Nebulization using ZnO/Si surface acoustic wave devices with focused interdigitated transducers

Propagation of surface acoustic waves (SAWs) on bulk piezoelectric substrates such as LiNbO3 and quartz, exhibits an in-plane anisotropic effect due to their crystal cut orientations. Thin film SAW devices, such as those based on ZnO or AlN, offer potential advantages, including isotropic wave velocities in all in-plane directions, higher power handling capability, and potentially lower failure rates. This paper reports experimental and simulation results of nebulization behaviour for water droplets using ZnO/Si surface acoustic wave devices with focused interdigital transducers (IDTs). Post-deposition annealing of the films at various temperatures was applied to improve the quality of the sputtering-deposited ZnO films, and 500 °C was found to be the optimal annealing temperature. Thin film ZnO/Si focused SAW devices were fabricated using the IDT designs with arc angles ranging from 30° to 90°. Nebulization was significantly enhanced with increasing the arc angles of the IDTs, e.g., increased nebulization rate, reduced critical powers required to initialise nebulization, and concentration of the nebulised plume into a narrower size of spray. Effects of applied RF power and droplet size have been systematically studied, and increased RF power and reduced droplet size significantly enhanced the nebulization phenomena

FCP-Net: A Feature-Compression-Pyramid Network Guided by Game-Theoretic Interactions for Medical Image Segmentation

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multireceptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Clinical research on smart healthcare has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform for smart healthcare, which is designed to boost translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To facilitate clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. SenseCare provides several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc. In this paper, we will present an overview of SenseCare as an efficient platform providing comprehensive toolkits and high extensibility for intelligent image analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure

Automatic Blastomere Recognition from a Single Embryo Image

The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient

Strategies for Giant Mass Sensitivity Using Super-High-Frequency Acoustic Waves

Surface acoustic wave (SAW) devices are powerful platforms for mass sensing, chemical vapor or gas detection, and biomolecular identification. Great efforts have been made to achieve high sensitivities by using super-high-frequency SAW devices. Conventional SAW sensing is based on mass-loading effects at the acoustic wave propagation (or delay line) region between two interdigitated transducers (IDTs). However, for many super-high-frequency SAW devices with their small sizes, there is a huge challenge that the sensitivity is difficult to be further increased, simply because there are very limited areas between the IDTs to deposit a sensing layer. Herein, we proposed a novel strategy based on giant mass-sensitivity effects generated on the global area of acoustic wave device (defined as areas of both delay line region and IDTs), which significantly enhances sensitivity and reduces the detection limit of the SAW device. Both theoretical analysis and experimental results proved this new strategy and mechanism, which are mainly attributed to the efficient energy confinement at the IDTs' region for the super-high-frequency SAW devices. The achieved mass sensitivity using this new strategy is as high as 2590 MHz · mm2·μg-1, which is about 500 times higher than that obtained from only using the acoustic wave propagation region with a SAW frequency of 4.43 GHz. Hypersensitive humidity detection has been demonstrated using this newly proposed sensing platform, achieving an extremely high sensitivity of 278 kHz/%RH and the fast response and recovery times of 37 and 35 s, respectively

Flexible thin-film acoustic wave devices with off-axis bending characteristics for multisensing applications

Flexible surface acoustic wave (SAW) devices have recently attracted tremendous attention for their widespread applications in sensing and microfluidics. However, for these applications, the SAW devices often need to be bent into off-axis deformations between the acoustic-wave propagation direction and bending direction. Currently there are few studies on this topic, and the bending mechanisms under off-axis bending deformations have remained unexplored for multi-sensing applications. Herein, we fabricated aluminum nitride (AlN) flexible SAW devices by using high quality AlN films deposited on flexible glass substrates and systematically investigated their complex deformation behaviors. A theoretical model was firstly developed using coupling wave equations and boundary condition method to analyze the device’s characteristics with bending and off-axis deformation under elastic strains. The relationships between frequency shifts of the SAW device with bending strain and off-axis angle were obtained which showed the identical results with those from the theoretical calculations. Finally, we performed proof-of-concept demonstrations of multi-sensing applications by monitoring human wrist movements at various off-axis angles and detecting UV light intensities on a curved surface, thus paving the ways for versatile flexible electronics applications

