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Transcutaneous auricular vagus nerve stimulation in anesthetized mice induces antidepressant effects by activating dopaminergic neurons in the ventral tegmental area
Get PDFDepression, a prevalent neuropsychiatric disorder, involves the dysregulation of neurotransmitters such as dopamine (DA). The restoration of DA balance is a pivotal therapeutic target for this condition. Recent studies have indicated that both antidepressant medications and non-pharmacological treatments, such as transcutaneous auricular vagus nerve stimulation (taVNS), can promote recovery from depressive symptoms. Despite the promise of taVNS as a non-invasive depression therapy, its precise mechanism remains unclear. We hypothesized that taVNS exerts antidepressant effects by modulating the DAergic system. To investigate this, we conducted experiments demonstrating that taVNS in anesthetized mice reduced depressive-like behaviors. However, this effect was abolished when DA neurons in the ventral tegmental area (VTADA) were inhibited. Additionally, taVNS in anesthetized mice enhanced VTADA activity, providing further evidence to support its antidepressant effects. Overall, our findings suggest that taVNS alleviates depression by augmenting VTADA activity, thereby contributing to a more comprehensive understanding of its therapeutic mechanisms. © The Author(s) 2024.TRUEsciescopu
통기성 고분자 나노메쉬 기판으로 고해상도 메탈 전극 트랜스퍼 프린팅
No full text"Nanomesh electronic, Breathable device, Photolithography, Transfer printing"List of Contents
Abstract i
List of contents iii
List of figures vi
Ⅰ. Introduction
1.1 Introduction of soft nanomesh electronics 1
1.2 Advantages of nanomesh devices 6
1.3 Limitations of nanomesh devices 10
Ⅱ. Experimental procedure
2.1 Fabrication method 15
Ⅲ. Result and Discussion
3.1 Result of transfer printing 17
3.1.1 After transfer printing image 17
3.1.2 Electrical performance 21
3.1.3 Durability test 23
3.2 Comparison of transfer printing method and conventional PVD method with shadow mask 26
3.2.1 Breathability test 29
Ⅳ. ConclusionMasterdCollectio
Improvement of magnetic properties by grain boundary diffusion using dry coating of Tb-(Pr, LRE)-Al_Cu alloys in Nd-Fe-B sintered magnets
No full textThe sintered Nd-Fe-B magnets have been widely used in the fields of a variety of applications, such as hard disc drives, magnetic sensor, wind power generators, efficient air-conditioner compressors and motors for electric vehicles. Nd-Fe-B sintered magnets exhibit a large maximum magnetic energy product, but in high-temperature environments such as the motors of eco-friendly cars, the coercivity decreases and performance deteriorates. To achieve high coercivity and remanence at high temperatures, heavy rare-earth (HRE) based grain boundary diffusion process (GBDP) is widely used. However, the high price of HRE makes it necessary to reduce the usage of HRE. In this study, we optimized the GBDP parameters by applying a dry coating method based on diffusion sources including light rare-earth (LRE) and low-melting-point metals to reduce the HRE usage. Addition of LRE and low melting point metals resulted in a dramatic reduction in the amount of Tb required in GBDP to increase coercivity. Dry coating has a significant impact on simplifying the process, reducing the amount of diffusion source usage, and improving magnetic properties. The magnetic properties and microstructure of Tb-(Pr, LRE)-Al-Cu diffusion magnets with various compositions are discussed
Low-Shot Prompt Tuning for Multiple Instance Learning Based Histology Classification
No full textIn recent years, prompting pre-trained visual-language (VL) models has shown excellent generalization to various downstream tasks in both natural and medical images. However, VL models are sensitive to the choice of input text prompts, requiring careful selection of templates. Moreover, prompt tuning in the weakly supervised/multiple-instance (MIL) setting is fairly under-explored, especially in the field of computational pathology. In this work, we present a novel prompt tuning framework leveraging frozen VL encoders with (i) residual visual feature adaptation, and (ii) text-based context prompt optimization for whole slide image (WSI) level tasks i.e., classification. In contrast with existing approaches using variants of attention-based instance pooling for slide-level representations, we propose synergistic prompt-based pooling of multiple instances as the weighted sum of learnable-context and slide features. By leveraging the mean learned-prompt vectors and pooled slide features, our design facilitates different slide-level tasks. Extensive experiments on public WSI benchmark datasets reveal significant gains over existing prompting methods, including standard baseline multiple instance learners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Computational Insights into Additive-Driven Improvements in Ionic Liquid Electrolytes for Lithium-Ion-Batteries.
