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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Self-oscillating chemoelectrical interface of solution-gated ion-sensitive field-effect transistor based on Belousov-Zhabotinsky reaction

The Belousov-Zhabotinsky (BZ) self-oscillation reaction is an important chemical model to elucidate nonequilibrium chemistry in an open system. However, there are only a few studies on the electrical behavior of pH oscillation induced by the BZ reaction, although numerous studies have been carried out to investigate the mechanisms by which the BZ reaction interacts with redox reactions, which results in potential changes. Needless to say, the electrical characteristic of a self-oscillating polymer gel driven by the BZ reaction has not been clarified. On the other hand, a solution-gated ion-sensitive field-effect transistor (ISFET) has a superior ability to detect ionic charges and includes capacitive membranes on the gate electrode. In this study, we carried out the electrical monitoring of self-oscillation behaviors at the chemoelectrical interface based on the BZ reaction using ISFET sensors, focusing on the pH oscillation and the electrical dynamics of the self-oscillating polymer brush. The pH oscillation induced by the BZ reaction is not only electrically observed using the ISFET sensor, the electrical signals of which results from the interfacial potential between the solution and the gate insulator, but also visualized using a large-scale and high-density ISFET sensor. Moreover, the N-isopropylacrylamide (NIPAAm)-based self-oscillating polymer brush with Ru(bpy)₃ as a catalyst clearly shows a periodic electrical response based on the swelling-deswelling behavior caused by the BZ reaction on the gate insulator of the ISFET sensor. Thus, the elucidation of the electrical self-oscillation behaviors induced by the BZ reaction using the ISFET sensor provides a solution to the problems of nonequilibrium chemistry.

Construction of a nano-phase-separated structure on a hydrogel surface

A hydrogel surface with a nano-phase-separated structure was successfully fabricated by grafting a fluorine-containing polymer using activators regenerated by electron transfer atom transfer radical polymerisation (ARGET ATRP). The modified hydrogel surface exhibits water repellency and high elasticity with maintaining transparency.

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

Effects of visual feedback of thoracoabdominal motion on oxygen consumption during hyperventilation - Pilot study

INTRODUCTION

Respiratory muscle oxygen consumption increases with the work of breathing. We hypothesized that reducing excessive respiratory muscle activity during exercise may improve exercise tolerance.

METHOD

We developed a device to provide real-time visual feedback of thoracoabdominal movement and used it to examine the influence of visual feedback of thoracoabdominal movement during diaphragmatic breathing on oxygen consumption in eight healthy men. While sitting on a wheelchair with the backrest reclined at 60°, oxygen consumption per body weight (VO₂/BW), minute ventilation (VE), tidal volume (VT), and breathing frequency (fR) were measured, breath-by-breath, using an expired-gas analyzer. The breathing pattern was analyzed by inductance plethysmography, with transducer bands over the chest and abdomen recording thoracoabdominal movements.

RESULTS

There was no significant difference in RatioTH-ABD and the ventilatory parameters between diaphragmatic breathing and diaphragmatic breathing with visual feedback. The average VO₂/BW during diaphragmatic breathing with visual feedback was 0.6 ml/kg lower than that during diaphragmatic breathing without visual feedback (p<0.05).

CONCLUSION

When visual feedback was used during diaphragmatic breathing, the RatioTH-ABD remained essentially unchanged, but VO₂/BW decreased significantly. This suggests that visual feedback of thoracoabdominal movement during diaphragmatic breathing may reduce respiratory muscle oxygen consumption.

Machine Learning to Predict Quasicrystals from Chemical Compositions

Quasicrystals have emerged as the third class of solid-state materials, distinguished from periodic crystals and amorphous solids, which have long-range order without periodicity exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, more than one hundred stable quasicrystals have been reported, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has lowered in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, it is shown that the discovery of new quasicrystals can be accelerated with a simple machine-learning workflow. With a list of the chemical compositions of known stable quasicrystals, approximant crystals, and ordinary crystals, a prediction model is trained to solve the three-class classification task and its predictability compared to the observed phase diagrams of ternary aluminum systems is evaluated. The validation experiments strongly support the superior predictive power of machine learning, with the overall prediction accuracy of the phase prediction task reaching ≈0.728. Furthermore, analyzing the input-output relationships black-boxed into the model, nontrivial empirical equations interpretable by humans that describe conditions necessary for stable quasicrystal formation are identified.

