A genome-wide association study for eumelanin pigmentation in chicken plumage using a computer vision approach.

Chicken plumage colouration is an important trait related to productivity in poultry industry. Therefore, the genetic basis for pigmentation in chicken plumage is an area of great interest. However, the colour trait is generally regarded as a qualitative trait and representing colour variations is difficult. In this study, we developed a method to quantify and classify colour using an F2 population crossed from two pure lines: White Leghorn and the Korean indigenous breed Yeonsan Ogye. Using red, green, and blue values in the cropped body region, we identified significant genomic regions on chromosomes 33:3 160 480-7 447 197 and Z:78 748 287-79 173 793. Furthermore, we identified two potential candidate genes (PMEL and MTAP) that might have significant effects on melanin-based plumage pigmentation. Our study presents a new phenotyping method using a computer vision approach and provides new insights into the genetic basis of melanin-based feather colouration in chickens.

Large-Area, Highly Ordered Array of Graphitic Carbon Materials Using Surface Active Chitosan Prepatterns.

We demonstrate that chitosan prepatterns can generate not only highly periodic DNA pattern but also various types of graphitic carbon materials such as single-walled carbon nanotubes (SWNTs), graphene oxide (GO) and reduced graphene oxide (RGO). Scanning electron microscopy (SEM), fluorescence imaging and Raman spectroscopic results revealed that the graphitic carbon materials were selectively deposited on the surface of the periodic chitosan patterns by the electrostatic interaction between protonated amine groups of chitosan and the negative charged carbon materials. One proof-of-concept application of the system to the fabrication of electrical devices based on the micropatterns of SWNTs and RGO was also demonstrated. The strategy to use highly surface active chitosan pattern that can easily fabricate highly periodic pattern via a variety of lithographic tools may pave the way for the production of periodic arrays of graphitic carbon materials for large area device integration.

Applying dynamic priority scheduling scheme to static systems of pinwheel task model in power-aware scheduling.

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task's priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity of O(n) reduces the energy consumption by 10-80% over the existing algorithms.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Enhanced luminescence of Cu-In-S nanocrystals by surface modification.

We have synthesized highly luminescent Cu-In-S nanocrystals by heating the mixture of metal carboxylates and alkylthiol under inert atmosphere. We modified the surface of CIS nanocrystals with zinc carboxylate and subsequent injection of alkylthiol. As a result of the surface modification, highly luminescent CIS@ZnS core/shell nanocrystals were synthesized. The luminescence quantum yield (QY) of best CIS@ZnS nanocrystals was above 50%, which is more than 10 times higher than the initial QY of CIS nanocrystals before surface modification (QY = 3%). Detailed study on the luminescence mechanism implies that etching of the surface of nanocrystals by dissociated carboxylate group (CH3COO-) and formation of epitaxial shell by Zn with sulfur from alkylthiol efficiently removed the surface defects which are major non-radiative recombination sites in semiconductor nanocrystals. In this study, we developed a novel surface modification route for monodispersed highly luminescent Cu-In-S nanocrystals with less toxic and highly stable precursors.

MSENet: Marbling score estimation network for automated assessment of Korean beef.

A novel beef marbling score estimation algorithm is proposed in this work. We develop a marbling score estimation network (MSENet), which simultaneously performs marbling score estimation and eye muscle area segmentation. The proposed MSENet includes a segmentation module, a bridge block, and a marbling scoring module. The segmentation module segments out eye muscle area from input images and the scoring module estimates marbling scores of input beef images. The proposed bridge block conveys the segmentation information for eye muscle area from the segmentation module to the scoring module. MSENet is trained on a new large-scale beef image dataset (more than 10,000), called the Hanwoo dataset. Experimental results demonstrate that the proposed MSENet achieves the reliable score estimation performance on the Hanwoo Dataset and the proposed bridge block effectively improves the estimation accuracy (Pearson's correlation coefficient: 0.952, Mean absolute error: 0.543).

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle.

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a "co-expression pattern recognizer" (CEPR) and "module classifier". The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach.

Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

The structural and magnetic properties of BaFe12O19 nanoparticles: effect of residual sodium ions.

