Background: Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear.Methods: Multi‐array electrodes were implanted in the dPAG nuclei of C57BL/6 mice. Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed.Results: The neural activity between flight and freezing behaviors showed different firing patterns, and the differences in the inter‐spike interval distribution were mainly reflected in the 2–10 ms period. The frequency band activities under both defensive behaviors were concentrated in the theta band; the active frequency of flight was ~8 to 10 Hz, whereas that of freezing behavior was ~6 to 8 Hz. The network connection density under both defense behaviors was significantly higher than the period before and after defensive behavior occurred, indicating that there was a high synchronization of neural activity during the defense process.Conclusions: The dPAG nuclei of mice have different coding features between flight and freezing behaviors; during strong looming stimulation, fast neuro‐instinctive decision making is required while encountering weak sweeping stimulation, and computable planning late behavior is predicted in the early stage. The frequency band activities under both defensive behaviors were concentrated in the theta band. There was a high synchronization of neural activity during the defense process, which may be a key factor triggering different defensive behaviors.

The microstructure and mechanical properties of X70 pipeline steel with a ferrite/martensite dual-phase microstructure produced by thermo-mechanical controlled processing were investigated by tensile tests, Charpy V-notched (CVN) impact tests, drop-weight tear tests, guided-bend tests, scanning electron microscopy and transmission electron microscopy combined with thermodynamic simulation analysis. All the mechanical properties met the strength, ductility, toughness and deformability properties requirements of X70 grade pipeline steel with strain-based design. The shear fracture area and absorbed energy of CVN at −10 °C were >97% and >205 J in base metal (BM), weld metal (WM) and heat affected zone (HAZ) with low transition temperature, indicating adequate resistance to propagating fracture. The microstructure of WM was mainly intragranular acicular ferrite that can guarantee high strength, toughness and over matching requirements of the welded joint. Because of being exposed to successive heat inputs, the ferrite plus martensite/bainite microstructure of BM was heated between A_c1 and T_s forming the HAZ. However, a high CVN impact toughness of 345 J at −10 °C in HAZ was obtained, which indicated that the excellent mechanical properties of BM would not be seriously deteriorated during the welding thermal cycles with the reasonable addition of Ti and Nb.

In this paper, a novel model for predicting anesthesia depth is put forward based on local field potentials (LFPs) in the primary visual cortex (V1 area) of rats. The model is constructed using a Support Vector Machine (SVM) to realize anesthesia depth online prediction and classification. The raw LFP signal was first decomposed into some special scaling components. Among these components, those containing higher frequency information were well suited for more precise analysis of the performance of the anesthetic depth by wavelet transform. Secondly, the characteristics of anesthetized states were extracted by complexity analysis. In addition, two frequency domain parameters were selected. The above extracted features were used as the input vector of the predicting model. Finally, we collected the anesthesia samples from the LFP recordings under the visual stimulus experiments of Long Evans rats. Our results indicate that the predictive model is accurate and computationally fast, and that it is also well suited for online predicting.

Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential (LFP) signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.