Plexin D1 negatively regulates macrophage-derived foam cell migration via the focal adhesion kinase/Paxillin pathway.

BACKGROUND: Macrophage-derived foam cell formation is a hallmark of atherosclerosis and is retained during plaque formation. Strategies to inhibit the accumulation of these cells hold promise as viable options for treating atherosclerosis. Plexin D1 (PLXND1), a member of the Plexin family, has elevated expression in atherosclerotic plaques and correlates with cell migration; however, its role in macrophages remains unclear. We hypothesize that the guidance receptor PLXND1 negatively regulating macrophage mobility to promote the progression of atherosclerosis. METHODS: We utilized a mouse model of atherosclerosis based on a high-fat diet and an ox-LDL- induced foam cell model to assess PLXND1 levels and their impact on cell migration. Through western blotting, Transwell assays, and immunofluorescence staining, we explored the potential mechanism by which PLXND1 mediates foam cell motility in atherosclerosis. RESULTS: Our study identifies a critical role for PLXND1 in atherosclerosis plaques and in a low-migration capacity foam cell model induced by ox-LDL. In the aortic sinus plaques of ApoE-/- mice, immunofluorescence staining revealed significant upregulation of PLXND1 and Sema3E, with colocalization in macrophages. In macrophages treated with ox-LDL, increased expression of PLXND1 led to reduced pseudopodia formation and decreased migratory capacity. PLXND1 is involved in regulating macrophage migration by modulating the phosphorylation levels of FAK/Paxillin and downstream CDC42/PAK. Additionally, FAK inhibitors counteract the ox-LDL-induced migration suppression by modulating the phosphorylation states of FAK, Paxillin and their downstream effectors CDC42 and PAK. CONCLUSION: Our findings indicate that PLXND1 plays a role in regulating macrophage migration by modulating the phosphorylation levels of FAK/Paxillin and downstream CDC42/PAK to promoting atherosclerosis.

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer.

Since labeled samples are typically scarce in real-world scenarios, self-supervised representation learning in time series is critical. Existing approaches mainly employ the contrastive learning framework, which automatically learns to understand similar and dissimilar data pairs. However, they are constrained by the request for cumbersome sampling policies and prior knowledge of constructing pairs. Also, few works have focused on effectively modeling temporal-spectral correlations to improve the capacity of representations. In this article, we propose the cross reconstruction transformer (CRT) to solve the aforementioned issues. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we obtain the frequency domain of the time series via the fast Fourier transform (FFT) and randomly drop certain patches in both time and frequency domains. Dropping is employed to maximally preserve the global context while masking leads to the distribution shift. Then a Transformer architecture is utilized to adequately discover the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose instance discrimination constraint (IDC) to reduce the mutual information between different time series samples and sharpen the decision boundaries. Additionally, a specified curriculum learning (CL) strategy is employed to improve the robustness during the pretraining phase, which progressively increases the dropping ratio in the training process. We conduct extensive experiments to evaluate the effectiveness of the proposed method on multiple real-world datasets. Results show that CRT consistently achieves the best performance over existing methods by 2%-9%. The code is publicly available at https://github.com/BobZwr/Cross-Reconstruction-Transformer.

Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network.

BACKGROUND: Critical values are commonly used in clinical laboratory tests to define health-related conditions of varying degrees. Knowing the values, people can quickly become aware of health risks, and the health professionals can take immediate actions and save lives. METHODS: In this paper, we propose a method that extends the concept of critical value to one of the most commonly used physiological signals in the clinical environment-Electrocardiogram (ECG). We first construct a mapping from common ECG diagnostic conclusions to critical values. After that, we build a 61-layer deep convolutional neural network named CardioV, which is characterized by an ordinal classifier. RESULTS: We conduct experiments on a large public ECG dataset, and demonstrate that CardioV achieves a mean absolute error of 0.4984 and a ROC-AUC score of 0.8735. In addition, we find that the model performs better for extreme critical values and the younger age group, while gender does not affect the performance. The ablation study confirms that the ordinal classification mechanism suits for estimating the critical values which contain ranking information. Moreover, model interpretation techniques help us discover that CardioV focuses on the characteristic ECG locations during the critical value estimation process. CONCLUSIONS: As an ordinal classifier, CardioV performs well in estimating ECG critical values that can help people quickly identify different heart conditions. We obtain ROC-AUC scores above 0.8 for all four critical value categories, and find that the extreme values (0 (no risk) and 3 (high risk)) have better model performance than the other two (1 (low risk) and 2 (medium risk)). Results also show that gender does not affect the performance, and the older age group has worse performance than the younger age group. In addition, visualization techniques reveal that the model pays more attention to characteristic ECG locations.

Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques.

Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.

Long-term stability of neural signals from microwire arrays implanted in common marmoset motor cortex and striatum.

Current neuroprosthetics rely on stable, high quality recordings from chronically implanted microelectrode arrays (MEAs) in neural tissue. While chronic electrophysiological recordings and electrode failure modes have been reported from rodent and larger non-human primate (NHP) models, chronic recordings from the marmoset model have not been previously described. The common marmoset is a New World primate that is easier to breed and handle compared to larger NHPs and has a similarly organized brain, making it a potentially useful smaller NHP model for neuroscience studies. This study reports recording stability and signal quality of MEAs chronically implanted in behaving marmosets. Six adult male marmosets, trained for reaching tasks, were implanted with either a 16-channel tungsten microwire array (five animals) or a Pt-Ir floating MEA (one animal) in the hand-arm region of the primary motor cortex (M1) and another MEA in the striatum targeting the nucleus accumbens (NAcc). Signal stability and quality was quantified as a function of array yield (active electrodes that recorded action potentials), neuronal yield (isolated single units during a recording session), and signal-to-noise ratio (SNR). Out of 11 implanted MEAs, nine provided functional recordings for at least three months, with two arrays functional for 10 months. In general, implants had high yield, which remained stable for up to several months. However, mechanical failure attributed to MEA connector was the most common failure mode. In the longest implants, signal degradation occurred, which was characterized by gradual decline in array yield, reduced number of isolated single units, and changes in waveform shape of action potentials. This work demonstrates the feasibility of longterm recordings from MEAs implanted in cortical and deep brain structures in the marmoset model. The ability to chronically record cortical signals for neural prosthetics applications in the common marmoset extends the potential of this model in neural interface research.

Common marmoset (Callithrix jacchus) as a primate model for behavioral neuroscience studies.

BACKGROUND: The common marmoset (Callithrix jacchus) has been proposed as a suitable bridge between rodents and larger primates. They have been used in several types of research including auditory, vocal, visual, pharmacological and genetics studies. However, marmosets have not been used as much for behavioral studies. NEW METHOD: Here we present data from training 12 adult marmosets for behavioral neuroscience studies. We discuss the husbandry, food preferences, handling, acclimation to laboratory environments and neurosurgical techniques. In this paper, we also present a custom built "scoop" and a monkey chair suitable for training of these animals. RESULTS: The animals were trained for three tasks: 4 target center-out reaching task, reaching tasks that involved controlling robot actions, and touch screen task. All animals learned the center-out reaching task within 1-2 weeks whereas learning reaching tasks controlling robot actions task took several months of behavioral training where the monkeys learned to associate robot actions with food rewards. COMPARISON TO EXISTING METHOD: We propose the marmoset as a novel model for behavioral neuroscience research as an alternate for larger primate models. This is due to the ease of handling, quick reproduction, available neuroanatomy, sensorimotor system similar to larger primates and humans, and a lissencephalic brain that can enable implantation of microelectrode arrays relatively easier at various cortical locations compared to larger primates. CONCLUSION: All animals were able to learn behavioral tasks well and we present the marmosets as an alternate model for simple behavioral neuroscience tasks.

Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

OBJECTIVE: Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. APPROACH: Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. MAIN RESULTS: The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. SIGNIFICANCE: By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI.

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

[Effect of carbachol on intestinal mucosa blood flow and absorption rate of glucose-electrolyte solution during enteral resuscitation for 50% total body surface area full-thickness burn injury in dog].

