Biotransformation of roxarsone by earthworms and subsequent risk of soil arsenic release: The role of gut bacteria.

The organoarsenical feed additive roxarsone (ROX) is a ubiquitous threat due to the unpredictable levels of arsenic (As) released by soil bacteria. The earthworms representing soil fauna communities provide hotspots for As biotransformation genes (ABGs). Nonetheless, the role of gut bacteria in this regard is unclear. In this study, the changes in As speciation, bacterial ABGs, and communities were analyzed in a ROX-contaminated soil (50 mg/kg As in ROX form) containing the earthworm Eisenia feotida. (RE vs. R treatment). After 56 d, earthworms reduced the levels of both ROX and total As by 59 % and 17 %, respectively. The available As content was 10 % lower in the RE than in R treatment. Under ROX stress, the total ABG abundance was upregulated in both earthworm gut and soil, with synergistic effects observed following RE treatment. Besides, the enrichment of arsM and arsB genes in earthworm gut suggested that gut bacteria may facilitate As removal by enhancing As methylation and transport function in soil. However, the bacteria carrying ABGs were not associated with the ABG abundance in earthworm gut indicating the unique strategies of earthworm gut bacteria compared with soil bacteria due to different microenvironments. Based on a well-fit structural equation model (P = 0.120), we concluded that gut bacteria indirectly contribute to ROX transformation and As detoxification by modifying soil ABGs. The positive findings of earthworm-induced ROX transformation shed light on the role of As biomonitoring and bioremediation in organoarsenical-contaminated environments.

A New Biocontrol Agent Bacillus velezensis SF334 against Rubber Tree Fungal Leaf Anthracnose and Its Genome Analysis of Versatile Plant Probiotic Traits.

Natural rubber is an important national strategic and industrial raw material. The leaf anthracnose of rubber trees caused by the Colletotrichum species is one of the important factors restricting the yields of natural rubber. In this study, we isolated and identified strain Bacillus velezensis SF334, which exhibited significant antagonistic activity against both C. australisinense and C. siamense, the dominant species of Colletotrichum causing rubber tree leaf anthracnose in the Hainan province of China, from a pool of 223 bacterial strains. The cell suspensions of SF334 had a significant prevention effect for the leaf anthracnose of rubber trees, with an efficacy of 79.67% against C. siamense and 71.8% against C. australisinense. We demonstrated that SF334 can lead to the lysis of C. australisinense and C. siamense mycelia by causing mycelial expansion, resulting in mycelial rupture and subsequent death. B. velezensis SF334 also harbors some plant probiotic traits, such as secreting siderophore, protease, cellulase, pectinase, and the auxin of indole-3-acetic acid (IAA), and it has broad-spectrum antifungal activity against some important plant pathogenic fungi. The genome combined with comparative genomic analyses indicated that SF334 possesses most genes of the central metabolic and gene clusters of secondary metabolites in B. velezensis strains. To our knowledge, this is the first time a Bacillus velezensis strain has been reported as a promising biocontrol agent against the leaf anthracnose of rubber trees caused by C. siamense and C. australisinense. The results suggest that B. velezensis could be a potential candidate agent for the leaf anthracnose of rubber trees.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

A toxicokinetics approach using Enchytraeus crypticus to evaluate the efficiency of hydroxyapatite to remediate soils contaminated with rare earth elements.

