Building Hierarchical Micro-Structure on the Carbon Fabrics to Improve Their Reinforcing Effect in the CFRP Composites

Nano-fibers grafted on carbon fibers (CFs) has been of one of the most popular methods used for the carbon fibers surface treatment, which could significantly influence the interfacial properties between polymer matrix and carbon fibers in composites. This chapter demonstrated three novel carbon fibers surface treatment methods, they are carbon nanotubes (CNTs) grafted on CFs using catalysts formed in an ethanol flame, carbon fiber forests (CFFs) by carbon fiber surface brushing and abrading and ZnO nanowire grown onto CFs though a facile hydrothermal method respectively. Based on metal catalyst particles or dopamine-based functionalization formed onto the nano-fiber/CF interface, a good interfacial bonding strength between the nano-fiber and CFs was observed by an instrumented tip of an atomic force microscope and further improvement of interfacial shear strength with epoxy as measured by the single fiber pull out/microbond test was realized. The hierarchical micro-fibers on CF fabrics were then utilized to fabricate the laminates to characterize anti-delamination capacity (the mode I and mode II interlaminar fracture toughness) of these composite laminates, wherein carbon fiber fabrics were grafted with CNTs, short CFs and ZnO nanowires respectively

Systematic optimization for production of the anti-MRSA antibiotics WAP-8294A in an engineered strain of Lysobacter enzymogenes

WAP-8294A is a group of cyclic lipodepsipeptides and considered as the first-in-class new chemical entity with potent activity against methicillin-resistant Staphylococcus aureus. One of the roadblocks in developing the WAP-8294A antibiotics is the very low yield in Lysobacter. Here, we carried out a systematic investigation of the nutritional and environmental conditions in an engineered L. enzymogenes strain for the optimal production of WAP-8294A. We developed an activity-based simple method for quick screening of various factors, which enabled us to optimize the culture conditions. With the method, we were able to improve the WAP-8294A yield by 10-fold in small-scale cultures and approximately 15-fold in scale-up fermentation. Additionally, we found the ratio of WAP-8294A2 to WAP-8294A1 in the strains could be manipulated through medium optimization. The development of a practical method for yield improvement in Lysobacter will facilitate the ongoing basic research and clinical studies to develop WAP- 8294A into true therapeutics

ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models

Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment. However, as medical image annotation is labour- and resource-intensive, the scarcity of annotated data limits the effectiveness and generalization of existing methods. Although recent research has focused on data generation and augmentation to address this issue, the quality of the generated data remains a challenge, which limits the contribution to the performance of subsequent tasks. Inspired by the superiority of diffusion models in fitting data distributions and generating high-quality data, in this paper, we propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks. Specifically, ArSDM utilizes the ground-truth segmentation mask as a prior condition during training and adjusts the diffusion loss for each input according to the polyp/background size ratio. Furthermore, ArSDM incorporates a pre-trained segmentation model to refine the training process by reducing the difference between the ground-truth mask and the prediction mask. Extensive experiments on segmentation and detection tasks demonstrate the generated data by ArSDM could significantly boost the performance of baseline methods.Comment: Accepted by MICCAI-202

On the Security Risks of Knowledge Graph Reasoning

Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e.g., cyber threat hunting). However, despite its surging popularity, the potential security risks of KGR are largely unexplored, which is concerning, given the increasing use of such capability in security-critical domains. This work represents a solid initial step towards bridging the striking gap. We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. Further, we present ROAR, a new class of attacks that instantiate a variety of such threats. Through empirical evaluation in representative use cases (e.g., medical decision support, cyber threat hunting, and commonsense reasoning), we demonstrate that ROAR is highly effective to mislead KGR to suggest pre-defined answers for target queries, yet with negligible impact on non-target ones. Finally, we explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries, which leads to several promising research directions.Comment: In proceedings of USENIX Security'23. Codes: https://github.com/HarrialX/security-risk-KG-reasonin

Use of facile mechanochemical method to functionalize carbon nanofibers with nanostructured polyaniline and their electrochemical capacitance

A facile approach to functionalize carbon nanofibers [CNFs] with nanostructured polyaniline was developed via in situ mechanochemical polymerization of polyaniline in the presence of chemically treated CNFs. The nanostructured polyaniline grafting on the CNF was mainly in a form of branched nanofibers as well as rough nanolayers. The good dispersibility and processability of the hybrid nanocomposite could be attributed to its overall nanostructure which enhanced its accessibility to the electrolyte. The mechanochemical oxidation polymerization was believed to be related to the strong Lewis acid characteristic of FeCl3 and the Lewis base characteristic of aniline. The growth mechanism of the hierarchical structured nanofibers was also discussed. After functionalization with the nanostructured polyaniline, the hybrid polyaniline/CNF composite showed an enhanced specific capacitance, which might be related to its hierarchical nanostructure and the interaction between the aromatic polyaniline molecules and the CNFs

Dissection of a QTL Hotspot on Mouse Distal Chromosome 1 that Modulates Neurobehavioral Phenotypes and Gene Expression

A remarkably diverse set of traits maps to a region on mouse distal chromosome 1 (Chr 1) that corresponds to human Chr 1q21–q23. This region is highly enriched in quantitative trait loci (QTLs) that control neural and behavioral phenotypes, including motor behavior, escape latency, emotionality, seizure susceptibility (Szs1), and responses to ethanol, caffeine, pentobarbital, and haloperidol. This region also controls the expression of a remarkably large number of genes, including genes that are associated with some of the classical traits that map to distal Chr 1 (e.g., seizure susceptibility). Here, we ask whether this QTL-rich region on Chr 1 (Qrr1) consists of a single master locus or a mixture of linked, but functionally unrelated, QTLs. To answer this question and to evaluate candidate genes, we generated and analyzed several gene expression, haplotype, and sequence datasets. We exploited six complementary mouse crosses, and combed through 18 expression datasets to determine class membership of genes modulated by Qrr1. Qrr1 can be broadly divided into a proximal part (Qrr1p) and a distal part (Qrr1d), each associated with the expression of distinct subsets of genes. Qrr1d controls RNA metabolism and protein synthesis, including the expression of ∼20 aminoacyl-tRNA synthetases. Qrr1d contains a tRNA cluster, and this is a functionally pertinent candidate for the tRNA synthetases. Rgs7 and Fmn2 are other strong candidates in Qrr1d. FMN2 protein has pronounced expression in neurons, including in the dendrites, and deletion of Fmn2 had a strong effect on the expression of few genes modulated by Qrr1d. Our analysis revealed a highly complex gene expression regulatory interval in Qrr1, composed of multiple loci modulating the expression of functionally cognate sets of genes

Systematic optimization for production of the anti-MRSA antibiotics WAP-8294A in an engineered strain of Lysobacter enzymogenes

WAP-8294A is a group of cyclic lipodepsipeptides and considered as the first-in-class new chemical entity with potent activity against methicillin-resistant Staphylococcus aureus. One of the roadblocks in developing the WAP-8294A antibiotics is the very low yield in Lysobacter. Here, we carried out a systematic investigation of the nutritional and environmental conditions in an engineered L. enzymogenes strain for the optimal production of WAP-8294A. We developed an activity-based simple method for quick screening of various factors, which enabled us to optimize the culture conditions. With the method, we were able to improve the WAP-8294A yield by 10-fold in small-scale cultures and approximately 15-fold in scale-up fermentation. Additionally, we found the ratio of WAP-8294A2 to WAP-8294A1 in the strains could be manipulated through medium optimization. The development of a practical method for yield improvement in Lysobacter will facilitate the ongoing basic research and clinical studies to develop WAP- 8294A into true therapeutics