Notes

Silencing of METTL3 efficiently slows down attack along with metastasis of prostate cancer tissues.
Objective.Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality.Approach.Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI).Main results.Throughout the study, continuous prosthesis usage increased (1% per week,p less then 0.001) and functional metrics improved up to 26% on control assessments and 76% on perceived workload evaluations. We observed increases in torque loading on the OI implant (up to 12.5% every month,p less then 0.001) and prosthesis control performance (0.5% every month,p less then 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (p less then 0.001), over time without degrading performance, demonstrating improved control efficiency with a machine learning-based myoelectric pattern recognition algorithm. The participant controlled the prosthesis up to one month without updating the pattern recognition algorithm. The participant customized prosthesis movements to perform specific tasks, such as individual finger control for piano playing and hand gestures for communication, which likely contributed to continued usage.Significance.This work demonstrates, in a single participant, the functional benefit of unconstrained use of a highly anthropomorphic prosthetic limb over an extended period. While hurdles remain for widespread use, including device reliability, results replication, and technical maturity beyond a prototype, this study offers insight as an example of the impact of advanced prosthesis technology for rehabilitation outside the laboratory.A novel co-spray method was proposed to fabricate a reduced graphene oxide (rGO)-poly (3-hexylthiophene) (P3HT) hybrid sensing device utilizing immiscible solution for ammonia detection at room temperature. The spectrum and Scanning Electron Microscopy (SEM) results revealed uniformly crimped morphology and favorable π-π interaction for the hybrid film. The hybrid film-based sensor showed obviously enhanced ammonia sensing performance, such as increased response, reduced response time, and reinforced sensitivity, in comparison to bare rGO, P3HT, and traditional rGO/P3HT layered film-based sensors, which could be attributed to an adsorption energy barrier and the p-n heterojunction effect. The synergetic strengthened sensing mechanism is discussed. Meanwhile, recovery ratio was introduced to evaluate the abnormal baseline drift induced high-response behavior. The excellent sensing properties of the hybrid sensor indicate that the co-spray method could be an alternative process for the preparation of hetero-affinity hybrid films or functional devices.Interpenetrated polymer network microgels, composed of crosslinked networks of poly(N-isopropylacrylamide) and polyacrylic acid (PAAc), have been investigated through rheological measurements at four different amounts of PAAc. Chidamide in vivo Both PAAc content and crosslinking degree modify particle dimensions, mass and softness, thereby strongly affecting the volume fraction and the system viscosity. Here the volume fraction is derived from the flow curves at low concentrations by fitting the zero-shear viscosity with the Einstein-Batchelor equation which provides a parameterkto shift weight concentration to volume fraction. We find that particles with higher PAAc content and crosslinker are characterized by a greater value ofkand therefore by larger volume fractions when compared to softer particles. The packing fractions obtained from rheological measurements are compared with those from static light scattering for two PAAc contents revealing a good agreement. Moreover, the behaviour of the viscosity as a function of packing fraction, at room temperature, has highlighted an Arrhenius dependence for microgels synthesized with low PAAc content and a Vogel-Fulcher-Tammann dependence for the highest investigated PAAc concentration. A comparison with the hard spheres behaviour indicates a steepest increase of the viscosity with decreasing particles softness. Finally, the volume fraction dependence of the viscosity at a fixed PAAc and at two different temperatures, below and above the volume phase transition, shows a quantitative agreement with the structural relaxation time measured through dynamic light scattering indicating that interpenetrated polymer network microgels softness can be tuned with PAAc and temperature and that, depending on particle softness, two different routes are followed.
Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery.

We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.

We first tested our solution using realistic Monte-Carlo simulations. Then, w solution offers a good decoder and a tool for investigating motor control neural mechanisms.
We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
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