Polydopamine nanoparticles coated with a metal-polyphenol network for enhanced photothermal/chemodynamic cancer combination therapy.

Polydopamine nanoparticles (PDA NPs) are commonly used for photothermal therapy (PTT) of cancer because of their good biocompatibility and photothermal conversion capability. However, it is difficult to achieve a good tumor inhibition effect with a single PTT of PDA. Therefore, in this work, we prepared a combined anticancer nanosystem for enhanced chemodynamic therapy (CDT)/PTT by coating PDAs with an (-)-epigallocatechin gallate (EGCG)/iron (Fe) metal-polyphenol network (MPN). The MPN shell of this nanosystem named EGCG@PDA is degraded by the weakly acidic environment intracellular, releasing EGCG and Fe3+. EGCG inhibits the expression of heat shock proteins (HSPs) in cancer cells, thus eliminating their thermal protection against cancer cells for enhanced PTT. Meanwhile, the reductive EGCG can also reduce Fe3+ to Fe2+, to catalyze the decomposition of overexpressed hydrogen peroxide (H2O2) in cancer cells to generate strong oxidative hydroxyl radicals (OH), i.e., catalyzing the Fenton reaction, for CDT. After the Fenton reaction, the re-oxidized Fe ions can be reduced again by EGCG and reused to catalyze the Fenton reaction, which can achieve enhanced CDT. Both in vitro and in vivo studies have shown that EGCG@PDA has low dark toxicity and good anticancer effects. It is expected to be used for precision cancer therapy.

Antibacterial and antibiofilm potential of Lacticaseibacillus rhamnosus YT and its cell-surface extract.

BACKGROUND: Foodborne pathogens and spoilage bacteria survived in the biofilm pose a serious threat to food safety and human health. It is urgent to find safe and effective methods to control the planktonic bacteria as well as the biofilm formation. Substances with antibacterial and antibiofilm activity found in lactic acid bacteria were mainly metabolites secreted in the cell-free supernatant. Previously, Lacticaseibacillus rhamnosus YT was isolated because its cell pellets displayed distinguished antibacterial activity under neutral conditions. This study aimed to investigate the antibacterial and antibiofilm properties of the L. rhamnosus YT cells and its crude cell-surface extract. RESULTS: The antibacterial activity of the L. rhamnosus YT cells constantly increased with cells growth and reached the peak value after the cells grew into stationary phase. After cocultivation with the L. rhamnosus YT cells, the biofilm formation of B. subtilis and S. enterica was reduced. The antibacterial activity of the L. rhamnosus YT cells was varied along with various culture conditions (carbon sources, nitrogen sources, medium pH and cultural temperatures) and the antibacterial intensity (antibacterial activity per cell) was disproportional to the biomass. Furthermore, the cell-surface extract was isolated and displayed broad antimicrobial spectrum with a bacteriostatic mode of action. The antibiofilm activity of the extract was concentration-dependent. In addition, the extract was stable to physicochemical treatments (heat, pH and protease). The extract performed favorable emulsifying property which could reduce the water surface tension from 72.708 mN/m to 51.011 mN/m and the critical micelle concentration (CMC) value was 6.88 mg/mL. Besides, the extract was also able to emulsify hydrocarbon substrates with the emulsification, index (E24) ranged from 38.55% (for n-hexane) to 53.78% (for xylene). The E24 for xylene/extract emulsion was merely decreased by 5.77% after standing for 120 h. The main components of the extract were polysaccharide (684.63 µg/mL) and protein (120.79 µg/mL). CONCLUSION: The properties of the extract indicated that it might be a kind of biosurfactant. These data suggested that L. rhamnosus YT and the cell-surface extract could be used as an alternative antimicrobial and antibiofilm agent against foodborne pathogens and spoilage bacteria in food industry.

A Markerless 2D Video, Facial Feature Recognition-Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study.

BACKGROUND: Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible. OBJECTIVE: The study aimed to develop a markerless 2D video, facial feature recognition-based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis. METHODS: We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists. RESULTS: The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD. CONCLUSIONS: PD patients commonly exhibit masked facial features. Videos of a facial feature recognition-based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient's condition, especially during the COVID-19 pandemic.

A weak post-acidification Lactobacillus helveticus UV mutant with improved textural properties.

To derive a mutant of L. helveticus SH2-1 with the capacity of weak postacidification and high texturing, first, taking L. delbrueckii frs4-1 and S. thermophilus grx02 as the controls, H+-ATPase activity was demonstrated to be highly related to the postacidification of L. helveticus SH2-1. Then, by detecting H+-ATPase activity, the weak postacidify mutant of L. helveticus SH2-1 (renamed as L. helveticus sh2-5-66) was selected from 80 UV mutants. The pH and acidity of the milk fermented with L. helveticus sh2-5-66 were separately 0.57 pH units higher and 57.1 °T lower than that of L. helveticus SH2-1. The acidification of L. helveticus sh2-5-66 was further demonstrated to be genetically stable during 100 generations cultivation. Moreover, the milk fermented with L. helveticus sh2-5-66 showed improvement in textural and rheological properties and flavor during storage which could be further improved by coculture with the commercial starter S. thermophilus st447.

Task-Oriented Deep Reinforcement Learning for Robotic Skill Acquisition and Control.

Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep neural networks, have been widely studied for autonomous robotic skill acquisition and control tasks. However, these methods and their extensions require extensive environmental interactions during training, which greatly prevents them from being applied to real-world robots. To alleviate this problem, we present an efficient model-free off-policy actor-critic algorithm for robotic skill acquisition and continuous control, by fusing the task reward with a task-oriented guiding reward, which is formulated by leveraging few and imperfect expert demonstrations. In this framework, the agent can explore the environment more intentionally, thus sampling efficiency can be achieved; moreover, the agent can also exploit the experience more effectively, thereby substantially improved performance can be realized simultaneously. The empirical results on robotic locomotion tasks show that the proposed scheme can lower sample complexity by 2-10 times in contrast with the state-of-the-art baseline deep RL (DRL) algorithms, while achieving performance better than that of the expert. Furthermore, the proposed algorithm achieves significant improvement in both sampling efficiency and asymptotic performance on tasks with sparse and delayed reward, wherein those baseline DRL algorithms struggle to make progress. This takes a substantial step forward to implement these methods to acquire skills autonomously for real robots.

Two-step system for image receiving in OAM-SK-FSO link.

Turbulence resistance is a significant research area for orbital angular momentum shift keying-based free space optical communication (OAM-SK-FSO). We put forward a two-step combinational system to receive high fidelity image data from the atmospheric turbulence (AT) channels. Firstly, the AT-detector-based multi-CNN (ATDM-CNN) demodulator is proposed which is very different from the traditional single-CNN (S-CNN) demodulator. The AT detector detects the AT strength and then an AT-determined CNN-based demodulator is activated to recognize the incident OAM modes. Sufficient numeral simulations compare the recognition rates of ATDM-CNN and S-CNN. The results indicate a tremendous improvement owing to the ATDM-CNN demodulator. Base on the ATDM-CNN's significant advantage in OAM recognition, the significant optimization of image data quality is possible in the further correction. As an option, the residual information errors are corrected by jointly using the rank-order adaptive median filter (RAMF) and the very-deep super-resolution (VDSR) network with minor information loss in severe ATs. The data increase resulting from RAMF-VDSR is tested. In conclusion, the proposed two-step system can provide a much higher quality of receiving image data in the OAM-SK-FSO link.

Output feedback stabilization of uncertain nonholonomic systems with external disturbances via active disturbance rejection control.

This paper investigates a class of nonholonomic systems with multifarious uncertainties including unmodeled dynamics, linear or nonlinear parameterization and unknown external disturbances. The objective is to deal with all uncertainties in a unified framework and simultaneously realize the global stabilization of the nonholonomic system. In terms of the nonholonomic constraints, a concise switching strategy is employed for stabilizing the whole system. Active disturbance rejection control (ADRC) is designed for an uncertain lower-triangular subsystem which is transformed from the nonholonomic system by using an input-state scaling technique. All system uncertainties are involved as the equivalent total disturbance which can be estimated by extended state observer (ESO) and compensated for by a feedback control input. Closed-loop performance analysis clarifies that the uncertain subsystem can be stabilized and the observation errors can be regulated to zero by tuning the bandwidth of the ESO. Simulations on a bilinear model illustrate the effectiveness of the proposed method.

Isolation of novel Lactobacillus with lipolytic activity from the vinasse and their preliminary potential using as probiotics.

Lactobacillus casei f1, L. paracasei f2 and L. paracasei f3 with lipolytic activity were isolated and identified from vinasses according to the morphological-physiological properties detection and 16S rDNA analysis. These three strains showed obvious lipase activities to olive oil and L. casei f1 performed highest enzyme activity of 17.8 U/mL. L. casei f1, L. paracasei f2 and L. paracasei f3 could lipolyze the blending oils, peanut oil and sesame oil with diverse degrading rates. The degrading rates to the preferred oils, L. casei f1 to blending oils, L. paracasei f2 to peanut oil and L. paracasei f3 to sesame oil, were 21.2%, 27.3% and 39.6%, respectively. The corresponding oil degrading rates increased as the cell growth and the highest degrading rates were obtained at the stationary phase with the viable count more than 7.5 LogCFU/mL. By GC-MS analysis, L. casei f1, L. paracasei f2 and L. paracasei f3 performed diverse lipolytic capacities to the 12 kinds of fat acids and all of them preferred to hydrolyze the linoleic acid with the degrading rate of 49.11%, 31.83% and 64.44%, respectively. These three strains showed considerable probiotic properties, displaying higher than 106 CFU/mL desirable viable count though the simulated gastrointestinal tract, as well as inhibiting six indicator bacteria. These results suggested that the three isolated strains could be considered as novel probiotic candidates and applied in the food industry.

Broad-host-range application of the srfA promoter from Bacillus subtilis in Escherichia coli.

The promoter of the srf operon (PsrfA) had been used to construct a cell-density-dependent expression system in B. subtilis in our previous work. The PsrfA and its derivative PsrfA12 showed good performance of heterologous protein expression in B. subtilis. In this work, using green fluorescent protein (GFP) and ß-galactosidase (LacZ) as the reporter proteins, the host feasibility and expression characteristics of the PsrfA and PsrfA12 in E. coli were identified. The prominent green fluorescence shooted by laser scanning confocal microscope, fluorescence intensity measured by spectrophotometer and the distinct protein bands detected by SDS-PAGE demonstrated that the GFP could be largely expressed under the control of the PsrfA and PsrfA12 in the E. coli host strain of BL21 (DE3) and JM109 and the expression of GFP in strain BL21 (DE3) was much higher than that of in strain JM109. Meanwhile, the promoter PsrfA 12 was much stronger than PsrfA to the extent that the GFP controlled by PsrfA12 in strain BL21 (DE3) was leaked into the supernatant. And the fluorescence intensity detected in the supernatant of the recombinant strain BL21 (DE3) containing PsrfA12 was 10.25-fold higher than that of strain JM109 containing PsrfA. Moreover, the LacZ could also be produced by PsrfA and PsrfA12 in strain BL21 (DE3) and JM109 and the strain JM109 showed better performance than BL21 (DE3) in expressing LacZ. The LacZ activity controlled by PsrfA and PsrfA12 in JM109 were separately 2.47-fold and 2.36-fold higher than that of in strain BL21 (DE3). This work will broaden the applied range of the PsrfA and enrich the efficient toolbar for cross-species gene expression or module construction in E. coli and B. subtilis.

A Heuristic algorithm for robustly stable generalized disturbance observer synthesis with closed loop consideration.

This paper investigate the problem of disturbance observer (DOB) based disturbance rejection control with closed loop performance guaranteed. A generalized DOB based control framework is presented. Specifically, the novel DOB framework if obtained by taking advantage of Youla factorization of a two degree-of-freedom controller in a nontraditional way. Closed loop analysis clarifies that generalized DOB inherits advantages of the traditional one, while mitigating its restrictions. Through appropriate reconfiguration, the Q-filter synthesizing is transformed into reduced-order controller designing. By taking advantage of the Kalman-Yakubovich-Popov (KYP) lemma and projection lemma, a two-stage heuristic algorithm is proposed: an initial full information controller is firstly established for the reconfigured system, which is used to heuristically obtain the reduced-order controller by alternately solving LMIs in the second stage. Finally, we illustrate the application of these results to minimum phase and non-minimum phase plants. Elaborated comparisons between the two-stage heuristic algorithm based generalized DOB and the state-of-art H∞ based DOB are conducted, the results verify the effectiveness and advantages.

Composite disturbance rejection control based on generalized extended state observer.

