The Alive Particle Filter

In the following article we develop a particle filter for approximating Feynman-Kac models with indicator potentials. Examples of such models include approximate Bayesian computation (ABC) posteriors associated with hidden Markov models (HMMs) or rare-event problems. Such models require the use of advanced particle filter or Markov chain Monte Carlo (MCMC) algorithms e.g. Jasra et al. (2012), to perform estimation. One of the drawbacks of existing particle filters, is that they may 'collapse', in that the algorithm may terminate early, due to the indicator potentials. In this article, using a special case of the locally adaptive particle filter in Lee et al. (2013), which is closely related to Le Gland & Oudjane (2004), we use an algorithm which can deal with this latter problem, whilst introducing a random cost per-time step. This algorithm is investigated from a theoretical perspective and several results are given which help to validate the algorithms and to provide guidelines for their implementation. In addition, we show how this algorithm can be used within MCMC, using particle MCMC (Andrieu et al. 2010). Numerical examples are presented for ABC approximations of HMMs

A number-based inventory of size-resolved black carbon particle emissions by global civil aviation

With the rapidly growing global air traffic, the impacts of the black carbon (BC) in the aviation exhaust on climate, environment and public health are likely rising. The particle number and size distribution are crucial metrics for toxicological analysis and aerosol-cloud interactions. Here, a size-resolved BC particle number emission inventory was developed for the global civil aviation. The BC particle number emission is approximately (10.9 ± 2.1) × 1025 per year with an average emission index of (6.06 ± 1.18) × 1014 per kg of burned fuel, which is about 1.3% of the total ground anthropogenic emissions, and 3.6% of the road transport emission. The global aviation emitted BC particles follow a lognormal distribution with a geometric mean diameter (GMD) of 31.99 ± 0.8 nm and a geometric standard deviation (GSD) of 1.85 ± 0.016. The variabilities of GMDs and GSDs for all flights are about 4.8 and 0.08 nm, respectively. The inventory provides new data for assessing the aviation impacts. Size-resolved Black Carbon (BC) particle number emission inventory is not available for global civil aviation. Here the authors converted BC mass emission inventory into number emission inventory and found that aviation BC number emission contributes to 1.3% of total ground anthropogenic emissions and 3.6% on global average. Document type: Articl

EFFECTS OF SHOD AND BAREFOOT CONDITIONS ON MEDIAL LONGITUDINAL ARCH ANGLE DURING RUNNING

The structure of the medial longitudinal arch (MLA) affects the spring-like function of the foot and is crucial to running performance. The purpose of this study was to investigate the differences in the MLA angle between barefoot and shod conditions by using a high-speed dual fluoroscopic imaging system (DFIS). Computed tomography was taken of each participant’s right foot for the construction of 3D models and local coordinate systems. Fifteen participants ran with or without running shoes at 3 m/s±5% speed. We recorded foot kinematics using DFIS. After the process of 3D-2D registration, MLA angles were calculated. Compared to barefoot, wearing shoes 1) decreased the initial landing MLA angle, maximum MLA angle and range of motion of the MLA angle (p \u3c 0.05); 2) decreased the MLA angles during 0%-70% of the stance phase (p \u3c 0.05). It suggests that shoes limit the MLA compression and recoil and its spring-like function

EFFECTS OF SHOD AND BAREFOOT RUNNING ON THE IN VIVO KINEMATICS OF THE FIRST METATARSOPHALANGEAL JOINT

The purpose of this study is to investigate the differences of the first metatarsophalangeal joint’s 6 degree-of-freedom (6DOF) kinematics during shod and barefoot conditions by using a high-speed dual fluoroscopic imaging system (DFIS). Fifteen healthy male runners were recruited. Computed tomography (CT) scans were taken of each participant’s right foot for the construction of 3D models and local coordinate system. The fluoroscopic images of the right foot during the stance period were acquired under shod and barefoot condition with rearfoot strike pattern Radiographic images were acquired at 100 Hz while the participants ran at a speed of 3±5% m/s in a track and 6DOF kinematics were calculated by 2D-3D registration. Paired sample t-test was used to compare the kinematic characteristics of the first MTPJ 6DOF kinematics between shod and barefoot. Compared with barefoot, wearing shoes 1) decreased the peak medial, posterior, and superior translation of the first MTPJ during stance (P ＜ 0.05); 2) decreased maximum extension angle, minimum extension angle, and flexion/extension range of motion of the first MTPJ during stance (P ＜ 0.05); 3) increased minimum adduction angle of the first MTPJ during stance (P ＜ 0.05). It suggests that shoes may affect the function of the first MTPJ and increase the risk of hallux valgus. Our study makes up for the deficiency of traditional motion measurement methods that only focus on the sagittal flexion and extension movement of the first MTPJ and provides a more comprehensive understanding of the potential relationship between joint motion and injurie

DiNuP: a systematic approach to identify regions of differential nucleosome positioning

Motivation: With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available. Results:: We describe a novel computational approach, DiNuP, that compares nucleosome profiles generated by high-throughput sequencing under various conditions. DiNuP provides a statistical P-value for each identified RDNP based on the difference of read distributions. DiNuP also empirically estimates the false discovery rate as a cutoff when two samples have different sequencing depths and differentiate reliable RDNPs from the background noise. Evaluation of DiNuP showed it to be both sensitive and specific for the detection of changes in nucleosome location, occupancy and fuzziness. RDNPs that were identified using publicly available datasets revealed that nucleosome positioning dynamics are closely related to the epigenetic regulation of transcription. Availability and implementation: DiNuP is implemented in Python and is freely available at http://www.tongji.edu.cn/~zhanglab/DiNuP