Delayed Mechanical Response to Chemical Kinetics in Self-Oscillating Hydrogels Driven by the Belousov-Zhabotinsky Reaction

We show experimentally that chemical and mechanical self-oscillations in Belousov–Zhabotinsky hydrogels are inherently asynchronous, that is, there is a detectable delay in swelling–deswelling response after a change in the chemical redox state. This phenomenon is observable in many previous experimental studies and potentially has far-reaching implications for the functionality and response time of the material in future applications; however, so far, it has not been quantified or reported systematically. Here, we provide a comprehensive qualitative and quantitative description of the chemical-to-mechanical delay, and we propose to explain it as a consequence of the slow nonequilibrium swelling–deswelling dynamics of the polymer material. Specifically, standard hydrogel pieces are large enough that transport processes, for example, counterion migration and water diffusion, cannot occur instantaneously throughout the entire gel piece, as opposed to previous theoretical considerations. As a result, the volume response of the polymer to a chemical change may be governed by a characteristic response time, which leads to the emergence of delay in mechanical oscillation. This is supported by our theoretical calculations.

Efficacy and safety of distal radial approach for cardiac catheterization: A systematic review and meta-analysis

BACKGROUND

The traditional radial approach (RA) is recommended as the standard method for coronary angiography (CAG), while a distal RA (DRA) has been recently used for CAG.

AIM

To assess the efficacy and safety of the DRA vs RA during CAG.

METHODS

The following databases were searched through December 2020: MEDLINE, the Cochrane Central Register of Controlled Trials, EMBASE, the World Health Organization International Clinical Trials Platform Search Portal, and ClinicalTrials.gov. Individual randomized-controlled trials for adult patients undergoing cardiac catheterization were included. The primary outcomes were the successful cannulation rate and the incidence of radial artery spasm (RAS) and radial artery occlusion (RAO). Study selection, data abstraction and quality assessment were independently performed using the Grading of Recommendations, Assessment, Development, and Evaluation approach.

RESULTS

Three randomized control trials and 13 registered trials were identified. The two approaches showed similar successful cannulation rates [risk ratio (RR) 0.90, 95% confidence interval (CI): 0.72-1.13]. The DRA did not decrease RAS (RR 0.43, 95%CI: 0.08-2.49) and RAO (RR 0.48, 95%CI: 0.18-1.29). Patients with the DRA had a shorter hemostasis time in comparison to those with the RA (mean difference -6.64, 95%CI: -10.37 to -2.90). The evidence of certainty was low.

CONCLUSION

For CAG, the DRA would be safer than the RA with comparable cannulation rates. Given the limited data, additional research, including studies with standard protocols, is necessary.

Autonomous Nanoscale Chemomechanical Oscillation on the Self-Oscillating Polymer Brush Surface by Precise Control of Graft Density

As a novel functional surface, a self-oscillating polymer brush that undergoes autonomous, periodic swelling/deswelling during the Belousov-Zhabotinsky (BZ) reaction has been developed. Although extensive research has revealed how the fundamental aspects of the BZ reaction can be regulated based on the surface design of the self-oscillating polymer brush, design strategies for the induction of mechanical oscillation remain unexplored. Herein, we investigated the graft density effects on the phase transition behavior, which is an important design parameter for the mechanical oscillation of the modified polymer. The self-oscillating polymer-modified substrates with controlled graft densities were prepared by immobilizing various compositions of an initiator and a noninitiator followed by surface-initiated atom transfer radical polymerization of the self-oscillating polymer chains. In addition to the characterization of each prepared substrate, atomic force microscopy (AFM) and digital holographic microscopy (DHM) were employed to evaluate the density effects on the static and dynamic surface structures. AFM revealed that equilibrium swelling as well as thermoresponsive behavior is profoundly affected by the graft density. Moreover, using DHM, autonomous mechanical oscillation was captured only on the self-oscillating polymer brush with adequate graft density. Notably, the oscillation amplitude (150 nm) and the period (20 s) in this study were superior to those in a previous report on the self-oscillating polymer modified through the grafting-to method by 10- and 3-fold, respectively. This study presents design guidelines for future applications, such as autonomous transport devices.

