Attenuation compensation has been introduced in the field of linearized inverse scattering problems for the restoration of geological structures and material properties within viscoacoustic media, which are characterized by P-wave velocity and the quality factor Q. It relies on the solution of a true-amplitude asymptotic inversion, incorporating a single-scattering propagation operator. Nonetheless, traditional mathematical treatments of asymptotic inversion often overlook the viscous properties of the medium. In this study, we investigate the application of the Gaussian-beam depth migration technique, which takes into account multiple wave arrivals, to address the complexities associated with true-amplitude viscoacoustic inverse scattering. This method presents a precise and adaptable alternative to conventional single- arrival ray-based migration techniques. Our focus is on viscoacoustic inversion imaging using the single-scattering Hessian operator, deemed essential for full waveform inversion. In this situation, we demonstrate how to derive an appropriate weighting filter that allows the dominant part of the weighted Hessian operator to effectively approximate the identity operator. As a result, we develop a new form of pseudoinverse operator linked to the Born modeling operator for a single-beam-center slowness component of the wavefield. This operator facilitates the implementation of viscoacoustic Gaussian beam prestack depth migration on common-shot gathers, thereby offering a robust solution for imaging complex structures where single-arrival ray-based approaches are insufficient. Numerical results derived from the analysis of 2D realistic synthetic datasets substantiate the effectiveness of the proposed methodology. This work not only advances the understanding of viscoacoustic imaging techniques but also significantly enhances their practical application in challenging geological scenarios.

The accurate imaging of shallow marine subsurface structures is important for oil and gas exploration and geotechnical applications. Guided waves from a dense ocean-bottom seismometer acquisition program over the Qiuyue areas of the East China Sea were used to estimate Pand S-wave velocities. A data-driven dispersion spectra inversion network (DSINet) that directly maps the dispersion spectra to Pand S-wave velocities (VP and VS) models is presented. The DSINet was trained with synthetic inputs that consisted of S-and P-wave velocity models with their corresponding theoretical dispersion spectra. Transfer learning was then introduced to enhance the applicability of the network to the dispersion spectra generated from seismic shot gathers. A well-trained DSINet was used on synthetic data. The results enable a detailed assessment of the network performance. Subsequently, the field data from the Qiuyue demonstrated that the DSINet has the ability to retrieve plausible subsurface Pand S-wave velocity structures. The improvements in the migration image demonstrate that the guided-wave dispersion spectra can reconstruct reliable VP and VS results. Agreements between the predicted data and the well information highlight the potential of our approach as an alternative method for generating VP and VS models and can provide a site-specific VP-VS relationship.

Multicomponent ocean-bottom node (OBN) data, which includes both pressure and multicomponent displacement measurements, can offer significant insights into the elastic properties of the subsurface in deep-water areas. To enhance imaging quality and better understand these insights, angle gathers are potentially beneficial. However, many OBN surveys involve roughly sampled receiver locations, resulting in sparse and irregular geometries. This presents a significant challenge when attempting to extract high-quality angle gathers due to issues such as low-fold and pronounced aliasing, especially for converted PS waves. To tackle this problem, we introduce an angle-domain common-receiver Gaussian-beam migration (GBM) method of multimeasurement data aimed at producing elastic angle gathers. The GBM is well-suited for this due to its automatic denoising, enabled by the local slant stacking typically used in GBM, and its angular interpolation capabilities from the local plane-wave nature of Gaussian beams. Moreover, performing migration in the common-receiver domain can handle sparse and irregular sampling better. By processing multimeasurement data simultaneously, we present the imaging condition with mode decomposition in the angle domain, incorporating deghosting processes on the receiver side. We applied our method to synthetic 4C OBN data sets and demonstrated its effectiveness in generating accurate elastic angle gathers. Artifact removal is well observed in angle gathers using our 4C approach. We also explored the effects of increasing the node spacing by up to 800 m. The results indicate that even with such sparse data, our method still has the ability to produce relatively high-quality angle gathers.

