Measurement of Lead Localization Accuracy Based on Magnetic Resonance Imaging

Post-implantation localization of deep brain stimulation (DBS) lead based on a magnetic resonance (MR) image is widely used. Existing localization methods use artifact center method or template registration method, which may lead to a considerable deviation of > 2 mm, and result in severe side effects or even surgical failure. Accurate measurement of lead position can instantly inform surgeons of the imprecise implantation. This study aimed to identify the influencing factors in DBS lead post-implantation localization approach, analyze their influence, and describe a localization approach that uses the individual template method to reduce the deviation. We verified that reconstructing direction should be parallel or perpendicular to lead direction, instead of the magnetic field. Besides, we used simplified relationship between magnetic field angle and deviation error to correct the localization results. The mean localization error can be reduced after correction and favors the feasibility of direct localization of DBS lead using MR images. We also discussed influence of in vivo noise on localization frequency and the possibility of using only MR images to localize the contacts.

A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

Traditional two-step passive localization methods need to extract the parameters like the direction of arrival (DOA), time of arrival (TOA), and time difference of arrival (TDOA) from the original data to determine the source position, which causes the poor positioning accuracy due to error accumulation. In this paper, a direct position determination (DPD) method is proposed to improve the positioning accuracy and robustness, which is based on a correlation algorithm. Firstly, the cost function directly related to the location of the source can be established by synthesizing the data received by multiantenna in the frequency domain. Then, the position of the source is estimated by the correlation DPD method to search the monitoring area. Compared to the improved TDOA algorithm and Least Squares DPD algorithm, the proposed method shows better localization accuracy of different SNRs. Finally, based on real measured data, it can be seen that the results of the proposed algorithm are better than the improved TDOA algorithm.

Non-Cooperative Passive Direct Localization Based on Waveform Estimation

This paper considers a non-cooperative passive localization system wherein widely distributed receivers are used to localize a transmitter radiating a periodical pulse pair signal. Two possible pulse modulation models, noncoherent and coherent pulses, are fully considered for practical application, and are effectively unified as a general model for the algorithm design. To achieve highly accurate and robust localization performance, an enhanced direct position determination (DPD) algorithm based on waveform estimation (WE) is devised to jointly estimate the transmitter position and the waveform profile. The optimal objective function based on a least square (LS) principle is first derived to directly determine the position of the transmitter. Due to the complete lack of knowledge on the transmitted signal, the processing center calculates the objective function at each searched grid of interest by using estimated pulses instead of the real ones, while extraction of pulse samples and estimation of waveform are executed. Theoretical derivation gives the solution to estimate the non-parameterized waveform with a structure of maximum Rayleigh quotient. Additionally, simulation results verify the effectiveness of the proposed algorithm for many common waveform types in the cases of transmitting noncoherent and coherent pulses, and also show the excellent advantage over the classical DPD algorithm at low signal-to-noise ratio (SNR).

Motifs of the C-terminal Domain of MCM9 Direct Localization to Sites of Mitomycin-C Damage for RAD51 Recruitment

The MCM8/9 complex is implicated in aiding fork progression and facilitating homologous recombination (HR) in response to several DNA damage agents. MCM9 itself is an outlier within the MCM family containing a long C-terminal extension (CTE) comprising 42% of the total length, but with no known functional components and high predicted disorder. In this report, we identify and characterize two unique motifs within the primarily unstructured CTE that are required for localization of MCM8/9 to sites of mitomycin C (MMC) induced DNA damage. First, an unconventional ‘bipartite-like’ nuclear localization (NLS) motif consisting of two positively charged amino acid stretches separated by a long intervening sequence is required for the nuclear import of both MCM8 and MCM9. Second, a variant of the BRC motif (BRCv), similar to that found in other HR helicases, is necessary for localization to sites of MMC damage. The MCM9-BRCv directly interacts with and recruits RAD51 downstream to MMC-induced damage to aid in DNA repair. Patient lymphocytes devoid of functional MCM9 and discrete MCM9 knockout cells have a significantly impaired ability to form RAD51 foci after MMC treatment. Therefore, the disordered CTE in MCM9 is functionally important in promoting MCM8/9 activity and in recruiting downstream interactors; thus, requiring full length MCM9 for proper DNA repair.

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.