Programmable directional color dynamics using plasmonics.

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual devices. However, the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural designs. Here, we present a multicolor nanofilter consisting of multilayered 'active' plasmonic nanocomposites, wherein metallic nanoparticles are embedded within a conductive polymer nanofilm. These nanocomposites are fabricated with a total thickness below 100 nm using a 'lithography-free' method at the wafer level, and they inherently exhibit three prominent optical modes, accompanying scattering phenomena that produce distinct dichroic reflection and transmission colors. Here, a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed, encompassing the entire visible spectrum. Furthermore, this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum (3250 K-6250 K). This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun's elevation and prevailing weather conditions.

Post-injury born oligodendrocytes incorporate into the glial scar and contribute to the inhibition of axon regeneration.

Failure of central nervous system projection neurons to spontaneously regenerate long-distance axons underlies irreversibility of white matter pathologies. A barrier to axonal regenerative research is that the axons regenerating in response to experimental treatments stall growth before reaching post-synaptic targets. Here, we test the hypothesis that the interaction of regenerating axons with live oligodendrocytes, which were absent during developmental axon growth, contributes to stalling axonal growth. To test this hypothesis, first, we used single cell RNA-seq (scRNA-seq) and immunohistology to investigate whether post-injury born oligodendrocytes incorporate into the glial scar after optic nerve injury. Then, we administered demyelination-inducing cuprizone and stimulated axon regeneration by Pten knockdown (KD) after optic nerve crush. We found that post-injury born oligodendrocyte lineage cells incorporate into the glial scar, where they are susceptible to the demyelination diet, which reduced their presence in the glial scar. We further found that the demyelination diet enhanced Pten KD-stimulated axon regeneration and that localized cuprizone injection promoted axon regeneration. We also present a resource for comparing the gene expression of scRNA-seq-profiled normal and injured optic nerve oligodendrocyte lineage cells.

Effects of Oxygen-Containing Functional Groups on the Electrochemical Performance of Activated Carbon for EDLCs.

Activated carbon (AC) is used in commercial electric double-layer capacitors (EDLC) as electrode active material owing to its favorable properties. However, oxygen functional groups (OFGs) present in AC reduce the lifespan of EDLCs. Thus, we investigated the correlation between the OFGs in AC and their electrochemical characteristics. Samples were prepared by heat-treating commercial AC at 300 °C-900 °C for 1 h under two gas atmospheres (N2 and 4% H2/N2 mixed gas). The textural properties were studied, and the reduction characteristics of AC under Ar and H2/Ar mixed gas atmospheres were investigated. Additionally, changes in the OFGs with respect to the heat-treatment conditions were examined via X-ray photoelectron spectroscopy. The specific surface areas of AC-N and AC-H were 2220-2040 and 2220-2090 m2/g, respectively. Importantly, the samples treated in hydrogen gas exhibited a higher yield than those treated in nitrogen while maintaining their pore characteristics. Additionally, the electrochemical performance of the AC was significantly enhanced after the reduction process; the specific capacitance increased from 62.1 F/g to 81.6 F/g (at 0.1 A/g). Thus, heat treatment in hydrogen gas improves the electrochemical performance of EDLCs without destroying the pore characteristics of AC.

Plasmonic Nanostructure Engineering with Shadow Growth.

Physical shadow growth is a vacuum deposition technique that permits a wide variety of 3D-shaped nanoparticles and structures to be fabricated from a large library of materials. Recent advances in the control of the shadow effect at the nanoscale expand the scope of nanomaterials from spherical nanoparticles to complex 3D shaped hybrid nanoparticles and structures. In particular, plasmonically active nanomaterials can be engineered in their shape and material composition so that they exhibit unique physical and chemical properties. Here, the recent progress in the development of shadow growth techniques to realize hybrid plasmonic nanomaterials is discussed. The review describes how fabrication permits the material response to be engineered and highlights novel functions. Potential fields of application with a focus on photonic devices, biomedical, and chiral spectroscopic applications are discussed.

Impact of 17ß-Estradiol on the Shape, Survival, Osteogenic Transformation, and mRNA Expression of Gingiva-Derived Stem Cell Spheroids.

Background and Objectives: Mesenchymal stem cells hold promise for tissue regeneration, given their robust growth and versatile differentiation capabilities. An analysis of bone marrow-sourced mesenchymal stem cell proliferation showed that 17ß-estradiol could enhance their growth. This study aims to investigate the influence of 17ß-estradiol on the shape, survival, osteogenic differentiation, and mineralization of human mesenchymal stem cells. Materials and Methods: Spheroids made from human gingiva-derived stem cells were cultivated with varying concentrations of 17ß-estradiol: 0, 0.01, 0.1, 1, and 10 nM. Morphology was assessed on days 1, 3, and 5. The live/dead kit assay was employed on day 3 for qualitative cell viability, while cell counting kit-8 was used for quantitative viability assessments on days 1, 3, and 5. To evaluate the osteogenic differentiation of the spheroids, a real-time polymerase chain reaction assessed the expressions of RUNX2 and COL1A1 on day 7. Results: The stem cells formed cohesive spheroids, and the inclusion of 17ß-estradiol did not noticeably alter their shape. The spheroid diameter remained consistent across concentrations of 0, 0.01, 0.1, 1, and 10 nM of 17ß-estradiol. However, cellular viability was boosted with the addition of 1 and 10 nM of 17ß-estradiol. The highest expression levels for RUNX2 and COL1A1 were observed with the introduction of 17ß-estradiol at 0.1 nM. Conclusions: In conclusion, from the results obtained, it can be inferred that 17ß-estradiol can be utilized for differentiating stem cell spheroids. Furthermore, the localized and controlled use, potentially through localized delivery systems or biomaterials, can be an area of active research. While 17ß-estradiol holds promise for enhancing stem cell applications, any clinical use requires a thorough understanding of its mechanisms, careful control of its dosage and delivery, and extensive testing to ensure safety and efficacy.

SYNCRIP controls miR-137 and striatal learning in animal models of methamphetamine abstinence.

Abstinence from prolonged psychostimulant use prompts stimulant withdrawal syndrome. Molecular adaptations within the dorsal striatum have been considered the main hallmark of stimulant abstinence. Here we explored striatal miRNA-target interaction and its impact on circulating miRNA marker as well as behavioral dysfunctions in methamphetamine (MA) abstinence. We conducted miRNA sequencing and profiling in the nonhuman primate model of MA abstinence, followed by miRNA qPCR, LC-MS/MS proteomics, immunoassays, and behavior tests in mice. In nonhuman primates, MA abstinence triggered a lasting upregulation of miR-137 in the dorsal striatum but a simultaneous downregulation of circulating miR-137. In mice, aberrant increase in striatal miR-137-dependent inhibition of SYNCRIP essentially mediated the MA abstinence-induced reduction of circulating miR-137. Pathway modeling through experimental deduction illustrated that the MA abstinence-mediated downregulation of circulating miR-137 was caused by reduction of SYNCRIP-dependent miRNA sorting into the exosomes in the dorsal striatum. Furthermore, diminished SYNCRIP in the dorsal striatum was necessary for MA abstinence-induced behavioral bias towards egocentric spatial learning. Taken together, our data revealed circulating miR-137 as a potential blood-based marker that could reflect MA abstinence-dependent changes in striatal miR-137/SYNCRIP axis, and striatal SYNCRIP as a potential therapeutic target for striatum-associated cognitive dysfunction by MA withdrawal syndrome.

Comparison of Pore Structures of Cellulose-Based Activated Carbon Fibers and Their Applications for Electrode Materials.

