petrophysical Logs by Rescaled Windowed R/S Analyses and Discrete Fracture Network (DFN) of Yanchang Formation in Mahuangshan-Hongliugou Area, Ordos Basin, China

Structural fractures generally develop in the upper crust strata and are usually distributed in a convergent pattern, forming structural fracture zones with a specific strike. Fracture zones control the reservoir seepage system and seriously affect the migration and accumulation of oil and gas in tight sandstone reservoirs. Therefore, characterizing the characteristics of the fracture zones for tight oil exploration and development effectively is essential. In this paper, the variable scale fractal method is introduced to calculate the petrophysical log, and a new curve H is built. An intensity log is to characterize the intensity of structural fracture development. The H curve is in a good linear relationship with the intensity curve after the comparison of the H curve and intensity curve in 32 wells. A quantitative relationship between H and the intensity curve is established. Based on the parameters obtained from the core and image logs, the discrete fracture network model was established using H curves from more than 300 wells, and the structural fracture zone was analyzed. The model shows that the fracture zones formed by structural fractures are in S-N and NW-SE directions in the study area. The orientation of the structural fracture zone is consistent with that of the fractured fault zone and fault, and the development of the fractured zone is consistent with the regional tectonic evolution characteristics. The characteristics of the fracture zone explain the distribution law of oil accumulation and groundwater salinity in the study area.

PolyQ length-based molecular encoding of vocalization frequency in FOXP2

The transcription factor FOXP2, a regulator of vocalization- and speech/language-related phenotypes, contains two long polyQ repeats (Q1 and Q2) displaying marked, still enigmatic length variation across mammals. We found that the Q1/Q2 length ratio quantitatively encodes vocalization frequency ranges, from the infrasonic to the ultrasonic, displaying striking convergent evolution patterns. Thus, species emitting ultrasonic vocalizations converge with bats in having a low ratio, whereas species vocalizing in the low-frequency/infrasonic range converge with elephants and whales, which have higher ratios. Similar, taxon-specific patterns were observed for the FOXP2-related protein FOXP1. At the molecular level, we observed that the FOXP2 polyQ tracts form coiled coils, assembling into condensates and fibrils, and drive liquid-liquid phase separation (LLPS). By integrating evolutionary and molecular analyses, we found that polyQ length variation related to vocalization frequency impacts FOXP2 structure, LLPS, and transcriptional activity, thus defining a novel form of polyQ length-based molecular encoding of vocalization frequency.

The influence of Mach number and overpressure on the structure of supersonic gas jets

ABSTRACT The Mach number is the primary parameter in determining the intrinsic driving properties, and effect on the environment of a supersonic jet. In the most basic form, a jet is released when a wall of a large high-pressure gas reservoir is punctured. The resulting high-pressure jet contains a configuration of shocks that continue to disturb the environment after the initial bow shock has passed. Here, we perform numerical simulations to determine the properties attributable to pure adiabatic hydrodynamic effects, taking a uniform stream out of a circular nozzle. We take a range of Mach numbers that, along with the jet overpressure, determine the flow pattern and shock locations. We distinguish conditions that generate Mach shock discs rather than a diamond pattern of oblique regular reflections. Potential observational diagnostics explored include the disc size, the distance from the nozzle, and oscillations in shock positions. Rapid oscillations occur in the divergent–convergent pattern through a feedback/hysteresis effect promoted by the ambient medium. The underlying flow patterns are independent of relative jet density, but heavy jets display both lower amplitude and lower frequency oscillations. We also study the energy transferred into the environment. Overpressured jets may contribute to noise and sound wave generation through screeching. However, these oscillations in the near-field are not sufficiently significant to regulate star and galaxy formation. We expect that upcoming high dynamic range and resolution observations will increasingly detect the shock patterns as jet gas transits from protostellar and galactic cores.

