Decoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis.

Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the "vision for action"; The dim condition significantly reduced the shooter's performance, whereas the noisy condition had less effect on the shooter's performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4' parameters decreased significantly while microstate 3' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target's center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.

Deep Sequencing Uncovers Rice Long siRNAs and Its Involvement in Immunity Against Rhizoctonia solani.

Small RNA (sRNA) is a class of noncoding RNA that can silence the expression of target genes. In rice, the majority of characterized sRNAs are within the range of 21 to 24 nucleotides (nt) long, whose biogenesis and function are associated with a specific sets of components, such as Dicer-like (OsDCLs) and Argonaute proteins (OsAGOs). Rice sRNAs longer than 24 nt are occasionally reported, with biogenesis and functional mechanism uninvestigated, especially in a context of defense responses against pathogen infection. By using deep sequencing, we identified a group of rice long small interfering RNAs (lsiRNAs) that are within the range of 25 to 40 nt in length. Our results show that some rice lsiRNAs are differentially expressed upon infection of Rhizoctonia solani, the causal agent of the rice sheath blight disease. Bioinformatic analysis and experimental validation indicate that some rice lsiRNAs can target defense-related genes. We further demonstrate that rice lsiRNAs are neither derived from RNA degradation nor originated as secondary small interfering RNAs (siRNAs). Moreover, lsiRNAs require OsDCL4 for biogenesis and OsAGO18 for function. Therefore, our study indicates that rice lsiRNAs are a unique class of endogenous sRNAs produced in rice, which may participate in response against pathogens.

Bacillus cereus AR156 activates PAMP-triggered immunity and induces a systemic acquired resistance through a NPR1-and SA-dependent signaling pathway.

Induced resistance responses play a potent role in plant defense system against pathogen attack. Bacillus cereus AR156 is a plant growth promoting rhizobacterium (PGPR) that installs induced systemic resistance (ISR) to Pseudomonas syringae pv. tomato (Pst) in Arabidopsis. Here, we show that AR156 leaf infiltration enhances disease resistance in Arabidopsis through the activation of a systemic acquired resistance (SAR). PR1 protein expression and reactive oxygen species (ROS) burst are strongly induced in plants treated with AR156 and inoculated with Pst than that in plants inoculated with Pst only. Moreover, AR156 can trigger SAR in jar1 or ein2 mutants, but not in the NahG transgenic and NPR1 mutant plants. Our results indicate that AR156-induced SAR depends on SA-signaling pathway and NPR1, but not JA and ET. Also, AR156-treated plants are able to rapidly activate MAPK signaling and FRK1 gene expression, which are involved in pathogen associated molecular pattern (PAMP)-triggered immunity (PTI). Altogether, our results indicate that AR156 can induce SAR by the SA-signaling pathways in an NPR1-dependent manner and involves multiple PTI components.