Recreation of the periodic table with an unsupervised machine learning algorithm

In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for a tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping, which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev’s periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces.

Investigation of inspiratory intercostal muscle activity in patients with spinal cord injury: a pilot study using electromyography, ultrasonography, and respiratory inductance plethysmography

[Purpose] The respiratory function in patients with cervical spinal cord injury is influenced by inspiratory intercostal muscle function. However, inspiratory intercostal muscle activity has not been conclusively evaluated. We evaluated the inspiratory intercostal muscle activity in patients with cervical spinal cord injury by using inspiratory intercostal electromyography, respiratory inductance plethysmography, and ultrasonography. [Participants and Methods] Three patients with cervical spinal cord injury were assessed. The change in mean amplitude (rest vs. maximum inspiration) was calculated by using intercostal muscle electromyography. Changes in intercostal muscle thickness (resting expiration and maximum inspiration) were also evaluated on ultrasonography. The waveform was converted to spirometry ventilation with respiratory inductance plethysmography, and the waveform at the xiphoid was considered to determine the rib cage volume. Each index was compared with the inspiratory capacities in each case. [Results] Intercostal muscle electromyography failed to measure the notable myoelectric potential in all the patients. The rib cage volume was higher at higher inspiratory capacities. The changes in muscle thickness were not significantly different between the patients. [Conclusion] The rib cage volume (measured with inductance plethysmography) was greater in the patients with cervical spinal cord injury when inspiratory intercostal muscle activity was high. Respiratory inductance plethysmography can capture inspiratory intercostal muscle function in patients with cervical spinal cord injury.

Construction of an artificial biosynthetic pathway for hyperextended archaeal membrane lipids in the bacterium Escherichia coli

Archaea produce unique membrane lipids, which possess two fully saturated isoprenoid chains linked to the glycerol moiety via ether bonds. The isoprenoid chain length of archaeal membrane lipids is believed to be important for some archaea to thrive in extreme environments because the hyperthermophilic archaeon Aeropyrum pernix and some halophilic archaea synthesize extended C25,C25-archaeal diether-type membrane lipids, which have isoprenoid chains that are longer than those of typical C20,C20-diether lipids. Natural archaeal diether lipids possessing longer C30 or C35 isoprenoid chains, however, have yet to be isolated. In the present study, we attempted to synthesize such hyperextended archaeal membrane lipids. We investigated the substrate preference of the enzyme sn-2,3-(digeranylfarnesyl)glycerol-1-phosphate synthase from A. pernix, which catalyzes the transfer of the second C25 isoprenoid chain to the glycerol moiety in the biosynthetic pathway of C25,C25-archaeal membrane lipids. The enzyme was shown to accept sn-3-hexaprenylglycerol-1-phosphate, which has a C30 isoprenoid chain, as a prenyl acceptor substrate to synthesize sn-2-geranylfarnesyl-3-hexaprenylglycerol-1-phosphate, a supposed precursor for hyperextended C25,C30-archaeal membrane lipids. Furthermore, we constructed an artificial biosynthetic pathway by introducing 4 archaeal genes and 1 gene from Bacillus subtilis in the cells of Escherichia coli, which enabled the E. coli strain to produce hyperextended C25,C30-archaeal membrane lipids, which have never been reported so far.

Bayesian Algorithm for Retrosynthesis

The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry.

