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We present a novel in situ liquid-cell transmission electron microscopy (TEM) approach to study the behavior of metal nanoparticles under high-energy electron irradiation. By utilizing a radically-inert liquid environment, we aim to minimize radiolysis effects and explore the influence of charge-induced transformations. We observed complex dynamics in nanoparticle behavior, including morphological changes and transitions between amorphous and crystalline states. These transformations are attributed to the delicate interplay between charge accumulation on the nanoparticles and enhanced radiolysis, suggesting a significant role for charge-assisted processes in nanoparticle evolution. Our findings provide valuable insights into the fundamental mechanisms driving nanoparticle behavior at the nanoscale and demonstrate the potential of liquid-cell TEM for studying complex physicochemical processes in controlled environments.

In this study, crystallization in highly supersaturated aqueous urea solutions was investigated using in situ infrared thermography facilitated by an acoustic levitation apparatus. A notable contribution of this thermographic approach is the identification of a transient heat release signature, particularly pronounced beyond the solubility limit, indicating the enhanced formation of bonds between urea molecules in the supersaturated states. Surprisingly, the temporal evolution of the heat release measurements on an acoustically levitated droplet strongly suggests a two-stage process for urea crystallization. A comprehensive statistical analysis based on classical nucleation theory is used to further investigate the exceptionally high degree of supersaturation and the emergence of prominent heat signatures observed toward the onset of crystallization.

We investigate the coherence properties of a transmission electron microscope by analyzing nano-diffraction speckles originating from bulk metallic glass. The spatial correlation function of the coherent diffraction patterns, obtained in the transmission geometry, reveals the highly coherent nature of the electron probe beam and its spatial dimension incident on the sample. Quantitative agreement between the measured speckle contrast and an analytical model yields estimates for the transverse and longitudinal coherence lengths of the source. We also demonstrate that the coherence can be controlled by changing the beam convergence angle. Our findings underscore the preservation of electron beam coherence throughout the electron optics, as evidenced by the high-contrast speckles observed in the scattering patterns of the amorphous system. This study paves the way for the application of advanced coherent diffraction methodologies to investigate local structures and dynamics occurring at atomic-length scales across a diverse range of materials.

Pre-harvest sprouting (PHS) of rice (Oryza sativa L.) causes severe economic problems due to reduced grain quality and yield. Fine mapping was carried out to identify genes associated with PHS; the detected quantitative trait locus (QTL) was narrowed down to 50 Kbp using F3:4 populations, four polymorphic insertion and deletion (InDel) markers, and two cleaved amplified polymorphic sequence (CAPS) markers. In one region, five candidate genes were detected, and the SNP and InDel in each gene (Os01g0111400 and Os01g0111600) were confirmed to show the differences and resulting amino acid changes between parent plants. Based on haplotype, expression, and co-segregation analysis, the InDel in Os01g0111600 was confirmed to be associated with the PHS trait. The results of this study could be applied to improve the PHS tolerance of Japonica rice varieties, and they also improved our understanding of the genetic basis underlying PHS tolerance.

Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.

Direct seeding is considered an efficient cultivation technology that reduces water use and labor costs. Mesocotyl length is one of the significant traits in cultivation; long mesocotyl is beneficial for the rate and uniformity of seedling emergence. In this study, we used a core collection of 137 rice accessions to identify quantitative trait loci (QTL) for mesocotyl elongation. A genome-wide association study (GWAS), combined with a principal component analysis (PCA) and a kinship matrix analysis, was conducted for the genotype analysis of 2 million, high-quality single nucleotide polymorphisms (SNPs). Through this GWAS analysis, 11 lead SNPs were confirmed to be associated with mesocotyl length, and a linkage disequilibrium (LD) decay analysis identified the 230 kb exploratory range for the detection of QTLs and candidate genes. Based on the gene expression database and haplotype analysis, five candidate genes (Os01g0269800, Os01g0731100, Os08g0136700, Os08g0137800, and Os08g0137900) were detected to be significantly associated with phenotypic variation. Five candidate gene expressions are reported to be associated with various plant hormone responses. Interestingly, two biotic stress response genes and two copper-containing redox proteins were detected as the candidate genes. The results of this study provide associated SNPs in candidate genes for mesocotyl length and strategies for developing direct seeding in breeding programs.

