Synergistic Interaction between Ruthenium Catalysts and Grafted Niobium on SBA-15 for 2,5-Furandicarboxylic Acid Production Using 5-Hydroxymethylfurfural.

This study entailed the synthesis of Ru nanocatalyst decorated on Nb-grafted SBA-15. A Nb-grafted SBA-15 support with varying Nb contents was utilized as a support for the Ru nanoparticles. The effect of Nb grafting on the immobilized Ru nanoparticle catalyst was systematically investigated, and its catalytic performance in the synthesis of furandicarboxylic acid using 5-hydroxymethylfurfural under base-free reaction conditions was evaluated. The results indicate the increased productivity of the Ru@Nb-grafted SBA-15 catalyst with a yield exceeding 95%, representing a significant advancement in catalysis. This study also affords insights into the complex relationship between the catalytic activity and selectivity and its unique surface attributes. Moreover, acidic sites were created, and the electron density within the active sites was modulated by monomeric Nb oxide species on the SBA-15. Additionally, the role of high-electron-density Ru atoms in facilitating the efficient adsorption and activation of the reactant, resulting in enhanced catalytic efficacy, was highlighted.

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address deficiencies in the model's predictive accuracy and generalizability; this can be addressed by expanding and integrating experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be trained separately, and they should not be simply combined without reconciling the discrepancies from different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the experimental database. Dataset augmentation is performed from 11 637 experimental solubilities to >900 000 data points in the integrated database, while correcting for the discrepancies between experiment and computation. Our model was successfully applied to study solvent selection in organic reactions and separation processes. The accuracy (mean absolute error around 0.2 kcal mol-1 for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.

Drug Discovery Perspectives of Antisense Oligonucleotides.

The era of innovative RNA therapies using antisense oligonucleotides (ASOs), siRNAs, and mRNAs is beginning. Since the emergence of the concept of ASOs in 1978, it took more than 20 years before they were developed into drugs for commercial use. Nine ASO drugs have been approved to date. However, they target only rare genetic diseases, and the number of chemistries and mechanisms of action of ASOs are limited. Nevertheless, ASOs are accepted as a powerful modality for next-generation medicines as they can theoretically target all disease-related RNAs, including (undruggable) protein-coding RNAs and non-coding RNAs. In addition, ASOs can not only downregulate but also upregulate gene expression through diverse mechanisms of action. This review summarizes the achievements in medicinal chemistry that enabled the translation of the ASO concept into real drugs, the molecular mechanisms of action of ASOs, the structure-activity relationship of ASO-protein binding, and the pharmacology, pharmacokinetics, and toxicology of ASOs. In addition, it discusses recent advances in medicinal chemistry in improving the therapeutic potential of ASOs by reducing their toxicity and enhancing their cellular uptake.

Short and scalable synthesis of cynandione A.

A two-step gram-scale synthesis of cynandione A is described. The key to success is the one-pot tandem oxidation/regioselective arylation of 1,4-hydroquinone in the presence of an excess amount of oxidant. Natural bond orbital charge analysis was performed in order to understand the regioselectivity of the arylation step. The highly practical and scalable synthesis developed herein is expected to assist the in-depth biological evaluation of cynandione A in various animal models.

Developing CRISPR/Cas9-Mediated Fluorescent Reporter Human Pluripotent Stem-Cell Lines for High-Content Screening.

Application of the CRISPR/Cas9 system to knock in fluorescent proteins to endogenous genes of interest in human pluripotent stem cells (hPSCs) has the potential to facilitate hPSC-based disease modeling, drug screening, and optimization of transplantation therapy. To evaluate the capability of fluorescent reporter hPSC lines for high-content screening approaches, we targeted EGFP to the endogenous OCT4 locus. Resulting hPSC-OCT4-EGFP lines generated expressed EGFP coincident with pluripotency markers and could be adapted to multi-well formats for high-content screening (HCS) campaigns. However, after long-term culture, hPSCs transiently lost their EGFP expression. Alternatively, through EGFP knock-in to the AAVS1 locus, we established a stable and consistent EGFP-expressing hPSC-AAVS1-EGFP line that maintained EGFP expression during in vitro hematopoietic and neural differentiation. Thus, hPSC-AAVS1-EGFP-derived sensory neurons could be adapted to a high-content screening platform that can be applied to high-throughput small-molecule screening and drug discovery campaigns. Our observations are consistent with recent findings indicating that high-frequency on-target complexities appear following CRISPR/Cas9 genome editing at the OCT4 locus. In contrast, we demonstrate that the AAVS1 locus is a safe genomic location in hPSCs with high gene expression that does not impact hPSC quality and differentiation. Our findings suggest that the CRISPR/Cas9-integrated AAVS1 system should be applied for generating stable reporter hPSC lines for long-term HCS approaches, and they underscore the importance of careful evaluation and selection of the applied reporter cell lines for HCS purposes.

