This study conducted a completely autotrophic nitrogen removal over nitrite (CANON) process in a continuous anoxic upflow bioreactor to treat synthetic wastewater with TMAH (tetramethylammonium hydroxide) ranging from 200 to 1000mg/L. The intermediates were analyzed for understanding the metabolic pathway of TMAH biodegradation in CANON process. In addition, (15)N-labeled TMAH was used as the substrate in a batch anoxic bioreactor to confirm that TMAH was converted to nitrogen gas in CANON process. The results indicated that TMAH was almost completely biodegraded in CANON system at different influent TMAH concentrations of 200, 500, and 1000mg/L. The average removal efficiencies of total nitrogen were higher than 90% during the experiments. Trimethylamine (TMA) and methylamine (MA) were found to be the main biodegradation intermediates of TMAH in CANON process. The production of nitrogen gas with (15)N-labeled during the batch anaerobic bioreactor indicated that CANON process successfully converted TMAH into nitrogen gas.
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http://dx.doi.org/10.1016/j.biortech.2016.01.127 | DOI Listing |
Neuroradiol J
January 2025
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shanghai Artificial Intelligence Laboratory, Shanghai, China. Electronic address:
Radiation therapy is a primary and effective treatment strategy for NasoPharyngeal Carcinoma (NPC). The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Despite that deep learning has achieved remarkable performance on various medical image segmentation tasks, its performance on OARs and GTVs of NPC is still limited, and high-quality benchmark datasets on this task are highly desirable for model development and evaluation.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
Front Radiol
December 2024
Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Brain Lang
January 2025
Dept. of Speech-Language Pathology, SUNY Buffalo State, Buffalo, NY, USA.
This study presents an examination of the neural connectivity associated with processing speech in noisy environments, an ability that declines with age. We correlated subjects' speech-in-noise (SIN) ability with resting-state MRI scans and Fractional Anisotropy (FA) values from the auditory section of the corpus callosum, both with and without correcting for age. The results revealed that subjects who performed poorly on the right ear SIN test (QuickSIN, MedRx) had higher correlations between the primary auditory cortex and regions of the brain that process language.
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