Wearable sweat sensors have emerged as promising tools for noninvasive health monitoring, yet the low analyte concentrations in sweat compared to blood pose significant challenges for the limit of detection. In this study, we developed a high-sensitivity electrochemical biosensor using carbon nanotube (CNT)-induced enzyme polymerization to detect uric acid and glucose with ultralow detection limits. The CNTs were functionalized via EDC/NHS to achieve covalent enzyme immobilization, enhancing catalytic efficiency, electron transfer, and sensor stability. To enable multifunctional sensing, we integrated glucose and uric acid detection with a pH sensor into a single wearable platform. A radially symmetric microfluidic module was designed through finite element analysis to optimize sweat flow and minimize refresh time, ensuring real-time biomarker tracking. The system also incorporated pH-based signal correction to improve detection accuracy in complex sweat environments. Finally, the sensing performance was validated through on-body sweat collection and analysis from six human volunteers, demonstrating its robustness, reliability, and potential for advancing next-generation personalized healthcare applications. This work provides a framework for designing multifunctional wearable sweat sensors and highlights the role of material and device innovations in overcoming key challenges in this field.

5-hydroxymethylcytosine (5hmC) plays a pivotal role in the DNA demethylation pathway and transcriptional regulation. While sequencing-based methods such as TET-assisted bisulfite sequencing offer single-base resolution, they are not ideal for dynamic, time-sensitive quantification. Here, we present a novel enzymatic biosensing strategy leveraging T7 endonuclease I for rapid and locus-specific 5hmC detection with a single-base resolution. This electrochemical platform captures double-tagged dsDNA and detects 5hmC by monitoring the signal reduction upon T7 endonuclease cleavage of A-C mismatches. The method achieved high sensitivity, detecting as little as 10 pg of hydroxymethylated DNA amid a 100,000-fold excess of methylated or unmethylated DNA. Furthermore, we demonstrated its ability to quantify real-time 5hmC variation during umbilical cord mesenchymal stem cell differentiation. This approach offers a powerful tool for 5hmC analysis in dynamic biological processes.

Traditional viscoacoustic migration methods effectively compensate for amplitude loss and phase distortion caused by viscous attenuation. However, these methods predominantly focus on achieving accurate structural imaging. Preserving relative-amplitude information in migrated images is crucial for accurately restoring medium properties while imaging geometric structures under viscous attenuation conditions. In this article, we present an efficient and stable method for viscoacoustic true-amplitude Gaussian beam migration in the common-offset domain, specifically designed to tackle this challenge. Our approach utilizes the multiparameter viscoacoustic Born scattering mechanism to recover the velocity and quality factor $Q$ . The incorporation of $Q$ into the migration process is achieved by defining $Q$ -related complex-valued travel time to compensate for attenuation effects. We illustrate the forward single scattering integral for the central beam component of the viscoacoustic common-offset scattered pressure wavefield, using attenuation-compensated Gaussian-beam expansions of the viscoacoustic Green's function. This description, along with the estimation of the kernel of the single-scattering Hessian operator, enables us to develop a viscoacoustic Gaussian-beam pseudoinverse migration operator for common-offset data. This operator includes a Beylkin determinant and an invertible normal matrix associated with a causality term. Employing this derived pseudoinverse operator enhances subsurface structure imaging accuracy and preserves amplitude response information of material parameters throughout the viscoacoustic migration process. Numerical experiments and field data tests demonstrate that, with the proposed attenuation compensation method, it is possible to align the estimated parameters with their true values, yielding more accurate imaging and parameter estimations in viscoacoustic media compared to the traditional acoustic approach without attenuation compensation.

Seismic imaging from sparsely acquired data faces challenges such as low image quality, discontinuities, and migration swing artifacts. Existing convolutional neural network (CNN)-based methods struggle with complex feature distributions and cannot effectively assess uncertainty, making it hard to evaluate the reliability of their processed results. To address these issues, we propose a new method using a generative diffusion model (GDM). Here, in the training phase, we use the imaging results from sparse data as conditional input, combined with noisy versions of dense data imaging results, for the network to predict the added noise. After training, the network can predict the imaging results for test images from sparse data acquisition, using the generative process with conditional control. This GDM not only improves image quality and removes artifacts caused by sparse data, but also naturally evaluates uncertainty by leveraging the probabilistic nature of the GDM. To overcome the decline in generation quality and the memory burden of large-scale images, we develop a patch fusion strategy that effectively addresses these issues. Synthetic and field data examples demonstrate that our method significantly enhances imaging quality and provides effective uncertainty quantification.