Salinity is one of the major constraints causing soil problems and is considered a limitation to increased rice production in rice-growing countries. This genome-wide association study (GWAS) experiment was conducted to understand the genetic basis of salt tolerance at the seedling stage in Korean rice. After 10 days of salt stress treatment, salt tolerance was evaluated with a standard evaluation system using a visual salt injury score. With 191 Korean landrace accessions and their genotypes, including 266,040 single-nucleotide polymorphisms (SNPs), using a KNU Axiom Oryza 580K Genotyping Array, GWAS was conducted to detect three QTLs with significant SNPs with a −log10(P) threshold of ≥3.66. The QTL of qSIS2, showed −log10(P) = 3.80 and the lead SNP explained 7.87% of total phenotypic variation. The QTL of qSIS4, showed −log10(P) = 4.05 and the lead SNP explained 10.53% of total phenotypic variation. The QTL of qSIS8 showed −log10(P) = 3.78 and the lead SNP explained 7.83% of total phenotypic variation. Among the annotated genes located in these three QTL regions, five genes were selected as candidates (Os04g0481600, Os04g0485300, Os04g0493000, Os04g0493300, and Os08g0390200) for salt tolerance in rice seedlings based on the gene expression database and their previously known functions.

Oxidative stress is a pathological condition occurring due to an imbalance between the oxidants and antioxidant defense systems in the body. Nuclear factor E2-related factor 2 (NRF2), encoded by the gene NFE2L2, is the master regulator of phase II antioxidant enzymes that protect against oxidative stress and inflammation. NRF2/ARE signaling has been considered as a promising target against oxidative stress-mediated diseases like diabetes, fibrosis, neurotoxicity, and cancer. The consumption of dietary phytochemicals acts as an effective modulator of NRF2/ARE in various acute and chronic diseases. In the present review, we discussed the role of NRF2 in diabetes, Alzheimer’s disease (AD), Parkinson’s disease (PD), cancer, and atherosclerosis. Additionally, we discussed the phytochemicals like curcumin, quercetin, resveratrol, epigallocatechin gallate, apigenin, sulforaphane, and ursolic acid that have effectively modified NRF2 signaling and prevented various diseases in both in vitro and in vivo models. Based on the literature, it is clear that dietary phytochemicals can prevent diseases by (1) blocking oxidative stress-inhibiting inflammatory mediators through inhibiting Keap1 or activating Nrf2 expression and its downstream targets in the nucleus, including HO-1, SOD, and CAT; (2) regulating NRF2 signaling by various kinases like GSK3beta, PI3/AKT, and MAPK; and (3) modifying epigenetic modulation, such as methylation, at the NRF2 promoter region; however, further investigation into other upstream signaling molecules like NRF2 and the effect of phytochemicals on them still need to be investigated in the near future.

A genome-wide association study (GWAS) was used to investigate the genetic basis of chilling tolerance in a collection of 117 rice accessions, including 26 Korean landraces and 29 weedy rices, at the reproductive stage. To assess chilling tolerance at the early young microspore stage, plants were treated at 12 °C for 5 days, and tolerance was evaluated using seed set fertility. GWAS, together with principal component analysis and kinship matrix analysis, revealed five quantitative trait loci (QTLs) associated with chilling tolerance on chromosomes 3, 6, and 7. The percentage of phenotypic variation explained by the QTLs was 11–19%. The genomic region underlying the QTL on chromosome 3 overlapped with a previously reported QTL associated with spikelet fertility. Subsequent bioinformatic and haplotype analyses suggested three candidate chilling-tolerance genes within the QTL linkage disequilibrium block: Os03g0305700, encoding a protein similar to peptide chain release factor 2; Os06g0495700, encoding a beta tubulin, autoregulation binding-site-domain-containing protein; and Os07g0137800, encoding a protein kinase, core-domain-containing protein. Further analysis of the detected QTLs and the candidate chilling-tolerance genes will facilitate strategies for developing chilling-tolerant rice cultivars in breeding programs.

Grain size affects the yield and quality of rice. The large grain line (LGL), showing a large grain size and japonica-like genome, was selected in the breeding field. The 94 F2 plants derived from a cross between LGL and Hanareum (a high-yielding tongil-type variety) were used for the quantitative trait loci (QTL) analysis of grain length (GL), grain width (GW), and grain thickness (GT). A linkage map of the F2 population, covering 1312 cM for all 12 chromosomes, was constructed using 123 Fluidigm SNP markers. A total of nine QTLs for the three traits were detected on chromosomes two, three, four, six, and seven. Two QTLs for GL on chromosomes two and six explained 17.3% and 16.2% of the phenotypic variation, respectively. Two QTLs were identified for GW on chromosomes two and three, and explained 24.3% and 23.5% of the phenotypic variation, respectively. The five QTLs for GT detected on chromosomes two, three, five, six and seven, explained 13.2%, 14.5%, 16.6%, 10.9%, and 10.2% of the phenotypic variation, respectively. A novel QTL for GT, qGT2, was validated on the same region of chromosome two in the selected F3 population. The QTLs identified in this study, and LGL, could be applied to the development of large-grain rice varieties.

