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High-angle annular dark-field (HAADF) imaging and elemental mapping at the atomic scale by scanning transmission electron microscopy (STEM) combined with electron energy-loss spectroscopy (EELS) are widely used for material characterization, in which quantitative understanding of the contrast of the image is required. Here, we report an unexpected image contrast in the elemental mapping of rutile TiO2, where the Ti L2,3 map shows an anisotropic elliptical shape that extends along the long axis in the octahedral structure, while the atomic contrast of Ti columns in the HAADF image is almost circular. Multi-slice simulation reveals that unique electron channeling related to the rutile structure and the difference of the potentials between HAADF and EELS cause the different atomic contrasts in the two images., (© The Author(s) 2022. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journa ls.permissions@oup.com.)

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV., (© The Author(s) 2021. Published by Oxford University Press.)

Methylation of DNA N6-methyladenosine (6mA) is a type of epigenetic modification that plays pivotal roles in various biological processes. The accurate genome-wide identification of 6mA is a challenging task that leads to understanding the biological functions. For the last 5 years, a number of bioinformatics approaches and tools for 6mA site prediction have been established, and some of them are easily accessible as web application. Nevertheless, the accurate genome-wide identification of 6mA is still one of the challenging works that lead to understanding the biological functions. Especially in practical applications, these tools have implemented diverse encoding schemes, machine learning algorithms and feature selection methods, whereas few systematic performance comparisons of 6mA site predictors have been reported. In this review, 11 publicly available 6mA predictors evaluated with seven different species-specific datasets (Arabidopsis thaliana, Tolypocladium, Diospyros lotus, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli). Of those, few species are close homologs, and the remaining datasets are distant sequences. Our independent, validation tests demonstrated that Meta-i6mA and MM-6mAPred models for A. thaliana, Tolypocladium, S. cerevisiae and D. melanogaster achieved excellent overall performance when compared with their counterparts. However, none of the existing methods were suitable for E. coli, C. elegans and D. lotus. A feasibility of the existing predictors is also discussed for the seven species. Our evaluation provides useful guidelines for the development of 6mA site predictors and helps biologists selecting suitable prediction tools., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naïve Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures., (© The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

The coupled surface plasmon-polariton (SPP) modes excited in an Al/SiO2/Al multilayer structure were analyzed using angle-resolved electron energy-loss spectroscopy (AREELS) with a relativistic electron probe. The dispersion relations for the coupled SPP modes were then directly observed and compared with predicted relations obtained via calculations. Good agreement was noted between the experimental and calculated results. In the multilayer structures, the dispersion relation for the coupled SPP modes was found to be sensitive to the thickness of each film, which could be interpreted qualitatively by the electron energy-loss probability calculated for thin aluminum (Al) films and narrow Al gaps using Kröger's formula. It was demonstrated that significant differences in the excitation probability for SPPs could be observed depending on the coupling modes.

ONO-4641 is a next-generation sphingosine 1-phosphate (S1P) receptor agonist selective for S1P receptors 1 and 5. The objective of the study was to characterize the immunomodulatory effects of ONO-4641 using preclinical data. ONO-4641 was tested in both in-vitro pharmacological studies as well as in-vivo models of transient or relapsing-remitting experimental autoimmune encephalomyelitis (EAE). In vitro, ONO-4641 showed highly potent agonistic activities versus S1P receptors 1 and 5 [half maximal effective concentration (EC(50) ) values of 0·0273 and 0·334 nM, respectively], and had profound S1P receptor 1 down-regulating effects on the cell membrane. ONO-4641 decreased peripheral blood lymphocyte counts in rats by inhibiting lymphocyte egress from secondary lymphoid tissues. In a rat experimental autoimmune encephalomyelitis (EAE) model, ONO-4641 suppressed the onset of disease and inhibited lymphocyte infiltration into the spinal cord in a dose-dependent manner at doses of 0·03 and 0·1 mg/kg. Furthermore, ONO-4641 prevented relapse of disease in a non-obese diabetic mouse model of relapsing-remitting EAE. These observations suggest that ONO-4641 may provide therapeutic benefits in the treatment of multiple sclerosis., (© 2012 ONO Pharmaceutical Co., Ltd Clinical and Experimental Immunology © 2012 British Society for Immunology.)

Motivation: Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level., Results: To overcome such existing limitations, we develop a novel algorithm that predicts the flux distribution of the mutants with a broad range of genetic modification, based on elementary mode analysis. It is denoted as genetic modification of flux (GMF), which couples two algorithms that we have developed: modified control effective flux (mCEF) and enzyme control flux (ECF). mCEF is proposed based on CEF to estimate the gene expression patterns in genetically modified mutants in terms of specific biological functions. GMF is demonstrated to predict the flux distribution of not only gene deletion mutants, but also the mutants with underexpressed and overexpressed genes in Escherichia coli and Corynebacterium glutamicum. This achieves breakthrough in the a priori flux prediction of a broad range of genetically modified mutants., Supplementary Information: Supplementary file and programs are available at Bioinformatics online or http://www.cadlive.jp.

