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Nanoplastics represent a global environmental concern due to their ubiquitous presence and potential adverse impacts on public and environmental health. There is a growing need to advance the mechanistic understanding of their reactivity as they interact with biological and environmental systems. Herein, for the first time, we report that polystyrene nanoplastics (PSNPs) have intrinsic peroxidase-like activity and are able to mediate oxidative stress. The peroxidase-like activity is dependent on temperature and pH, with a maximum at pH 4.5 and 40 °C. The catalytic activity exhibits saturation kinetics, as described by the Michaelis-Menten model. The peroxidase-like activity of PSNPs is attributed to their ability to mediate electron transfer from peroxidase substrates to H2O2. Ozone-induced PSNP aging can introduce oxygen-containing groups and disrupt aromatic structures on the nanoplastic surface. While ozonation initially enhances peroxidase-like activity by increasing oxygen-containing groups without degrading many aromatic structures, extended ozonation destroys aromatic structures, significantly reducing this activity. The peroxidase-like activity of PSNPs can mediate oxidative stress, which is generally positively correlated with their aromatic structures, as suggested by the ascorbic acid assay. These results help explain the reported oxidative stress exerted by nanoplastics and provide novel insights into their environmental and public health implications.
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Early detection of polyps is essential to decrease colorectal cancer(CRC) incidence. Therefore, developing an efficient and accurate polyp segmentation technique is crucial for clinical CRC prevention. In this paper, we propose an end-to-end training approach for polyp segmentation that employs diffusion model. The images are considered as priors, and the segmentation is formulated as a mask generation process. In the sampling process, multiple predictions are generated for each input image using the trained model, and significant performance enhancements are achieved through the use of majority vote strategy. Four public datasets and one in-house dataset are used to train and test the model performance. The proposed method achieves mDice scores of 0.934 and 0.967 for datasets Kvasir-SEG and CVC-ClinicDB respectively. Furthermore, one cross-validation is applied to test the generalization of the proposed model, and the proposed methods outperformed previous state-of-the-art(SOTA) models to the best of our knowledge. The proposed method also significantly improves the segmentation accuracy and has strong generalization capability.
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Pólipos del Colon , Neoplasias Colorrectales , Humanos , Pólipos del Colon/diagnóstico por imagen , Neoplasias Colorrectales/diagnóstico por imagen , Modelos Estadísticos , Interpretación de Imagen Asistida por Computador/métodos , AlgoritmosRESUMEN
Frequent cyanobacterial blooms pose a serious threat to the aquatic ecosystem and human health, so developing an efficient algae removal method is a long-term goal for bloom management. Current technologies for algal bloom control need urgent improvement in terms of algicide recovery, eco-friendliness and cost. Here we propose a contact-electro-catalytic method, using polytetrafluoroethylene (PTFE) film as a reusable catalyst. This contact-electro-catalytic approach involves the generation of reactive oxygen species (e.g., O2â¢-, HOâ¢, 1O2 and H2O2) through water-PTFE contact electrification under the low-frequency ultrasonic waves, facilitating the inactivation of algae. The removal rate of the cyanobacterium Microcystis aeruginosa (M. aeruginosa) exposured to the water-PTFE contact-electro-catalytic system is almost five times greater than that of ultrasound alone after 5 h. A mechanistic investigation revealed that the contact-electro-catalytic system damaged the photosynthetic activity, antioxidant system and membrane integrity of the cells. Additionally, LC-MS metabolomic analysis indicated that this system caused substantial significant disruptions in the TCA cycle, amino acid metabolism, purine metabolism and phospholipid metabolism. Three-dimensional fluorescence spectroscopy suggested contact-electro-catalysis could further availably degrade the organic matter. We anticipate that this method can provide an eco-friendly, highly efficient and economic approach for effective control of harmful algal blooms.
