Spiral-Driven Vertical Conductivity in Nanocrystalline Graphene.

The structure of graphene grown in chemical vapor deposition (CVD) is sensitive to the growth condition, particularly the substrate. The conventional growth of high-quality graphene via the Cu-catalyzed cracking of hydrocarbon species has been extensively studied; however, the direct growth on noncatalytic substrates, for practical applications of graphene such as current Si technologies, remains unexplored. In this study, nanocrystalline graphene (nc-G) spirals are produced on noncatalytic substrates by inductively coupled plasma CVD. The enhanced out-of-plane electrical conductivity is achieved by a spiral-driven continuous current pathway from bottom to top layer. Furthermore, some neighboring nc-G spirals exhibit a homogeneous electrical conductance, which is not common for stacked graphene structure. Klein-edge structure developed at the edge of nc-Gs, which can easily form covalent bonding, is thought to be responsible for the uniform conductance of nc-G aggregates. These results have important implications for practical applications of graphene with vertical conductivity realized through spiral structure.

MnO2-Induced Oxidation of Iodide in Frozen Solution.

Metal oxides play a critical role in the abiotic transformation of iodine species in natural environments. In this study, we investigated iodide oxidation by manganese dioxides (ß-MnO2, γ-MnO2, and δ-MnO2) in frozen and aqueous solutions. The heterogeneous reaction produced reactive iodine (RI) in the frozen phase, and the subsequent thawing of the frozen sample induced the gradual transformation of in situ-formed RI to iodate or iodide, depending on the types of manganese dioxides. The freezing-enhanced production of RI was observed over the pH range of 5.0-9.0, but it decreased with increasing pH. Fulvic acid (FA) can be iodinated by I-/MnO2 in aqueous and frozen solutions. About 0.8-8.4% of iodide was transformed to organoiodine compounds (OICs) at pH 6.0-7.8 in aqueous solution, while higher yields (10.4-17.8%) of OICs were obtained in frozen solution. Most OICs generated in the frozen phase contained one iodine atom and were lignin-like compounds according to Fourier transform ion cyclotron resonance/mass spectrometry analysis. This study uncovers a previously unrecognized production pathway of OICs under neutral conditions in frozen environments.

Invasion and interaction determine population composition in an open evolving ecological system.

It is well-known that interactions between species determine the population composition in an ecosystem. Conventional studies have focused on fixed population structures to reveal how interactions shape population compositions. However, interaction structures are not fixed but change over time due to invasions. Thus, invasion and interaction play an important role in shaping communities. Despite its importance, however, the interplay between invasion and interaction has not been well explored. Here, we investigate how invasion affects the population composition with interactions in open evolving ecological systems considering generalized Lotka-Volterra-type dynamics. Our results show that the system has two distinct regimes. One is characterized by low diversity with abrupt changes of dominant species in time, appearing when the interaction between species is strong and invasion slowly occurs. On the other hand, frequent invasions can induce higher diversity with slow changes in abundances despite strong interactions. It is because invasion happens before the system reaches its equilibrium, which drags the system from its equilibrium all the time. All species have similar abundances in this regime, which implies that fast invasion induces regime shift. Therefore, whether invasion or interaction dominates determines the population composition.

Power-grid stability predictions using transferable machine learning.

Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms-random forest, support vector machine, and artificial neural network-training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution rather than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.

Reproducibility of Crude Oil Spectra Obtained with Ultrahigh Resolution Mass Spectrometry.

