Small grazing angle reflection and the sound siphon effect over a low velocity layer of sediments.

The low-order normal modes with small grazing angles (SGA) often control long-range sound field characteristics in shallow water. The SGA reflection loss from a half-space low-velocity bottom (LVB) is independent of the sound attenuation, except around the angle of complete transmission; the SGA bottom reflection loss (BRL) from a seafloor with a top low-velocity layer is very insensitive to the LVB attenuation also, except around a few selected frequencies. Thus, the "seafloor velocity-attenuation coupling" problem will be more fatal for LVB geo-acoustic inversions. The dispersion equation of the normal modes in the LVB layer is coincidentally the same as the singularity expression of the SGA reflection coefficient in the water column, resulting in a sound siphon effect that causes the abnormally high SGA BRL and transmission loss in the water at the siphon frequencies. The siphon effect is very sensitive to seafloor acoustic parameters, might offer a physical base for geo-acoustic inversion, and show a dim light in a "gray area" for inverting the LVB sound attenuation at low to mid frequencies. As an example, the acoustic siphon effect and related seafloor geophysical parameters forming it in the Yellow Sea are reported in this paper.

Sound attenuation at low to mid frequencies in low velocity seabottoms.

Attenuation is the most difficult seafloor acoustic property to get, particularly at low to mid frequencies. For low velocity bottoms (LVB), it becomes even more challenging, due to its small attenuation and lower velocity (relative to the velocity of the adjacent water). The latter one causes a fatal "seafloor velocity-attenuation couplings" in geo-acoustic inversions. Thus, attenuation inversions for the LVB require an accurate seafloor velocity profile, especially the velocity in the LVB layer. The propagation of explosive sound in the Yellow Sea with a strong thermocline and a top LVB layer exhibits many prominent characteristics: modal dispersion (the ground wave, water wave, Airy phase), two groups of water waves at high frequencies, and the siphon effect which causes abnormally large sound transmission loss at selected frequencies, etc. These observations are used to precisely measure the critical frequency, the Airy frequency, Airy wave velocity, 1st mode group velocity, and to derive the velocities in the LVB layer and in the basement. Using inverted seafloor parameters, the source level-normalized transmission loss and the first mode decay rate in ranges up to 27.66 km, the sound attenuations in the LVB are derived for a frequency range of 13-5000 Hz.

Theoretical and numerical study on transient acoustic wave propagation across ice layers in the Arctic Ocean.

The study of transient acoustic wave propagation across the Arctic Ocean ice layer provides theoretical guidance for the design of trans-ice acoustic communication systems. In this study, the Arctic Ocean was modeled as an ice-water composite structure, where the ice and water are regarded as an elastic solid and liquid, respectively. An analytical transient solution for acoustic wave propagation in this structure was derived using the eigenfunction expansion method. Further, the numerical procedures were presented and used to analyze the acoustic wave propagation characteristics across the ice layer. The results show that waveforms corresponding to the radial displacements are more severely distorted than the axial displacements. The amplitudes of the radial and axial displacements decreased rapidly with increasing propagation distance. The ice thickness had a greater impact on the radial displacement than axial displacement; the thicker the ice, the greater the distortion for both radial and axial displacements.

Porous-carbon/manganese composite catalyst transformed from waste biomass as peroxymonosulfate activator for carbamazepine degradation.

Activation of peroxymonosulfate (PMS) with solid catalysts for organic pharmaceutical degradation still faces challenge due to the demand of inexpensive catalysts. In this study, manganese-oxidizing microalgae (MOM) and its associated biogenic manganese oxides (BMO) were employed to prepare biomass-transformed porous-carbon/manganese (B-PC/Mn) catalyst through high-temperature calcination (850 °C). Remarkably, 100 % of carbamazepine (CBZ) was degraded within 30 min in the B-PC/Mn/PMS system. The degradation kinetic constant was 0.1718 min-1, which was 44.0 times higher than that of the biomass-transformed porous carbon mixed with MnOx activated PMS system. 1O2 was generated in the B-PC/Mn/PMS system, which is responsible for CBZ degradation. The MOM-BMO-associated structure greatly increased the specific surface areas and the contents of the C = O and pyrrolic-N groups, which facilitated PMS activation. The structure also induced the generation of Mn5C2, which exhibited a strong adsorption towards PMS. This study provides a novel strategy for preparing catalysts by using waste biomass.

MonoAux: Fully Exploiting Auxiliary Information and Uncertainty for Monocular 3D Object Detection.

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

Source depth estimation with feature matching using convolutional neural networks in shallow water.

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes.

Double-enzyme active MnO2@BSA mediated lab-on-paper dual-modality aptasensor for di(2-ethylhexyl)phthalate.

Di(2-ethylhexyl)phthalate (DEHP), as an environmental endocrine disruptor, has adverse effects on eco-environments and health. Thus, it is crucial to highly sensitive on-site detect DEHP. Herein, a double-enzyme active MnO2@BSA mediated dual-modality photoelectrochemical (PEC)/colorimetric aptasensing platform with the cascaded sensitization structures of ZnIn2S4 and TiO2 as signal generators was engineered for rapid and ultrasensitive detection of DEHP using an all-in-one lab-on-paper analytical device. Benefitting from cascaded sensitization effect, the ZnIn2S4/TiO2 photosensitive structures-assembled polypyrrole paper electrode gave an enhanced photocurrent signal. The MnO2@BSA nanoparticles (NPs) with peroxidase-mimic and oxidase-mimic double-enzymatic activity induced multiple signal quenching effects and catalyzed color development. Specifically, the MnO2@BSA NPs acted as peroxidase mimetics to generate catalytic precipitates, which not only obstructed interfacial electron transfer but also served as electron acceptors to accept photogenerated electrons. Besides, the steric hindrance effect from MnO2@BSA NPs-loaded branchy polymeric DNA duplex structures further decreased photocurrent signal. The target recycling reaction caused the detachment of MnO2@BSA NPs to increase PEC signal, realizing the ultrasensitive detection of DEHP with a low detection limit of 27 fM. Ingeniously, the freed MnO2@BSA NPs flowed to colorimetric zone with the aid of fluid channels and acted as oxidase mimetics to induce color intensity enhancement, resulting in the rapid visual detection of DEHP. This work provided a prospective paradigm to develop field-based paper analytical tool for DEHP detection in aqueous environment.

Noninvasive Nonlinear Optical Computational Histology.

Cancer remains a global health challenge, demanding early detection and accurate diagnosis for improved patient outcomes. An intelligent paradigm is introduced that elevates label-free nonlinear optical imaging with contrastive patch-wise learning, yielding stain-free nonlinear optical computational histology (NOCH). NOCH enables swift, precise diagnostic analysis of fresh tissues, reducing patient anxiety and healthcare costs. Nonlinear modalities are evaluated, including stimulated Raman scattering and multiphoton imaging, for their ability to enhance tumor microenvironment sensitivity, pathological analysis, and cancer examination. Quantitative analysis confirmed that NOCH images accurately reproduce nuclear morphometric features across different cancer stages. Key diagnostic features, such as nuclear morphology, size, and nuclear-cytoplasmic contrast, are well preserved. NOCH models also demonstrate promising generalization when applied to other pathological tissues. The study unites label-free nonlinear optical imaging with histopathology using contrastive learning to establish stain-free computational histology. NOCH provides a rapid, non-invasive, and precise approach to surgical pathology, holding immense potential for revolutionizing cancer diagnosis and surgical interventions.

Deploying Cationic Cellulose Nanofiber Confinement to Enable High Iodine Loadings Towards High Energy and High-Temperature Zn-I2 Battery.

High iodine loading and high-temperature adaptability of the iodine cathode are prerequisites to achieving high energy density at full battery level and promoting the practical application for the zinc-iodine (Zn-I2 ) battery. However, it would aggravate the polyiodide shuttle effect when employing high iodine loading and working temperature. Here, a sustainable cationic cellulose nanofiber (cCNF) was employed to confine the active iodine species through strong physiochemical adsorption to enlarge the iodine loading and stabilize it even at high temperatures. The cCNF could accommodate dual-functionality by enlarging the iodine loading and suppressing the polyiodide shuttle effect, owing to the unique framework structure with abundant surface positive charges. As a result, the iodine cathode based on the cCNF could deliver high iodine mass loading of 14.1 mg cm-2 with a specific capacity of 182.7 mAh g-1 , high areal capacity of 2.6 mAh cm-2 , and stable cycling over 3000 cycles at 2 A g-1 , thus enabling a high energy density of 34.8 Wh kg-1 and the maximum power density of 521.2 W kg-1 at a full Zn-I2 battery level. In addition, even at a high temperature of 60 °C, the Zn-I2 battery could still deliver a stable cycling.

An inflammation-responsive double-layer microneedle patch for recurrent atopic dermatitis therapy.

Seeking a potent therapeutic strategy for alleviating atopic dermatitis (AD) attack and preventing its recurrence is highly desired but remains challenging in clinical practice. Here, we propose an inflammation-responsive double-layer microneedle (IDMN) patch in situ delivering VD3 for recurrent AD therapy. IDMN comprises the backing layer part and the double-layer microneedle part, in which the inner layer is gelatin methacryloyl (GelMA) loaded with VD3 while the outer layer is composed of hyaluronic acid (HA). Introduction of the HA backing layer and outer layer around the GelMA tips can not only provide sufficient mechanical strength to penetrate into hardened AD skin with minimal invasiveness, but also exert a strong moisturizing effect after being rapidly dissolved. The inner layer of GelMA is degraded by the matrix metalloproteinase (MMP) in a dose dependent manner, which is secreted according to the disease progression of AD. The responsive degradation of GelMA tips result in corresponding release of VD3 to treat AD, triggering negative feedback against GelMA degradation. The IDMN administration on AD-bearing mice reveals efficient "curing" performances (including suppress erythema, scaling and lichenification, reduce epidermal thickness, inhibit mast cells infiltration, and down-regulate inflammatory factor secretion), which are basically realized through synergistic effect of the released VD3 and the dissolved HA molecules. Importantly, the residual tips of IDMN with VD3 are retained in the skin after the first AD relief, showing promising "warning" ability to inhibit the recurrence of AD. Hence, the developed IDMN patch is expected to be one of the excellent candidates for AD therapy and other relapsing diseases in clinical fields.

Hydraulic-electric analogy for design and operation of microfluidic systems.

Microfluidic systems have been investigated as practical tools in the fields of biomedical engineering, analytical chemistry, materials science, and biological research. Yet the widespread applications of microfluidic systems have been hindered by the complexity of microfluidic design and the reliance on bulky external controllers. Hydraulic-electric analogy provides a powerful method to design and operate microfluidic systems with minimal requirement of control equipment. Here, we summarize recent development of microfluidic components and circuits based on the hydraulic-electric analogy. In a manner similar to electric circuits, microfluidic analogue circuits with a continuous flow or pressure input actuate fluids in a predetermined way to enable singular tasks such as flow- or pressure-driven oscillators. Microfluidic digital circuits consisting of logic gates are activated by a programmable input to perform complex tasks including on-chip computation. In this review, the design principles and applications of a variety of microfluidic circuits are overviewed. The challenges and future directions of the field are also discussed.

Risk factors of early mortality in patients with small cell lung cancer: a retrospective study in the SEER database.

BACKGROUND: Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine cancer with a high risk of early mortality (i.e., survival time less than 1 month). This study aimed to identify relevant risk factors and predict early mortality in SCLC patients. METHODS: A total of 27,163 SCLC cases registered between 2010 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) data. Significant independent risk factors were identified by univariate and multivariate logistic regression analyses. Nomograms for all-causes and cancer-specific early death were constructed and evaluated. RESULTS: Age, sex, clinical stage, presence of metastasis (liver and lung), and absence of treatment (surgery, radiotherapy and chemotherapy) were identified for significant association with all-causes and cancer-specific early death. Nomograms based on these predictors exhibited high accuracy (area under ROC curve > 0.850) and potential clinical practicality in the prediction of early mortality. CONCLUSION: We identified a set of factors associated with early mortality from SCLC and developed a clinically useful nomogram to predict high-risk patients. This nomogram could aid oncologists in the administration of individualized treatment regimens, potentially improving clinical outcomes of SCLC patients.

Passive synthetic aperture for direction-of-arrival estimation using sparse Bayesian learning.

Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion.

Difference frequency coherent matched autoproduct processing for source localization in deep ocean.

Matched autoproduct processing (MAP) refers to a matched field processing (MFP) style array signal processing technique for passive source localization, which interrogates frequency-difference autoproduct instead of genuine acoustic pressure. Due to frequency downshifting, MAP is less sensitive to environmental mismatch, but it suffers from low spatial resolution and a low peak-to-sidelobe ratio of ambiguity surface. These source localization metrics are herein improved with coherent approaches. Specifically, the coherent normalized MFP is extended to coherent matched autoproduct processing (CMAP), a difference frequency coherent algorithm that exploits correlations among the autoproducts at various difference frequencies and eliminates the phase factor of the source spectrum for passive source localization. Phase-only coherent matched autoproduct processing is a CMAP derivation technique that only uses phase information. Through simulations in a Munk sound-speed profile environment, sensitivity analysis in the South China Sea environment, and high signal-to-noise ratio experimental measurements, these two algorithms are validated as compared to the conventional MFP and incoherent MAP. Simulation investigations demonstrate that difference frequency coherent algorithms can suppress sidelobes while simultaneously enhancing the localization resolution and robustness. The experimental results generally support the findings of the simulations.

Cascaded microfluidic circuits for pulsatile filtration of extracellular vesicles from whole blood for early cancer diagnosis.

Tumor-derived extracellular vesicles (EVs) hold the potential to substantially improve noninvasive early diagnosis of cancer. However, analysis of nanosized EVs in blood samples has been hampered by lack of effective, rapid, and standardized methods for isolating and detecting EVs. To address this difficulty, here we use the electric-hydraulic analogy to design cascaded microfluidic circuits for pulsatile filtration of EVs via integration of a cell-removal circuit and an EV-isolation circuit. The microfluidic device is solely driven by a pneumatic clock pulse generator, allowing for preprogrammed, clog-free, gentle, high-yield, and high-purity isolation of EVs directly from blood within 30 minutes. We demonstrate its clinical utility by detecting protein markers of isolated EVs from patient blood using a polyethylene glycol-enhanced thermophoretic aptasensor, with 91% accuracy for diagnosis of early-stage breast cancer. The cascaded microfluidic circuits can have broad applications in the field of EV research.

Reversing inflammatory microenvironment by a single intra-articular injection of multi-stimulus responsive lipogel to relieve rheumatoid arthritis and promote joint repair.

Rheumatoid arthritis (RA) is a common chronic disease dominated by inflammatory synovitis, which is characterized with hyperplastic synovium, up-regulated matrix metalloproteinase (MMP) expression, hypoxic joint cavity and excessive reactive oxygen species (ROS) accumulation. Such local adverse microenvironment in RA joints further exacerbates the infiltration of synovial inflammatory cells, especially M1-type macrophages. Regulating intra-articular pathological conditions, eliminating excess M1 macrophages or converting them to an anti-inflammatory M2 phenotype may break the vicious progression circle. Herein, we develop a multi-stimulus responsive lipogel as effective platform to relieve RA symptoms and promote articular cartilage recovery via reversing its inflammatory microenvironment. The injectable lipogel is fabricated by loading polydopamine nanoparticles and methotrexate into a thermosensitive gel, and intra-articularly injected to form the therapeutic depot (PDA/MTX@TSG) in situ. The gel degrades slowly under esterase hydrolysis, and maintains sustained drug release in physiological conditions. Meanwhile, it can 1) induce a reversible gel-sol phase transition upon mild photothermal treatment (external NIR light control), and 2) specifically respond to MMP-rich RA microenvironment (internal enzymatic hydrolysis effect). Such stimulus-responsive system can deliver therapeutic components in a controllable manner, and significantly reverse adverse inflammatory microenvironment of RA joints through ROS eliminating, hypoxia alleviating, and M1-M2 macrophage polarization effects. Animal experiments indicate that observable RA relief and joint repair are realized after a single lipogel injection combined with NIR irradiation. Our study highlights the importance of altering local RA microenvironment via anti-inflammatory macrophage polarization, and therefore presents a potent therapeutic strategy for RA treatment in clinical intervention.

Implementation of Bartlett matched-field processing using interpretable complex convolutional neural network.

Neural networks have been applied to underwater source localization and achieved better performance than the conventional matched-field processing (MFP). However, compared with MFP, the neural networks lack physical interpretability. In this work, an interpretable complex convolutional neural network based on Bartlett processor (BC-CNN) for underwater source localization is designed, the output and structure of which have clear physical meanings. The relationship between the convolution weights of BC-CNN and replica pressure of MFP is discussed, which effectively presents the interpretability of the BC-CNN. Simulation experiments using two kinds of labels demonstrate the equivalence between the Bartlett MFP and BC-CNN.

3D Printed Nitrogen-Doped Thick Carbon Architectures for Supercapacitor: Ink Rheology and Electrochemical Performance.

The 3D printing technique offers huge opportunities for customized thick-electrode designs with high loading densities to enhance the area capacity in a limited space. However, key challenges remain in formulating 3D printable inks with exceptional rheological performance and facilitating electronic/ion transport in thick bulk electrodes. Herein, a hybrid ink consisting of woody-derived cellulose nanofibers (CNFs), multiwalled carbon nanotubes (MWCNTs), and urea is formulated for the 3D printing nitrogen-doped thick electrodes, in which CNFs serve as both dispersing and thickening agents for MWCNTs, whereas urea acts as a doping agent. By systematically tailoring the concentration-dependent rheological performance and 3D printing process of the ink, a variety of gel architectures with high geometric accuracy and superior shape fidelity are successfully printed. The as-printed gel architecture is then transformed into a nitrogen-doped carbon block with a hierarchical porous structure and superior electrochemical performance after freeze-drying and annealing treatments. Furthermore, a quasi-solid-state symmetric supercapacitor assembled with two interdigitated carbon blocks obtained by a 3D printing technique combined with a nitrogen-doping strategy delivers an energy density of 0.10 mWh cm-2 at 0.56 mW cm-2 . This work provides guidance for the formulation of the printable ink used for 3D printing of high-performance thick carbon electrodes.

Pre-programmed microdroplet generator to control wide-ranging chemical concentrations.

Controlling the chemical concentration in droplets is an essential function in numerous applications of biology, chemistry, and engineering. However, it is difficult to perform an automated control of wide-ranging chemical concentrations accurately without any high-precision off-chip controllers. In this study, we present a preprogrammed microdroplet generator that controls the chemical concentration of droplets by more than three orders of magnitude. The device used constant water-head pressure as the sole driving force and regulated the concentration in a periodic and stepwise manner while keeping the droplet size constant. For this process, an autonomous microfluidic circuit controlled the flow rate ratio of two solutions, and an external mechanical capacitor tuned the step size of the control pressure over a wide range of 0.31-237 Pa. The utility of the device was demonstrated using a DNA-mediated homogeneous binding assay.