First case of glyphosate resistance in Polypogon fugax: possible involvement of P450-based mechanisms.

Background: Polypogon fugax has evolved resistance to multiple herbicides in China, yet there has been no documented case of glyphosate resistance. A putative glyphosate-resistant P. fugax (HN-R) population was collected from canola fields in Hunan Province, China, surviving glyphosate treatment at the field-recommended rate [540 g acid equivalent (a.

PfGSTF2 endows resistance to quizalofop-p-ethyl in Polypogon fugax by GSH conjugation.

Populations of Polypogon fugax have developed resistance to many acetyl-CoA carboxylase (ACCase)-inhibiting herbicides. This resistance threats the effectiveness and sustainability of herbicide use. In our previous research, a field P.

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

has become a global nuisance weed and has evolved resistance to glufosinate. The involvement of target-site resistance (TSR) in glufosinate resistance in 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.

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.

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.

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 CoO and the mesoporous hollow microsphere is improved in morphology homogeneity with the decrease of Co/Mn ratio in the range of 1.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.