Foghtherkelsen9824

Cochlear implants (CIs) allow good perception of speech while music listening is unsatisfactory, leading to reduced music enjoyment. Hence, a number of ongoing efforts aim to improve music perception with a CI. Regardless of the nature of these efforts, effect measurements must be valid and reliable. While auditory skills are typically examined by behavioral methods, recording of the mismatch negativity (MMN) response, using electroencephalography (EEG), has recently been applied successfully as a supplementary objective measure. Eleven adult CI users and 14 normally hearing (NH) controls took part in the present study. To measure their detailed discrimination of fundamental features of music we applied a new multifeature MMN-paradigm which presented four music deviants at four levels of magnitude, incorporating a novel "no-standard" approach to be tested with CI users for the first time. A supplementary test measured behavioral discrimination of the same deviants and levels. The MMN-paradigm elicited signifit tool in future CI research. For clinical use, future studies should investigate the possibility of applying the paradigm with the purpose of assessing discrimination skills not only at the group level but also at the individual level. Copyright © 2020 Petersen, Andersen, Haumann, Højlund, Dietz, Michel, Riis, Brattico and Vuust.Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and facilitate downstream network analysis. The existing embedding methods commonly endeavor to capture structure information in a network, but lack of consideration of subsequent tasks and synergies between these tasks, which are of equal importance for learning desirable network representations. To address this issue, we propose a novel multi-task network representation learning (MTNRL) framework, which is end-to-end and more effective for underlying tasks. The original network and the incomplete network share a unified embedding layer followed by node classification and link prediction tasks that simultaneously perform on the embedding vectors. By optimizing the multi-task loss function, our framework jointly learns task-oriented embedding representations for each node. Besides, our framework is suitable for all network embedding methods, and the experiment results on several benchmark datasets demonstrate the effectiveness of the proposed framework compared with state-of-the-art methods. Copyright © 2020 Xie, Jin, Gong, Zhang and Yu.Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters. Copyright © 2020 Samani, Alappatt, Parker, Ismail and Verma.There is increasing attention to sprint interval exercise (SIE) training as a time-efficient exercise regime. Recent studies, including our own (Kujach et al., 2018), have shown that acute high-intensity intermittent exercise can improve cognitive function; however, the neurobiological mechanisms underlying the effect still remain unknown. We thus examined the effects of acute SIE on cognitive function by monitoring the peripheral levels of growth and neurotrophic factors as well as blood lactate (LA) as potential mechanisms. Thirty-six young males participated in the current study and were divided into two groups SIE (n = 20; mean age 21.0 ± 0.9 years) and resting control (CTR) (n = 16; mean age 21.7 ± 1.3 years). The SIE session consisted of 5 min of warm-up exercise and six sets of 30 s of all-out cycling exercise followed by 4.5 min of rest on a cycling-ergometer. Blood samples to evaluate the changes of serum concentrations of brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor (VEGF), and blood LA were obtained at three time points before, immediately after, and 60 min after each session. A Stroop task (ST) and trail making test (TMT) parts A and B were used to assess cognitive functions. Acute SIE shortened response times for both the ST and TMT A and B. Meanwhile, the peripheral levels of BDNF, IGF-1, and VEGF were significantly increased after an acute bout of SIE compared to those in CTR. In response to acute SIE, blood LA levels significantly increased and correlated with increased levels of BDNF, IGF-1, and VEGF. Furthermore, cognitive function and BDNF are found to be correlated. The current results suggest that SIE could have beneficial effects on cognitive functions with increased neuroprotective factors along with peripheral LA concentration in humans. Copyright © 2020 Kujach, Olek, Byun, Suwabe, Sitek, Ziemann, Laskowski and Soya.Recent findings from rodent studies suggest that high-fat diet (HFD) increases hyperalgesia independent of obesity status. Furthermore, weight loss interventions such as voluntary physical activity (PA) for adults with obesity or overweight was reported to promote pain reduction in humans with chronic pain. However, regardless of obesity status, it is not known whether HFD intake and sedentary (SED) behavior is underlies chronic pain susceptibility. Moreover, differential gene expression in the nucleus accumbens (NAc) plays a crucial role in chronic pain susceptibility. Thus, the present study used an adapted model of the inflammatory prostaglandin E2 (PGE2)-induced persistent hyperalgesia short-term (PH-ST) protocol for mice, an HFD, and a voluntary PA paradigm to test these hypotheses. selleck chemicals Therefore, we performed an analysis of differential gene expression using a transcriptome approach of the NAc. We also applied a gene ontology enrichment tools to identify biological processes associated with chronic pain susceptibility and to investigate the interaction between the factors studied diet (standard diet vs.