MicroRNAs (miRNAs) have attracted significant attention in the field of cancer research as a promising class of biomarkers. However, precise, sensitive, and specific detection of miRNAs still confronts challenges due to their dynamic expression, low abundance, and high sequence similarity among families. The highly sensitive silicon nanowires (SiNWs) biosensors are limited by little interface discrepancy within the SiNWs, which has the potential to affect the final output results. In this study, the calibration of the SiNWs biosensor was initially suggested to be conducted through photo response. This approach successfully mitigated the impact of preparation and modification procedures, resulting in an enhanced correlation between the biosensor and q-PCR for the identification of breast cancer miRNA. Specifically, the correlation coefficient was raised from below 0.5 to above 0.8. Furthermore, the uncharged peptide nucleic acid (PNA) was used for device modification in order to address the issue of detecting miRNAs with a total length in below 0.01×PBS buffer. It was shown that the PNA probe exhibited greater sensitivity compared to the DNA probe in 0.001×PBS buffer. Significantly, the improved biosensor exhibited favorable selectivity and was capable of identifying single base mismatch. The sensor exhibited a high level of sensitivity in detecting miRNA within a concentration range of 1 fM to 10 pM when applied to actual human blood samples. The biosensor exhibited an excellent level of reproducibility in the analysis of spiked samples, achieving a recovery rate of 91 %, without requiring RNA extraction or amplification procedures. The biosensor has the capability to directly detect miRNA in authentic clinical samples, hence demonstrating significant promise for the timely diagnosis of cancer via the use of miRNA as biomarkers.

The ocean-bottom node (OBN) seismic acquisition system is designed to gather high-fidelity, wide-azimuth, and long-offset four-component (4C) data, which includes shear waves and enables the use of the elastic assumption in imaging and inversion. However, deploying geophysical instruments on the seafloor is difficult and costly, leading to the usual adoption of sparse node spacing. This can, however, lead to poor illumination and imaging challenges, especially in the shallow subsurface near the seafloor. To address these issues in the context of 4C elastic imaging, we propose a deep learning-based method using a multiscale convolutional neural network (Ms-CNN) to improve the imaging quality of OBN surveys with sparse data acquisition. As an alternative to interpolating the sparse seismic data in the data domain, which can be a challenging task due to the limitations attributed to sampling theorem and the often larger amounts of data compared to the image, we train an Ms-CNN in a supervised fashion to map from sparse data images of P-wave to P-wave (PP) and P-wave to S-wave (PS) sections produced by 4C Gaussian beam migration to the equivalent dense data images, allowing for the direct processing of sparse data to improve imaging quality. Here, we combine the mean absolute error and multiscale structure similarity index measure in the loss function to optimize the network's training process and to help improve the performance. The effectiveness of the method is demonstrated through experiments on synthetic and field data, resulting in improved event continuity and reduced noise in migration results from sparse OBN acquisitions.

Multicomponent elastic Gaussian-beam migration (EGBM) is relatively accurate, efficient, and flexible. However, a large amount of land seismic data is recorded by single-component geophones, which means that only vertical ground motions can be measured. In this case, the multicomponent EGBM cannot be applied. Conventionally, the vertical component is simply interpreted as the P-wave to P-wave reflected waves (PP) component and migrated using an acoustic migration method. But in fact, the vertical component also records the shear wave information. To perform elastic migration using only vertical-component data (in cases where multicomponent data are not available), we propose a single-component EGBM method for PP and P-wave to S-wave reflected waves (PS) imaging. Based on the Kirchhoff-Helmholtz integral, we give an effective formula for the downward extrapolation of multimode waves. Using our method, different wave modes are separated during migration by applying a decomposition vector to single-component data without prior data separation, resulting in better elimination of crosstalk artifacts and lower processing costs. Numerical experiments on 3-D land seismic data and 2-D vertical seismic profile (VSP) data are provided to demonstrate the performance of the method. Results show that PS-wave imaging is also feasible when we only have vertical-component data.

Four-component ocean-bottom node (OBN) surveys allow for the imaging of subsurface elastic properties for oil and gas exploration in deepwater environments. However, sparse acquisition sampling for the high-quality imaging of OBN data is challenging. To alleviate this problem, a common-receiver domain 4C elastic Gaussian beam migration method based on elastic reciprocity transformation that considers the monopole/dipole characters of sources and receivers is developed. Common-receiver migration also is computationally efficient in an OBN survey wherein the number of shots usually exceeds the number of geophones. P/S and up-/downgoing wavefield decompositions are accomplished on the “virtual source side” during migration. A decomposition matrix and a wavefield extrapolation formula are derived from the elastic Kirchhoff-Helmholtz integral with the representation of the Green’s function as a superposition of Gaussian beams. The local slant stack is performed on the common-receiver recordings that are subjected to more optimized sampling, which is less sensitive to aliasing. The performance of the method on synthetic data is validated using the coarse sampling of OBNs in a deepwater and ultradeepwater environment.

