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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Degree distributions under general node removal: Power-law or Poisson?

Perturbations made to networked systems may result in partial structural loss, such as a blackout in a power-grid system. Investigating the resulting disturbance in network properties is quintessential to understand real networks in action. The removal of nodes is a representative disturbance, but previous studies are seemingly contrasting about its effect on arguably the most fundamental network statistic, the degree distribution. The key question is about the functional form of the degree distributions that can be altered during node removal or sampling. The functional form is decisive in the remaining subnetwork's static and dynamical properties. In this work, we clarify the situation by utilizing the relative entropies with respect to the reference distributions in the Poisson and power-law form, to quantify the distance between the subnetwork's degree distribution and either of the reference distributions. Introducing general sequential node removal processes with continuously different levels of hub protection to encompass a series of scenarios including uniform random removal and preferred or protective (i.e., biased random) removal of the hub, we classify the altered degree distributions starting from various power-law forms by comparing two relative entropy values. From the extensive investigation in various scenarios based on direct node-removal simulations and by solving the rate equation of degree distributions, we discover in the parameter space two distinct regimes, one where the degree distribution is closer to the power-law reference distribution and the other closer to the Poisson distribution.

Complete genome sequencing and comparative genomic analysis of Lactobacillus acidophilus C5 as a potential canine probiotics.

Lactobacillus acidophilus is a gram-positive, microaerophilic, and acidophilic bacterial species. L. acidophilus strains in the gastrointestinal tracts of humans and other animals have been profiled, but strains found in the canine gut have not been studied yet. Our study helps in understanding the genetic features of the L. acidophilus C5 strain found in the canine gut, determining its adaptive features evolved to survive in the canine gut environment, and in elucidating its probiotic functions. To examine the canine L. acidophilus C5 genome, we isolated the C5 strain from a Korean dog and sequenced it using PacBio SMRT sequencing technology. A comparative genomic approach was used to assess genetic relationships between C5 and six other strains and study the distinguishing features related to different hosts. We found that most genes in the C5 strain were related to carbohydrate transport and metabolism. The pan-genome of seven L. acidophilus strains contained 2,254 gene families, and the core genome contained 1,726 gene families. The phylogenetic tree of the core genes in the canine L. acidophilus C5 strain was very close to that of two strains (DSM20079 and NCFM) from humans. We identified 30 evolutionarily accelerated genes in the L. acidophilus C5 strain in the ratio of non-synonymous to synonymous substitutions (dN/dS) analysis. Five of these thirty genes were associated with carbohydrate transport and metabolism. This study provides insights into genetic features and adaptations of the L. acidophilus C5 strain to survive the canine intestinal environment. It also suggests that the evolution of the L. acidophilus genome is closely related to the host's evolutionary adaptation process.

Characterization and Functional Test of Canine Probiotics.

Probiotics can modulate the composition of gut microbiota and benefit the host animal health in multiple ways. Lactic acid bacteria (LAB), mainly Lactobacillus and Bifidobacterium species, are well-known microbes with probiotic potential. In the present study, 88 microbial strains were isolated from canine feces and annotated. Among these, the four strains CACC517, 537, 558, and 566 were tested for probiotic characteristics, and their beneficial effects on hosts were evaluated both in vitro and in vivo; these strains exhibited antibiosis, antibiotic activity, acid and bile tolerance, and relative cell adhesion to the HT-29 monolayer cell line. Byproducts of these strains increased the viability and decreased oxidative stress in mouse and dog cell lines (RAW264.7 and DH82, respectively). Subsequently, when the probiotics were applied to the clinical trial, changes in microbial composition and relative abundance of bacterial strains were clearly observed in the experimental animals. Experimental groups before and after the application were obviously separated from PCA analysis of clinical results. Conclusively, these results could provide comprehensive understanding of the effects of probiotic strains (CACC517, 537, 558, and 566) and their industrial applications.

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.

Detecting the differential genomic variants using cross-population phenotype-associated variant (XP-PAV) of the Landrace and Yorkshire pigs in Korea.

