Daegu Gyeongbuk Institute of Science and Technology
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Role of the circadian nuclear receptor REV-ERBα in dorsal raphe serotonin synthesis in mood regulation
Get PDFAffective disorders are frequently associated with disrupted circadian rhythms. The existence of rhythmic secretion of central serotonin (5-hydroxytryptamine, 5-HT) pattern has been reported; however, the functional mechanism underlying the circadian control of 5-HTergic mood regulation remains largely unknown. Here, we investigate the role of the circadian nuclear receptor REV-ERBα in regulating tryptophan hydroxylase 2 (Tph2), the rate-limiting enzyme of 5-HT synthesis. We demonstrate that the REV-ERBα expressed in dorsal raphe (DR) 5-HTergic neurons functionally competes with PET-1—a nuclear activator crucial for 5-HTergic neuron development. In mice, genetic ablation of DR 5-HTergic REV-ERBα increases Tph2 expression, leading to elevated DR 5-HT levels and reduced depression-like behaviors at dusk. Further, pharmacological manipulation of the mice DR REV-ERBα activity increases DR 5-HT levels and affects despair-related behaviors. Our findings provide valuable insights into the molecular and cellular link between the circadian rhythm and the mood-controlling DR 5-HTergic systems. © The Author(s) 2024.TRUEsciescopu
Neuron-astrocyte interaction-inspired percolative networks with metal microdendrites and nanostars for ultrasensitive and transparent electronic skins
No full textBiological systems provide innovative designs for electronic devices, optimizing network configurations for high-performance signal transmission with minimal energy consumption. The brain, as one of the most complex biological structures, demonstrates efficient network design through the multiscale radial networks of neurons and astrocytes. Emulating these brain networks offers a blueprint for the development of ultrasensitive pressure sensors for electronic skin, aiming to provide a more intuitive and sensitive mode of interaction between humans and machines. Herein, we propose a neuromorphic percolative network inspired by neuron-astrocyte interactions for ultrasensitive pressure sensors employing metal microdendrites and nanostars. Electromechanical investigation through representative volume elements simulation reveals that the optimized arrangement of microdendrites and nanostars in the neuromorphic percolative system enhances the percolation threshold and probability. Following these simulation results, we developed a neuromorphic percolative polyurethane (NP-PU) matrix utilizing the metal microdendrite-nanostar networks. The augmented quantum tunneling effect in the NP-PU matrix was investigated through electrochemical impedance spectroscopy and capacitance analysis. The fabricated piezoresistive pressure sensor with the NP-PU matrix shows ultrahigh sensitivity (160.3 kPa−1) at a low pressure range and a low limit of detection resolution (4 Pa), enabled by multi-channel quantum tunneling in the metal particle networks. Furthermore, the sensor maintains excellent mechanical flexibility and high optical transparency (75.4 %), improving its efficacy in applications like electronic skin and force touch panel. Our study highlights the potential of leveraging biological system-inspired network designs for crafting advanced electronic devices. © 2024 Elsevier B.V.FALSEsciescopu
Oblong-shaped piezoelectric ultrasound energy harvester for high-performance wireless power charging
No full textWireless power charging for implantable biomedical electronics (IBEs) can potentially eliminate the need for frequent battery-replacement surgeries, ultimately improving patients’ quality of life. Ultrasound wireless power transfer (US-WPT) based on piezoelectric receivers has demonstrated significant potential, particularly for deep-seated IBEs. However, most studies on US-WPT have overlooked the optimization of piezoelectric receiver dimensions relative to transmitted ultrasound beam profiles for maximizing battery-charging efficiency for IBEs. This study revealed that a piezoelectric receiver should have dimensions corresponding to the main lobe width of the transmit beam profile within a focal area. Moreover, its output power should be proportional to the cube of the receiver area. Considering these findings, an oblong-shaped ultrasound transmitter and receiver (OsUTR) was developed for efficient WPT, capable of fully charging commercial batteries. In water, the OsUTR produced an output power per unit area of 246.93 mW/cm2, which is 6.588 times higher than that achieved by previously reported methods. This resulted in an average charging rate of 1.64 mC/s, enabling the fully charging of a 30 mAh commercial battery in 1.33 h. Experiments with a 50 mm thick porcine tissue demonstrated that the OsUTR provided an output voltage and current of 38.4 Vp-p and 103.4 mAp-p, respectively. Consequently, full charging of the battery was successfully achieved in 1.80 h. This high-performance OsUTR can enable long-term use of IBEs. This innovation reduces the burden of battery replacement and expands the applicability of implantable devices, driving significant advancements in the IBEs industry. © 2024FALSEsciescopu