Extensive rare earth element (REE) mining activities pose threats to agricultural soils surrounding the mining areas. Here, low and high REE-contaminated soils from farmlands around mine tailings were remediated with hydroxyapatite. A toxicokinetic approach was applied to assess whether the use of hydroxyapatite reduced the bioavailability of REEs and thus inhibited their accumulation in the terrestrial organism Enchytraeus crypticus. Our results showed that addition of hydroxyapatite increased soil pH, DOC and anion contents. CaCl2-extractable REE concentrations significantly decreased, indicating the stabilization by hydroxyapatite. The influence of hydroxyapatite on the REE accumulation in enchytraeids was quantified by fitting a toxicokinetic model to dynamic REE body concentrations. The estimated uptake (Ku) and elimination rate constants (Ke), and bioaccumulation factor (BAF) for REEs were in the range of 0.000821 - 0.122 kgsoil/kgworm day-1, 0.0224 - 0.136 day-1, and 0.00135 - 1.96, respectively. Both Ku and BAF were significantly reduced by over 80% by hydroxyapatite addition, confirming the decreased REE bioavailability. Low atomic number REEs had higher BAFs in slightly contaminated soil, suggesting a higher bioaccumulation potential of light REEs in soil organisms. Overall, chemical stabilization with amendments can attenuate the bioavailability of REEs and reduce the potential ecological risk of contaminated agricultural soils near REE mining areas.

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis. PURPOSE: To develop a quality-checked and physics-constrained deep learning (DL) method to estimate material basis images from single-kV CT data without resorting to dual-energy CT acquisition schemes. METHODS: Single-kV CT images are decomposed into two material basis images using a deep neural network. The role of this network is to generate a feature space with 64 template features with the same matrix dimensions of the input single-kV CT image. These 64 template image features are then combined to generate the desired material basis images with different sets of combination coefficients, one for each material basis image. Dual-energy CT image acquisitions with two separate kVs were curated to generate paired training data between a single-kV CT image and the corresponding two material basis images. To ensure the obtained two material basis images are consistent with the encoded spectral information in the actual projection data, two physics constraints, that is, (1) effective energy of each measured projection datum that characterizes the beam hardening in data acquisitions and (2) physical factors of scanners such as detector and tube characteristics, are incorporated into the end-to-end training. The entire architecture is referred to as Deep-En-Chroma in this paper. In the application stage, the generated material basis images are sent to a deep quality check (Deep-QC) network to assess the quality of estimated images and to report the pixel-wise estimation errors for users. The models were developed using 5592 training and validation pairs generated from 48 clinical cases. Additional 1526 CT images from another 13 patients were used to evaluate the quantitative accuracy of water and iodine basis images estimated by Deep-En-Chroma. RESULTS: For the iodine basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is -0.25 mg/mL, and the agreement limits are [-0.75 mg/mL, +0.24 mg/mL]. For the water basis images estimated by Deep-En-Chroma, the mean difference with respect to dual-energy CT is 0.0 g/mL, and the agreement limits are [-0.01 g/mL, 0.01 g/mL]. Across the test cohort, the median [25th, 75th percentiles] root mean square errors between the Deep-En-Chroma and dual-energy material images are 14 [12, 16] mg/mL for the water images and 0.73 [0.64, 0.80] mg/mL for the iodine images. When significant errors are present in the estimated material basis images, Deep-QC can capture these errors and provide pixel-wise error maps to inform users whether the DL results are trustworthy. CONCLUSIONS: The Deep-En-Chroma network provides a new pathway to estimating the clinically relevant material basis images from single-kV CT data and the Deep-QC module to inform end-users of the accuracy of the DL material basis images in practice.

Study of the Heat Transfer Performance of Laminated Paper Honeycomb Panels.

To apply functional honeycomb panels (FHPs) in actual engineering projects, the heat transfer performance and intrinsic heat transfer mechanism of laminated honeycomb panels (LHPs, total thickness of 60 mm) with different structural parameters were investigated in this study by a heat flow meter. The results showed that (1) the equivalent thermal conductivity λequ of the LHP was almost independent of the cell size, when it consisted of a small single-layer thickness. Thus, LHP panels with a single-layer thickness of 15-20 mm are recommended. (2) A heat transfer model of LHPs was developed, and it was concluded that the heat transfer performance of LHPs depends greatly on the performance of their honeycomb core. Then, an equation was derived for the steady state temperature distribution of the honeycomb core. (3) The contribution of each heat transfer method to the total heat flux of the LHP was calculated using the theoretical equation. According to the theoretical results, the intrinsic heat transfer mechanism affecting the heat transfer performance of LHPs was revealed. The results of this study laid the foundation for the application of LHPs in building envelopes.

