Biology subjects

Abe, M. S.

Publications and source records attributed to Abe, M. S..

11 recordsLinked to original sources

bioRxiv2024

How neural network structure alters the brain's self-organized criticality

The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary between order and disorder, where it acquires its information-processing capabilities. The mechanism that maintains this critical state has been proposed as a feedback system known as self-organized criticality (SOC); brain parameters, such as synaptic plasticity, are regulated internally without external adjustment. Therefore, clarifying how SOC occurs can help us to understand the mechanisms that maintain brain function and cause brain disorders. From the standpoint of neural network structures, the topology of neural circuits also plays a crucial role in information processing, with healthy neural networks exhibiting small-world, scale-free, and modular characteristics. However, how these network structures affect SOC remains poorly understood. In this study, we numerically investigated the possibility that the structure of neural networks contributes to the brains critical state and dysfunction using a mathematical model. Our results reveal that the time scales at which synaptic plasticity operates to achieve a critical state differ depending on the network structure. Additionally, we observed Dragon king phenomena associated with abnormal neural activity, depending on the network structure and synaptic plasticity time scales. Notably, Dragon king was observed over a wide range of synaptic plasticity time scales in scale-free networks with high-degree hub nodes. This study emphasizes the importance of neural network topology in neuroscience from the perspective of SOC.

neuroscience↗

bioRxiv2023

Facilitative interaction networks in experimental microbial community dynamics

Understanding potential roles of facilitative interactions between species is one of the major challenges in ecology and microbiology. However, we still have limited knowledge of entangled webs of facilitative interactions in ecosystems. By compiling whole-genome shotgun metagenomic data of an experimental microbial community, we tested the hypothesis that architecture of facilitative interaction networks could change through time. A metabolic modeling approach for estimating dependence between microbial genomes (species) allowed us to infer the network structure of potential facilitative interactions at 13 time points through the 110-day monitoring of experimental microbiomes. We then found that positive feedback loops, which were theoretically predicted to promote cascade breakdown of ecological communities, existed within the inferred networks of metabolic interactions prior to the drastic community-compositional shift observed in the microbiome time-series. We further applied "directed-graph" analyses to pinpoint potential keystone species located at the "upper stream" positions of such feedback loops. These analyses on facilitative interactions will help us understand key mechanisms causing catastrophic shifts in microbial community structure.

ecology↗

bioRxiv2023

Metagenomic analysis of ecological niche overlap and community collapse in microbiome dynamics

Species utilizing the same resources often fail to coexist for extended periods of time. Such competitive exclusion mechanisms potentially underly microbiome dynamics, causing breakdowns of communities composed of species with similar genetic backgrounds of resource utilization. Although genes responsible for competitive exclusion among a small number of species have been investigated in pioneering studies, it remains a major challenge to integrate genomics and ecology for understanding stable coexistence in species-rich communities. Here, we show that community-scale analyses of functional gene redundancy can provide a useful platform for interpreting and predicting collapse of bacterial communities. Through 110-day time-series of experimental microbiome dynamics, we analyzed the metagenome-assembled genomes of co-occurring bacterial species. We then inferred ecological niche space based on the multivariate analysis of the genome compositions. The analysis allowed us to evaluate potential shifts in the level of niche overlap between species through time. We hypothesized that community-scale pressure of competitive exclusion could be evaluated by quantifying overlap of genetically determined resource-use profiles (metabolic pathway profiles) among coexisting species. We found that the degree of community compositional changes observed in the experimental microbiome was explained by the magnitude of gene-repertoire overlaps among bacterial species. The metagenome-based analysis of genetic potential for competitive exclusion will help us forecast major events in microbiome dynamics such as sudden community collapse (i.e., dysbiosis).

microbiology↗

bioRxiv2022

Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

Microbiome dynamics are both crucial indicators and drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes. We here integrate theoretical and empirical bases for anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We then confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the energy landscape analysis of statistical physics or that of a stability index of nonlinear mechanics. These results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems.

ecology↗

bioRxiv2020

Individual Ants Do Not Show Active/rest Rhythms in Natural Nest Conditions

Circadian rhythms, which respond to the day/night cycle on the earth, arise from the endogenous timekeeping system within organisms, called the biological clock. For accurate circadian rhythms, endogenous oscillations are synchronized to light and temperature. In social insects, both abiotic and biotic factors (i.e., social interactions) play a significant role in active/rest rhythm regulation. However, it is challenging to monitor individual active-rest rhythms in a colony because of the large group size and small body size. Therefore, it is unclear how social interactions coordinate each individuals active/rest rhythms. This study developed an image-based tracking system using 2D barcodes for Diacamma sp. (a monomorphic ant) and measured the locomotor activities of all colony members under laboratory colony conditions. We also investigated the effect of broods on active/rest rhythms by removing all broods under colony conditions. Active/rest rhythms appeared only in solitary ants, not under colony conditions. In addition, arrhythmic active-rest rhythms were retained after brood removal. Therefore, a mixture of social interactions, not abiotic factors, induces the loss of active/rest rhythms. These results contribute to the knowledge of the diversity pattern of circadian rhythms in social insects.

animal behavior and cognition↗

bioRxiv2020

Amplitude of circadian rhythms becomes weaker in the north, but there is no cline in the period of rhythm in a beetle

