Method For Rapid Identification Research Articles

Accuracy of various matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based rapid identification methods—As a tool to augment diagnostic stewardship in blood culture laboratory, South India

BackgroundBlood stream infection is a medical emergency associated with high morbidity and mortality. Prompt identification of bloodstream infection-causing microorganisms directly from positive blood culture will significantly enhance patient care by reducing the turnaround time of pathogen recognition. MethodsA total of 256 freshly flagged positive blood culture bottles were subjected to Gram staining. Direct MALDI-TOF MS analysis was performed following sample preparation techniques such as lysis centrifugation, lysis filtration and VITEK® MS BC kit to directly identify microorganisms from positive blood cultures. Along with these short-term incubation methods of Choco spot and minute colony(8–10h) were also performed. All those positive bottles were identified by the routine (reference) laboratory method. Results177 isolates (69.14 %) were correctly identified by Lysis centrifugation, 163 isolates (63.67 %) were correctly identified by Lysis filtration, 206 isolates (80.47 %) were correctly identified by Choco spot,250 isolates (97.65 %) were correctly identified from minute colony (8–10h) of incubation. Of 162 isolates,115 isolates (70.99 %) were correctly identified by VITEK® MS Blood culture kit, (BioMérieux). VITEK® MS BC kit method revealed higher agreement with the kappa value of 0.697 than lysis centrifugation (0.672) followed by lysis filtration (0.611). ConclusionsIn house method of lysis centrifugation is found to be equivalent to VITEK® MS BC kit method and superior to lysis filtration method in correct direct identification of bacteria from positive blood cultures by MALDI-TOF MS analysis. As lysis centrifugation requires only 10 min of processing time as compared to overnight incubation, thus it offers a less expensive substitute for the VITEK® MS BC kit in the clinical laboratory. As a consequence of this study, we have implemented direct MALDI-TOF-based identification from positive BCs in our daily routine diagnostic management.

Investigating the Quantification Capabilities of a Nanopore-Based Sequencing Platform for Food Safety Application via External Standards of Lambda DNA and Lambda Spiked Beef.

Six hundred million cases of disease and roughly 420,000 deaths occur globally each year due to foodborne pathogens. Current methods to screen and identify pathogens in swine, poultry, and cattle products include immuno-based techniques (e.g., immunoassay integrated biosensors), molecular methods (e.g., DNA hybridization and PCR assays), and traditional culturing. These methods are often used in tandem to screen, quantify, and characterize samples, prolonging real-time comprehensive analysis. Next-generation sequencing (NGS) is a relatively new technology that combines DNA-sequencing chemistry and bioinformatics to generate and analyze large amounts of short- or long-read DNA sequences and whole genomes. The goal of this project was to evaluate the quantitative capabilities of the real-time NGS Oxford Nanopore Technologies' MinION sequencer through a shotgun-based sequencing approach. This investigation explored the correlation between known amounts of the analyte (lambda DNA as a pathogenic bacterial surrogate) with data output, in both the presence and absence of a background matrix (Bos taurus DNA). A positive linear correlation was observed between the concentration of analyte and the amount of data produced, number of bases sequenced, and number of reads generated in both the presence and absence of a background matrix. In the presence of bovine DNA, the sequenced data were successfully mapped to the NCBI lambda reference genome. Furthermore, the workflow from pre-extracted DNA to target identification took less than 3 h, demonstrating the potential of long-read sequencing in food safety as a rapid method for screening, identification, and quantification.

