Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences

Comprehensive structural model of the mechanochemical cycle of a mitotic motor highlights molecular adaptations in the kinesin family

Kinesins are responsible for a wide variety of microtubule-based, ATP-dependent functions. Their motor domain drives these activities but the molecular adaptations that specify these diverse and essential cellular activities are poorly understood. It has been assumed that the first identified kinesin - the transport motor kinesin-1 – is the mechanistic paradigm for the entire superfamily, but accumulating evidence suggests that this is not the case. To address the deficits in our understanding of the molecular basis of functional divergence within the kinesin superfamily, we studied kinesin-5s, which are essential mitotic motors whose inhibition blocks cell division. Using cryo-electron microscopy and subnanometer resolution structure determination, we have visualised conformations of microtubule-bound human kinesin-5 motor domain at successive steps in its ATPase cycle. Following ATP hydrolysis, nucleotide-dependent conformational changes in the active site are allosterically propagated into rotations of the motor domain and uncurling of the drugbinding loop L5. In addition, the mechanical neck-linker element that is crucial for motor stepping undergoes discrete, ordered displacements. We also observed large reorientations of the motor N-terminus that indicate its importance for kinesin-5 function through control of neck-linker conformation. A kinesin-5 mutant lacking this N-terminus is enzymatically active, and ATP-dependent neck-linker movement and motility is defective although not ablated. All these aspects of kinesin-5 mechanochemistry are distinct from kinesin-1. Our findings directly demonstrate the regulatory role of the kinesin-5 N-terminus in collaboration with the motor’s structured neck-linker, and highlight the multiple adaptations within kinesin motor domains that tune their mechanochemistries according to distinct functional requirements

Secretory vesicles are preferentially targeted to areas of low molecular SNARE density

Intercellular communication is commonly mediated by the regulated fusion, or exocytosis, of vesicles with the cell surface. SNARE (soluble N-ethymaleimide sensitive factor attachment protein receptor) proteins are the catalytic core of the secretory machinery, driving vesicle and plasma membrane merger. Plasma membrane SNAREs (tSNAREs) are proposed to reside in dense clusters containing many molecules, thus providing a concentrated reservoir to promote membrane fusion. However, biophysical experiments suggest that a small number of SNAREs are sufficient to drive a single fusion event. Here we show, using molecular imaging, that the majority of tSNARE molecules are spatially separated from secretory vesicles. Furthermore, the motilities of the individual tSNAREs are constrained in membrane micro-domains, maintaining a non-random molecular distribution and limiting the maximum number of molecules encountered by secretory vesicles. Together our results provide a new model for the molecular mechanism of regulated exocytosis and demonstrate the exquisite organization of the plasma membrane at the level of individual molecular machines

Low-energy cross section of the 7Be(p,g)8B solar fusion reaction from Coulomb dissociation of 8B

Final results from an exclusive measurement of the Coulomb breakup of 8B into 7Be+p at 254 A MeV are reported. Energy-differential Coulomb-breakup cross sections are analyzed using a potential model of 8B and first-order perturbation theory. The deduced astrophysical S_17 factors are in good agreement with the most recent direct 7Be(p,gamma)8B measurements and follow closely the energy dependence predicted by the cluster-model description of 8B by Descouvemont. We extract a zero-energy S_17 factor of 20.6 +- 0.8 (stat) +- 1.2 (syst) eV b.Comment: 14 pages including 16 figures, LaTeX, accepted for publication in Physical Review C. Minor changes in text and layou