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Karlsson Herndon posted an update 6 months ago
With the aid of the learned high-quality parameters, MTs can independently extract semantically aligned part-level features from relevant channels in the testing stage. Systematic experiments on four large-scale ReID databases demonstrate that MPN consistently outperforms state-of-the-art approaches by significant margins.Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular diseases. However, deep learning models that are commonly used and trained in end-to-end fashion are not able to provide good interpretability. In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative knowledge representation. Our approach first employs an AutoEncoder to encode electrocardiogram signals into two parts hand-encoding knowledge and machine-encoding knowledge. A classifier then takes as input the encoded knowledge to classify arrhythmia heartbeats with or without human in the loop (HIL). Experiments and evaluation on the MIT-BIH Arrhythmia Database demonstrate that our new approach not only can effectively classify arrhythmia while offering interpretability, but also can improve the classification accuracy by adjusting the hand-encoding knowledge with our HIL mechanism.
A transcranial magnetic stimulation system with programmable stimulus pulses and patterns is presented. The stimulus pulses of the implemented system expand beyond conventional damped cosine or near-rectangular pulses and approach an arbitrary waveform.
The desired stimulus waveform shape is defined as a reference signal. This signal controls the semiconductor switches of an H-bridge inverter to generate a high-power imitation of the reference. The design uses a new paradigm for TMS, applying pulse-width modulation with a non-resonant, high-frequency switching architecture to synthesize waveforms that leverages the low-pass filtering properties of neuronal cells. The modulation technique enables control of the waveform, frequency, pattern, and intensity of the stimulus.
A system prototype was developed to demonstrate the technique. The experimental measurements demonstrate that the system is capable of generating stimuli up to 4 kHz with peak voltage and current values of ±1000 V and ±3600 A, respectively. The maximum transferred energy measured in the experimental validation was 100.4 Joules. To characterize repetitive TMS modalities, the efficiency of generating consecutive pulse triplets and quadruplets with interstimulus intervals of 1 ms was tested and verified.
The implemented TMS device can generate consecutive rectangular pulses with a predetermined time interval, widths and polarities, enables the synthesis of a wide range of magnetic stimuli.
New waveforms promise functional advantages over the waveforms generated by current-generation TMS systems for clinical neuroscience research.
New waveforms promise functional advantages over the waveforms generated by current-generation TMS systems for clinical neuroscience research.Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarker immediately after treatment from contrast-enhanced MRI. The NPV has variable accuracy, and the use of contrast agent prevents continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model. A deep convolutional neural network was trained on noncontrast multiparametric MR images using the NPV biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue. A novel volume-conserving registration algorithm yielded a voxel-wise correlation between treatment and follow-up NPV, providing a rigorous validation of the biomarker. VY-3-135 price The learned noncontrast multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, substantially outperforming the current clinical standard (DICE coefficient = 0.53). Noncontrast multiparametric MR imaging integrated with a deep convolutional neural network provides a more accurate prediction of MRgFUS treatment outcome than current contrast-based techniques.In single-port access surgeries, robot size is crucial due to the limited workspace. Thus, a robot may be designed under-actuated. Suturing, in contrast, is a complicated task and requires full actuation. This study aims to overcome this shortcoming by implementing an optimization-based algorithm for autonomous suturing for an under-actuated robot. The proposed algorithm approximates the ideal suturing trajectory by slightly reorienting the needle while deviating as little as possible from the ideal, full degree-of-freedom suturing case. The deviation of the path taken by a custom robot with respect to the ideal trajectory varies depending on the suturing starting location within the workspace as well as the needle size. A quantitative analysis reveals that in 13% of the investigated workspace, the accumulative deviation was less than 10 mm. In the remaining workspace, the accumulative deviation was less than 30 mm. Likewise, the accumulative deviation of a needle with a radius of 10 mm was 2.2 mm as opposed to 8 mm when the radius was 20 mm. The optimization-based algorithm maximized the accuracy of a four-DOF robot to perform a path-constrained trajectory and illustrates the accuracy-workspace correlation.Presents a missing funding acknowledgment for Dr. Mihail Popescu in the above named paper.Presents a corrected version of Table A-II in the above named paper.Centrioles are characterized by a nine-fold arrangement of microtubule triplets held together by an inner protein scaffold. These structurally robust organelles experience strenuous cellular processes such as cell division or ciliary beating while performing their function. However, the molecular mechanisms underlying the stability of microtubule triplets, as well as centriole architectural integrity remain poorly understood. Here, using ultrastructure expansion microscopy for nanoscale protein mapping, we reveal that POC16 and its human homolog WDR90 are components of the microtubule wall along the central core region of the centriole. We further found that WDR90 is an evolutionary microtubule associated protein. Finally, we demonstrate that WDR90 depletion impairs the localization of inner scaffold components, leading to centriole structural abnormalities in human cells. Altogether, this work highlights that WDR90 is an evolutionary conserved molecular player participating in centriole architecture integrity.
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