In this study, the reproducibility of crude oil analyzed with (+) atmospheric pressure photoionization ultrahigh resolution mass spectrometry was evaluated. Three sets of data were obtained at intervals of approximately a month for a span of three months. For each monthly data set, four oil samples were analyzed with four replicates in 1 day. The obtained 48 spectra were processed to examine the reproducibility of the class, double bond equivalent (DBE), and individual peak distributions. The reproducibility of the relative abundance was better than that of the absolute abundance. The distributions of major classes were consistent within the three sets with a less than 1% relative standard deviation (RSD). The DBE distribution for each data set was reproducible within 1% RSD, whereas the DBE distributions for the combined data sets had RSD values of 1%-6%. The RSD values were higher for minor components, suggesting that care must be taken in the use of minor values for quantitative or semiquantitative evaluation. The relative abundances of individual peaks in the major classes were reproducible within 1%-3% RSD for each data set. However, the RSD values of the combined data sets were over 10%, even for abundant peaks. The smaller RSD of the class and DBE distributions than that of individual peaks for combined data sets strongly suggest that variations observed from individuals were caused by random errors. The data presented in this study provide guidelines for evaluating petroleomic data obtained in the laboratory at different times or laboratories.

Comparison of characteristics of long noncoding RNA in Hanwoo according to sex.

OBJECTIVE: Cattle were some of the first animals domesticated by humans for the production of milk, meat, etc. Long noncoding RNA (lncRNA) is defined as longer than 200 bp in non-protein coding transcripts. lncRNA is known to function in regulating gene expression and is currently being studied in a variety of livestock including cattle. The purpose of this study is to analyze the characteristics of lncRNA according to sex in Hanwoo cattle. METHODS: This study was conducted using the skeletal muscles of 9 Hanwoo cattle include bulls, steers and cows. RNA was extracted from skeletal muscle of Hanwoo. Sequencing was conducted using Illumina HiSeq2000 and mapped to the Bovine Taurus genome. The expression levels of lncRNAs were measured by DEGseq and quantitative trait loci (QTL) data base was used to identify QTLs associated with lncRNA. The python script was used to match the nearby genes. RESULTS: In this study, the expression patterns of transcripts of bulls, steers and cows were identified. And we identified significantly differentially expressed lncRNAs in bulls, steers and cows. In addition, characteristics of lncRNA which express differentially in muscles according to the sex of Hanwoo were identified. As a result, we found differentially expressed lncRNAs according to sex were related to shear force and body weight. CONCLUSION: This study was classified and characterized lncRNA which differentially expressed by sex in Hanwoo cattle. We believe that the characterization of lncRNA by sex of Hanwoo will be helpful for future studies of the physiological mechanisms of Hanwoo cattle.

Application of Online Liquid Chromatography 7 T FT-ICR Mass Spectrometer Equipped with Quadrupolar Detection for Analysis of Natural Organic Matter.

In this study, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), combined with quadrupolar detection (QPD), was applied for online liquid chromatography (LC) MS analysis of natural organic matter (NOM). Although FT-ICR MS has emerged as an important analytical technique to study NOM, there are few previous reports on online LC FT-ICR MS analysis of NOM due to the long acquisition time (2-8 s) required to obtain high-resolution mass spectra. The QPD technique provides a critical advantage over the conventional dipolar detection (DPD) technique for LC-MS analysis because a spectrum with the same resolving power can be obtained in approximately half the acquisition time. QPD FT-ICR MS provides resolving powers ( mΔm50% ) of ∼300000 and 170000 at m/ z 400 with acquisition times per scan of 1.2 and 0.8 s, respectively. The reduced acquisition time per scan allows increased number of acquisitions in a given LC analysis time, resulting in improved signal to noise ( S/ N) ratio and dynamic range in comparison to conventional methods. For example, 40% and 100% increases in the number of detected peaks were obtained with LC QPD FT-ICR MS, in comparison to conventional LC DPD FT-ICR MS and direct-injection FT-ICR MS. It is also possible to perform more quantitative comparison and molecular level investigation of NOMs with 2 µg of a NOM sample. The data presented herein demonstrate a proof of principle that QPD combined with LC FT-ICR MS is a sensitive analytical technique that can provide comprehensive information about NOM.

On structural and dynamical factors determining the integrated basin instability of power-grid nodes.

