Publications by authors named "N Boccardo"

In recent years, the rapid alkalinization factor (RALF) family of cysteine-rich peptides has been reported to be crucial for several plant signaling mechanisms, including cell growth, plant immunity and fertilization. RALF4 and RALF19 (RALF4/19) pollen peptides redundantly regulate the pollen tube integrity and growth through binding to their receptors ANXUR1/2 (ANX1/2) and Buddha's Paper Seal 1 and 2 (BUPS1/2), members of the RLK1-like (CrRLK1L) family, and, thus, are essential for plant fertilization. However, the signaling mechanisms at the cellular level that follow these binding events remain unclear.

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Background: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions.

Methods: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively.

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We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes.

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Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change.

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The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches.

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