Eleusine indica Cytochrome P450 and Glutathione S-Transferase Are Linked to High-Level Resistance to Glufosinate.

Eleusine indica has become a global nuisance weed and has evolved resistance to glufosinate. The involvement of target-site resistance (TSR) in glufosinate resistance in E. indica has been elucidated, while the role of nontarget-site resistance (NTSR) remains unclear. Here, we identified a glufosinate-resistant (R) population that is highly resistant to glufosinate, with a resistance index of 13.5-fold. Molecular analysis indicated that the resistance mechanism of this R population does not involve TSR. In addition, pretreatment with two known metabolic enzyme inhibitors, the cytochrome P450 (CYP450) inhibitor malathion and the glutathione S-transferase (GST) inhibitor 4-chloro-7-nitrobenzoxadiazole (NBD-Cl), increased the sensitivity of the R population to glufosinate. The results of subsequent RNA sequencing (RNA-seq) and quantitative real-time PCR (RT-qPCR) suggested that the constitutive overexpression of a GST gene (GSTU3) and three CYP450 genes (CYP94s and CYP71) may play an important role in glufosinate resistance. This study provides new insights into the resistance mechanism of E. indica.

Electrospun Ce-Mn oxide as an efficient catalyst for soot combustion: Ce-Mn synergy, soot-catalyst contact, and catalytic oxidation mechanism.

Increasing the contact efficiency and improving the intrinsic activity are two effective strategies to obtain efficient catalysts for soot combustion. Herein, the electrospinning method is used to synthesize fiber-like Ce-Mn oxide with a strong synergistic effect. The slow combustion of PVP in precursors and highly soluble manganese acetate in spinning solution facilitates the formation of fibrous Ce-Mn oxides. The fluid simulation clearly indicates that the slender and uniform fibers provide more interwoven macropores to capture soot particles than the cubes and spheres do. Accordingly, electrospun Ce-Mn oxide exhibits better catalytic activity than reference catalysts, including Ce-Mn oxides by co-precipitation and sol-gel methods. The characterizations suggest that Mn3+ substitution into fluorite-type CeO2 enhances the reducibility through the acceleration of Mn-Ce electron transfer, improves the lattice oxygen mobility by weakening the Ce-O bonds, and induces oxygen vacancies for the activation of O2. The theoretical calculation reveals that the release of lattice oxygen becomes easy because of a low formation energy of oxygen vacancy, while the high reduction potential is beneficial for the activation of O2 on Ce3+-Ov (oxygen vacancies). Due to above Ce-Mn synergy, the CeMnOx-ES shows more active oxygen species and higher oxygen storage capacity than CeO2-ES and MnOx-ES. The theoretical calculation and experimental results suggest that the adsorbed O2 is more active than lattice oxygen and the catalytic oxidation mainly follows the Langmuir-Hinshelwood mechanism. This study indicates that electrospinning is a novel method to obtain efficient Ce-Mn oxide.

Effect of iron substitution in cryptomelane on the heterogeneous reaction with isoprene.

Biogenic isoprene is an important pollutant for regional air quality. Being ubiquitously distributed on the earth surface, manganese (hydr)oxides should play a vital role in the transformation of isoprene. Cryptomelane is a typical manganese oxide with isomorphous substitution of Fe for Mn, but less attention has been paid to its heterogeneous reaction with isoprene. When Fe3+ replaces Mn3+, K+ is depleted and Mn3+ is oxidized to Mn4+. In contrast, oxygen vacancies are formed when Fe3+ substitutes Mn4+. Fe substitution creates weak crystallites and abundant mesopores, resulting in the increase of isoprene adsorption. As found by theoretical calculations, the Mn4+-O2- bonds at the cross sections of the tunnels is more active than that on the outer wall of the tunnels. After the adsorption of isoprene, bridging carboxylate species and hydrogen-bonding water are produced and the surface octahedra are distorted, i.e., Mn4+O6 → Mn3+O6-δ. As the heat facilitates the breakage of Mn4+-O2-, the increase of environmental temperature enhances the oxidation of isoprene. The above findings shed light on the effect of Fe substitution in cryptomelane to enhance the oxidation of isoprene, and illustrates that heterogeneous reaction with isoprene impairs the transformation of other environmental substances on cryptomelane.

Insight into the effect of manganese substitution on mesoporous hollow spinel cobalt oxides for catalytic oxidation of toluene.

The cobalt oxides and manganese oxides have high-activity potential for catalytic oxidation of volatile organic compounds (VOCs), while the mesoporous hollow morphology is crucial to the mass transfer of reactant and product. Therefore, it is worth investigating the effect of manganese substitution in mesoporous hollow cobalt oxides on catalytic oxidation. Herein, a partially disordered spinel structure is formed by the Mn substitution in Co3O4 and the mesoporous hollow microsphere is improved in morphology homogeneity with the decrease of Co/Mn ratio in the range of 1.8-28.8. The 5Co1Mn (Mn-substituted Co3O4 with Co/Mn at 5.4) exhibits outstanding catalytic activity for toluene oxidation with 50% CO2 generation at 237 °C, which is 21 °C lower than Co3O4. Moreover, the 5Co1Mn displays satisfactory stability in reusability, lifetime, and water resistance. The small defective crystallite, mesoporous hollow morphology, and high specific surface area endow Mn-substituted Co3O4 with more surface chemical adsorbed oxygen, enhancing the catalytic oxidation of toluene. Theoretical calculation on (311) plane of Co3O4 reveals that Mn2+ or Mn3+ substitution increases the formation energy of oxygen vacancy and makes it difficult to adsorb gaseous oxygen on the defective surface. The interaction between Co and Mn impedes the improvement of toluene oxidation because the mobility of lattice oxygen, the surface distribution of Co3+, and the ratio of surface adsorbed oxygen to surface lattice oxygen are hindered by Mn substitution. The chemical adsorbed oxygen is more active than lattice oxygen in the oxidation of adsorbed intermediates (phenolate, benzoate species, etc.). The Langmuir-Hinshelwood mechanism dominates in the catalytic oxidation at 200-250 °C, while the catalytic oxidation follows both the Langmuir-Hinshelwood mechanism and Mars-van Krevelen mechanism above 250 °C. This work provides some enlightenment for exploring the role of surface oxygen species in VOCs oxidation and uncovering the interaction in binary spinel oxides.

A novel thixotropic magnesium phosphate-based bioink with excellent printability for application in 3D printing.

The emergence of 3D bioprinting is expected to solve the present puzzle in the field of regenerative medicine. However, the appropriate bioink was lacking due to the rigorous requirement of high printability and biocompatibility, which was often contradictory. In this study, a novel thixotropic magnesium phosphate-based gel (TMP-BG) was prepared and its application in 3D printing was explored. The stable gel could be synthesized by adjusting the ratio of ternary reactants (NaOH, Mg(OH)2, and H3PO4). Moreover, the structure, morphology, particle size and composition of TMP-BG were characterized. Furthermore, the rheological and thixotropic behaviors and degradation of TMP-BG were investigated. The printability of TMP-BG was tested by using the extrusion-based 3D printer. The biocompatibility of TMP-BG was evaluated in vitro. The composition of TMP-BG was MgNa3H(PO4)2, which was of nanometer and sub-micro scale and easily formed a complex three-dimensional porous structure. Rheological results showed that the gel had notable shear thinning behavior and good thixotropy, which could provide the TMP-BG with injectability and formability simultaneously. In addition, the thixotropic mechanisms of TMP-BG were speculated to be a model of "house of cards". Finally, TMP-BG could be printed into large-sized and different complex three-dimensional structures. Results of the MG-63 cell viability and cell proliferation confirmed the biocompatibility of TMG-BG. The present newly developed TMP-BG has the potential to be used as 3D printing bioink involving living cells for future applications in regenerative medicine.

Quantized Attention-Gated Kernel Reinforcement Learning for Brain-Machine Interface Decoding.

Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret dynamic neural activity without patients' real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require finding a good policy in very few trials, which impose a limit on the complexity of the tasks that can be learned before the animal quits. Therefore, this paper explores the possibility of letting the agent infer potential goals through actions over space with multiple objects, using the instantaneous reward to assign credit spatially. A previous method, attention-gated RL employs a multilayer perceptron trained with backpropagation, but it is prone to local minima entrapment. We propose a quantized attention-gated kernel RL (QAGKRL) to avoid the local minima adaptation in spatial credit assignment and sparsify the network topology. The experimental results show that the QAGKRL achieves higher successful rates and more stable performance, indicating its powerful decoding ability for more sophisticated BMI tasks as required in clinical applications.

Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain-Machine Interfaces.

Classic brain-machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces.

OBJECTIVE: Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. APPROACH: In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat's motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. MAIN RESULTS: Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. SIGNIFICANCE: These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

Prediction of switching time between movement preparation and execution by neural activity in monkey premotor cortex.

Premotor cortex is a higher level cortex than primary motor cortex in movement controlling hierarchy, which contributes to the motor preparation and execution simultaneously during the planned movement. The mediation mechanism from movement preparation to execution has attracted many scientists' attention. Gateway hypothesis is one possible explanation that some neurons act as "gating" to release the movement intention at the "on-go" cue. We propose to utilize a local-learning based feature extraction method to target the neurons in premotor cortex, which functionally contribute mostly to the discrimination between motor preparation and execution without tuning information to either target or movement trajectory. Then the support vector machine is utilized to predict the single trial switching time. With top three functional "gating" neurons, the prediction accuracy rate of the switching time is above 90%, which indicates the potential of asynchronous BMI control using premotor cortical activity.

Neural decoding using a parallel sequential Monte Carlo method on point processes with ensemble effect.

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Information analysis on neural tuning in dorsal premotor cortex for reaching and grasping.

Previous studies have shown that the dorsal premotor cortex (PMd) neurons are relevant to reaching as well as grasping. In order to investigate their specific contribution to reaching and grasping, respectively, we design two experimental paradigms to separate these two factors. Two monkeys are instructed to reach in four directions but grasp the same object and grasp four different objects but reach in the same direction. Activities of the neuron ensemble in PMd of the two monkeys are collected while performing the tasks. Mutual information (MI) is carried out to quantitatively evaluate the neurons' tuning property in both tasks. We find that there exist neurons in PMd that are tuned only to reaching, tuned only to grasping, and tuned to both tasks. When applied with a support vector machine (SVM), the movement decoding accuracy by the tuned neuron subset in either task is quite close to the performance by full ensemble. Furthermore, the decoding performance improves significantly by adding the neurons tuned to both tasks into the neurons tuned to one property only. These results quantitatively distinguish the diversity of the neurons tuned to reaching and grasping in the PMd area and verify their corresponding contributions to BMI decoding.

Decoding grasp types with high frequency of local field potentials from primate primary dorsal premotor cortex.

Recently, local field potentials (LFPs) have been successfully used to extract information of arm and hand movement in some brain-machine interfaces (BMIs) studies, which suggested that LFPs would improve the performance of BMI applications because of its long-term stability. However, the performance of LFPs in different frequency bands has not been investigated in decoding hand grasp types. Here, the LFPs from the monkey's dorsal premotor cortices were collected by microelectrode array when monkey was performing grip-specific grasp task. A K-nearest neighbor classifier performed on the power spectrum of LFPs was used to decode grasping movements. The decoding powers of LFPs in different frequency bands, channels and trials used for training were also studied. The results show that the broad high frequency band (200-400Hz) LFPs achieved the best performance with classification accuracy reaching over 0.9. It infers that high frequency band LFPs in PMd cortex could be a promising source of control signals in developing functional BMIs for hand grasping.

Mutual information analysis on non-stationary neuron importance for brain machine interfaces.

Decoding with the important neuron subset has been widely used in brain machine interfaces (BMIs), as an effective strategy to reduce computational complexity. Previous works usually assume stationary of neuron importance, which may not be true according to recent research. We propose to conduct a mutual information evaluation to track the time-varying neuron importance over time. We found worth noting changes both in information amount and space distribution in our experiment. When the method is applied with a Kalman filter, the decoding performance achieve is better (with higher correlation coefficient) than when a fixed subset, which shows that time-varying neuron importance should be considered in adaptive algorithms.