Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction

The task of action-driven human motion prediction aims to forecast future human motion from the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most of the datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as Walk or Run. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive methods on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness

Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we propose a novel approach to predicting future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce the energy-based formulation to flexibly impose prior losses over the learnable parameters of MDN to maintain motion coherence as well as improve the prediction accuracy by customizing the energy functions. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method in terms of prediction diversity and accuracy

Preparation and in vivo evaluation of gel-based nasal delivery system for risperidone

The aim of this study was to prepare a nasal gel of risperidone and to investigate the pharmacokinetics and relative bioavailability of the drug in rats. Compared with oral dosing, the risperidone nasal gel exhibited very fast absorption and high bioavailability. Maximal plasma concentration (cmax ) and the time to reach cmax (tmax) were 15.2 µg mL–1 and 5min for the nasal gel, 3.6 µg mL–1and 30 min for the oral drug suspension, respectively. Pharmacokinetic parameters such as tmax, cmax and AUC between oral and nasal routes were significantly different (p ＜ 0.01). Relative bioavailability of the drug nasal preparation to the oral suspension was up to 1600.0%. Further, the in vitro effect of the risperidone nasal gel on nasal mucociliary movement was also investigated using a toad palate model. The risperidone nasal formulation showed mild ciliotoxicity, but the adverse effect was temporary and reversible

A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization

Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural language processing. Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons. However, the single-output design limits its capability to handle multi-output tasks, significantly lowering its applications. In this paper, we propose a novel multi-in and multi-out dendritic neuron model (MODN) to tackle multi-output tasks. Our core idea is to introduce a filtering matrix to the soma layer to adaptively select the desired dendrites to regress each output. Because such a matrix is designed to be learnable, MODN can explore the relationship between each dendrite and output to provide a better solution to downstream tasks. We also model a telodendron layer into MODN to simulate better the real neuron behavior. Importantly, MODN is a more general and unified framework that can be naturally specialized as the DNM by customizing the filtering matrix. To explore the optimization of MODN, we investigate both heuristic and gradient-based optimizers and introduce a 2-step training method for MODN. Extensive experimental results performed on 11 datasets on both binary and multi-class classification tasks demonstrate the effectiveness of MODN, with respect to accuracy, convergence, and generality

Preparation and characterization of simvastatin/DMβCD complex and its pharmacokinetics in rats

Simvastatin is poorly bioavailable because it is practically insoluble in water and shows dissolution rate-limited absorption. Solubilizing effects of several β-cyclodextrin （βCD） derivatives such as HPβCD, SBEβCD and DMβCD on simvastatin in aqueous solution were investigated using the phase solubility technique. The solubility diagram of simvastatin with each βCD derivative could be classified as AL-type, indicating soluble complex formation of 1:1 stoichiometry. Among the above βCD derivatives DMβCD was found to be the ideal complexing agent for improving drug solubility. The simvastatin complex with DMβCD was prepared using the co-evaporation method and was then characterized by differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FT-IR) and in vitro dissolution. Dissolution and pharmacokinetic studies indicated that the simvastatin/DMβCD complex exhibited an increased dissolution rate, rapid absorption, and improved bioavailability in rats compared to free drug. Maximum plasma concentration (cmax) and the time to reach it (tmax) were 21.86 µg mL–1 and 1.4 h for the drug complex, 8.25 µg mL–1 and 3.0 h for free drug, respectively. Main pharmacokinetic parameters such as tmax, cmax were significantly different (p < 0.01) between the simvastatin complex and free drug. Bioavailability of the simvastatin complex relative to free drug was up to 167.0 %

Preparation and characterization of simvastatin/DMβCD complex and its pharmacokinetics in rats

Simvastatin is poorly bioavailable because it is practically insoluble in water and shows dissolution rate-limited absorption. Solubilizing effects of several β-cyclodextrin (βCD) derivatives such as HPβCD, SBEβCD and DMβCD on simvastatin in aqueous solution were investigated using the phase solubility technique. The solubility diagram of simvastatin with each βCD derivative could be classified as AL-type, indicating soluble complex formation of 1:1 stoichiometry. Among the above βCD derivatives DMβCD was found to be the ideal complexing agent for improving drug solubility. The simvastatin complex with DMβCD was prepared using the co-evaporation method and was then characterized by differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FT-IR) and in vitro dissolution. Dissolution and pharmacokinetic studies indicated that the simvastatin/DMβCD complex exhibited an increased dissolution rate, rapid absorption, and improved bioavailability in rats compared to free drug. Maximum plasma concentration (cmax) and the time to reach it (tmax) were 21.86 μg mL−1 and 1.4 h for the drug complex, 8.25 μg mL−1 and 3.0 h for free drug, respectively. Main pharmacokinetic parameters such as tmax, cmax were significantly different (p < 0.01) between the simvastatin complex and free drug. Bioavailability of the simvastatin complex relative to free drug was up to 167.0 %

Preparation and in vivo evaluation of a gel-based nasal delivery system for risperidone

The aim of this study was to prepare a nasal gel of risperidone and to investigate the pharmacokinetics and relative bioavailability of the drug in rats. Compared with oral dosing, the risperidone nasal gel exhibited very fast absorption and high bioavailability. Maximal plasma concentration (cmax) and the time to reach cmax (tmax) were 15.2 μg mL-1 and 5 min for the nasal gel, 3.6 μg mL-1 and 30 min for the oral drug suspension, respectively. Pharmacokinetic parameters such as tmax′, cmax and AUC of oral and nasal routes were significantly different (p < 0.01). Relative bioavailability of the drug nasal preparation to the oral suspension was up to 1600.0 %. Further, the in vitro effect of the risperidone nasal gel on nasal mucociliary movement was also investigated using a toad palate model. The risperidone nasal formulation showed mild ciliotoxicity, but the adverse effect was temporary and reversible

Preparation, characterization, and in vivo pharmacokinetics of thermosensitive in situ nasal gel of donepezil hydrochloride

Donepezil hydrochloride thermosensitive in situ gel for nasal delivery was prepared by using Poloxamer 407 and Poloxamer 188 as thermoreversible polymers, hydroxypropyl-β-cyclodextrin and ethylparaben as permeation enhancer and preservative, respectively. The gelation temperature and time, pH value of the gel formulation were found to meet the requirements for nasal administration. The in vitro erosion and in vitro release tests exhibited obvious drug sustained release behavior. Meantime, main pharmacokinetic parameters such as tmax, cmax and AUC in plasma as well as in brain were significantly different between the nasal gel formulation and intragastric drug solution in rats (p < 0.01). The relative bioavailability and drug targeting efficiency of the gel formulation were calculated to be 385.6 and 151.2 %, respectively. Thus, the drug gel formulation might be a potential new delivery system for treatment of Alzheimer’s disease due to its higher bioavailability and better distribution to brain when compared to oral route