CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.Comment: Under revision in Computer Communication Journa

Propofol inhibits the voltage-gated sodium channel NaChBac at multiple sites.

Voltage-gated sodium (NaV) channels are important targets of general anesthetics, including the intravenous anesthetic propofol. Electrophysiology studies on the prokaryotic NaV channel NaChBac have demonstrated that propofol promotes channel activation and accelerates activation-coupled inactivation, but the molecular mechanisms of these effects are unclear. Here, guided by computational docking and molecular dynamics simulations, we predict several propofol-binding sites in NaChBac. We then strategically place small fluorinated probes at these putative binding sites and experimentally quantify the interaction strengths with a fluorinated propofol analogue, 4-fluoropropofol. In vitro and in vivo measurements show that 4-fluoropropofol and propofol have similar effects on NaChBac function and nearly identical anesthetizing effects on tadpole mobility. Using quantitative analysis by 19F-NMR saturation transfer difference spectroscopy, we reveal strong intermolecular cross-relaxation rate constants between 4-fluoropropofol and four different regions of NaChBac, including the activation gate and selectivity filter in the pore, the voltage sensing domain, and the S4-S5 linker. Unlike volatile anesthetics, 4-fluoropropofol does not bind to the extracellular interface of the pore domain. Collectively, our results show that propofol inhibits NaChBac at multiple sites, likely with distinct modes of action. This study provides a molecular basis for understanding the net inhibitory action of propofol on NaV channels. © 2018 Wang et al

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN