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.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics.

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

The Evolution of Rag Gene Enhancers and Transcription Factor E and Id Proteins in the Adaptive Immune System.

Adaptive immunity relies on the V(D)J DNA recombination of immunoglobulin (Ig) and T cell receptor (TCR) genes, which enables the recognition of highly diverse antigens and the elicitation of antigen-specific immune responses. This process is mediated by recombination-activating gene (Rag) 1 and Rag2 (Rag1/2), whose expression is strictly controlled in a cell type-specific manner; the expression of Rag1/2 genes represents a hallmark of lymphoid lineage commitment. Although Rag genes are known to be evolutionally conserved among jawed vertebrates, how Rag genes are regulated by lineage-specific transcription factors (TFs) and how their regulatory system evolved among vertebrates have not been fully elucidated. Here, we reviewed the current body of knowledge concerning the cis-regulatory elements (CREs) of Rag genes and the evolution of the basic helix-loop-helix TF E protein regulating Rag gene CREs, as well as the evolution of the antagonist of this protein, the Id protein. This may help to understand how the adaptive immune system develops along with the evolution of responsible TFs and enhancers.

Medusavirus, a Novel Large DNA Virus Discovered from Hot Spring Water.

Recent discoveries of new large DNA viruses reveal high diversity in their morphologies, genetic repertoires, and replication strategies. Here, we report the novel features of medusavirus, a large DNA virus newly isolated from hot spring water in Japan. Medusavirus, with a diameter of 260 nm, shows a T=277 icosahedral capsid with unique spherical-headed spikes on its surface. It has a 381-kb genome encoding 461 putative proteins, 86 of which have their closest homologs in Acanthamoeba, whereas 279 (61%) are orphan genes. The virus lacks the genes encoding DNA topoisomerase II and RNA polymerase, showing that DNA replication takes place in the host nucleus, whereas the progeny virions are assembled in the cytoplasm. Furthermore, the medusavirus genome harbored genes for all five types of histones (H1, H2A, H2B, H3, and H4) and one DNA polymerase, which are phylogenetically placed at the root of the eukaryotic clades. In contrast, the host amoeba encoded many medusavirus homologs, including the major capsid protein. These facts strongly suggested that amoebae are indeed the most promising natural hosts of medusavirus, and that lateral gene transfers have taken place repeatedly and bidirectionally between the virus and its host since the early stage of their coevolution. Medusavirus reflects the traces of direct evolutionary interactions between the virus and eukaryotic hosts, which may be caused by sharing the DNA replication compartment and by evolutionarily long lasting virus-host relationships. Based on its unique morphological characteristics and phylogenomic relationships with other known large DNA viruses, we propose that medusavirus represents a new family, MedusaviridaeIMPORTANCE We have isolated a new nucleocytoplasmic large DNA virus (NCLDV) from hot spring water in Japan, named medusavirus. This new NCLDV is phylogenetically placed at the root of the eukaryotic clades based on the phylogenies of several key genes, including that encoding DNA polymerase, and its genome surprisingly encodes the full set of histone homologs. Furthermore, its laboratory host, Acanthamoeba castellanii, encodes many medusavirus homologs in its genome, including the major capsid protein, suggesting that the amoeba is the genuine natural host from ancient times of this newly described virus and that lateral gene transfers have repeatedly occurred between the virus and amoeba. These results suggest that medusavirus is a unique NCLDV preserving ancient footprints of evolutionary interactions with its hosts, thus providing clues to elucidate the evolution of NCLDVs, eukaryotes, and virus-host interaction. Based on the dissimilarities with other known NCLDVs, we propose that medusavirus represents a new viral family, Medusaviridae.

Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement.

Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society.

Finite Element Analysis of Interface Dependence on Nanomechanical Sensing.

