Biology subjects

Shinohara, S.

Publications and source records attributed to Shinohara, S..

5 recordsLinked to original sources

bioRxiv2025

Muc6-expressing gastric isthmus progenitors contribute to regeneration and metaplasia supported by myeloid-mesenchymal interactions

Gastric mucosal homeostasis is maintained by tissue-resident stem and progenitor cells residing in the isthmus region. Following mucosal injury, surviving cells contribute to regeneration, coinciding with characteristic pathological changes such as atrophic gastritis and metaplasia. To comprehensively understand the cellular dynamics involved in this process, we performed single-cell and spatial transcriptomics using newly generated transgenic mice. In human samples and mouse models, loss of gastric chief cells precedes, and even induces, loss of parietal cells during the progression of atrophy and metaplasia, validating the causal relationship underlying the decrease of these two lineages. Single-cell analysis confirmed robust stemness and metaplastic changes in the Muc6-expressing neck lineage following either chief or parietal cell ablation, and lineage-tracing experiments revealed that Muc6-expressing isthmus progenitors serve as a source of metaplasia and regeneration. Mechanistically, mucosal injury recruits IL-1-expressing myeloid cells, which stimulates NRG1 production in stromal fibroblasts, leading to mucosal proliferation and regeneration mediated by Myc activation in isthmus progenitors. These findings highlight the injury-responsible stem cell-like function of Muc6-expressing isthmal progenitors, which play a critical role in mucosal homeostasis and disease progression. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/648856v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@10fc2eaorg.highwire.dtl.DTLVardef@1c5b82corg.highwire.dtl.DTLVardef@1be747dorg.highwire.dtl.DTLVardef@d1d722_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

A generalized distribution interpolated between the exponential and power law distributions and applied to the walking data of the pill bug (Armadillidium vulgare)

The Levy walk, a type of random walk in which the frequency of linear step-lengths follows a power-law distribution, can be observed in the migratory behavior of organisms at various levels, from bacteria and T cells to humans. Compared to the Brownian walk, which is also a type of random walk (characterized by an exponential distribution of the frequency of occurrence of step-length), the Levy walk is characterized by the occasional appearance of linear movements over very long distances. In this paper, we propose a general distribution that includes the power-law and exponential distributions as special cases. This distribution has two parameters: the first parameter represents the exponent, similar to the power-law and exponential distributions and the second is a shape parameter representing the shape of the distribution. By introducing this distribution, an intermediate distribution model can be interpolated between the power-law and exponential distributions. The shape parameter measures whether a distribution assimilates a power-law or exponential distribution. In this study, the proposed distribution was fitted to the frequency distribution of the step-length calculated from the walking data of pill bugs. The autocorrelation coefficients were also calculated from the time-series data of the step-length, and the relationship between the shape parameter and time dependency was investigated. The results showed a significant negative correlation between the two (r=-0.61, n=30, t=4.04, and p=0.00037). This means that pill bugs with gait patterns closer to Levy than Brownian walks have a stronger time dependence with respect to step-length changes.

animal behavior and cognition↗

bioRxiv2021

Simulation of foraging behavior using a decision-making agent with Bayesian and inverse Bayesian inference: Levy and Brownian walk-like search patterns derived from differences in prey distributions

It has been stated that in human migratory behavior, the step length series may have temporal correlation and that there is some relationship between this time dependency and the fact that the frequency distribution of step length follows a power-law distribution. Furthermore, the frequency of occurrence of the step length in some large marine organisms has been found to switch between power-law and exponential distributions, depending on the difficulty of prey acquisition. However, to date it has not been clarified how the aforementioned three phenomena arise: the positive correlation created in the step length series, the relation between the positive correlation of the step length series and the form of an individuals step length distribution, and the switching between power-law and exponential distributions depending on the abundance of prey. This study simulated foraging behavior using the Bayesian decision-making agent simultaneously performing both knowledge learning and knowledge-based inference to analyze how the aforementioned three phenomena arise. In the agent with learning and inference, past experiences were stored as hypotheses (knowledge) and they were used in current foraging behavior; at the same time, the hypothesis continued to be updated based on new experiences. The simulation results show that the agent with both learning and inference has a mechanism that simultaneously causes all the phenomena.

animal behavior and cognition↗

bioRxiv2019

A role of causal inference in non-stationary environments

Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.

animal behavior and cognition↗

bioRxiv2019

Extended Bayesian inference incorporating symmetry bias

In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment with high accuracy. We further propose a human-like Bayesian inference method to replace the conditional probability in Bayesian inference with the aforementioned causal induction model. In this method, two components coexist: the component of Bayesian inference, which updates the degree of confidence for each hypothesis, and the component of inverse Bayesian inference that modifies the model of each hypothesis. In other words, this method allows not only inference but also simultaneous learning. Our study demonstrates that the method with both Bayesian inference and inverse Bayesian inference enables us to deal flexibly with unsteady situations where the target of inference changes occasionally.

animal behavior and cognition↗