THERMAL CROSS-LINKABLE HOLE TRANSPORT MATERIAL, LIGHT EMITTING DIODE INCLUDING SAME AND METHOD FOR MANUFACTURING THE SAME
No full textMultichannel Carbon Nanofibers: Pioneering the Future of Energy Storage
No full textMultichannel carbon nanofibers (MCNFs), characterized by complex hierarchical structures comprising multiple channels or compartments, have attracted considerable attention owing to their high porosity, large surface area, good directionality, tunable composition, and low density. In recent years, electrospinning (ESP) has emerged as a popular synthetic technique for producing MCNFs with exceptional properties from various polymer blends, driven by phase separation between polymers. These interactions, including van der Waals forces, covalent bonding, and ionic interactions, are crucial for MCNF production. Over time, the applications of MCNFs have expanded, making them one of the most intriguing topics in material research. MCNFs with tailored porous channels, controllable dimensions, confined spaces, high surface areas, designed architectures, and easy electrolyte access to active walls are considered optimal for electrochemical energy storage (EES) technologies. This review provides an exhaustive overview of the working principle, synthesis methods, and structural properties of MCNFs, and examines their advantages, limitations, and potential for producing multichannel architectures. Furthermore, this review explores the relationship between the composition of MCNF electrode materials for EES devices (supercapacitors and batteries) and their electrochemical performance. This review also addresses future directions and challenges in the development and utilization of MCNFs and provides insights into potential research avenues for advancing this exciting field.FALSEsciescopu
Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model
Get PDFSegmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types of nuclei. In this work, we introduce category descriptors to perform nuclei segmentation and classification by prompting the SAM model. We close the domain gap between histopathology and natural scene images by aligning features in low-level space while preserving the high-level representations of SAM. We performed extensive experiments on the Lizard dataset, validating the ability of our model to perform automatic nuclei segmentation and classification, especially for rare nuclei types, where achieved a significant detection improvement in the F1 score of up to 12%. Our model also maintains compatibility with manual point prompts for interactive refinement during inference without requiring any additional training.TRUEsciescopu
리더의 경계확장행위는 언제 그리고 어떻게 구성원의 직무성과를 높이는가? 이중 매개 메커니즘과 향상초점의 조절 효과
No full textActive resourcing and buffering across formal organizational boundaries are becoming increasingly important in today’s highly interconnected organizations. These boundary spanning activities can be particularly effective and successful when performed by leaders who can leverage formal authority and networks to cross these boundaries. In this context, we highlight the benefits of leader boundary spanning behavior, focusing on their influence on task performance within leader-follower dyads. Drawing on Job Demands-Resources theory, we propose that personal resources, both cognitive (i.e., self-efficacy) and socio-emotional (i.e., psychological safety), augmented by boundary spanning leaders, serve to enhance follower task performance.
Furthermore, we propose that the effects of leader boundary spanning behavior will be more pronounced for followers with high promotion focus, as the work environment fostered by boundary spanning leaders will be more consistent with their regulatory fit.
Results from an empirical investigation of 694 leader-follower dyads largely confirm our moderated mediation model. By extending beyond existing team-level findings, we elucidate the theoretical and managerial implications of leader boundary spanning behavior at the individual level and within the dyadic framework.