OBJECTIVE: To investigate the effect of carbachol (CAR) on blood flow of intestinal mucosa and absorption rate of glucose-electrolyte solution (GES) during enteral resuscitation of burn shock in dog. METHODS: Eighteen male Beagle dogs were subjected to a (51.2+/-2.6)% total body surface area (TBSA) full-thickness flame injury, and fluid resuscitation was given according to Parkland formula 0.5 hour after burn. Animals were randomly divided into intravenous infusion of GES group (VGES group, n=6), enteral infusion of GES group (EGES group, n=6) and EGES containing 0.25 microg/kg of CAR group (EGES/CAR group n=6). In the first 8 hours post burn, intestinal absorption rate of water and Na+, intestinal mucosa blood flow (IBF), the plasma volume (PV) and plasma concentration of Na+ were continuously determined without anesthesia. At the end of 8 hours animals were sacrificed, and specimens of gut tissue were taken to determine the activity of Na+-K+-ATPase. RESULTS: The intestinal absorption rate of water and Na+ was reduced markedly after burn in two enteral resuscitation groups and much lower than pre-injury levels and the expected infusing rate according to Parkland formula. It was found that the absorption rate of water and Na+ from 1.5 hours and 2.5 hours in EGES/CAR group were significantly higher compared with those in EGES group (all P<0.05). During 8 hours after burn, only 47.1% and 63.8% of fluids enterally infused in EGES and EGES/CAR groups were absorbed by the gut. The volume of fluid absorbed and the fluid absorption rate were significantly higher in EGES/CAR group than those in EGES group (P<0.05). Incidence of gut intolerance (diarrhea) was 83% in EGES group, which was higher than that of in EGES/CAR group (50%). IBF was significantly decreased compared with pre-injury levels in all groups. Enteral infusion of CAR led to a significant elevation of IBF in EGES/CAR compared with GES group from 4 hours after burn, but it was still lower than pre-injury levels and those in VGES group. The Na+-K+-ATPase activity between three groups ranked as follows: VGES group>EGES/CAR group>EGES group (P<0.05). Within 8 hours post injury, PV and plasma concentration of Na+ in two enteral resuscitation groups were much lower than those in VGES group, but from 4 hours after burn the values in EGES/CAR group were higher than those in EGES group (all P<0.05). CONCLUSION: 50%TBSA full-thickness flame injury led to a markedly decrease in intestinal absorption rate of water and Na+. The total volume of fluid absorbed by intestine in 8 hours was significantly lower in enteral resuscitation groups compared to the regime of the Parkland formula. CAR promoted intestinal absorption rate and PV by increasing the intestinal blood flow and Na+-K+-ATPase activity, and it seems to exert a helpful effect on the resuscitation of burn shock with electrolyte solution per oral route.

[Effect of carbachol on gastric emptying and gastric mucosa partial pressure of carbon dioxide in oral resuscitation of burn shock with a glucose-electrolyte solution in dogs].

OBJECTIVE: To investigate the effect of carbachol (CAR) on gastric emptying and gastric mucosa partial pressure of carbon dioxide (PgCO2) in the resuscitation of burn shock with oral administration of glucose-electrolyte solution (GES) in dogs. METHODS: Twenty-four adult male Beagle dogs were randomly divided into 4 groups: 35% total body surface area (TBSA) III degree burn resuscitated with oral GES (35% TBSA GES, n=6), 35% TBSA III degree burn with oral GES containing 20 microg/kg of CAR (35% TBSA GES/CAR, n=6), 50% TBSA III degree burn with oral GES (50% TBSA GES, n=6) and 50% TBSA III degree burn with oral GES containing 20 microg/kg of CAR (50% TBSA GES/CAR, n=6). Dogs were subjected to 35% TBSA or 50% TBSA full-thickness flame injury respectively. Thirty minutes after burn, dogs were given GES or GES containing CAR according to Parkland formula (1/2 of 4 mlxkg(-1)x1% TBSA(-1) within 8 hours post burn, and the remaining 1/2 within next 16 hours post burn) by gavage. The gastric emptying rate, PgCO2 and intolerance symptoms were determined at 2, 4, 8 and 24 hours post burn. RESULTS: The gastric emptying rate was significantly decreased in all groups after the burn (P<0.05), and it was 51.5% at 2 hours after burn in 35% TBSA GES group and 39.2% at 4 hours after burn in 50% TBSA GES group. It was gradually ameliorated, but still much lower than pre-injury levels (both P<0.05). The gastric emptying rate in GES/CAR group were significantly higher at all time points after injury than those in 35% GES group (P<0.05), and it was higher than that in 50% GES group at 8 hours and 24 hours (both P<0.05). The gastric emptying rate restored to pre-injury levels (P>0.05) in 35% GES/CAR group, and it was still lower than pre-injury level in 50% GES/CAR group (P<0.05). The PgCO2 were significantly elevated in all groups post burn (all P<0.05), and could not return to pre-injury levels. The PgCO2 in GES/CAR group were significantly higher at all time points after injury than those in 35% GES group (P<0.05), and it was higher than that in 50% GES group at 4 hours and 24 hours (P<0.05). The degree of gastric intolerance symptoms could be ranked as follows: 50%TBSA GES group (83.3%, 5/6)>50% TBSA GES/CAR group (50.0%, 3/6)>35%TBSA GES group (16.7%, 1/6)>35%TBSA GES/CAR group (0, 0/6). CONCLUSION: The results indicate that CAR has a significant effect in improving gastric emptying and gastric ischemia during oral resuscitation of burn shock with a glucose electrolyte solution.