Rib cage contributions to inspiratory capacity in patients with cervical spinal cord injury.

Background: Cervical spinal cord injury (CSI) often leads to impaired respiratory function, affecting the overall well-being of patients. This study aimed to investigate the influence of rib cage motion on inspiratory capacity in CSI patients. Methods: We conducted a study with 11 CSI patients, utilising respiratory inductance plethysmography (RIP). We measured ventilatory volume by spirometry concurrently with RIP. Participants were instructed to perform maximal inspiratory efforts. Inspiratory capacity (IC) was calculated from spirometry waveforms. We converted the respiratory waveforms of the chest and abdomen into inspiratory volume measured by a spirometer. The inspiratory volume measured by the chest sensor was defined as VRIP-rib cage (VRIP-rc), and the inspiratory volume measured by the abdominal sensor was defined as VRIP-abdomen (VRIP-ab). Subsequently, the relationships of IC with VRIP-rc and VRIPab were assessed. Results: The mean IC was 1.828 ± 0.459 L, with the mean VRIP-rc at 1.343 ± 0.568 L and the mean VRIP-ab at 0.485 ± 0.427 L. A significant correlation was observed between IC and VRIP-rc (r = 0.67, p = 0.02), indicating that rib cage motion significantly influences IC in CSI patients. Conclusion: This study highlights the importance of rib cage motion in assessing inspiratory capacity in patients with CSI.

Emergent Synchronous Volumetric Oscillation in Hierarchically Structured Self-Oscillating Gel Clusters.

Emergent properties accompanying synchronization among oscillators are vital characteristics in biological systems. Belousov-Zhabotinsky (BZ) oscillators are an artificial model to study the emergence and synchronization in life. This research represents a self-oscillating gel system with clusterable properties to experimentally examine synchronous and emergent properties at a fundamental hierarchical level. Incorporating acrylic acid (AAc) moieties within the gel network facilitates cluster formation through hydrogen bonding in an acidic BZ substrate solution. Upon clustering, both homogeneous and heterogeneous gel assemblies─ranging from double to quadruple clusters─exhibit increased and synchronized periods and amplitudes during the BZ reaction. Notably, in heterogeneous clusters, gel units with initially short periods and small volumetric amplitudes display a significant increase, aligning with the lonfger periods and larger amplitudes of other elements within the cluster, an emergent property. This research can pave the way for a better understanding of synchronous and emergent properties in biological oscillators such as cardiomyocytes.

Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars.

In computational neurolinguistics, it has been demonstrated that hierarchical models such as recurrent neural network grammars (RNNGs), which jointly generate word sequences and their syntactic structures via the syntactic composition, better explained human brain activity than sequential models such as long short-term memory networks (LSTMs). However, the vanilla RNNG has employed the top-down parsing strategy, which has been pointed out in the psycholinguistics literature as suboptimal especially for head-final/left-branching languages, and alternatively the left-corner parsing strategy has been proposed as the psychologically plausible parsing strategy. In this article, building on this line of inquiry, we investigate not only whether hierarchical models like RNNGs better explain human brain activity than sequential models like LSTMs, but also which parsing strategy is more neurobiologically plausible, by developing a novel fMRI corpus where participants read newspaper articles in a head-final/left-branching language, namely Japanese, through the naturalistic fMRI experiment. The results revealed that left-corner RNNGs outperformed both LSTMs and top-down RNNGs in the left inferior frontal and temporal-parietal regions, suggesting that there are certain brain regions that localize the syntactic composition with the left-corner parsing strategy.

Temperature-Adaptative Self-Oscillating Gels: Toward Autonomous Biomimetic Soft Actuators with Broad Operating Temperature Region.

Self-oscillating gel systems exhibiting an expanded operating temperature and accompanying functional adaptability are showcased. The developed system contains nonthermoresponsive main-monomers, such as N,N-dimethylacrylamide (DMAAm) or 2-acrylamido-2-methylpropane sulfonic acid (AMPS) or acrylamide (AAm) or 3-(methacryloylamino)propyl trimethylammonium chloride (MAPTAC). The gels volumetrically self-oscillate within the range of the conventional (20.0 °C) and extended (27.0 and 36.5 °C) temperatures. Moreover, the gels successfully adapt to the environmental changes; they beat faster and smaller as the temperature increases. The period and amplitude are also controlled by tuning the amount of main-monomers and N-(3-aminopropyl) acrylamide. Furthermore, the record amplitude in the bulk gel system consisting of polymer strand and cross-linker at 36.5 °C is achieved (≈10.8%). The study shows new self-oscillation systems composed of unprecedented combinations of materials, giving the community a robust material-based insight for developing more life-like autonomous biomimetic soft robots with various operating temperatures and beyond.

