Robust Compensation of Electromechanical Delay during Neuromuscular Electrical Stimulation of Antagonistic Muscles

Neuromuscular electrical stimulation (NMES) can potentially be used to restore the limb function in persons with neurological disorders, such as spinal cord injury (SCI), stroke, etc. Researches on control system design has so far focused on relatively simple unidirectional NMES applications requiring stimulation of single muscle group. However, for some advanced tasks such as pedaling or walking, stimulation of multiple muscles is required. For example, to extend as well as flex a limb joint requires electrical stimulation of an antagonistic muscle pair. This is due to the fact that muscles are unidirectional actuators. The control challenge is to allocate control inputs to antagonist muscles based on the system output, usually a limb angle error to achieve a smooth and precise transition between antagonistic muscles without causing discontinuities. Furthermore, NMES input to each muscle is delayed by an electromechanical delay (EMD), which arises due to the time lag between the electrical excitation and the force development in muscle. And EMD is known to cause instability or performance loss during closed-loop control of NMES. In this thesis, a robust delay compensation controller for EMDs in antagonistic muscles is presented. A Lyapunov stability analysis yields uniformly ultimately bounded tracking for a human limb joint actuated by antagonistic muscles. The simulation results indicate that the controller is robust and effective in switching between antagonistic muscles and compensating EMDs during a simulated NMES task. Further experiments on a dual motor testbed shows its feasibility as an NMES controller for human antagonistic muscles

Dendritic cells contribute to perivascular adipose tissue dysfunction in type 2 diabetes

T2DM is a chronic disease characterized by low-grade inflammation in adipose tissue. Recent investigations have shown that a variety of immune cells can accumulate in adipose tissue contributing to the development of chronic inflammation. To date focus has been placed on specific immune cell populations including B and T lymphocytes, M1 macrophages, neutrophils, mast cells and natural killer cells. However, it remains uncertain about the exact immune cell populations in adipose tissue during T2DM. The dendritic cell is a potent antigen presenting cell that has been demonstrated to participate in the chronic inflammation associated with multiple diseases, including autoimmune disease, atherosclerosis and type 1 diabetes. Thus, it was hypothesized that dendritic cells would also play a role in the development of chronic inflammation elicited by T2DM. Firstly, our data obtained in db/db mice (T2DM murine model) provide evidence that dendritic cells do, indeed, accumulate in multiple depots of perivascular and visceral adipose tissue. Importantly, the dendritic cells target the adipose tissue rather than accumulating within the vascular wall, accompanied with increased production of pro-inflammatory factors TNF-[alpha] and IL-6 in adipose tissue. Secondly, depletion of dendritic cells within adipose tissue in db/db (db[subscript Flt3l-]/ db[subscript Flt3l-]) mice attenuated the pro-inflammatory environment. As perivascular adipose tissue exerts anti-contractile actions and potentiates vasorelaxation under physiological conditions, we examined the effects of fat from db/db mice on vascular function. The data showed that in db/db mice, both of these 'vaso-protective' effects were lost at early (6-10 weeks) and later (18-22 weeks) stages of T2DM in the presence of inflamed mesenteric adipose tissue. Depletion of dendritic cells in db[subscript Flt3l-]/ db[subscript Flt3l-]- mice greatly attenuated inflammation in perivascular adipose tissue (decreased secretion of TNF-[alpha] and IL-6) compared to the db/db and partially restored vascular function. Collectively, our studies demonstrate that the accumulation of dendritic cells in adipose tissue contributes to the pathogenesis of chronic inflammation in T2DM, resulting in impairment of anti-contractile and pro-relaxant actions of perivascular adipose tissue. Deletion of dendritic cells restores these physiological actions of adipose tissue.Includes biblographical reference

Measuring Policy Distance for Multi-Agent Reinforcement Learning

Diversity plays a crucial role in improving the performance of multi-agent reinforcement learning (MARL). Currently, many diversity-based methods have been developed to overcome the drawbacks of excessive parameter sharing in traditional MARL. However, there remains a lack of a general metric to quantify policy differences among agents. Such a metric would not only facilitate the evaluation of the diversity evolution in multi-agent systems, but also provide guidance for the design of diversity-based MARL algorithms. In this paper, we propose the multi-agent policy distance (MAPD), a general tool for measuring policy differences in MARL. By learning the conditional representations of agents' decisions, MAPD can computes the policy distance between any pair of agents. Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects. Based on the online deployment of MAPD, we design a multi-agent dynamic parameter sharing (MADPS) algorithm as an example of the MAPD's applications. Extensive experiments demonstrate that our method is effective in measuring differences in agent policies and specific behavioral tendencies. Moreover, in comparison to other methods of parameter sharing, MADPS exhibits superior performance.Comment: 9 pages, 6 figure

