Emergence of integrated behaviors through direct optimization for homeostasis.

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment.

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.

Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability.

An encompassed representation of timescale hierarchies in first-order reaction network.

Complex networks are pervasive in various fields such as chemistry, biology, and sociology. In chemistry, first-order reaction networks are represented by a set of first-order differential equations, which can be constructed from the underlying energy landscape. However, as the number of nodes increases, it becomes more challenging to understand complex kinetics across different timescales.

Fast computational approach with prior dimension reduction for three-dimensional chemical component analysis using CT data of spectral imaging.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components.

Exact solution to quantum dynamical activity.

The quantum dynamical activity constitutes a thermodynamic cost in trade-off relations such as the quantum speed limit and the quantum thermodynamic uncertainty relation. However, calculating the quantum dynamical activity has been a challenge. In this paper, we present the exact solution for the quantum dynamical activity by deploying the continuous matrix product state method.

Information geometric bound on general chemical reaction networks.

We investigate the convergence of chemical reaction networks (CRNs), aiming to establish an upper bound on their reaction rates. The nonlinear characteristics and discrete composition of CRNs pose significant challenges in this endeavor. To circumvent these complexities, we adopt an information geometric perspective, utilizing the natural gradient to formulate a nonlinear system.

Binding Mechanism between Platelet Glycoprotein and Cyclic Peptide Elucidated by McMD-Based Dynamic Docking.

The cyclic peptide OS1 (amino acid sequence: CTERMALHNLC), which has a disulfide bond between both termini cysteine residues, inhibits complex formation between the platelet glycoprotein Ibα (GPIbα) and the von Willebrand factor (vWF) by forming a complex with GPIbα. To study the binding mechanism between GPIbα and OS1 and, therefore, the inhibition mechanism of the protein-protein GPIbα-vWF complex, we have applied our multicanonical molecular dynamics (McMD)-based dynamic docking protocol starting from the unbound state of the peptide. Our simulations have reproduced the experimental complex structure, although the top-ranking structure was an intermediary one, where the peptide was bound in the same location as in the experimental structure; however, the β-switch of GPIbα attained a different conformation.

Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance.

Electrodes are crucial for controlling the movements of biohybrid robots, but their external placement outside muscle tissue often leads to inefficient and non-selective stimulation of nearby biohybrid actuators. To address this, we propose embedding pillar electrodes within the skeletal muscle tissue, resulting in enhanced contraction of the target muscle without affecting the neighbor tissue with a 4 mm distance. We use finite element method simulations to establish a selectivity model, correlating the VI(volume integration of electric field intensity within muscle tissue) with actual contractile distances under different amplitudes of electrical pulses.

Odd Response-Induced Phase Separation of Active Spinners.

Due to the breaking of time-reversal and parity symmetries and the presence of non-conservative microscopic interactions, active spinner fluids and solids respectively exhibit nondissipative odd viscosity and nonstorage odd elasticity, engendering phenomena unattainable in traditional passive or active systems. Here, we study the effects of odd viscosity and elasticity on phase behaviors of active spinner systems. We find the spinner fluid under a simple shear experiences an anisotropic gas-liquid phase separation driven by the odd-viscosity stress.

Bodily maps of uncertainty and surprise in musical chord progression and the underlying emotional response.

Music has profoundly shaped the human experience across cultures and generations, yet its impact on our minds and bodies remains elusive. This study examined how the perception of musical chord elicits bodily sensations and emotions through the brain's predictive processing. By deploying body-mapping tests and emotional evaluations on 527 participants exposed to chord progressions, we unveiled the intricate interplay between musical uncertainty, prediction error in eliciting specific bodily sensations and emotions.

A method to analyze gene expression profiles from hippocampal neurons electrophysiologically recorded .

Hippocampal pyramidal neurons exhibit diverse spike patterns and gene expression profiles. However, their relationships with single neurons are not fully understood. In this study, we designed an electrophysiology-based experimental procedure to identify gene expression profiles using RNA sequencing of single hippocampal pyramidal neurons whose spike patterns were recorded in living mice.

Detecting Leadership Opportunities in Group Discussions Using Off-the-Shelf VR Headsets.

The absence of some forms of non-verbal communication in virtual reality (VR) can make VR-based group discussions difficult even when a leader is assigned to each group to facilitate discussions. In this paper, we discuss if the sensor data from off-the-shelf VR devices can be used to detect opportunities for facilitating engaging discussions and support leaders in VR-based group discussions. To this end, we focus on the detection of suppressed speaking intention in VR-based group discussions by using personalized and general models.

Chaotic neural dynamics facilitate probabilistic computations through sampling.

Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation.

Embedding Bifurcations into Pneumatic Artificial Muscle.

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing.

Genomic Evidence for the Complex Evolutionary History of Macaques (Genus Macaca).

The genus Macaca is widely distributed, occupies a variety of habitats, shows diverse phenotypic characteristics, and is one of the best-studied genera of nonhuman primates. Here, we reported five re-sequencing Macaca genomes, including one M. cyclopis, one M.

Noise reduction of electron holography observations for a thin-foiled Nd-Fe-B specimen using the wavelet hidden Markov model.

In this study, we investigate the effectiveness of noise reduction in electron holography, based on the wavelet hidden Markov model (WHMM), which allows the reasonable separation of weak signals from noise. Electron holography observations from a NdFeB thin foil showed that the noise reduction method suppressed artificial phase discontinuities generated by phase retrieval. From the peak signal-to-noise ratio, it was seen that the impact of denoising was significant for observations with a narrow spacing of interference fringes, which is a key parameter for the spatial resolution of electron holography.

Modulatory Effects on Laminar Neural Activity Induced by Near-Infrared Light Stimulation with a Continuous Waveform to the Mouse Inferior Colliculus In Vivo.

Infrared neural stimulation (INS) is a promising area of interest for the clinical application of a neuromodulation method. This is in part because of its low invasiveness, whereby INS modulates the activity of the neural tissue mainly through temperature changes. Additionally, INS may provide localized brain stimulation with less tissue damage.

Plasma proteomics profile-based comparison of torso versus brain injury: A prospective cohort study.

Background: Trauma-related deaths and posttraumatic sequelae are a global health concern, necessitating a deeper understanding of the pathophysiology to advance trauma therapy. Proteomics offers insights into identifying and analyzing plasma proteins associated with trauma and inflammatory conditions; however, current proteomic methods have limitations in accurately measuring low-abundance plasma proteins. This study compared plasma proteomics profiles of patients from different acute trauma subgroups to identify new therapeutic targets and devise better strategies for personalized medicine.

Enhancement of neural crest formation by mechanical force in development.

In vertebrate development, ectoderm is specified into neural plate (NP), neural plate border (NPB), and epidermis. Although such patterning is thought to be achieved by molecular concentration gradients, it has been revealed, mainly by analysis, that mechanical force can regulate cell specification. During patterning, cells deform and migrate, and this applies force to surrounding tissues, shaping the embryo.