Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials.
Ontology highlight
ABSTRACT:
SUBMITTER: Boiko DA
PROVIDER: S-EPMC8171319 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures. The photo-induced changes of LC molecules around topological defects observed by the time-resolved measurement was converted into the image sequences of the orientation angle and the order parameter change. We found that each pair of brushes with different colors around topological defects showed different orientation angle and ordering changes. The photo-induced change was triggered by the photoisomerization reaction of molecules, and one pair of brushes increased in its order parameter just after light irradiation, causing gradual rotation in the brush. The molecules in the other pair of brushes were disordered and rotated by the effect of the initially affected region. This combination approach of the time-resolved phase/polarization microscopy and the NN function can provide detailed information on the molecular alignment dynamics around the topological defects.
| S-EPMC8079417 | biostudies-literature
Project description:Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles' structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.
| S-EPMC8703294 | biostudies-literature
Project description:Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.
| S-EPMC9601165 | biostudies-literature
Project description:The compression of the layered carbon nitride C6N9H3 · HCl was studied experimentally and with density functional theory (DFT) methods. This material has a polytriazine imide structure with Cl(-) ions contained within C12N12 voids in the layers. The data indicate the onset of layer buckling accompanied by movement of the Cl(-) ions out of the planes beginning above 10-20 GPa followed by an abrupt change in the diffraction pattern and c axis spacing associated with formation of a new interlayer bonded phase. The transition pressure is calculated to be 47 GPa for the ideal structures. The new material has mixed sp(2)-sp(3) hybridization among the C and N atoms and it provides the first example of a pillared-layered carbon nitride material that combines the functional properties of the graphitic-like form with improved mechanical strength. Similar behavior is predicted to occur for Cl-free structures at lower pressures.
| S-EPMC3698513 | biostudies-other
Project description:Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often has severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, radiographs in emergency settings are often read out of necessity by emergency medicine clinicians who lack subspecialized expertise in orthopedics, and misdiagnosed fractures account for upward of four of every five reported diagnostic errors in certain EDs. In this work, we developed a deep neural network to detect and localize fractures in radiographs. We trained it to accurately emulate the expertise of 18 senior subspecialized orthopedic surgeons by having them annotate 135,409 radiographs. We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. The average clinician's sensitivity was 80.8% (95% CI, 76.7-84.1%) unaided and 91.5% (95% CI, 89.3-92.9%) aided, and specificity was 87.5% (95 CI, 85.3-89.5%) unaided and 93.9% (95% CI, 92.9-94.9%) aided. The average clinician experienced a relative reduction in misinterpretation rate of 47.0% (95% CI, 37.4-53.9%). The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care.
| S-EPMC6233134 | biostudies-other
Project description:Implantable brain electrophysiology electrodes are valuable tools in both fundamental and applied neuroscience due to their ability to record neural activity with high spatiotemporal resolution from shallow and deep brain regions. Their use has been hindered, however, by the challenges in achieving chronically stable operations. Furthermore, implantable depth neural electrodes can only carry out limited data sampling within predefined anatomical regions, making it challenging to perform large-area brain mapping. Minimizing inflammatory responses and associated gliosis formation, and improving the durability and stability of the electrode insulation layers are critical to achieve long-term stable neural recording and stimulation. Combining electrophysiological measurements with simultaneous whole-brain imaging techniques, such as magnetic resonance imaging (MRI), provides a useful solution to alleviate the challenge in scalability of implantable depth electrodes. In recent years, various carbon-based materials have been used to fabricate flexible neural depth electrodes with reduced inflammatory responses and MRI-compatible electrodes, which allows structural and functional MRI mapping of the whole brain without obstructing any brain regions around the electrodes. Here, we conducted a systematic comparative evaluation on the electrochemical properties, mechanical properties, and MRI compatibility of different kinds of carbon-based fiber materials, including carbon nanotube fibers, graphene fibers, and carbon fibers. We also developed a strategy to improve the stability of the electrode insulation without sacrificing the flexibility of the implantable depth electrodes by sandwiching an inorganic barrier layer inside the polymer insulation film. These studies provide us with important insights into choosing the most suitable materials for next-generation implantable depth electrodes with unique capabilities for applications in both fundamental and translational neuroscience research.
| S-EPMC8730365 | biostudies-literature
Project description:A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.
| S-EPMC4896428 | biostudies-other
Project description:Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in physiological and pathological conditions. Nowadays, manual stereological cell counting or semi-automatic segmentation techniques are widely used for the quantitative analysis of microscopy images. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. We have developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides precision comparable to those of human experts. Additionally, the runtime of cell detection is significantly less than that of other three computational methods analysed, and it is faster than human observers by orders of magnitude. We applied our DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT), as morphine tolerance is believed to activate glia. We have demonstrated a strong positive correlation between manual and DCNN-based quantification of astrocytes in rat brain.
| S-EPMC6110828 | biostudies-other
Project description:Local density approximation plus on-site Coulomb interaction U electronic structure calculations reveal that layered perovskite oxide Sr2RuO4 exhibits the ferromagnetic (FM) half-metallic ground state, which is nearly degenerate with the antiferromagnetic (AFM) phase with a slightly higher total energy. The nearly degenerate FM/AFM total energies provide a reasonable explanation for the experimentally observed spin-fluctuation. In addition, a dumbbell-shape 4d - t2g recombined dxz?-?dyz orbital ordering on the Ru sublattice is obtained owing to the on-site Coulomb interaction U associated with the elongated RuO6 octahedron local structure. The discovered orbital ordering is robust against the spin-orbit interaction as well as the surface terminations. Our findings unravel the on-site Coulomb correlation as the driving force of the Ru-4d orbital ordering as well as the inherent magnetic degeneracy.
| S-EPMC7184627 | biostudies-literature
Project description:Atomic dopants and defects play a crucial role in creating new functionalities in 2D transition metal dichalcogenides (2D TMDs). Therefore, atomic-scale identification and their quantification warrant precise engineering that widens their application to many fields, ranging from development of optoelectronic devices to magnetic semiconductors. Scanning transmission electron microscopy with a sub-Å probe has provided a facile way to observe local dopants and defects in 2D TMDs. However, manual data analytics of experimental images is a time-consuming task, and often requires subjective decisions to interpret observed signals. Therefore, an approach is required to automate the detection and classification of dopants and defects. In this study, based on a deep learning algorithm, fully convolutional neural network that shows a superior ability of image segmentation, an efficient and automated method for reliable quantification of dopants and defects in TMDs is proposed with single-atom precision. The approach demonstrates that atomic dopants and defects are precisely mapped with a detection limit of ≈1 × 1012 cm-2 , and with a measurement accuracy of ≈98% for most atomic sites. Furthermore, this methodology is applicable to large volume of image data to extract atomic site-specific information, thus providing insights into the formation mechanisms of various defects under stimuli.
| S-EPMC8373156 | biostudies-literature