A very long chain fatty acid responsive transcription factor, MYB93, regulates lateral root development in Arabidopsis.

Lateral roots (LRs) are critical to root system architecture development in plants. Although the molecular mechanisms by which auxin regulates LR development have been extensively studied, several additional regulatory systems are hypothesized to be involved. Recently, the regulatory role of very long chain fatty acids (VLCFAs) has been shown in LR development. Our analysis showed that LTPG1 and LTPG2, transporters of VLCFAs, are specifically expressed in the developing LR primordium (LRP), while the number of LRs is reduced in the ltpg1/ltpg2 double mutant. Moreover, late LRP development was hindered when the VLCFA levels were reduced by the VLCFA synthesis enzyme mutant, kcs1-5. However, the details of the regulatory mechanisms of LR development controlled by VLCFAs remain unknown. In this study, we propose a novel method to analyze the LRP development stages with high temporal resolution using a deep neural network and identify a VLCFA-responsive transcription factor, MYB93, via transcriptome analysis of kcs1-5. MYB93 showed a carbon chain length-specific expression response following treatment of VLCFAs. Furthermore, myb93 transcriptome analysis suggested that MYB93 regulated the expression of cell wall organization genes. In addition, we also found that LTPG1 and LTPG2 are involved in LR development through the formation of root cap cuticle, which is different from transcriptional regulation by VLCFAs. Our results suggest that VLCFA is a regulator of LRP development through transcription factor-mediated regulation of gene expression and the transportation of VLCFAs is also involved in LR development through root cap cuticle formation.

Analysis of Plant-Specific ANTH Domain-Containing Protein in Marchantia polymorpha.

Membrane trafficking is a fundamental mechanism for protein and lipid transport in eukaryotic cells and exhibits marked diversity among eukaryotic lineages with distinctive body plans and lifestyles. Diversification of the membrane trafficking system is associated with the expansion and secondary loss of key machinery components, including RAB GTPases, soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and adaptor proteins, during plant evolution. The number of AP180 N-terminal homology (ANTH) proteins, an adaptor family that regulates vesicle formation and cargo sorting during clathrin-mediated endocytosis, increases during plant evolution. In the genome of Arabidopsis thaliana, 18 genes for ANTH proteins have been identified, a higher number than that in yeast and animals, suggesting a distinctive diversification of ANTH proteins. Conversely, the liverwort Marchantia polymorpha possesses a simpler repertoire; only two genes encoding canonical ANTH proteins have been identified in its genome. Intriguingly, a non-canonical ANTH protein is encoded in the genome of M. polymorpha, which also harbors a putative kinase domain. Similar proteins have been detected in sporadic lineages of plants, suggesting their ancient origin and multiple secondary losses during evolution. We named this unique ANTH group phosphatidylinositol-binding clathrin assembly protein-K (PICALM-K) and characterized it in M. polymorpha using genetic, cell biology-based and artificial intelligence (AI)-based approaches. Our results indicate a flagella-related function of MpPICALM-K in spermatozoids, which is distinct from that of canonical ANTH proteins. Therefore, ANTH proteins have undergone significant functional diversification during evolution, and PICALM-K represents a plant-unique ANTH protein that is delivered by neofunctionalization through exon shuffling.

Wound-inducible WUSCHEL-RELATED HOMEOBOX 13 is required for callus growth and organ reconnection.

