Invited - Temporal information processing for in-sensor computing based on amorphous IGZO phototransistor

On facing the massive and unstructured data processing, it is imperative to emulate artificial neural networks with new physical hardware architectures in addition to software-based approaches, to overcome the barrier of the von Neumann bottleneck. By mimicking the human visual sensing system, the optoelectronic devices, which can perform data compression and reduce the network size through the reconstruction of input signals, are promising to develop the neuromorphic in-sensor computing for minimizing the time latency as well as improving the energy efficiency. In this work, we demonstrate an amorphous indium-gallium-zinc-oxide (a-IGZO) phototransistor with ZrOx high-k dielectric layer with distinct responses to various optical stimulation inputs. Due to the persistent photoconductivity (PPC) effect of a-IGZO after lighting, our device is able to exhibit synaptic functions via the application of 405 nm light spikes, such as paired-pulse facilitation (PPF) and short-term memory (STM). Furthermore, in order to perform the temporal optical signals processing, the a-IGZO phototransistor is stimulated by four-timeframe temporal pulse streams composed of 405 nm light spikes and it expresses the different temporal responses. The distinct output photocurrent response reveals that the a-IGZO phototransistor can be applied to distinguish the time-series input light signals. Accordingly, the a-IGZO phototransistor have a promising potential for processing optical temporal information and can possibly be implemented for visual in-sensor computing techniques. Please click Download on the upper right corner to see the full abstract

Comparison of Radical Scavenging Activity, Cytotoxic Effects and Apoptosis Induction in Human Melanoma Cells by Taiwanese Propolis from Different Sources

Propolis is a sticky substance that is collected from plants by honeybees. We previously demonstrated that propolins A, B, C, D, E and F, isolated from Taiwanese propolis (TP), could effectively induce human melanoma cell apoptosis and were strong antioxidant agents. In this study, we evaluated TP for free radical scavenging activity by DPPH (1,2-diphenyl-2-picrylhydrazyl). The phenolic concentrations were quantified by the Folin–Ciocalteu method. The apoptosis trigger activity in human melanoma cells was evaluated. TP contained a higher level of phenolic compounds and showed strong capability to scavenge free radicals. Additionally, TP1g, TP3, TP4 and TP7 exhibited a cytotoxic effect on human melanoma cells, with an IC(50) of ∼2.3, 2.0, 3.3 and 3.3 μg/ml, respectively. Flow cytometric analysis for DNA fragmentation indicated that TP1g, TP2, TP3 and TP7 could induce apoptosis in human melanoma cells and there is a marked loss of cells from the G2/M phase of the cell cycle. To address the mechanism of the apoptosis effect of TP, we evaluated its effects on induction of apoptosis-related proteins in human melanoma cells. The levels of procaspase-3 and PARP [poly(ADP-ribose) polymerase] were markedly decreased. Furthermore, propolins A, B, C, D, E and F in TP were determined using HPLC. The results indicate that TP is a rich source of these compounds. The findings suggest that TP induces apoptosis in human melanoma cells due to its high level of propolins

Activation of β-Adrenoceptors by Dobutamine May Induce a Higher Expression of Peroxisome Proliferator-Activated Receptors δ (PPARδ) in Neonatal Rat Cardiomyocytes

Recent evidence showed the role of peroxisome proliferator-activated receptors (PPARs) in cardiac function. Cardiac contraction induced by various agents is critical in restoring the activity of peroxisome proliferator-activated receptors δ (PPARδ) in cardiac myopathy. Because dobutamine is an agent widely used to treat heart failure in emergency setting, this study is aimed to investigate the change of PPARδ in response to dobutamine. Neonatal rat cardiomyocytes were used to examine the effects of dobutamine on PPARδ expression levels and cardiac troponin I (cTnI) phosphorylation via Western blotting analysis. We show that treatment with dobutamine increased PPARδ expression and cTnI phosphorylation in a time- and dose-dependent manner in neonatal rat cardiomyocytes. These increases were blocked by the antagonist of β1-adrenoceptors. Also, the action of dobutamine was related to the increase of calcium ions and diminished by chelating intracellular calcium. Additionally, dobutamine-induced action was reduced by the inhibition of downstream messengers involved in this calcium-related pathway. Moreover, deletion of PPARδ using siRNA generated the reduction of cTnI phosphorylation in cardiomyocytes treated with dobutamine. Thus, we concluded that PPARδ is increased by dobutamine in cardiac cells

Scalable photonic sources using two-dimensional lead halide perovskite superlattices

Miniaturized photonic sources based on semiconducting two-dimensional (2D) materials offer new technological opportunities beyond the modern III-V platforms. For example, the quantum-confined 2D electronic structure aligns the exciton transition dipole moment parallel to the surface plane, thereby outcoupling more light to air which gives rise to high-efficiency quantum optics and electroluminescent devices. It requires scalable materials and processes to create the decoupled multi-quantum-well superlattices, in which individual 2D material layers are isolated by atomically thin quantum barriers. Here, we report decoupled multi-quantum-well superlattices comprised of the colloidal quantum wells of lead halide perovskites, with unprecedentedly ultrathin quantum barriers that screen interlayer interactions within the range of 6.5 Å. Crystallographic and 2D k-space spectroscopic analysis reveals that the transition dipole moment orientation of bright excitons in the superlattices is predominantly in-plane and independent of stacking layer and quantum barrier thickness, confirming interlayer decoupling

Enhanced photo-excitation and angular-momentum imprint of gray excitons in WSe2_{2} monolayers by spin-orbit-coupled vector vortex beams

A light beam can be spatially structured in the complex amplitude to possess orbital angular momentum (OAM), which introduces a new degree of freedom alongside the intrinsic spin angular momentum (SAM) associated with circular polarization. Moreover, super-imposing two twisted lights with distinct SAM and OAM produces a vector vortex beam (VVB) in non-separable states where not only complex amplitude but also polarization are spatially structured and entangled with each other. In addition to the non-separability, the SAM and OAM in a VVB are intrinsically coupled by the optical spin-orbit interaction and constitute the profound spin-orbit physics in photonics. In this work, we present a comprehensive theoretical investigation, implemented on the first-principles base, of the intriguing light-matter interaction between VVBs and WSe$_{2}$ monolayers (WSe$_{2}$-MLs), one of the best-known and promising two-dimensional (2D) materials in optoelectronics dictated by excitons, encompassing bright exciton (BX) as well as various dark excitons (DXs). One of the key findings of our study is the substantial enhancement of the photo-excitation of gray excitons (GXs), a type of spin-forbidden dark exciton, in a WSe$_2$-ML through the utilization of a twisted light that possesses a longitudinal field associated with the optical spin-orbit interaction. Our research demonstrates that a spin-orbit-coupled VVB surprisingly allows for the imprinting of the carried optical information onto gray excitons in 2D materials, which is robust against the decoherence mechanisms in materials. This observation suggests a promising method for deciphering the transferred angular momentum from structured lights to excitons

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy

Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the “Set-Based gene-EnviRonment InterAction test” (SBERIA), “gene-environment set association test” (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10−7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 × 10−5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses