PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior.

Objective.The evaluation of animals' motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation methods are unreliable, unpractical, and inaccurate.Approach.Data augmentation (random scaling, random standard deviation Gaussian blur, random contrast, and random uniform color quantization) is adopted to augment image dataset. For the key points recognition, we present a novel efficient convolutional deep learning framework (PMotion), which combines modified ConvNext using multi-kernel feature fusion and self-defined stacked Hourglass block with SiLU activation function.Main results.PMotion is useful to predict the key points of dynamics of unmarked animal body joints in real time with high spatial precision. Gait quantification (step length, step height, and joint angle) was performed for the study of lateral lower limb movements with rats on a treadmill.Significance.The performance accuracy of PMotion on rat joint dataset was improved by 1.98, 1.46, and 0.55 pixels compared with deepposekit, deeplabcut, and stacked hourglass, respectively. This approach also may be applied for neurobehavioral studies of freely moving animals' behavior in challenging environments (e.g.Drosophila melanogasterand openfield-Pranav) with a high accuracy.

Unexpected Terrain Induced Changes in Cortical Activity in Bipedal-Walking Rats.

Humans and other animals can quickly respond to unexpected terrains during walking, but little is known about the cortical dynamics in this process. To study the impact of unexpected terrains on brain activity, we allowed rats with blocked vision to walk on a treadmill in a bipedal posture and then walk on an uneven area at a random position on the treadmill belt. Whole brain EEG signals and hind limb kinematics of bipedal-walking rats were recorded. After encountering unexpected terrain, the θ band power of the bilateral M1, the γ band power of the left S1, and the θ to γ band power of the RSP significantly decreased compared with normal walking. Furthermore, when the rats left uneven terrain, the ß band power of the bilateral M1 and the α band power of the right M1 decreased, while the γ band power of the left M1 significantly increased compared with normal walking. Compared with the flat terrain, the θ to low ß (3-20 Hz) band power of the bilateral S1 increased after the rats contacted the uneven terrain and then decreased in the single- or double- support phase. These results support the hypothesis that unexpected terrains induced changes in cortical activity.

Opsonic Activity of Conservative Versus Variable Regions of the Group A Streptococcus M Protein.

Group A Streptococcus (GAS) and GAS-associated infections are a global challenge, with no licensed GAS vaccine on the market. The GAS M protein is a critical virulence factor in the fight against GAS infection, and it has been a primary target for GAS vaccine development. Measuring functional opsonic antibodies against GAS is an important component in the clinical development path for effective vaccines. In this study, we compared the opsonic activity of two synthetic, self-adjuvanting subunit vaccines containing either the J8- or 88/30-epitope in Swiss outbred mice using intranasal administration. Following primary immunization and three boosts, sera were assessed for IgG activity using ELISA, and opsonization activity against seven randomly selected clinical isolates of GAS was measured. Vaccine constructs containing the conservative J8-epitope showed significant opsonic activity against six out of the seven GAS clinical isolates, while the vaccine containing the variable 88/30-epitope did not show any significant opsonic activity.

Application of Fmoc-SPPS, Thiol-Maleimide Conjugation, and Copper(I)-Catalyzed Alkyne-Azide Cycloaddition "Click" Reaction in the Synthesis of a Complex Peptide-Based Vaccine Candidate Against Group A Streptococcus.

Fmoc solid-phase peptide synthesis (SPPS) is the most common approach used to synthesize natural and unnatural peptides. However, the synthesis of sequences longer than 30-60 amino acids is associated with a drastic reduction in peptide quality. Thus, large and complex peptides are normally synthesized as fragments, which are then conjugated together. Here, we describe the synthesis of a large, branched peptide, a multi-epitope vaccine candidate against Group A Streptococcus, with the help of microwave-assisted Fmoc-SPPS, thiol-maleimide conjugation, and copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) "click" reaction.

A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object.

A rodent real-time tracking framework is proposed to automatically detect and track multi-objects in real time and output the coordinates of each object, which combines deep learning (YOLO v3: You Only Look Once, v3), the Kalman Filter, improved Hungarian algorithm, and the nine-point position correction algorithm. A model of a Rat-YOLO is trained in our experiment. The Kalman Filter model is established in an acceleration model to predict the position of the rat in the next frame. The predicted data is used to fill the losing position of rats if the Rat-YOLO doesn't work in the current frame, and to associate the ID between the last frame and current frame. The Hungarian assigned algorithm is used to show the relationship between the objects of the last frame and the objects of the current frame and match the ID of the objects. The nine-point position correction algorithm is presented to adjust the correctness of the Rat-YOLO result and the predicted results. As the training of deep learning needs more datasets than our experiment, and it is time-consuming to process manual marking, automatic software for generating labeled datasets is proposed under a fixed scene and the labeled datasets are manually verified in term of their correctness. Besides this, in an off-line experiment, a mask is presented to remove the highlight. In this experiment, we select the 500 frames of the data as the training datasets and label these images with the automatic label generating software. A video (of 2892 frames) is tested by the trained Rat model and the accuracy of detecting all the three rats is around 72.545%, however, the Rat-YOLO combining the Kalman Filter and nine-point position correction arithmetic improved the accuracy to 95.194%.