Self-powered semitransparent WS2/LaVO3vertical-heterostructure photodetectors by employing interfacial hexagonal boron nitride.

Two-dimensional (2D) semiconductor and LaVO3materials with high absorption coefficients in the visible light region are attractive structures for high-performance photodetector (PD) applications. Insulating 2D hexagonal boron nitride (h-BN) with a large band gap and excellent transmittance is a very attractive material as an interface between 2D/semiconductor heterostructures. We first introduce WS2/h-BN/LaVO3semitransparent PD. The photo-current/dark current ratio of the device exhibits a delta-function characteristic of 4 × 105at 0 V, meaning 'self-powered'. The WS2/h-BN/LaVO3PD shows up to 0.27 A W-1responsivity (R) and 4.6 × 1010cm Hz1/2W-1detectivity (D*) at 730 nm. Especially, it was confirmed that theD* performance improved by about 5 times compared to the WS2/LaVO3device at zero bias. Additionally, it is suggested that the PD maintains 87% of its initialRfor 2000 h under the atmosphere with a temperature of 25 °C and humidity of 30%. Based on the above results, we suggest that the WS2/h-BN/LaVO3heterojunction is promising as a self-powered optoelectronic device.

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most important preprocessing steps prior to any subsequent analysis. For rs-fMRI denoising, a common trend is to decompose rs-fMRI data into multiple components and then regress out noise-related components. Therefore, various machine learning techniques have been used in such analyses with predefined procedures and manually engineered features. However, the lack of a universal definition of a noise-related source or artifact complicates manual feature engineering. Manual feature selection can result in the failure to capture unknown types of noise. Furthermore, the possibility that the hand-crafted features will only work for the broader population (e.g., healthy adults) but not for "outliers" (e.g., infants or subjects that belong to a disease cohort) is quite high. In practice, we have limited knowledge of which features should be extracted; thus, multi-classifier assembly must be implemented to improve performance, although this process is quite time-consuming. However, in real rs-fMRI applications, fast and accurate automatic identification of noise-related components on different datasets is critical. To solve this problem, we propose a novel, automatic, and end-to-end deep learning framework dedicated to noise-related component identification via a faster and more effective multi-layer feature extraction strategy that learns deeply embedded spatio-temporal features of the components. In this study, we achieved remarkable performance on various rs-fMRI datasets, including multiple adult rs-fMRI datasets from different rs-fMRI studies and an infant rs-fMRI dataset, which is quite heterogeneous and differs from that of adults. Our proposed framework also dramatically increases the noise detection speed owing to its inherent ability for deep learning (< 1s for single-component classification). It can be easily integrated into any preprocessing pipeline, even those that do not use standard procedures but depend on alternative toolboxes.

Highly stable semitransparent multilayer graphene/LaVO3vertical-heterostructure photodetectors.

A heterostructure composed of a combination of semi-metallic graphene (Gr) and high-absorption LaVO3is ideal for high-performance translucent photodetector (PD) applications. Here, we present multilayer Gr/LaVO3vertical-heterostructure semitransparent PDs with various layer numbers (Ln). AtLn= 2, the PD shows the best performance with a responsivity (R) of 0.094 A W-1and a specific detectivity (D*) of 7.385 × 107cm Hz1/2W-1at 532 nm. Additionally, the average visible transmittance of the PD is 63%, i.e. it is semitransparent. We increased photocurrent (PC) by approximately 13%, from 0.564 to 0.635µA cm-2by using an Al reflector on the semitransparent PD. The PC of an unencapsulated PD maintains about 86% (from 0.571 to 0.493µA cm-2) of its initial PC value after 2000 h at 25 °C temperature/30% relative humidity, showing good stability. This behavior is superior to that of previously reported graphene-based PDs. These results show that these PDs have great potential for semitransparent optoelectronic applications.

High-performance and -stability graphene quantum dots-mixed conducting polymer/porous Si hybrid solar cells with titanium oxide passivation layer.

