Distributed shape detection for an acoustic sensitive optical cable with DAS.

Acoustic sensitive optical cables (ASOCs) and their shape detection are vital in underwater acoustic monitoring, and a distributed ASOC shape detection method is demonstrated with distributed acoustic sensing (DAS) technology. The accurate three-dimensional (3D) position of each ASOC unit is obtained from DAS signals and the prior position information of auxiliary acoustic sources by using a proposed adaptive peak allocation algorithm. Preliminary work has demonstrated single-point 3D localization and distributed ASOC shape detection, and the error is 6.53 cm. To the best of our knowledge, it is the first time that distributed ASOC shape detection is achieved with DAS. This method will promote the development of ASOC applications, such as underwater target detection and towed array correction.

Voiceprint and position information detection of non-cooperative ship with Φ-OTDR and suspended sensitized optical cable.

Target detection is significant in many fields, including oceanic security, marine ecology, etc. In this paper, phase sensitive optical time domain reflectometry (Φ-OTDR) is introduced for the non-cooperative ship detection, with large-scale diversity technology and suspended sensitized optical cable. In outfield experiments, the ship's voiceprint information is obtained in high fidelity, the ship's power spectrum is analyzed, and the over-top detection is achieved. Moreover, an array orientation method based on adaptive phase difference correction (APDC) is proposed to track the ship, suppressing the delay jitter influence of acoustic transmission underwater. This is the first time that voiceprint information of the non-cooperative ship is high-fidelity acquired and deeply analyzed with Φ-OTDR and suspended sensitized optical cable, which is conducive to the detection and identification of marine targets, and proves the potential of Φ-OTDR in hydroacoustic detection applications.

Charting Aging Trajectories of Knee Cartilage Thickness for Early Osteoarthritis Risk Prediction: An MRI Study from the Osteoarthritis Initiative Cohort.

Knee osteoarthritis (OA), a prevalent joint disease in the U.S., poses challenges in terms of predicting of its early progression. Although high-resolution knee magnetic resonance imaging (MRI) facilitates more precise OA diagnosis, the heterogeneous and multifactorial aspects of OA pathology remain significant obstacles for prognosis. MRI-based scoring systems, while standardizing OA assessment, are both time-consuming and labor-intensive. Current AI technologies facilitate knee OA risk scoring and progression prediction, but these often focus on the symptomatic phase of OA, bypassing initial-stage OA prediction. Moreover, their reliance on complex algorithms can hinder clinical interpretation. To this end, we make this effort to construct a computationally efficient, easily-interpretable, and state-of-the-art approach aiding in the radiographic OA (rOA) auto-classification and prediction of the incidence and progression, by contrasting an individual's cartilage thickness with a similar demographic in the rOA-free cohort. To better visualize, we have developed the toolset for both prediction and local visualization. A movie demonstrating different subtypes of dynamic changes in local centile scores during rOA progression is available at https://tli3.github.io/KneeOA/. Specifically, we constructed age-BMI-dependent reference charts for knee OA cartilage thickness, based on MRI scans from 957 radiographic OA (rOA)-free individuals from the Osteoarthritis Initiative cohort. Then we extracted local and global centiles by contrasting an individual's cartilage thickness to the rOA-free cohort with a similar age and BMI. Using traditional boosting approaches with our centile-based features, we obtain rOA classification of KLG ≤ 1 versus KLG = 2 (AUC = 0.95, F1 = 0.89), KLG ≤ 1 versus KLG ≥ 2 (AUC = 0.90, F1 = 0.82) and prediction of KLG2 progression (AUC = 0.98, F1 = 0.94), rOA incidence (KLG increasing from < 2 to ≥ 2; AUC = 0.81, F1 = 0.69) and rOA initial transition (KLG from 0 to 1; AUC = 0.64, F1 = 0.65) within a future 48-month period. Such performance in classifying KLG ≥ 2 matches that of deep learning methods in recent literature. Furthermore, its clinical interpretation suggests that cartilage changes, such as thickening in lateral femoral and anterior femoral regions and thinning in lateral tibial regions, may serve as indicators for prediction of rOA incidence and early progression. Meanwhile, cartilage thickening in the posterior medial and posterior lateral femoral regions, coupled with a reduction in the central medial femoral region, may signify initial phases of rOA transition.

Generative appearance replay for continual unsupervised domain adaptation.

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on three datasets with different organs and modalities, where it substantially outperforms existing techniques. Our code is available at: https://github.com/histocartography/generative-appearance-replay.

Inhomogeneous antibiotic distribution in sediment profiles in anthropogenically impacted lakes: Source apportionment, fate drivers, and risk assessment.

Antibiotic residues in lake ecosystems have been widely reported; however, the vertical distribution of antibiotics in lake sediment profiles have rarely been examined. This study systematically revealed the vertical distribution pattern, sources, and risks of antibiotics in sediments of four typical agricultural lakes in central China. Nine of 33 target antibiotics were detected with a total concentration range of 39.3-18,250.6 ng/g (dry weight), and the order of average concentration was erythromycin (1447.4 ng/g) > sulfamethoxazole (443.7 ng/g) > oxytetracycline (62.6 ng/g) > enrofloxacin (40.7 ng/g) > others (0.1-2.1 ng/g). The middle-layer sediments (9-27 cm) had significantly higher antibiotic detected number and concentration than those in the top layer (0-9 cm) and bottom layer (27-45 cm) (p < 0.05). Correlation analysis showed that significant relationships existed between antibiotic concentrations and the octanol-water partition coefficients (Kow) of antibiotics (p < 0.05). Redundancy analysis indicated that Pb, Co, Ni, water content, and organic matter (p < 0.05) jointly affected the distribution of antibiotics in sediment profiles. Risk assessment showed that the highest potential ecological and resistance selection risks of antibiotics occurred in the middle-layer sediments, and oxytetracycline, tetracycline, and enrofloxacin had the most extensive potential risks in the sediment profiles. Additionally, the positive matrix factorization model revealed that human medical wastewater (54.5%) contributed more antibiotic pollution than animal excreta (45.5%) in sediment. This work highlights the inhomogeneous distribution of antibiotics in sediment profiles and provides valuable information for the prevention and control of antibiotic contamination in lakes.

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. Methods: Baseline data were from 3321 Osteoarthritis Initiative (OAI) participants with available cartilage thickness maps (6265 knees) and 77 clinical features. Cartilage maps were obtained from 3D DESS MR images using a deep-learning based segmentation approach and an atlas-based analysis developed by our group. Angle-based Joint and Individual Variation Explained (AJIVE) was used to capture and quantify variation, both shared among multiple data blocks and individual to each block, and to determine statistical significance. Results: Three major modes of variation were shared across the three data blocks. Mode 1 reflected overall thicker cartilage among men, those with higher education, and greater knee forces; Mode 2 showed associations between worsening Kellgren-Lawrence Grade, medial cartilage thinning, and worsening symptoms; and Mode 3 contrasted lateral and medial-predominant cartilage loss associated with BMI and malalignment. Each data block also demonstrated individual, independent modes of variation consistent with the known discordance between symptoms and structure in knee OA and reflecting the importance of features such as physical function, symptoms, and comorbid conditions independent of structural damage. Conclusions: This exploratory analysis, combining the rich OAI dataset with novel methods for determining and visualizing cartilage thickness, reinforces known associations in knee OA while providing insights into the potential for data integration in knee OA phenotyping.

Assessing the contribution of global wildfire biomass burning to BaP contamination in the Arctic.

Polycyclic aromatic hydrocarbons (PAHs) have become cause for growing concern in the Arctic ecosystems, partly due to their stable levels despite global emission reduction. Wildfire is considered one of the primary sources that influence PAH levels and trends in the Arctic, but quantitative investigations of this influence are still lacking. This study estimates the global emissions of benzo[a]pyrene (BaP), a congener of PAHs with high carcinogenicity, from forest and grassland fires from 2001 to 2020 and simulates the contributions of wildfire-induced BaP emissions from different source regions to BaP contamination in the Arctic. We find that global wildfires contributed 29.3% to annual averaging BaP concentrations in the Arctic from 2001 to 2020. Additionally, we show that wildfires contributed significantly to BaP concentrations in the Arctic after 2011, enhancing it from 10.1% in 2011 to 83.9% in 2020. Our results reveal that wildfires accounted for 94.2% and 50.8% of BaP levels in the Asian Arctic during boreal summer and autumn, respectively, and 74.2% and 14.5% in the North American Arctic for the same seasons. The source-tagging approach identified that local wildfire biomass emissions were the largest source of BaP in the Arctic, accounting for 65.7% of its concentration, followed by those of Northern Asia (17.8%) and Northern North America (13.7%). Our findings anticipate wildfires to play a larger role in Arctic PAH contaminations alongside continually decreasing anthropogenic emissions and climate warming in the future.

Assessing Safety of Market-Sold Fresh Fish: Tracking Fish Origins and Toxic Chemical Origins.