Nanomechanical sensors and their arrays have been attracting significant attention for detecting, discriminating and identifying target analytes. The sensing responses can be partially explained by the physical properties of the receptor layers coated on the sensing elements. Analytical solutions of nanomechanical sensing are available for a simple cantilever model including the physical parameters of both a cantilever and a receptor layer. These analytical solutions generally rely on the simple structures, such that the sensing element and the receptor layer are fully attached at their boundary. However, an actual interface in a real system is not always fully attached because of inhomogeneous coatings with low affinity to the sensor surface or partial detachments caused by the exposure to some analytes, especially with high concentration. Here, we study the effects of such macroscopic interfacial structures, including partial attachments/detachments, for static nanomechanical sensing, focusing on a Membrane-type Surface stress Sensor (MSS), through finite element analysis (FEA). We simulate various macroscopic interfacial structures by changing the sizes, numbers and positions of the attachments as well as the elastic properties of receptor layers (e.g., Young's modulus and Poisson's ratio) and evaluate the effects on the sensitivity. It is found that specific interfacial structures lead to efficient sensing responses, providing a guideline for designing the coating films as well as optimizing the interfacial structures for higher sensitivity including surface modification of the substrate.

Effects of Center Metals in Porphines on Nanomechanical Gas Sensing.

Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing. By omitting the substituents on the tetrapyrrole macrocycle of porphyrin, the peripheral interference by substituents can be avoided. Zinc, nickel, and iron were chosen for the center metals as these metalloporphines show different properties compared to free-base porphine. The present study revealed that iron insertion enhanced sensitivity to various gases, while zinc and nickel insertion led to equivalent or less sensitivity than free-base porphine. Based on the experimental results, we discuss the role of center metals for gas uptake from the view point of molecular interaction. We also report the high robustness of the iron porphine to humidity, showing the high feasibility of porphine-based nanomechanical sensor devices for practical applications in ambient conditions.

Effects of coating materials on two dimensional stress-induced deflection of nanomechanical sensors.

Nanomechanical sensors, which have been expected as a promising platform for various applications, will not generate any measureable signal without an appropriate coating (receptor) layer. In spite of this critical dependence on the coating layer, there is almost no guideline for the optimization of these layers in terms of their material properties; such as, Young's modulus, Poisson's ratio, and geometrical parameters. In the present study, the effects of coating layer thickness [3 nm-10 microm], Young's modulus [100 Pa to approximately 1 TPa], and Poisson's ratio [0.10 to approximately 0.45] are investigated with the finite element analysis (FEA), focusing on systems with two dimensional (2D) stress induced either on top of the coating layer (i.e., surface stress) or at the interface between the coating layer and the silicon cantilever. It is found that the coating layer can either enhance or reduce the deflection of nanomechanical sensors depending on its material properties. These results provide guidelines for designing a coating layer to achieve higher sensitivity.

Sorption Kinetic Parameters from Nanomechanical Sensing for Discrimination of 2-Nonenal from Saturated Aldehydes.

Nanomechanical sensors have gained significant attention as promising platforms for artificial olfaction. Since sorption kinetic parameters that can be estimated from the sensing signals of nanomechanical sensors reflect the chemical and physicochemical interactions between the odorant and receptor material, the parameters can be utilized for the direct discrimination of each odorant. In this study, we demonstrated the discrimination of 20 vapors, including hydrocarbons, alcohols, organic acids, ketones, and aldehydes, which are reported as human body odor components, using the parameters extracted in the analytical solution of nanomechanical sensors based on sorption kinetics with viscoelastic behaviors. By using one of the specific nanomechanical sensors─membrane-type surface stress sensor─as a sensing unit, we successfully discriminated trans-2-nonenal known as an aging marker from other saturated aldehydes along with quantifying their concentrations.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array.

INTRODUCTION: Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. METHODS: Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. RESULTS: Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. CONCLUSION: Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Double-side-coated nanomechanical membrane-type surface stress sensor (MSS) for one-chip-one-channel setup.

