Emissions of greenhouse gases carbon dioxide and methane from duckweed systems for stormwater treatment

This study determined the greenhouse gas emission from lab-scale duckweed treatment systems that were used for stormwater treatment. By using the static chamber technique, the fluxes of CO2 emission from the duplicate duckweed systems were 1472 -- 721 and 626 -- 234 mg m-2 d-1, respectively. After the complete removal of duckweeds, CO2 emission from the systems decreased to 492 -- 281 and 395 -- 53 mg m-2 d-1, respectively. A thin-film model was successfully applied to predict the increasing CO2 concentrations approaching saturation in the static chamber. In contrast, the concentrations of methane in the closed chamber fluctuated a lot with time, which were attributed to complex methane production and consumption reactions at the soil-water interface. The CH4 flux from the two duckweed systems were 299 -- 74 mg m-2 d-1 and 180 -- 91 mg m-2 d-1, respectively. After the removal of duckweeds, the flux were 559 -- 215 mg m-2 d-1 and 328 -- 114 mg m-2 d-1, respectively. The higher CO2 emission in the duckweed systems was linked to more biomass debris formation on the soil surface due to duckweed growth and decay. As a result of duckweed growth, the duplicated duckweed systems removed 54 -- 13 % COD, 94 -- 4 % NH4 + -N, 87 -- 7 % NO3- -N, 34 -- 7 % PO4 3- -P at the hydraulic retention time of 10 days. When the duckweeds were removed, the nutrient removal efficiencies decreased significantly: 68 -- 3 % for NH4 + -N, 43 -- 7 % for NO3 - -N, 10 -- 6 % for PO4 3- -P. The COD removal efficiency without duckweeds was 47 -- 6 %, which did not change significantly

Molecularly imprinted polymers and their application as environmental sensors

Molecularly imprinted polymers are specialty polymers with ability of selectively capturing target molecules. They show great potential to be environmental sensors for the detection of specific contaminant. The overall research objective is to investigate the sensing ability of MIPs based on two mechanisms fluorescence quenching and reflectance for two example contaminants 2, 4-dinitrotoluene and 2-butoxylethanol, which are fingerprinting contaminant of explosive manufacturing and hydraulic fracking. The water chemistry effects are explored on MIPs for their potential use as in-situ sensors in complex aquatic environments. Fluorescent carbon dots with different surface functionality were fabricated and their environmental fate was explored. Amino-functionalized carbon dots (AC-dots) were applied to fluorescently label a molecularly imprinted polymer (MIP) for 2, 4-dinitrotoluene (DNT) as a template. DNT is specifically captured by the cavities in the MIP and interact with AC-dots on the surface, resulting in quenching of the fluorescence of the AC-dots. Response to DNT reaches equilibrium within [about]30 min. The method has a dynamic range that extends from 1 to 15 ppm, and allows for quantitation of DNT in aqueous solutions, with a detection limit of 0.28 ppm. Selectivity tests conducted in presence of DNT analogs demonstrated the specific recognition of DNT. The effect of sample water chemistry on carbon dots labeled molecularly imprinted polymer (AC-MIP) sensor the detection of 2, 4-dinitrotoluene (DNT) was investigated. With the increase of ionic strength from 1 mM to 100 mM, the quenching amount of MIPs decreased about 19% and 30% with NaCl and CaCl2 respectively. In the range of pH from 4 to 9, quenching effect is slightly higher at basic environment for both MIPs and non-imprinted polymers (NIPs) resulting from swelling properties of the films. NOM added the quenching amount to the sensor with a modified equation developed with NOM as a variable. In both lake water and tap water, DNT concentrations read by the sensors were very close to the HPLC measured DNT concentrations with the range from 72% to 105%. Molecularly imprinted polymers (MIPs) sensors for detection of 2-butoxyethanol (2BE), a pollutant associated with hydraulic fracturing contamination, were developed based on the combination of a colloidal crystal templating method and a molecular imprinting technique. MIPs exhibited higher binding than non-imprinted films (NIPs) due to the specific adsorption provided by molecular imprinting with imprinting efficiencies around 2. Optical tests were performed because of the uniformly ordered porous structure. The reflectance spectra of the sensors showed Bragg's peaks, which responded to the presence of 2BE; peaks presented increasing red shifts up to 50 nm with 2BE concentrations in the range of 1 ppb to 100 ppm, which allowed quantitative estimates of present 2BE concentration in aqueous solutions. The material has the potential for early detection of hydraulic fracturing sites contamination.Includes biblographical reference