Reliability of voluntary cough assessments using respiratory flow waveform

[Purpose] Voluntary cough can be assessed by recording flow waves. The purpose of this study was to examine the reliability of the measurements of respiratory flow waveforms, using equipment that recorded flow waves during cough. [Participants and Methods] Twenty healthy participants were recruited for this study. They underwent spirometry on them and, subsequently, their flow waves during single and consecutive voluntary cough tasks in the sitting position were recorded. The intra-class correlation coefficient was used to assess the intra-rater and inter-rater reliabilities for the voluntary cough data. [Results] The intra-class correlation coefficients were 0.6 to 0.8 for 'intra-rater reliability' and higher than 0.9 for 'inter-rater reliability', for single and consecutive cough tasks. The first assessment of cough peak flow was significantly higher than the second, during consecutive cough tasks. Similarly, the first assessment of cough volume acceleration was significantly higher than the second. [Conclusion] Our results demonstrated high intra-rater and inter-rater reliabilities for single and consecutive cough tasks. Following additional procedures and valuations, including the storage of data and standard range decisions, this method of cough assessment will be applied to patients with reduced cough function.

Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study

RATIONALE AND OBJECTIVES

To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematical model observer with a focus on low-contrast detectability.

MATERIALS AND METHODS

The CCT189 MITA CT IQ Low-Contrast Phantom was used and helical scans were performed using a 64-detector CT scanner. Tube voltage was set at 120 kVp and tube current was adjusted from 45 to 600 mA. Images were reconstructed at slice thicknesses of 0.625 and 5.0 mm with FBP and five types of iterative progressive reconstruction with visual modeling (IPV) algorithms. The noise power spectrum (NPS) and normalized NPS were calculated. To evaluate low-contrast detectability, the model observer with the channelized Hotelling observer model was applied using low-contrast modules in the phantom.

RESULTS

The NPS and normalized NPS for IPV images had similar curves as that for FBP images. At a slice thickness of 0.625 mm and equivalent radiation dose level, the mean improvement of low-contrast detectability for IPV images was 1.19-2.15-fold greater than FBP images with corresponding noise reduction levels. At equivalent noise levels of 5.0-8.0 HU, low-contrast detectability of the IPVstd2 to IPVstr2 images as almost the same or better than that of the FBP images. However, the detectability of the IPVstr4 image was lower than that of the FBP image (p = 0.02).

CONCLUSION

Low-contrast detectability of the IPV images was improved with a similar normalized NPS as with FBP images. Furthermore, a radiation reduction of >50% was achieved for the IPV images, while maintaining similar low-contrast detectability.

Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing

Motivation

Sequencing total RNA without poly-A selection enables us to obtain a transcriptomic profile of nascent RNAs undergoing transcription with co-transcriptional splicing. In general, the RNA-seq reads exhibit a sawtooth pattern in a gene, which is characterized by a monotonically decreasing gradient across introns in the 5’–3’ direction, and by substantially higher levels of RNA-seq reads present in exonic regions. Such patterns result from the process of underlying transcription elongation by RNA polymerase II, which traverses the DNA strand in a 5’–3’ direction as it performs a complex series of mRNA synthesis and processing. Therefore, data of sequenced total RNAs could be utilized to infer the rate of transcription elongation by solving the inverse problem.

Results

Though solving the inverse problem in total RNA-seq has the great potential, statistical methods have not yet been fully developed. We demonstrate what extent the newly developed method can be useful. The objective is to reconstruct the spatial distribution of transcription elongation rates in a gene from a given noisy, sawtooth-like profile. It is necessary to recover the signal source of the elongation rates separately from several types of nuisance factors, such as unobserved modes of co-transcriptionally occurring mRNA splicing, which exert significant influences on the sawtooth shape. The present method was tested using published total RNA-seq data derived from mouse embryonic stem cells. We investigated the spatial characteristics of the estimated elongation rates, focusing especially on the relation to promoter-proximal pausing of RNA polymerase II, nucleosome occupancy and histone modification patterns.