A Novel Method for Rapid Screening of Salmonidae Ingredients and Accurate Detection of Atlantic Salmon (Salmo salar) Simultaneously Using Duplex Real-Time PCR Coupled with Melting Curve Analysis

The substitution of ingredients with Salmonidae, particularly Salmo salar, has led to widespread reports of financial losses and health risks globally, emphasizing the urgent need for the development of a rapid and precise method for species identification. The aim of the present study was to develop a novel method for the rapid screening of Salmonidae ingredients and the accurate detection of S. salar simultaneously using multiplex real-time PCR coupled with melting curve analysis. Specifically, primer sets specific for S. salar and Salmonidae were cross-confirmed. Moreover, the reaction system and conditions of a real-time duplex PCR were optimized, and the proposed methodology was verified, proving that the assay has good specificity and sensitivity. Clear and distinguishable melting peaks, with expected Tm values of around 80 °C (S. salar) and 84 °C (Salmonidae), were observed for twelve products, proving the presence of S. salar. However, four products were not derived from S. salar, but they could have belonged to another species within the Salmonidae family due to the presence of only one specific melting peak at a Tm value of about 84 °C. Therefore, the novel assay in the present study allows for the fast and accurate screening of Salmonidae ingredients and the detection of S. salar simultaneously.

Rapid identification of an effective bauxite gas reservoir by principal component analysis.

In recent years, industrial gas flow has been obtained from the bauxite gas reservoir in the southwestern Ordos Basin, which has made the identification of aluminium-bearing rock reservoirs a popular topic. To accelerate the exploration and development of this type of gas reservoir, major element testing, rock thin section identification and principal component analysis (PCA) were conducted, and a method for rapid and accurate identification of bauxite reservoirs via conventional logging was established. The test results clearly revealed the vertical stratification of major elements and three lithologies in the aluminium (Al)-bearing rock series in the study area. The log response characteristics of effective gas reservoirs were summarized, providing a basis for subsequent research on identifying effective bauxite reservoirs via mathematical dimensionality reduction of logging curves. The porosity comparison of strata with different lithologies suggests that dissolution pores are more developed in Al-rich layers, providing insight for identifying effective reservoirs by Al2O3 content. On the basis of the above findings, a lithological identification chart of Al-bearing rock series was established via principal component analysis (PCA), and an effective bauxite reservoir logging identification model based on Al2O3 content prediction was developed. The results show that using the dimensionality reduction method for principal component analysis of logging curves with overlapping information can avoid model distortion caused by multicollinearity. The research results can be used to identify bauxite reservoirs quickly and accurately without other test data.

A rapid and reliable method for early Legionella pneumophila identification and characterization in support of the epidemiology study.

Legionnaires' disease is a severe pneumonia predominantly caused by Legionella pneumophila (Lp), whose major reservoirs are artificial water systems. As most human infections are caused by L. pneumophila serogroup 1 (Lp1), a reliable method for Lp distinction can be crucial for bacterial spread prevention. As the ability to withstand in environments and to cause the waterborne disease is strongly related to specific genes, the identification of virulent strains can be of great relevance to implement water environmental monitoring and to contain harmful outbreaks to public health. We aimed to test an assay for Lp identification among different Legionella species, and to determine the serogroups. Additionally, we investigated the carriage of virulence and antimicrobial resistance genes. A total of 90 Legionella spp. isolates identified by phenotypic tests were subjected to the designed quantitative PCR assay targeting specific mip for Lp, wzm for Lp1, pvcA and ahpD for biofilm production. Eleven serogroups were investigated in all our isolates tested positive for mip gene, subsequently analyzed for 12 virulence and 8 antimicrobial resistance genes. Only the 70 Lp isolates were positive for mip. Out of 27 Lp isolates belonging to serogroup 1 based on agglutination test, 23 (85.2%) carried wzm. The presence of ahpD and pvcA was found in 94.3 and 98.6% of Lp isolates, respectively. By multiplex PCR, all 23 wzm-positive strains were confirmed as serogroup 1 that was the most predominant (33%). At least one virulence gene was detected in all Lp isolates. The most frequent gene was ispE (100%), followed by issD (96%), icmK and enhC (93%), cpxA (91%), rtxA2 (74%), lvhB8-B9 (61%), and prpA (54%). The other genes were less diffused in Lp strains (rtxA1, 44%; lvhB3-B4, 47%; pvcB, 27%; lvrE, 24%). Of the macrolide resistance genes, the ereA was found in 84% of Lp strains, while only 14 (20%) harbored the lpeAB among the efflux pump genes. The assays validated in this study enable the simultaneous Lp and Lp1 detection. The differentiation of Lp strains according to their virulence properties could be useful to predict the bacterial ability to survive and to cause the disease.