Highly efficient tissue repair is pivotal for surviving damage-associated stress. Plants generate callus upon injury to heal wound sites, yet regulatory mechanisms of tissue repair remain elusive. Here, we identified WUSCHEL-RELATED HOMEOBOX 13 (WOX13) as a key regulator of callus formation and organ adhesion in Arabidopsis (Arabidopsis thaliana). WOX13 belongs to an ancient subclade of the WOX family, and a previous study shows that WOX13 orthologs in the moss Physcomitrium patens (PpWOX13L) are involved in cellular reprogramming at wound sites. We found that the Arabidopsis wox13 mutant is totally defective in establishing organ reconnection upon grafting, suggesting that WOX13 is crucial for tissue repair in seed plants. WOX13 expression rapidly induced upon wounding, which was partly dependent on the activity of an AP2/ERF transcription factor, WOUND-INDUCED DEDIFFERENTIATION 1 (WIND1). WOX13 in turn directly upregulated WIND2 and WIND3 to further promote cellular reprogramming and organ regeneration. We also found that WOX13 orchestrates the transcriptional induction of cell wall-modifying enzyme genes, such as GLYCOSYL HYDROLASE 9Bs, PECTATE LYASE LIKEs and EXPANSINs. Furthermore, the chemical composition of cell wall monosaccharides was markedly different in the wox13 mutant. These data together suggest that WOX13 modifies cell wall properties, which may facilitate efficient callus formation and organ reconnection. Furthermore, we found that PpWOX13L complements the Arabidopsis wox13 mutant, suggesting that the molecular function of WOX13 is partly conserved between mosses and seed plants. This study provides key insights into the conservation and functional diversification of the WOX gene family during land plant evolution.

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab.

We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed.In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki-Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction.While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.

A Novel Morphological Marker for the Analysis of Molecular Activities at the Single-cell Level.

For more than a century, hematoxylin and eosin (H&E) staining has been the de facto standard for histological studies. Consequently, the legacy of histological knowledge is largely based on H&E staining. Due to the recent advent of multi-photon excitation microscopy, the observation of live tissue is increasingly being used in many research fields. Adoption of this technique has been further accelerated by the development of genetically encoded biosensors for ions and signaling molecules. However, H&E-based histology has not yet begun to fully utilize in vivo imaging due to the lack of proper morphological markers. Here, we report a genetically encoded fluorescent marker, NuCyM (Nucleus, Cytosol, and Membrane), which is designed to recapitulate H&E staining patterns in vivo. We generated a transgenic mouse line ubiquitously expressing NuCyM by using a ROSA26 bacterial artificial chromosome (BAC) clone. NuCyM evenly marked the plasma membrane, cytoplasm and nucleus in most tissues, yielding H&E staining-like images. In the NuCyM-expressing cells, cell division of a single cell was clearly observed as five basic phases during M phase by three-dimensional imaging. We next crossed NuCyM mice with transgenic mice expressing an ERK biosensor based on the principle of Förster resonance energy transfer (FRET). Using NuCyM, ERK activity in each cell could be extracted from the FRET images. To further accelerate the image analysis, we employed machine learning-based segmentation methods, and thereby automatically quantitated ERK activity in each cell. In conclusion, NuCyM is a versatile cell morphological marker that enables us to grasp histological information as with H&E staining.Key words: in vivo imaging, histology, machine learning, molecular activity.

Adaptive t-vMF dice loss: An effective expansion of dice loss for medical image segmentation.

Dice loss is widely used for medical image segmentation, and many improved loss functions have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter κ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMF Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve adaptive training based on the accuracy of each class. We evaluated binary segmentation datasets of CVC-ClinicDB and Kvasir-SEG, and multi-class segmentation datasets of Automated Cardiac Diagnosis Challenge and Synapse multi-organ segmentation. Through experiments conducted on four datasets using a five-fold cross-validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.

Automatic enhancement preprocessing for segmentation of low quality cell images.

We present a novel automatic preprocessing and ensemble learning technique for the segmentation of low-quality cell images. Capturing cells subjected to intense light is challenging due to their vulnerability to light-induced cell death. Consequently, microscopic cell images tend to be of low quality and it causes low accuracy for semantic segmentation. This problem can not be satisfactorily solved by classical image preprocessing methods. Therefore, we propose a novel approach of automatic enhancement preprocessing (AEP), which translates an input image into images that are easy to recognize by deep learning. AEP is composed of two deep neural networks, and the penultimate feature maps of the first network are employed as filters to translate an input image with low quality into images that are easily classified by deep learning. Additionally, we propose an automatic weighted ensemble learning (AWEL), which combines the multiple segmentation results. Since the second network predicts segmentation results corresponding to each translated input image, multiple segmentation results can be aggregated by automatically determining suitable weights. Experiments on two types of cell image segmentation confirmed that AEP can translate low-quality cell images into images that are easy to segment and that segmentation accuracy improves using AWEL.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

Expanded tube attention for tubular structure segmentation.