Dynamics of bacterial community in the foregut and hindgut of earthworms with the nutrition supplied by kitchen waste during vermicomposting.

Earthworm gut microbiota is vital in degrading bio-waste during vermicomposting. However, microbial dynamics in earthworm gut during this process are unclear. Thus, the aim is to firstly report the bacterial dynamics in both foregut and hindgut of earthworms over a 28 days' timeframe of vermicomposting by Eisenia foetida with the nutrition supplied by kitchen waste. Results showed that except the changing of the bacterial diversity, composition and structure, dynamics of the foregut and hindgut bacteria also differed during vermicomposting which related to the changes of nutrient provision. Day 3 was a turning point. The abundant bacteria of the top 20 % genera nearly did not overlap between the foregut and hindgut. In the end of vermicomposting, a remarkable stable bacterial structure appeared in the hindgut compared to somewhat muddled one in the foregut. Understanding the dynamics of earthworm gut microbiota enables the improvements to regulate the efficiency of organic waste vermicomposting.

Automatic cerebral computed tomography venographic imaging based on the prior knowledge of cerebral blood circulation.

BACKGROUND AND PURPOSE: Current clinical computed tomography venographic (cCTV) images present limited cerebral venous profiles. Therefore, this study aimed to develop an automatic cerebral CTV imaging technique using computed tomographic perfusion (CTP) images in a cohort of patients with stroke. MATERIALS AND METHODS: We retrospectively evaluated 10 (intracerebral hemorrhage) and 2 (acute ischemic stroke) patients who underwent institutional CTP imaging. CTV images were processed with the proposed CTV (pCTV) technique, and pCTV and cCTV images were then independently evaluated by two experienced neuroradiologists blinded to all clinical information using a novel scoring method that considered overall image quality, venous visibility, and arterial mis-segmentation. Venous visibility was separately evaluated for the dural sinus, superficial vein, and deep vein. Then, statistical analysis was performed to determine whether the pCTV technique was superior to the cCTV technique. RESULTS: In total, 14 sets of pCTV images were generated and compared with cCTV images. The overall image quality and venous visibility scores of pCTV images were significantly higher than those of cCTV images (all values of p<0.05), especially for the dural sinus (median [25th, 75th percentiles], 14.00 [13.63, 15.50] vs. 7.50 [7.00, 10.88]), and superficial vein (9.00 [8.88, 10.00] vs. 3.25 [1.63, 8.25]), while the difference in arterial mis-segmentation was not statistically significant (p= 0.164). CONCLUSIONS: This study proposed an automatic cerebral CTV imaging technique to eliminate residual bone and soft tissues, minimize the impact of the cerebral arterial system, and present a relatively comprehensive cerebral venous system, which would help physicians assess cerebral venous outflow profiles after stroke and seek imaging markers associated with clinical outcomes.

Discovery and Mechanism of Action of a Novel Antimicrobial Peptide from an Earthworm.