Systems signatures reveal unique remission-path of Type 2 diabetes following Roux-en-Y gastric bypass surgery

Roux-en-Y Gastric bypass surgery (RYGB) is emerging as a powerful tool for treatment of obesity and may also cause remission of type 2 diabetes. However, the molecular mechanism of RYGB leading to diabetes remission independent of weight loss remains elusive. In this study, we profiled plasma metabolites and proteins of 10 normal glucose-tolerant obese (NO) and 9 diabetic obese (DO) patients before and 1-week, 3-months, 1-year after RYGB. 146 proteins and 128 metabolites from both NO and DO groups at all four stages were selected for further analysis. By analyzing a set of bi-molecular associations among the corresponding network of the subjects with our newly developed computational method, we defined the represented physiological states (called the edge-states that reflect the interactions among the bio-molecules), and the related molecular networks of NO and DO patients, respectively. The principal component analyses (PCA) revealed that the edge states of the post-RYGB NO subjects were significantly different from those of the post-RYGB DO patients. Particularly, the time-dependent changes of the molecular hub-networks differed between DO and NO groups after RYGB. In conclusion, by developing molecular network-based systems signatures, we for the first time reveal that RYGB generates a unique path for diabetes remission independent of weight loss

The effects of vegetation type on ecosystem carbon storage and distribution in subtropical plantations

Establishing plantation forests significantly increases the carbon (C) storage of terrestrial ecosystems. However, how vegetation types affect the ecosystem C sequestration capacity is not completely clear. Here, a slash pine plantation (SPP), a Schima superba plantation (SSP), and a Masson pine plantation (MPP), which have been planted for 30 years, were selected in subtropical China. The C storage and distribution patterns of plant, litter, and soil were investigated and calculated. The ecosystem C density was 17.7, 21.6, and 15.3 kg m–2 for SPP, SSP, and MPP, respectively. Ecosystem C stocks were mainly contributed by tree aboveground (39.9–46.0%) and soil C stocks (41.6–44.2%). The ecosystem C density of SSP was higher than that of SPP and MPP, and significant differences were found among three plantations for both aboveground and underground C densities. The aboveground and underground ecosystem C storage of SSP was 27.4 and 53.4% higher than that of MPP, respectively. Meanwhile, root C storage of MPP was lower than that of SPP and SSP, while soil C storage of MPP was lower than that of SSP. In the understory layer, SPP had the highest C density, followed by MPP, and there was a significant difference in C density among three plantations. However, no significant difference was found for the ecosystem C distribution among three plantations. Our results show that vegetation types significantly affect C storage but not C distribution in forest ecosystems and establishing the broad-leaved plantation has the highest ecosystem C storage in the subtropics. This study provides a theoretical basis for us to choose appropriate forest management measures

Machine Learning Empowered Thin Film Acoustic Wave Sensing

Thin film based surface acoustic wave (SAW) technology has been extensively explored for physical, chemical and biological sensors. However, these sensors often show inferior performance for a specific sensing in complex environments, as they are affected by multiple influencing parameters and their coupling interferences. To solve these critical issues, we propose a methodology to extract critical information from the scattering parameter and combine machine learning method to achieve multi-parameter decoupling. We used AlScN film-based SAW device as an example, in which highly c-axis orientated and low stress AlScN film was deposited on silicon substrate. The AlScN/Si SAW device showed a Bode quality factor value of 228 and an electro-mechanical coupling coefficient of ~2.3. Two sensing parameters (i.e., ultraviolet or UV and temperature) were chosen for demonstration and the proposed machine-learning method was used to distinguish their influences. Highly precision UV sensing and temperature sensing were independently achieved without their mutual interferences. This work provides an effective solution for decoupling of multi-parameter influences and achieving anti-interference effects in thin film based SAW sensing