Amplitude-preserving migration is very important for reservoir characterization, which can faithfully provide information on the strength of the reflectors. However, conventional migration algorithms do not compensate for variable illumination effects and can hardly obtain true amplitudes of medium parameter. Least-squares migration (LSM) is an effective method to address this issue. Unfortunately, there is a key problem with LSM methods: most LSM methods only consider illumination compensation but not consider the accuracy of migration velocity model. The accuracy of the migration velocity model directly affects the quality of migrated images. Moreover, changes in velocity are more indicative of reservoir properties than reflectivity. Therefore, it is necessary to incorporate velocity estimation into migration imaging to realize joint inversions. Based on these facts, we present an iterative reweighted LSM method by approximating the local Hessian using point spread functions. Then, we related the LSM results to the scattering potential, simultaneously achieving velocity update with illumination compensation. Based on the gradually changing characteristics of rock properties, we adopted a sparse derivative constraint rather than requiring the result to be sparse. Consequently, this processing caused the results to contain broader bandwidths, giving the image a more continuous and textured appearance. Next, we evaluated the proposed method using the Marmousi2 model. The results had a higher resolution and a more reliable amplitude than the initial migration images. Hence, we efficaciously completed the velocity model update, with our method achieving encouraging results under both relatively accurate migration velocity and highly smoothed migration velocity model tests.

The extension of Gaussian-beam migration (GBM) from isotropic media to anisotropic media is a straightforward process that involves adapting the existing framework to incorporate anisotropic effects. However, the true challenge for GBM lies in simultaneously performing geometric structure imaging and accurately restoring medium properties in the presence of anisotropy. In this article, we propose a novel approach for true-amplitude GBM in acoustic transversely isotropic (TI) media with a vertical axis of symmetry (VTI). The recovered parameters include the normal moveout (NMO) velocity parameter $v_{n}$ , the anelliptic parameter $\eta $ , and Thomsen’s parameter $\delta $ . We employ Gaussian-beam expansions for the acoustic VTI Green’s function to represent the forward single scattering integral for a single-beam-center component of the common-offset scattered pressure wavefield. Based on this representation and estimation of the kernel of the single-scattering Hessian operator, we construct an acoustic VTI Gaussian-beam pseudoinverse operator. This operator includes a Beylkin determinant and a normal matrix integral over the local dip vectors chosen from the local slant stacks of the common-offset data. By utilizing this derived pseudoinverse operator, we can improve the accuracy of subsurface structure imaging and preserve the amplitude response information of material parameters during the acoustic VTI migration process. We apply the present approach to synthetic data examples and demonstrate its effectiveness in terms of imaging accuracy and amplitude fidelity.

Correction for 'A smartphone-based three-in-one biosensor for co-detection of SARS-CoV-2 viral RNA, antigen and antibody' by Yanzhi Dou et al., Chem. Commun., 2022, DOI: https://doi.org/10.1039/d2cc01297a.

Mycobacterium tuberculosis (M. tb) is an intracellular pathogen persisting in phagosomes that has the ability to escape host immune surveillance causing tuberculosis (TB). Lipoarabinomannan (LAM), as a glycolipid, is one of the complex outermost components of the mycobacterial cell envelope and plays a critical role in modulating host responses during M. tb infection. Different species within the Mycobacterium genus exhibit distinct LAM structures and elicit diverse innate immune responses. However, little is known about the mechanisms. In this study, we first constructed a LAM-truncated mutant with fewer arabinofuranose (Araf) residues named M. sm-ΔM_6387 (Mycobacterium smegmatis arabinosyltransferase EmbC gene knockout strain). It exhibited some prominent cell wall defects, including tardiness of mycobacterial migration, loss of acid-fast staining, and increased cell wall permeability. Within alveolar epithelial cells (A549) infected by M. sm-ΔM_6387, the uptake rate was lower, phagosomes with bacterial degradation appeared, and microtubule-associated protein light chain 3 (LC3) recruitment was enhanced compared to wild type Mycobacterium smegmatis (M. smegmatis). We further confirmed that the variability in the removal capability of M. sm-ΔM_6387 resulted from host cell responses rather than the changes in the mycobacterial cell envelope. Moreover, we found that M. sm-ΔM_6387 or its glycolipid extracts significantly induced expression changes in some genes related to innate immune responses, including Toll-like receptor 2 (TLR2), class A scavenger receptor (SR-A), Rubicon, LC3, tumor necrosis factor alpha (TNF-α), Bcl-2, and Bax. Therefore, our studies suggest that nonpathogenic M. smegmatis can deposit LC3 on phagosomal membranes, and the decrease in the quantity of Araf residues for LAM molecules not only impacts mycobacterial cell wall integrity but also enhances host defense responses against the intracellular pathogens and decreases phagocytosis of host cells.