PURPOSE: Semantic segmentation of tubular structures, such as blood vessels and cell membranes, is a very difficult task, and it tends to break many predicted regions in the middle. This problem is due to the fact that tubular ground truth is very thin, and the number of pixels is extremely unbalanced compared to the background. METHODS: We present a novel training method using pseudo-labels generated by morphological transformation. Furthermore, we present an attention module using thickened pseudo-labels, called the expanded tube attention (ETA) module. By using the ETA module, the network learns thickened regions based on pseudo-labels at first and then gradually learns thinned original regions while transferring information in the thickened regions as an attention map. RESULTS: Through experiments conducted on retina vessel image datasets using various evaluation measures, we confirmed that the proposed method using ETA modules improved the clDice metric accuracy in comparison with the conventional methods. CONCLUSIONS: We demonstrated that the proposed novel expanded tube attention module using thickened pseudo-labels can achieve easy-to-hard learning.

A Materials Acceleration Platform for Organic Laser Discovery.

Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, an automated platform is introduced for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. This platform encompasses automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, discovered eight potential candidates for organic lasers is discovered. The lasing threshold of four molecules in thin-film devices and find two molecules with state-of-the-art performance is tested. These promising results show the potential of automated synthesis and screening for accelerated materials development.

Creation of synthetic contrast-enhanced computed tomography images using deep neural networks to screen for renal cell carcinoma.

In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter renal tumors. This retrospective, multicenter study included 155 patients (artificial intelligence training cohort [n = 99], validation cohort [n = 56]) who underwent surgery for small-diameter (≤40 mm) renal tumors, with the pathological diagnosis of renal cell carcinoma, during 2010-2020. We created a learned deep neural networks using pix2pix. We examined the quality of the synthetic enhanced computed tomography images created using this deep neural networks and compared them with real enhanced computed tomography images using the zero-mean normalized cross-correlation parameter. We assessed concordance rates between real and synthetic images and diagnoses according to 10 urologists by creating a receiver operating characteristic curve and calculating the area under the curve. The synthetic computed tomography images were highly concordant with the real computed tomography images, regardless of the existence or morphology of the renal tumor. Regarding the concordance rate, a greater area under the curve was obtained with synthetic computed tomography (area under the curve = 0.892) than with only computed tomography (area under the curve = 0.720; p < 0.001). In conclusions, this study is the first to use deep neural networks to create a high-quality synthetic computed tomography image that was highly concordant with a real computed tomography image. Our synthetic computed tomography images could be used for urological diagnoses and clinical screening.

Enhanced fluorescence in a nanoporous waveguide and its quantitative analysis.

Fluorescence behavior was examined for fluorophore-labeled protein (BSA-AF) adsorbed on the nanopore surface of a nanoporous waveguiding film. The waveguiding film has a bilayer structure of a porous anodic alumina (PAA) layer on a metallic aluminum (Al) layer, and this structure allows efficient interaction of fluorophores entrapped in the nanoporous waveguiding film with a hotspot of the enhanced electromagnetic field of the waveguide modes. Fluorescence response of BSA-AF depends on the enhanced field within the waveguiding film and the enlarged adsorbed amount in the PAA layer where most of the light is confined. Enhancement of the field in the waveguiding film can be controlled by the refractive index of the PAA layer and enlargement of the pore size efficiently affects the enhancement of the fluorescence response. Compared to the film without a PAA layer, the PAA/Al film exhibits more than 140-fold larger fluorescence response due to the large adsorption capacity of the PAA nanopores and the enhanced field formed by the waveguide modes in the PAA layer with a low refractive index.

Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning.

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

Development of a Novel Evaluation Method for Endoscopic Ultrasound-Guided Fine-Needle Biopsy in Pancreatic Diseases Using Artificial Intelligence.