Harmonic resonance and entrainment of propagating chemical waves by external mechanical stimulation in BZ self-oscillating hydrogels.

Smart polymer materials that are nonliving yet exhibit complex "life-like" or biomimetic behaviors have been the focus of intensive research over the past decades, in the quest to broaden our understanding of how living systems function under nonequilibrium conditions. Identification of how chemical and mechanical coupling can generate resonance and entrainment with other cells or external environment is an important research question. We prepared Belousov-Zhabotinsky (BZ) self-oscillating hydrogels which convert chemical energy to mechanical oscillation. By cyclically applying external mechanical stimulation to the BZ hydrogels, we found that when the oscillation of a gel sample entered into harmonic resonance with the applied oscillation during stimulation, the system kept a "memory" of the resonant oscillation period and maintained it post stimulation, demonstrating an entrainment effect. More surprisingly, by systematically varying the cycle length of the external stimulation, we revealed the discrete nature of the stimulation-induced resonance and entrainment behaviors in chemical oscillations of BZ hydrogels, i.e., the hydrogels slow down their oscillation periods to the harmonics of the cycle length of the external mechanical stimulation. Our theoretical model calculations suggest the important roles of the delayed mechanical response caused by reactant diffusion and solvent migration in affecting the chemomechanical coupling in active hydrogels and consequently synchronizing their chemical oscillations with external mechanical oscillations.

Ensemble dynamics and information flow deduction from whole-brain imaging data.

The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.

Anisotropically self-oscillating gels by spatially patterned interpenetrating polymer network.

Here we introduce sub-millimeter self-oscillating gels that undergo the Belousov-Zhabotinsky (BZ) reaction and can anisotropically oscillate like cardiomyocytes. The anisotropically self-oscillating gels in this study were realized by spatially patterning an acrylic acid-based interpenetrating network (AA-IPN). We found that the patterned AA-IPN regions, locally introduced at both ends of the gels through UV photolithography, can constrain the horizontal gel shape deformation during the BZ reaction. In other words, the two AA-IPN regions could act as a physical barrier to prevent isotropic deformation. Furthermore, we controlled the anisotropic deformation behavior during the BZ reaction by varying the concentration of acrylic acid used in the patterning process of the AA-IPN. As a result, a specific directional deformation behavior (66% horizontal/vertical amplitude ratio) was fulfilled, similar to that of cardiomyocytes. Our study can provide a promising insight to fabricating robust gel systems for cardiomyocyte modeling or designing novel autonomous microscale soft actuators.

Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns.

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X-ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase-identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al-Si-Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well-trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al-Si-Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions.

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

A surface-grafted hydrogel demonstrating thermoresponsive adhesive strength change.

Here, we designed a surface-grafted hydrogel (SG gel) that exhibits thermoresponsive changes in surface properties. Quantitative measurements using a self-made device showed that the adhesive strength between the SG gel surface and a Bakelite plate due to hydrophobic interaction changed significantly with temperature.

Fabrication of submillimeter-sized spherical self-oscillating gels and control of their isotropic volumetric oscillatory behaviors.

In this study, we established a fabrication method and analyzed the volumetric self-oscillatory behaviors of submillimeter-sized spherical self-oscillating gels. We validated that the manufactured submillimeter-sized spherical self-oscillating gels exhibited isotropic volumetric oscillations during the Belousov-Zhabotinsky (BZ) reaction. In addition, we experimentally elucidated that the volumetric self-oscillatory behaviors (i.e., period and amplitude) and the oscillatory profiles depended on the following parameters: (1) the molar composition of N-(3-aminopropyl)methacrylamide hydrochloride (NAPMAm) in the gels and (2) the concentration of Ru(bpy)3-NHS solution containing an active ester group on conjugation. These clarified relationships imply that controlling the amount of Ru(bpy)3 in the gel network could influence the gel volumetric oscillation during the BZ reaction. These results of submillimeter-sized and spherical self-oscillating gels bridge knowledge gaps in the current field because the gels with corresponding sizes and shapes have not been systematically explored yet. Therefore, our study could be a cornerstone for diverse applications of (self-powered) gels in various scales and shapes, including soft actuators exhibiting life-like functions.

