Automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism.

Wild bird repulsion is critical in agriculture because it helps avoid agricultural food losses and mitigates the risk of avian influenza. Wild birds transmit avian influenza in poultry farms and thus cause large economic losses. In this study, we developed an automatic wild bird repellent system that is based on deep-learning-based wild bird detection and integrated with a laser rotation mechanism. When a wild bird appears at a farm, the proposed system detects the bird's position in an image captured by its detection unit and then uses a laser beam to repel the bird. The wild bird detection model of the proposed system was optimized for detecting small pixel targets, and trained through a deep learning method by using wild bird images captured at different farms. Various wild bird repulsion experiments were conducted using the proposed system at an outdoor duck farm in Yunlin, Taiwan. The statistical test results of our experimental data indicated that the proposed automatic wild bird repellent system effectively reduced the number of wild birds in the farm. The experimental results indicated that the developed system effectively repelled wild birds, with a high repulsion rate of 40.3% each day.

Single-Particle Spectroscopic Chromatography Reveals Heterogeneous RNA Loading and Size Correlations in Lipid Nanoparticles.

Lipid nanoparticles (LNP) have emerged as pivotal delivery vehicles for RNA therapeutics. Previous research and development usually assumed that LNPs are homogeneous in population, loading density, and composition. Such perspectives are difficult to examine due to the lack of suitable tools to characterize these physicochemical properties at the single-nanoparticle level. Here, we report an integrated spectroscopy-chromatography approach as a generalizable strategy to dissect the complexities of multicomponent LNP assembly. Our platform couples cylindrical illumination confocal spectroscopy (CICS) with single-nanoparticle free solution hydrodynamic separation (SN-FSHS) to simultaneously profile population identity, hydrodynamic size, RNA loading levels, and distributions of helper lipid and PEGylated lipid of LNPs at the single-particle level and in a high-throughput manner. Using a benchmark siRNA LNP formulation, we demonstrate the capability of this platform by distinguishing seven distinct LNP populations, quantitatively characterizing size distribution and RNA loading level in wide ranges, and more importantly, resolving composition-size correlations. This SN-FSHS-CICS analysis provides critical insights into a substantial degree of heterogeneity in the packing density of RNA in LNPs and size-dependent loading-size correlations, explained by kinetics-driven assembly mechanisms of RNA LNPs.

Digitized Kinetic Analysis Enhances Genotyping Capacity of CRISPR-Based Biosensing.

CRISPR/Cas systems have been widely employed for nucleic acid biosensing and have been further advanced for mutation detection by virtue of the sequence specificity of crRNA. However, existing CRISPR-based genotyping methods are limited by the mismatch tolerance of Cas effectors, necessitating a comprehensive screening of crRNAs to effectively distinguish between wild-type and point-mutated sequences. To circumvent the limitation of conventional CRISPR-based genotyping, here, we introduce Single-Molecule kinetic Analysis via a Real-Time digital CRISPR/Cas12a-assisted assay (SMART-dCRISPR). SMART-dCRISPR leverages the differential kinetics of the signal increase in CRISPR/Cas systems, which is modulated by the complementarity between crRNA and the target sequence. It employs single-molecule digital measurements to discern mutations based on kinetic profiles that could otherwise be obscured by variations in the target concentrations. We applied SMART-dCRISPR to genotype notable mutations in SARS-CoV-2, point mutation (K417N) and deletion (69/70DEL), successfully distinguishing wild-type, Omicron BA.1, and Omicron BA.2 SARS-CoV-2 strains from clinical nasopharyngeal/nasal swab samples. Additionally, we introduced a portable digital real-time sensing device to streamline SMART-dCRISPR and enhance its practicality for point-of-care settings. The combination of a rapid and sensitive isothermal CRISPR-based assay with single-molecule kinetic analysis in a portable format significantly enhances the versatility of CRISPR-based nucleic acid biosensing and genotyping.

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform.

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR.

Cancer Methylation Biomarker Detection in an Automated, Portable, Multichannel Magnetofluidic Platform.

Early detection of cancer is critical to improving clinical outcomes, especially in territories with limited healthcare resources. DNA methylation biomarkers have shown promise in early cancer detection, but typical workflows require highly trained personnel and specialized equipment for manual and lengthy processing, limiting use in resource-constrained areas. As a potential solution, we introduce the Automated Cartridge-based Cancer Early Screening System (ACCESS), a compact, portable, multiplexed, automated platform that performs droplet magnetofluidic- and methylation-specific qPCR-based assays for the detection of DNA methylation cancer biomarkers. Development of ACCESS focuses on esophageal cancer, which is among the most prevalent cancers in low- and middle-income countries with extremely low survival rates. Upon implementing detection assays for two esophageal cancer methylation biomarkers within ACCESS, we demonstrated successful detection of both biomarkers from esophageal tumor tissue samples from eight esophageal cancer patients while showing specificity in paired normal esophageal tissue samples. These results illustrate ACCESS's potential as an amenable epigenetic diagnostic tool for resource-constrained areas toward early detection of esophageal cancer and potentially other malignancies.

Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration.

BACKGROUND: Bacterial infections, especially polymicrobial infections, remain a threat to global health and require advances in diagnostic technologies for timely and accurate identification of all causative species. Digital melt - microfluidic chip-based digital PCR combined with high resolution melt (HRM) - is an emerging method for identification and quantification of polymicrobial bacterial infections. Despite advances in recent years, existing digital melt instrumentation often delivers nonuniform temperatures across digital chips, resulting in nonuniform digital melt curves for individual bacterial species. This nonuniformity can lead to inaccurate species identification and reduce the capacity for differentiating bacterial species with similar digital melt curves. RESULTS: We introduce herein a new temperature calibration method for digital melt by incorporating an unamplified, synthetic DNA fragment with a known melting temperature as a calibrator. When added at a tuned concentration to an established digital melt assay amplifying the commonly targeted 16S V1 - V6 region, this calibrator produced visible low temperature calibrator melt curves across-chip along with the target bacterial melt curves. This enables alignment of the bacterial melt curves and correction of heating-induced nonuniformities. Using this calibration method, we were able to improve the uniformity of digital melt curves from three causative species of bacteria. Additionally, we assessed calibration's effects on identification accuracy by performing machine learning identification of three polymicrobial mixtures comprised of two bacteria with similar digital melt curves in different ratios. Calibration greatly improved mixture composition prediction. SIGNIFICANCE: To the best of our knowledge, this work represents the first DNA calibrator-supplemented assay and calibration method for nanoarray digital melt. Our results suggest that this calibration method can be flexibly used to improve identification accuracy and reduce melt curve variabilities across a variety of pathogens and assays. Therefore, this calibration method has the potential to elevate the diagnostic capabilities of digital melt toward polymicrobial bacterial infections and other infectious diseases.