This study presents the first investigation of cellulose-based activated carbon fibers (RACFs) prepared as electrode materials for the electric double-layer capacitor (EDLC) in lieu of activated carbon, to determine its efficacy as a low-cost, environmentally friendly enhancement alternative to nanocarbon materials. The RACFs were prepared by steam activation and their textural properties were studied by Brunauer-Emmett-Teller and non-localized density functional theory equations with N2/77K adsorption isotherms. The crystallite structure of the RACFs was observed by X-ray diffraction. The RACFs were applied as an electrode material for an EDLC and compared with commercial activated carbon (YP-50F). The electrochemical performance of the EDLC was analyzed using galvanostatic charge/discharge curves, cyclic voltammetry, and electrochemical impedance spectroscopy. The results show that the texture properties of the activated carbon fibers were influenced by the activation time. Crucially, the specific surface area, total pore volume, and mesopore volume ratio of the RACF with a 70-min activation time (RACF-70) were 2150 m2/g, 1.03 cm3/g and 31.1%, respectively. Further, electrochemical performance analysis found that the specific capacitance of RACF-70 increased from 82.6 to 103.6 F/g (at 2 mA/cm2). The overall high specific capacitance and low resistance of the RACFs were probably influenced by the pore structure that developed outstanding impedance properties. The results of this work demonstrate that RACFs have promising application value as performance enhancing EDLC electrode materials.

Bamboo-Based Mesoporous Activated Carbon for High-Power-Density Electric Double-Layer Capacitors.

Demand for hybrid energy storage systems is growing, but electric double-layer capacitors (EDLCs) have insufficient output characteristics because of the microporous structure of the activated carbon electrode material. Commercially, activated carbon is prepared from coconut shells, which yield an activated carbon material (YP-50F) rich in micropores, whereas mesopores are desired in EDLCs. In this study, we prepared mesoporous activated carbon (PB-AC) using a readily available, environmentally friendly resource: bamboo. Crucially, modification using phosphoric acid and steam activation was carried out, which enabled the tuning of the crystal structure and the pore characteristics of the product. The structural characteristics and textural properties of the PB-AC were determined, and the specific surface area and mesopore volume ratio of the PB-AC product were 960-2700 m2/g and 7.5-44.5%, respectively. The high specific surface area and mesopore-rich nature originate from the phosphoric acid treatment. Finally, PB-AC was used as the electrode material in EDLCs, and the specific capacitance was found to be 86.7 F/g for the phosphoric-acid-treated sample steam activated at 900 °C for 60 min; this capacitance is 35% better than that of the commercial YP-50F (64.2 F/g), indicating that bamboo is a suitable material for the production of activated carbon.

Predicting potentially hazardous chemical reactions using an explainable neural network.

Predicting potentially dangerous chemical reactions is a critical task for laboratory safety. However, a traditional experimental investigation of reaction conditions for possible hazardous or explosive byproducts entails substantial time and cost, for which machine learning prediction could accelerate the process and help detailed experimental investigations. Several machine learning models have been developed which allow the prediction of major chemical reaction products with reasonable accuracy. However, these methods may not present sufficiently high accuracy for the prediction of hazardous products which particularly requires a low false negative result for laboratory safety in order not to miss any dangerous reactions. In this work, we propose an explainable artificial intelligence model that can predict the formation of hazardous reaction products in a binary classification fashion. The reactant molecules are transformed into substructure-encoded fingerprints and then fed into a convolutional neural network to make the binary decision of the chemical reaction. The proposed model shows a false negative rate of 0.09, which can be compared with 0.47-0.66 using the existing main product prediction models. To provide explanations for what substructures of the given reactant molecules are important to make a decision for target hazardous product formation, we apply an input attribution method, layer-wise relevance propagation, which computes the contributions of individual inputs per input data. The computed attributions indeed match some of the existing chemical intuitions and mechanisms, and also offer a way to analyze possible data-imbalance issues of the current predictions based on relatively small positive datasets. We expect that the proposed hazardous product prediction model will be complementary to existing main product prediction models and experimental investigations.

Developmentally upregulated transcriptional elongation factor a like 3 suppresses axon regeneration after optic nerve injury.

Projection neurons of the mammalian central nervous system (CNS) do not spontaneously regenerate axons which have been damaged by an injury or disease, often leaving patients with permanent disabilities that affect motor, cognitive, or sensory functions. Although several molecular targets which promote some extent of axon regeneration in animal models have been identified, the resulting recovery is very limited, and the molecular mechanisms underlying the axonal regenerative failure in the CNS are still poorly understood. One of the most studied targets for axon regeneration in the CNS is the mTOR pathway. A number of developmentally regulated genes also have been found to play a role in CNS axon regeneration. Here, we found that Transcriptional Elongation Factor A Like 3 (Tceal3), belonging to the Bex/Tceal transcriptional regulator family, which also modulates the mTOR pathway, is developmentally upregulated in retinal ganglion cell (RGCs) projection CNS neurons, and suppresses their capacity to regenerate axons after injury.

Differential Demographic and Clinical Characteristics between MMR Vaccinated and Unvaccinated Children in South Korea: A Nationwide Study.

In the context of recent measles outbreaks, substantial factors associated with measles-mumps-rubella (MMR) unvaccination need to be clarified. This study aimed to identify differential demographic and clinical characteristics between MMR vaccinated and unvaccinated groups. We used a large-linked database to identify children born between 2008 and 2016 by combining data from the Korea Immunization Registry Information System and National Health Information database. The MMR vaccination status was ascertained up to the age of 2 to define MMR vaccinated and unvaccinated groups. We conducted a multivariate logistic regression to estimate odds ratios (ORs) with 95% confidence intervals (CIs) to identify factors associated with MMR unvaccination. Of 3,973,253 children, 75,674 (1.9%) did not receive the MMR vaccine. Compared with the MMR vaccinated group, the underutilization of healthcare resources was more notable in the MMR unvaccinated group (number of outpatient visits (5.73 ± 12.1 vs. 25.8 ± 17.06); days hospitalized (1.69 ± 14.5 vs. 2.32 ± 6.90)). Children were less likely to receive the MMR vaccine if they were born with congenital anomaly (OR 2.12; 95% CI 1.90-2.36), were never admitted to an intensive care unit (1.88; 1.78-1.98), or never visited an emergency room (3.57; 3.53-3.72). There were substantial factors associated with MMR unvaccination, underscoring a need to optimize targeted interventions tailored to the subset of children in South Korea.

Modal decomposition of fiber modes based on direct far-field measurements at two different distances with a multi-variable optimization algorithm.

We present a novel method for modal decomposition of a composite beam guided by a large-mode-area fiber by means of direct far-field pattern measurements with a multi-variable optimization algorithm. For reconstructing far-field patterns, we use finite-number bases of Hermite Gaussian modes that can be converted from all the guided modes in the given fiber and exploit a stochastic parallel gradient descent (SPGD)-based multi-variable optimization algorithm equipped with the D4σ technique in order for completing the modal decomposition with compensating the centroid mismatch between the measured and reconstructed beams. We measure the beam intensity profiles at two different distances, which justifies the uniqueness of the solution obtained by the SPGD algorithm. We verify the feasibility and effectiveness of the proposed method both numerically and experimentally. We have found that the fractional error tolerance in terms of the beam intensity overlap could be maintained below 1 × 10-7 and 3.5 × 10-3 in the numerical and experimental demonstrations, respectively. As the modal decomposition is made uniquely and reliably, such a level of the error tolerance could be maintained even for a beam intensity profile measured at a farther distance.

Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation.

