Understanding the toxicity mechanism of gelsemine in zebrafish.

Gelsemium elegans (GE), also known as Duanchangcao, is a plant associated with toxic symptoms related to the abdomen; however, the toxicity caused by GE remains unknown. Gelsemine (GEL) is an alkaloid extracted from GE and is one of the most toxic alkaloids. This study used zebrafish as an animal model and employed high-throughput gene sequencing to identify genes and signaling pathways related to GEL toxicity. Exposure to GEL negatively impacted heart rate, swim bladder development, and activity in zebrafish larvae. Transcriptomics data revealed the enrichment of inflammatory and phagocyte signaling pathways. RT-PCR analysis revealed a decrease in the expression of pancreas-related genes, including the pancreatic coagulation protease (Ctr) family, such as Ctrl, Ctrb 1, and Ctrc, due to GEL exposure. Furthermore, GEL exposure significantly reduced Ctrb1 protein expression while elevating trypsin and serum amylase activities in zebrafish larvae. GEL also resulted in a decrease in pancreas-associated fluorescence area and an increase in neutrophil-related fluorescence area in transgenic zebrafish. This study revealed that GEL toxicity in zebrafish larvae is related to acute pancreatic inflammation.

Real-Time 3D Facial Tracking via Cascaded Compositional Learning.

We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models with each is a boosted ferns initially trained to predict partial motion parameters of the same modality, and then concatenated together via a global optimization step to form a singular strong boosted ferns that can effectively handle the whole regression target. It can explicitly cope with the modality variety in output variables, while manifesting increased fitting power and a faster learning speed comparing against the conventional boosted ferns. By further cascading a sequence of GoMBFs (GoMBF-Cascade) to regress facial motion parameters, we achieve competitive tracking performance on a variety of in-the-wild videos comparing to the state-of-the-art methods which either have higher computational complexity or require much more training data. It provides a robust and highly elegant solution to real-time 3D facial tracking using a small set of training data and hence makes it more practical in real-world applications. We further deeply investigate the effect of synthesized facial images on training non-deep learning methods such as GoMBF-Cascade for 3D facial tracking. We apply three types synthetic images with various naturalness levels for training two different tracking methods, and compare the performance of the tracking models trained on real data, on synthetic data and on a mixture of data. The experimental results indicate that, i) the model trained purely on synthetic facial imageries can hardly generalize well to unconstrained real-world data, ii) involving synthetic faces into training benefits tracking in some certain scenarios but degrades the tracking model's generalization ability. These two insights could benefit a range of non-deep learning facial image analysis tasks where the labelled real data is difficult to acquire.

Long-Term Organic Farming Manipulated Rhizospheric Microbiome and Bacillus Antagonism Against Pepper Blight (Phytophthora capsici).

Soil-borne diseases are often less severe in organic farms, possibly because of the recruitment of beneficial microorganisms by crops. Here, the suppressiveness of organic, integrated, and conventionally managed soils to pepper blight (Phytophthora capsici) was studied in growth chamber experiments. Disease incidence was 41.3 and 34.1% lower in the soil from an organic farming system than in either the soil from the integrated or from the conventional farming systems, respectively. Beta-diversity of rhizospheric microbial communities differed among treatments, with enrichment of Bacillus, Sporosarcina, Acidobacteria Gp5, Gp6, Gp22, and Ignavibacterium by the organic soil. Cultivation-dependent analysis indicated that 50.3% of in vitro antagonists of P. capsici isolated from the rhizosphere of healthy peppers were affiliated to Bacillus. An integration of in vitro antagonists and bacterial diversity analyses indicated that Bacillus antagonists were higher in the rhizosphere of pepper treated by the organic soil. A microbial consortium of 18 in vitro Bacillus antagonists significantly increased the suppressiveness of soil from the integrated farming system against pepper blight. Overall, the soil microbiome under the long-term organic farming system was more suppressive to pepper blight, possibly owing to Bacillus antagonism in the rhizosphere. This study provided insights into microbiome management for disease suppression under greenhouse conditions.

Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA) and Singular Value Decomposition (SVD) to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.