With their capability for real-time and label-free detection of targets ranging from gases to biological molecules, nanomechanical sensors are expected to contribute to various fields, such as medicine, security, and environmental science. For practical applications, one of the major issues of nanomechanical sensors is the difficulty of coating receptor layers on their surfaces to which target molecules adsorb or react. To have measurable deflection, a single-side coating is commonly applied to cantilever-type geometry, and it requires specific methods or protocols, such as inkjet spotting or gold-thiol chemistry. If we can apply a double-side coating to nanomechanical sensors, it allows almost any kind of coating technique including dip coating methods, making nanomechanical sensors more useful with better user experiences. Here we address the feasibility of the double-side coating on nanomechanical sensors demonstrated by a membrane-type surface stress sensor (MSS) and verify its working principle by both finite element analysis (FEA) and experiments. In addition, simple hand-operated dip coating is demonstrated as a proof of concept, achieving practical receptor layers without any complex instrumentation. Because the double-side coating is compatible with batch protocols such as dip coating, double-side-coated MSS represents a new paradigm of one-chip-one-channel (channels on a chip are all coated with the same receptor layers) shifting from the conventional one-chip-multiple-channel (channels on a chip are coated with different receptor layers) paradigm.

Repetitive Direct Comparison Method for Odor Sensing.

Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.

Measurement of Volatile Fatty Acids in Silage through Odors with Nanomechanical Sensors.

The measurement of volatile fatty acids (VFAs) is of great importance in the fields of food and agriculture. There are various methods to measure VFAs, but most methods require specific equipment, making on-site measurements difficult. In this work, we demonstrate the measurements of VFAs in a model sample, silage, through its vapor using an array of nanomechanical sensors-Membrane-type Surface stress Sensors (MSS). Focusing on relatively slow desorption behaviors of VFAs predicted with the sorption kinetics of nanomechanical sensing and the dissociation nature of VFAs, the VFAs can be efficiently measured by using features extracted from the decay curves of the sensing response, resulting in sufficient discrimination of the silage samples. Since the present sensing system does not require expensive, bulky setup and pre-treatment of samples, it has a great potential for practical applications including on-site measurements.

Effects of fermentative quality of corn silage on lactation, blood metabolites, and rumen fermentation in dairy cows.

We investigated the effect of fermentation quality of corn silage on dry matter intake (DMI), milk yield, ruminal fermentation, methane (CH4 ) emissions, and plasma metabolites in lactating cows. Six lactating Holstein cows were used in a 2 × 2 crossover design with two dietary treatments containing high quality corn silage with lower pH (high group) or low quality corn silage with higher pH (low group). The cows were fed partial mixed ration (PMR containing 50%DM of each corn silage) ad libitum plus 0.7 kg/day of concentrates at milking. The DMI of cows in the low group (24.8 kg/day) tended to be lower (p < 0.10) than that in the high group (26.8 kg/day). The dietary treatment did not affect milk yield or milk fat, protein, or lactose concentrations. The ruminal acetic acid proportion of the low group was significantly higher (p < 0.05) than that of the high group. The CH4 emission per DMI of the low group tended to be higher (p < 0.10) than that of the high group. The plasma concentration of the total cholesterol (TCHO) and the activity of aspartate aminotransferase and alanine aminotransferase of the low group were significantly higher than those of the high group.

Optimization of DNA hybridization efficiency by pH-driven nanomechanical bending.

The accessibility and binding affinity of DNA are two key parameters affecting the hybridization efficiency in surface-based biosensor technologies. Better accessibility will result in a higher hybridization efficiency. Often, mixed ssDNA and mercaptohexanol monolayers are used to increase the hybridization efficiency and accessibility of surface-bound oligonucleotides to complementary target DNA. Here, no mercaptohexanol monolayer was used. We demonstrate by differential microcantilever deflection measurements at different pH that the hybridization efficiency peaks between pH 7.5 and 8.5. At low pH 4.5, hydration and electrostatic forces led to tensile surface stress, implying the reduced accessibility of the bound ssDNA probe for hybridization. In contrast, at high pH 8.5, the steric interaction between neighboring ssDNA strands was decreased by higher electrostatic repulsive forces, bending the microcantilever away from the gold surface to provide more space for the target DNA. Cantilever deflection scales with pH-dependent surface hybridization efficiency because of high target DNA accessibility. Hence, by changing the pH, the hybridization efficiency is adjusted.

Two dimensional array of piezoresistive nanomechanical Membrane-type Surface Stress Sensor (MSS) with improved sensitivity.