Although there have been many genome-wide association studies (GWAS) and selective sweep analyses to understand pig genomic regions related to growth performance, these methods considered only the gene effect and selection signal, respectively. In this study, we suggest the cross-population phenotype associated variant (XP-PAV) analysis as a novel method to determine the genomic variants with different effects between the two populations. XP-PAV analysis could reveal the differential genetic variants between the two populations by considering the gene effect and selection signal simultaneously. In this study, we used daily weight gain (DWG) and back fat thickness (BF) as phenotypes and the Landrace and Yorkshire populations were used for XP-PAV analysis. The main aim was to reveal the differential selection by considering the gene effect between Landrace and Yorkshire pigs. In the gene ontology analysis of XP-PAV results, differential selective genes in DWG analysis were involved in the regulation of interleukin-2 production and cell cycle G2/M transition. The protein modification and glycerophospholipid biosynthetic processes were the most enriched terms in the BF analysis. Therefore, we could identify genetic differences for immune and several metabolic pathways between Landrace and Yorkshire breeds using the XP-PAV analysis. In this study, we expect that XP-PAV analysis will play a role in determining useful selective variants with gene effects and provide a new interpretation of the genetic differences between the two populations.

Spontaneous Formation of a ZnO Monolayer by the Redox Reaction of Zn on Graphene Oxide.

Graphene-based two-dimensional heterostructures are of substantial interest both for fundamental studies and their various potential applications. Particularly interesting are atomically thin semiconducting oxides on graphene, which uniquely combine a wide band gap and optical transparency. Here, we report the atomic-scale investigation of a novel self-formation of a ZnO monolayer from the Zn metal on a graphene oxide substrate. The spontaneous oxidation of the ultrathin Zn metal occurs by a reaction with oxygen supplied from the graphene oxide substrate, and graphene oxide is deoxygenated by a transfer of oxygen from O-containing functional groups to the zinc metal. The ZnO monolayer formed by this spontaneous redox reaction shows a graphene-like structure and a band gap of about 4 eV. This study demonstrates a unique and straightforward synthetic route to atomically thin two-dimensional heterostructures made from a two-dimensional metal oxide and graphene, formed by the spontaneous redox reaction of a very thin metal layer directly deposited on graphene oxide.

Comparative genomics of canine Lactobacillus reuteri reveals adaptation to a shared environment with humans.

BACKGROUND: Lactobacillus reuteri is a gram-positive, non-motile bacterial species that has been used as a representative microorganism model to describe the ecology and evolution of vertebrate gut symbionts. OBJECTIVE: Because the genetic features and evolutionary strategies of L. reuteri from the gastrointestinal tract of canines remain unknown, we tried to construct draft genome canine L. reuteri and investigate modified, acquired, or lost genetic features that have facilitated the evolution and adaptation of strains to specific environmental niches by this study. METHODS: To examine canine L. reuteri, we sequenced an L. reuteri strain isolated from a dog in Korea. A comparative genomic approach was used to assess genetic diversity and gain insight into the distinguishing features related to different hosts based on 27 published genomic sequences. RESULTS: The pan-genome of 28 L. reuteri strains contained 7,369 gene families, and the core genome contained 1070 gene families. The ANI tree based on the core genes in the canine L. reuteri strain (C1) was very close to those for three strains (IRT, DSM20016, JCM1112) from humans. Evolutionarily, these four strains formed one clade, which we regarded as C1-clade in this study. We could investigate a total of 32,050 amino acid substitutions among the 28 L. reuteri strain genomes. In this comparison, 283 amino acid substitutions were specific to strain C1 and four strains in C1-clade shared most of these 283 C1-strain specific amino acid substitutions, suggesting strongly similar selective pressure. In accessory genes, we could identify 127 C1-clade host-specific genes and found that several genes were closely related to replication, recombination, and repair. CONCLUSION: This study provides new insights into the adaptation of L. reuteri to the canine intestinal habitat, and suggests that the genome of L. reuteri from canines is closely associated with their living and shared environment with humans.

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.