We aimed to develop a new artificial intelligence (AI)-based method for evaluating endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) specimens in pancreatic diseases using deep learning and contrastive learning. We analysed a total of 173 specimens from 96 patients who underwent EUS-FNB with a 22 G Franseen needle for pancreatic diseases. In the initial study, the deep learning method based on stereomicroscopic images of 98 EUS-FNB specimens from 63 patients showed an accuracy of 71.8% for predicting the histological diagnosis, which was lower than that of macroscopic on-site evaluation (MOSE) performed by EUS experts (81.6%). Then, we used image analysis software to mark the core tissues in the photomicrographs of EUS-FNB specimens after haematoxylin and eosin staining and verified whether the diagnostic performance could be improved by applying contrastive learning for the features of the stereomicroscopic images and stained images. The sensitivity, specificity, and accuracy of MOSE were 88.97%, 53.5%, and 83.24%, respectively, while those of the AI-based diagnostic method using contrastive learning were 90.34%, 53.5%, and 84.39%, respectively. The AI-based evaluation method using contrastive learning was comparable to MOSE performed by EUS experts and can be a novel objective evaluation method for EUS-FNB.

Properties of a metal clad waveguide sensor based on a nanoporous-metal-oxide/metal multilayer film.

A metal-clad waveguide (MCWG) sensor comprised of a nanoporous waveguiding layer on a metal cladding layer is advantageous in sensing of biomolecules because of a high surface area of nanopores and a sharp dip in the reflection spectrum due to characteristics of the MCWG mode. Here, a porous anodic alumina (PAA)/aluminum (Al) film was fabricated on a glass substrate as a MCWG sensor with the Kretschmann geometry, and the sensor response was examined for both colorless bovine serum albumin (BSA) and colored metal complexes by measurements of reflection spectra and Fresnel calculations. The BSA adsorption on the PAA layer induced a parallel redshift of the waveguide coupling dip in the reflection spectrum. The experimental results were well simulated by the five-phase Fresnel calculations which indicated that the redshift of the dip was linearly dependent on the adsorbed amount of BSA. When the response of a MCWG sensor with a PAA layer was compared with that of a MCWG sensor with a nonporous alumina layer, the former showed larger redshift than the latter, due to a large adsorbed amount of BSA in the PAA layer with high surface area. For the adsorption of colored Ru[Bphen(3)](2+) and Fe[Phen(3)](2+), the effect of both the real and imaginary parts of the complex refractive index on the sensor response was examined. As a result, a redshift of the waveguide coupling dip was observed for both metal complexes irrespective of the wavelength region examined; this could be ascribed to the changes in the real part of the refractive index due to the adsorption of metal complexes on the PAA layer. Meanwhile, an increase in the reflectivity was observed when the coupling wavelength was close to that of the absorption bands of the metal complexes; this could be ascribed to the changes in the imaginary part of the refractive index of the PAA layer. Using the sensor response caused by the changes in the imaginary part, absorption spectral profiles of metal complexes could be reproduced.

Optical waveguide sensor based on a porous anodic alumina/aluminum multilayer film.

An optical waveguide sensor was fabricated by forming a multilayer film made by porous anodic alumina (PAA) and Al layers on a glass substrate. The fabricated sensor system was based on the monitoring of a waveguide coupling mode, which is sensitive to the change in the refractive index of the PAA layer caused by collection of target molecules into the pores of the PAA layer. The PAA/Al multilayer film was formed by partial anodization of an Al film deposited on the glass substrate, and the waveguide coupling mode was examined by measuring angular spectra (reflectivity dependence on the incident angle of monitoring light; green He-Ne laser, 534.5 nm). A deep and sharp waveguide coupling dip was obtained for the PAA/Al multilayer system where the thicknesses of the PAA and Al layers were 200 and 17 nm, respectively. The optical sensor response of the PAA/Al multilayer system was compared to the responses of a surface plasmon resonance (SPR) sensor made by a Au thin film on a SF10 glass substrate. It was inferred that the optical waveguide sensor made by the PAA/Al multilayer could detect a smaller change in the refractive index of a solution, and it provided higher resolution than the SPR sensor. The sensor response for a change in the complex refractive index of the PAA layer was examined next, and it was found that the optical waveguide sensor was sensitive to the change in the imaginary part of the complex refractive index rather than the change in the real part. This result indicated that the sensitivity of the optical waveguide sensor could be improved by using the light absorption of a target compound.

Robust cell particle detection to dense regions and subjective training samples based on prediction of particle center using convolutional neural network.

In recent years, finding the cause of pathogenesis is expected by observing the cell images. In this paper, we propose a cell particle detection method in cell images. However, there are mainly two kinds of problems in particle detection in cell image. The first is the different properties between cell images and standard images used in computer vision researches. Edges of cell particles are ambiguous, and overlaps between cell particles are often occurred in dense regions. It is difficult to detect cell particles by simple detection method using a binary classifier. The second is the ground truth made by cell biologists. The number of training samples for training a classifier is limited, and incorrect samples are included by the subjectivity of observers. From the background, we propose a cell particle detection method to address those problems. In our proposed method, we predict the center of a cell particle from the peripheral regions by convolutional neural network, and the prediction results are voted. By using the obvious peripheral edges, we can robustly detect overlapped cell particles because all edges of overlapping cell particles are not ambiguous. In addition, voting from peripheral views enables reliable detection. Moreover, our method is useful in practical applications because we can prepare many training samples from a cell particle. In experiments, we evaluate our detection methods on two kinds of cell detection datasets. One is challenging dataset for synthetic cells, and our method achieved the state-of-the-art performance. The other is real dataset of lipid droplets, and our method outperformed the conventional detector using CNN with binary outputs for particles and non-particles classification.

Automated adherent cell elimination by a high-speed laser mediated by a light-responsive polymer.

Conventional cell handling and sorting methods require manual labor, which decreases both cell quality and quantity. To purify adherent cultured cells, cell purification technologies that are high throughput without dissociation and can be utilized in an on-demand manner are expected. Here, we developed a Laser-induced, Light-responsive-polymer-Activated, Cell Killing (LiLACK) system that enables high-speed and on-demand adherent cell sectioning and purification. This system employs a visible laser beam, which does not kill cells directly, but induces local heat production through the trans-cis-trans photo-isomerization of azobenzene moieties. Using this system in each passage for sectioning, human induced pluripotent stem cells (hiPSCs) maintained their pluripotency and self-renewal during long-term culture. Furthermore, combined with deep machine-learning analysis on fluorescent and phase contrast images, a label-free and automatic cell processing system has been developed by eliminating unwanted spontaneously differentiated cells in undifferentiated hiPSC culture conditions.

Nanoporous Waveguide Spectroscopy for the Estimation of Enzyme Adsorption on Mesoporous Silica.

Mesoporous silica is considered as promising host material for enzymes due to its uniform pore size of enzyme dimensions and tunable surface chemical properties. In this study, we applied nanoporous waveguide (NPWG) spectroscopy to observe adsorption dynamics of heme proteins with different molecular size within mesoporous silica film modified with different surface functional groups. Since NPWG spectroscopy provides kinetic information and rough quantification of adsorption amount, it is useful to study the adsorption process of enzymes within inorganic nanoporous materials.

Encapsulation of PEG-modified myoglobin in hydrophobic mesoporous silica as studied by optical waveguide spectroscopy.

The purpose of this study is to apply optical waveguide (OWG) spectroscopy to characterize the encapsulation behavior of enzymes modified with polyethylene glycol (PEG), i.e. pegylation, in a hydrophobic mesoporous silica film. For that purpose, pegylated myoglobin (PEG-Mb) was introduced into the silica mesopores modified with octadecylsilyl (ODS) groups and studied by OWG spectroscopy. OWG spectroscopy confirmed that the hydrophobic interaction between the PEG group and the surface ODS group promoted the encapsulation of PEG-Mb into the hydrophobic silica mesopores. The surface density of ODS affected the adsorbed amount of PEG-Mb and the higher surface density of the ODS group resulted in the suppression of adsorption and diffusion of PEG-Mb inside the pore. Since the desorption rate of PEG-Mb was found to be much slower than the adsorption rate, the pegylation of an enzyme could be effective for the enzyme encapsulation into the hydrophobic mesoporous silica host.