We present a new generation of piezoresistive nanomechanical Membrane-type Surface stress Sensor (MSS) chips, which consist of a two dimensional array of MSS on a single chip. The implementation of several optimization techniques in the design and microfabrication improved the piezoresistive sensitivity by 3~4 times compared to the first generation MSS chip, resulting in a sensitivity about ~100 times better than a standard cantilever-type sensor and a few times better than optical read-out methods in terms of experimental signal-to-noise ratio. Since the integrated piezoresistive read-out of the MSS can meet practical requirements, such as compactness and not requiring bulky and expensive peripheral devices, the MSS is a promising transducer for nanomechanical sensing in the rapidly growing application fields in medicine, biology, security, and the environment. Specifically, its system compactness due to the integrated piezoresistive sensing makes the MSS concept attractive for the instruments used in mobile applications. In addition, the MSS can operate in opaque liquids, such as blood, where optical read-out techniques cannot be applied.

Nanomechanical membrane-type surface stress sensor.

Nanomechanical cantilever sensors have been emerging as a key device for real-time and label-free detection of various analytes ranging from gaseous to biological molecules. The major sensing principle is based on the analyte-induced surface stress, which makes a cantilever bend. In this letter, we present a membrane-type surface stress sensor (MSS), which is based on the piezoresistive read-out integrated in the sensor chip. The MSS is not a simple "cantilever," rather it consists of an "adsorbate membrane" suspended by four piezoresistive "sensing beams," composing a full Wheatstone bridge. The whole analyte-induced isotropic surface stress on the membrane is efficiently transduced to the piezoresistive beams as an amplified uniaxial stress. Evaluation of a prototype MSS used in the present experiments demonstrates a high sensitivity which is comparable with that of optical methods and a factor of more than 20 higher than that obtained with a standard piezoresistive cantilever. The finite element analyses indicate that changing dimensions of the membrane and beams can substantially increase the sensitivity further. Given the various conveniences and advantages of the integrated piezoresistive read-out, this platform is expected to open a new era of surface stress-based sensing.

Detection of Trace Amounts of Water in Organic Solvents by DNA-Based Nanomechanical Sensors.

The detection of trace amounts of water in organic solvents is of great importance in the field of chemistry and in the industry. Karl Fischer titration is known as a classic method and is widely used for detecting trace amounts of water; however, it has some limitations in terms of rapid and direct detection because of its time-consuming sample preparation and specific equipment requirements. Here, we found that a DNA-based nanomechanical sensor exhibits high sensitivity and selectivity to water vapor, leading to the detection and quantification of trace amounts of water in organic solvents as low as 12 ppm in THF, with a ppb level of LoD through their vapors. Since the present method is simple and rapid, it can be an alternative technique to the conventional Karl Fischer titration.

Recent Advances in Nanomechanical Membrane-Type Surface Stress Sensors towards Artificial Olfaction.

Nanomechanical sensors have gained significant attention as powerful tools for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Nanomechanical sensors and their arrays are a promising platform for artificial olfaction in combination with data processing technologies, including machine learning techniques. This paper reviews the background of nanomechanical sensors, especially conventional cantilever-type sensors. Then, we focus on one of the optimized structures for static mode operation, a nanomechanical Membrane-type Surface stress Sensor (MSS), and discuss recent advances in MSS and their applications towards artificial olfaction.

Visualization of Flow-Induced Strain Using Structural Color in Channel-Free Polydimethylsiloxane Devices.

Measuring flow of gases is of fundamental importance yet is typically done with complex equipment. There is, therefore, a longstanding need for a simple and inexpensive means of flow measurement. Here, gas flow is measured using an extremely simple device that consists of an Ar plasma-treated polydimethylsiloxane (PDMS) slab adhered on a glass substrate with a tight seal. This device does not even have a channel, instead, gas can flow between the PDMS and the glass by deforming the PDMS wall, in other words, by making an interstice as a temporary path for the flow. The formation of the temporary path results in a compressive bending stress at the inner wall of the path, which leads to the formation of well-ordered wrinkles, and hence, the emergence of structural color that changes the optical transmittance of the device. Although it is very simple, this setup works sufficiently well to measure arbitrary gases and analyzes their flow rates, densities, and viscosities based on the change in color. It is also demonstrated that this technique is applicable to the flow-induced display of a pattern such as a logo for advanced applications.