Tuberculosis (TB), caused by infection with airborne Mycobacterium tuberculosis (MTB), seriously threatens human health and has become a public health problem of worldwide concern. To achieve effective control of TB, rapid and sensitive detection of MTB is particularly important. At present, the common detection methods for MTB cannot meet the requirements of speed, flexibility and portability simultaneously. In this work, a multichannel microfluidic chip was developed and packaged with an ultra-sensitive silicon nanowire field-effect-transistor biosensor. The fluid system was tested and optimized through simulation, and the best conditions were determined: the flow rate was 0.3 mL min-1 and the flow direction was perpendicular to a silicon nanowire. A one-way valve, a switching valve and a peristaltic pump were combined to establish a biosensor detection system to realize the automatic detection of TB samples. Then we systematically explained the factors affecting simulated exhaled breath condensate (SEBC) collection, and established and optimized the method for collection of SEBC from the perspective of collection volume and biological activity. The best collection conditions were determined for a 5 mm pipe diameter at 0 °C, and a sufficient sample volume was obtained in only 2 minutes for microfluidic detection. Then, the actual application value of the established collection method was further evaluated. Volunteers were recruited and this method was used to collect their exhaled breath condensate to analyze the collection effect. The system detected MTB in SEBC with good sensitivity (∼4 × 104 particles per mL). It is expected to be further integrated and miniaturized in the future to realize point-of-care testing.

Tuberculosis (TB) remains a public health problem that cannot be ignored. The portable and efficient detection of Mycobacterium tuberculosis (MTB) is important for the effective control of this disease. However, current detection techniques do not meet the requirements for MTB detection in the actual environment and often require cumbersome detection steps that are time consuming and inflexible. In this study, a portable immunosensor to detect MTB in sputum was prepared and then subjected to interface characterizations, such as scanning electron microscopy, hydrophilic angle test, and fluorescence characterization. The source and gate voltage of the device were optimized and tested using a non-contact photoresponse. The results showed that the sensitivity of the sensor to luminance increases with the decrease in source voltage. The gate voltage can substantially improve the response of the immunosensor to the normalized current of protein and amplify the signal at least 1.6 times. The optimal voltage detection conditions of source voltage (0.3 V) and gate voltage (0.1 V) were also determined. Several common proteins present in simulated saliva were used for anti-interference tests, and the sensor exhibited good specificity. Finally, the dilution gradient of an actual TB sputum sample was optimized. In the absence of preconditioning, a double-blind experiment was used to distinguish between the sputum from patients with TB and healthy individuals to shorten the TB detection time to a few minutes. Compared with the hospital's conventional detection method using cultures, the proposed method can complete the detection in a shorter time. This study provides a new strategy for the portable diagnosis of TB.

Elastic least-squares migration (ELSM) has the potential to produce high-resolution images. It can be implemented in either data-domain or image-domain but is much faster in the image domain. A critical step of image-domain ELSM is the calculation of the Hessian. However, it is impractical to directly calculate the Hessian due to its high storage and costs. In this letter, the Hessian is efficiently constructed with elastic point spread functions (PSFs) calculated by a combination of multicomponent Gaussian beam Born modeling (of scattering points with elastic parameters perturbation) and elastic Gaussian beam migration. Based on this, we propose a fast image-domain ELSM method. A hyper-Laplacian priori regularization is used to produce sparse solutions. We evaluate the proposed method with the Marmousi2 model, and the results demonstrate the capability of the method to image complex structures with improved resolution relative to the initial migrated image.

Rapid and comprehensive diagnostic methods are necessary for early identification and monitoring of SARS-CoV-2. Here, we have developed a universal and portable three-in-one biosensor linked to a smartphone for co-detection of SARS-CoV-2 viral RNA, antigen, and antibody. In combination with a smartphone, the online monitoring of SARS-CoV-2 virus-infected patients from infection to immunization could be intelligently achieved.