Biosynthesis of Enfumafungin-type Antibiotic Reveals an Unusual Enzymatic Fusion Pattern and Unprecedented C-C Bond Cleavage.

Enfumafungin-type antibiotics, represented by enfumafungin and fuscoatroside, belong to a distinct group of triterpenoids derived from fungi. These compounds exhibit significant antifungal properties with ibrexafungerp, a semisynthetic derivative of enfumafungin, recently gaining FDA's approval as the first oral antifungal drug for treating invasive vulvar candidiasis. Enfumafungin-type antibiotics possess a cleaved E-ring with an oxidized carboxyl group and a reduced methyl group at the break site, suggesting unprecedented C-C bond cleavage chemistry involved in their biosynthesis. Here, we show that a 4-gene (fsoA, fsoD, fsoE, fsoF) biosynthetic gene cluster is sufficient to yield fuscoatroside by heterologous expression in Aspergillus oryzae. Notably, FsoA is an unheard-of terpene cyclase-glycosyltransferase fusion enzyme, affording a triterpene glycoside product that relies on enzymatic fusion. FsoE is a P450 enzyme that catalyzes successive oxidation reactions at C19 to facilitate a C-C bond cleavage, producing an oxidized carboxyl group and a reduced methyl group that have never been observed in known P450 enzymes. Our study thus sets the important foundation for the manufacture of enfumafungin-type antibiotics using biosynthetic approaches.

Multi-Omics Revealed Peanut Root Metabolism Regulated by Exogenous Calcium under Salt Stress.

High salinity severely inhibits plant seedling root development and metabolism. Although plant salt tolerance can be improved by exogenous calcium supplementation, the metabolism molecular mechanisms involved remain unclear. In this study, we integrated three types of omics data (transcriptome, metabolome, and phytohormone absolute quantification) to analyze the metabolic profiles of peanut seedling roots as regulated by exogenous calcium under salt stress. (1) exogenous calcium supplementation enhanced the allocation of carbohydrates to the TCA cycle and plant cell wall biosynthesis rather than the shikimate pathway influenced by up-regulating the gene expression of antioxidant enzymes under salt stress; (2) exogenous calcium induced further ABA accumulation under salt stress by up-regulating the gene expression of ABA biosynthesis key enzymes AAO2 and AAO3 while down-regulating ABA glycosylation enzyme UGT71C5 expression; (3) exogenous calcium supplementation under salt stress restored the trans-zeatin absolute content to unstressed levels while inhibiting the root cis-zeatin biosynthesis.

A clustering-based method for estimating pennation angle from B-mode ultrasound images.

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20 Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Feedback control of upright seating with functional neuromuscular stimulation during a reaching task after spinal cord injury: a feasibility study.

BACKGROUND: Restoring or improving seated stability after spinal cord injury (SCI) can improve the ability to perform activities of daily living by providing a dynamic, yet stable, base for upper extremity motion. Seated stability can be obtained with activation of the otherwise paralyzed trunk and hip musculature with neural stimulation, which has been shown to extend upper limb reach and improve seated posture. METHODS: We implemented a proportional, integral, derivative (PID) controller to maintain upright seated posture by simultaneously modulating both forward flexion and lateral bending with functional neuromuscular stimulation. The controller was tested with a functional reaching task meant to require trunk movements and impart internal perturbations through rapid changes in inertia due to acquiring, moving, and replacing objects with one upper extremity. Five subjects with SCI at various injury levels who had received implanted stimulators targeting their trunk and hip muscles participated in the study. Each subject was asked to move a weighted jar radially from a center home station to one of three target stations. The task was performed with the controller active, inactive, or with a constant low level of neural stimulation. Trunk pitch (flexion) and roll (lateral bending) angles were measured with motion capture and plotted against each other to generate elliptical movement profiles for each task and condition. Postural sway was quantified by calculating the ellipse area. Additionally, the mean effective reach (distance between the shoulder and wrist) and the time required to return to an upright posture was determined during reaching movements. RESULTS: Postural sway was reduced by the controller in two of the subjects, and mean effective reach was increased in three subjects and decreased for one. Analysis of the major direction of motion showed return to upright movements were quickened by 0.17 to 0.32 s. A 15 to 25% improvement over low/no stimulation was observed for four subjects. CONCLUSION: These results suggest that feedback control of neural stimulation is a viable way to maintain upright seated posture by facilitating trunk movements necessary to complete reaching tasks in individuals with SCI. Replication of these findings on a larger number of subjects would be necessary for generalization to the various segments of the SCI population.