업무의 복잡성과 상호 연계성이 높아진 현대 조직에서는, 공식적인 부서 간 경계를 넘어 적극적으로 자원을 확보하고, 부서 외부로부터의 압력을 완화하는 노력의 중요성이 높아지고 있다. 이러한 적극적 경계확장행위는 조직 내부의 경계를 보다 용이하게 초월할 수 있는 공식적 권한과 인적 네트워크를 지닌 부서의 리더가 수행할 때, 그 효과성과 잠재력이 더욱 크게 발현될 것으로 기대된다. 이러한 관점에서 우리는 리더의 경계확장행위가 갖는 긍정적 효과를 구성원의 직무성과를 중심으로 밝히고자 한다. 직무 요구-자원 이론 관점에서, 본 연구는 리더의 적극적인 경계확장행위가 휘하에서 일하는 구성원들에게 인지적 자원(즉, 자기효능감) 그리고 사회-정서적 자원(즉, 심리적 안전감)을 제공함으로써 직무성과를 높이는 데 도움이 되리라고 가정한다. 또한, 이러한 긍정적인 효과는 환경과 개인의 적합성 관점에서 향상초점이 높은 구성원에게 두드러지게 나타날 것으로 기대한다. 694쌍의 상사-구성원으로 구성된 실증 데이터를 분석한 결과, 우리의 이중 매개모델과 조절된 매개 효과에 대한 지지를 확인할 수 있었다. 기존의 팀 수준에서 주로 논의되어왔던 경계확장행위 문헌을 보완하며, 본 연구는 리더의 경계 확장행위가 개인 수준에서 그리고 구성원과의 관계에서 갖는 긍정적 효과에 관하여 이론적, 실무적 시사점을 제시한다.FALSEkc
Unravelling the effect of Ti3+/Ti4+ active sites dynamic on reaction pathways in direct gas-solid-phase CO2 photoreduction
No full textConverting CO2 to CH4 by solar-powered catalysis involves complex steps that produce a range of by-products. Therefore, designing efficient heterostructures for a particular chemical synthesis is challenging. The optimisation of photocatalyst surfaces can achieve the desired CO2 photoreduction pathway. Herein, we developed TiO2/CdSe nanocrystals with both amorphous and crystalline TiO2 surfaces. In situ EXAFS analysis revealed that the amorphous surface contains abundant active Ti3+ sites, while the crystalline surface is limited. Moreover, the amorphous surface of TiO2/CdSe exhibits self-regenerating Ti3+ active sites, which enable a novel CH4 cycle. Density functional theory calculation showed that an amorphous structure enhances electron transfer and localisation to Ti3+, favouring CO2 adsorption. In situ DRIFTS analysis showed different CO2 to CH4 pathways on both surfaces. These results show the potential for enhanced photocatalytic CO2 reduction through surface engineering, which has far-reaching implications for sustainable energy conversion. © 2024 Elsevier B.V.FALSEsciescopu
Electrode Placement Optimization for Electrical Impedance Tomography Using Active Learning
Get PDFElectrical impedance tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. Herein, a novel systematic framework is presented that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user-defined metrics. Given the many-to-one mapping between electrode configuration and the performance metric, the approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN's prediction accuracy for unseen electrode configurations, performance metrics are maximized while iteratively updating the NN via active learning during the optimization process. Transfer learning is employed to expedite optimization of electrode placements for time-consuming iterative reconstruction techniques by fine-tuning a NN initially trained on one-step reconstruction data. The method is validated using two representative reconstruction methods: one-step reconstruction with Newton's one-step error reconstructor prior and the iterative total variation method. This research underscores the potential of the proposed framework in addressing EIT's inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of noninvasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT. © 2024 The Authors. Advanced Engineering Materials published by Wiley-VCH GmbH.TRUEsciescopu
