Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data. In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to the respiratory sound classification task. In addition, we introduce a straightforward Patch-Mix augmentation, which randomly mixes patches between different samples, with Audio Spectrogram Transformer (AST). We further propose a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space. Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.Comment: INTERSPEECH 2023, Code URL: https://github.com/raymin0223/patch-mix_contrastive_learnin

Wearables in medicine

Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches

Contact Angle Effects on Pore and Corner Arc Menisci in Polygonal Capillary Tubes Studied with the Pseudopotential Multiphase Lattice Boltzmann Model

In porous media, pore geometry and wettability are determinant factors for capillary flow in drainage or imbibition. Pores are often considered as cylindrical tubes in analytical or computational studies. Such simplification prevents the capture of phenomena occurring in pore corners. Considering the corners of pores is crucial to realistically study capillary flow and to accurately estimate liquid distribution, degree of saturation and dynamic liquid behavior in pores and in porous media. In this study, capillary flow in polygonal tubes is studied with the Shan-Chen pseudopotential multiphase lattice Boltzmann model (LBM). The LB model is first validated through a contact angle test and a capillary intrusion test. Then capillary rise in square and triangular tubes is simulated and the pore meniscus height is investigated as a function of contact angle θ. Also, the occurrence of fluid in the tube corners, referred to as corner arc menisci, is studied in terms of curvature versus degree of saturation. In polygonal capillary tubes, the number of sides leads to a critical contact angle θc which is known as a key parameter for the existence of the two configurations. LBM succeeds in simulating the formation of a pore meniscus at θ > θc or the occurrence of corner arc menisci at θ < θc. The curvature of corner arc menisci is known to decrease with increasing saturation and decreasing contact angle as described by the Mayer and Stoewe-Princen (MS-P) theory. We obtain simulation results that are in good qualitative and quantitative agreement with the analytical solutions in terms of height of pore meniscus versus contact angle and curvature of corner arc menisci versus saturation degree. LBM is a suitable and promising tool for a better understanding of the complicated phenomena of multiphase flow in porous media

Contact Angle Effects on Pore and Corner Arc Menisci in Polygonal Capillary Tubes Studied with the Pseudopotential Multiphase Lattice Boltzmann Model

In porous media, pore geometry and wettability are determinant factors for capillary flow in drainage or imbibition. Pores are often considered as cylindrical tubes in analytical or computational studies. Such simplification prevents the capture of phenomena occurring in pore corners. Considering the corners of pores is crucial to realistically study capillary flow and to accurately estimate liquid distribution, degree of saturation and dynamic liquid behavior in pores and in porous media. In this study, capillary flow in polygonal tubes is studied with the Shan-Chen pseudopotential multiphase lattice Boltzmann model (LBM). The LB model is first validated through a contact angle test and a capillary intrusion test. Then capillary rise in square and triangular tubes is simulated and the pore meniscus height is investigated as a function of contact angle θ. Also, the occurrence of fluid in the tube corners, referred to as corner arc menisci, is studied in terms of curvature versus degree of saturation. In polygonal capillary tubes, the number of sides leads to a critical contact angle θc which is known as a key parameter for the existence of the two configurations. LBM succeeds in simulating the formation of a pore meniscus at θ > θc or the occurrence of corner arc menisci at θ < θc. The curvature of corner arc menisci is known to decrease with increasing saturation and decreasing contact angle as described by the Mayer and Stoewe-Princen (MS-P) theory. We obtain simulation results that are in good qualitative and quantitative agreement with the analytical solutions in terms of height of pore meniscus versus contact angle and curvature of corner arc menisci versus saturation degree. LBM is a suitable and promising tool for a better understanding of the complicated phenomena of multiphase flow in porous media

Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media

International audienceFine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications

Advantages of the phosphatidylserine-recognizing peptide PSP1 for molecular imaging of tumor apoptosis compared with annexin V.

A number of peptide-based indicators have been identified and reported as potential apoptosis probes, offering great promise for early assessment of therapeutic efficacy in several types of cancer. Direct comparison of the newly developed probes with previously used ones would be an important step in assessing possible applications. Here, we compared the newly identified peptide-based phosphatidylserine (PS) indicator PSP1 (CLSYYPSYC) with annexin V, a common probe for molecular imaging of apoptotic cells, with respect to PS binding kinetics, apoptotic cell-targeting ability, and the efficacy of homing to apoptotic tumor cells in a mouse model after treatment with the anticancer agent camptothecin. Our results indicate that PSP1 efficiently targeted apoptotic cells and generated apoptosis/tumor-specific signals after cancer treatment in the animal model, whereas a similar dose of annexin V showed weak signals. The formation of a stable complex of PSP1 with PS might be one reason for the efficient in vivo targeting. We suggest that PSP1 has potential advantages for in vivo apoptotic cell imaging and could serve as a platform for the development of de novo peptide-based probes for apoptosis

Design of a Multicomponent Peptide-Woven Nanocomplex for Delivery of siRNA

<div><p>We developed and tested a multicomponent peptide-woven siRNA nanocomplex (PwSN) comprising different peptides designed for efficient cellular targeting, endosomal escape, and release of siRNA. To enhance tumor-specific cellular uptake, we connected an interleukin-4 receptor-targeting peptide (I4R) to a nine-arginine peptide (9r), yielding I4R-9r. To facilitate endosomal escape, we blended endosomolytic peptides into the I4R-9r to form a multicomponent nanocomplex. Lastly, we modified 9r peptides by varying the number and positions of positive charges to obtain efficient release of siRNA from the nanocomplex in the cytosol. Using this step-wise approach for overcoming the biological challenges of siRNA delivery, we obtained an optimized PwSN with significant biological activity <i>in vitro</i> and <i>in vivo</i>. Interestingly, surface plasmon resonance analyses and three-dimensional peptide models demonstrated that our designed peptide adopted a unique structure that was correlated with faster complex disassembly and a better gene-silencing effect. These studies further elucidate the siRNA nanocomplex delivery pathway and demonstrate the applicability of our stepwise strategy to the design of siRNA carriers capable of overcoming multiple challenges and achieving efficient delivery.</p></div

<i>In vivo</i> imaging of tumors after apoptosis induction.

<p>(A) Cy7.5-labelled PSP1peptide, control peptide, or annexin V was systematically injected into the tail vein of tumor-bearing nude mice treated with camptothecin 24 hrs before injection. The same amount of annexin V was also injected into non-camptothecin-treated tumor-bearing mice as a control. The homing of the peptides was examined after the indicated times using an optical imaging system. The white dotted circles indicate the tumors. (B) Histological examination of tumor tissues after homing of PSP1 and annexin V, performed under a fluorescence microscope. Tumor cell apoptosis was observed by TUNEL staining.</p