An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton.

A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions via FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this paper, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.

Astramalabaricosides A-T, Highly Oxygenated Malabaricane Triterpenoids with Migratory Inhibitory Activity from Astragalus membranaceus var. mongholicus.

Twenty new malabaricane triterpenoids, astramalabaricosides A-T (1-20), were isolated from the roots of Astragalus membranaceus var. mongholicus (Astragali Radix). Their structures were determined by spectroscopic analysis, and the use of the circular dichroism exciton chirality method, quantum chemical calculations, and chemical methods. Malabaricane triterpenoids, an unusual group with the 6-6-5-tricyclic core, are distributed in plants (e.g., Simaroubaceae, Polypodiaceae, and Fabaceae), a marine sponge, and fungi, and their number obtained to date is limited. Compounds 1-20 were characterized as glycosides with a highly oxygenated side chain, and 13-20 were the first cyclic carbonate derivatives among the malabaricane triterpenoids. The stereocluster formed from the continuous hydroxylated chiral carbons in each highly oxygenated side chain and the 6-6-5-tricyclic core system were entirely segregated, and the independent identification of their stereoconfigurations required considerable effort. The migratory inhibitory and antiproliferative activities of 1-20 were evaluated by wound-healing and cell-viability assays, respectively. Most compounds showed significant migratory inhibitory activity, and a preliminary structure-activity relationship was developed. Malabaricane triterpenoids are being reported in the genus Astragalus for the first time.

Bioactive pterocarpans from the root of Astragalus membranaceus var. mongholicus.

Eleven undescribed and three known pterocarpans were isolated and identified from the traditional Chinese medicine "Huang-qi", Astragali Radix (the root of Astragalus membranaceus var. mongholicus (Bunge) P.K.Hsiao). The structures of these pterocarpans were determined using spectroscopic, X-ray crystallographic, quantum chemical calculation, and chemical methods. Pterocarpans, almost exclusively distributed in the family of Leguminosae, are the second largest subgroup of isoflavanoids. However, pterocarpan glycoside number is limited, most of which are glucosides, and only one pterocarpan apioside was isolated from nature. Notably, nine rare apiosyl-containing pterocarpan glycosides were isolated and identified. The hypoglycemic activities of all these compounds were evaluated using α-glucosidase and DPP-IV inhibitory assays respectively, and some isolates displayed the α-glucosidase inhibitory function. The antioxidant activities of all compounds were evaluated using the ORAC and DPPH radical scavenging assays, respectively. All compounds exhibited varying degrees of oxygen radical absorbance capacity, and some compounds displayed DPPH radical scavenging ability.

Robust Control of the Human Trunk Posture Using Functional Neuromuscular Stimulation: A Simulation Study.

The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by functional neuromuscular stimulation (FNS), which applies low-level current to the motor nerves to activate the paralyzed muscles to generate useful torques, to actuate the trunk. FNS can be modulated to vary the biotorques to drive the trunk to follow a user-defined reference motion and maintain it at a desired postural set-point. However, a stabilizing modulation policy (i.e., control law) is difficult to derive as the biomechanics of the spine and pelvis are complex and the neuromuscular dynamics are highly nonlinear, nonautonomous, and input redundant. Therefore, a control method that can stabilize it with FNS without knowing the accurate skeletal and neuromuscular dynamics is desired. To achieve this goal, we propose a control framework consisting of a robust control module that generates stabilizing torques while an artificial neural network-based mapping mechanism with an anatomy-based updating law ensures that the muscle-generated torques converge to the stabilizing values. For the robust control module, two sliding-mode robust controllers (i.e., a high compensation controller and an adaptive controller), were investigated. System stability of the proposed control method was rigorously analyzed based on the assumption that the skeletal dynamics can be approximated by Euler-Lagrange equations with bounded disturbances, which enables the generalization of the control framework. We present experiments in a simulation environment where an anatomically realistic three-dimensional musculoskeletal model of the human trunk moved in the anterior- posterior and medial-lateral directions while perturbations were applied. The satisfactory simulation results suggest the potential of this control technique for trunk tracking tasks in a typical clinical environment.

A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control.

Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging's region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( < .001) and increased the prediction coefficient of determination by 20.13% ( < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.

Feedback Control of Upright Seating with Functional Neuromuscular Stimulation during a Functional Task after Spinal Cord Injury: A Case Study.

