Time difference detection based on sliding window all-phase FFT and Kalman filtering for precise flow measurement.

Coriolis mass flowmeter (CMF) measures the mass flow rate by detecting the time difference, typically using frequency domain methods. However, the spectrum leakage is the primary challenge. To address this issue, a new time difference detection method is proposed utilizing sliding window and all-phase fast Fourier transform. The computational complexity is reduced considering the changes in signal frequency. To further improve the stability and response speed of the measured value, a Kalman filtering algorithm based on variance detection is also proposed for post-processing. A transmitter system is developed to validate the proposed methods. The results demonstrate that, for single phase fluids, the accuracy is better than 0.5‰ and the repeatability is better than 0.2‰, thereby offering an improvement in the accuracy of CMF and supporting for industrial applications.

Subthreshold micropulse laser therapy for early postoperative macular thickening following surgical removal of epiretinal membrane.

BACKGROUND: This study aimed to investigate the functional and anatomical outcomes of subthreshold micropulse laser (SMPL) therapy in eyes with early postoperative macular thickening after idiopathic epiretinal membrane (iERM) removal. METHODS: This was a prospective and interventional study. Forty-eight eyes from 48 patients with macular edema at 1 month after iERM removal were randomly divided into two groups. Patients in the SMPL group (n = 24) received SMPL therapy while no special intervention was used for the observation group (n = 24). Baseline demographic data and clinical findings before and at 1 and 3 months after SMPL treatment or observation, including best-corrected visual acuity (BCVA) and the changes in central subfield thickness (CST) and average macular thickness (AMT), were analyzed. RESULTS: An improvement in BCVA with a decrease in CST and AMT from baseline to the 3-month follow-ups were observed in both SMPL and observation groups. No significant difference in BCVA was observed between the SMPL group and observation group either in the 1-month (0.26 [0.15, 0.52] vs. 0.26 [0.15, 0.39], P = 0.852) or the 3-month (0.15 [0.10, 0.30] vs. 0.23 [0.15, 0.30], P = 0.329) follow-up. There was a greater reduction in CST in the SMPL group versus observation group between baseline and the 3-month follow-up (-77.8 ± 72.3 µm vs. -45.0 ± 46.9 µm, P = 0.049). The alteration in AMT did not differ between the two groups in either 1-month (-16.5 ± 20.1 µm vs. -19.7 ± 16.3 µm, P = 0.547) or 3-month (-36.9 ± 26.9 µm vs. -34.0 ± 20.1 µm, P = 0.678) follow-up. CONCLUSIONS: SMPL therapy led to a significant decrease in CST at the 3-month follow-up while did not significantly improve the visual acuity in patients with postoperative macular thickening following iERM surgery. TRIAL REGISTRATION: The study was registered on Aug 27, 2020 (Trial Registration Number: ChiCTR 2000037227).

MEMS-based portable confocal Raman spectroscopy rapid imaging system.

Aiming at the miniaturization and rapid imaging requirements of a portable confocal Raman system, a MEMS-based portable confocal Raman spectroscopy rapid imaging method is proposed in this study. This method combines the dual 2D MEMS mirror scanning method and the grid-by-grid scanning method. The dual 2D MEMS mirror scanning method is used for the miniaturization design of the system, and the grid-by-grid scanning method is used for rapid imaging of Raman spectroscopy. Finally, the rapid imaging and miniaturization design of a portable confocal Raman spectroscopy system are realized. Based on this method, a portable confocal Raman spectroscopy rapid imaging system with an optical probe size of just 98m m×70m m×40m m is constructed. The experimental results show that the imaging speed of the system is 45 times higher than that of the traditional point-scan confocal Raman system, and the imaging speed can be further improved according to the requirements. In addition, the system is used to swiftly identify agate ore, and the material composition distribution image over a 126µm 2×126µm 2 region is obtained in just 16 min. This method provides a new solution for the rapid imaging and miniaturization design of the confocal Raman system, as well as a new technical means for rapid detection in deep space exploration, geological exploration, and field detection.

A deep supervised transformer U-shaped full-resolution residual network for the segmentation of breast ultrasound image.

PURPOSE: Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors' workload. In this article, we proposed a deep supervised transformer U-shaped full-resolution residual network (DSTransUFRRN) to segment BUS images. METHODS: In the proposed method, a full-resolution residual stream and a deep supervision mechanism were introduced into TransU-Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples. RESULTS: Cross-validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04 ± 0.86%) than all compared methods (p < 0.05). For the dataset B, the Dice was lower than that for the dataset A due to the small number of samples, but the Dice of DSTransUFRRN (88.15% ± 2.11%) was significantly higher than that of other compared methods (p < 0.05). CONCLUSIONS: In this study, we proposed DSTransUFRRN for BUS image segmentation. The proposed methods achieved significantly higher accuracy than the compared previous methods.

High-resolution single-photon imaging with physics-informed deep learning.

High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance.

Robust Kramers-Kronig holographic imaging with Hilbert-Huang transform.

Holography based on Kramers-Kronig relations (KKR) is a promising technique due to its high-space-bandwidth product. However, the absence of an iterative process limits its noise robustness, primarily stemming from the lack of a regularization constraint. This Letter reports a generalized framework aimed at enhancing the noise robustness of KKR holography. Our proposal involves employing the Hilbert-Huang transform to connect the real and imaginary parts of an analytic function. The real part is initially processed by bidimensional empirical mode decomposition into a series of intrinsic mode functions (IMFs) and a residual term. They are then selected to remove the noise and bias terms. Finally, the imaginary part can be obtained using the Hilbert transform. In this way, we efficiently suppress the noise in the synthetic complex function, facilitating high-fidelity wavefront reconstruction using ∼20% of the exposure time required by existing methods. Our work is expected to expand the applications of KKR holography, particularly in low phototoxicity biological imaging and other related scenarios.

Complex Transformer Network for Single-Angle Plane-Wave Imaging.

OBJECTIVE: Plane-wave imaging (PWI) is a high-frame-rate imaging technique that sacrifices image quality. Deep learning can potentially enhance plane-wave image quality, but processing complex in-phase and quadrature (IQ) data and suppressing incoherent signals pose challenges. To address these challenges, we present a complex transformer network (CTN) that integrates complex convolution and complex self-attention (CSA) modules. METHODS: The CTN operates in a four-step process: delaying complex IQ data from a 0° single-angle plane wave for each pixel as CTN input data; extracting reconstruction features with a complex convolution layer; suppressing irrelevant features derived from incoherent signals with two CSA modules; and forming output images with another complex convolution layer. The training labels are generated by minimum variance (MV). RESULTS: Simulation, phantom and in vivo experiments revealed that CTN produced comparable- or even higher-quality images than MV, but with much shorter computation time. Evaluation metrics included contrast ratio, contrast-to-noise ratio, generalized contrast-to-noise ratio and lateral and axial full width at half-maximum and were -11.59 dB, 1.16, 0.68, 278 µm and 329 µm for simulation, respectively, and 9.87 dB, 0.96, 0.62, 357 µm and 305 µm for the phantom experiment, respectively. In vivo experiments further indicated that CTN could significantly improve details that were previously vague or even invisible in DAS and MV images. And after being accelerated by GPU, the CTN runtime (76.03 ms) was comparable to that of delay-and-sum (DAS, 61.24 ms). CONCLUSION: The proposed CTN significantly improved the image contrast, resolution and some unclear details by the MV beamformer, making it an efficient tool for high-frame-rate imaging.

A high-precision multi-dimensional microspectroscopic technique for morphological and properties analysis of cancer cell.

Raman and Brillouin scattering are sensitive approaches to detect chemical composition and mechanical elasticity pathology of cells in cancer development and their medical treatment researches. The application is, however, suffering from the lack of ability to synchronously acquire the scattering signals following three-dimensional (3D) cell morphology with reasonable spatial resolution and signal-to-noise ratio. Herein, we propose a divided-aperture laser differential confocal 3D Geometry-Raman-Brillouin microscopic detection technology, by which reflection, Raman, and Brillouin scattering signals are simultaneously in situ collected in real time with an axial focusing accuracy up to 1 nm, in the height range of 200 µm. The divided aperture improves the anti-noise capability of the system, and the noise influence depth of Raman detection reduces by 35.4%, and the Brillouin extinction ratio increases by 22 dB. A high-precision multichannel microspectroscopic system containing these functions is developed, which is utilized to study gastric cancer tissue. As a result, a 25% reduction of collagen concentration, 42% increase of DNA substances, 17% and 9% decrease in viscosity and elasticity are finely resolved from the 3D mappings. These findings indicate that our system can be a powerful tool to study cancer development new therapies at the sub-cell level.