In electric power systems delivering alternating current, it is essential to maintain its synchrony of the phase with the rated frequency. The synchronization stability that quantifies how well the power-grid system recovers its synchrony against perturbation depends on various factors. As an intrinsic factor that we can design and control, the transmission capacity of the power grid affects the synchronization stability. Therefore, the transition pattern of the synchronization stability with the different levels of transmission capacity against external perturbation provides the stereoscopic perspective to understand the synchronization behavior of power grids. In this study, we extensively investigate the factors affecting the synchronization stability transition by using the concept of basin stability as a function of the transmission capacity. For a systematic approach, we introduce the integrated basin instability, which literally adds up the instability values as the transmission capacity increases. We first take simple 5-node motifs as a case study of building blocks of power grids, and a more realistic IEEE 24-bus model to highlight the complexity of decisive factors. We find that both structural properties such as gate keepers in network topology and dynamical properties such as large power input/output at nodes cause synchronization instability. The results suggest that evenly distributed power generation and avoidance of bottlenecks can improve the overall synchronization stability of power-grid systems.

Atomic Scale Study on Growth and Heteroepitaxy of ZnO Monolayer on Graphene.

Atomically thin semiconducting oxide on graphene carries a unique combination of wide band gap, high charge carrier mobility, and optical transparency, which can be widely applied for optoelectronics. However, study on the epitaxial formation and properties of oxide monolayer on graphene remains unexplored due to hydrophobic graphene surface and limits of conventional bulk deposition technique. Here, we report atomic scale study of heteroepitaxial growth and relationship of a single-atom-thick ZnO layer on graphene using atomic layer deposition. We demonstrate atom-by-atom growth of zinc and oxygen at the preferential zigzag edge of a ZnO monolayer on graphene through in situ observation. We experimentally determine that the thinnest ZnO monolayer has a wide band gap (up to 4.0 eV), due to quantum confinement and graphene-like structure, and high optical transparency. This study can lead to a new class of atomically thin two-dimensional heterostructures of semiconducting oxides formed by highly controlled epitaxial growth.

Anomalous rapid defect annihilation in self-assembled nanopatterns by defect melting.

Molecular self-assembly commonly suffers from dense structural defect formation. Spontaneous defect annihilation in block copolymer (BCP) self-assembly is particularly retarded due to significant energy barrier for polymer chain diffusion and structural reorganization. Here we present localized defect melting induced by blending short neutral random copolymer chain as an unusual method to promote the defect annihilation in BCP self-assembled nanopatterns. Chemically neutral short random copolymer chains blended with BCPs are specifically localized and induce local disordered states at structural defect sites in the self-assembled nanopatterns. Such localized "defect melting" relieves the energy penalty for polymer diffusion and morphology reorganization such that spontaneous defect annihilation by mutual coupling is anomalously accelerated upon thermal annealing. Interestingly, neutral random copolymer chain blending also causes morphology-healing self-assembly behavior that can generate large-area highly ordered 10 nm scale nanopattern even upon poorly defined defective prepatterns. Underlying mechanisms of the unusual experimental findings are thoroughly investigated by three-dimensional self-consistent field theory calculation.

Impact of nsSNPs in human AIM2 and IFI16 gene: a comprehensive in silico analysis.

AIM2 and IFI16 are the most studied members of AIM2-like receptors (ALRs) in humans and share a common N-Terminal PYD domain and C-terminal HIN domain. The HIN domain binds to dsDNA in response to the invasion of bacterial and viral DNA, and the PYD domain directs apoptosis-associated speck-like protein via protein-protein interactions. Hence, activation of AIM2 and IFI16 is crucial for protection against pathogenic assaults, and any genetic variation in these inflammasomes can dysregulate the human immune system. In this study, different computational tools were used to identify the most deleterious and disease-causing non-synonymous single nucleotide polymorphisms (nsSNPs) in AIM2 and IFI16 proteins. Molecular dynamic simulation was performed for the top damaging nsSNPs to study single amino acid substitution-induced structural alterations in AIM2 and IFI16. The observed results suggest that the variants G13V, C304R, G266R, and G266D for AIM2, and G13E and C356F are deleterious and affect structural integrity. We hope that the suggested deleterious nsSNPs and structural dynamics of AIM2 and IFI16 variants will guide future research to better understand the function of these variants with large-scale studies and may assist in fresher therapeutics focusing on these polymorphisms.Communicated by Ramaswamy H. Sarma.

