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ABSTRACT Molecular transport through polymer networks, including hydrogels and biological matrices, underpins many applications ranging from water filtration and gas separation to drug delivery and cell culture. Conventional strategies for regulating transport in polymer networks primarily focus on tuning molecular diffusion through network mesh size and polymer chemistry, whereas convection is often considered negligible because nanoscale‐mesh networks typically exhibit low fluid permeability. Although hydraulic pressure is a well‐established driving force for convection in porous media, extending pressure‐driven convection to non‐porous polymer networks has remained fundamentally challenging because they can undergo substantial deformation or fracture under pressure gradients. Here, we demonstrate that hydraulic pressure applied across mechanically tough and grid‐supported hydrogels enables robust and tunable solute transport while maintaining structural integrity. The characteristic transport time can be experimentally modulated by up to 65‐fold, consistent with a coupled diffusion–convection model. Beyond tuning transport kinetics, applied pressure enhances size‐ and charge‐dependent transport selectivity by up to 5.4‐fold compared to pressure‐free conditions. As a proof of concept, we demonstrate pressure‐programmed antimicrobial delivery that dynamically controls doxorubicin transport while blocking bacterial penetration. These findings identify pressure‐regulated convection as an underexplored mechanism for controlling transport in polymer networks. 

Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed about the rapidly evolving threat landscape. However, current CTI knowledge extraction methods lack flexibility and generalizability, often resulting in inaccurate and incomplete knowledge extraction. Syntax parsing relies on fixed rules and dictionaries, while model fine-tuning requires large annotated datasets, making both paradigms challenging to adapt to new threats and ontologies. To bridge the gap, we propose CTINexus, a novel framework leveraging optimized in-context learning (ICL) of large language models (LLMs) for data-efficient CTI knowledge extraction and high-quality cybersecurity knowledge graph (CSKG) construction. Unlike existing methods, CTINexus requires neither extensive data nor parameter tuning and can adapt to various ontologies with minimal annotated examples. This is achieved through: (1) a carefully designed automatic prompt construction strategy with optimal demonstration retrieval for extracting a wide range of cybersecurity entities and relations; (2) a hierarchical entity alignment technique that canonicalizes the extracted knowledge and removes redundancy; (3) an long-distance relation prediction technique to further complete the CSKG with missing links. Our extensive evaluations using 150 real-world CTI reports collected from 10 platforms demonstrate that CTINexus significantly outperforms existing methods in constructing accurate and complete CSKG, highlighting its potential to transform CTI analysis with an efficient and adaptable solution for the dynamic threat landscape. 

Genetic markers play a crucial role in identifying crop types, especially in sample types unsuitable for chemical analysis (e.g., trace residues, small leaf fragments, immature crops). Cannabinoid synthase genes are key enzymes in determining a crop’s chemical composition or chemotype. However, existing markers often overlook the analysis of pseudogenes in the cannabis genome. Cannabinoid synthase genes are found to be highly repetitive in the cannabis genome and contain many incomplete coding sequences (pseudogenes) that can have 91–95% nucleotide identity to functional synthase genes. The risk of amplification of pseudogenes needs to be carefully evaluated in all synthase-gene targeting methods for genetic testing of cannabis. To address this, we previously developed a custom NGS panel targeting all synthase genes (THCAS, CBDAS, and CBCAS) as well as their pseudogenes. To ensure a comprehensive analysis, a diverse array of sample types was incorporated. These samples included reference C. sativa samples sourced from the National Institute on Drug Abuse (NIDA) and the National Institute of Standards and Technology (NIST), along with CBD, CBG, delta-8-THC, delta-10-THC, THCO, and CBDV hemp samples containing. Additionally, the study included hemp seed samples and extracts from seized marijuana provided by the Drug Enforcement Agency (DEA). The identified novel polymorphisms were then incorporated into two fast genotyping assays. The study developed a SNaPshot™ assay and a TaqMan™ quantitative Polymerase Chain Reaction (qPCR) Single Nucleotide Polymorphism (SNP) genotyping assay for hemp-marijuana differentiation. SNaPshot™, with four markers on the THCAS gene, THCAS pseudogene, and CBDAS gene successfully differentiated five hemp types and hemp seeds from seized marijuana across various locations in the United States, Chile, and the Mexico-United States border. Furthermore, this assay yielded valuable CBD information in the marijuana samples. On the other hand, the TaqMan™ qPCR SNP genotyping assays, using two markers on the THCAS gene and THCAS pseudogene, showed efficient differentiation of the majority of hemp samples from marijuana, offering the benefits of reduced analysis time and lower costs. However, these assays were unable to distinguish hemp seeds from marijuana or provide CBD information of the sample. In summary, this presentation showcases the extensive findings from the custom-target NGS panel, leading to the development of two robust, rapid genotyping assays. The comprehensive evaluation of synthase genes enhances researchers’ understanding of the diversity within and between Cannabis sativa crop types. This research offers valuable insights for forensic science and law enforcement in their efforts to accurately differentiate hemp and marijuana varieties. 

Cannabis sativa, which can be grown as marijuana or hemp, has been one of the most contentious plants in the US. Law enforcement is now tasked with identifying the two types of crops as a result of the 2018 Agricultural Improvement Act, which legalized hemp. Genetic markers are desirable for determining crop type in sample types that are inconducive to chemical analysis, such as trace residues, small leaf fragments, immature crops, aged samples, seeds, and root material. Cannabinoid synthase genes are considered the key enzymes that determine the chemical composition or chemotype of a particular cultivar. Several researchers have suggested the use of cannabinoid synthase genes, particularly tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS), to forecast the chemotype of Cannabis sativa. A wider range of cannabis types must be taken into account when evaluating cannabis-related genetic markers because existing techniques for crop type identification based on previous theories may lack specificity and precision. The cannabis genome has been revealed to include several copies of cannabinoid synthase genes. The majority of homologs are pseudogenes with nucleotide identities between 91 and 95 percent to the functioning synthase genes. Therefore, the risk of amplification of pseudogenes cannot be overlooked in all synthase-gene targeting methods for the genetic testing of cannabis. This study evaluated not only THCAS and CBDAS but also synthase-like genes (pseudogenes), as well as another commonly seen synthase gene, cannabichromene acid synthase (CBCAS). A custom next generation sequencing (NGS) panel was developed that allowed us to target the three cannabinoid synthase genes: THCAS, CBDAS, and CBCAS, as well as the pseudo synthase genes: THCAS-Like, CBDAS-Like, and CBCAS-Like, in a broad variety of C. sativa samples. Samples including reference C. sativa samples from the National Institute on Drug Abuse (NIDA) and National Institute of Standards and Technology (NIST), CBD, CBG, delta-8-THC, delta-10-THC, THCO, and CBDV hemp samples, hemp seed samples, and seized marijuana extracts from the Drug Enforcement Agency (DEA). This presentation will be demonstrating the development and analysis process using the NGS panel to discover novel polymorphisms and comprehensively evaluate the relationship between the cannabis chemotype and the synthase gene copies. The study suggested a novel flow chart for genetic distinction between hemp and marijuana. The knowledge gained from this study will also be incorporated to develop a fast-genotyping assay that could potentially assist law enforcement agencies in distinguish legal hemp and illicit marijuana.