Increasing global and domestic food trade and required logistics create uncertainties in food safety inspection due to uncertainties in food origins and extensive trade activities. Modern blockchain techniques have been developed to inform consumers of food origins but do not provide food safety information in many cases. A novel food safety tracking and modeling framework for quantifying toxic chemical levels in the food and the food origins was developed. By integrating chemicals' multimedia environment exchange, food web, and source tracking systems, the framework was implemented to identify short-chain chlorinated paraffin (SCCP) contamination of fresh hairtail fish sold by a Walmart supermarket in Xi'an, northwestern China, and sourced in Eastern China Sea coastal waters. The framework was shown to successfully predict SCCP level with a mean of 17.8 ng g-1 in Walmart-sold hairtails, which was comparable to lab-analyzed 21.9 ng g-1 in Walmart-sold hairtails. The framework provides an alternative and cost-effective approach for safe food inspection compared to traditional food safety inspection techniques. These encouraging results suggest that the approach and rationale reported here could add additional information to the food origin tracking system to enhance transparency and consumers' confidence in the traded food they consumed.

Topical delivery of gambogic acid assisted by the combination of low-frequency ultrasound and chemical enhancers for chemotherapy of cutaneous melanoma.

Anti-cutaneous melanoma activity of the skin-delivered gambogic acid (GA) has been reported in our previous study. However, it is difficult for GA to diffuse passively through intact skin without any enhancement means. In this study, a combination of chemical enhancers (EN: azone and propylene glycol) and physical ultrasound (US) was used to improve the percutaneous permeation of GA and enhance the anti-melanoma activity. The enhancement effect of the combination of EN and US (EN-US) on GA in vitro and in vivo was studied, and the enhancement mechanism and skin irritation were also evaluated. We showed that the parameters of US application at a constant frequency (30 kHz) with a duty cycle of 100% and intensity of 1.75 W/cm2 for 20 min were optimal. In vitro, EN-US showed a considerable enhancement of the permeation of GA, and the enhancement effect was stronger than that with the use of EN or US alone. In vivo antitumor study showed that the tumor growth was significantly inhibited after percutaneous administration of GA by EN-US, more than in the intravenous injection group. The penetration enhancement mechanism revealed that EN-US not only altered the structure of lipid bilayers and keratins to reduce the barrier effect of the stratum corneum but also produced diffusion channels in the skin under the cavitation effect of US, thereby promoting the skin penetration of GA. In addition, there was no observable skin irritation in mice after treatment with EN-US. Our study demonstrated that the combination of EN and US improved the skin permeation and retention of GA to enhance the anti-melanoma activity. This method also provides technical guidance for the future development of topical and transdermal therapeutic system of GA.

3D printed graphene/polyurethane wearable pressure sensor for motion fitness monitoring.

The structural design of three-dimensional (3D) flexible wearable sensors using conductive polymer composites is a hot spot in current research. In this paper, honeycomb-shaped flexible resistive pressure sensors with three different support structures were manufactured by using thermoplastic polyurethane and graphene nanoplatelets composites based on fused deposition 3D printing technology. Based on the various 3D conductive network of the sensors, the flexible sensor exhibit excellent piezoresistive performance, such as adjustable gauge factor (GF) (13.70-54.58), exceptional durability and stability. A combination of representative volume element and finite element simulations was used to simulate the stress distribution of sensors with different structures to predict the structure's effect on the sensor GF. In addition, the sensor can be attached to human body to monitor the body's swallowing and walking behaviors. The sensor has prospective process applications for intelligent wearable devices.

The increased binding affinity of curcumin with human serum albumin in the presence of rutin and baicalin: A potential for drug delivery system.

The impacts of rutin and baicalin on the interaction of curcumin (CU) with human serum albumin (HSA) were investigated by fluorescence and circular dichroism (CD) spectroscopies under imitated physiological conditions. The results showed that the fluorescence quenching of HSA by CU was a simultaneous static and dynamic quenching process, irrespective of the presence or absence of flavonoids. The binding constants between CU and HSA in the absence and presence of rutin and baicalin were 2.268×10(5)M(-1), 3.062×10(5)M(-1), and 3.271×10(5)M(-1), indicating that the binding affinity was increased in the case of two flavonoids. Furthermore, the binding distance determined according to Förster's theory was decreased in the presence of flavonoids. Combined with the fact that flavonoids and CU have the same binding site (site I), it can be concluded that they may simultaneously bind in different regions in site I, and formed a ternary complex of flavonoid-HSA-CU. Meanwhile, the results of fluorescence quenching, CD and three-dimensional fluorescence spectra revealed that flavonoids further strengthened the microenvironmental and conformational changes of HSA induced by CU binding. Therefore, it is possible to develop a novel complex involving CU, flavonoid and HSA for CU delivery. The work may provide some valuable information in terms of improving the poor bioavailabiliy of CU.