Waltherclark7827

Besides, PerAF was increased in the right middle temporal gyrus in these children. The results regarding ReHo, fALFF and PerAF in the typical band was similar to those in slow-5 band, respectively. A correlation was found between the PerAF value of the right middle temporal gyrus and scores of the urinary intention-related wakefulness. Results in other bands were either negative or in white matter. NE children might have abnormal intrinsic neural oscillations mainly on slow-5 bands.Recent evidence supports involvement of the acute phase protein haptoglobin in numerous events during mammalian reproduction. The present study represents an in-depth investigation of haptoglobin expression and secretion in the porcine oviduct and uterus, and assesses its effect on porcine in vitro embryo production. A systematic study was made of sows in different oestrous stages late follicular, early luteal and late luteal stages. Relative haptoglobin mRNA abundance was quantified by RT-qPCR. In addition, expression of the protein was analysed by immunohistochemistry and the results were complemented by Western-blot and proteomic analyses of the oviductal and uterine fluids. In vitro porcine fertilization and embryo culture were carried out in the presence of haptoglobin. The results indicate that haptoglobin mRNA expression in the porcine oviduct and uterus is most abundant during the late luteal stage of the oestrous cycle. By means of Western blot and proteomic analyses haptoglobin presence was demonstrated in the oviduct epithelium and in the oviductal and uterine fluids in different stages of the oestrous cycle. The addition of haptoglobin during gamete co-incubation had no effect on sperm penetration, monospermy or efficiency rates; however, compared with the control group, blastocyst development was significantly improved when haptoglobin was present (haptoglobin 64.50% vs. control 37.83%; p  less then  0.05). In conclusion, the presence of haptoglobin in the oviduct and uterus of sows at different stages of the oestrous cycle suggests that it plays an important role in the reproduction process. The addition of haptoglobin during in vitro embryo production improved the blastocyst rates.Dual-energy CT provides enhanced diagnostic power with similar or even reduced radiation dose as compared to single-energy CT. Its principle is based on the distinct physical properties of low and high energetic photons, which, however, may also affect the biological effectiveness and hence the extent of CT-induced cellular damage. Therefore, a comparative analysis of biological effectiveness of dual- and single-energy CT scans with focus on early gene regulation and frequency of radiation-induced DNA double strand breaks (DSBs) was performed. Blood samples from three healthy individuals were irradiated ex vivo with single-energy (80 kV and 150 kV) and dual-energy tube voltages (80 kV/Sn150kV) employing a modern dual source CT scanner resulting in Volume Computed Tomography Dose Index (CTDIvol) of 15.79-18.26 mGy and dose length product (DLP) of 606.7-613.8 mGy*cm. Non-irradiated samples served as a control. Differential gene expression in peripheral blood mononuclear cells was analyzed 6 h after irradiation using whole transcriptome sequencing. DSB frequency was studied by 53BP1 + γH2AX co-immunostaining and microscopic evaluation of their focal accumulation at DSBs. Neither the analysis of gene expression nor DSB frequency provided any evidence for significantly increased biological effectiveness of dual-energy CT in comparison to samples irradiated with particular single-energy CT spectra. Relative to control, irradiated samples were characterized by a significantly higher rate of DSBs (p  less then  0.001) and the shared upregulation of five genes, AEN, BAX, DDB2, FDXR and EDA2R, which have already been suggested as radiation-induced biomarkers in previous studies. Despite steadily decreasing doses, CT diagnostics remain a genotoxic stressor with impact on gene regulation and DNA integrity. However, no evidence was found that varying X-ray spectra of CT impact the extent of cellular damage.The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. WZ811 ic50 We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25-56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.Successful responding to acutely threatening situations requires adequate approach-avoidance decisions. However, it is unclear how threat-induced states-like freezing-related bradycardia-impact the weighing of the potential outcomes of such value-based decisions. Insight into the underlying computations is essential, not only to improve our models of decision-making but also to improve interventions for maladaptive decisions, for instance in anxiety patients and first-responders who frequently have to make decisions under acute threat. Forty-two participants made passive and active approach-avoidance decisions under threat-of-shock when confronted with mixed outcome-prospects (i.e., varying money and shock amounts). Choice behavior was best predicted by a model including individual action-tendencies and bradycardia, beyond the subjective value of the outcome. Moreover, threat-related bradycardia (high-vs-low threat) interacted with subjective value, depending on the action-context (passive-vs-active). Specifically, in action-contexts incongruent with participants' intrinsic action-tendencies, stronger bradycardia related to diminished effects of subjective value on choice across participants. These findings illustrate the relevance of testing approach-avoidance decisions in relatively ecologically valid conditions of acute and primarily reinforced threat. These mechanistic insights into approach-avoidance conflict-resolution may inspire biofeedback-related techniques to optimize decision-making under threat. Critically, the findings demonstrate the relevance of incorporating internal psychophysiological states and external action-contexts into models of approach-avoidance decision-making.The reflection coefficient of a microwave surface wave incident at the termination of a metasurface is explored. Two different surface types are examined. One is a square array of square metallic patches on a dielectric-coated metallic ground plane, the other a Sievenpiper 'mushroom' array. In the latter the surface wave fields are more confined within the structure. Comparison of the measured surface-wave reflection spectra is made with that obtained from analytic theory and numerical modelling. The reflection coefficient is shown to be dependent on both the momentum mismatch between the surface wave and the freely propagating modes as well as the different field distributions of the two modes.Urban flooding can be predicted by using different modeling approaches. This study considered different methods of modeling the dynamic flow interactions between pipe systems and surface flooding in urban areas. These approaches can be divided into two categories based on surface runoff collection units. This paper introduces a new hydrodynamic model that couples the storm water management model and the 2D overland model. The model's efficiency was validated based on the aforementioned experimental dataset; agreement was verified by correlation values above 0.6. Additionally, this study used different approaches and compared their accuracy in predicting flooding patterns. The results show that the use of sub-catchments to model the collection of surface runoff was not predictive of the inundation process, indicating a lower goodness of fit with the recorded values than that of adopting cells. Moreover, to determine which method of adopting cells to collect runoff could better predict rainstorm-induced inundation, an error and correlation analysis was conducted.