A user-friendly system to measure electromyographic activity of dancers

A data acquisition system aided by virtual instrumentation was developed to measure electromyographic activity of the dancers. The system is composed of three main components: (1) Analog front-end with signal conditioning, (2) USB serial interface based A/D conversion, and (3) virtual instrumentation designed in LabView tools. The proposed system is able to accurately collect the magnitude of jump force and displayed the data using virtual instruments with alarm functions. The signal path is well-conditioned and processed, which makes the device suitable for feasibility studies for future research

Magnetic Resonance imaging (MRI) in detection of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeled with superparamagnetic iron oxide (SPIO) nanoparticle

*Purpose:* To investigate the MR imaging of _Bifidobacterium longum_ and _Clostridium novyi-NT_ labeling with superparamagnetic iron oxide (SPIO) nanoparticles.

*Materials and methods:* Tubes containing _B. longum_-SPIO, Free-SPIO, _B. longum_ and PYG Medium were incubated under anaerobic condition in _in vitro_ experiment. Transmission electron microscope and Prussian blue staining were used to demonstrate intra-bacteria nanoparticles. R~2~^*^ mapping and R~2~ mapping were reconstructed after MR scanning. _B. longum_-SPIO and _C. novyi_-NT-SPIO were injected respectively _in vivo_ to show whether it might be traced by MR imaging.

*Results:* Magnetosomes in bacteria were observed by electron microscopic and stained by Prussian blue staining. At the same concentration of SPIOs, the R~2~^*^ value of _B. longum_-SPIO was significantly higher than that of Free-SPIO (P<0.001), however, the R~2~ value was lower comparing with Free-SPIO (P<0.001). After injection with _B. longum_-SPIO, they could present in tumor and shorten T~2~^*^.

*Conclusion:* _B. longum_ and _C. novyi_-NT could be labeled by SPIO and then traced by MRI

Linear active disturbance rejection control of waste heat recovery systems with organic Rankine cycles

In this paper, a linear active disturbance rejection controller is proposed for a waste heat recovery system using an organic Rankine cycle process, whose model is obtained by applying the system identification technique. The disturbances imposed on the waste heat recovery system are estimated through an extended linear state observer and then compensated by a linear feedback control strategy. The proposed control strategy is applied to a 100 kW waste heat recovery system to handle the power demand variations of grid and process disturbances. The effectiveness of this controller is verified via a simulation study, and the results demonstrate that the proposed strategy can provide satisfactory tracking performance and disturbance rejection

Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling

This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well