Recently, conducting polymer/Si hybrid solar cells (HSCs) based on simple fabrication processes have become highly attractive due to their low cost, but low conductivity of the polymer, high reflection index of Si, and large recombination loss on the Si back contact are major drawbacks that should be solved for the practical applications. Here, we first report HSCs composed of graphene quantum dots (GQDs)-mixed poly (3,4-ethylenedioxythiophene) (PEDOT:GQDs)/ porous Si (PSi)/n-Si/titanium oxide (TiO x , back passivation layer). Maximum power conversion efficiency (PCE) of 10.49% is obtained from the HSCs at an active area of 5 mm2, resulting from the enhanced conductivity of the PEDOT:GQDs, the reduced reflectivity of Si (the increased absorption) by the formation of PSi, and the prevented recombination loss at the Si backside due to the passivation. In addition, the HSCs of 16 mm2 active area maintain ∼78% (absolutely from 8.03% to 6.28%) of the initial PCE even while kept under ambient conditions for 15 d.

New Pyrene Derivatives Having Rigid Imidazole Moiety for Blue Emitters.

Pyrene, imidazole and dibenzofuran were used to synthesize new blue emitters of 1-(4-(dibenzo[b,d]furan-4-yl)phenyl)-2-(pyren-1-yl)-1H-phenanthro[9,10-d]imidazole (BFP-PI) and 1-(4-(dibenzo[b,d]furan-4-yl)phenyl)-4,5-diphenyl-2-(pyren-1-yl)-1H-imidazole (BFP-DPI). In the film state, BFP-PI and BFP-DPI show photoluminescence (PL) maximum values of 462 nm and 459 nm. The relative PL quantum efficiency (PLQY) of BFP-PI and BFP-DPI is 89.16% and 79.2% by using reference compound of 9,10-diphenylanthracene. The device using BFP-PI in the non-doped state as emitting material showed current efficiency (C.E.) of 3 cd/A and external quantum efficiency (E.Q.E.) of 2.15%, and the device using BFP-DPI as emitting material exhibited C.E. of 2.64 cd/A and E.Q.E. of 1.6%.

A potent hydroxamic acid-based, small-molecule inhibitor A452 preferentially inhibits HDAC6 activity and induces cytotoxicity toward cancer cells irrespective of p53 status.

HDAC6-selective inhibitors are novel epigenetic anticancer agents. However, their precise mechanisms of action are incompletely understood. We investigated the anticancer mechanisms of the novel potent and selective HDAC6 inhibitor A452 compared with current clinically tested HDAC6 inhibitor ACY-1215. We demonstrate that A452 effectively inhibits the cell growth and viability of various cancer cell types, irrespective of p53 status. A452-induced apoptosis as evidenced by activated caspase 3 and PARP, increased Bak and Bax and decreased Bcl-xL. Moreover, A452 shifted cells away from antiapoptotic (AKT and ERK) pathways and toward proapoptotic (p38) pathways. A452 triggered DNA damage via increased γH2AX and activation of the checkpoint kinase Chk2. A452 induced the suppression of cell migration and invasion. Interestingly, A452 upregulated the expression of PD-L1, which regulates the PD-1 inhibitory pathway in T cells. Overall, our results suggest that A452 is more effective as an anticancer agent than ACY-1215. Therefore, therapeutically targeting HDAC6 may represent a novel strategy for cancer treatment irrespective of the p53 mutation status.

A452, an HDAC6-selective inhibitor, synergistically enhances the anticancer activity of chemotherapeutic agents in colorectal cancer cells.

Although histone deacetylase inhibitors (HDACi) alone could be clinically useful, these are most recently used in combination with other anticancer agents in clinical trials for cancer treatment. Recently, we reported the anticancer activity of an HDAC6-selective inhibitor A452 toward various cancer cell types. This study aims to present a potent synergistic antiproliferative effect of A452/anticancer agent treatment in colorectal cancer cells (CRC) cells, independently of the p53 status. A452 in combination with irinotecan, or SAHA is more potent than either drug alone in the apoptotic pathway as evidenced by activated caspase-3 and PARP, increased Bak and pp38, decreased Bcl-xL, pERK, and pAKT, and induced apoptotic cells. Furthermore, A452 enhances DNA damage induced by anticancer agents as indicated by the increased accumulation of γH2AX and the activation of the checkpoint kinase Chk2. The silencing of HDAC6 enhances the cell growth inhibition and cell death caused by anticancer agents. In addition, A452 induces the synergistic suppression of cell migration and invasion. This study suggests a mechanism by which HDAC6-selective inhibition can enhance the efficacy of specific anticancer agents in CRC cells.

