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Integrating longitudinal pediatric patient-centered result way of measuring to the scientific work-flows using a commercial electronic digital well being document: one step towards growing value to the affected individual.
Given the importance of the EC as one of the first regions to be affected by AD pathology in humans, the observation that the EC is susceptible to differential bioenergetic regulation in response to a metabolic stressor such as APOE4 may point to a causative factor in the pathogenesis of AD.Empirical kinetic models such as the Monod equation have been widely applied to relate the cell growth with substrate availability. The Monod equation shares a similar form with the mechanistically-based Michaelis-Menten kinetics for enzymatic processes, which has provoked long-standing and un-concluded conjectures on their relationship. In this work, we integrated proteome allocation principles into a Flux Balance Analysis (FBA) model of Escherichia coli, which quantitatively revealed potential mechanisms that underpin the phenomenological Monod parameters the maximum specific growth rate could be dictated by the abundance of growth-controlling proteome and growth-pertinent proteome cost; more importantly, the Monod constant (Ks) was shown to relate to the Michaelis constant for substrate transport (Km,g), with the link being dependent on the cell's metabolic strategy. Besides, the proposed model was able to predict glucose uptake rate at given external glucose concentration through the size of available proteome resource for substrate transport and its enzymatic cost, while growth rate and acetate overflow were accurately simulated for two E. coli strains. Bridging the enzymatic kinetics of substrate intake and overall growth phenotypes, this work offers a mechanistic interpretation to the empirical Monod law, and demonstrates the potential of coupling local and global cellular constrains in predictive modelling.Melinjo seed extract (MSE) contains large amounts of polyphenols, including dimers of trans-resveratrol (e.g. gnetin C, L, gnemonoside A, B and D), and has been shown to potentially improve obesity. However, there is no clinical evidence regarding the anti-obesity effects of MSE, and its mechanisms are also unclear. We investigated the hypothesis that MSE supplementation increases the adiponectin (APN) multimerization via the up-regulation of disulfide bond A oxidoreductase-like protein (DsbA-L) under either or both physiological and obese conditions. To investigate the effect of MSE on the physiological condition, 42 healthy young volunteers were enrolled in a randomized, double-blind placebo-controlled clinical trial for 14 days. The participants were randomly assigned to the MSE 150 mg/day, MSE 300 mg/day or placebo groups. Furthermore, in order to investigate the effect of MSE on APN levels under obese conditions, we administered MSE powder (500 or 1000 mg/kg/day) to control-diet- or high-fat-diet (HFD)-fed C57BL/6 mice for 4 weeks. All participants completed the clinical trial. The administration of MSE 300 mg/day was associated with an increase in the ratio of HMW/total APN in relation to the genes regulating APN multimerization, including DsbA-L. Furthermore, this effect of MSE was more pronounced in carriers of the DsbA-L rs191776 G/T or T/T genotype than in others. In addition, the administration of MSE to HFD mice suppressed their metabolic abnormalities (i.e. weight gain, increased blood glucose level and fat mass accumulation) and increased the levels of total and HMW APN in serum and the mRNA levels of ADIPOQ and DsbA-L in adipose tissue. The present study suggests that MSE may exert beneficial effects via APN multimerization in relation to the induction of DsbA-L under both physiological and obese conditions.In recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong's subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. selleck As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability.
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