PPARα activation partially drives NAFLD development in liver-specific Hnf4a-null mice.

HNF4α regulates various genes to maintain liver function. There have been reports linking HNF4α expression to the development of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis. In this study, liver-specific Hnf4a-deficient mice (Hnf4aΔHep mice) developed hepatosteatosis and liver fibrosis, and they were found to have difficulty utilizing glucose. In Hnf4aΔHep mice, the expression of fatty acid oxidation-related genes, which are PPARα target genes, was increased in contrast to the decreased expression of PPARα, suggesting that Hnf4aΔHep mice take up more lipids in the liver instead of glucose. Furthermore, Hnf4aΔHep/Ppara-/- mice, which are simultaneously deficient in HNF4α and PPARα, showed improved hepatosteatosis and fibrosis. Increased C18:1 and C18:1/C18:0 ratio was observed in the livers of Hnf4aΔHep mice, and the transactivation of PPARα target gene was induced by C18:1. When the C18:1/C18:0 ratio was close to that of Hnf4aΔHep mouse liver, a significant increase in transactivation was observed. In addition, the expression of Pgc1a, a coactivator of PPARs, was increased, suggesting that elevated C18:1 and Pgc1a expression could contribute to PPARα activation in Hnf4aΔHep mice. These insights may contribute to the development of new diagnostic and therapeutic approaches for NAFLD by focusing on the HNF4α and PPARα signaling cascade.

Capsule self-oscillating gels showing cell-like nonthermal membrane/shape fluctuations.

A primary interest in cell membrane and shape fluctuations is establishing experimental models reflecting only nonthermal active contributions. Here we report a millimeter-scaled capsule self-oscillating gel model mirroring the active contribution effect on cell fluctuations. In the capsule self-oscillating gels, the propagating chemical signals during a Belousov-Zhabotinsky (BZ) reaction induce simultaneous local deformations in the various regions, showing cell-like shape fluctuations. The capsule self-oscillating gels do not fluctuate without the BZ reaction, implying that only the active chemical parameter induces the gel fluctuations. The period and amplitude depend on the gel layer thickness and the concentration of the chemical substrate for the BZ reaction. Our results allow for a solid experimental platform showing actively driven cell-like fluctuations, which can potentially contribute to investigating the active parameter effect on cell fluctuations.

Region-dependent volumetric oscillation of self-oscillating gels with gradient transducers.

Heartbeats with different ventricular contractions vary with heart regions, which can be described as anisotropy. Herein, we report self-oscillating gels which exhibit region-dependent anisotropic volumetric oscillation behavior similar to that of the heart. We installed a (Ru(bpy)3) gradient transducer on self-oscillating gels by employing slow and unidirectional diffusion in the gels and dipping part of the gel into a Ru(bpy)3-NHS solution. We found that the spatial distribution of Ru(bpy)3 in the gel caused region-dependent swelling/deswelling behavior depending on the redox state. We also confirmed that gel regions with smaller Ru(bpy)3 amounts exhibit lower amplitudes than those with larger amounts of Ru(bpy)3 during the Belousov-Zhabotinsky (BZ) reaction. These results are important in the design of self-oscillating soft actuators or machines, such as a biomimetic pump with desirable anisotropic oscillating behavior.

The ratio of 12α to non-12-hydroxylated bile acids reflects hepatic triacylglycerol accumulation in high-fat diet-fed C57BL/6J mice.

In our previous study, enterohepatic 12α-hydroxylated (12α) bile acid (BA) levels were found to be correlated with hepatic triacylglycerol concentration in rats fed high-fat (HF) diet. Since BA composition is diverse depending on animal species, we evaluated whether such a relationship is applicable in mice in response to an HF diet. C57BL/6JJmsSLC (B6) male mice were fed HF diet for 13 weeks and analyzed for triacylglycerol, cholesterol, oxysterols, and other metabolites in the liver. The BA composition was determined in the liver, small intestinal contents, portal plasma, aortic plasma, and feces. Neutral sterols were also measured in the feces. The ratio of 12α BA/non-12 BA increased in the liver, portal plasma, small intestinal contents, and feces of HF-fed B6 mice. Moreover, a positive correlation was observed between the ratio of fecal 12α BAs/non-12 BAs and hepatic triacylglycerol concentration. The concentration of 7α-hydroxycholesterol was increased in the liver of HF-fed B6 mice, whereas no increase was observed in the hepatic expression of cytochrome P450 family 7 subfamily A member 1. The present study showed that the ratio of 12α BA/non-12 BA in feces is closely associated with hepatic triacylglycerol accumulation in B6 mice fed HF diet.

