In Situ Cas12a-Based Allele-Specific PCR for Imaging Single-Nucleotide Variations in Foodborne Pathogenic Bacteria.

In situ profiling of single-nucleotide variations (SNVs) can elucidate drug-resistant genotypes with single-cell resolution. The capacity to directly "see" genetic information is crucial for investigating the relationship between mutated genes and phenotypes. Fluorescence in situ hybridization serves as a canonical tool for genetic imaging; however, it cannot detect subtle sequence alteration including SNVs. Herein, we develop an in situ Cas12a-based amplification refractory mutation system-PCR (ARMS-PCR) method that allows the visualization of SNVs related to quinolone resistance inside cells. The capacity of discriminating SNVs is enhanced by incorporating optimized mismatched bases in the allele-specific primers, thus allowing to specifically amplify quinolone-resistant related genes. After in situ ARMS-PCR, we employed a modified Cas12a/CRISPR RNA to tag the amplicon, thereby enabling specific binding of fluorophore-labeled DNA probes. The method allows to precisely quantify quinolone-resistant Salmonella enterica in the bacterial mixture. Utilizing this method, we investigated the survival competition capacity of quinolone-resistant and quinolone-sensitive bacteria toward antimicrobial peptides and indicated the enrichment of quinolone-resistant bacteria under colistin sulfate stress. The in situ Cas12a-based ARMS-PCR method holds the potential for profiling cellular phenotypes and gene regulation with single-nucleotide resolution at the single-cell level.

Artificial Intelligence-Enhanced Waveguide "Photonic Nose"- Augmented Sensing Platform for VOC Gases in Mid-Infrared.

On-chip nanophotonic waveguide sensor is a promising solution for miniaturization and label-free detection of gas mixtures utilizing the absorption fingerprints in the mid-infrared (MIR) region. However, the quantitative detection and analysis of organic gas mixtures is still challenging and less reported due to the overlapping of the absorption spectrum. Here,an Artificial-Intelligence (AI) assisted waveguide "Photonic nose" is presented as an augmented sensing platform for gas mixture analysis in MIR. With the subwavelength grating cladding supported waveguide design and the help of machine learning algorithms, the MIR absorption spectrum of the binary organic gas mixture is distinguished from arbitrary mixing ratio and decomposed to the single-component spectra for concentration prediction. As a result, the classification of 93.57% for 19 mixing ratios is realized. In addition, the gas mixture spectrum decomposition and concentration prediction show an average root-mean-square error of 2.44 vol%. The work proves the potential for broader sensing and analytical capabilities of the MIR waveguide platform for multiple organic gas components toward MIR on-chip spectroscopy.

Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for rural middle school students.

This paper investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among rural middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from rural middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing rural education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in rural settings.

Development of Photonic In-Sensor Computing Based on a Mid-Infrared Silicon Waveguide Platform.

Neuromorphic in-sensor computing has provided an energy-efficient solution to smart sensor design and on-chip data processing. In recent years, various free-space-configured optoelectronic chips have been demonstrated for on-chip neuromorphic vision processing. However, on-chip waveguide-based in-sensor computing with different data modalities is still lacking. Here, by integrating a responsivity-tunable graphene photodetector onto the silicon waveguide, an on-chip waveguide-based in-sensor processing unit is realized in the mid-infrared wavelength range. The weighting operation is achieved by dynamically tuning the bias of the photodetector, which could reach 4 bit weighting precision. Three different neural network tasks are performed to demonstrate the capabilities of our device. First, image preprocessing is performed for handwritten digits and fashion product classification as a general task. Next, resistive-type glove sensor signals are reversed and applied to the photodetector as an input for gesture recognition. Finally, spectroscopic data processing for binary gas mixture classification is demonstrated by utilizing the broadband performance of the device from 3.65 to 3.8 µm. By extending the wavelength from near-infrared to mid-infrared, our work shows the capability of a waveguide-integrated tunable graphene photodetector as a viable weighting solution for photonic in-sensor computing. Furthermore, such a solution could be used for large-scale neuromorphic in-sensor computing in photonic integrated circuits at the edge.

Colorimetric and chemiluminescent enzyme immunoassays based on the alkaline phosphatase-tagged single-chain variable fragment fusion tracer for detecting zearalenone in agro-products.

Zearalenone (ZEN) is a non-steroidal estrogenic mycotoxin and seriously threatens food safety, which requires rapid and sensitive detection methods for monitoring ZEN in agro-products. Herein, an alkaline phosphatase-tagged single-chain variable fragment fusion protein (ALP-scFv) was used as a bifunctional tracer to develop a colorimetric enzyme immunoassay (CEIA) and a chemiluminescent enzyme immunoassay (CLEIA) for ZEN. In addition, the interactions between scFv and ZEN were exploited by computer-assisted simulation, and four key amino acid sites were preliminarily identified. After optimization, the CEIA and CLEIA exhibited a limit of detection of 0.02 and 0.006 ng/mL, respectively. Furthermore, both methods showed favorable accuracy in recovery experiments and good selectivity in cross reactions. Moreover, the detection results of the actual samples from both methods correlated well with those from high-performance liquid chromatography. Overall, the ALP-scFv fusion tracer-based CEIA and CLEIA are demonstrated as reliable tools for ZEN detection in food.