Skin barrier dysfunction and filaggrin.

Skin barrier dysfunction caused by endogenous or exogenous factors can lead to various disorders such as xerosis cutis, ichthyoses, and atopic dermatitis. Filaggrin is a pivotal structural protein of the stratum corneum (SC) and provides natural moisturizing factors that play a role in skin barrier functions. Filaggrin aggregates keratin filaments, resulting in the formation of a keratin network, which binds cornified envelopes and collapse keratinocytes to flattened corneocytes. This complex network contributes to the physical strength of the skin. Filaggrin is degraded by caspase-14, calpain 1, and bleomycin hydrolases into amino acids and amino acid metabolites such as trans-urocanic acid and pyrrolidone carboxylic acid, which are pivotal natural moisturizing factors in the SC. Accordingly, filaggrin is important for the pathophysiology of skin barrier disorders, and its deficiency or dysfunction leads to a variety of skin disorders. Here, the roles and biology of filaggrin, related skin diseases, and a therapeutic strategy targeting filaggrin are reviewed. In addition, several drug candidates of different mode of actions targeting filaggrin, along with their clinical efficacy, are discussed.

Acid-treated waste red mud as an efficient catalyst for catalytic fast copyrolysis of lignin and polyproylene and ozone-catalytic conversion of toluene.

In this study, red mud (RM), a highly alkaline waste generated from alumina production industries, was used as a catalytic material for both fast copyrolysis of organosolv lignin (OL) and polypropylene (PP) and toluene removal under ozone at room temperature. The RM was pretreated with HCl to investigate the effect of alkalinity. In the catalytic fast copyrolysis of the OL and PP, the acid-treated RM (HRM) produced more aromatics, phenolics, and light olefins (C3 to C5) but less oxygenates and heavy olefins (C6 to C46) than the RM. The difference in pyrolytic performance between the RM and HRM was likely attributed to the concentrated Fe2O3 species in the HRM catalyst. In addition, more efficient toluene removal was observed over MnOx/HRM than over MnOx/RM owing to the large Brunauer-Emmett-Teller surface area, high amounts of Al and Fe, and optimal Mn3+/Mn4+ ratio. This study demonstrates that the RM, an industrial waste, can be reused as an effective catalytic material for not only biofuel production but also pollutant removal.

Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules.

The stabilities of radicals play a central role in determining the thermodynamics and kinetics of many reactions in organic chemistry. In this data descriptor, we provide consistent and validated quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules containing C, H, N and O atoms. These data consist of optimized 3D geometries, enthalpies, Gibbs free energy, vibrational frequencies, Mulliken charges and spin densities calculated at the M06-2X/def2-TZVP level of theory, which was previously found to have a favorable trade-off between experimental accuracy and computational efficiency. We expect this data to be useful in the further development of machine learning techniques to predict reaction pathways, bond strengths, and other phenomena closely related to organic radical chemistry.

Publisher Correction: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A perspective on biomass-derived biofuels: From catalyst design principles to fuel properties.

The hazards to health and the environment associated with the transportation sector include smog, particulate matter, and greenhouse gas emissions. Conversion of lignocellulosic biomass into biofuels has the potential to provide significant amounts of infrastructure-compatible liquid transportation fuels that reduce those hazardous materials. However, the development of these technologies is inefficient, due to: (i) the lack of a priori fuel property consideration, (ii) poor shared vocabulary between process chemists and fuel engineers, and (iii) modern and future engines operating outside the range of traditional autoignition metrics such as octane or cetane numbers. In this perspective, we describe an approach where we follow a "fuel-property first" design methodology with a sequence of (i) identifying the desirable fuel properties for modern engines, (ii) defining molecules capable of delivering those properties, and (iii) designing catalysts and processes that can produce those molecules from a candidate feedstock in a specific conversion process. Computational techniques need to be leveraged to minimize expenses and experimental efforts on low-promise options. This concept is illustrated with current research information available for biomass conversion to fuels via catalytic fast pyrolysis and hydrotreating; outstanding challenges and research tools necessary for a successful outcome are presented.