Functional Output Regression for Machine Learning in Materials Science.

In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from an electron microscope, which can be represented as a two- or three-dimensional function in the image coordinate system. In this study, we consider two unified frameworks to handle such multidimensional or functional output regressions, which are applicable to a wide range of predictive analyses in material science. The first approach employs generative adversarial networks, which are known to exhibit outstanding performance in various computer vision tasks such as image generation, style transfer, and video generation. We also present another type of statistical modeling inspired by a statistical methodology referred to as functional data analysis. This is an extension of kernel regression to deal with functional outputs, and its simple mathematical structure makes it effective in modeling even with small amounts of data. We demonstrate the proposed methods through several case studies in materials science.

Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study.

Background: Brain CT needs more attention to improve the extremely low image contrast and image texture. Purpose: To evaluate the performance of iterative progressive reconstruction with visual modeling (IPV) for the improvement of low-contrast detectability (IPV-LCD) compared with filtered backprojection (FBP) and conventional IPV. Materials and methods: Low-contrast and water phantoms were used. Helical scans were conducted with the use of a CT scanner with 64 detectors. The tube voltage was set at 120 kVp; the tube current was adjusted from 60 to 300 mA with a slice thickness of 0.625 mm and from 20 to 150 mA with a slice thickness of 5.0 mm. Images were reconstructed with the FBP, conventional IPV, and IPV-LCD algorithms. The channelized Hotelling observer (CHO) model was applied in conjunction with the use of low-contrast modules in the low-contrast phantom. The noise power spectrum (NPS) and normalized NPS were calculated. Results: At the same standard and strong levels, the IPV-LCD method improved low-contrast detectability compared with the conventional IPV, regardless of contrast-rod diameters. The mean CHO values at a slice thickness of 0.625 mm were 1.83, 3.28, 4.40, 4.53, and 5.27 for FBP, IPV STD, IPV-LCD STD, IPV STR, and IPV-LCD STR, respectively. The normalized NPS for the IPV-LCD STD and STR images were slightly shifted to the higher frequency compared with that for the FBP image. Conclusion: IPV-LCD images further improve the low-contrast detectability compared with FBP and conventional IPV images while maintaining similar FBP image appearances.

Fabrication of Self-Oscillating Gels by Polymer Crosslinking Method and Analysis on Their Autonomous Swelling-Deswelling Behaviors.

We have developed a new methodology for fabricating self-oscillating gels by a post-polymerization crosslinking. The method enables us to make the self-oscillating gels easily just by mixing two kinds of polymer solutions at room temperature with fast gelation. Moreover, the polymer crosslinking method has the advantage that the self-oscillating gels could be fabricated from well-defined linear polymers. We revealed that the dynamic swelling-deswelling behavior of the gels was simply affected by the net amount of the catalyst for the Belousov-Zhabotinsky reaction in the whole gels, although the equilibrium swelling behavior was influenced by the properties of the constituent linear polymers. Our results offer the opportunity to access the origin of the dynamic and equilibrium behavior of materials by the hierarchical assembly as well as enable easy microfabrication of the self-oscillating gel.

Effects of breathing control using visual feedback of thoracoabdominal movement on aerobic exercise.

PURPOSE: To clarify whether voluntary respiratory movement control strategy is sustainable during exercise and to determine its effect on aerobic exercise. MATERIAL AND METHODS: Ten healthy men were enrolled in this study. We developed a device that can convert information on thoracoabdominal changes from an inductance plethysmograph and display the Konno-Mead diagram on the monitor in real time for each breath. On the first day, an incremental load test (ILT) was performed under two conditions. On the second day, a constant load test (CLT) was performed under two conditions using the load 1 min before the anaerobic threshold (AT). RESULTS: In the ILT, a significant prolongation of AT time was observed with voluntary respiratory movement control. In the CLT, carbon dioxide excretion showed no significant interaction, but the gas exchange ratio did. CONCLUSION: Voluntary respiratory movement control using a combination of chest band and visual feedback of thoracoabdominal movements may be a respiratory strategy to enhance aerobic exercise.