A DOCK8-WIP-WASp complex links T cell receptors to the actin cytoskeleton

Wiskott-Aldrich syndrome (WAS) is associated with mutations in the WAS protein (WASp), which plays a critical role in the initiation of T cell receptor–driven (TCR-driven) actin polymerization. The clinical phenotype of WAS includes susceptibility to infection, allergy, autoimmunity, and malignancy and overlaps with the symptoms of dedicator of cytokinesis 8 (DOCK8) deficiency, suggesting that the 2 syndromes share common pathogenic mechanisms. Here, we demonstrated that the WASp-interacting protein (WIP) bridges DOCK8 to WASp and actin in T cells. We determined that the guanine nucleotide exchange factor activity of DOCK8 is essential for the integrity of the subcortical actin cytoskeleton as well as for TCR-driven WASp activation, F-actin assembly, immune synapse formation, actin foci formation, mechanotransduction, T cell transendothelial migration, and homing to lymph nodes, all of which also depend on WASp. These results indicate that DOCK8 and WASp are in the same signaling pathway that links TCRs to the actin cytoskeleton in TCR-driven actin assembly. Further, they provide an explanation for similarities in the clinical phenotypes of WAS and DOCK8 deficiency.United States. Public Health Service (RO1AI114588)United States. Public Health Service (K08AI114968

Chromosomal-level assembly of the Asian Seabass genome using long sequence reads and multi-layered scaffolding

We report here the ~670 Mb genome assembly of the Asian seabass (Lates calcarifer), a tropical marine teleost. We used long-read sequencing augmented by transcriptomics, optical and genetic mapping along with shared synteny from closely related fish species to derive a chromosome-level assembly with a contig N50 size over 1 Mb and scaffold N50 size over 25 Mb that span ~90% of the genome. The population structure of L. calcarifer species complex was analyzed by re-sequencing 61 individuals representing various regions across the species' native range. SNP analyses identified high levels of genetic diversity and confirmed earlier indications of a population stratification comprising three clades with signs of admixture apparent in the South-East Asian population. The quality of the Asian seabass genome assembly far exceeds that of any other fish species, and will serve as a new standard for fish genomics

Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis

The twelve papers in this special section focus on learning systems with shared information for computer vision and multimedia communication analysis. In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes containing a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with shared information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different levels of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems

Antibiotic Resistance in a Coastal River in Mississippi, USA – Potential Drivers

Wastewater treatment plants (WWTPs) are major sources of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in water bodies. Most studies on the impact of WWTPs on antibiotic resistance have focused on freshwater systems, with little information on coastal and estuarine waters with variable salinity. This study monitored seasonal levels of ARGs at the effluent and downstream of the Pascagoula— Moss Point WWTP in the lower Pascagoula River, a coastal river in southeastern Mississippi, USA. Surface water samples were collected seasonally at upstream, outflow, and 3 downstream sites from February to November 2016. Bacterial resistance to sulfamethazine, tetracycline, and ciprofloxacin was quantified using selective culture and qPCR. Mixed—effects models were developed to identify potential driving factors of ARG concentrations related to the WWTP and local environmental conditions (salinity, water temperature, and pH). The best model was selected based on the lowest Akaike Information Criterion (AIC) corrected for small sample size. The results show that the genes sul1, sul2, and intI1 were detected, with intI1 having the highest relative concentration. The qPCR analysis suggests a negative relation between ARG levels and temperature and salinity. ARG concentrations peaked immediately downstream of the WWTP and decreased gradually further downstream in some months, but the spatial pattern varied widely between sampling months. The study highlights the complex patterns of environmental ARGs and the importance of accounting for the impact of WWTPs, local environmental factors, and other anthropogenic influences to understand their potential drivers

Self-correcting LLM-controlled Diffusion Models

Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images, current text-to-image diffusion models still often struggle to accurately interpret and follow complex input text prompts. In contrast to existing models that aim to generate images only with their best effort, we introduce Self-correcting LLM-controlled Diffusion (SLD). SLD is a framework that generates an image from the input prompt, assesses its alignment with the prompt, and performs self-corrections on the inaccuracies in the generated image. Steered by an LLM controller, SLD turns text-to-image generation into an iterative closed-loop process, ensuring correctness in the resulting image. SLD is not only training-free but can also be seamlessly integrated with diffusion models behind API access, such as DALL-E 3, to further boost the performance of state-of-the-art diffusion models. Experimental results show that our approach can rectify a majority of incorrect generations, particularly in generative numeracy, attribute binding, and spatial relationships. Furthermore, by simply adjusting the instructions to the LLM, SLD can perform image editing tasks, bridging the gap between text-to-image generation and image editing pipelines. We will make our code available for future research and applications.Comment: 16 pages, 10 figure

ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.Comment: Accepted by AAAI 202

RFID localization using special antenna technique

In this paper, a RFID localization method using special antenna technique is presented. By using an active RFID system with external dipole antenna the angle and the distance from the antenna to the RFID tag can be found based on the principle of null steering. Compared with other techniques, this method has a number of advantages such as simple design, easy to implement, low cost and high reliability

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset

Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit driving protocol, we collect the Berkeley DeepDrive Drone dataset. The dataset contains 1) a set of aerial videos recording understructured driving, 2) a collection of images and annotations to train vehicle detection models, and 3) a kit of development scripts for illustrating typical usages. We believe that the dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and, of secondary interest, for computer vision in remote sensing settings.Comment: 6 pages, 10 figures, 1 tabl

Lectin-Dependent Enhancement of Ebola Virus Infection via Soluble and Transmembrane C-type Lectin Receptors

Mannose-binding lectin (MBL) is a key soluble effector of the innate immune system that recognizes pathogen-specific surface glycans. Surprisingly, low-producing MBL genetic variants that may predispose children and immunocompromised individuals to infectious diseases are more common than would be expected in human populations. Since certain immune defense molecules, such as immunoglobulins, can be exploited by invasive pathogens, we hypothesized that MBL might also enhance infections in some circumstances. Consequently, the low and intermediate MBL levels commonly found in human populations might be the result of balancing selection. Using model infection systems with pseudotyped and authentic glycosylated viruses, we demonstrated that MBL indeed enhances infection of Ebola, Hendra, Nipah and West Nile viruses in low complement conditions. Mechanistic studies with Ebola virus (EBOV) glycoprotein pseudotyped lentiviruses confirmed that MBL binds to N-linked glycan epitopes on viral surfaces in a specific manner via the MBL carbohydrate recognition domain, which is necessary for enhanced infection. MBL mediates lipid-raft-dependent macropinocytosis of EBOV via a pathway that appears to require less actin or early endosomal processing compared with the filovirus canonical endocytic pathway. Using a validated RNA interference screen, we identified C1QBP (gC1qR) as a candidate surface receptor that mediates MBL-dependent enhancement of EBOV infection. We also identified dectin-2 (CLEC6A) as a potentially novel candidate attachment factor for EBOV. Our findings support the concept of an innate immune haplotype that represents critical interactions between MBL and complement component C4 genes and that may modify susceptibility or resistance to certain glycosylated pathogens. Therefore, higher levels of native or exogenous MBL could be deleterious in the setting of relative hypocomplementemia which can occur genetically or because of immunodepletion during active infections. Our findings confirm our hypothesis that the pressure of infectious diseases may have contributed in part to evolutionary selection of MBL mutant haplotypes

Correction: Type III Effector Activation via Nucleotide Binding, Phosphorylation, and Host Target Interaction

The Pseudomonas syringae type III effector protein avirulence protein B (AvrB) is delivered into plant cells, where it targets the Arabidopsis RIN4 protein (resistance to Pseudomonas maculicula protein 1 [RPM1]–interacting protein). RIN4 is a regulator of basal host defense responses. Targeting of RIN4 by AvrB is recognized by the host RPM1 nucleotide-binding leucine-rich repeat disease resistance protein, leading to accelerated defense responses, cessation of pathogen growth, and hypersensitive host cell death at the infection site. We determined the structure of AvrB complexed with an AvrB-binding fragment of RIN4 at 2.3 Å resolution. We also determined the structure of AvrB in complex with adenosine diphosphate bound in a binding pocket adjacent to the RIN4 binding domain. AvrB residues important for RIN4 interaction are required for full RPM1 activation. AvrB residues that contact adenosine diphosphate are also required for initiation of RPM1 function. Nucleotide-binding residues of AvrB are also required for its phosphorylation by an unknown Arabidopsis protein(s). We conclude that AvrB is activated inside the host cell by nucleotide binding and subsequent phosphorylation and, independently, interacts with RIN4. Our data suggest that activated AvrB, bound to RIN4, is indirectly recognized by RPM1 to initiate plant immune system function