The robust innate immune system of the earthworm provides a potential source of natural antimicrobial peptides (AMPs). However, the cost and high rediscovery rate of direct separation and purification limits their discovery. Genome sequencing of numerous earthworm species facilitates the discovery of new antimicrobial peptides. Through predicting potential antimicrobial peptides in the open reading frames of the Eisenia andrei genome and sequence optimization, a novel antimicrobial peptide, named EWAMP-R (RIWWSGGWRRWRW), was identified. EWAMP-R demonstrated good activity against various bacteria, including drug-resistant strains. The antibacterial mechanisms of EWAMP-R were explored through molecular simulation and wet-laboratory experiments. These experiments demonstrated that the bacterial membrane may be one of the targets of EWAMP-R but that there may be different interactions with Gram-negative and Gram-positive bacterial membranes. EWAMP-R can disrupt bacterial membrane integrity; however, at low concentrations, it appears that EWAMP-R may get through the membrane of Escherichia coli instead of damaging it directly, implying the existence of a secondary response. Gene expression studies identified that in E. coli, only the apoptosis-like cell death (ALD) pathway was activated, while in Staphylococcus aureus, the MazEF pathway was also upregulated, limiting the influence of the ALD pathway. The different antimicrobial actions against Gram-positive and -negative bacteria can provide important information on the structure-activity relationship of AMPs and facilitate AMP design with higher specificity. This study identified a new source of antibacterial agents that has the potential to address the increasingly serious issue of antibiotic resistance. IMPORTANCE Drug-resistant bacteria are a great threat to public health and drive the search for new antibacterial agents. The living environment of earthworms necessitates a strong immune system, and therefore, they are potentially a rich resource of novel antibiotics. A novel AMP, EWAMP-R, with high antibacterial activity was found through in silico analysis of the Eisenia andrei genome. Molecular analysis investigating the interactions between EWAMP-R and the cell membrane demonstrated the importance of tryptophan and arginine residues to EWAMP-R activity. Additionally, the different secondary responses found between E. coli and S. aureus were in accordance with a common phenomenon where some antibacterial agents only target specific species of bacteria. These results provided useful molecular information to support further AMP research and design. Our study expands the sources of antimicrobial peptides and also helps to explain the adaptability of earthworms to their environment.

Multiomics analyses uncover nanoceria triggered oxidative injury and nutrient imbalance in earthworm Eisenia fetida.

The toxic stress caused by nanoceria remains vague owing to the limited efforts scrutinizing its molecular mechanisms. Herein, we investigated the impacts of nanoceria on earthworm Eisenia fetida, at the molecular level using the multiomics-based profiling approaches (transcriptomics, metabolomics, and 16 S rRNA sequencing). Nanoceria (50 and 500 mg/kg) significantly increased the contents of malondialdehyde (MDA), Fe, and K in worms, suggesting oxidative injury and nutrient imbalance. This was corroborated by the transcriptomic and metabolomic analyses. Nanoceria decreased the levels of certain genes and metabolites associated with glycerolipid and glycerophospholipid metabolisms, suggesting the production of reactive oxygen species and subsequent oxidative stress. Additionally, the ABCD3 gene belonging to ABC transporter family was upregulated, facilitating Fe uptake by worms. Moreover, the higher contents of MDA, Fe, and K after exposure were tightly associated with the imbalanced intestinal flora. Specifically, a higher relative abundance of Actinobacteriota and a lower relative abundance of Proteobacteria and Patescibacteria were induced. This study, for the first time, revealed that nanoceria at nonlethal levels caused oxidative stress and nutrient imbalance of earthworms from the perspective of genes, metabolites, and gut microbiome perturbations, and also established links between the gut microbiome and the overall physiological responses of the host.

Eco-risk management of tylosin fermentation residues using vermicomposting.

Tylosin fermentation residues (TFR) pose an ecotoxicological risk through antibiotic resistant bacteria (ARBs) and their corresponding genes (ARGs). This study evaluated the ecotoxicity of TFR to soil biological activity, and further explored the mechanisms of vermicomposting to reduce the toxicological risk. The results showed that tylosin (TYL) was moderately degradable with a half-life (t1/2) of 37.5 d, inducing 28-44% inhibition rate of nitrogen transformation in soil, and the EC50 of earthworm avoidance was 880 mg/kg. The 30-d vermicomposting reduced the pH and OM content, while increased the EC and TN content, accelerated compost maturation (C/N ratio up to 20), and enriched the microbial community. ARGs were reduced by earthworm through removal of TYL (>70% degradation, t1/2 of <20 d), inhibiting abundance of intI1 and ARBs. We conclude that vermicomposting is an efficient method for TFR treatment and its eco-risk management.

Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.

