Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Robotic perception is currently at a cross-roads between modern methods which operate in an efficient latent space, and classical methods which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer which explicitly performs Bayesian inference within a depthwise separable convolution layer to simultaneously maximize efficiency while maintaining reliability. We apply our layer to the task of 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information in real time. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, and demonstrate improved latency with comparable semantic results

Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by changing a few pixel values on the original remote sensing image, could result in a collapse for the well-trained deep learning based SOD model. Different with existing methods adding perturbation to original images, we propose to jointly tune adversarial exposure and additive perturbation for attack and constrain image close to cloudy image as Adversarial Cloud. Cloud is natural and common in remote sensing images, however, camouflaging cloud based adversarial attack and defense for remote sensing images are not well studied before. Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model. By considering both regular and generalized adversarial examples, the proposed DefenseNet can defend the proposed Adversarial Cloud in white-box setting and other attack methods in black-box setting. Experimental results on a synthesized benchmark from the public remote sensing SOD dataset (EORSSD) show the promising defense against adversarial cloud attacks

ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty

In this paper, we develop a modular neural network for real-time semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel Convolutional Bayesian Kernel Inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a Robot Operating System (ROS) package of ConvBKI and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066

Exploration of the Regulatory Mechanism of Secondary Metabolism by Comparative Transcriptomics in Aspergillus flavus

Mycotoxins cause a huge threaten to agriculture, food safety, and human and animal life. Among them, aflatoxins (AFs) have always been considered the most potent carcinogens, and filamentous fungi from Aspergillus genus are their major producers, especially A. flavus. Although the biosynthesis path of these chemicals had been well-identified, the regulatory mechanisms controlling expression of AF gene cluster were poorly understood. In this report, genome-wide transcriptome profiles of A. flavus from AF conducing [yeast sucrose media (YES)] and non-conducing [yeast peptone media (YEP)] conditions were compared by using deep RNA sequencing (RNA-seq), and the results revealed that AF biosynthesis pathway and biosynthesis of amino acids were significantly upregulated in YES vs. YEP. Further, a novel LaeA-like methyltransferase AFLA_121330 (Lael1) was identified for the first time, to play a specific role in the regulation of AF biosynthesis. Contrary to LaeA, which gene deletion reduced the level, lael1 deletion resulted in a significant increase in AF production. Further, co-expression network analysis revealed that mitochondrial pyruvate transport and signal peptide processing were potentially involved in AF synthesis for the first time, as well as biological processes of ribosome, branched-chain amino acid biosynthetic process and translation were co-regulated by AfRafA and AfStuA. To sum up, our analyses could provide novel insights into the molecular mechanism for controlling the AF and other secondary metabolite synthesis, adding novel targets for plant breeding and making fungicides

3D printed grafts with gradient structures for organized vascular regeneration

Synthetic vascular grafts suitable for small-diameter arteries (<6 mm) are in great need. However, there are still no commercially available small-diameter vascular grafts (SDVGs) in clinical practice due to thrombosis and stenosis after in vivo implantation. When designing SDVGs, many studies emphasized reendothelization but ignored the importance of reconstruction of the smooth muscle layer (SML). To facilitate rapid SML regeneration, a high-resolution 3D printing method was used to create a novel bilayer SDVG with structures and mechanical properties mimicking natural arteries. Bioinspired by the collagen alignment of SML, the inner layer of the grafts had larger pore sizes and high porosity to accelerate the infiltration of cells and their circumferential alignment, which could facilitate SML reconstruction for compliance restoration and spontaneous endothelialization. The outer layer was designed to induce fibroblast recruitment by low porosity and minor pore size and provide SDVG with sufficient mechanical strength. One month after implantation, the arteries regenerated by 3D-printed grafts exhibited better pulsatility than electrospun grafts, with a compliance (8.9%) approaching that of natural arteries (11.36%) and significantly higher than that of electrospun ones (1.9%). The 3D-printed vascular demonstrated a three-layer structure more closely resembling natural arteries while electrospun grafts showed incomplete endothelium and immature SML. Our study shows the importance of SML reconstruction during vascular graft regeneration and provides an effective strategy to reconstruct blood vessels through 3D-printed structures rapidly

Absolute configuration, stability, and interconversion of 6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine valine adducts and their phenylthiohydantoin derivatives

Pyrrolizidine alkaloid-containing plants are widespread in the world and probably the most common poisonous plants affecting livestock, wildlife, and humans. Pyrrolizidine alkaloids require metabolic activation to form dehydropyrrolizidine alkaloids that bind to cellular proteins and DNA leading to hepatotoxicity, genotoxicity, and tumorigenicity. At present, it is not clear how dehydropyrrolizidine alkaloids bind to cellular amino acids and proteins to induced toxicity. We previously reported that reaction of dehydromonocrotaline with valine generated four highly unstable 6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine (DHP)-derived valine (DHP-valine) adducts that upon reaction with phenyl isothiocyanate (PITC) formed four DHP-valine-PITC adduct isomers. In this study, we report the absolute configuration and stability of DHP-valine and DHP-valine-PITC adducts, and the mechanism of interconversion between DHP-valine-PITC adducts

Two DNA Aptamers against Avian Influenza H9N2 Virus Prevent Viral Infection in Cells

<div><p>New antiviral therapy for pandemic influenza mediated by the H9N2 avian influenza virus (AIV) is increasingly in demand not only for the poultry industry but also for public health. Aptamers are confirmed to be promising candidates for treatment and prevention of influenza viral infections. Thus, we studied two DNA aptamers, A9 and B4, selected by capillary electrophoresis-based systemic evolution of ligands by exponential enrichment (CE-SELEX) procedure using H9N2 AIV purified haemagglutinin (HA) as target. Both aptamers had whole-virus binding affinity. Also, an enzyme-linked aptamer assay (ELAA) confirmed binding affinity and specificity against other AIV subtypes. Finally, we studied aptamer-inhibitory effects on H9N2 AIV infection in Madin–Darby canine kidney (MDCK) cells and quantified viral load in supernatant and in cell with quantitative PCR (qPCR). Our data provide a foundation for future development of innovative anti-influenza drugs.</p></div

Pyrrolizidine Alkaloid-Protein Adducts: Potential Non-invasive Biomarkers of Pyrrolizidine Alkaloid-Induced Liver Toxicity and Exposure

Pyrrolizidine alkaloids (PAs) are phytochemicals present in hundreds of plant species from different families widely distributed in many geographical regions around the world. PA-containing plants are probably the most common type of poisonous plants affecting livestock, wildlife, and humans. There have been many large-scale human poisonings caused by the consumption of food contaminated with toxic PAs. PAs require metabolic activation to generate pyrrolic metabolites to exert their toxicity. In this study, we developed a novel method to quantify pyrrole-protein adducts present in the blood. This method involves the use of AgNO₃ in acidic ethanol to cleave the thiol linkage of pyrrole-protein (DHP-protein) adducts, and the resulting 7,9-di-C₂H₅O–DHP is quantified by HPLC-ES-MS/MS multiple reaction monitoring analysis in the presence of a known quantity of isotopically labeled 7,9-di-C₂D₅O–DHP internal standard. Using this method, we determined that diester-type PAs administered to rats produced higher levels of DHP-protein adducts than other types of PAs. The results suggest that DHP-protein adducts can potentially serve as minimally invasive biomarkers of PA exposure