Many species show rhythmicity in activity, from the timing of flowering in plants to that of foraging behaviour in animals. The free-running periods and amplitude (sometimes called strength or power) of circadian rhythms are often used as indicators of biological clocks. Many reports have shown that these traits highly geographically variable, and interestingly, they often show latitudinal or altitudinal clines. In many cases, the higher the latitude is, the longer the free-running circadian period (i.e., period of rhythm) in insects and plants. However, reports of positive correlations between latitude or longitude and circadian rhythm traits, including free-running periods, the power of the rhythm and locomotor activity, are limited to certain taxonomic groups. Therefore, we collected a cosmopolitan stored-product pest species, the red flour beetle Tribolium castaneum, in various parts of Japan and examined its rhythm traits, including the power of the rhythm and period of the rhythm, which were calculated from locomotor activity. The analysis revealed that power was significantly lower for beetles collected in northern areas compared with southern areas in Japan. However, it is worth noting that the period of circadian rhythm did not show any clines; specifically, it did not vary among the sampling sites, despite the very large sample size (n = 1585). We discuss why these cline trends were observed in T. castaneum.

animal behavior and cognition↗

bioRxiv2020

Bipartite network analysis of task-ant associations reveals task groups and absence of colonial diurnal activity

Social insects are one of the best examples of complex self-organised systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterise task allocation patterns due to behavioural complexity, such as the individual variation, context dependency, and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterise individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module, and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e., task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.

animal behavior and cognition↗

bioRxiv2020

Optimal Random Avoidance Strategy in Prey-Predator Interactions

It has recently been reported that individual animals, ranging from insects to birds and mammals, exhibit a special class of random walks, known as Levy walks, which can lead to higher search efficiency than normal random walks. However, the role of randomness or unpredictability in animal movements is not very well understood. In the present study, we used a theoretical framework to explore the advantage of Levy walks in terms of avoidance behaviour in prey-predator interactions and analysed the conditions for maximising the preys survival rate. We showed that there is a trade-off relationship between the predictability of the preys movement and the length of time of exposure to predation risk, suggesting that it is difficult for prey to decrease both parameters in order to survive. Then, we demonstrated that the optimal degree of randomness in avoidance behaviour could change depending on the predators ability. In particular, Levy walks resulted in higher survival rates than normal random walks and straight movements when the physical ability of the predators was high. This indicates that the advantage of Levy walks may also be present in random avoidance behaviour and provides new insights into why Levy walks can evolve in terms of randomness.

animal behavior and cognition↗

bioRxiv2020

Levy walks emerging near a critical point

AO_SCPLOWBSTRACTC_SCPLOWA special class of random walks, so-called Levy walks, has been observed in a variety of organisms ranging from cells, insects, fishes, and birds to mammals, including humans. Although their prevalence is considered to be a consequence of natural selection for higher search efficiency, some findings suggest that Levy walks might also be epiphenomena that arise from interactions with the environment. Therefore, why they are common in biological movements remains an open question. Based on some evidence that Levy walks are spontaneously generated in the brain and the fact that power-law distributions in Levy walks can emerge at a critical point, we hypothesized that the advantages of Levy walks might be enhanced by criticality. However, the functional advantages of Levy walks are poorly understood. Here, we modeled nonlinear systems for the generation of locomotion and showed that Levy walks emerging near a critical point had optimal dynamic ranges for coding information. This discovery suggested that Levy walks could change movement trajectories based on the magnitude of environmental stimuli. We then showed that the high flexibility of Levy walks enabled switching exploitation/exploration based on the nature of external cues. Finally, we analyzed the movement trajectories of freely moving Drosophila larvae and showed empirically that the Levy walks may emerge near a critical point and have the large dynamic range and high flexibility. Our results suggest that the commonly observed Levy walks emerge near a critical point and could be explained on the basis of these functional advantages.

ecology↗

bioRxiv2019

Scaling laws in spoken language associated with cognitive functions

A decline in cognitive functions due to aging has led to critical problems in modern society, and it is imperative to develop a method to predict the decline or related diseases, including dementia. Although it has been expected that language could be a sign of the cognitive decline, it remains less understood, especially in natural conditions. In this study, we recorded the large-scale data of one million words from group conversations among healthy elderly people and analysed the relationship between spoken language and cognitive functions based on scaling laws, namely, Zipfs law and Heaps law. We found that word patterns followed these scaling laws irrespective of cognitive function, but the variations in scaling laws were associated with cognitive functions. Moreover, using generative models, we uncovered the relationship between the variations and cognitive functions. These results indicate that scaling laws in language can be a biomarker for the cognitive decline.

neuroscience↗

bioRxiv2018

Genetic distance and social compatibility in the aggregation behavior of Japanese toad tadpoles

From microorganism to vertebrates, living things often exhibit social aggregation. One of anuran larvae, dark-bodied toad tadpoles (genus Bufo) are known to aggregate against predators. When individuals share genes from a common ancestor for whom social aggregation was a functional trait, they are also likely to share common recognition cues regarding association preferences, while greater genetic distances make cohesive aggregation difficult. In this study, we conducted quantitative analyses to examine aggregation behavior among three lineages of toad tadpoles: Bufo japonicus japonicus, B. japonicus formosus, and B. gargarizans miyakonis. To determine whether there is a correlation between cohesiveness and genetic similarity among group members, we conducted an aggregation test using 42 cohorts consisting of combinations drawn from a laboratory-reared set belonging to distinct clutches. As genetic indices, we used mitochondrial DNA (mtDNA) and major histocompatibility complex (MHC) class II alleles. The results clearly indicated that aggregation behavior in toad tadpoles is directly influenced by genetic distances based on mtDNA sequences and not on MHC haplotypes. Cohesiveness among heterogeneous tadpoles is negatively correlated with the geographic dispersal of groups. Our findings suggest that social incompatibility among toad tadpoles reflects phylogenetic relationships.

animal behavior and cognition↗