Methods: A method of the pulsation for a pVAD is proposed (AP-pVAD Model). AP-pVAD Model consists of two parts: NPQ Model and LSTM-Transformer Model. (1)The NPQ Model determines the mathematical relationship between motor speed, pressure, and flow rate for the pVAD. (2)The Attention module of Transformer neural network is integrated into the LSTM neural network to form the new LSTM-Transformer Model to predict the pulsation time characteristic points for adjusting the motor speed of the pVAD. Results: The AP-pVAD Model is validated in three hydraulic experiments and an animal experiment. (1)The pressure provided by pVAD calculated with the NPQ Model has a maximum error of only 2.15 mmHg compared to the expected values. (2)The pulsation time characteristic points predicted by the LSTM-Transformer Model shows a maximum prediction error of 1.78ms, which is significantly lower than other methods. (3)The in-vivo test of pVAD in animal experiment has significant improvements in aortic pressure. Animals survive for over 27 hours after the initiation of pVAD operation. Conclusion: (1)For a given pVAD, motor speed has a linear relationship with pressure and a quadratic relationship with flow. (2)Deep learning can be used to predict pulsation characteristic time points, with the LSTM-Transformer Model demonstrating minimal prediction error and better robust performance under conditions of limited dataset sizes, elevated noise levels, and diverse hyperparameter combinations, demonstrating its feasibility and effectiveness.

Lawmakers have slammed the Food and Drug Administration for ignoring'troubling evidence' of Elon Musk's Neuralink practices and pushing the brain chip to human trials. Rep. Earl Blumenauer (D-Oregon) penned a letter to the FDA, criticizing the agency for not expecting the company's long list of animal abuse allegations that span back to at least 2019. The Democrat cited 2022 reports that described employees' complaints of'hack jobs' of animal experiments due to a rushed schedule, causing needless suffering and deaths. The open letter also stated'these alleged failures to follow standard operating procedures potentially endangered animal welfare and compromised data collection for human trials.' Blumenauer is now demanding the FDA explain how it reconciled reports of such lapses with its decision to authorize Neuralink's human trial.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.

Snakes can traverse almost all types of environments by bending their elongate bodies in 3-D to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes traverse natural environments, but also allow snake robots to achieve similar performance. How snakes and snake robots move on flat surfaces has been understood well. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability in 3-D terrain, except for some studies on arboreal snake locomotion and on robots using geometric planning. To bridge the knowledge gap, we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with the amplitude of vertical bending. The instability can be reduced by body compliance that statistically improves body-terrain contact. Despite this, vertical body bending can potentially allow snakes to push against terrain for propulsion, as demonstrated by corn snakes traversing an uneven arena. A snake robot can generate large propulsion like this if contact is well maintained. Contact feedback control can help accommodate perturbations such as novel terrain geometry or excessive external forces by improving contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the 3-D world stably.

Research by neuroscientists at the University of Chicago shows how short-term, working memory uses networks of neurons differently depending on the complexity of the task at hand. The researchers used modern artificial intelligence (AI) techniques to train computational neural networks to solve a range of complex behavioral tasks that required storing information in short term memory. The AI networks were based on the biological structure of the brain and revealed two distinct processes involved in short-term memory. One, a "silent" process where the brain stores short-term memories without ongoing neural activity, and a second, more active process where circuits of neurons fire continuously. The study, led by Nicholas Masse, PhD, a senior scientist at UChicago, and senior author David Freedman, PhD, professor of neurobiology, was published this week in Nature Neuroscience.

Personalized medicine targets the customization of treatment strategies to patients' individual characteristics. Here we consider the problem of optimizing personalized pharmacological treatment strategies for cancer. We focus primarily on developing effective strategies to collect the data necessary for the construction of personalized treatments. We formulate this problem as a contextual bandit and present a new algorithm based on repeated sub-sampling for robust data collection in this framework. We present a case study showing experiments on a simulation setting, built from real data collected in a previous animal experiments. Promising results in this case study have since lead us to deploy this strategy in a partner wet lab to allocate treatments for the next phase of animal experiments.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.