A-264 Validation of a Novel Real-Time PCR Assay for Identification of Mycobacterium species Isolates Recovered from Clinical Culture

Abstract Background Historically, clinical mycobacteriology laboratories used the nucleic acid hybridization-based AccuProbe assay manufactured by Hologic, Inc., to rapidly rule in or rule out Mycobacterium tuberculosis (MTB) and members of the M. avium-intracellulare complex (MAC). The clinical value of this test was in its ability to rapidly rule in/out MTB from positive broth cultures and allow the initiation of appropriate antimicrobial therapy and infection prevention practices. Recent discontinuation of this commercial assay left a gap in testing available for the rapid identification of these organisms, which allowed us to develop an in-house real-time PCR test. Methods A replacement assay was developed by Labcorp R & D based on real-time PCR amplification of extracted nucleic acids from positive culture growth. Aliquots from positive AFB VersaTREK broth cultures were heat inactivated, and nucleic acids were extracted using the MagNA Pure 24 (MP24) instrument (Roche). Real-time PCR using published primer probe sets (Rocchetti, et al 2016) was performed on the QuantStudio™ 7 (QS7) instrument (Applied Biosystems®). The assay was designed with three independent primer/probe sequence targets: one for MTB Complex (IS6110), one for MAC (ITS), and one pan-target for the Mycobacterium genus (16s rRNA). Results Procedural verification was performed using 100 residual clinical specimens previously identified using the Hologic AccuProbe® assay. Categorical concordance was 97% (97/100). The three discrepant results were identified as MAC positive by PCR; however, these were not identified as MAC on the predicate assay. Analytical sensitivity for the PCR assay was established to be 1,000 CFU/mL for each target. Analytical specificity was assessed in vitro for nine Mycobacterium species, with no cross-reactivity observed. In vitro analysis of target detection in the presence of yeast (Candida glabrata) was also performed, and we saw no inhibition. Analysis of the primer/probe sets for all targets was performed in silico using XploreDB database of microbial genomes, with confirmed 100% matches for both MAC and MTB genomes compared to hundreds of species. By comparison, the PAN-Myco primer set showed decreased performance, having three mismatches (two on forward primer and one on the probe). As this target sequence is used as an Internal Control (IC) this was not problematic for overall assay performance. The assay demonstrated excellent precision (%CV) for each target: MTB = 0.74%, MAC = 1.10%, PAN-Myco = 3.19%. Conclusions Utilizing the depth of molecular methodologies available in our laboratories, we developed a rapid method for identification of MAC and MTB from positive broth cultures, which had excellent performance compared to the predicate assay. The PCR ID LDT test is now available and allows testing to continue with no interruption in patient care.

An investigation of scattered light integrating collector technology for rapid blood culture sensitivity testing.

Introduction. Sepsis rates are increasing, with Gram-negative organisms representing a large proportion of bloodstream infections. Rapid antibiotic administration, alongside diagnostic investigations, is required for the effective management of these patients.Gap statement. Current diagnostics take ~48 h for a final report; therefore, rapid diagnostics are required.Aim. This study investigated a novel antibiotic sensitivity method, the scattered light integrating collector (SLIC), combined with a rapid identification method using matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) technology to determine if an accurate identification and susceptibility result can be provided within 4 h of a positive blood culture report.Methodology. A total of 47 blood cultures containing Gram-negative bacteria from 46 patients were processed using the MALDI-TOF Biotyper Sepsityper for identification directly from the blood and the SLIC instrument for susceptibility testing. All organisms were also tested using the current standard workflow used in the host laboratory. Categorical agreement (CA), major errors (MaEs) and very major errors (VMEs) were determined.Results. SLIC produced susceptibility results with a 71.9% CA, 30.6% MaE and 17.5% VME. The median difference in time to the final result was 44.14 (43 : 05-45 : 15) h earlier compared to the current method.Conclusion. We conclude that SLIC was unable to consistently provide sufficiently accurate antibiotic susceptibility results compared to the current standard method.

