Gate Modulation of Dissipationless Nonlinear Quantum Geometric Current in 2D Te.

Two-dimensional trigonal tellurium (2D Te), a narrow-bandgap semiconductor with a bandgap of approximately 0.3 eV, hosts Weyl points near the band edge and exhibits a narrow, strong Berry curvature dipole (BCD). By applying a back-gate bias to align the Fermi level with the BCD, a sharp increase in the dissipationless transverse nonlinear Hall response is observed in 2D Te. Gate modulation of the BCD demonstrates an on/off ratio of 104 and a responsivity of nearly 106 V/W, while the longitudinal current induced by band modulation reaches an on/off ratio of about 10. This current is sustained up to 200 K, exhibiting a change of 3 orders of magnitude. The inclusion of both transistor action and rectification enhances the temperature sensitivity of the dissipationless Hall current, offering potential applications in electrothermal detectors and sensors and highlighting the significance of topological properties in advancing electronic applications.

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.

A Brief Review of Transparent Conducting Oxides (TCO): The Influence of Different Deposition Techniques on the Efficiency of Solar Cells.

Global-warming-induced climate changes and socioeconomic issues increasingly stimulate reviews of renewable energy. Among energy-generation devices, solar cells are often considered as renewable sources of energy. Lately, transparent conducting oxides (TCOs) are playing a significant role as back/front contact electrodes in silicon heterojunction solar cells (SHJ SCs). In particular, the optimized Sn-doped In2O3 (ITO) has served as a capable TCO material to improve the efficiency of SHJ SCs, due to excellent physicochemical properties such as high transmittance, electrical conductivity, mobility, bandgap, and a low refractive index. The doped-ITO thin films had promising characteristics and helped in promoting the efficiency of SHJ SCs. Further, SHJ technology, together with an interdigitated back contact structure, achieved an outstanding efficiency of 26.7%. The present article discusses the deposition of TCO films by various techniques, parameters affecting TCO properties, characteristics of doped and undoped TCO materials, and their influence on SHJ SC efficiency, based on a review of ongoing research and development activities.

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.

Wafer-Scale Epitaxial Growth of an Atomically Thin Single-Crystal Insulator as a Substrate of Two-Dimensional Material Field-Effect Transistors.

As the electron mobility of two-dimensional (2D) materials is dependent on an insulating substrate, the nonuniform surface charge and morphology of silicon dioxide (SiO2) layers degrade the electron mobility of 2D materials. Here, we demonstrate that an atomically thin single-crystal insulating layer of silicon oxynitride (SiON) can be grown epitaxially on a SiC wafer at a wafer scale and find that the electron mobility of graphene field-effect transistors on the SiON layer is 1.5 times higher than that of graphene field-effect transistors on typical SiO2 films. Microscale and nanoscale void defects caused by heterostructure growth were eliminated for the wafer-scale growth of the single-crystal SiON layer. The single-crystal SiON layer can be grown on a SiC wafer with a single thermal process. This simple fabrication process, compatible with commercial semiconductor fabrication processes, makes the layer an excellent replacement for the SiO2/Si wafer.

Monolayer Kagome metals AV3Sb5.

Recently, layered kagome metals AV3Sb5 (A = K, Rb, and Cs) have emerged as a fertile platform for exploring frustrated geometry, correlations, and topology. Here, using first-principles and mean-field calculations, we demonstrate that AV3Sb5 can crystallize in a mono-layered form, revealing a range of properties that render the system unique. Most importantly, the two-dimensional monolayer preserves intrinsically different symmetries from the three-dimensional layered bulk, enforced by stoichiometry. Consequently, the van Hove singularities, logarithmic divergences of the electronic density of states, are enriched, leading to a variety of competing instabilities such as doublets of charge density waves and s- and d-wave superconductivity. We show that the competition between orders can be fine-tuned in the monolayer via electron-filling of the van Hove singularities. Thus, our results suggest the monolayer kagome metal AV3Sb5 as a promising platform for designer quantum phases.

Supervised chemical graph mining improves drug-induced liver injury prediction.

Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.

Topological phase transitions without symmetry indication in NaZnSb[Formula: see text]Bi[Formula: see text].

We study topological phase transitions in tetragonal NaZnSb[Formula: see text]Bi[Formula: see text], driven by the chemical composition x. Notably, we examine mirror Chern numbers that change without symmetry indicators. First-principles calculations are performed to show that NaZnSb[Formula: see text]Bi[Formula: see text] experiences consecutive topological phase transitions, diagnosed by the strong [Formula: see text] topological index [Formula: see text] and two mirror Chern numbers [Formula: see text] and [Formula: see text]. As the chemical composition x increases, the topological invariants ([Formula: see text]) change from (000), (020), (220), to (111) at x = 0.15, 0.20, and 0.53, respectively. A simplified low-energy effective model is developed to examine the mirror Chern number changes, highlighting the central role of spectator Dirac fermions in avoiding symmetry indicators. Our findings suggest that NaZnSb[Formula: see text]Bi[Formula: see text] can be an exciting testbed for exploring the interplay between the topology and symmetry.

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.

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.

Correction: On-demand quantum spin Hall insulators controlled by two-dimensional ferroelectricity.

Correction for 'On-demand quantum spin Hall insulators controlled by two-dimensional ferroelectricity' by Jiawei Huang et al., Mater. Horiz., 2022, DOI: https://doi.org/10.1039/d2mh00334a.

On-demand quantum spin Hall insulators controlled by two-dimensional ferroelectricity.

We propose a new class of quantum materials, type-II two-dimensional ferroelectric topological insulators (2DFETIs), which allow non-volatility and an on-off switch of quantum spin Hall states. A general strategy is developed to realize type-II 2DFETIs using only topologically trivial 2D ferroelectrics. The built-in electric field arising from the out-of-plane polarization across the bilayer heterostrucuture of 2D ferroelectrics enables robust control of the band gap size and band inversion strength, which can be utilized to manipulate the topological phase transitions on-demand. Using first-principles calculations with hybrid density functionals, we demonstrate that a series of bilayer heterostructures are type-II 2DFETIs characterized with a direct coupling between the band topology and polarization state. We propose a few 2DFETI-based quantum electronics, including domain-wall quantum circuits and topological memristors.

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).

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.

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.

BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud.

MOTIVATION: Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline. RESULTS: We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools. AVAILABILITY AND IMPLEMENTATION: http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A review on compound-protein interaction prediction methods: Data, format, representation and model.

There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.

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.

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.

Decelerated Hot Carrier Cooling in Graphene via Nondissipative Carrier Injection from MoS2.

One key to improve the performance of advanced optoelectronic devices and energy harvesting in graphene is to understand the predominant carrier scattering via optical phonons. Nevertheless, low light absorbance in graphene yields a limited photoexcited carrier density, hampering the hot carrier effect, which is strongly correlated to the hot optical phonon bottleneck effect as the energy-loss channel. Here, by integrating graphene with monolayer MoS2 possessing stronger light absorbance, we demonstrate an efficient interfacial hot carrier transfer between graphene and MoS2 in their heterostructure with a prolonged relaxation time using broadband transient differential transmittance spectroscopy. We observe that the carrier relaxation time of graphene in the heterostructure is 4 times slower than that of bare graphene. This is explained by nondissipative interlayer transfer from MoS2 to graphene, which is attributed to the enhanced hot optical phonon bottleneck effect of graphene in the heterostructure by an increased photoexcited carrier population. A significant reduction of both amplitude and relaxation time in A- and B-excitons is another evidence of the interlayer transfer from MoS2 to graphene. The nondissipative interlayer charge transfer from MoS2 to graphene is confirmed by density functional calculations. This provides a different platform to further study the photoinduced hot carrier effect in graphene heterostructures for photothermoelectric detectors or hot carrier solar cells.