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AIM: To assess the retinal vasculature alterations in indirect traumatic optic neuropathy (ITON) patients following craniofacial trauma by optic coherence tomography angiography (OCTA). METHODS: Patients diagnosed of monocular ITON were recruited from August 2016 to May 2020. OCTA was performed using the AngioVue OCT-A system for two cube scans centered at the optic nerve head and fovea. OCTA data included thicknesses of peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC), as well as proportion of capillary perfusion and data were analyzed for correlation with post-injury timepoints: within 7, 8-30, 31-90, and 91-365d. RESULTS: A total of 73 ITON patients were studied. Significant thinning of RNFL and GCC layers and attenuation of microvascular perfusion were observed in ITON eyes as compared to contralateral unaffected eyes (for most of the analyzed sectors and quadrants, P

This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-based end-to-end perception–planning–execution method to overcome the challenges associated with training and learning in end-to-end approaches that directly output control forces. In this approach, the state set is established based on the environment information, the action set is established based on the motion characteristics of the AUV, and the control execution force set is established based on the control constraints. The mapping relations between each set are trained using deep reinforcement learning, enabling the AUV to perform the corresponding action in the current state, thereby accomplishing tasks in an end-to-end manner. Furthermore, we introduce the hindsight experience replay (HER) method in the perception planning mapping process to enhance stability and sample efficiency during training. Finally, we conduct simulation experiments encompassing planning, execution, and end-to-end performance evaluation. Simulation training demonstrates that our proposed method exhibits improved decision-making capabilities and real-time obstacle avoidance during planning. Compared to global planning, the end-to-end algorithm comprehensively considers constraints in the AUV planning process, resulting in more realistic AUV actions that are gentler and more stable, leading to controlled tracking errors.

Abstract. Background:. Colorectal cancer is the third most common cancer worldwide and still lack of effective therapy so far. Petasin, a natural product found in plants of the genus Petasites, has been reported to possess anticancer activity. The present study aimed to investigate the anticolon cancer activity of petasin both in vitro and in vivo. The molecular mechanism of petasin was also further explored. Methods:. Caco-2, LoVo, SW-620, and HT-29 cell lines were used to detect the inhibitory effect of petasin on colon cancer proliferation. Cell viability was determined using the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide) assay. Cell apoptosis was analyzed by flow cytometry. Hoechst 33258 staining was used to visualize morphological changes. Cell migration was assessed using a wound-healing migration assay, and cell invasion was investigated using Transwell chambers. Western blotting assays were employed to evaluate the expression levels of proteins in the protein kinase B/mammalian target of rapamycin (Akt/mTOR) signaling pathway. Finally, in vivo activity of petasin was evaluated using the SW-620 subcutaneous tumor model established in Balb/c nude mice. Twelve rats were randomly divided into control group and 10 mg/kg petasin group. The tumor volume was calculated every 7 days for 28 days. Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay was performed to assess the apoptotic effect of petasin. Differences between two groups were assessed by analysis of independent-sample t tests. Results:. Petasin significantly inhibited the proliferation of human colon carcinoma cell lines, induced apoptosis, and suppressed migration and invasion in SW-620 cells. Western blotting results showed that petasin decreased the phosphorylation of Akt (1.01 ± 0.16 vs. 0.74 ± 0.06, P = 0.042), mTOR (0.71 ± 0.12 vs. 0.32 ± 0.11, P = 0.013), and P70S6K (1.23 ± 0.21 vs. 0.85 ± 0.14, P = 0.008), elevated the expression of caspase-3 (0.41 ± 0.09 vs. 0.74 ± 0.12, P = 0.018) and caspase-9 (1.10 ± 0.27 vs. 1.98 ± 0.22, P = 0.009), decreased the Bcl-2 protein (2.75 ± 0.47 vs. 1.51 ± 0.36, P = 0.008), downregulated the expression of matrix metalloproteinase (MMP)-3 (1.51 ± 0.31 vs. 0.82 ± 0.11, P = 0.021) and MMP-9 (1.56 ± 0.32 vs. 0.94 ± 0.15, P = 0.039) in SW-620 cell. In vivo, 10 mg/kg petasin inhibited tumor growth in Balb/c nude mice (924.18 ± 101.23 vs. 577.67 ± 75.12 mm3 at day 28, P = 0.001) and induced apoptosis (3.6 ± 0.7% vs. 36.0 ± 4.9%, P = 0.001) in tumor tissues. Conclusions:. Petasin inhibits the proliferation of colon cancer SW-620 cells via inactivating the Akt/mTOR pathway. Our findings suggest petasin as a potential candidate for colon cancer therapy.

New types of artificial intelligence products are gradually transferring to voice interaction modes with the demand for intelligent products expanding from communication to recognizing users' emotions and instantaneous feedback. At present, affective acoustic models are constructed through deep learning and abstracted into a mathematical model, making computers learn from data and equipping them with prediction abilities. Although this method can result in accurate predictions, it has a limitation in that it lacks explanatory capability; there is an urgent need for an empirical study of the connection between acoustic features and psychology as the theoretical basis for the adjustment of model parameters. Accordingly, this study focuses on exploring the differences between seven major “acoustic features” and their physical characteristics during voice interaction with the recognition and expression of “gender” and “emotional states of the pleasure-arousal-dominance (PAD) model.” In this study, 31 females and 31 males aged between 21 and 60 were invited using the stratified random sampling method for the audio recording of different emotions. Subsequently, parameter values of acoustic features were extracted using Praat voice software. Finally, parameter values were analyzed using a Two-way ANOVA, mixed-design analysis in SPSS software. Results show that gender and emotional states of the PAD model vary among seven major acoustic features. Moreover, their difference values and rankings also vary. The research conclusions lay a theoretical foundation for AI emotional voice interaction and solve deep learning's current dilemma in emotional recognition and parameter optimization of the emotional synthesis model due to the lack of explanatory power.