Robust phase unwrapping via non-local regularization.

Phase unwrapping is an indispensable step in recovering the true phase from a modulo-2π phase. Conventional phase unwrapping methods suffer from error propagation under severe noise. In this Letter, we propose an iterative framework for robust phase unwrapping with high fidelity. The proposed method utilizes the transport-of-intensity equation to solve the phase unwrapping problem with high computational efficiency. To further improve reconstruction accuracy, we take advantage of non-local structural similarity using low-rank regularization. Meanwhile, we use an adaptive iteration strategy that dynamically and automatically updates the denoising parameter to avoid over-smoothing and preserve image details. A set of simulation and experimental results validates the proposed method, which can provide satisfying results under severe noise conditions, and outperform existing state-of-the-art phase unwrapping methods with at least 6 dB higher peak SNR (PSNR).

Diminished AdipoR1/APPL1 Interaction Mediates Reduced Cardioprotective Actions of Adiponectin against Myocardial Ischemia/Reperfusion Injury in Type-2 Diabetic Mice.

Background: Obesity-related diseases have important implications for the occurrence, severity, and outcome of ischemic heart disease. Patients with obesity, hyperlipidemia, and diabetes mellitus (metabolic syndrome) are at increased risk of heart attack with decreased plasma lipocalin levels, and lipocalin is negatively correlated with heart attack incidence. APPL1 is a signaling protein with multiple functional structural domains and plays an important role in the APN signaling pathway. There are two known subtypes of lipocalin membrane receptors, AdipoR1 and AdipoR2. AdioR1 is mainly distributed in skeletal muscle while AdipoR2 is mainly distributed in the liver. Objective: To clarify whether the AdipoR1-APPL1 signaling pathway mediates the effect of lipocalin in reducing myocardial ischemia/reperfusion injury and its mechanism will provide us with a new approach to treat myocardial ischemia/reperfusion injury using lipocalin as an intervention and therapeutic target. Methods: (1) Induction of hypoxia/reoxygenation in SD mammary rat cardiomyocytes to simulate myocardial ischemia/reperfusion; (2) downregulation of APPL1 expression in cardiomyocytes to observe the effect of lipocalin on myocardial ischemia/reperfusion and its mechanism of action. Results: (1) Primary mammary rat cardiomyocytes were isolated and cultured and induced to simulate MI/R by hypoxia/reoxygenation; (2) lipocalin inhibited H/R-induced apoptosis in cardiomyocytes; and (3) APN attenuated MI/R injury through AdipoR1-APPL1 and the possible mechanism. Conclusion: This study demonstrates for the first time that lipocalin can attenuate myocardial ischemia/reperfusion injury through the AdipoR1-APPL1 signaling pathway and that the reduction of AdipoR1/APPL1 interaction plays an important role in cardiac APN resistance to MI/R injury in diabetic mice.

A unified model for large-scale thickness measurement of lubricating film based on ultrasonic lag phase slope.

A unified model based on the ultrasonic lag phase slope is developed for measuring the lubricating film thickness at a large scale. The ultrasonic lag phase of adjacent waves instead of the phase of overlapped waves is calculated as a function of the ultrasonic frequency and film thickness. The slope of the ultrasonic lag phase is determined correspondingly, which is linearly proportional to the lubricating film thickness. Both the finite element analysis and tests on the lubricating film thickness are performed to verify the proposed method. The results show that despite the fluctuations of the lag phase, the lag phase slope can be used for measuring the lubricating film thickness at a large scale from 0.1 µm to 170 µm.

Pathological Roles of Oxidative Stress in Cardiac Microvascular Injury.

Cardiac microvascular injury can be a fundamental pathological process that causes high incidence cardiovascular diseases such heart failure, diabetic cardiomyopathy, and hypertension. It is also an independent risk factor for cardiovascular disease. Oxidative stress is a significant pathological process in which the body interferes with the balance of the endogenous antioxidant defense system by producing reactive oxygen species, leading to property changes and dysfunction. It has been demonstrated that oxidative stress is one of the major causes of cardiac microvascular disease. Therefore, additional investigation into the relationship between oxidative stress and cardiac microvascular injury will direct clinical management in the future. In order to give suggestions and support for future in-depth studies, we give a basic overview of the cardiac microvasculature in relation to physiopathology in this review. We also summarize the role of oxidative stress of mitochondrial and non-mitochondrial origin in cardiac microvascular injury and related drug studies.