Availability and implementation

A C implementation of PolSter and sample data are available at https://github.com/yoshida-lab/PolSter.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Belousov-Zhabotinsky Reaction in Thermoresponsive Core-Shell Hydrogel Microspheres with a Tris(2,2'-bipyridyl)ruthenium Catalyst in the Core

The Belousov-Zhabotinsky (BZ) reaction shows temporal or spatiotemporal structures such as redox oscillation of the catalyst, [ruthenium(II)tris(2,2'-bipyridine)][PF₆]₂ ([Ru(bpy)₃][PF₆]₂). In this study, autonomously oscillating hydrogel microspheres (microgels) were investigated, which show swelling/deswelling oscillation induced by the redox oscillation of the BZ reaction inside the gel. Despite the periodically and autonomously induced oscillation that does not require an external stimulus, it has not been possible to perform any manipulation of the oscillatory behavior over time. The results of the present study show that it is possible to reversibly switch the microgel oscillations from an "on" active state of the BZ reaction to an "off" inactive state by changing the temperature in combination with thermoresponsive microgels. To realize on-demand switching, the construction of double-shell structures is crucial; the thermoresponsive first shell allows the microgels to modulate the diffusion of the substrates or intermediates in the BZ reaction, while the second shell maintains colloidal stability under high temperatures and high ion concentrations. The functionalized double-shell microgels were prepared via multistep seeded precipitation polymerization. The oscillatory switching behavior of the BZ reaction was observed directly and evaluated by ultraviolet-visible (UV-vis) spectroscopy. The central concept of this study, i.e., "on-off switching" can be expected to benefit the development of advanced bioinspired materials.

Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans

BACKGROUND

Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons.

RESULTS

Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging.

CONCLUSION

Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.

Fabrication of Self-Oscillating Micelles with a Built-In Oxidizing Agent

Various biological behaviors are fueled by "respiration", which is an example of catabolism. So far, we have reported various self-oscillating soft materials exhibiting bioinspired dynamic movements. These autonomous polymer systems are driven by the Belousov-Zhabotinsky (BZ) reaction, which is analogous to the tricarboxylic acid (TCA) cycle that is an integral part of respiration. However, in the BZ reaction, the external addition of an oxidizing agent is necessary to initiate the oxidation process, which is realized by intracellular moieties such as ubiquinone in living systems. Herein, we realized self-oscillating micelles that are driven without the external addition of an oxidizing agent. This was achieved by embedding the oxidizing source into the structure of the self-oscillating AB diblock copolymers. This strategy introduces a new function equivalent to intracellular oxidizing moieties, and is useful for the design of completely autonomous bioinspired materials.

Design of two complementary copolymers that work as a glue for cell-laden collagen gels

This study realised cytocompatible “in situ” surface functionalization of collagen gel for adding the property of gel-to-gel adhesion.

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

iQSPR is an inverse molecular design algorithm based on Bayesian inference that was developed in our previous study. Here, the algorithm is integrated in Python as a new module called iQSPR-X in the all-in-one materials informatics platform XenonPy. Our new software provides a flexible, easy-to-use, and extensible platform for users to build customized molecular design algorithms using pre-set modules and a pre-trained model library in XenonPy. In this paper, we describe key features of iQSPR-X and provide guidance on its use, illustrated by an application to a polymer design that targets a specific range of bandgap and dielectric constant.

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140 000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

Reactivity Control of Polymer Functional Groups by Altering the Structure of Thermoresponsive Triblock Copolymers

A thermoresponsive ABA triblock copolymer bearing an aldehyde group on the thermoresponsive A segments was synthesized. The polymer formed a micellar assembly due to the hydrophobic interactions of the thermoresponsive segment above the lower critical solution temperature (LCST). In contrast, the ABA polymer assembly decomposed upon lowering the temperature below the LCST. Using this structural change, the reactivity of the aldehyde group toward primary amines of albumin and poly(allylamine) was investigated. When the ABA polymer assembly and reactant were mixed above the LCST, Schiff base formation was suppressed because of the aldehyde group being protected by the hydrophobic thermoresponsive core. In contrast, Schiff base formation between the ABA triblock copolymer and the primary amine moiety on the molecules was confirmed below the LCST. The reactivity of the aldehyde functional group can therefore be controlled by altering the structure of the thermoresponsive ABA polymer.

Stable and Prolonged Autonomous Oscillation in a Self-Oscillating Polymer Brush Prepared on a Porous Glass Substrate

We developed an autonomous functional surface, named a "self-oscillating polymer brush surface", which exhibits swelling-deswelling of the modified polymer chains synchronized with the Belousov-Zhabotinsky (BZ) reaction. The grafted polymer chain is a random copolymer composed of thermoresponsive N-isopropylacrylamide, N-(3-aminopropyl)methacrylamide, and ruthenium tris(2,2'-bipyridine) [Ru(bpy)₃]. To provide stable oscillations over a long period of time, suppression of the dilution of the BZ reactants inside the polymer surface and the increase in the amount of immobilized Ru(bpy)₃ are important. Here, we modified the self-oscillating polymer brush on a porous glass substrate and characterized its dynamic behavior. The increased surface area of the porous glass allowed for an efficient introduction of the metal catalyst, which resulted in a stable BZ reaction observable by optical microscopy. Compared with an aqueous BZ solution and the self-oscillating polymer modified on a glass coverslip, the wave velocity and diffusion coefficient were significantly lower for the porous glass system, which suggested that the reaction-diffusion of the reactants was markedly different than those of the other two systems. Moreover, the wave velocity was unchanged on the porous glass system for 1 h, whereas that of the solution dropped by 30 μm s^-1. Waveform analyses based on the Field-Körös-Noyes mechanism revealed that densely packed Ru(bpy)₃ in the porous glass system affects the duration of the key processes in the BZ reaction. These findings can help with understanding the dynamic behavior of the self-oscillating polymer brush on a porous glass substrate. Stable self-oscillations on the polymer brush-grafted porous glass substrate will aid future applications such as mass transport systems.

Ral GTPase Activation by Downregulation of RalGAP Enhances Oral Squamous Cell Carcinoma Progression

Ral small GTPases, consisting of RalA and RalB, are members of the Ras family. Their activity is upregulated by RalGEFs. Since several RalGEFs are downstream effectors of Ras, Ral is activated by the oncogenic mutant Ras. Ral is negatively regulated by RalGAP complexes that consist of a catalytic α1 or α2 subunit and its common partner β subunit and similarly regulate the activity of RalA as well as RalB in vitro. Ral plays an important role in the formation and progression of pancreatic and lung cancers. However, the involvement of Ral in oral squamous cell carcinoma (OSCC) is unclear. In this study, we investigated OSCC by focusing on Ral. OSCC cell lines with high Ral activation exhibited higher motility. We showed that knockdown of RalGAPβ increased the activation level of RalA and promoted the migration and invasion of HSC-2 OSCC cells in vitro. In contrast, overexpression of wild-type RalGAPα2 in TSU OSCC cells attenuated the activation level of RalA and inhibited cell migration and invasion. Real-time quantitative polymerase chain reaction analysis of samples from patients with OSCC showed that RalGAPα2 was downregulated in oral cancer tissues as compared with normal epithelia. Among patients with OSCC, those with a lower expression of RalGAPα2 showed a worse overall survival rate. A comparison of DNA methylation and histone modifications of the RalGAPα2 gene in OSCC cell lines suggested that crosstalk among DNA methylation, histone H4Ac, and H3K27me2 was involved in the downregulation of RalGAPα2. Thus, activation of Ral GTPase by downregulation of RalGAP expression via a potential epigenetic mechanism may enhance OSCC progression.

Effective wound bed preparation using maggot debridement therapy for patients with critical limb ischaemia

OBJECTIVE

Patients with critical limb ischaemia (CLI) lack sufficient blood flow in to the limb, which leads to difficulties in the normal wound healing process. Therefore, maggot debridement therapy (MDT) has not generally been recommended for CLI patients. We evaluated the effectiveness of wound bed preparation by MDT in CLI patients who had undergone mid-foot amputation.