We studied the structural and magnetic properties of BaFe12O19 (BaM) nanoparticles synthesized by a co-precipitation route based on a modified citrate process. The lattice contraction of co-precipitated BaM nanoparticles is detected after prolonged sintering at 900 °C. In addition, investigation with X-ray photoelectron spectroscopy (XPS) provides evidence of a highly enhanced population of oxygen vacancies. The lattice distortion and formation of oxygen vacancies are attributed to a direct result of substitutional incorporation of smaller Na ions into Ba2+ sites in the lattice of BaM during prolonged annealing. The aliovalent impurities are assumed to be originated from NaOH which has been incorporated as a pH modifier. Magnon scattering is detected at 1640 cm-1 in the low temperature Raman spectra of BaM. Minimization of the magnon peak is also identified after prolonged annealing, which indicates oxygen vacancy-induced collapse of strong anti-ferromagnetic interaction between Fe3+ ions in the bipyramidal sites and the octahedral sites in BaM nanoparticles. As a result, the saturation magnetization (Ms) of BaM nanoparticles is enhanced by the onset of local ferromagnetic interaction induced by the collapse of antiferromagnetic interaction between oppositely aligned spins. In this study, we re-investigated the evolution of structural and magnetic properties with prolonged annealing of BaM nanoparticles and the effects of residual Na ions are also discussed.

Enhanced Crystallinity and Luminescence Characteristics of Hexagonal Boron Nitride Doped with Cerium Ions According to Tempering Temperatures.

Hexagonal boron nitride was synthesized by pyrolysis using boric acid and melamine. At this time, to impart luminescence, rare earth cerium ions were added to synthesize hexagonal boron nitride nanophosphor particles exhibiting deep blue emission. To investigate the changes in crystallinity and luminescence according to the re-heating temperature, samples which had been subjected to pyrolysis at 900 °C were subjected to re-heating from 1100 °C to 1400 °C. Crystallinity and luminescence were enhanced according to changes in the reheating temperature. The synthesized cerium ion-doped hexagonal boron nitride nanoparticle phosphor was applied to the anti-counterfeiting field to prepare an ink that can only be identified under UV light.

Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs.

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

Automated Classification of Normal Control and Early-Stage Dementia Based on Activities of Daily Living (ADL) Data Acquired from Smart Home Environment.

With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.

Hybrid Nanoparticle Layers Toward Enhanced Luminescence of Phosphor Plates for White LEDs.

MgF2 porous nanoparticle-silica hybrid coating layers were utilized to enhance luminescence efficiency of phosphor plates for white light-emitting diodes (wLEDs). Silica and chemically synthesized porous MgF2 nanoparticles were sequentially deposited by spin coating on the phosphor plate with phosphor-in-glass (PiG) configuration. Microscopic investigation the hybrid film suggested intermixing of silica and porous MgF2 nanoparticles. Numerical estimation based on transfer matrix method expected a significant reduction in reflectance and enhancement of transmission spectra by the hybrid coating. In accordance, the photoluminescence intensity of phosphor plates showed a drastic improvement and quantum yield were enhanced from 30% (for bare PiG plate) to 45% (for hybrid-layer-coated PiG plate). When assembled with blue LEDs, hybrid nanoparticle coating on the PiG phosphor plates improved luminescence efficacy from 40 to 49 lm/W without deterioration of chromaticity during operation. In addition, mechanical durability of the hybrid thin film was confirmed by an abrasion test. Conformal coating of matching layer consisting of porous MgF2 nanoparticles and silica can provide an efficient route to highly luminescent white LEDs with mechanical stability.

Tailoring the morphology of CdSe nanocrystals by incorporation of Pb.

We have investigated the effects of Pb2+ addition on the morphological development of CdSe nanocrystals. We show that the addition of Pb ions in the initial precursor solution changed the morphology of CdSe nanocrystals to branched rods with high aspect ratio. The branched nanocrystals are mainly composed of wurzite phase grown along the [001] direction and the length of rods in each branched nanocrystal can be increased by increasing the amount of Pb2+ addition to accelerate the anisotropic growth of the nanocrystals. The luminescence, however, mostly arises from trap-related recombination and is significantly red-shifted by Pb2+ addition. Surface passivations of the branched nanocrystals with ZnS were proved to be effective in eliminating trap emission and enhancing band-edge emission, leading to a larger quantum yield.

High-Energy, Short-Duration Bursts of Coherent Terahertz Radiation from an Embedded Plasma Dipole.

Emission of radiation from electrons undergoing plasma oscillations (POs) at the plasma frequency has attracted interest because of the existence of intriguing and non-trivial coupling mechanism between the electrostatic PO and the emitted electromagnetic wave. While broadband emission from plasma waves in inhomogeneous plasma is well known, the underlying physics of narrowband emission at the plasma frequency observed in experiments and in solar radio-bursts is obscure. Here we show that a spatially-localized plasma dipole oscillation (PDO) can be generated when electrons are trapped in a moving train of potential wells produced by the ponderomotive force of two slightly detuned laser pulses that collide in plasma and give rise to a burst of quasi-monochromatic radiation. The energy radiated in the terahertz spectral region can reach an unprecedented several millijoules, which makes it suitable for applications requiring short pulses of high-intensity, narrowband terahertz radiation.