BACKGROUND: A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus, may limit the accuracy of intraview or organ-specific dose modulations in emerging CT technologies. PURPOSE: The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans. METHODS: CT images and the corresponding two-view scout images were retrospectively collected from 4214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55 136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold standard) for both fluence-field-modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of-the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity. RESULTS: The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. (2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition; compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference. (3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient body compared to tube current-modulated CT. (4) ScoutCT-NET 3D patient models enabled accurate prescription of organ-specific CT to boost image quality for a given body region-of-interest under a given radiation dose constraint. CONCLUSION: 3D tomographic attenuation models generated by ScoutCT-NET from two-view scout images can be used to prescribe fluence-field-modulated or organ-specific CT scans with high accuracy for the overall objective of radiation dose reduction or image quality improvement for a given imaging task.

Mitochondrial bioenergetic, oxidative stress and burrowing responses in earthworm exposed to roxarsone in soil.

The eco-risk of roxarsone (ROX) was evaluated using multiple responses of earthworm biomarkers under different ROX concentrations for 28 d. With the increasing total arsenic accumulation (TAs-E), biological responses in earthworm generally showed a two-stage changes of homeostasis dysregulation and dose-dependent alterations. At the early periods, ROX stress increased the reactive oxygen species (ROS) and lipid peroxidation (LPO) in a similar manner, and apparently disrupted mitochondrial calcium ([Ca2+]m). But earthworms regulated their mitochondrial and redox homeostasis through stable mitochondrial membrane potential (MMP) and increase of ATP level, superoxide dismutase (SOD) and catalase (CAT). After 14 d, the positively correlated mitochondrial effects of ROS, [Ca2+]m, MMP and ATP were related to the behavioral inhibition of burrow length, depth and reuse rate as well as antioxidant up-regulation of Nrf2, HO-1, sod1 and cat. These results contributed possible biomarkers from the dose-dependent relationship between mitochondrial, antioxidant and behavioral responses. Multiple biological detection in earthworms can better reflect the sub-chronic ecotoxicity of phenylarsenic pollutants in soil.

Quantitative lung perfusion blood volume using dual energy CT-based effective atomic number (Zeff ) imaging.

BACKGROUND: Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Zeff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects. PURPOSE: (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images. METHODS: The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Zeff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Zeff as PB V Z eff ( x ) = a Z eff ß ( x ) + b , where a, b, and ß are three constants. Namely, quantitative PB V Z eff is determined by Zeff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBViodine ) derived from two-material decompositions and the proposed PB V Z eff method. RESULTS: Among patients with pulmonary emboli, the proposed PB V Z eff maps clearly show the perfusion defects while the PBViodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist. CONCLUSION: Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects.

Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical cohorts. PURPOSE: The purpose of this work is to address the previously mentioned challenges in current deep learning methods. METHODS: A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL-PICCS method in terms of reconstruction accuracy and generalizability. RESULTS: Extensive evaluation studies have demonstrated that: (1) quantitative reconstruction accuracy of DL-PICCS for individual patient is improved when it is compared with the deep learning methods and CS-based methods; (2) the false-positive lesion-like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL-PICCS reconstructed images; and (3) DL-PICCS enables a deep learning scheme to relax its working conditions to enhance its generalizability. CONCLUSIONS: DL-PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.

Toxicity assessment of earthworm exposed to arsenate using oxidative stress and burrowing behavior responses and an integrated biomarker index.

