Notes

ESC functioning party on heart cellular electrophysiology situation papers: significance, possibilities, as well as limitations regarding fresh versions with regard to heart failure electrophysiology research.
Time series-based modeling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill's model using MATLAB, which can predict biomethane production with time series. This model can predict the biomethane production for both batch and continuous process, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between literature and the developed model was less than ± 7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary to an industrial perspective, which usually requires extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.Nitrogen has a vital influence on the properties of the microwave-assisted hydrothermal carbonization (MHTC) products of Spirulina platensis (SP). The effects of hydrothermal temperature (140-220 °C) and time (1-4 h) on the product distribution and nitrogen migration of SP in MHTC were studied. Increasing temperature led to an increase in the carbon content, and a decrease in the nitrogen content in hydrochar. Protein-N was the major nitrogen-containing species in hydrochar. The total nitrogen in liquid phase increased significantly with increasing temperature. Carbon dots were found to be one of the valuable products in the liquid phase. Higher temperatures improved the amine-N level and reduced the quaternary-N content in carbon dots. A close correspondence was found between the N-containing species and the luminescence centers of carbon dots. A possible nitrogen migration mechanism was proposed to provide guidance for the potential application of the products.The preservation efficiency of mainstream (M-ANA) and sidestream anaerobic ammonium oxidation (anammox) (S-ANA) were evaluated based on their activation energy (Ea). The Ea of M-ANA cultivated under low nitrogen loads was lower than that of S-ANA, which greatly contributed to enhancing the viability of anammox during preservation at 4 °C. After preservation for 140 d, the decay rate (bAN) of M-ANA ranged from 0.0012 to 0.0013/d; the bAN of S-ANA was 0.0036-0.0041/d. The addition of hydrazine, which requires minimal energy to activate anammox metabolism, is highly beneficial for the viability of microorganisms. The low Ea of anammox contributes to efficient reactivation with rapid reactivation of heme c, and the addition of hydrazine makes the process more beneficial. Although the specific nitrogen removal rate of the M-SNA seed sludge was much lower than that of S-ANA, the rate of M-ANA became higher after 48 days of reactivation.Unlike other non-lymphoid tissues monocytes comprise a large proportion of mononuclear phagocytes present within the gingiva. Their functions and fate remain poorly understood. The oral mucosa faces challenges common to all barrier surfaces, including constant exposure to antigens and the resident commensal bacteria, but also experiences ongoing mechanical damage from mastication. Gingiva monocytes may therefore possess both myeloid functions observed at other barrier sites, such as hypo-responsiveness to bacterial stimulation, and distinctive functions tailored by their unique environment. In this review, we discuss the establishment and function of monocytes and macrophages at several mucosal tissues, and posit potential functions of monocytes within the gingiva tissue.The cerebellum is involved in the acquisition and consolidation of learned fear responses. Knowledge about its contribution to extinction learning, however, is sparse. Extinction processes likely involve erasure of memories, but there is ample evidence that at least part of the original memory remains. We asked the question whether memory persists within the cerebellum following extinction training. The renewal effect, that is the reoccurrence of the extinguished fear memory during recall in a context different from the extinction context, constitutes one of the phenomena indicating that memory of extinguished learned fear responses is not fully erased during extinction training. MK-2206 We performed a differential AB-A/B fear conditioning paradigm in a 7-Tesla (7T) MRI system in 31 young and healthy men. On day 1, fear acquisition training was performed in context A and extinction training in context B. On day 2, recall was tested in contexts A and B. As expected, participants learned to predict that the CS+ was folbut only on a trend level. Thus, part of the original memory likely remains within the cerebellum following extinction training. We found cerebellar activations related to the CS+ and CS- during fear acquisition training which likely reflect associative and non-associative aspects of the task. Cerebellar activations, however, were not significantly different for CS+ and CS-. Since the CS- was never followed by an electric shock, the cerebellum may contribute to associative learning related to the CS, for example as a safety cue.
We aimed at evaluating a Gjedde-Patlak plot and non-invasive image-derived input functions (IDIF) from the aorta to quantify cerebral glucose metabolic rate (CMRglc) in comparison to the reference standard based on sampling the arterial input function (AIF).

Six healthy subjects received 200 MBq [
F]FDG simultaneously with the initiation of a three-part dynamic PET recording consisting of a 15min-recording of the aorta, a 40min-recording of the brain and finally 2min-recording of the aorta. Simultaneously, the arterial
F concentration was measured via arterial cannulation. Regions of interest were drawn in the aorta and the brain and time-activity curves extracted. The IDIF was obtained by fitting a triple exponential function to the aorta time-activity curve after the initial peak including the late aorta frame, thereby interpolating the arterial blood activity concentration during the brain scan. CMRglc was calculated from Gjedde-Patlak plots using AIF and IDIF, respectively and the predictive value eement between CMRglc obtained with an IDIF from the aorta and the reference standard with AIF. A non-invasive three-part dynamic [18F]FDG PET recording is feasible as a non-invasive alternative for reliable quantification of cerebral glucose metabolism in all scanner systems. This is useful in patients with presumed global cerebral changes owing to systemic disease or for the monitoring of treatment effects.Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.Recent neuroimaging evidence suggests that there might be an anterior-posterior functional differentiation of the hippocampus along the long-axis. The HERNET (hippocampal encoding/retrieval and network) model proposed an encoding/retrieval dichotomy with the anterior hippocampus more connected to the dorsal attention network (DAN) during memory encoding, and the posterior portions more connected to the default mode network (DMN) during retrieval. Evidence both for and against the HERNET model has been reported. In this study, we test the validity of the HERNET model non-invasively in humans by computing functional connectivity (FC) in layer-specific cortico-hippocampal microcircuits. This was achieved by acquiring sub-millimeter functional magnetic resonance imaging (fMRI) data during encoding/retrieval tasks at 7T. Specifically, FC between infra-granular output layers of DAN with hippocampus during encoding and FC between supra-granular input layers of DMN with hippocampus during retrieval were computed to test the predictions of the HERNET model. Our results support some predictions of the HERNET model including anterior-posterior gradient along the long axis of the hippocampus. While preferential relationships between the entire hippocampus and DAN/DMN during encoding/retrieval, respectively, were observed as predicted, anterior-posterior specificity in these network relationships could not be confirmed. The strength and clarity of evidence for/against the HERNET model were superior with layer-specific data compared to conventional volume data.Large slow oscillations (SO, 0.5-2 Hz) characterise slow-wave sleep and are crucial to memory consolidation and other physiological functions. Manipulating slow oscillations may enhance sleep and memory, as well as benefitting the immune system. Closed-loop auditory stimulation (CLAS) has been demonstrated to increase the SO amplitude and to boost fast sleep spindle activity (11-16 Hz). Nevertheless, not all such stimuli are effective in evoking SOs, even when they are precisely phase locked. Here, we studied what factors of the ongoing activity patterns may help to determine what oscillations to stimulate to effectively enhance SOs or SO-locked spindle activity. Hence, we trained classifiers using the morphological characteristics of the ongoing SO, as measured by electroencephalography (EEG), to predict whether stimulation would lead to a benefit in terms of the resulting SO and spindle amplitude. Separate classifiers were trained using trials from spontaneous control and stimulated datasets, and we evaluated their performance by applying them to held-out data both within and across conditions.
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