Seated stability is a major concern of individuals with trunk paralysis. Trunk paralysis is commonly caused by spinal cord injuries (SCI) at or above the thoracic spine. Current methods to improve stability restrict the movement of the user by constraining their trunk to an upright position. Feedback control of functional neuromuscular stimulation (FNS) can help maintain seated stability while still allowing the user to perform movements to accomplish functional tasks. In this study, an individual with a SCI (C7, AIS B) and an implanted stimulator capable of recruiting trunk and hip musculature unilaterally moved a weighted jar on a countertop to and from three prescribed stations directly in front, laterally, and across midline. For comparison, the tasks were performed with constant baseline stimulation and with feedback modulated stimulation based on the tilt of the trunk obtained from an external accelerometer fed into two PID controllers; one for forward trunk pitch and the other for lateral roll. The trunk pitch and roll angles were obtained through motion capture cameras and various measures of postural sway (95% fitted ellipse area, root mean squared (RMS), path length) and the repeatability (coefficient of variation (CoV), variance ratio (VR)) were calculated. Feedback control significantly increased RMS of trunk movement along the major axis of the fitted ellipse, but decreased RMS values during bending along the minor axis of motion. As a result, the fitted ellipse area decreased when deploying the jar to one of the stations and increased with the other two. The CoV indicated reduced variation in the presence of feedback controlled stimulation for all stations, and VR showed higher repeatability in trunk pitch. Plots of the trunk pitch and roll revealed a faster return to upright motion due to feedback stimulation.Clinical relevance- Feedback control in combination with FNS is a viable method to improve seated stability while still allowing dynamic movements in individuals with a SCI, thus addressing a major concern of the population.

Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.

A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network-based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton's knee motors based on the muscle fatigue and recovery characteristics of a participant's quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.

Synergistic effects of copper and ethylene on resveratrol synthesis in peanuts.

This study aimed to investigate the effects of exogenous copper and ethylene on resveratrol biosynthesis in peanut buds. In this study, different concentrations of copper sulfate and ethephon were used to induce peanut bud roots. Resveratrol content was determined using high-performance liquid chromatography (HPLC). The interaction effects of the two inducers were analyzed using regression and response surface methodology. Results showed that resveratrol biosynthesis increased with the increase in copper sulfate concentration. Resveratrol synthesis showed an increasing trend when the concentration of ethephon was from 5 to 7 mmol/L. The resveratrol content reached a maximum value of 7.7 µg/g when the concentration of ethephon was 7 mmol/L and then decreased with an increase in ethephon. Response surface analysis revealed that the combination of 0.1 mmol/L copper sulfate and 5 mmol/L ethephon was effective and resulted in the induction of resveratrol biosynthesis in peanut buds.

Cladosporine A, a new indole diterpenoid alkaloid with antimicrobial activities from Cladosporium sp.

Cladosporine A (1), a new indole diterpenoid alkaloid, was isolated from the extract of a fungal strain Cladosporium sp. JNU17DTH12-9-01. Its structure was elucidated by extensive spectroscopic analysis, and the absolute configurations were determined by electronic circular dichroism (ECD) experiments. This is the first report of the presence of indole diterpenoid alkaloid in the genus Cladosporium. The antimicrobial activities against Staphylococcus aureus 209P, Escherichia coli ATCC0111, Aspergillus niger R330, and Candida albicans FIM709 were evaluated. Compound 1 showed MICs of 4 µg/mL and 16 µg/mL against S. aureus 209P and C. albicans FIM709, respectively.

A Tube-based Model Predictive Control Method to Regulate a Knee Joint with Functional Electrical Stimulation and Electric Motor Assist.

A hybrid neuroprosthesis system is a promising rehabilitation technology to restore lower-limb function in persons with paraplegia. The technology combines functional electrical stimulation (FES) and a powered lower limb exoskeleton to produce movements for walking and standing. The main control challenge in the hybrid neuroprosthesis is to achieve an optimal coordination between FES and electric motors. Model-based optimal control methods have been suggested for the control of the hybrid neuroprosthesis. However, it is often difficult to effect robust control performance with model-based optimal control methods due to modeling uncertainties. A tube-based model predictive control (MPC) method is developed to obtain robust and optimal coordination between FES and an electric motor during a knee regulation task. An external feedback control is used to limit the error between the actual position and the MPC-computed nominal position. The tube-based MPC method is proven to have recursive feasibility, compliance to input constraints, and exponentially bounded stability. The experimental results obtained from an able-bodied participant and a participant with spinal cord injury validate the controller's ability to allocate control inputs to FES and the electric motor as well as method's robustness to modeling uncertainties.

Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton - Preliminary Results.

Currently controllers that dynamically modulate functional electrical stimulation (FES) and a powered exoskeleton at the same time during standing-up movements are largely unavailable. In this paper, an optimal shared control of FES and a powered exoskeleton is designed to perform sitting to standing (STS) movements with a hybrid exoskeleton. A hierarchical control design is proposed to overcome the difficulties associated with developing an optimal real-time solution for the highly nonlinear and uncertain STS control model with multiple degrees of freedom. A higher-level robust nonlinear control design is derived to exponentially track a time-invariant desired STS movement profile. Then, a lower-level optimal control allocator is designed to distribute control between FES and the knee electric motors. The allocator uses a person's muscle fatigue and recovery dynamics to determine an optimal ratio between the FES-elicited knee torque and the exoskeleton assist. Experiments were performed on human participants, two persons without disability and one person with spinal cord injury (SCI), to validate the feedback controller and the optimal torque allocator. The muscles of the participant with SCI did not actively contract to FES, so he was only tested with the powered exoskeleton controller. The experimental results show that the proposed hierarchical control design is a promising method to effect shared control in a hybrid exoskeleton.

Cycloartane-type triterpenoids from the root of Astragalus membranaceus var. mongholicus.

Three new (1-3) and 11 known (4-14) cycloartane-type triterpenoids were isolated from the root of Astragalus membranaceus var. mongholicus. Their structures were determined by spectroscopic analyses and chemical methods. Cycloartane-type triterpenoids are a class of major bioactive constituents in the root of A. membranaceus var. mongholicus, and the discovery of compounds 1-3 added new members of this kind of natural product. [Formula: see text].

Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network Structure.

Functional electrical stimulation (FES) has recently been proposed as a supplementary torque assist in lower-limb powered exoskeletons for persons with paraplegia. In the combined system, also known as a hybrid neuroprosthesis, both FES-assist and the exoskeleton act to generate lower-limb torques to achieve standing and walking functions. Due to this actuator redundancy, we are motivated to optimally allocate FES-assist and exoskeleton torque based on a performance index that penalizes FES overuse to minimize muscle fatigue while also minimizing regulation or tracking errors. Traditional optimal control approaches need a system model to optimize; however, it is often difficult to formulate a musculoskeletal model that accurately predicts muscle responses due to FES. In this paper, we use a novel identification and control structure that contains a recurrent neural network (RNN) and several feedforward neural networks (FNNs). The RNN is trained by supervised learning to identify the system dynamics, while the FNNs are trained by a reinforcement learning method to provide sub-optimal control actions. The output layer of each FNN has its unique activation functions, so that the asymmetric constraint of FES and the symmetric constraint of exoskeleton motor control input can be realized. This new structure is experimentally validated on a seated human participant using a single joint hybrid neuroprosthesis.

Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty.

Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.

Model-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis.

A hybrid neuroprosthesis that combines human muscle power, elicited through functional electrical stimulation (FES), with a powered orthosis may be advantageous over a sole FES or a powered exoskeleton-based rehabilitation system. The hybrid system can conceivably overcome torque reduction due to FES-induced muscle fatigue by complementarily using torque from the powered exoskeleton. The second advantage of the hybrid system is that the use of human muscle power can supplement the powered exoskeleton's power (motor torque) requirements; thus, potentially reducing the size and weight of a walking restoration system. To realize these advantages, however, it is unknown how to concurrently optimize desired control performance and allocation of control inputs between FES and electric motor. In this paper, a model predictive control-based dynamic control allocation (DCA) is used to allocate control between FES and the electric motor that simultaneously maintain a desired knee angle. The experimental results, depicting the performance of the DCA method while the muscle fatigues, are presented for an able-bodied participant and a participant with spinal cord injury. The experimental results showed that the motor torque recruited by the hybrid system was less than that recruited by the motor-only system, the algorithm can be easily used to allocate more control input to the electric motor as the muscle fatigues, and the muscle fatigue induced by the hybrid system was found to be less than the fatigue induced by sole FES. These results validate the aforementioned advantages of the hybrid system; thus implying the hybrid technology's potential use in walking rehabilitation.