Enhancing ToF-SIMS OLED Data Analysis with Neural Networks and Mathematical Spectral Mixing.

This study presents a method employing artificial neural networks (ANN) for automated interpretation and depth profiling of organic multilayers using a limited set of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra. To overcome the challenges of acquiring massive data sets for OLEDs, training data was generated by combining existing ToF-SIMS data sets with mathematically generated spectra. The classification model achieved an impressive 99.9% accuracy in identifying the mixed layers of the OLED dyes. The study demonstrates the synergy of ToF-SIMS and ANN analysis for effective classification and depth profiling of the OLED layers, providing valuable insights for the development and optimization of organic electronic devices.

Comparative analysis of organic chemical compositions in airborne particulate matter from Ulaanbaatar, Beijing, and Seoul using UPLC-FT-ICR-MS and artificial neural network.

This paper presents comparative study on the composition and sources of PM2.5 in Ulaanbaatar, Beijing, and Seoul. Ultrahigh performance liquid chromatography (UPLC) combined with ultrahigh resolution mass spectrometry (UHR-MS) were employed to analyze 85 samples collected in winter. The obtained 340 spectra were interpreted with artificial neural network (ANN). PM2.5 mass concentrations in Ulaanbaatar were significantly higher than those in Beijing and Seoul. ANN based interpretation of UPLC UHR-MS data showed that aliphatic/lipid derived organo­sulfur compounds, polycyclic aromatic and organo­oxygen compounds were characteristic to Ulaanbaatar. Whereas, aliphatic/lipid-derived organo­oxygen compounds were major components in Beijing and Seoul. Aromatic organo­nitrogen compounds were the main contributors to differentiating the spectra obtained from Beijing from the other cities. Based on two-dimensional gas chromatography/high resolution mass spectrometric (GCxGC/HRMS) data, it was determined that the concentrations of the polycyclic aromatic hydrocarbon (PAH) and polycyclic aromatic sulfur heterocycle (PASH) containing sulfur were highest in Ulaanbaatar, followed by Beijing and Seoul. Coal/biomass combustion was identified as the primary source of contamination in Ulaanbaatar, while petroleum combustion was the main contributor to PM2.5 in Beijing and Seoul. The conclusion that diesel-powered heavy-duty trucks and buses are the main contributors to NOx emissions in Beijing is consistent with previous reports. This study provides a more comprehensive understanding of the composition and sources of PM2.5 in the three cities, with a focus on the differences in their atmospheric pollution profiles based on the UPLC UHR-MS and ANN analysis. It is notable that this study is the first to utilize this method on a large-scale sample set, providing a more detailed and molecular-level understanding of the compositional differences among PM2.5. Overall, the study contributes to a better understanding of the sources and composition of PM2.5 in Northeast Asia, which is essential for developing effective strategies to reduce air pollution and improve public health.

Micropyramidal Flexible Ion Gel Sensor for Multianalyte Discrimination and Strain Compensation.

A highly sensitive and flexible gas sensor that can detect a wide range of chemicals is crucial for wearable applications. However, conventional single resistance-based flexible sensors face challenges in maintaining chemical sensitivity under mechanical stress and can be affected by interfering gases. This study presents a versatile approach for fabricating a micropyramidal flexible ion gel sensor, which accomplishes sub-ppm sensitivity (<80 ppb) at room temperature and discrimination capability between various analytes, including toluene, isobutylene, ammonia, ethanol, and humidity. The discrimination accuracy of our flexible sensor is as high as 95.86%, enhanced by using machine learning-based algorithms. Moreover, its sensing capability remains stable with only a 2.09% change from the flat state to a 6.5 mm bending radius, further amplifying its universal usage for wearable chemical sensing. Therefore, we envision that a micropyramidal flexible ion gel sensor platform assisted by machine learning-based algorithms will provide a new strategy toward next-generation wearable sensing technology.