Traditional extended state observer (ESO) design method does not focus on analysis of system reconstruction strategy. The prior information of the controlled system cannot be used for ESO implementation to improve the control accuracy. In this paper, composite disturbance rejection control strategy is proposed based on generalized ESO. First, the disturbance rejection performance of traditional ESO is analyzed to show the essence of the reconstruction strategy. Then, the system is reconstructed based on the equivalent disturbance model. The generalized ESO is proposed based on the reconstructed model, while convergence of the proposed ESO is analyzed along with the outer loop feedback controller. Simulation results on a second order mechanical system show that the proposed generalized ESO can deal with the external disturbance with known model successfully. Experiment of attitude tracking task on an aircraft is also carried out to show the effectiveness of the proposed method.

Robust uncalibrated visual servoing control based on disturbance observer.

In this paper, an uncalibrated visual servoing scheme with optimal disturbance rejection performance is proposed based on disturbance observer (DOB). Comparing with traditional uncalibrated methods, which estimate online the hand-eye relationship characterized by varying image Jacobian, the uncertainty of image Jacobian is eliminated via DOB to approach a given nominal model in this paper. By solving a constrained optimization problem transformed into an H∞ control framework, the disturbance rejection performance is optimized while ensuring the robust stability of the closed-loop visual servoing system. The controller is based on the nominal image Jacobian matrix, thus avoiding singularities and local minima. Simulations and experiments show that the proposed scheme performs better in tracking an object than traditional algorithms. The disturbance and image noise rejection performance is verified.

Disturbance rejection control for non-minimum phase systems with optimal disturbance observer.

This paper investigates the disturbance rejection control for stable non-minimum phase (NMP) systems with time delay. A robust disturbance observer (DOB) based control structure is proposed. Specifically, the robust DOB is employed to compensate the uncertain plant into a nominal one, based on which a prefilter is adopted to acquire desired performance. Then, a novel DOB configuration strategy for stable NMP systems is proposed. This strategy synthesizes the internal and robust stability, relative order and mixed sensitivity design requirements together to establish the optimization function. The optimal solution is obtained by standard H∞ theory under the condition of guarantying the presented requirements. We also investigate how the DOB can compensate the uncertain plant into a nominal one. The specifical design procedure is presented for an uncertain plant with both unstable zeros and time delay.

Motion planning and coordination for robot systems based on representation space.

This paper proposes a general motion planning and coordination strategy for robot systems. The representation space (RS) of a robot system is constructed to describe the distributions of system attributes. The reachable area in the RS, denoting the attribute set that the system can be of, indicates the system's ability to accomplish tasks. Moreover, it also describes the influences of the internal and external constraints on the system's capability. Task realization is transformed to finding a trajectory in the RS for the system attributes to transit along under constraints. Meanwhile, the realizable conditions of a prescribed task by the robot system of specific configurations are discussed. If the task is realizable, the optimal strategy for task execution could further be figured out. Otherwise, it could be transformed to be realizable via task reassignment or system reconfigurations so that a connected path could be found for the transition of the system attributes from the starting point to the goal in the RS. The proposed scheme contributes to designing, planning, and coordination of the robotic tasks. Experiments on path planning of a robot manipulator and formation movement of a multirobot system, as well as coordination of a mobile manipulator system, are conducted to show the validity and generalization of the proposed method.

Nonlinear visual mapping model for 3-D visual tracking with uncalibrated eye-in-hand robotic system.

A new control scheme for uncalibrated robotic visual tracking problem is proposed that compromises the computational expenses of overall system with offline modeling and online control. A nonlinear visual mapping model for the uncalibrated hand-eye coordination is first proposed with an artificial neural network implementation. An online visual tracking controller is then developed together with a real-time motion planner. To improve the system performance, the control scheme is also integrated with a feedforward controller to compensate unknown object motions. Extensive simulations and experiments demonstrate the effectiveness of the proposed control scheme.

Incremental learning with balanced update on receptive fields for multi-sensor data fusion.

This paper addresses multi-sensor data fusion with incremental learning ability. A new cost function is proposed for the receptive field weighted regression (RFWR) algorithm based on the idea of back propagation (BP), so that the computation efficiency and the learning strategy of the modified RFWR are much more applicable for multi-sensor data fusion problem. Thus a new fusion structure and algorithm with incremental learning ability is constructed by adopting the modified RFWR algorithm together with the weighted average algorithm. Experiments of a two-camera unified positioning system are implemented successfully to test the proposed computation structure and algorithms.