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

We conducted a genome-wide association study (GWAS) of cold tolerance in a collection of 127 rice accessions, including 57 Korean landraces at the seedling stage. Cold tolerance of rice seedlings was evaluated in a growth chamber under controlled conditions and scored on a 0–9 scale, based on their low-temperature response and subsequent recovery. GWAS, together with principal component analysis (PCA) and kinship matrix analysis, revealed four quantitative trait loci (QTLs) on chromosomes 1, 4, and 5 that explained 16.5% to 18.5% of the variance in cold tolerance. The genomic region underlying the QTL on chromosome four overlapped with a previously reported QTL associated with cold tolerance in rice seedlings. Similarly, one of the QTLs identified on chromosome five overlapped with a previously reported QTL associated with seedling vigor. Subsequent bioinformatic and haplotype analyses revealed three candidate genes affecting cold tolerance within the linkage disequilibrium (LD) block of these QTLs: Os01g0357800, encoding a pentatricopeptide repeat (PPR) domain-containing protein; Os05g0171300, encoding a plastidial ADP-glucose transporter; and Os05g0400200, encoding a retrotransposon protein, Ty1-copia subclass. The detected QTLs and further evaluation of these candidate genes in the future will provide strategies for developing cold-tolerant rice in breeding programs.

A 307-m tall meteorological tower was used to evaluate meteorological observation data obtained using a rotary-wing unmanned aerial vehicle (UAV). A comparative study between the tower and UAV observations was conducted during the daytime (06:00 to 19:00 local time (LT)) in the summer of 2017 (16–18th August). Hourly vertical profiles of air temperature, relative humidity, black carbon (BC), and ozone (O3) concentrations were obtained for up to 300 m height. Statistical metrics for evaluating the accuracy of UAV observations against the tower observation showed positive (potential temperature) and negative (relative humidity) biases, which were within acceptable ranges. The daytime evolution of the lower atmospheric boundary layer (ABL) was successfully captured by the hourly UAV observations. During the early morning, a large vertical slope of potential temperature was observed between 100 and 140 m, corresponding to the stable ABL height. The large vertical slope coincided with the large differences in BC and O3 concentrations between altitudes below and above the height. The transition from stable to convective ABL was observed at 10–11 LT, indicated by the ABL height higher than 300 m in the convective ABL. Finally, we provide several recommendations to reduce uncertainties of UAV observation.

To increase coverage of protein identification of an Agaricus bisporus fruiting body, we analyzed the crude protein fraction of the fruiting body by using a shotgun proteomics approach where 7 MudPIT (Multi-Protein identification Technology) runs were conducted and the MS/MS spectra from the 7 MudPIT runs were merged. Overall, 3093 non-redundant proteins were identified to support the expression of those genes annotated in the genome database of Agaricus bisporus. The physicochemical properties of the identified proteins, i.e., wide pI value range and molecular mass range, were indicative of unbiased protein identification. The relative quantification of the identified proteins revealed that K5XI50 (Aldedh domain-containing protein) and K5XEW1 (Ubiquitin-like domain-containing protein) were highly abundant in the fruiting body. Based on the information in the Uniprot (Universal Protein Resource) database for A. bisporus, only approximately 53% of the 3093 identified proteins have been functionally described and approximately 47% of the proteins remain uncharacterized. Gene Ontology analysis revealed that the majority of proteins were annotated with a biological process, and proteins associated with coiled-coil (12.8%) and nucleotide binding (8.21%) categories were dominant. The Kyoto Encyclopedia of Genes and Genome analysis revealed that proteins involved in biosynthesis of secondary metabolites and tyrosine metabolism were enriched in a fruiting body of Agaricus bisporus, suggesting that the proteins are associated with antioxidant metabolites.

The Yeongdong region, located east of the Taebaek Mountains, South Korea, often experiences severe windstorms in spring, causing a lot of damages, especially when forest fires spread out rapidly by strong winds. Here, the characteristics and generation mechanisms of the windstorms in the Yeongdong region on 8 April 2012 are examined through a high-resolution Weather Research and Forecasting (WRF) model simulation. In the Yangyang area, the steep descent of the isentropes on the lee slope of the mountain and their recovery farther leeward are seen. Inversion layers and incoming flow in hydraulic jump regime suggest that the hydraulic jump is responsible for the downslope windstorm. In the Jangjeon area, the plume-shaped wind pattern extending seaward from the gap exit is seen when the sea-level pressure difference between the gap inside and the gap exit, being responsible for the gap winds, is large. In the Uljin area, downslope windstorms pass over the region with weak wind, low Richardson number, and deep planetary boundary layer (PBL), making banded pattern in the wind and PBL height fields. This study demonstrates that the characteristics of the windstorms in the lee of the Taebaek Mountains and their generation mechanisms differ depending on local topographic features.