METHODS

Patients who underwent mid-foot amputation after angioplasty between April 2014 and October 2016 were retrospectively investigated by classifying them into an MDT group or a conventional treatment group. The primary outcome was defined as achievement of wound healing. Secondary outcomes were the proportions of amputation-free survival (AFS) and successful ambulatory improvement. Propensity scores were used to evaluate treatment outcomes based on five factors: ankle-brachial index, skin perfusion pressure of the foot, nutritional status, experience with dialysis and age.

RESULTS

A total of 39 patients (39 legs) were included, seven within the MDT group and 32 in the conventional treatment group. Clinical backgrounds of the two groups showed no significant differences except for higher albumin levels for the MDT group (3.5±0.4g/dl; p=0.014). The wound healing proportion was significantly higher in the MDT group (86%) than in the control group (38%) (p=0.035). At 6 months after amputation, no significant differences were found between the two groups for AFS (71% versus 47%; p=0.41) or ambulatory capability (43% versus 28%; p=0.65). This result was also similar to the propensity score adjustment analysis.

CONCLUSIONS

The efficacy of MDT with favourable wound bed preparation was shown in our CLI patients based on effective debridement and granulation formation by maggots, avoiding the loss of their heels. Wound-healing rates after MDT were higher for patients than for those receiving conventional treatment. MDT is considered a valid adjuvant treatment strategy for patients with CLI after revascularisation treatment is conducted. More favourable wound bed preparation and successful graft take were achieved in the MDT group, suggesting the effectiveness of MDT for wound healing in CLI patients.

Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis

Vascular endothelial cells (ECs) in angiogenesis exhibit inhomogeneous collective migration called “cell mixing”, in which cells change their relative positions by overtaking each other. However, how such complex EC dynamics lead to the formation of highly ordered branching structures remains largely unknown. To uncover hidden laws of integration driving angiogenic morphogenesis, we analyzed EC behaviors in an in vitro angiogenic sprouting assay using mouse aortic explants in combination with mathematical modeling. Time-lapse imaging of sprouts extended from EC sheets around tissue explants showed directional cohesive EC movements with frequent U-turns, which often coupled with tip cell overtaking. Imaging of isolated branches deprived of basal cell sheets revealed a requirement of a constant supply of immigrating cells for ECs to branch forward. Anisotropic attractive forces between neighboring cells passing each other were likely to underlie these EC motility patterns, as evidenced by an experimentally validated mathematical model. These results suggest that cohesive movements with anisotropic cell-to-cell interactions characterize the EC motility, which may drive branch elongation depending on a constant cell supply. The present findings provide novel insights into a cell motility-based understanding of angiogenic morphogenesis.

Pattern Formation in Heterostructured Gel by the Ferrocyanide-Iodate-Sulfite Reaction

Pattern formation in the reaction-diffusion systems for the ferrocyanide-iodate-sulfite reaction has been investigated. Previous studies have been conducted in a uniform medium. However, in this study, we reported the pattern formation in heterostructured gels with different network densities. The chemical states of the gel depend on the diffusivity, which in turn depends on the network density of the gel. Consequently, a pH pattern reflecting the heterostructured gel emerged. Furthermore, adjusting the condition produces novel patterns in the heterostructured gel.

Polymeric Design for Electron Transfer in Photoinduced Hydrogen Generation through a Coil-Globule Transition

To realize a renewable energy society, a polymeric system for photoinduced hydrogen generation utilizing a copolymer containing an electron acceptor was designed. In this system, the redox changes of viologen introduced into poly(N-isopropylacrylamide) cause cyclic conformational changes owing to the shifting of the phase transition temperature (PTT). The polymeric coil-globule transitions with hydrophilic/hydrophobic changes accelerate the electron transfer for hydrogen generation. In particular, hydrogen generation using visible-light energy with high efficiency is achieved around the PTT. In contrast to conventional solution systems, our polymeric system enables efficient hydrogen generation in a close molecular arrangement without the aggregation of catalytic nanoparticles. The utilization of conformational changes will provide a new strategy for synthesizing artificial photosynthetic hydrogels that split water to generate both hydrogen and oxygen.