No full textIonic liquid electrolyte, RDF, Lithium-ion-batteries, Additives, Molecular Dynamic simulationLocally concentrated ionic liquid (LCIL) electrolytes significantly influence electrochemical processes and battery performance. In this study, we use Molecular Dynamics simulations to examine how a diluent affects the structural, dynamic, and transport properties of Li+ in an LCIL electrolyte for lithium-ion batteries (LIBs). The LCIL is composed of lithium bis(fluorosulfonyl)imide (LiFSI) serving as the salt, 1-methyl-1-propylpyrrolidinium bis(fluorosulfonyl)imide (pyr13FSI) functioning as the solvent, and 1,1,2,2-tetrafluoroethylene 2,2,3,3-tetrafluoropropyl ether (TTE) acting as the diluent. Adding TTE decreases the quantity of free solvent molecules and enhances the coordination between Li+ and FSI- ions, leading to a more stable Li+ solvation sheath. This also improves Li+ transport properties, such as the diffusion coefficient and transference number, which are critical for designing effective electrolytes for LIBs. However, we also analyzed the effects of different salts and solvation ratios on the characteristics of Li-ion coordination environments and their dynamic and transport properties. Notably, adding a minimal amount of lithium hexafluorophosphate (LiPF6) salt to the electrolyte significantly reduces the number of free solvent molecules in the system. This addition enhances the dynamic properties of Li-ions. Our findings offer a deeper atomic-level understanding of Li+ dynamics and transport properties, contributing to developing advanced electrolytes for future lithium-ion batteries.|국소적으로 농축된 이온성 액체(LCIL) 전해질은 전기화학적 과정과 배터리 성능에 큰 영향을 미칩니다. 본 연구에서는 분자 동력학 시뮬레이션을 사용하여 희석제가 리튬 이온배터리(LIB)를 위한 LCIL 전해질 내에서 Li+의 구조적, 동적 및 전송 특성에 어떻게 영향을 미치는지 조사합니다. LCIL 은 염으로 리튬 비스(플루오로설포닐)이미드(LiFSI), 용매로 1-메틸-1-프로필피롤리디늄 비스(플루오로설포닐)이미드(pyr13FSI), 희석제로 1,1,2,2-테트라플루오로에틸렌 2,2,3,3-테트라플루오로프로필 에테르(TTE)로 구성됩니다. TTE 를 첨가하면 자유 용매 분자의 양이 감소하고 Li+와 FSI- 이온 간의 배위수가 향상되어 보다 안정적인 Li+ 용매화층이 형성됩니다. 이는 확산 계수 및 이동도 수 같은 Li+ 전송 특성을 향상시키며, 이는 LIB 를 위한 효과적인 전해질 설계에 중요합니다. 우리는 또한 다양한 염과 용매화 비율이 Li 이온 배위 환경의 특성과 동적 및 전송 특성에 미치는 영향을 분석했습니다. 특히, 전해질에 소량의 리튬 헥사플루오로포스페이트(LiPF6) 염을 첨가하면 시스템 내 자유 용매 분자의 수가 크게 감소합니다. 이 염의 첨가는 Li 이온의 동적 특성을 향상시킵니다. 우리의 연구 결과는 Li+의 동역학 및 전송 특성에 대한 더 깊은 원자 수준의
이해를 제공하며, 미래의 리튬 이온 배터리를 위한 진보한 전해질 개발에 기여할 것입니다.Chapter 1: Introduction 1
1.1. Background 1
1.2. Research Objectives 2
Chapter 2: Methodology 4
2.1. Introduction to MD simulation and its capabilities 4
2.2. Explanation of simulation setup 5
2.3. Energy Minimization 5
2.4. Force Field 6
Chapter 3: Electrolyte Preparation 8
Chapter 4: Result & Discussion 10
4.1. Effect of TTE 10
4.1.1. Structural Properties 10
4.1.2. Transport Properties: 12
4.2. Effect of different salt/ionic liquid ratios 16
4.2.1. Structural Properties 16
4.2.2. Transport Properties: 17
4.3. Effect of different salts 19
4.3.1. Structural Properties 19
4.3.2. Transport Properties: 21
Chapter 5: Conclusion 23
References: 25
요 약 문 29MasterdCollectio
MPC-Based Exponential Weight Laguerre Function With Non-Singular Terminal SMC for Four-Wheel Independent Drive Electric Vehicles
Get PDFThis article describes a complete control method that uses Laguerre exponentially weighted model predictive control (LEMPC) to help four-wheel independent drive electric vehicles stay stable and follow their paths. The proposed method incorporates an enhanced direct yaw moment control using a robust non-singular terminal sliding mode control framework. We evaluated traditional, Laguerre, and exponentially weighted model predictive control methodologies (TMPC, LMPC, and LEMPC), respectively, with comparisons of reduced computational load and complexity while maintaining path tracking. The weighted Laguerre model predictive control exhibits improved robustness and reduced computational time and load. The suggested strong non-singular terminal sliding mode control (NTSMC) combined with LEMPC improved control and stability in a wide range of maneuvering situations and levels of uncertainty. The synergistic impact of NTMSC with LEMPC was examined to improve path tracking efficacy and dynamic stability under diverse road conditions and disturbances. The effectiveness of the control strategy in handling and stability of vehicle at high speed while maintaining efficient path tracking was validated by simulation conducted in MATLAB/Simulink along with high-fidelity co-Simulink Carsim environment. © IEEE.TRUEsciescopu