In-silico Antigenicity Determination and Clustering of Dengue Virus Serotypes

Emerging or re-emerging dengue virus (DENV) causes dengue fever epidemics globally. Current DENV serotypes are defined based on genetic clustering, while discrepancies are frequently observed between the genetic clustering and the antigenicity experiments. Rapid antigenicity determination of DENV mutants in high-throughput way is critical for vaccine selection and epidemic prevention during early outbreaks, where accurate prediction methods are seldom reported for DENV. Here, a highly accurate and efficient in-silico model was set up for DENV based on possible antigenicity-dominant positions (ADPs) of envelope (E) protein. Independent testing showed a high performance of our model with AUC-value of 0.937 and accuracy of 0.896 through quantitative Linear Regression (LR) model. More importantly, our model can successfully detect those cross-reactions between inter-serotype strains, while current genetic clustering failed. Prediction cluster of 1,143 historical strains showed new DENV clusters, and we proposed DENV2 should be further classified into two subgroups. Thus, the DENV serotyping may be re-considered antigenetically rather than genetically. As the first algorithm tailor-made for DENV antigenicity measurement based on mutated sequences, our model may provide fast-responding opportunity for the antigenicity surveillance on DENV variants and potential vaccine study

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe

Proteochemometric Modeling of the Antigen-Antibody Interaction : New Fingerprints for Antigen, Antibody and Epitope-Paratope Interaction

Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance (R-2 = 0: 91; Q(test)(2) = 0: 68) among peers. Further, together with EPIF as a new cross-term, our model (R-2 = 0: 92; Q(2) test = 0: 74) can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (R2Peer reviewe

Arbuscular mycorrhizal fungal interactions bridge the support of root-associated microbiota for slope multifunctionality in an erosion-prone ecosystem

The role of diverse soil microbiota in restoring erosion-induced degraded lands is well recognized. Yet, the facilitative interactions among symbiotic arbuscular mycorrhizal (AM) fungi, rhizobia, and heterotrophic bacteria, which underpin multiple functions in eroded ecosystems, remain unclear. Here, we utilized quantitative microbiota profiling and ecological network analyses to explore the interplay between the diversity and biotic associations of root-associated microbiota and multifunctionality across an eroded slope of a Robinia pseudoacacia plantation on the Loess Plateau. We found explicit variations in slope multifunctionality across different slope positions, associated with shifts in limiting resources, including soil phosphorus (P) and moisture. To cope with P limitation, AM fungi were recruited by R. pseudoacacia, assuming pivotal roles as keystones and connectors within cross-kingdom networks. Furthermore, AM fungi facilitated the assembly and composition of bacterial and rhizobial communities, collectively driving slope multifunctionality. The symbiotic association among R. pseudoacacia, AM fungi, and rhizobia promoted slope multifunctionality through enhanced decomposition of recalcitrant compounds, improved P mineralization potential, and optimized microbial metabolism. Overall, our findings highlight the crucial role of AM fungal-centered microbiota associated with R. pseudoacacia in functional delivery within eroded landscapes, providing valuable insights for the sustainable restoration of degraded ecosystems in erosion-prone regions

Nanocone‐Modified Surface Facilitates Gas Bubble Detachment for High‐Rate Alkaline Water Splitting

Abstract: The significant amount of gas bubbles generated during high‐rate alkaline water splitting (AWS) can be detrimental to the process. The accumulation of bubbles will block the active catalytic sites and hinder the ion and electrolyte diffusion, limiting the maximum current density. Furthermore, the detachment of large bubbles can also damage the electrode's surface layer. Here, a general strategy for facilitating bubble detachment is demonstrated by modifying the nickel electrode surface with nickel nanocone nanostructures, which turns the surface into underwater superaerophobic. Simulation and experimental data show that bubbles take a considerably shorter time to detach from the nanocone‐modified nickel foil than the unmodified foil. As a result, these bubbles also have a smaller detachment size and less chance for bubble coalescence. The nanocone‐modified electrodes, including nickel foil, nickel foam, and 3D‐printed nickel lattice, all show substantially reduced overpotentials at 1000 mA cm−2 compared to their pristine counterpart. The electrolyzer assembled with two nanocone‐modified nickel lattice electrodes retains >95% of the performance after testing at ≈900 mA cm−2 for 100 h. The surface NC structure is also well preserved. The findings offer an exciting and simple strategy for enhancing the bubble detachment and, thus, the electrode activity for high‐rate AWS