Anti-inflammatory effects, nuclear magnetic resonance identification, and high-performance liquid chromatography isolation of the total flavonoids from Artemisia frigida

AbstractThe aerial parts of Artemisia frigida Willd. are used to treat joint swelling, renal heat, abnormal menstruation, and sore carbuncle. The anti-inflammatory effects of A. frigida have been well-known in folk medicine, suggesting that components extracted from A. frigida could potentially treat inflammatory disease. With the aim of discovering bioactive compounds, in this study, we extracted total flavonoids from the aerial parts of A. frigida and investigated their anti-inflammatory effects against inflammation induced by carrageenan and egg albumin in rats. At the doses studied, total flavonoids (100 mg/kg, 200 mg/kg, and 400 mg/kg) and some isolated compounds (30 mg/kg) showed significant and dose-dependent anti-inflammatory effects. According to the high-performance liquid chromatography analysis of the total flavonoids from A. frigida, there are five major compounds, namely, 5-hydroxy-3′,4′-dimethoxy-7-O-β-d-glucuronide (F1), 5-hydroxy-3′,4′,5′-trimethoxy-7-O-β-d-glucuronide (F2), 5,7,3′-trihydroxy-6,4′-dimethoxyflavone (F3), 5,3′-dihydroxy-6,7,4′-trimethoxyflavone (F4), and 5,3′-dihydroxy-3,6,7,4′-tetramethoxyflavone (F5), which may explain the anti-inflammatory activity

Three-Dimensional Medical Image Fusion with Deformable Cross-Attention

Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. However, this approach often neglects the fundamental commonalities and disparities between multimodal information. Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images. In this study, we introduce an innovative unsupervised feature mutual learning fusion network designed to rectify these limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB) module that facilitates the dual modalities in discerning their respective similarities and differences. We have applied our model to the fusion of 3D MRI and PET images obtained from 660 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB module, our network generates high-quality MRI-PET fusion images. Experimental results demonstrate that our method surpasses traditional 2D image fusion methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Importantly, the capacity of our method to fuse 3D images enhances the information available to physicians and researchers, thus marking a significant step forward in the field. The code will soon be available online

SVFormer: Semi-supervised Video Transformer for Action Recognition

Semi-supervised action recognition is a challenging but critical task due to the high cost of video annotations. Existing approaches mainly use convolutional neural networks, yet current revolutionary vision transformer models have been less explored. In this paper, we investigate the use of transformer models under the SSL setting for action recognition. To this end, we introduce SVFormer, which adopts a steady pseudo-labeling framework (ie, EMA-Teacher) to cope with unlabeled video samples. While a wide range of data augmentations have been shown effective for semi-supervised image classification, they generally produce limited results for video recognition. We therefore introduce a novel augmentation strategy, Tube TokenMix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis. In addition, we propose a temporal warping augmentation to cover the complex temporal variation in videos, which stretches selected frames to various temporal durations in the clip. Extensive experiments on three datasets Kinetics-400, UCF-101, and HMDB-51 verify the advantage of SVFormer. In particular, SVFormer outperforms the state-of-the-art by 31.5% with fewer training epochs under the 1% labeling rate of Kinetics-400. Our method can hopefully serve as a strong benchmark and encourage future search on semi-supervised action recognition with Transformer networks

ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and the variable tumor shape and size bring challenges to the accurate segmentation of the breast lesion. Motivated by the selective kernel convolution, we introduce an enhanced selective kernel convolution for breast tumor segmentation, which integrates multiple feature map region representations and adaptively recalibrates the weights of these feature map regions from the channel and spatial dimensions. This region recalibration strategy enables the network to focus more on high-contributing region features and mitigate the perturbation of less useful regions. Finally, the enhanced selective kernel convolution is integrated into U-net with deep supervision constraints to adaptively capture the robust representation of breast tumors. Extensive experiments with twelve state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure