In Vitro and In Vivo Studies Identify Important Features of Dengue Virus pr-E Protein Interactions

Flaviviruses bud into the endoplasmic reticulum and are transported through the secretory pathway, where the mildly acidic environment triggers particle rearrangement and allows furin processing of the prM protein to pr and M. The peripheral pr peptide remains bound to virus at low pH and inhibits virus-membrane interaction. Upon exocytosis, the release of pr at neutral pH completes virus maturation to an infectious particle. Together this evidence suggests that pr may shield the flavivirus fusion protein E from the low pH environment of the exocytic pathway. Here we developed an in vitro system to reconstitute the interaction of dengue virus (DENV) pr with soluble truncated E proteins. At low pH recombinant pr bound to both monomeric and dimeric forms of E and blocked their membrane insertion. Exogenous pr interacted with mature infectious DENV and specifically inhibited virus fusion and infection. Alanine substitution of E H244, a highly conserved histidine residue in the pr-E interface, blocked pr-E interaction and reduced release of DENV virus-like particles. Folding, membrane insertion and trimerization of the H244A mutant E protein were preserved, and particle release could be partially rescued by neutralization of the low pH of the secretory pathway. Thus, pr acts to silence flavivirus fusion activity during virus secretion, and this function can be separated from the chaperone activity of prM. The sequence conservation of key residues involved in the flavivirus pr-E interaction suggests that this protein-protein interface may be a useful target for broad-spectrum inhibitors

The Domain I-Domain III Linker Plays an Important Role in the Fusogenic Conformational Change of the Alphavirus Membrane Fusion Protein▿

The alphavirus Semliki Forest virus (SFV) infects cells through a low-pH-dependent membrane fusion reaction mediated by the virus fusion protein E1. Acidic pH initiates a series of E1 conformational changes that culminate in membrane fusion and include dissociation of the E1/E2 heterodimer, insertion of the E1 fusion loop into the target membrane, and refolding of E1 to a stable trimeric hairpin conformation. A highly conserved histidine (H3) on the E1 protein was previously shown to promote low-pH-dependent E1 refolding. An SFV mutant with an alanine substitution at this position (H3A) has a lower pH threshold and reduced efficiency of virus fusion and E1 trimer formation than wild-type SFV. Here we addressed the mechanism by which H3 promotes E1 refolding and membrane fusion. We identified E1 mutations that rescue the H3A defect. These revertants implicated a network of interactions that connect the domain I-domain III (DI-DIII) linker region with the E1 core trimer, including H3. In support of the importance of these interactions, mutation of residues in the network resulted in more acidic pH thresholds and reduced efficiencies of membrane fusion. In vitro studies of truncated E1 proteins demonstrated that the DI-DIII linker was required for production of a stable E1 core trimer on target membranes. Together, our results suggest a critical and previously unidentified role for the DI-DIII linker region during the low-pH-dependent refolding of E1 that drives membrane fusion

ToTCompute: A Novel EEG-Based TimeOnTask Threshold Computation Mechanism for Engagement Modelling and Monitoring

Engagement influences participation, progression and retention in game-based e-learning (GBeL). Therefore, GBeL systems should engage the players in order to support them to maximize their learning outcomes, and provide the players with adequate feedback to maintain their motivation. Innovative engagement monitoring solutions based on players’ behaviour are needed to enable engagement monitoring in a non-disturbing way, without interrupting the game-play and game flow. Furthermore, generic metrics and automatic mechanisms for their engagement monitoring and modelling are needed. One important metric that was used for engagement modelling is TimeOnTask, which represents the duration of time required by the player to complete a task. This paper proposes ToTCompute (TimeOnTask Threshold Computation), a novel mechanism that automatically computes - in a task-dependent manner - TimeOnTask threshold values after which student engagement decreases with a given percentage from his initial level of engagement (e.g., after 2 min student engagement will fall with 10 % from his initial level). In this way the mechanism enables engagement modelling at a higher granularity and further enables engagement-based adaptation in GBeL systems. ToTCompute makes use of game-playing information and EEG signals collected through an initial testing session. The results of an experimental case study have shown that ToTCompute can be used to automatically compute threshold values for the TimeOnTask generic engagement metric, which explains up to 76.2 % of the variance in engagement change. Furthermore, the results confirmed the usefulness of the mechanism as the TimeOnTask threshold value is highly task-dependent, and setting its value manually for multiple game tasks would be a laborious process

Emotional states detection approaches based on physiological signals for healthcare applications: A review

Mood disorders, anxiety, depression, and stress affect people’s quality of life and increase the vulnerability to diseases and infections. Depression, e.g., can carry undesirable consequences such as death. Hence, emotional states detection approaches using wearable technology are gaining interest in the last few years. Emerging wearable devices allow monitoring different physiological signals in order to extract useful information about people’s health status and provide feedback about their health condition. Wearable applications include e.g., patient monitoring, stress detection, fitness monitoring, wellness monitoring, and assisted living for elderly people, to name a few. This increased interests in wearable applications have allowed the development of new approaches to assist people in everyday activities and emergencies that can be incorporated into the smart city concept. Accurate emotional state detection approaches will allow an effective assistance, thus improving people’s quality of life and well-being. With these issues in mind, this chapter discusses existing emotional states’ approaches using machine and/or deep learning techniques, the most commonly used physiological signals in these approaches, existing physiological databases for emotion recognition, and highlights challenges and future research directions in this field. © Springer Nature Switzerland AG 2020

Kinesins and cancer

Kinesins are a family of molecular motors that travel unidirectionally along microtubule tracks to fulfil their many roles in intracellular transport or cell division. Over the past few years kinesins that are involved in mitosis have emerged as potential targets for cancer drug development. Several compounds that inhibit two mitotic kinesins (EG5 (also known as KIF11) and centromere-associated protein E (CENPE)) have entered Phase I and II clinical trials either as monotherapies or in combination with other drugs. Additional mitotic kinesins are currently being validated as drug targets, raising the possibility that the range of kinesin-based drug targets may expand in the future