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive “separation-enrichment-detection” rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform “hot spots” and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of “screening before cultivating” for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of “cultivating before screening”. It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

Commercial Trade and Conservation: DNA Barcoding of Wild Reticulated Python (Malayopython reticulatus) (Schneider, 1801) for Sustainable Management

Abstract The reticulated python, scientifically known as Malayopython reticulatus, is a prominent member of the Pythonidae family, exhibiting a wide geographic distribution across Southeast Asia. Like most python species, it is listed on Appendix II of CITES, allowing for regulated harvest and commercial export. However, inadequate monitoring of snakeskin origins within Malaysia has facilitated illegal trade, exacerbating concerns over population declines. Thus, this study aims to document the DNA barcoding of M. reticulatus in Pantai Penyabong, Johor, utilizing the COI gene marker of mitochondrial DNA (mtDNA). Observations revealed 11 reticulated pythons, with two successfully captured for blood sample extraction and amplification. Sequences obtained exhibited a high identity percentage in the NCBI GenBank database, supported by similar topologies in phylogenetic trees. These findings demonstrate the efficacy of molecular identification techniques in combating illegal wildlife trade. Governments and law enforcement agencies can enforce conservation laws and protect wild populations by verifying mislabeled python species. This study highlights the crucial role of DNA barcoding in wildlife forensics and conservation management, offering a rapid and reliable method for species identification and monitoring in the face of escalating threats.

A spectrum correlation matrix-based rapid damage identification method for joints in hinged slab bridges by sparse measurement

Hinged slab bridges are widely used in the medium and small-span bridges on highways and the joints are highly susceptible to damage under the action of the increasing traffic. However, current methods for damage identification of hinged joints, whether rely on static or dynamic testing, typically require substantial manpower, resources and time, which presents challenges for the rapid evaluation of numerous bridges. In view of this, a novel damage index named spectrum correlation matrix (SCM) is proposed and its feature extraction technology is established as well as the potential for sparse measurement is theoretically explained. Furthermore, a rapid, robust, and convenient damage identification methodology using SCM and Levenberg-Marquardt algorithm is proposed. An optimal strategy for the layout of sparse measuring points has been explored, the sensitivity and the effectiveness of the proposed method is validated through both numerical simulations and the in-situ test of a bridge in service. The results indicate that the SCM contains abundant vibration information and shows a high sensitivity to damage. The strategy for the layout of measuring points is highly efficient and the proposed methodology greatly reduces the number of measuring points compared to previous vibration-based methods, which eases rapid identification of damage for joints in hinged slab bridges.

A Rapid Circuit Phase Error Identification and Compensation Method for MEMS QMG Achieving 99.7% Reduction in ZRO Drift

A Rapid Circuit Phase Error Identification and Compensation Method for MEMS QMG Achieving 99.7% Reduction in ZRO Drift

Construction of pyrimidine derivatives-copper enzyme mimics as colorimetric sensing elements for efficient detection of phenolic compounds and hydrogen peroxide

As concerns about environmental pollution grow, the rapid identification and quantification of pollutants have become increasingly vital. In this work, a series of pyrimidine derivatives-Cu enzyme mimics (Cytosine-Cu, Cytidine-Cu, and CMP-Cu) with laccase- and peroxidase-like activity were prepared through the coordination of Cu2+ with different pyrimidine derivatives (PDs). The PDs-Cu enzyme mimics contain high levels of Cu+ and N−Cu coordination structures, which provide sufficient catalytic sites for the substrates. Compared with natural enzymes and other nanozymes, PDs-Cu demonstrate superior substrate affinity, catalytic efficiency, stability, and resistance to interference. It was found that PDs-Cu enzyme mimics have different catalytic activities towards different phenolic compounds. Therefore, a three-channel colorimetric sensor array (CSA) was successfully developed utilizing PDs-Cu as the sensing elements. The CSA can accurately identify different phenolic compounds and their mixtures in seawater and simulated wastewater. Additionally, a colorimetric method for detecting H2O2 in eye drops was developed, featuring a detection range of 0.1–10.0μM and a limit of quantification of 0.1μM. This research not only provides a flexible protocol for regulating the catalytic activity of enzyme mimics, but also provides important inspiration for the development of methods for rapid identification and detection of contaminants in the environmental water.