Research on airflow convergence patterns and air distribution characteristics under multi-column attachment ventilation

Multi-column attachment ventilation (M-CAV) is usually delivered to the occupied zone by floor-attached airflows in large spaces. However, after the floor-attached airflow converges, the original air distribution will change, which will have an important impact on the traditional air distribution design. In this paper, airflow convergence patterns and air distribution characteristics in the most common double-column and tri-column layouts are studied by experiments and computational fluid dynamics (CFD) simulations. A 1:3 scaled-down model was established for velocity experiments and flow field visualization, and the results were used to verify and optimize the CFD model. The results indicated a seagull-like convergence flow pattern under the symmetric double-column layout. Under the axisymmetric layout with different column spacings, the floor-attached airflow in the half-open air outlet mode exhibited full convergence with the merging point (point MP) and combined point (point CP), partial convergence with point MP, and non-convergence without point MP and point CP. Under the centrosymmetric layout, an M-shaped velocity distribution was formed, and no airflow converged. For equal-velocity conditions, the dimensionless convergence axial velocity distributions for different air supply velocities was approximately consistent. In the fully-open air outlet mode, the floor-attached airflow could be divided into the collision-dominated region, the combined collision and convergence-dominated region, and the convergence-dominated region based on the forms of airflow interaction. For unequal-velocity conditions, the high-velocity airflow restricted the diffusion of the low-velocity airflow.

When tainted money should fund public goods: fundraising professional and public moral preferences.

Philanthropy is essential to public goods such as education and research, arts and culture, and the provision of services to those in need. Providers of public goods commonly struggle with the dilemma of whether to accept donations from morally tainted donors. Ethicists also disagree on how to manage tainted donations. Forgoing such donations reduces opportunities for societal well-being and advancement; however, accepting them can damage institutional and individual reputations. Half of professional fundraisers have faced tainted donors, but only around a third of their institutions had relevant policies (n = 52). Here, we draw on two large samples of US laypeople (ns = 2,019; 2,566) and a unique sample of experts (professional fundraisers, n = 694) to provide empirical insights into various aspects of tainted donations that affect moral acceptability: the nature of the moral taint (criminal or morally ambiguous behavior), donation size, anonymity, and institution type. We find interesting patterns of convergence (rejecting criminal donations), divergence (professionals' aversion to large tainted donations), and indifference (marginal role of anonymity) across the samples. Laypeople also applied slightly higher standards to universities and museums than to charities. Our results provide evidence of how complex moral trade-offs are resolved differentially, and can thus motivate and inform policy development for institutions dealing with controversial donors.

Multimodal meaning-making of aggression in English song narrative: A cognitive-pragmatic perspective

This article addresses cognitive and pragmatic aspects of the multimodal meaning-making of AGGRESSION in the English song narrative. The cognitive-pragmatic foundations integrate cognitive and pragmatic aspects with multimodal ones, which allows to explain how signs of different modes and semiotic resources instantiate the concept and which cognitive features are decisive for (re)construction of meaning by performers and viewers. Cognitive features have verbal and non-verbal implementation through visual and auditory modes. The verbal semiotic resource, implemented through auditory and visual modes, contains lexical and pragmatic means of direct/indirect realization of aggression. Lexical means include words that name/describe/express aggression, as well as lexemes that acquire the aggressive meaning in a certain context. Pragmatic means are represented by expressive statements realizing threat, order, reproach, and accusation. The non-verbal semiotic resource is also represented through both modes and is characterized by semiotic heterogeneity, including prosodic/mimic/gestural features, various aspects of performers’ appearance, music clip design and lighting. They include hoarse voice/high exalted vocals/screaming; aggressive look/bulging eyes/wide open mouth/tense distorted face; active movements/fight; dark clothes/ dark make-up/tattoos. The musical component is characterized by the lower register, alternating consonance and dissonance, and a fast tempo. The lighting includes the play of light and shadow, and flashes. The construction of aggression appears as a result of the integration of semiotic elements into a multimodal blend, which is an emergent entity. Semantic relations between the blend components are based on the complementarity or contradiction of their elements, which makes it possible to single out convergent and divergent constructive patterns.