Distributed Nash Equilibrium Seeking in Consistency-Constrained Multicoalition Games.

The distributed Nash equilibrium (NE) seeking problem for multicoalition games has attracted increasing attention in recent years, but the research mainly focuses on the case without agreement demand within coalitions. This article considers a class of networked games among multiple coalitions where each coalition contains multiple agents that cooperate to minimize the sum of their costs, subject to the demand of reaching an agreement on their state values. Furthermore, the underlying network topology among the agents does not need to be balanced. To achieve the goal of NE seeking within such a context, two estimates are constructed for each agent, namely, an estimate of partial derivatives of the cost function and an estimate of global state values, based on which, an iterative state updating law is elaborately designed. Linear convergence of the proposed algorithm is demonstrated. It is shown that the consistency-constrained multicoalition games investigated in this article put the well-studied networked games among individual players and distributed optimization in a unified framework, and the proposed algorithm can easily degenerate into solutions to these problems.

Anatomical and visual outcomes of fovea-sparing internal limiting membrane peeling with or without inverted flap technique for myopic foveoschisis.

BACKGROUND: Vitrectomy and peeling of the internal limiting membrane (ILM) was an effective therapeutic approach for myopic foveoschisis with progressive visual loss. This study investigated the anatomical and visual outcomes of fovea-sparing ILM peeling with or without the inverted flap technique for patients with symptomatic myopic foveoschisis (MF). METHODS: We retrospectively reviewed the clinical data of patients with MF. Vitrectomy with fovea-sparing ILM peeling and air tamponade was performed in all patients. The primary outcome measures included best-corrected visual acuity (BCVA), mean macular thickness (MMT), and central foveal thickness (CFT). Depending on whether an inverted ILM flap technique was utilized, further subgroup comparisons between the inverted flap group and the non-inverted flap group were conducted. RESULTS: Twenty-six eyes of 22 patients were included. Fifteen eyes were underwent fovea-sparing ILM peeling without inverted ILM flap and 11 of the 26 eyes were treated with fovea-sparing ILM peeling and an inverted ILM flap technique. In the mean follow-up period of 10.74 ± 4.58 months, a significant improvement in BCVA was observed from 0.97 ± 0.45 logMAR to 0.58 ± 0.51 logMAR (P < 0.01), during which the BCVA of 20 eyes (76.92%) improved and remained stable in 5 eyes (19.23%). Moreover, a positive correlation was also found between the preoperative BCVA and the postoperative BCVA (r = 0.50, P = 0.01). At the last visit, the final MMT decreased from 492.69 ± 209.62 µm to 234.73 ± 86.09 µm, and the CFT reduced from 296.08 ± 209.22 µm to 138.31 ± 73.92 µm (all P < 0.01). A subgroup analysis found no significant differences in BCVA, MMT, or CFT between the inverted and non-inverted flap groups (all P > 0.05). CONCLUSION: Fovea-sparing ILM peeling with or without inverted flap technique resulted in favorable visual and anatomical outcomes for the treatment of MF. An important factor affecting the postoperative visual outcome was the preoperative visual acuity. Our study found no significant difference between the presence and absence of the inverted ILM flap.

Integration of Massive Open Online Course (MOOC) in Ophthalmic Skills Training for Medical Students: Outcomes and Perspectives.

PURPOSE: To integrate a massive open online course (MOOC) into conventional clinical ophthalmology teaching and investigate its impact on the skills of medical students. METHODS: This was a nonrandomized, prospective, and comparative study. Seventy-six medical students were assigned to 2 groups before their clinical teaching. Some were asked to follow a MOOC for slitlamp microscope examination but used textbook for preview of visual acuity test (SLMM group, n=39), while others were required to take a MOOC for visual acuity test and previewed slitlamp microscopy using textbook (VATM group, n=37). All the students then underwent conventional clinical ophthalmology teaching on slitlamp microscopy and visual acuity test. Their performance was evaluated using Direct Observation of Procedural Skills (DOPS). Students were also asked to complete a 5-item questionnaire about their learning experience and comment on the MOOC. RESULTS: Students in the SLMM group obtained overall higher scores in the slitlamp practical skills (47.64±4.01 vs 44.68±5.99, P=0.013), while those in the VATM group performed better in the visual acuity test (46.45±4.90 vs 43.78±4.94, P=0.004). MOOC was deemed to increase learning interests (4.13 of 5 points) and motivation (4.01 of 5 points) but was more preferred as an additional tool to traditional teaching methods (4.34 of 5 points) rather than to replace them (2.92 of 5 points). CONCLUSIONS: MOOC offers an added benefit in improving clinical skills and is worth advocating as an additional tool for clinical ophthalmic education.

