This study aims to develop a predictive framework to assess the removal and fate of trace organic chemicals (TrOCs) during wastewater treatment by anaerobic membrane bioreactor (AnMBR). The fate of 27 TrOCs in both the liquid and sludge phases during AnMBR treatment was systematically investigated. The results demonstrate a relationship between hydrophobicity and specific molecular features of TrOCs and their removal efficiency. These molecular features include the presence of electron withdrawing groups (EWGs) or donating groups (EDGs), especially those containing nitrogen and sulphur. All seven hydrophobic contaminants were well removed (>70%) by AnMBR treatment. Most hydrophilic TrOCs containing EDGs were also well removed (>70%). In contrast, hydrophilic TrOCs containing EWGs were mostly poorly removed and could accumulate in the sludge phase. The removal of several nitrogen/sulphur bearing TrOCs (e.g., linuron and caffeine) by AnMBR was higher than that by aerobic treatment, possibly due to nitrogen or sulphur reducing bacteria.
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http://dx.doi.org/10.1016/j.biortech.2015.04.034 | DOI Listing |
ASN Neuro
January 2025
Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA.
In light of the increasing importance for measuring myelin ratios - the ratio of axon-to-fiber (axon + myelin) diameters in myelin internodes - to understand normal physiology, disease states, repair mechanisms and myelin plasticity, there is urgent need to minimize processing and statistical artifacts in current methodologies. Many contemporary studies fall prey to a variety of artifacts, reducing study outcome robustness and slowing development of novel therapeutics. Underlying causes stem from a lack of understanding of the myelin ratio, which has persisted more than a century.
View Article and Find Full Text PDFElife
December 2024
Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, Austria.
Phantom perceptions like tinnitus occur without any identifiable environmental or bodily source. The mechanisms and key drivers behind tinnitus are poorly understood. The dominant framework, suggesting that tinnitus results from neural hyperactivity in the auditory pathway following hearing damage, has been difficult to investigate in humans and has reached explanatory limits.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Psychology, Theoretical Cognitive Science Group, Philipps-Universität Marburg, Marburg, Germany.
Introduction: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, South Tyrol, Italy.
Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity.
View Article and Find Full Text PDFPLoS One
January 2025
School of Marxism, Central South University, Changsha, China.
Since the dissemination of information is more rapid and the scale of users on online platforms is enormous, the public opinion risk is more visible and harder to tackle for universities and authorities. Improving the accuracy of predictions regarding online public opinion crises, especially those related to campuses, is crucial for maintaining social stability. This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy.
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