Biology subjects

Xiao, B.

Publications and source records attributed to Xiao, B..

4 recordsLinked to original sources

bioRxiv2018

RNAi screening identifies a new Toll from shrimp that restricts WSSV infection through activating Dorsal to induce antimicrobial peptides

The function of Toll pathway defense against bacterial infection has been well established in shrimp, however how this pathway responds to viral infection is still largely unknown. In this study, we report the Toll4-Dorsal-AMPs cascade restricts the white spot syndrome virus (WSSV) infection of shrimp. A total of nine Tolls from Litopenaeus vannamei namely Toll1-9 are identified, and RNAi screening in vivo reveals the Toll4 is important for shrimp to oppose WSSV infection. Knockdown of Toll4 results in elevated viral loads and renders shrimp more susceptible to WSSV. Furthermore, Toll4 could be a one of upstream pattern recognition receptor (PRR) to detect WSSV, and thereby leading to nuclear translocation and phosphorylation of Dorsal, the known NF-{kappa}B transcription factor of the canonical Toll pathway. More importantly, silencing of Toll4 and Dorsal contributes to impaired expression of a specific set of antimicrobial peptides (AMPs) such as anti-LPS-factor (ALF) and lysozyme (LYZ) family, which exert potent anti-WSSV activity. Two AMPs of ALF1 and LYZ1 as representatives are demonstrated to have the ability to interact with several WSSV structural proteins. Taken together, we therefore identify the Toll4-Dorsal pathway mediates strong resistance to WSSV infection by inducing some specific AMPs.\n\nAuthor summaryThe TLR pathway mediated antiviral immune response is well identified in mammals, yet, Toll pathway governing this protection in invertebrates remains unknown. In the present study, we uncover that a shrimp Toll4 from a total of nine Tolls in L. vannamei confers resistance to WSSV thought inducing the NF-{kappa}B transcription factor Dorsal to inspiring the production of some antimicrobial peptides (AMPs) with antiviral activity. The anti-LPS-factor (ALF) and lysozyme (LYZ) family are identified as the Toll4-Dorsal pathway targeted genes with the ability to interact with viral structural proteins in response to WSSV infection. These results suggest that the Toll receptor induces the expression of AMPs with antiviral activity could be a general antiviral mechanism in invertebrates and Toll pathway established antiviral defense could be conserved during evolution.

immunology↗

bioRxiv2018

Interaction between static visual cues and force-feedback on the perception of mass of virtual objects

We use force-feedback device and a game engine to measure the effects of material appearance on the perception of mass of virtual objects. We discover that the perceived mass is mainly determined by the ground-truth mass output by the force-feedback device. Different from the classic Material Weight Illusion (MWI), however, heavy-looking objects (e.g. steel) are consistently rated heavier than light-looking ones (e.g. fabric) with the same ground-truth mass. Analysis of the initial accelerated velocity of the movement trajectories of the virtual probe shows greater acceleration for materials with heavier rated mass. This effect is diminished when the participants lift the object for the second time, meaning that the influence of visual appearance disappears in the movement trajectories once it is calibrated by the force-feedback. We also show how the material categories are affected by both the visual appearance and the weight of the object. We conclude that visual appearance has a significant interaction with haptic force-feedback on the perception of mass and also affects the kinematics of how participants manipulate the object.\n\nCCS CONCEPTS* Human-centered computing [->] Empirical studies in HCI; Empirical studies in interaction design; Empirical studies in visualization;\n\nACM Reference FormatWenyan Bi, Jonathan Newport, and Bei Xiao. 2018. Interaction between static visual cues and force-feedback on the perception of mass of virtual objects. In Proceedings of. ACM, New York, NY, USA, 5 pages.

neuroscience↗

bioRxiv2018

Estimating mechanical properties of cloth from videos using dense motion trajectories: human psychophysics and machine learning

Humans can visually estimate the mechanical properties of deformable objects (e.g. cloth stiffness). While much of the recent work on material perception has focused on static image cues (e.g., textures and shape), little is known whether humans can integrate information over time to make a judgment. Here, we investigate the effect of spatiotemporal information across multiple frames (multi-frame motion) on estimating the bending stiffness of cloth. Using high-fidelity cloth animations, we first examined how the perceived bending stiffness changed as a function of the physical bending stiffness defined in the simulation model. Using maximum likelihood difference scaling methods (MLDS) we found that the perceived stiffness and the physical bending stiffness were highly correlated. A second experiment in which we scrambled the frame sequences diminished this correlation. This suggests that multi-frame motion plays an important role. To provide further evidence for this finding, we extracted dense motion trajectories from the videos across 15 consecutive frames and used the trajectory descriptors to train a machine-learning model with the measured perceptual scales. The model can predict human perceptual scales in new videos with varied winds, optical properties of cloth, and scene setups. When the correct multi-frame was removed (using either scrambled videos or 2-frame optical flow to train the model), the predictions significantly worsened. Our findings demonstrate that multi-frame motion information is important for both humans and machines to estimate the mechanical properties. In addition, we show that dense motion trajectories are effective features to build a successful automatic cloth estimation system.

neuroscience↗

bioRxiv2018

Utility of Attention-Deficit/Hyperactivity Disorder Trait Measure in Population Genetics: A Polygenic Risk Study

BackgroundValid and genetically-informative trait measures of psychopathology collected in the general population would provide a powerful complement to case/control genetic designs. We report the convergent, predictive and discriminant validity of the parent- and the self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN) for attention-deficit/hyperactivity disorder (ADHD) traits. We tested if SWAN ADHD scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as with traits and polygenic risk for co-occurring disorders such as anxiety and obsessive-compulsive disorder (OCD).\n\nMethodsWe collected parent- and self-report SWAN scores in a community sample (n=15,560; 6-18 years of age) and created norms. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with a community ADHD diagnosis (n=972) from those without a community diagnosis. We validated cut-points from the community sample in a clinical sample (266 ADHD cases; 36 controls). We tested if SWAN scores were associated with anxiety and obsessive-compulsive (OC) traits and polygenic risk for ADHD, OCD and anxiety disorders.\n\nResultsBoth the parent- and the self-report SWAN measures showed high convergent validity with established ADHD measures and distinguished ADHD participants with high sensitivity and specificity in the community sample. Cut-points established in the community sample discriminated ADHD clinic cases from controls with a sensitivity of 86% and specificity of 94%. High parent- and self-report SWAN scores and scores above the community-based cut-points were associated with polygenic risk for ADHD. High ADHD traits were associated with high anxiety traits, but not OC traits. SWAN scores were not associated with OCD or anxiety disorder polygenic risk.\n\nConclusionThe parent- and self-report SWAN are potentially useful in genetic research because they predict ADHD diagnoses and are associated with ADHD polygenic risk.

genetics↗