The extraordinary mass activity of jagged Pt nanowires can substantially improve the economics of the hydrogen evolution reaction (HER). However, it is a great challenge to fully unveil the HER kinetics driven by the jagged Pt nanowires with their multiscale morphology. Herein we present an end-to-end framework that combines experiment, machine learning, and multiscale advances of the past decade to elucidate the HER kinetics catalyzed by jagged Pt nanowires under alkaline conditions. The bifunctional catalysis conventionally refers to the synergistic increase in activity by the combination of two different catalysts. We report that monometals, such as jagged Pt nanowires, can exhibit bifunctional characteristics owing to its complex surface morphology, where one site prefers electrochemical proton adsorption and another is responsible for activation, resulting in a 4-fold increase in the activity. We find that the conventional design guideline that the sites with a 0 eV Gibbs free energy of adsorption are optimal for HER does not hold under alkaline conditions, and rather, an energy between -0.2 and 0.0 eV is shown to be optimal. At the reaction temperatures, the high activity arises from low-coordination-number (≤7) Pt atoms exposed by the jagged surface. Our current demonstration raises an emerging prospect to understand highly complex kinetic phenomena on the nanoscale in full by implementing end-to-end multiscale strategies.

An aza-Diels-Alder approach to chlorinated quinolines, benzoquinolines, and polybenzoquinolines.

Quinolines and quinoline-containing macromolecules are renowned for their valuable biological activities and excellent materials properties. Herein, we validate a general strategy for the synthesis of chloro-containing quinoline, benzoquinoline and polybenzoquinoline variants via the aza-Diels-Alder reaction. The described findings could be ultimately implemented in other synthetic pathways and may open new opportunities for analogous quinoline-derived materials.

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning.

Predicting the synthesizability of inorganic materials is one of the major challenges in accelerated material discovery. A widely employed approximate approach is to consider the thermodynamic decomposition stability due to its simplicity of computing, but it is notorious for either producing too many candidates or missing important metastable materials. These results, however, are not unexcepted since the synthesizability is a complex phenomenon, and the thermodynamic stability is just one contributor. Here, we suggest a machine-learning model to quantify the probability of synthesis based on the partially supervised learning of materials database. We adapted the positive and unlabeled machine learning (PU learning) by implementing the graph convolutional neural network as a classifier in which the model outputs crystal-likeness scores (CLscore). The model shows 87.4% true positive (CLscore > 0.5) prediction accuracy for the test set of experimentally reported cases (9356 materials) in the Materials Project. We further validated the model by predicting the synthesizability of newly reported experimental materials in the last 5 years (2015-2019) with an 86.2% true positive rate using the model trained with the database as of the end of year 2014. Our analysis shows that our model captures the structural motif for synthesizability beyond what is possible by Ehull. We find that 71 materials among the top 100 high-scoring virtual materials have indeed been previously synthesized in the literature. With the proposed data-driven metric of the crystal-likeness score, high-throughput virtual screenings and generative models can benefit significantly by effectively reducing the chemical space that needs to be explored experimentally in the future toward more rational materials design.

Formation of Large Crystalline Domains in a Semiconducting Polymer with Semi-fluorinated Alkyl Side Chains and Application to High-Performance Thin-Film Transistors.

The semi-fluorinated alkyl (SFA) side chain introduced thienylenevinylene (TV)-based p-type polymer, PC12TVC5F7T, was synthesized for use in organic thin-film transistors (OTFTs). Herein, we investigated the influence of SFA side chains on the morphology, molecular orientation, and crystalline structure using a combination of atomic force microscopy (AFM), scanning electron microscopy (SEM), two-dimensional (2D) grazing-incidence wide-angle X-ray scattering (GIWAXS), and density functional theory (DFT) calculations. Interestingly, the incorporation of SFA side chains led to the evolution of plate-like large-sized domains and also strongly intermolecular stacked high crystalline structures. Furthermore, due to the strong interactions between SFA side chains, several (00h) peaks could be observed for PC12TVC5F7T, in spite of their fairly large dihedral angle. As a result, due to the well-developed microstructure of PC12TVC5F7T, the OTFT devices based on it exhibited a high hole mobility of 1.91 cm2 V-1 s-1, which is an outstanding value among the poly(thiophene) derivative polymers. These observations indicate that large-sized domains and strongly intermolecular stacked high crystalline structures, which are beneficial for charge carrier transport, could be attained by the introduction of SFA side chains, further enhancing the performance of the OTFTs.

