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The bench design and the DAS allowed measuring the outflow of GFs in different spreaders and demonstrated that this parameter was influenced by particle segregation. The segregation of GF1 was higher than that of GF2. The outflow variability at the speed of 1.11 m s-1 was lower, and the spreader with a fluted roller had the highest uniformity and was the most suitable for application with variable rates.Niemann-Pick disease type C is a rare, fatal neurodegenerative disorder characterized by massive intracellular accumulation of cholesterol. In most cases, loss-of-function mutations in the NPC1 gene that encodes lysosomal cholesterol transporter NPC1 are responsible for the disease, and more than half of the mutations are considered to interfere with the biogenesis or folding of the protein. We previously identified a series of oxysterol derivatives and phenanthridine-6-one derivatives as pharmacological chaperones, i.e., small molecules that can rescue folding-defective phenotypes of mutated NPC1, opening up an avenue to develop chaperone therapy for Niemann-Pick disease type C. Here, we present an improved image-based screen for NPC1 chaperones and we describe its application for drug-repurposing screening. We identified some azole antifungals, including itraconazole and posaconazole, and a kinase inhibitor, lapatinib, as probable pharmacological chaperones. find more A photo-crosslinking study confirmed direct binding of itraconazole to a representative folding-defective mutant protein, NPC1-I1061T. Competitive photo-crosslinking experiments suggested that oxysterol-based chaperones and itraconazole share the same or adjacent binding site(s), and the sensitivity of the crosslinking to P691S mutation in the sterol-sensing domain supports the hypothesis that their binding sites are located near this domain. Although the azoles were less effective in reducing cholesterol accumulation than the oxysterol-derived chaperones or an HDAC inhibitor, LBH-589, our findings should offer new starting points for medicinal chemistry efforts to develop better pharmacological chaperones for NPC1.Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.A widening evidence base across low- and middle-income countries (LMICs) points towards mutually reinforcing linkages between poverty and mental health problems. The use of validated and culturally relevant measures of mental health outcomes is crucial to the expansion of evidence. At present, there is a paucity of measures that have been tested and validated in contexts of extreme poverty. Using data from adult women living in extreme poverty in rural Haiti this study assesses the cross-cultural validity of the widely used Rosenberg Self-Esteem Scale (RSES) and its applicability in assessing linkages between poverty and mental health outcomes. We find no evidence for a one-dimensional 10-factor structure of the RSES within our data and agree with other authors that the standard self-esteem model does not fit well in this cultural context. Comparisons with another widely used measure of mental health-the K6 measure-indicate that the RSES cannot be used as a proxy for mental health outcomes. We conclude that the use of the RSES in different cultural contexts and with samples with different socioeconomic characteristics should be undertaken with caution; and that greater consideration of the validity of psychosocial constructs and their measurement is vital for gaining robust and replicable insights into breaking the cycle between poverty and mental health problems.Humans are notoriously bad at understanding probabilities, exhibiting a host of biases and distortions that are context dependent. This has serious consequences on how we assess risks and make decisions. Several theories have been developed to replace the normative rational expectation theory at the foundation of economics. These approaches essentially assume that (subjective) probabilities weight multiplicatively the utilities of the alternatives offered to the decision maker, although evidence suggest that probability weights and utilities are often not separable in the mind of the decision maker. In this context, we introduce a simple and efficient framework on how to describe the inherently probabilistic human decision-making process, based on a representation of the deliberation activity leading to a choice through stochastic processes, the simplest of which is a random walk. Our model leads naturally to the hypothesis that probabilities and utilities are entangled dual characteristics of the real human decision making process.
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