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Landscape wildfires generate a substantial amount of dissolved black carbon (DBC) annually, yet the molecular nitrogen (N) structures in DBC are poorly understood. Here, we systematically compared the chemodiversity of N-containing molecules among three different DBC samples from rice straw biochar pyrolyzed at 300, 400, and 500 °C, one leached dissolved organic carbon (LDOC) sample from composted rice straw, and one fire-affected soil dissolved organic matter (SDOMFire) sample using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). N-Containing molecules contributed 20.0%, 36.1%, and 43.7% of total compounds in Combined DBC (pooling together the three DBC), LDOC, and SDOMFire, respectively, and molecules with fewer N atoms had higher proportions (i.e., N1 > N2 > N3). The N-containing molecules in Combined DBC were dominated by polycyclic aromatic (62.2%) and aromatic (14.4%) components, while those in LDOC were dominated by lignin-like (50.4%) and aromatic (30.1%) components. The composition and structures of N-containing molecules in SDOMFire were more similar to those in DBC than in LDOC. As the temperature rose, the proportion of the nitrogenous polycyclic aromatic component in DBC significantly increased with concurrent enhanced oxidation and unsaturation of N. As indicated by density functional theory (DFT)-based thermodynamic calculations, the proportion of aliphatic amide N decreased from 23.2% to 7.9%, whereas that of nitroaromatic N increased from 10.0% to 39.5% as the temperature increased from 300 to 500 °C; alternatively, the proportion of aromatic N in the 5/6 membered ring remained relatively stable (â¼31%) and that of aromatic amide N peaked at 400 °C (32.7%). Our work first provides a comprehensive and thorough description of molecular N structures of DBC, which helps to better understand and predict their fate and biogeochemical behavior.
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Espectrometría de Masas , Nitrógeno , Termodinámica , Nitrógeno/química , Estructura Molecular , Carbono/química , Suelo/químicaRESUMEN
Iodo-phenolic disinfection byproducts (DBPs) widely occur in disinfected water, posing potential risks to human health and the ecosystem as they possess higher toxicity than the bromo- and chloro-analogs. Herein, we elucidated the catalytic hydrodehalogenation (HDH) activity and selectivity of polyiodinated phenolic DBPs on supported noble metal catalysts at ambient conditions. Both 2,4,6-triiodophenol and 4-chloro-2,6-diiodophenol can be efficiently eliminated on Pd/TiO2 and Rh/TiO2 within 20 min, with Pd/TiO2 exhibiting higher turnover frequency. The HDH reactions proceeded in both stepwise and concerted pathways on Pd/TiO2, while they were dominantly stepwise on Rh/TiO2. Experimental results and theoretical calculations revealed that the HDH selectivity depends on the position and the bond energy of halo-substitutions. For the HDH of 2,4,6-triiodophenol, the para-substituted iodine was more favorable to be dehalogenated than the ortho-substituted ones due to the steric hindrance of the hydroxyl group. For the HDH of 4-chloro-2,6-diiodophenol, the ortho-substituted iodine was removed before the para-substituted chlorine as CI bond had higher reactivity than CCl bond. Significant catalyst deactivation was observed for the HDH of 4-chloro-2,6-diiodophenol on Pd/TiO2 due to iodine poisoning, resulting in 4-chlorophenol as the dominant product. In contrast, Rh/TiO2 can completely hydrodehalogenate 4-chloro-2,6-diiodophenol into cyclohexanone with little iodine poisoning. Our results suggest that HDH is an efficient process for abating iodo-phenolic DBPs. Rh/TiO2 is a more promising HDH catalyst for iodinated DBP removal than Pd/TiO2 with excellent resistance to iodine poisoning.
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Soil dissolved organic matter (SDOM) is an important part of the DOM pool in terrestrial systems, influencing the transport and fate of many pollutants. In this study, SDOMs from different regions across China were compared by a series of spectroscopic methods, including UV-vis spectroscopy, fluorescence spectroscopy, and Fourier transform infrared (FTIR) spectroscopy, and the hydrophobicity was quantified by partition coefficients of SDOM in the aqueous two-phase system (KATPS). The molecular weight, aromaticity, and hydrophobicity of SDOM from different regions exhibited strong heterogeneity, KATPS combined with UV-vis and fluorescence indices can be readily used for differentiating heterogeneous SDOM, and SDOMs were compositionally and structurally different from DOMs in aquatic systems based on spectral characterization. Importantly, the two-phase system (TPS) model has only been validated by DOMs in freshwater systems, and good organic carbonâwater partition coefficient (KOC) predictive power (RMSE = 0.11) could be provided by the TPS model when applied to heterogeneous SDOM without calibration, showing its broad applicability. Our results demonstrate the applicability of the TPS model for predicting the sorption behavior of terrestrial DOM, broadening the application scope of the TPS model and indicating its potential as a routine model for the risk assessment of hydrophobic organic compounds (HOCs) in organic contaminated sites.
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Interacciones Hidrofóbicas e Hidrofílicas , Compuestos Orgánicos , Contaminantes del Suelo , Suelo , China , Suelo/química , Compuestos Orgánicos/análisis , Compuestos Orgánicos/química , Contaminantes del Suelo/análisis , Contaminantes del Suelo/química , Sustancias Húmicas/análisis , Modelos Químicos , Monitoreo del Ambiente/métodos , Espectroscopía Infrarroja por Transformada de FourierRESUMEN
Iron-based materials are effective for the reductive removal of the disinfection byproduct bromate in water, while the construction of highly stable and active Fe-based materials with wide pH adaptability remains greatly challenging. In this study, highly dispersed iron phosphide-decorated porous carbon (Fe2P(x)@P(z)NC-y) was prepared via the thermal hydrolysis of Fe@ZIF-8, followed by phosphorus doping (P-doping) and pyrolysis. The reduction performances of Fe2P(x)@P(z)NC-y for bromate reduction were evaluated. Characterization results showed that the Fe, P, and N elements were homogeneously distributed in the carbonaceous matrix. P-doping regulated the coordination environment of Fe atoms and enhanced the conductivity, porosity, and wettability of the carbonaceous matrix. As a result, Fe2P(x)@P(1.0)NC-950 exhibited enhanced reactivity and stability with an intrinsic reduction kinetic constant (kint) 1.53-1.85 times higher than Fe(x)@NC-950 without P-doping. Furthermore, Fe2P(0.125)@P(1.0)NC-950 displayed superior reduction efficiency and prominent stability with very low Fe leaching (4.53-22.98 µg L-1) in a wide pH range of 4.0-10.0. The used Fe2P(0.125)@P(1.0)NC-950 could be regenerated by phosphating, and the regenerated Fe2P(0.125)@P(1.0)NC-950 maintained 85% of its primary reduction activity after five reuse cycles. The study clearly demonstrates that Fe2P-decorated porous carbon can be applied as a robust and stable Fe-based material in aqueous bromate reduction.
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Fluorescence array technologies have attracted great interest in the sensing field because of their high sensitivity, low cost, and capability of multitarget detection. However, traditional array sensing relies on multiple independent sensors and thus often requires time-consuming and laborious measurement processes. Herein, we introduce a novel fluorescence array strategy of the array-on-a-metal-organic framework (MOF), which integrates multiple array elements into a single MOF matrix to achieve facile sensing and discrimination of multiple target analytes. As a proof-of-concept system, we constructed a luminescent MOF containing three different emitting channels, including a lanthanide ion (europium/Eu3+, red emission), a fluorescent dye (7-hydroxycoumarin-4-acetic acid/HCAA, blue emission), and the MOF itself (UiO-66-type MOF, blue-violet emission). Five structurally similar nitroaromatic compounds (NACs) were chosen as the targets. All three channels of the array-on-a-MOF displayed rapid and stable fluorescence quenching responses to NACs (response equilibrium achieved within 30 s). Different responses were generated for each channel against each NAC due to the various quenching mechanisms, including photoinduced electron transfer, energy competition, and the inner filter effect. Using linear discriminant analysis, the array-on-a-MOF successfully distinguished the five NACs and their mixtures at varying concentrations and demonstrated good sensitivity to quantify individual NACs (detect limit below the advisory concentration in drinking water). Moreover, the array also showed feasibility in the sensing and discrimination of multiple NACs in real water samples. The proposed "array-on-a-MOF" strategy simplifies multitarget discrimination procedures and holds great promise for various sensing applications.