Application of electrospray-scanning mobility particle sizer for the measurement of sub-10 nm chemical mechanical planarization slurry abrasive size distribution.

A scanning mobility particle sizer (SMPS) was used for measuring the size of a sub-10 nm chemical mechanical planarization slurry abrasive. An atomizer and an electrospray were used for aerosolization of slurry abrasives. It was difficult to measure the exact particle size distribution using the atomizer due to the agglomeration peak generated by the relatively large droplet size. However, the electrospray-SMPS (ES-SMPS) measurement result well matched with that of the transmission electron microscopy analysis without the agglomeration peak as ES is known to generate a relatively small droplet during aerosolization. The particle size distribution of the two sub-10 nm ceria slurries was measured using the ES-SMPS. To avoid the dispersion stability issue due to the pH change, pH adjustment was required when the sample was diluted.

WNT11 is a direct target of early growth response protein 1.

WNT11 is a member of the non-canonical Wnt family and plays a crucial role in tumor progression. However, the regulatory mechanisms underlying WNT11 expression are unclear. Tumor necrosis factor-alpha (TNFα) is a major inflammatory cytokine produced in the tumor microenvironment and contributes to processes associated with tumor progression, such as tumor invasion and metastasis. By using site-directed mutagenesis and introducing a serial deletion in the 5'-regulatory region of WNT11, we observed that TNFα activates the early growth response 1 (EGR1)-binding sequence (EBS) in the proximal region of WNT11 and that the transcription factor EGR1 is necessary for the TNFα-induced transcription of WNT11. EGR1 bound directly to the EBSs within the proximal 5'-regulatory region of WNT11 and ectopic expression of EGR1 stimulated WNT11 promoter activity, whereas the knockdown of EGR1 expression by RNA interference reduced TNFα-induced WNT11 expression in T47D breast cancer cells. We also observed that mitogen-activated protein kinases (MAPK), extracellular signalregulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 kinase mediated TNFα-induced transcription of WNT11 via EGR1. Our results suggest that EGR1 directly targets WNT11 in response to TNFα stimulation in breast cancer cells. [BMB Reports 2020; 53(12): 628-633].

Transcriptome Profiling Reveals Novel Candidate Genes Related to Hippocampal Dysfunction in SREBP-1c Knockout Mice.

Lipid homeostasis is an important component of brain function, and its disturbance causes several neurological disorders, such as Huntington's, Alzheimer's, and Parkinson's diseases as well as mood disorders. Sterol regulatory element-binding protein-1c (SREBP-1c) is a key modulatory molecule involved in lipid homeostasis in the central nervous system. However, little is known about the biological effects of SREBP-1c in the brain. Our previous study uncovered that mice deficient in SREBP-1c exhibit schizophrenia-like behaviors. To investigate whether there are novel molecular mechanisms involved in the neurological aberrations caused by SREBP-1c deficiency, we analyzed the transcriptomes of the hippocampus of SREBP-1c knockout (KO) mice and wild-type mice. We found seven differentially expressed genes (three up-regulated and four down-regulated genes) in the hippocampus of SREBP-1c KO mice. For further verification, we selected the three most significantly changed genes: glucagon-like peptide 2 receptors (GLP2R) involved in hippocampal neurogenesis and neuroplasticity as well as in cognitive impairments; necdin (NDN) which is related to neuronal death and neurodevelopmental disorders; and Erb-B2 receptor tyrosine kinase 4 (ERBB4) which is a receptor for schizophrenia-linked protein, neuregulin-1. The protein levels of GLP2R and NDN were considerably decreased, but the level of ERBB4 was significantly increased in the hippocampus of SREBP-1c KO mice. However, further confirmation is warranted to establish the translatability of these findings from this rodent model into human patients. We suggest that these data provide novel molecular evidence for the modulatory role of SREBP-1c in the mouse hippocampus.