Safflower bud inhibits RANKL-induced osteoclast differentiation and prevents bone loss in ovariectomized mice.

BACKGROUND: The powder and extract of safflower seeds are known to be effective in the prevention of bone loss in ovariectomized animals. However, the inhibitory effect and molecular mechanisms of safflower bud (SB), the germinated safflower, on bone destruction is unclear. PURPOSE: The present study was designed to investigate the inhibitory effect and molecular mechanism of SB on osteoclastic differentiation and on bone loss in ovarietomized (OVX) mice. METHODS: Osteoclastogenesis was determined by TRAP staining, F-actin ring formation, and bone resorption assay. NF-κB and MAPKs activation was analyzed by transfection assay and Western blot, respectively. Real-time PCR was performed to examine the expression of osteoclastogenesis-related genes. Histological changes, increases in TRAP-positive cells, and cathepsin K expression were examined in the metaphysis of OVX mice. Density of bone marrow was evaluated by µCT. RESULTS: SB inhibited the RANKL-induced differentiation of BMDMs into osteoclasts in a dose-dependent manner. F-actin ring formation and bone resorption were also reduced by SB in RANKL-treated BMDMs. In addition, SB decreased the activation of NF-κB and MAPKs and the expression of osteoclastogenesis-related genes in BMDMs treated with RANKL. Feeding of SB-included diet prevented bone loss in OVX mice. The number of TRAP-positive cells and level of protein expression of cathepsin K was reduced and bone mineral density was increased in the metaphysis of mice fed SB compared with OVX mice. CONCLUSION: These findings suggest that SB can be a preventive and therapeutic candidate for destructive bone diseases.

Effects of laser polarizations on shock generation and shock ion acceleration in overdense plasmas.

The effects of laser-pulse polarization on the generation of an electrostatic shock in an overdense plasma were investigated using particle-in-cell simulations. We found, from one-dimensional simulations, that total and average energies of reflected ions from a circular polarization- (CP) driven shock front are a few times higher than those from a linear polarization- (LP) driven one for a given pulse energy. Moreover, it was discovered that the pulse transmittance is the single dominant factor for determining the CP-shock formation, while the LP shock is affected by the plasma scale length as well as the transmittance. In two-dimensional simulations, it is observed that the transverse instability, such as Weibel-like instability, can be suppressed more efficiently by CP pulses.

Shock ion acceleration by an ultrashort circularly polarized laser pulse via relativistic transparency in an exploded target.

We investigated ion acceleration by an electrostatic shock in an exploded target irradiated by an ultrashort, circularly polarized laser pulse by means of one- and three-dimensional particle-in-cell simulations. We discovered that the laser field penetrating via relativistic transparency (RT) rapidly heated the upstream electron plasma to enable the formation of a high-speed electrostatic shock. Owing to the RT-based rapid heating and the fast compression of the initial density spike by a circularly polarized pulse, a new regime of the shock ion acceleration driven by an ultrashort (20-40 fs), moderately intense (1-1.4 PW) laser pulse is envisaged. This regime enables more efficient shock ion acceleration under a limited total pulse energy than a linearly polarized pulse with crystal laser systems of λ∼1µm.

Amine-based polar solvent treatment for highly efficient inverted polymer solar cells.

The interfacial dipolar polarization in inverted structure polymer solar cells, which arises spontaneously from the absorption of ethanolamine end groups, such as amine and hydroxyl groups on ripple-structure zinc oxide (ZnO-R), lowers the contact barrier for electron transport and extraction and leads to enhanced electron mobility, suppression of bimolecular recombination, reduction of the contact resistance and series resistance, and remarkable enhancement of the power conversion efficiency.

Bulk synthesis of ordered macroporous silica particles for superhydrophobic coatings.

We report the bulk synthesis of ordered macroporous ceramic particles by the emulsion templating process. Large polystyrene (PS) beads and small silica nanoparticles were assembled simultaneously inside an emulsion, which formed composite structured particles during the evaporation of droplets. Then, by burning out PS beads, macroporous ceramic particle films were produced on substrate. The size of ordered macroporous particles could be controlled by adjusting the emulsification condition for confining emulsions. As well, the pore size could be controlled by changing the diameter of PS beads. The surface of the ordered macroporous particle films was coated with fluorinated molecules, which have shown superhydrophobic property due to multi-scale roughness.