Multifunctional biomaterial platforms for blocking the fibrosis process and promoting cellular restoring effects in myocardial fibrosis therapy.

Myocardial fibrosis is the result of abnormal healing after acute and chronic myocardial damage and is a direct cause of heart failure and cardiac insufficiency. The clinical approach is to preserve cardiac function and inhibit fibrosis through surgery aimed at dredging blood vessels. However, this strategy does not adequately address the deterioration of fibrosis and cardiac function recovery. Therefore, numerous biomaterial platforms have been developed to address the above issues. In this review, we summarize the existing biomaterial delivery and restoring platforms, In addition, we also clarify the therapeutic strategies based on biomaterial platforms, including general strategies to block the fibrosis process and new strategies to promote cellular restoring effects. The development of structures with the ability to block further fibrosis progression as well as to promote cardiomyocytes viability should be the main research interests in myocardial fibrosis, and the reestablishment of structures necessary for normal cardiac function is central to the treatment of myocardial fibrosis. Finally, the future application of biomaterials for myocardial fibrosis is also highlighted.

Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study.

Although brain-computer interface (BCI) shows promising prospects to help post-stroke patients recover their motor function, its decoding accuracy is still highly dependent on feature extraction methods. Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. This approach can enhance the decoding accuracy by learning a low dimensional embedding to extract distance-based representations from pair-wise EEG data. To enhance training efficiency and algorithm performance, a temporal-spectral distance weighted sampling method is proposed to select more informative input samples. In addition, an adaptive training strategy is adopted to address the session-to-session non-stationarity by progressively updating the subject-specific model. The proposed method is applied on both upper limb and lower limb neurorehabilitation datasets acquired from 33 stroke patients, with a total of 358 sessions. Results indicate that using k-Nearest Neighbor as the classification algorithm, the proposed method yielded 72.8% and 66.0% accuracies for the two datasets respectively, significantly better than the other state-of-the-arts ( ). Without losing generality, we also evaluated the proposed method on two publicly available datasets acquired from healthy subjects, wherein the proposed algorithm demonstrated superior performance at most cases as well. Our results support, for the first time, the use of a metric learning based feature extractor to learn representations from non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.

Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.

Ultrasound sound-speed tomography (USST) is a promising technology for breast imaging and breast cancer detection. Its reconstruction is a complex non-linear mapping from the projection data to the sound-speed image (SSI). The traditional reconstruction methods include mainly the ray-based methods and the waveform-based methods. The ray-based methods with linear approximation have low computational cost but low reconstruction quality; the full wave-based methods with the complex non-linear model have high quality but high cost. To achieve both high quality and low cost, we introduced traditional linear approximation as prior knowledge into a deep neural network and treated the complex non-linear mapping of USST reconstruction as a combination of linear mapping and non-linear mapping. In the proposed method, the linear mapping was seamlessly implemented with a fully connected layer and initialized using the Tikhonov pseudo-inverse matrix. The non-linear mapping was implemented using a U-shape Net (U-Net). Furthermore, we proposed the Tikhonov U-shape net (TU-Net), in which the linear mapping was done before the non-linear mapping, and the U-shape Tikhonov net (UT-Net), in which the non-linear mapping was done before the linear mapping. Moreover, we conducted simulations and experiments for evaluation. In the numerical simulation, the root-mean-squared error was 6.49 and 4.29 m/s for the UT-Net and TU-Net, the peak signal-to-noise ratio was 49.01 and 52.90 dB, the structural similarity was 0.9436 and 0.9761 and the reconstruction time was 10.8 and 11.3 ms, respectively. In this study, the SSIs obtained with the proposed methods exhibited high sound-speed accuracy. Both the UT-Net and the TU-Net achieved high quality and low computational cost.