ZymoSoups: A high-throughput forward genetics method for rapid identification of virulence genes in Zymoseptoria tritici.

Septoria tritici blotch is caused by the fungus Zymoseptoria tritici and poses a major threat to wheat productivity. There are over twenty mapped loci in wheat that confer strong (gene-for-gene) resistance against this pathogen, however the corresponding genes in Z. tritici that confer virulence against distinct R genes remain largely unknown. In this study, we developed a rapid forward genetics methodology to identify genes that enable Z. tritici to gain virulence on previously resistant wheat varieties. We used the known gene-for-gene interaction between Stb6 and AvrStb6 as a proof-of-concept that this method could quickly recover single candidate virulence genes. We subjected the avirulent Z. tritici strain IPO323, which carries the recognized AvrStb6 allele, to UV mutagenesis and generated a library of over 66,000 mutants. We screened these mutants on leaves of the resistant wheat variety Cadenza, in mixtures (soups) ranging from 100-500 mutants per soup. We identified five soups with a gain-of-virulence (GoV) phenotype relative to the IPO323 parental strain and re-sequenced 18 individual isolates, including four control isolates and two mutants lacking virulence, when screened individually. Of the 12 confirmed GoV mutants, one had a single nucleotide polymorphism (SNP) in the AvrStb6 coding region. The other 11 GoV mutants exhibited large (~70Kb) deletions at the end of chromosome 5, including the AvrStb6 locus. Our findings demonstrate the efficiency of this forward genetic approach in elucidating the genetic basis of qualitative resistance to Z. tritici and the potential to rapidly identify other, currently unknown, Avr genes in this pathogen.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing.

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Research on wheat impurity identification method based on terahertz imaging technology

The traditional detection of impurities in wheat has difficulties such as low precision, time-consuming, and cumbersome, therefore, it is important to study the method of rapid and accurate detection of impurities in wheat for correctly assessing the quality grade of wheat. Terahertz (THz) technology has many superior properties such as transient, broadband, low-energy, and penetrating, which can realize rapid and nondestructive detection of wheat quality. In this study, a classification and recognition algorithm AHA-RetinaNet-X for wheat impurity terahertz images based on RetinaNet and Artificial hummingbird algorithm (AHA) is proposed.A THz three-dimensional tomography imaging system is used to image wheat and its impurities, and two THz image datasets, respectively the wheat and impurity dataset for verifying the classification effect of wheat and impurities and the impurity dataset for verifying the classification effect of impurities. The experimental results show that the AHA-RetinaNet-X model outperforms other detection and classification models in terms of accuracy, F1-score, precision, recall, and specificity, and is able to achieve 96.1%, 94.9%, 95.2%, 95.8%, 95.5%, 95.3%, and 93.3% for the wheat and impurity dataset and the impurity dataset, respectively, 95.6%, 96.3%, and 95.2%, and the mAP value of AHA-RetinaNet-X is also higher than the other models and can reach 92.1%. The combination of THz imaging technology and AHA-RetinaNet-X can realize the classification and identification of wheat and impurities, which provides a new method for the non-contact rapid nondestructive detection and identification of wheat and impurities, and also provides a reference for the research of the identification and detection methods of other substances.

Validation of short culture method for rapid bacterial identification of blood cultures via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

Development of rapid bacterial identification from blood cultures has been an area of intense study in diagnostic microbiology. Shortened turnaround time coupled with antimicrobial stewardship interventions have been shown to improve patient outcomes and decrease healthcare-associated costs. We report the validation of a short incubation method for Gram-positive and Gram-negative bacterial identification utilizing MALDI-TOF MS without additional instrumentation, processing or cost compared with current practice. Prospective, observational, single-centre study in a quaternary care academic hospital encompassing 376 blood cultures subjected to bacterial identification after short incubation periods of 3-4 and 6-8 h. There was 97.5% species-level identification agreement with tests undertaken after 3-4 h incubation with 83.6% isolates identified, and 99.7% species-level identification agreement after 6-8 h incubation with 96.7% isolates identified. The short incubation method provides a rapid MALDI-TOF MS bacterial identification method, reducing turnaround time by 10-18 h compared with standard practice without additional cost, processing or instrumentation.