Highly-flexible perovskite photodiodes employing doped multilayer-graphene transparent conductive electrodes.

We first report highly-flexible perovskite photodiodes, using AuCl3-doped multilayer-graphene transparent conducting electrodes. The doping effect of the AuCl3 is more effective when the number of layers (L n ) = 1 and 2 rather than 3 and 4, as analyzed by Raman scattering and sheet resistance. The photodiodes optimized at L n  = 2 exhibit a 105 photo-/dark-current ratio, 0.4 AW-1 responsivity, 80% external quantum efficiency, 5.3 × 1010 cm Hz1/2/W detectivity, 90 dB linear dynamic range, and ∼1.1 µs response time. In addition, the photodiodes show excellent bending stabilities, maintaining a responsivity at about 70% of its initial value, even after 1000 bending cycles at a bending curvature of 4 mm.

Strong enhancement of emission efficiency in GaN light-emitting diodes by plasmon-coupled light amplification of graphene.

Recently, we have demonstrated that excitation of plasmon-polaritons in a mechanically-derived graphene sheet on the top of a ZnO semiconductor considerably enhances its light emission efficiency. If this scheme is also applied to device structures, it is then expected that the energy efficiency of light-emitting diodes (LEDs) increases substantially and the commercial potential will be enormous. Here, we report that the plasmon-induced light coupling amplifies emitted light by ∼1.6 times in doped large-area chemical-vapor-deposition-grown graphene, which is useful for practical applications. This coupling behavior also appears in GaN-based LEDs. With AuCl3-doped graphene on Ga-doped ZnO films that is used as transparent conducting electrodes for the LEDs, the average electroluminescence intensity is 1.2-1.7 times enhanced depending on the injection current. The chemical doping of graphene may produce the inhomogeneity in charge densities (i.e., electron/hole puddles) or roughness, which can play a role as grating couplers, resulting in such strong plasmon-enhanced light amplification. Based on theoretical calculations, the plasmon-coupled behavior is rigorously explained and a method of controlling its resonance condition is proposed.

Highly-stable and -flexible graphene/(CF3SO2)2NH/graphene transparent conductive electrodes for organic solar cells.

We first employ highly-stable and -flexible (CF3SO2)2NH-doped graphene (TFSA/GR) and GR-encapsulated TFSA/GR (GR/TFSA/GR) transparent conductive electrodes (TCEs) prepared on polyethylene terephthalate substrates for flexible organic solar cells (OSCs). Compared to conventional indium tin oxide (ITO) TCEs, the TFSA-doped-GR TCEs show higher optical transmittance and larger sheet resistance. The TFSA/GR and GR/TFSA/GR TCEs show work functions of 4.89 ± 0.16 and 4.97 ± 0.18 eV, respectively, which are not only larger than those of the ITO TCEs but also indicate p-type doping of GR, and are therefore more suitable for anode TCEs of OSCs. In addition, typical GR/TFSA/GR-TCE OSCs are much more mechanically flexible than the ITO-TCE ones with their photovoltaic parameters being similar, as proved by bending tests as functions of cycle and curvature.

Resonance effects in thickness-dependent ultrafast carrier and phonon dynamics of topological insulator Bi2Se3.

Resonance effects in the thickness-dependent ultrafast carrier and phonon dynamics of topological insulator Bi2Se3 are found irrespective of the kind of substrate by measuring thickness-dependent abrupt changes of pump-probe differential-reflectivity signals (ΔR/R) from Bi2Se3 thin films on four different substrates of poly- and single-crystalline (sc-) ZnO, sc-GaN and SiO2. The absolute peak intensity of the ΔR/R is maximized at ∼t C (6 ∼ 9 quintuple layers), which is not directly related to but is very close to the critical thickness below which the energy gap opens. The intensities of the two phonon modes deduced from the oscillatory behaviors superimposed on the ΔR/R profiles are also peaked at ∼t C for the four kinds of substrates, consistent with the thickness-dependent Raman-scattering behaviors. These resonant effects and others are discussed based on possible physical mechanisms including the effects of three-dimensional carrier depletion and intersurface coupling.

Spatially-resolved exploration of the mouse brain glycome by tissue glyco-capture (TGC) and nano-LC/MS.