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost.

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

Graph theory-based reaction pathway searches and DFT calculations for the mechanism studies of free radical-initiated peptide sequencing mass spectrometry (FRIPS MS): a model gas-phase reaction of GGR tri-peptide.

Graph theory-based reaction pathway searches (ACE-Reaction program) and density functional theory calculations were performed to shed light on the mechanisms for the production of [an + H]+, xn+, yn+, zn+, and [yn + 2H]+ fragments formed in free radical-initiated peptide sequencing (FRIPS) mass spectrometry measurements of a small model system of glycine-glycine-arginine (GGR). In particular, the graph theory-based searches, which are rarely applied to gas-phase reaction studies, allowed us to investigate reaction mechanisms in an exhaustive manner without resorting to chemical intuition. As expected, radical-driven reaction pathways were favorable over charge-driven reaction pathways in terms of kinetics and thermodynamics. Charge- and radical-driven pathways for the formation of [yn + 2H]+ fragments were carefully compared, and it was revealed that the [yn + 2H]+ fragments observed in our FRIPS MS spectra originated from the radical-driven pathway, which is in contrast to the general expectation. The acquired understanding of the FRIPS fragmentation mechanism is expected to aid in the interpretation of FRIPS MS spectra. It should be emphasized that graph theory-based searches are powerful and effective methods for studying reaction mechanisms, including gas-phase reactions in mass spectrometry.

Relationship of sociodemographic and anthropometric characteristics, and nutrient and food intakes with osteoarthritis prevalence in elderly subjects with controlled dyslipidaemia: a cross-sectional study.

BACKGROUND AND OBJECTIVES: Several studies have suggested that abnormal levels of serum cholesterol may be a major risk factor for osteoarthritis. However, no studies have been conducted to prevent osteoarthritis under controlled conditions of serum cholesterol. This study aimed to examine the relationship of sociodemographic and anthropometric characteristics, and nutrient and food intakes with osteoarthritis prevalence in Korean elderly subjects with controlled dyslipidaemia. METHODS AND STUDY DESIGN: This study included 314 subjects aged ≥65 years who were diagnosed and treated for dyslipidaemia (data from the Seventh Korea National Health and Nutrition Examination Survey, 2016). Among them, 108 were also diagnosed with osteoarthritis. Sociodemographic, health, and nutritional data were analysed. RESULTS: Osteoarthritis prevalence was higher in females, highereducated subjects, unmarried subjects, non-smokers, and subjects with high body mass index (p<0.05). After adjusting for the multiple variables, the non-osteoarthritis group had significantly higher vitamin C intake (132±11.0 vs 93.1±11.1 mg/day), fish intake (172±30.0 vs 79.0±12.9 g/day), and seaweed intake (93.7±19.3 vs 38.3±13.4 mg/day) than the osteoarthritis group. Furthermore, the lowest vitamin C, fish, seaweed intake group (quartile 1) each had 3.20, 2.76, 9.93 times higher risk of osteoarthritis than the highest vitamin C, fish, seaweed intake group (quartile 4) (p<0.05). CONCLUSIONS: Among Korean elderly subjects with controlled dyslipidaemia, those with osteoarthritis had lower vitamin C, fish, seaweed intakes than those without osteoarthritis. Although our results do not prove that low vitamin C, fish, seaweed intakes cause osteoarthritis, such relationship is worth exploring for a preventive perspective.

Common features of F-18 FDG PET/CT findings in Scrub Typhus: prospective study before and after antibiotics therapy.

Scrub typhus is an acute febrile illness caused by obligate intracellular organism Orientia tsutsugamushi. While there have been many reports on the evaluation of disease activity and infectious diseases using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT), the clinical value of FDG PET/CT in scrub typhus has not been fully investigated. We enrolled 17 patients who were 18 years of age or older and clinically suspected of having scrub typhus with eschar. Clinical assessments, blood samples, and FDG PET/CT images were obtained at enrolment and again after 3 weeks. The median age of the patients was 65 years; 9 (52.9%) patients were male. On initial FDG PET/CT, the eschars showed markedly increased FDG uptake on PET imaging that improved after treatment. Generalized lymphadenopathy and splenomegaly with high FDG uptake were observed in all patients. On follow-up FDG PET/CT after appropriate therapy, FDG uptake and sizes of eschar, lymph nodes, and spleen were markedly decreased. As far as we are aware, this is the first investigation with multiple patients of FDG PET/CT in scrub typhus and the demonstration of clinical utility. FDG PET/CT imaging of scrub typhus could provide useful information about the clinical features before and after antibiotic treatment.

Performance of ACE-Reaction on 26 Organic Reactions for Fully Automated Reaction Network Construction and Microkinetic Analysis.

Accurate analysis of complex chemical reaction networks is necessary for reliable prediction of reaction mechanism. Though quantum chemical methods provide a desirable accuracy, large computational costs are unavoidable as considering numerous reaction pathways on the networks. We proposed a graph-theoretic approach combined with chemical heuristics (named ACE-Reaction) in previous work [ Chem. Sci. 2018 , 9 , 825 ], which automatically and rapidly finds out the most essential part of reaction networks just from reactants and products, and here we extended it by incorporating a stochastic approach for microkinetic modeling. To show its performance and broad applicability, we applied it to 26 organic reactions, which include 16 common functional groups. As a result, we could demonstrate that ACE-Reaction successfully found the accepted mechanism of all reactions, most within a few hours on a single workstation, and additional microkinetic modeling automatically discovered new competitive paths as well as a major path.

Poly(amide-imide) materials for transparent and flexible displays.

The key component currently missing for the next generation of transparent and flexible displays is a high-performance polymer material that is flexible, while showing optical and thermal properties of glass. It must be transparent to visible light and show a low coefficient of thermal expansion (CTE). While specialty plastics such as aromatic polyimides are promising, reducing their CTE and improving transparency simultaneously proved challenging, with increasing coloration the main problem to be resolved. We report a new poly(amide-imide) material that is flexible and displays glass-like behavior with a CTE value of 4 parts per million/°C. This novel polymer was successfully used as a substrate to fabricate transparent and flexible indium-gallium-zinc oxide thin-film transistors.

Efficient prediction of reaction paths through molecular graph and reaction network analysis.

Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. This can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. Finally, the resulting minimal network is subjected to quantum chemical calculations to determine kinetically the most favorable reaction path at the predictable accuracy. As example studies, our method was able to successfully find the accepted mechanisms of Claisen ester condensation and cobalt-catalyzed hydroformylation reactions.

Efficient structural elucidation of microhydrated biomolecules through the interrogation of hydrogen bond networks.

Microhydration of biomolecules is an important structural factor associated with their biological processes. However, there is no general way to elucidate stable hydrated structures even for simple amino acids because of the high complexity of chemical space increasing rapidly with the number of water molecules. Here, we propose a very efficient computational method to selectively sample the most stable structures of microhydrated molecules. The key idea is to utilize the unique structural patterns of H-bond networks obtained from their energetic features, i.e. their tendency to form more H-bonds. As a proof of concept, we could identify the new global minima of glycine·10(H2O) and for the first time, we found the minimum number of water molecules required to stabilize the zwitterionic form of tyrosine. Furthermore, the most stable structures of hydrated glycine and tyrosine indeed had common features, which were consistent with the X-ray data of proteins in water.

Feasibility of Activation Energy Prediction of Gas-Phase Reactions by Machine Learning.

Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas-phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features without resorting to specific reaction types so as to maintain the most general input form for broad applicability. The tree boosting method showed the best performance among others in terms of the coefficient of determination, mean absolute error, and root mean square error, the values of which were 0.89, 1.95, and 4.49 kcal mol-1 , respectively. Computation time for the prediction of activation energies for 2541 test reactions was about one second on a single computing node without using accelerators.

Retargeting Human-Object Interaction to Virtual Avatars.

In augmented reality (AR) applications, a virtual avatar serves as a useful medium to represent a human in a different place. This paper deals with the problem of retargeting a human motion to an avatar. In particular, we present a novel method that retargets a human motion with respect to an object to that of an avatar with respect to a different object of a similar shape. To achieve this, we developed a spatial map that defines the correspondences between any points in the 3D spaces around the respective objects. The key advantage of the spatial map is that it identifies the desired locations of the avatar's body parts for any input motion of a human. Once the spatial map is created offline, the motion retargeting can be performed in real-time. The retargeted motion preserves important features of the original motion such as the human pose and the spatial relation with the object. We report the results of a number of experiments that demonstrate the effectiveness of the proposed method.