Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving

Aiming towards a holistic understanding of multiple downstream tasks simultaneously, there is a need for extracting features with better transferability. Though many latest self-supervised pre-training methods have achieved impressive performance on various vision tasks under the prevailing pretrain-finetune paradigm, their generalization capacity to multi-task learning scenarios is yet to be explored. In this paper, we extensively investigate the transfer performance of various types of self-supervised methods, e.g., MoCo and SimCLR, on three downstream tasks, including semantic segmentation, drivable area segmentation, and traffic object detection, on the large-scale driving dataset BDD100K. We surprisingly find that their performances are sub-optimal or even lag far behind the single-task baseline, which may be due to the distinctions of training objectives and architectural design lied in the pretrain-finetune paradigm. To overcome this dilemma as well as avoid redesigning the resource-intensive pre-training stage, we propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training, where the off-the-shelf pretrained models can be effectively adapted without increasing the training overhead. During the adapt stage, we utilize learnable multi-scale adapters to dynamically adjust the pretrained model weights supervised by multi-task objectives while leaving the pretrained knowledge untouched. Furthermore, we regard the vision-language pre-training model CLIP as a strong complement to the pretrain-adapt-finetune paradigm and propose a novel adapter named LV-Adapter, which incorporates language priors in the multi-task model via task-specific prompting and alignment between visual and textual features.Comment: Accepted at NeurIPS 202

Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography

This paper presents a closed-loop magnetic manipulation framework for robotic transesophageal echocardiography (TEE) acquisitions. Different from previous work on intracorporeal robotic ultrasound acquisitions that focus on continuum robot control, we first investigate the use of magnetic control methods for more direct, intuitive, and accurate manipulation of the distal tip of the probe. We modify a standard TEE probe by attaching a permanent magnet and an inertial measurement unit sensor to the probe tip and replacing the flexible gastroscope with a soft tether containing only wires for transmitting ultrasound signals, and show that 6-DOF localization and 5-DOF closed-loop control of the probe can be achieved with an external permanent magnet based on the fusion of internal inertial measurement and external magnetic field sensing data. The proposed method does not require complex structures or motions of the actuator and the probe compared with existing magnetic manipulation methods. We have conducted extensive experiments to validate the effectiveness of the framework in terms of localization accuracy, update rate, workspace size, and tracking accuracy. In addition, our results obtained on a realistic cardiac tissue-mimicking phantom show that the proposed framework is applicable in real conditions and can generally meet the requirements for tele-operated TEE acquisitions.Comment: Accepted by IEEE Transactions on Robotics. Copyright may be transferred without notice, after which this version may no longer be accessibl

Ksak: A high-throughput tool for alignment-free phylogenetics

Phylogenetic tools are fundamental to the studies of evolutionary relationships. In this paper, we present Ksak, a novel high-throughput tool for alignment-free phylogenetic analysis. Ksak computes the pairwise distance matrix between molecular sequences, using seven widely accepted k-mer based distance measures. Based on the distance matrix, Ksak constructs the phylogenetic tree with standard algorithms. When benchmarked with a golden standard 16S rRNA dataset, Ksak was found to be the most accurate tool among all five tools compared and was 19% more accurate than ClustalW2, a high-accuracy multiple sequence aligner. Above all, Ksak was tens to hundreds of times faster than ClustalW2, which helps eliminate the computation limit currently encountered in large-scale multiple sequence alignment. Ksak is freely available at https://github.com/labxscut/ksak

Style Transfer Enabled Sim2Real Framework for Efficient Learning of Robotic Ultrasound Image Analysis Using Simulated Data

Robotic ultrasound (US) systems have shown great potential to make US examinations easier and more accurate. Recently, various machine learning techniques have been proposed to realize automatic US image interpretation for robotic US acquisition tasks. However, obtaining large amounts of real US imaging data for training is usually expensive or even unfeasible in some clinical applications. An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a Sim2Real framework to efficiently learn robotic US image analysis tasks based only on simulated data for real-world deployment. A style transfer module is proposed based on unsupervised contrastive learning and used as a preprocessing step to convert the real US images into the simulation style. Thereafter, a task-relevant model is designed to combine CNNs with vision transformers to generate the task-dependent prediction with improved generalization ability. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic transesophageal echocardiography (TEE). Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed

Protective Effects of <i>Atractylodis lancea</i> Rhizoma on Lipopolysaccharide-Induced Acute Lung Injury via TLR4/NF-κB and Keap1/Nrf2 Signaling Pathways In Vitro and In Vivo

Acute lung injury (ALI) is a syndrome caused by an excessive inflammatory response characterized by intractable hypoxemia both inside and outside the lung, for which effective therapeutic drugs are lacking. Atractylodis rhizoma, a traditional Chinese medicine, has excellent anti-inflammatory and antiviral properties in addition to protecting the integrity of the cellular barrier. However, few studies of Atractylodis rhizoma for the treatment of ALI have been published, and its mechanism of action remains unclear. In the present study, the chemical composition of the ethanolic extract of Atractylodis rhizoma (EEAR) was initially clarified by high performance liquid chromatography (HPLC), after which it was studied in vivo using a lipopolysaccharide (LPS)-induced ALI rat model. Treatment with EEAR significantly reduced the lung wet/dry (W/D) ratio, neutrophil infiltration, and malondialdehyde (MDA) and myeloperoxidase (MPO) formation, and enhanced superoxide dismutase (SOD) and glutathione (GSH) depletion in rats with ALI, thereby improving lung barrier function and effectively reducing lung injury. In addition, EEAR significantly reduced histopathological changes, decreased the expression of inflammatory factors (such as tumor necrosis factor-α (TNF-α), interleukin-1 beta (IL-1β), inducible nitric oxide synthase (INOS), and cyclooxygenase-2 (COX-2)), and inhibited the activation of the NF-κB signaling pathway, thus reducing inflammation. In addition, EEAR was found to also reduce oxidative stress in ALI by upregulating the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream proteins heme oxygenase-1 (HO-1) and NADPH quinone acceptor oxidoreductase 1 (NQO-1). EEAR also reduced LPS-induced inflammatory factor expression in THP-1 cells in vitro by inhibition of the NF-κB signaling pathway, and reduced damage from lipopolysaccharide (LPS)-induced oxidative stress in THP-1 cells by promoting the expression of Nrf2 and its downstream targets HO-1 and NQO-1, the molecular mechanism of which was consistent with in vivo observations. Therefore, we conclude that EEAR attenuates oxidative stress and inflammatory responses via TLR4/NF-κB and Keap1/Nrf2 signaling pathways to alleviate LPS-induced ALI, suggesting that Atractylodis rhizoma is a potential drug candidate for the treatment of ALI

Probiotic Lactobacillus casei Expressing Porcine Antimicrobial Peptide PR39 Elevates Antibacterial Activity in the Gastrointestinal Tract

PR39, a 4.7-kDa proline-rich antimicrobial peptide, acts as a cationic host defense peptide. In addition to killing bacteria, PR39 mediates inflammatory reactions, including cell proliferation, migration, wound healing, and angiogenesis. Here, we examined the antibacterial effects of this peptide. The synthetic gene fragment PR39 was inserted into the secretory expression vector plasmid pPG:612 of Lactobacillus casei, yielding the recombinant strain pPG:612-PR39/L. casei 393. In vitro antibacterial tests showed that expression of the PR39 peptide in recombinant L. casei resulted in antibacterial activity against Escherichia coli and Salmonella, but had only minor antibacterial effects in Staphylococcus aureus. In addition, BALB/c mice fed the recombinant pPG:612-PR39/L. casei 393 grew better and had increased peripheral blood lymphocyte percentages, white blood cell numbers, and spleen indices compared with those of the control group. Scanning electron microscopy showed that jejunum and duodenum villus height, crypt depth, and the ratio of V/C in the intestinal villi also increased. Moreover, mice fed the recombinant strain showed significantly lower mortality rates than the control group when challenged with the enterotoxigenic E. coli K88+ (ETEC K88+). Thus, this recombinant expression system had beneficial characteristics of both Lactobacillus casei and PR39, supporting its potential as an animal feed additive.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author