Tissue glyco-capture (TGC), a highly sensitive MS-compatible method for extraction of glycans from tissue, was combined with structure-specific nano-LC/MS for sensitive and detailed profiling of the mouse brain glycome. Hundreds of glycan structures were directly detected by accurate mass MS and structurally elucidated by MS/MS, revealing the presence of novel glycan motifs such as antennary fucosylation, sulfation, and glucuronidation that are potentially associated with cellular signaling and adhesion. Microgram-level sensitivity enabled glycomic analysis of specific regions of the brain, as demonstrated on not only brain sections (with a one-dimensional spatial resolution of 20 µm) but also isolated brain structures (e.g., the hippocampus). Reproducibility was extraordinarily high (R > 0.98) for both method and instrumental replicates. The pairing of TGC with structure-specific nano-LC/MS was found to be an exceptionally powerful platform for qualitative and quantitative exploration of the brain glycome.

Aceroside VIII is a new natural selective HDAC6 inhibitor that synergistically enhances the anticancer activity of HDAC inhibitor in HT29 cells.

The identification of new isoform-specific histone deacetylase inhibitors is important for revealing the biological functions of individual histone deacetylase and for determining their potential use as therapeutic agents. Among the 11 zinc-dependent histone deacetylases that have been identified in humans, histone deacetylase 6 is a structurally and functionally unique enzyme. Here, we tested the inhibitory activity of diarylheptanoids isolated from Betula platyphylla against histone deacetylase 6. Aceroside VIII selectively inhibited histone deacetylase 6 catalytic activity and the combined treatment of aceroside VIII or (-)-centrolobol with A452, another selective histone deacetylase 6 inhibitor, led to a synergistic increase in levels of acetylated α-tubulin. Aceroside VIII, paltyphyllone, and (-)-centrolobol synergistically enhanced the induction of apoptosis and growth inhibition by A452. Consistent with these results, A452 in combination with aceroside VIII, paltyphyllone, or (-)-centrolobol was more potent than either drug alone for the induction of apoptosis. Together, these findings indicate that aceroside VIII is a specific histone deacetylase 6 inhibitor and points to a mechanism by which natural histone deacetylase 6-selective inhibitors may enhance the efficacy of other histone deacetylase 6 inhibitors in colon cancer cells.

Therapeutic effects of mouse bone marrow-derived clonal mesenchymal stem cells in a mouse model of inflammatory bowel disease.

Mouse bone marrow-derived clonal mesenchymal stem cells (mcMSCs), which were originated from a single cell by a subfractionation culturing method, are recognized as new paradigm for stem cell therapy featured with its homogenous cell population. Next to proven therapeutic effects against pancreatitis, in the current study we demonstrated that mcMSCs showed significant therapeutic effects in dextran sulfate sodium (DSS)-induced experimental colitis model supported with anti-inflammatory and restorative activities. mcMSCs significantly reduced the disease activity index (DAI) score, including weight loss, stool consistency, and intestinal bleeding and significantly increased survival rates. The pathological scores were also significantly improved with mcMSC. We have demonstrated that especial mucosal regeneration activity accompanied with significantly lowered level of apoptosis as beneficiary actions of mcMSCs in UC models. The levels of inflammatory cytokines including TNF-α, IFN-γ, IL-1ß, IL-6, and IL-17 were all significantly concurrent with significantly repressed NF-κB activation compared to the control group and significantly decreased infiltrations of responsible macrophage and neutrophil. Conclusively, our findings provide the rationale that mcMSCs are applicable as a potential source of cell-based therapy in inflammatory bowel diseases, especially contributing either to prevent relapse or to accelerate healing as solution to unmet medical needs in IBD therapy.

Enhancement of the effectiveness of graphene as a transparent conductive electrode by AgNO3 doping.

Single-layer graphene sheets have been synthesized by using chemical vapor deposition, and subsequently doped with AgNO3 at various doping concentrations (n(D)) from 5 to 50 mM. Atomic force microscopy and field emission scanning electron microscopy images reveal the formation of ∼10-100 nm Ag particles on the graphene surface after doping. The type of n doping is confirmed by analyzing the n(D)-dependent behaviors of Raman scattering and the work function of the doped graphene films. The sheet resistance monotonically decreases to ∼173 Ω/sq with the increase of n(D) to 50 mM, and the transmittance is reduced by only about 3% for the highest n(D). At n(D) = 10 mM optimized doped graphene layers with a sheet resistance of 202 Ω/sq and a transmittance of 96% are obtained, resulting in a maximum DC conductivity/optical conductivity ratio (σ(DC)/σ(OP)) of ∼45.5, much larger than the minimum industry standard (σ(DC)/σ(OP) = ∼35) for transparent conductive electrodes.

FTMMR: Fusion Transformer for Integrating Multiple Molecular Representations.

Molecular property prediction has gained substantial attention due to its potential for various bio-chemical applications. Numerous attempts have been made to enhance the performance by combining multiple molecular representations (1D, 2D, and 3D). However, most prior works only merged a limited number of representations or tried to embed multiple representations through a single network without using representation-specific networks. Furthermore, the heterogeneous characteristics of each representation made the fusion more challenging. Addressing these challenges, we introduce the Fusion Transformer for Multiple Molecular Representations (FTMMR) framework. Our strategy employs three distinct representation-specific networks and integrates information from each network using a fusion transformer architecture to generate fused representations. Additionally, we use self-supervised learning methods to align heterogeneous representations and to effectively utilize the limited chemical data available. In particular, we adopt a combinatorial loss function to leverage the contrastive loss for all three representations. We evaluate the performance of FTMMR using seven benchmark datasets, demonstrating that our framework outperforms existing fusion and self-supervised methods.

Sparse Graph Representation Learning based on Reinforcement Learning for Personalized Mild Cognitive Impairment (MCI) Diagnosis.

Resting-state functional magnetic resonance imaging (rs-fMRI) has gained attention as a reliable technique for investigating the intrinsic function patterns of the brain. It facilitates the extraction of functional connectivity networks (FCNs) that capture synchronized activity patterns among regions of interest (ROIs). Analyzing FCNs enables the identification of distinctive connectivity patterns associated with mild cognitive impairment (MCI). For MCI diagnosis, various sparse representation techniques have been introduced, including statistical- and deep learningbased methods. However, these methods face limitations due to their reliance on supervised learning schemes, which restrict the exploration necessary for probing novel solutions. To overcome such limitation, prior work has incorporated reinforcement learning (RL) to dynamically select ROIs, but effective exploration remains challenging due to the vast search space during training. To tackle this issue, in this study, we propose an advanced RL-based framework that utilizes a divide-and-conquer approach to decompose the FCN construction task into smaller subproblems in a subject-specific manner, enabling efficient exploration under each sub-problem condition. Additionally, we leverage the learned value function to determine the sparsity level of FCNs, considering individual characteristics of FCNs. We validate the effectiveness of our proposed framework by demonstrating its superior performance in MCI diagnosis on publicly available cohort datasets.

Graph-Based Conditional Generative Adversarial Networks for Major Depressive Disorder Diagnosis With Synthetic Functional Brain Network Generation.

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural Networks (DNN) architectures to construct Generative Adversarial Networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional Networks (GCN)-based Conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

DNA microarray profiling of genes differentially regulated by three heterochromatin protein 1 (HP1) homologs in Drosophila.

Heterochromatin protein 1 (HP1) is an epigenetic gene silencing protein that is regulated by lysine 9 methylation of histone H3. Most eukaryotes have at least three HP1 homologs with similar domain structures but with different localization patterns and functions in heterochromatin and euchromatin. However, little is known about the genome-wide effects of the three main HP1 homologs on gene expression. Here, to gain insight into the different gene expression effects of the three HP1 homologs, we performed a comprehensive and comparative microarray analysis of Drosophila HP1 homologs. Bioinformatic analysis of the microarray profiling revealed significant similarity and uniqueness in the genes altered in HP1-knockdown S2 cells in Drosophila. Although global changes of these transcripts were surprisingly subtle (4-6%), there were ~582 common target genes for the three HP1s that showed transcript levels either reduced or induced >1.5-fold. Depletion of HP1 resulted in up-regulated and down-regulated gene profiles, indicating that HP1 mediates both repression and activation of gene expression. This study is the first to systematically analyze the bioinformatics of HP1 paralogs and provide basic clues to the molecular mechanism by which HP1 might control gene expression in a homolog-specific manner.