Molecular Simulation of Surfactant Displacement of Residual Oil in Nanopores: Formation of Water Channels and Electrostatic Interaction.

The water-oil-rock system's surfactant and electrostatic interactions are essential for removing oil droplets from rock substrates. Our work illustrates the impact of surface charge on the oil contact angle in an ideal system comprising silica, water, and dodecane; smaller contact angles are observed for more polar substrates. Modifying the polarity of the model silica surface allows for the observation of the creation of heteromolecule channels and the process of stripping crude oil while accounting for the impacts of water flow and different types of surfactant molecules. In solutions containing ionic surfactants, the injection and diffusion of water molecules between the oil layer and the silica substrate are facilitated by the disturbance of the oil molecules by the surfactant molecules. By comparing different surfactants in water flow, the characterization of water molecular channels and the stripping process of crude oil can be observed. The disruption of oil molecules by the surfactant molecules has been found to enhance the injection and diffusion of water molecules between the oil layer and the silica substrate in solutions containing ionic surfactants. The size of the contact angle and the extension of the water channel are simultaneously greatly influenced by the surfactant's molecular characteristics and the substrate's polarity. These simulation results show that several factors influence the process of water molecule channel creation that water molecules diffuse, and the detachment of oil from the silica substrate is facilitated by the migration of surfactants to the bottom of the oil molecule and the electrostatic interactions between the water molecules and the silica substrate.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

Spherical DNNs and Their Applications in 360 ° Images and Videos.

Spherical images or videos, as typical non-euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projection, which is neither equal area nor conformal. The distortion caused by the projection limits the performance of vanilla Deep Neural Networks (DNNs) designed for traditional euclidean data. In this paper, we design a novel Spherical Deep Neural Network (DNN) to deal with the distortion caused by the equirectangular projection. Specifically, we customize a set of components, including a spherical convolution, a spherical pooling, a spherical ConvLSTM cell and a spherical MSE loss, as the replacements of their counterparts in vanilla DNNs for spherical data. The core idea is to change the identical behavior of the conventional operations in vanilla DNNs across different feature patches so that they will be adjusted to the distortion caused by the variance of sampling rate among different feature patches. We demonstrate the effectiveness of our Spherical DNNs for saliency detection and gaze estimation in 360° videos. For saliency detection, we take the temporal coherence of an observer's viewing process into consideration and propose to use a Spherical U-Net and a Spherical ConvLSTM to predict the saliency maps for each frame sequentially. As for gaze prediction, we propose to leverage a Spherical Encoder Module to extract spatial panoramic features, then we combine them with the gaze trajectory feature extracted by an LSTM for future gaze prediction. To facilitate the study of the 360° videos saliency detection, we further construct a large-scale 360° video saliency detection dataset that consists of 104 360° videos viewed by 20+ human subjects. Comprehensive experiments validate the effectiveness of our proposed Spherical DNNs for 360 ° handwritten digit classification and sport classification, saliency detection and gaze tracking in 360° videos. We also visualize the regions contributing to the classification decisions in our proposed Spherical DNNs via the Grad-CAM technique in the classification task, and the results show that our Spherical DNNs constantly leverage reasonable and important regions for decision making, regardless the large distortions. All codes and dataset are available on https://github.com/svip-lab/SphericalDNNs.

A novel gene LbHLH from the halophyte Limonium bicolor enhances salt tolerance via reducing root hair development and enhancing osmotic resistance.

BACKGROUND: Identifying genes involved in salt tolerance in the recretohalophyte Limonium bicolor could facilitate the breeding of crops with enhanced salt tolerance. Here we cloned the previously uncharacterized gene LbHLH and explored its role in salt tolerance. RESULTS: The 2,067-bp open reading frame of LbHLH encodes a 688-amino-acid protein with a typical helix-loop-helix (HLH) domain. In situ hybridization showed that LbHLH is expressed in salt glands of L. bicolor. LbHLH localizes to the nucleus, and LbHLH is highly expressed during salt gland development and in response to NaCl treatment. To further explore its function, we heterologously expressed LbHLH in Arabidopsis thaliana under the 35S promoter. The overexpression lines showed significantly increased trichome number and reduced root hair number. LbHLH might interact with GLABRA1 to influence trichome and root hair development, as revealed by yeast two-hybrid analysis. The transgenic lines showed higher germination percentages and longer roots than the wild type under NaCl treatment. Analysis of seedlings grown on medium containing sorbitol with the same osmotic pressure as 100 mM NaCl demonstrated that overexpressing LbHLH enhanced osmotic resistance. CONCLUSION: These results indicate that LbHLH enhances salt tolerance by reducing root hair development and enhancing osmotic resistance under NaCl stress.

Importin-ß From the Recretohalophyte Limonium bicolor Enhances Salt Tolerance in Arabidopsis thaliana by Reducing Root Hair Development and Abscisic Acid Sensitivity.

AIMS: To elucidate the genetics underlying salt tolerance in recretohalophytes and assess its relevance to non-halophytes, we cloned the Limonium bicolor homolog of Arabidopsis thaliana (Arabidopsis) SUPER SENSITIVE TO ABA AND DROUGHT2 (AtSAD2) and named it LbSAD2, an importin-ß gene associated with trichome initiation and reduced abscisic acid (ABA) sensitivity, and then we assessed the heterologously expressed LbSAD2 in Arabidopsis. METHODS: We examined LbSAD2 expression and assessed the effect of heterologous LbSAD2 expression in Arabidopsis on root hair/trichome induction; the expression levels of possible related genes in trichome/root hair development; some physiological parameters involved in salt tolerance including germination rate, root length, and contents of Na+, proline, and malondialdehyde; and the response of ABA at the germination stage. RESULTS: The LbSAD2 gene is highly expressed in the salt gland development stage and salt treatment, especially located in the salt gland by in situ hybridization, and the LbSAD2 protein contains some special domains compared with AtSAD2, which may suggest the involvement of LbSAD2 in salt tolerance. Compared with the SAD2/GL1 mutant CS65878, which lacks trichomes, CS65878-35S:LbSAD2 had higher trichome abundance but lower root hair abundance. Under 100 mM NaCl treatment, CS65878-35S:LbSAD2 showed enhanced germination and root lengths; improved physiological parameters, including high proline and low contents of Na+ and malondialdehyde; higher expression of the salt-tolerance genes Δ1-PYRROLINE-5-CARBOXYLATE SYNTHETASE 1 (P5CS1) and GST CLASS TAU 5 (GSTU5); reduced ABA sensitivity; and increased expression of the ABA signaling genes RESPONSIVE TO ABA 18 (RAB18) and SNF1-RELATED PROTEIN KINASE 2 (SRK2E), but not of the ABA biosynthesis gene 9-CIS-EPOXYCAROTENOID DIOXYGENASE 3 (NCED3). CONCLUSION: LbSAD2 enhances salt tolerance in Arabidopsis by specifically reducing root hair development, Na+ accumulation, and ABA sensitivity.

Multiview Multitask Gaze Estimation With Deep Convolutional Neural Networks.

Gaze estimation, which aims to predict gaze points with given eye images, is an important task in computer vision because of its applications in human visual attention understanding. Many existing methods are based on a single camera, and most of them only focus on either the gaze point estimation or gaze direction estimation. In this paper, we propose a novel multitask method for the gaze point estimation using multiview cameras. Specifically, we analyze the close relationship between the gaze point estimation and gaze direction estimation, and we use a partially shared convolutional neural networks architecture to simultaneously estimate the gaze direction and gaze point. Furthermore, we also introduce a new multiview gaze tracking data set that consists of multiview eye images of different subjects. As far as we know, it is the largest multiview gaze tracking data set. Comprehensive experiments on our multiview gaze tracking data set and existing data sets demonstrate that our multiview multitask gaze point estimation solution consistently outperforms existing methods.

Personalized Saliency and Its Prediction.

Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstances, especially a scene is composed of multiple salient objects. To study such heterogenous visual attention pattern across observers, we first construct a personalized saliency dataset and explore correlations between visual attention, personal preferences, and image contents. Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency. We then present two solutions towards predicting such discrepancy maps, i.e., a multi-task convolutional neural network (CNN) framework and an extended CNN with Person-specific Information Encoded Filters (CNN-PIEF). Extensive experimental results demonstrate the effectiveness of our models for PSM prediction as well their generalization capability for unseen observers.

Beneficial Effects of Salt on Halophyte Growth: Morphology, Cells, and Genes.

Halophytes can survive and complete their life cycle in the presence of ≥200 mM NaCl. These remarkable plants have developed various strategies to tolerate salinity and thrive in high-salt environments. At the appropriate levels, salt has a beneficial effect on the vegetative growth of halophytes but inhibits the growth of non-halophytes. In recent years, many studies have focused on elucidating the salt-tolerance mechanisms of halophytes at the molecular, physiological, and individual level. In this review, we focus on the mechanisms, from the macroscopic to the molecular, underlying the successful growth of halophytes in saline environments to explain why salt has beneficial effects on halophytes but harmful effects on non-halophytes. These mechanisms include the specialized organs of halophytes (for example, ion compartmentalization in succulent leaves), their unique structures (salt glands and hydrophobic barriers in roots), and their salt-tolerance genes. We hope to shed light on the use of halophytes for engineering salt-tolerant crops, soil conservation, and the protection of freshwater resources in the near future.