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In this study, anammox bacteria were rapidly enriched in sequencing batch biofilm reactors (SBBRs) with different inoculations. The activated sludge taken from a sequencing batch reactor was used and inoculated to SBBR1, while SBBR2 was seeded with stored anaerobic sludge from an upflow anaerobic fixed bed (2-year stored at 5-15 °C). Nitrogen removal performance, anammox activity, biofilm characteristics and variation of the microbial community were evaluated. The maximum total nitrogen loading rate (NLR) of SBBR1 gradually reached to 1.62 kgN/(m³/day) with a removal efficiency higher than 88% and the NLR of SBBR2 reached to 1.43 kgN/(m³/day) with a removal efficiency of 86%. SBBR2 was more stable compared to SBBR1. These results, combined with molecular techniques such as scanning electron microscope, fluorescence in situ hybridization, and terminal restriction fragment length polymorphism, indicated that different genera of anammox bacteria became dominant. This research also demonstrates that SBBR is a promising bioreactor for starting up and enriching anammox bacteria.
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http://dx.doi.org/10.1007/s00253-012-4427-z | DOI Listing |
Environ Technol
September 2025
School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, People's Republic of China.
To explore strategies for further reducing aeration energy consumption in the simultaneous nitrification and denitrification (SND) process, an SND reactor was constructed to treat low carbon-to-nitrogen (C/N) ratio domestic wastewater under ultra-low dissolved oxygen (DO) conditions (DO < 0.05 mg·L⁻). The effects of hydraulic retention time (HRT) and C/N ratio on nitrogen removal performance were systematically evaluated, and batch experiments were conducted to determine nitrification and denitrification rates.
View Article and Find Full Text PDFRev Sci Instrum
September 2025
Joint Quantum Institute, University of Maryland and National Institute of Standards and Technology, College Park, Maryland 20742, USA.
We describe an apparatus that efficiently produces 23Na Bose-Einstein condensates (BECs) in a hybrid trap that combines a quadrupole magnetic field with a far-detuned optical dipole trap. Using a Bayesian optimization framework, we systematically optimize all BEC production parameters in modest-sized batches of highly correlated parameters. Furthermore, we introduce a Lagrange multiplier-based technique to optimize the duration of different evaporation stages constrained to have a fixed total duration; this enables the progressive creation of increasingly rapid experimental sequences that still generate high-quality BECs.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, In
Understanding the structural and functional diversity of toxin proteins is critical for elucidating macromolecular behavior, mechanistic variability, and structure-driven bioactivity. Traditional approaches have primarily focused on binary toxicity prediction, offering limited resolution into distinct modes of action of toxins. Here, we present MultiTox, an ensemble stacking framework for the classification of toxin proteins based on their molecular mode of action: neurotoxins, cytotoxins, hemotoxins, and enterotoxins.
View Article and Find Full Text PDFMediators Inflamm
September 2025
Shengli Clinical Medical College of Fujian Medical University, Fuzhou 350001, Fujian, China.
Osteoporosis is a prevalent metabolic bone disorder with complex molecular underpinnings. Emerging evidence implicates endoplasmic reticulum stress (ERS) in its pathogenesis; however, systematic exploration of ERS-related genes (ERSRGs) remains limited. This study aimed to identify ERS-related differentially expressed genes (ERSRDEGs) in osteoporosis, construct a diagnostic model, and elucidate associated molecular mechanisms.
View Article and Find Full Text PDFiScience
September 2025
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, School of Computing and Information Technology, Great Bay University, Dongguan, China.
Distinguishing similar cancer subtypes and predicting responses to immune checkpoint blockade (ICB) are critical for improving clinical outcomes. However, existing gene expression signatures often suffer from batch effects and poor generalizability across cohorts. To address these limitations, we propose adaptive individualized gene pair signatures (AIGPS), a robust method that adaptively quantifies gene pair reversals and selects informative features using machine learning.
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