High efficiency and stable partial nitration achieved via gel immobilization.

Long-term high efficiency and stable partial nitrification (PN) performance was achieved using gel-immobilized partial nitrifying bacteria. The PN characteristics of the filler under high and low ammonia nitrogen concentrations and low temperature were comprehensively studied and the rapid reactivation was achieved after reactor breakdown or long stagnation period. The results showed that the maximum ammonia oxidation rate was 66.8 mg•(L•h)-1 and the nitrite accumulation rate was above 95 % for the filler. Efficient and stable PN performance depends on the high abundance of ammonia-oxidizing bacteria (AOB) inside the filler and dynamically microbial community. In addition, the oxygen-limited zone and competition between the microorganisms inside the filler effectively inhibited the growth of nitrite oxidizing bacteria, and the sludge outside the filler assisted in this process, which supported the dominant position of AOB in fillers. This study provides a reliable technology for the practical application of the PN nitrogen removal process.

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation.

BACKGROUND AND OBJECTIVES: Total Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. METHODS: In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. RESULTS: Our network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). CONCLUSIONS: The results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.

Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels.

Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.

Recovery of bio­sulfur and metal resources from mine wastewater by sulfide biological oxidation-alkali flocculation: A pilot-scale study.

Mine wastewater treatment using bio-sulfate reduction technology forms sulfur-containing wastewater that comprises sulfides (HS- and S2-) and metal ions. Bio­sulfur generated by sulfur-oxidizing bacteria in such wastewater is usually negatively charged hydrocolloidal particles. However, bio­sulfur and metal resource recovery are difficult using traditional methods. In this study, the sulfide biological oxidation-alkali flocculation (SBO-AF) method was investigated to recover the above resources, and to provide a technical reference for mine wastewater resource recovery and heavy metal pollution control. Specifically, the performance of SBO in forming bio­sulfur and the key parameters of SBO-AF were explored and then applied in a pilot-scale process to recover resources from wastewater. Results show that partial sulfide oxidation was achieved under a sulfide loading rate of 5.08 ± 0.39 kg/m3·d, dissolved oxygen of 2.9-3.5 mg/L and temperature of 27-30 °C. The average sulfide oxidation rate and sulfur selectivity ratio were 92.86 % and 90.22 %, respectively. At pH 10, metal hydroxide and bio­sulfur colloids co-precipitated through the precipitation catching and adsorption charge neutralization effect. The average manganese, magnesium and aluminum concentrations and turbidity in the wastewater were 53.93 mg/L, 522.97 mg/L, 34.20 mg/L and 505 NTU, respectively, and decreased to 0.49 mg/L, 80.65 mg/L, 1.00 mg/L and 23.33 NTU, respectively, after treatment. The recovered precipitate mainly contained sulfur, along with metal hydroxides. The average sulfur, manganese, magnesium and aluminum contents were 45.6 %, 29.5 %, 15.1 % and 6.5 %, respectively. Economic feasibility analysis and the above results show that SBO-AF has obvious technical and economic advantages in the recovery resources from mine wastewater.

Time-of-flight completion in ultrasound computed tomography based on the singular value threshold algorithm.

Ultrasound computed tomography (USCT) has been developed for breast tumor screening. The sound-speed modal of USCT can provide quantitative sound-speed values to help tumor diagnosis. Time-of-flight (TOF) is the critical input in sound-speed reconstruction. However, we found that the missing data problem in the detected TOF causes artifacts on the reconstructed sound-speed images, which may affect the tumor identification. In this study, to address the missing TOF data problem, we first adopted the singular value threshold (SVT) algorithm to complete the TOF matrix. The threshold value in SVT is difficult to determine, so we proposed a selection strategy, that is, to enumerate the threshold values as the multiples of the maximum singular value of the incomplete matrix and then evaluate the image quality to select the proper threshold value. In the numerical breast phantom experiment, the artifacts are eliminated, and the accuracy is higher than the accuracy of the compared methods. In the in vivo experiment, we reconstructed the sound-speed image of the breast of a volunteer with invasive breast cancer, and the SVT algorithm improved the image sharpness. The completion of DTOF based on SVT gives better accuracy than the compared methods, but too large a threshold value decreases the accuracy. In the future, the selection method of the threshold value needs further research, and more USCT cases should be included in the experiments.

Design and Implementation of a Modular and Scalable Research Platform for Ultrasound Computed Tomography.

Increasing attention has been attracted to the research of ultrasound computed tomography (USCT). This article reports the design considerations and implementation details of a novel USCT research system named UltraLucid, which aims to provide a user-friendly platform for researchers to develop new algorithms and conduct clinical trials. The modular design strategy is adopted to make the system highly scalable. A prototype has been assembled in our laboratory, which is equipped with a 2048-element ring transducer, 1024 transmit (TX) channels, 1024 receive (RX) channels, two servers, and a control unit. The prototype can acquire raw data from 1024 channels simultaneously using a modular data acquisition and a transfer system, consisting of 16 excitation and data acquisition (EDAQ) boards. Each EDAQ board has 64 independent TX and RX channels and 4-Gb Ethernet interfaces for raw data transmission. The raw data can be transferred to two servers at a theoretical rate of 64 Gb/s. Both servers are equipped with a 10.9-TB solid-state drive (SSD) array that can store raw data for offline processing. Alternatively, after processing by onboard field-programmable gate arrays (FPGAs), the raw data can be processed online using multicore central processing units (CPUs) and graphics processing units (GPUs) in each server. Through control software running on the host computer, the researchers can configure parameters for transmission, reception, and data acquisition. Novel TX-RX scheme and coded imaging can be implemented. The modular hardware structure and the software-based processing strategy make the system highly scalable and flexible. The system performance is evaluated with phantoms and in vivo experiments.

Zone-Shrinking Fresnel Zone Travel-Time Tomography for Sound Speed Reconstruction in Breast USCT.

Many studies have been carried out on ultrasound computed tomography (USCT) for its potential application in breast imaging. The sound speed (SS) image modality in USCT can help doctors diagnose the breast cancer, as the tumor usually has a higher sound speed than normal tissues. Travel time is commonly used to reconstruct SS image. Raypath travel-time tomography (RTT) assumes that the sound wave travels through a raypath. RTT is computationally efficient but with low contrast to noise ratio (CNR). Fresnel zone travel-time tomography (FZTT) is based on the assumption that the sound wave travels through an area called the Fresnel zone. FZTT can provide SS image with high CNR but low accuracy due to the wide Fresnel zone. Here, we propose a zone-shrinking Fresnel zone travel-time tomography (ZSFZTT), where a weighting factor is adopted to shrink the Fresnel zone during the inversion process. Numerical phantom and in vivo breast experiments were performed with ZSFZTT, FZTT, and RTT. In the numerical experiment, the reconstruction biases of size by ZSFZTT, FZTT, and RTT were 0.2%~8.3%, 2.3%~31.7%, and 1.8%~25%; the reconstruction biases of relative SS value by ZSFZTT, FZTT, and RTT were 24.7%~42%, 53%~60.8%, and 30.3%~47.8%; and the CNR by ZSFZTT, FZTT, and RTT were 67.7~96.6, 68.5~98, and 1.7~2.7. In the in vivo breast experiment, ZSFZTT provided the highest CNR of 8.6 compared to 8.1 by FZTT and 1.9 by RTT. ZSFZTT improved the reconstruction accuracy of size and the relative reconstruction accuracy of SS value compared to FZTT and RTT while maintaining a high CNR similar to that of FZTT.