Arsenate (As-V) is a ubiquitous contaminant in soil as a result of excessive use of veterinary drugs and pesticides, causing enormous environmental risks. Multiple biomarkers have been used to assess the ecotoxicity of arsenic, however, the mechanisms of toxicity remain unclear. This paper describes the exposure of the earthworm (Eisenia fetida) to natural soil with different As-V concentrations for 28 days, then biomarkers from oxidative stress and burrowing behavior were quantified to evaluate As-V stress. Dynamic changes in reactive oxygen species (ROS), lipid peroxidation (MDA), adenosine triphosphate (ATP) content and antioxidant enzymes activity (Gpx, SOD, CAT) implied two stages of intensified stress responses and physiological adaptability. The transcriptional expression and regulation of antioxidant enzymes showed different responses. The mRNA expression of sod1 was up-regulated, while that of cat showed no significant change. The related regulators, nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1), showed dose-dependent activation, suggesting antioxidant defense induced by Nrf2 signaling. The burrowing behavior after 14-day exposure indicated that As-V inhibited burrowing activity, especially the burrow length and maximum burrow depth. These multiple biomarkers were integrated using a biomarker response index (BRI) model, which showed significant dose-effect relationship especially on day 28, and suggested that ATP was a sensitive and representative biomarker. This study provided evidence that burrowing activity, Nrf2 and HO-1 were useful biomarkers warranting inclusion into the BRI model. Arsenic toxicity was comprehensively understood through redox homeostasis regulation, biochemical and behavioral changes, and these results suggested new strategies for soil pollutants diagnosis.

High Pitch Helical CT Reconstruction.

To avoid severe limited-view artifacts in reconstructed CT images, current multi-row detector CT (MDCT) scanners with a single x-ray source-detector assembly need to limit table translation speeds such that the pitch p (viz., normalized table translation distance per gantry rotation) is lower than 1.5. When , it remains an open question whether one can reconstruct clinically useful helical CT images without severe artifacts. In this work, we show that a synergistic use of advanced techniques in conventional helical filtered backprojection, compressed sensing, and more recent deep learning methods can be properly integrated to enable accurate reconstruction up to p=4 without significant artifacts for single source MDCT scans.

A Noise Control Method Using Adaptive Adjustable Parametric Array Loudspeaker to Eliminate Environmental Noise in Real Time.

Long-term exposure to environmental noise is dangerous to human health. Therefore, there is an urgent need to suppress or eliminate environmental noise. Due to the limitation of environmental space, the use of reverse sound waves emitted by loudspeakers for noise elimination has been widely used in noise control. However, because of the omni-directionality of sound propagation, a traditional voice coil loudspeaker (VCL) is used as a secondary source (emission reverse sound wave). It is easy to increase the sound pressure in non-target areas and form significant acoustic feedback to the reference source. Therefore, we propose an online secondary path modeling method using an adjustable parametric array loudspeaker (PAL) based on ultrasounds to eliminate environmental noise in real time. According to the different distance of the target, the size of the PAL is adjusted adaptively to realize the noise control of different long-distance targets. The distribution of quiet areas is discussed. The experimental results showed that a PAL as a secondary source had the same noise reduction effect as a traditional VCL, but it had longer propagation distance, smaller sound feedback and a more regular and controllable distribution of quiet areas. These research findings have great potential for improving environmental noise and creating a quiet environment.

Comparative study of vermicomposting of garden waste and cow dung using Eisenia fetida.

Vermicomposting is the process of composting using worms and is applied in waste management to produce high-quality organic fertilizer. Garden waste (GW) is often mixed with other raw materials for vermicomposting. In the present study, the feasibility of vermicomposting using only GW was investigated in comparison with cow dung (CD). The total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents and the electrical conductivity increased, while total organic carbon (TOC) and the C/N ratio decreased in both substrates after vermicomposting. The nutrient content (TN, TP, and TK) of the GW vermicompost was promoted less than that in CD. Scanning electron microscopy images and specific surface area analysis showed that the vermicompost was strongly disaggregated and became more compacted and fragmented compared with the raw substrates. No mortality of earthworms was observed in GW; however, the earthworms had a higher mean body weight and reproduction rate in CD than that in GW. There were higher bacterial community richness and diversity in the vermicompost than that in the raw materials, and the dominant phylum species were Proteobacteria, Actinobacteria, and Bacteroidetes. Redundancy analysis demonstrated that TN, C/N ratio, and TOC play an important role in bacterial community dynamics. These data indicate that vermicomposting is a robust process that is suitable for the management of GW.