We investigated the chemical shift of the electron energy-loss near-edge structure (ELNES) for the nitrogen K-edge and titanium L3-edge measured from the interface region between a titanium nitride layer and a titanium layer. Both the titanium nitride and titanium layers were prepared by a sputtering method. Elemental analysis for nitride and titanium in the vicinity of the interface region was performed using a standard technique in electron energy-loss spectroscopy. It was demonstrated that both the ELNES of nitrogen K-edge and titanium L3-edge presented the chemical shift, more or less, depending on the composition of TiNx. The experimental findings were interpreted using a first-principles band structure calculation. The chemical shifts of nitrogen K-edge and titanium L3-edge can be used as fingerprinting for readily distinguishing the composition of TiNx.

We have applied high-angle annular dark-field microscopy to the characterization of the structure of template-grown nanotubes composed of a polymer and a discotic liquid crystalline material. Selective staining of the liquid crystal phase with ruthenium tetroxide was used to develop adequate Z-contrast that allows us to distinguish between the two phases. At appropriate staining conditions, we could clearly visualize, in the annular dark-field mode, a 5-15-nm thin liquid crystalline layer precipitated on the inner surface of the polymer tubes. Cryo-electron diffraction has shown high alignment of the discotic columns within the layer parallel to the tube axis. However, although the polymer/liquid crystal phase separation is almost complete, the wetting behavior of the polymer in relation to the template appears to be sensitively influenced by kinetic factors.

The effect of peripheral halogenation is examined based on analytical transmission electron microscopy and thermal analyses of two chemical family structures, specifically the vanadyl-phthalocyanine family (VOPcX: X = H16, F14.5) and the copper-phthalocyanine family (CuPcX: X = H16, F16, Cl16, Cl8Br8), focusing on the process of molecular changes and crystalline disintegrations. To clarify the molecular transformations, electron energy-loss spectroscopy (EELS) is applied to two fluorinated phthalocyanines (VOPcF14.5 and CuPcF16), by monitoring mass changes as well as energy loss near edge structures (ELNES). The elemental mass of both VOPcF14.5 and CuPcF16 remain constant up to 0.5 C x cm(-2), except in the case of mass reduction attributed to oxygen loss occurring in VOPcF14.5. It is expected that the released oxygen will induce higher radiation damage in VOPcF14.5. Although mass variation is not observed in CuPcF16, it is found from ELNES that the pi resonant system of nitrogen is more radiation sensitive than that of carbon. These results imply that the electron sensitivity in VOPcX is triggered by eliminated oxygen or, thus, an induced larger empty space, whereas the sensitivity of CuPcX is dominated only by a large intermolecular empty space resulting in the following bond alterations. It is also found that the decomposition temperature (Td) measured by thermal analyses and the characteristic dose (D1/e) are exponentially correlated to the "effective molecular occupancy" (Oe) evaluated as a volume function of molecules in unit cells. By measuring Td and/or Oe, we discuss the durability of peripheral halogenation with respect to the radiation damage.

Duchenne muscular dystrophy (DMD) is a common X-linked disease resulting from the absence of dystrophin in muscle. Affected boys suffer from incurable progressive muscle weakness, leading to premature death. Stem cell transplantation may be curative, but is hampered by the need for systemic delivery and immune rejection. To address these barriers to stem cell therapy in DMD, we investigated a fetal-to-fetal transplantation strategy. We investigated intramuscular, intravascular, and intraperitoneal delivery of human fetal mesenchymal stem cells (hfMSCs) into embryonic day (E) 14-16 MF1 mice to determine the most appropriate route for systemic delivery. Intramuscular injections resulted in local engraftment, whereas both intraperitoneal and intravascular delivery led to systemic spread. However, intravascular delivery led to unexpected demise of transplanted mice. Transplantation of hfMSCs into E14-16 mdx mice resulted in widespread long-term engraftment (19 weeks) in multiple organs, with a predilection for muscle compared with nonmuscle tissues (0.71% vs. 0.15%, p < .01), and evidence of myogenic differentiation of hfMSCs in skeletal and myocardial muscle. This is the first report of intrauterine transplantation of ontologically relevant hfMSCs into fully immunocompetent dystrophic fetal mice, with systemic spread across endothelial barriers leading to widespread long-term engraftment in multiple organ compartments. Although the low-level of chimerism achieved is not curative for DMD, this approach may be useful in other severe mesenchymal or enzyme deficiency syndromes, where low-level protein expression may ameliorate disease pathology.

The biological properties of stem cells are key to the success of cell therapy, for which MSC are promising candidates. Although most therapeutic applications to date have used adult bone marrow MSC, increasing evidence suggests that MSC from neonatal and mid-gestational fetal tissues are more plastic and grow faster. Fetal stem cells have been isolated earlier in development, from first-trimester blood and hemopoietic organs, raising the question of whether they are biologically closer to embryonic stem cells and thus have advantages over adult bone marrow MSC. In this study, we show that human first-trimester fetal blood, liver, and bone marrow MSC but not adult MSC express the pluripotency stem cell markers Oct-4, Nanog, Rex-1, SSEA-3, SSEA-4, Tra-1-60, and Tra-1-81. In addition, fetal MSC, irrespective of source, had longer telomeres (p < .001), had greater telomerase activity (p < .01), and expressed more human telomerase reverse transcriptase (p < .01). Fetal MSC were also more readily expandable and senesced later in culture than their adult counterparts (p < .01). Compared with adult MSC, first-trimester fetal tissues constitute a source of MSC with characteristics that appear advantageous for cell therapy.

Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents 'Domain expansion' for proteins and RNAs, 'Virtual reaction and nodes' that are responsible for illustrating domain-based interaction and 'InnerLink' that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis.

Motivation: Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks., Results: We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE., Availability: All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid&#95;layout.htm; http://www.cadlive.jp/, Supplementary Information: http://kurata21.bio.kyutech.ac.jp/grid/grid&#95;layout.htm; http://www.cadlive.jp/

The further understanding of the mechanisms of gene regulatory networks requires comprehensive tools for both the representation of complicated signal transduction pathways and the in silico identification of genomic signals that govern the regulation of gene expression. Consequently, sophisticated notation must be developed to represent the signal transduction pathways in a form that can be readily processed by both computers and humans. We propose the regulator-reaction equations combined with detailed attributes including the associated cellular component, molecular function, and biological process and present the simulation-directed graphical notation that is derived from modification of Kohn's method. We have developed the software suite, CADLIVE (Computer-Aided Design of LIVing systEms), which features a graphical user interface (GUI) to edit large-scale maps of complicated signal transduction pathways using a conventional XML-based representation. The regulator-reaction equations represent not only mechanistic reactions, but also semantic models containing ambiguous and incomplete processes. In order to demonstrate the feasibility of CADLIVE, we constructed a detailed map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, in a really compact space. CADLIVE enables one to look at the whole view of a large-scale map, to integrate postgenomic data into the map, and to computationally simulate the signal transduction pathways, which greatly facilitates exploring novel or unexpected interactions.

The phenomenon known as RNA interference (RNAi) by double-stranded RNA (dsRNA) that was reported recently in the nematode Caenorhabditis elegans has been shown to operate by a mechanism that is widely conserved among species including plant cells. No quantitative analysis of the effects of RNAi on the expression of specific genes in plant cultured cells has been reported. An RNAi effect was observed 24 h after the introduction of dsRNA expression plasmids into tobacco BY-2 cells by electroporation. The simple system for suppression of specific genes in plant cells should be useful in attempts to elucidate the roles of individual genes in plant cells.

A biosensing system based on the response of fungal cells was used for the evaluation of antifungal activity of medicinal plants against Aspergillus niger. This system measured the hyphal growth rate in real time in the presence or absence of Chinese herbal extracts. The sensitivity of this system was 100-fold higher than that of conventional methods, and is advisable for the screening of antifungal compounds.

Mannan-binding proteins (MBPs) occur in two forms, both of which are synthesized in the liver. Although two different MBP cDNAs have been cloned and characterized for rat and mouse, only one form of human MBP cDNA has been isolated. In this study, two forms of human MBP, liver MBP (L-MBP) and serum MBP (S-MBP), were purified from liver and serum and characterized, respectively. The amino acid sequences of these two human MBPs were identical and consistent with those deduced from the cDNA sequence. The most significant difference between L-MBP and S-MBP was the number of subunits, which was about 9 in L-MBP and 18 in S-MBP. Furthermore, S-MBP but not L-MBP had the ability to activate the complement. These results suggested that a newly synthesized protein is processed post-translationally into two forms, S-MBP and L-MBP, in human liver. Recombinant MBP synthesized in COS-1 cells, after transfection with human MBP cDNA, was secreted into the medium, suggesting that COS-1 cells lack a mechanism for differentiating S-MBP and L-MBP. A mutant MBP synthesized in COS-1 cells which lacked a sequence comprising 9 amino acid residues at the beginning of the collagen-like domain had no ability to activate the complement, suggesting that this sequence plays an important role in the activation of the complement by human MBP.

This report shows the results of the reinvestigation of tRNA phosphorylation in E. coli. The phosphorylation did not occur on suppressor seryl-tRNA but occurred on other tRNA species. The activity of tRNA phosphorylation was found in E. coli extracts and partially purified. On DEAE-Sephadex A50 and PAGE gel, the phosphorylated-tRNA showed a pattern different from that the natural suppressor serine tRNA.

Two seryl-tRNAs, which were prepared from natural suppressor tRNA-rich fractions on a pattern of chromatography on Sephadex A-50, were phosphorylated by a tRNA kinase in Escherichia coli B. A part of phosphate on the tRNA was confirmed to be phosphoserine. Phosphoseryl-tRNA is universal in bacteria and vertebrates. Phosphoseryl-tRNA should be an intermediate from seryl-tRNA to selenocysteyl-tRNA.