Scaling laws between population and facility densities.

When a new facility like a grocery store, a school, or a fire station is planned, its location should ideally be determined by the necessities of people who live nearby. Empirically, it has been found that there exists a positive correlation between facility and population densities. In the present work, we investigate the ideal relation between the population and the facility densities within the framework of an economic mechanism governing microdynamics. In previous studies based on the global optimization of facility positions in minimizing the overall travel distance between people and facilities, it was shown that the density of facility D and that of population rho should follow a simple power law D approximately rho(2/3). In our empirical analysis, on the other hand, the power-law exponent alpha in D approximately rho(alpha) is not a fixed value but spreads in a broad range depending on facility types. To explain this discrepancy in alpha, we propose a model based on economic mechanisms that mimic the competitive balance between the profit of the facilities and the social opportunity cost for populations. Through our simple, microscopically driven model, we show that commercial facilities driven by the profit of the facilities have alpha = 1, whereas public facilities driven by the social opportunity cost have alpha = 2/3. We simulate this model to find the optimal positions of facilities on a real U.S. map and show that the results are consistent with the empirical data.

Automated analysis of human cardiomyocytes dynamics with holographic image-based tracking for cardiotoxicity screening.

This paper proposes a new non-invasive, low-cost, and fully automated platform to quantitatively analyze dynamics of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) at the single-cell level by holographic image-based tracking for cardiotoxicity screening. A dense Farneback optical flow method and holographic imaging informatics were combined to characterize the contractile motion of a single CM, which obviates the need for costly equipment to monitor a CM's mechanical beat activity. The reliability of the proposed platform was tested by single-cell motion characterization, synchronization analysis, motion speed measurement of fixed CMs versus live CMs, and noise sensitivity. The applicability of the motion characterization method was tested to determine the pharmacological effects of two cardiovascular drugs, isoprenaline (166 nM) and E-4031 (500 µM). The experiments were done using single CMs and multiple cells, and the results were compared to control conditions. Cardiomyocytes responded to isoprenaline by increasing the action potential (AP) speed and shortening the resting period, thus increasing the beat frequency. In the presence of E-4031, the AP speed was decreased, and the resting period was prolonged, thus decreasing the beat frequency. The findings offer insights into single hiPS-CMs' contractile motion and a deep understanding of their kinetics at the single-cell level for cardiotoxicity screening.

Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils.

This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R2 = 1.00 for model and R2 = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.

Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy.

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

Elucidating breed-specific variants of native pigs in Korea: insights into pig breeds' genomic characteristics.

Although conserving native pig breeds is important in Korea, research on the genomic aspects to identify breed-specific variations in native pig breeds is uncommon. Single nucleotide polymorphisms (SNPs) can be a powerful source for identifying breed-specific variants. We used whole genome sequencing data, including Jeju Native Pig (JNP), Korean Native Pig (KNP), Korean Wild Boar (KWB), and other western commercial pig breeds to determine native pig breed-specific SNPs. Furthermore, the goal was not only to determine the genomic specificity of native pig breeds but also to identify SNPs that carry breed-specific information (breed-informative SNPs) that can be related to breed characteristics. The representative characteristics of native pigs are their unique meat quality and disease resistance. We surveyed the gene ontology (GO) of native pigs with breed-specific SNPs. Examining the genes associated with GO may contribute to revealing the reasons for the unique characteristics of native pig breeds. The enriched GOs terms were neuron projection development, cell surface receptor signaling pathway, ion homeostasis in JNP, cell adhesion and wound healing in KNP, and DNA repair and reproduction in KWB. We expect that this study of breed-specific SNPs will enable us to gain a deeper understanding of native pigs in Korea.