DNA Aggregation and Condensation by Charged Agents
No full textWhile DNA is one of the longest and the stiffest molecules in nature and is negatively charged, it is strongly condensed in a tiny space of cell nuclei and in some virus thanks to polyvalent cations and/or small basic proteins. For example, protamine, a small arginine-rich basic protein plays an important role in packaging paternal genome into sperm nuclei during spermatogenesis, achieving a 106-fold compaction of DNA. In dilute solutions containing various charged condensing agents, short DNA fragments as well as long DNA chains form also aggregates and condensates, some of them being reminiscent of those found in vivo. We present molecular dynamics simulations of DNA aggregation and condensation using a simple idealized model roughly reproducing DNA grooves and incorporating solely long-range electrostatic and steric interactions. The DNA microscopic organization is analyzed and discussed in comparison to 3D high-resolution cryoTEM images
Machine-learning-based diabetes classification method using blood flow oscillations and Pearson correlation analysis of feature importance
Get PDFDiabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both accurate and fast. High measurement accuracy and fast measurement time are essential for effective noninvasive diabetes diagnosis; these can be achieved using diffuse speckle contrast analysis (DSCA) systems and artificial intelligence algorithms. In this study, we use a machine learning algorithm to analyze rat blood flow signals measured using a DSCA system with simple operation, easy fabrication, and fast measurement for helping diagnose diabetes. The results confirmed that the machine learning algorithm for analyzing blood flow oscillation data shows good potential for diabetes classification. Furthermore, analyzing the blood flow reactivity test revealed that blood flow signals can be quickly measured for diabetes classification. Finally, we evaluated the influence of each blood flow oscillation data on diabetes classification through feature importance and Pearson correlation analysis. The results of this study should provide a basis for the future development of hemodynamic-based disease diagnostic methods. © 2024 The Author(s). Published by IOP Publishing Ltd.TRUEsciescopu
Topographic analysis of retinal and choroidal vascular displacements after macular hole surgery
Get PDFIt has been reported that the retinal vessel and macular region of the retina are displaced after macular hole (MH) surgery. However, there is no detailed information for correlations between retinal and choroidal displacements. We obtained optical coherence tomography angiography (OCTA) and en-face optical coherence tomography (OCT) images from 24 eyes to measure the retinal and choroidal vascular displacement before and after surgery. These images were merged into infrared images using blood vessel patterns. The same vascular bifurcation points were automatically selected for each follow-up image, and the displacements of the bifurcation points were analyzed as a vector unit for prespecified grid regions in a semi-automated fashion. The results showed displacements of the choroidal intermediate vessels and retinal vessels following MH surgery (p = 0.002, p < 0.001). The topographic changes showed inferior, nasal, and centripetal displacement of the retina and inferiorly displaced choroid. The ILM peeling size and basal MH size were significantly associated with the retinal displacement (p < 0.001 and p = 0.010). Additionally, changes in the amount of the choroidal displacement were significantly correlated with that of the retinal displacements (p = 0.002). Clinicians should keep in mind that there might be topographic discrepancies of the displacement between retina and choroid when analyzing them following surgery.TRUEsciescopu