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(1) Background: Sanghuangporus baumii, a valuable medicinal fungus, has limited studies on its gene function due to the lack of a genetic transformation system. (2) Methods: This study aimed to establish an efficient Agrobacterium tumefaciens-mediated transformation (ATMT) system for S. baumii. This study involved cloning the promoter (glyceraldehyde-3-phosphate dehydrogenase, gpd) of S. baumii, reconstructing the transformation vector, optimizing the treatment of receptor tissues, and inventing a new method for screening positive transformants. (3) Results: The established ATMT system involved replacing the CaMV35S promoter of pCAMBIA-1301 with the gpd promoter of S. baumii to construct the pCAMBIA-SH-gpd transformation vector. The vectors were then transferred to A. tumefaciens (EHA105) for infection. This study found that the transformation efficiency was higher in the infection using pCAMBIA-SH-gpd vectors than using pCAMBIA-1301 vectors. The mycelia of S. baumii were homogenized for 20 s and collected as the genetic transformation receptor. After 20 min of co-culture and 48 h of incubation in 15 mL PDL medium at 25 °C, new colonies grew. (4) Conclusions: These colonies were transferred to PDA medium (hygromycin 4 µg/mL, cefotaxime 300 µg/mL), and the transformation efficiency was determined to be 33.7% using PCR.
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The design of efficient catalysts for catalytic ethylene (C2H4) oxidation is of crucial importance for extending the shelf life of fruits and vegetables. Herein, a carbon modified SBA-15 supported Pt catalyst (Pt/CSBA-15) was prepared in situ by a facile solid phase grinding-infiltration-inert atmosphere calcination method. Characterization results reveal that in the Pt/CSBA-15 catalysts thin carbon layers are successfully formed in the hexagonal pores of SBA-15. Additionally, Pt particles are well dispersed in the channels of SBA-15, and Pt/CSBA-15 has a smaller Pt particle size than the catalyst without carbon modification (i.e., Pt/SBA-15). O2 is more feasibly adsorbed and activated on small-sized Pt particles, and in situ formed carbon species enhance the hydrophobicity of catalysts. As a result, both 3Pt/CSBA-15 and 5Pt/CSBA-15 are able to maintain 100% conversion of 50 ppm of C2H4 for more than 7 h at 0 °C. 3Pt/CSBA-15 even achieves 81.5% C2H4 conversion and 71.6% CO2 yield after 20 h, exhibiting much more prominent catalytic performances than 3Pt/SBA-15. DFT calculations and in situ FTIR measurements confirm that small-sized Pt particles possess strong O2 affinity to promote O2 adsorption, and in situ formed hydrophobic carbon layers efficiently suppress competitive H2O adsorption. Such a unique one-step catalyst preparation method for regulating the size of metal particles and the hydrophobicity of catalysts can be perfectly utilized to develop simple and efficient hydrophobic catalysts applied in low-temperature oxidation of C2H4.
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Landscape fires annually generate large quantities of black carbon. The water-soluble fraction of black carbon (i.e., dissolved black carbon/DBC) is an important constituent of the dissolved organic carbon (DOC) pool, playing a crucial role in the global budget of refractory carbon and climate change. A key challenge in constraining the flux and fate of riverine DBC is to develop targeted and accurate quantification methods. Herein, we report that benzenepentacarboxylic acid (B5CA) intrinsically present in DBC can be used as an exclusive and holistic marker (representing both condensed aromatics and less-/nonaromatic fractions) for DBC quantification. B5CA was universally detected in water extractions of biochar and fire-affected soils with relatively large abundance but not produced by nonthermogenic processes. It has good mobility in the environment as it is not readily precipitated by cations or adsorbed by common geosorbents. B5CA also represents the recalcitrant components of DBC with excellent stability against photodegradation and biodegradation. Applying B5CA as the DBC marker in surface waters of the Changjiang River (i.e., the third largest river in the world), we calculate the DBC concentration in the downstream Changjiang River to be 4.8 ± 5.5% of the DOC flux. Our work provides a simple and reliable approach for the accurate quantification and source tracking of DBC in the soil and aquatic carbon pools.
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Carbono , Ácidos Carboxílicos , Suelo , Ríos , Hollín , AguaRESUMEN
Ultrasound computed tomography (USCT) with a ring array is an emerging diagnostic method for breast cancer. In the literature, synthetic aperture (SA) imaging has employed the delay-and-sum (DAS) beamforming technique for ring-array USCT to obtain isotropic resolution reflection images. However, the images obtained by the conventional DAS beamformer suffer from off-axis clutter and low resolution due to inhomogeneity of the medium and phase distortion. To address these issues, researchers have developed adaptive beamforming methods, such as coherence factor (CF) and convolutional beamforming algorithm (COBA), that improve image quality. In this study, we propose a joint method that combines CF with short-lag COBA (SLCOBA). First, we estimate the average sound speed using CF to address tissue inhomogeneity. Based on the corrected sound speed map, SLCOBA effectively suppresses side lobes and enhances image quality. Numerical results show that the proposed method reduces clutter and noise, improving resolution performance. These findings suggest that the proposed method may be a practical option for breast imaging in inhomogeneous mediums in the future.
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Procesamiento de Imagen Asistido por Computador , Tomografía Computarizada por Rayos X , Procesamiento de Imagen Asistido por Computador/métodos , Fantasmas de Imagen , Ultrasonografía/métodos , AlgoritmosRESUMEN
BACKGROUND: To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. METHODS: The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. RESULTS: The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was < 0.01. CONCLUSION: In the evaluation of cervical maturity, the results of machine learning method are more objective and significantly accurate compared with the traditional Bishop scoring method. The machine learning method is a better predictor of cervical maturity than the traditional Bishop method.
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Cuello del Útero , Pueblos del Este de Asia , Trabajo de Parto Inducido , Trabajo de Parto , Femenino , Humanos , Embarazo , Cuello del Útero/diagnóstico por imagen , Trabajo de Parto Inducido/métodos , Paridad , Valor Predictivo de las Pruebas , Maduración Cervical , Ultrasonografía , Aprendizaje AutomáticoRESUMEN
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.
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Médicos , Ultrasonografía Mamaria , Femenino , Humanos , Proyectos de Investigación , Procesamiento de Imagen Asistido por ComputadorRESUMEN
CO2 hydrogenation via the reverse water gas shift (RWGS) reaction is a promising strategy for CO2 utilization while constructing Ni-based catalysts with high catalytic activity and perfect CO selectivity remains a great challenging. Here, we demonstrate that the product selectivity for CO2 hydrogenation can be significantly tuned from CH4 to CO by phosphating of SiO2-supported Ni catalysts due to the geometric effect. Interestingly, nickel phosphide catalysts with different crystalline phases (Ni12P5 and Ni2P) differ sharply in CO2 conversion, and Ni12P5 is remarkably more active. Furthermore, we developed a facile strategy to confine small Ni12P5 nanoparticles in mesoporous SiO2 channels (Ni12P5@SBA-15). Enhanced activity is exhibited on Ni12P5@SBA-15, ascribed to the highly effective confinement effect. The in situ diffuse reflectance infrared Fourier transform spectroscopy and density functional theory calculations unveil that catalytic CO2 hydrogenation follows a direct CO2 dissociation route with adsorbed CO as the key intermediate. Notably, strong multibonded CO (threefold and bridge-bonded CO) is feasibly formed on the Ni catalyst accounting for CH4 as the dominant product whereas only weak linearly bonded CO exists on nickel phosphide catalysts resulting in almost 100% CO selectivity. The present results indicate that Ni12P5@SBA-15 combining the geometric effect and the confinement effect can achieve near-unity CO selectivity and enhanced activity for CO2 hydrogenation.
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The content, composition and molecular signatures of soil organic matter (SOM) have important influences on the cycle of soil organic carbon (SOC) and the partitioning of polycyclic aromatic hydrocarbons (PAHs) in soil. Seventy-nine soil samples from farmland, forest and urban areas were collected in Shenyang, China to investigate black carbon (BC) content, SOM molecular signatures varied with land use patterns, as well as the relationship with PAHs. The content of BC in urban soils was significantly higher than that of farmland and forest. BC was a key contributor of urban SOM which accounted for 0.35 ± 0.31 of SOC in urban soil. Based on BC/SOC ratio, the main sources of BC were identified as fossil fuel combustion for urban soils, while for farmland and forest soils, it is the mixed results of fossil fuel combustion and biomass burning. All categories of PAHs in urban soils showed the highest level compared to farmland and forest soils. Pearson's correlation analysis results showed there were significant positive correlations between BC and PAHs categories in urban soils, indicating the important role of BC in the accumulation of PAHs in soil. SOM from each of the two different land use patterns can be distinguished by molecular signatures. Urban SOM had abundant molecular markers derived from condensed organic carbon inputs, which was consistent with the BC/SOC value. Farmland SOM had abundant carbon from vegetation and microorganisms, and forest SOM was rich in organic carbon from fresh plant materials. The markers enriched in urban SOM showed significant correlations with most PAHs categories, highlighting the affinity of urban SOM for PAHs at the molecular level. This study contributed to understanding the impact of land management methods on SOM molecular composition signatures and its influence on PAHs occurrence in soil, providing a theoretical basis for regional soil pollution management.
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Lead chromate pigments are commonly used yellow inorganic pigments. They can pose environmental risks as they contain toxic heavy metals lead and chromium. Low molecular weight organic acids (LMWOAs), as widespread dissolved organic matter (DOM), affect the lead and chromium release from the pigment in water. In this work, the role of LMWOAs in the photodissolution of commercial lead chromate pigment was investigated. The pigment underwent significant photodissolution under simulated sunlight exposure with LMWOAs, and subsequently released Cr(III) and Pb(II). The photodissolution process is caused by the reduction of Cr(VI) by photogenerated electrons of the lead chromate pigment. The LMWOAs promoted photodissolution of the pigment by improving the electron-hole separation. The formation of Cr(III)-contained compounds leads to a slower release of chromium than lead. The photodissolution kinetics increase with decreasing pH and increasing LMWOAs concentration. The photodissolution of lead chromate pigment was basically positively related to the total number of hydroxyl and carboxyl groups in LMWOAs. The LMWOAs with stronger affinity to lead chromate pigment, lower adiabatic ionization potential (AIP) and higher energy of the highest occupied molecular orbital (EHOMO) are favorable to Cr(VI) reduction by photogenerated electrons and pigment photodissolution. 2.39% of chromium and 10.34% of lead released from the lead chromate pigment in natural conditions during a 6-h sunlight exposure. This study revealed the photodissolution mechanism of lead chromate pigment mediated by LMWOAs with different molecular structures, which helps understand the environmental photochemical behavior of the pigment. The present results emphasize the important role of DOM in the heavy metals release from commercial inorganic pigments.
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Cromatos , Metales Pesados , Plomo , Luz Solar , Peso Molecular , Cromo/química , Compuestos Orgánicos , ColorantesRESUMEN
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.
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Procesamiento de Imagen Asistido por Computador , Microburbujas , Procesamiento de Imagen Asistido por Computador/métodos , Ultrasonografía/métodos , Fantasmas de Imagen , Simulación por Computador , AlgoritmosRESUMEN
Dissolved black carbon (DBC) is an important constituent of the natural organic carbon pool, influencing the global carbon cycling and the fate processes of many pollutants. In this work, we discovered that DBC released from biochar has intrinsic peroxidase-like activity. DBC samples were derived from four biomass stocks, including corn, peanut, rice, and sorghum straws. All DBC samples catalyze H2O2 decomposition into hydroxyl radicals, as determined by the electron paramagnetic resonance and the molecular probe. Similar to enzymes that exhibit saturation kinetics, the steady-state reaction rates follow the Michaelis-Menten equation. The peroxidase-like activity of DBC is controlled by the ping-pong mechanism, as suggested by parallel Lineweaver-Burk plots. Its activity increases with temperature from 10 to 80 °C and has an optimum at pH 5. The peroxidase-like activity of DBC is positively correlated with its aromaticity as aromatics can stabilize the reactive intermediates. The active sites in DBC also involve oxygen-containing groups, as inferred by increased activity after the chemical reduction of carbonyls. The peroxidase-like activity of DBC has significant implications for biogeochemical processing of carbon and potential health and ecological impacts of black carbon. It also highlights the need to advance the understanding of the occurrence and role of organic catalysts in natural systems.