Prediction of Total Soluble Solids Content Using Tomato Characteristics: Comparison Artificial Neural Network vs. Multiple Linear Regression

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

Rapid identification method of milk powder from different animals based on Raman spectroscopy

This study developed an efficient method for identifying and quantitatively analyzing animal-origin milk powders using Raman spectroscopy combined with chemometrics. By employing the MultiClassClassifier model, the method achieved high accuracy in distinguishing various types of animal-origin milk powders, with sensitivity and specificity both exceeding 80% and an overall accuracy of 93%. Furthermore, the quantitative models based on Partial Least Squares Regression and Support Vector Machine Regression exhibited excellent linear correlations, with both Root Mean Square Error and Mean Relative Error below 0.2. These models successfully quantified adulteration in camel, mare, and donkey milk powders in comparison to goat and cow milk powders. The study's approach not only holds significant promise for detecting adulteration in specialty milk powders but also demonstrates wide applicability in analyzing other powdered adulterants.

QTL-Seq: Rapid, Cost-Effective, and Reliable Method for QTL Identification

QTL-seq is a powerful method that integrates whole-genome sequencing (WGS) with bulk-segregant analysis to rapidly and reliably identify quantitative trait loci (QTLs) associated with specific traits. This approach significantly advances traditional QTL mapping by eliminating the need for genome wide DNA markers such as SSR, RFLP, and INDELs, which are typically used in linkage-based QTL mapping. Instead, QTL-seq leverages WGS to detect all genetic variations such as SNPs, Indels, and Structural Variants across the entire genome, providing a comprehensive resource for marker development in marker-assisted selection. The QTL-seq process begins with the creation of genetically diverse mapping populations, such as F2 or RILs, followed by detailed phenotypic characterization. DNA from plants exhibiting similar phenotypes is pooled into bulk groups and sequenced, allowing for cost-effective and efficient QTL identification. Identified QTLs can be further validated through fine mapping using recombinant screenings and progeny testing, leading to the identification of candidate genes associated with traits of interest. In this study, we outline a user-friendly QTL-seq pipeline, from sequencing to data visualization, using the methodology and data provided by Takagi et al., 2013, to demonstrate its practical application. While the manuscript primarily focuses on describing the pipeline, we also conducted a case study analysis with real data to showcase its effectiveness. Our work contributes to the broader understanding of QTL-seq applications and offers practical recommendations for optimizing this method in future breeding programs.

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple pesticides. Herein, we first synthesize a Eu(III)-functionalized HOF-on-HOF composite (Eu@PFC-1@MA-TPA) and then utilize principal component analysis (PCA) and a machine learning (ML) algorithm to achieve simultaneous identification of the pesticides 2,6-dichloro-4-nitroaniline (DCN) and thiabendazole (TBZ) and their mixtures. Eu@PFC-1@MA-TPA displays high quantitative identification ability, which can distinguish single DCN and TBZ as low as 1 μM and their mixtures at 5 μM through PCA. In addition, the hydrogel film Eu@PFC-1@MA-TPA/AG is fabricated to monitor DCN and TBZ in drinking water, tap water, river water, and apple juice with high sensitivity. Furthermore, based on the obvious fluorescence color variance of pesticides, Eu@PFC-1@MA-TPA/AG achieves visual and in situ imaging detection of single DCN and TBZ and their mixtures. More importantly, we construct an intelligent artificial vision platform integrating Eu@PFC-1@MA-TPA/AG with a DenseNet algorithm, which can identify the concentrations and types of DCN and TBZ and their mixtures within 1 s with over 98% accuracy. This work develops a precise and rapid analysis method for simultaneous identification of multiple pesticides through combining a visualized fluorescence sensor and an ML algorithm.