Barcode Reading Research Articles

Photon-Upconversion Barcoding with Multiple Barcode Channels: Application for Droplet Microfluidics

Barcoding facilitates high-throughput analytical methods in complex matrixes with a reduced volume of sample, reagents, time, and cost. Because of orthogonality to fluorescence, photon-upconversion barcodes attracted considerable attention in recent years. We constructed an epiluminescence detector, which, for the first time, demonstrated the reading of photon-upconversion spectra from microdroplets in a microfluidic chip with frequency up to 10 Hz. Non-negative least-squares deconvolution enabled the reading of an unprecedented number of photon-upconversion barcode channels (six) from emission spectra (excitation 980 nm, emission 430-875 nm). The standard deviation of barcode reading from microdroplets was ∼1%. Described barcoding can be, for example, used for multiparameter titrations, multiplexed biological and chemical assays, optimizations on a microfluidic platform, and preparation of barcoded concentration gradients and libraries.

1D Barcode Detection via Integrated Deep-Learning and Geometric Approach

Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.

Reading Analysis for Barcode Scanner With Interference from LED-Based Lighting

This paper addresses reading failures of a barcode scanner interfered by light emitting diode (LED) lamps. It quantifies the reading performance in terms of timing signal-to-interference ratio (TSIR), in particular, as a function of modulation depth and frequency of the interference from modulated LED lighting. At the decision variable, the interference typically generated in the LED driver by a switched mode power supply (SMPS) is neither additive nor Gaussian. It has frequencies up to several MHz that can seriously affect the barcode reading performance. To calculate the signal and interference power for TSIR, the laser scanner physical channels are analyzed, including the laser beam path and the LED interference path. Since barcode scanners usually use peak or edge detection, the reading reliability is subject to the first and second derivatives of the LED inference, respectively. We validate the proposed TSIR for predicting scanner reading performance by experiments. For instance, we found that typical scanners reach a specified sensitivity at a TSIR value of around 50. We further investigate the reading performance under multi-frequency interference, e.g., interference from the harmonics of the SMPS control signal. In general, this paper presents a simple, yet realistic model to quantify the reading performance in terms of TSIR for barcode scanner subject to single-frequency LED interference, and it proposes an empirically verified flicker interference metric (FIM) for multi-frequency interference.

Evaluation of an Automated Tissue Sectioning Machine for Digital Pathology

Background: Automation and digital pathology are the trends for future anatomic pathology with the increasing workload in histology laboratories. While tissue process, embedding, staining and coverslipping, and digitizing have been available for automated use, tissue sectioning appears to be the biggest roadblock to a fully automated histology process. In this study we were aimed to investigate a tissue automated sectioning machine for both clinical and research use.Methods: Totally 77 surgical resection blocks of various organs embedded with clinical standard paraffin were sectioned automatically using AS-410 (Dainippon Seiki Co. LTD., Japan) at 5 μm thickness with the default setting (Setting A). 10 slides per block were sectioned and the last 5 slides were stained with H&E. All stained slides were digitized with whole-slide imaging scanner, and then evaluated by the image scientist and the pathologist. The image scientist scored the images base on the extent of imperfection (Evaluation I), while the pathologist scored the images based on the clinical diagnosis purpose (Evaluation II). Both scoring systems were scored from 1 to 5, with 1 the worst quality and 5 the highest quality. Tissues with unsatisfied score were sectioned with modified setting (Setting B), and evaluated again by the same image scientist and pathologist using the same scoring systems. And the scores from the two different settings were compared. Auto-trimming and barcode reading and printing of AS-410 were also evaluated.Results: The AS-410 provided auto-trimming function to detect exposed tissue for cutting, accomplished by the installed camera and calculation software. It read sample information and printed barcode as well as input text and automatically generated slide order information. It produced good quality of sections for most cases with median score more than 4 in both Evaluation I and Evaluation II using setting A. The scores of the unsatisfied blocks sectioned with setting A improved significantly when those blocks were sectioned with setting B.Conclusion: The AS-410 tissue sectioning machine produces high-quality sections with clinical standard paraffin tissue blocks of a variety of organs with proper settings. It promises high automation with sound sectioning quality in the era of digital pathology for both clinical and research use. Lung Breast Prostate

Alignment-free clustering of UMI tagged DNA molecules.

Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient. We introduce Calib, a computational tool that clusters paired-end reads from UMI tagged sequencing experiments generated by substitution-error-dominant sequencing platforms such as Illumina. Calib clusters are defined as connected components of a graph whose edges are defined in terms of both barcode similarity and read sequence similarity. The graph is constructed efficiently using locality sensitive hashing and MinHashing techniques. Calib's default clustering parameters are optimized empirically, for different UMI and read lengths, using a simulation module that is packaged with Calib. Compared to other tools, Calib has the best accuracy on simulated data, while maintaining reasonable runtime and memory footprint. On a real dataset, Calib runs with far less resources than alignment-based methods, and its clusters reduce the number of tentative false positive in downstream variation calling. Calib is implemented in C++ and its simulation module is implemented in Python. Calib is available at https://github.com/vpc-ccg/calib. Supplementary data are available at Bioinformatics online.

Carbamazepine, lamotrigine, levetiracetam and valproic acid in dried blood spots with liquid chromatography tandem mass spectrometry; method development and validation.

Monitoring of antiepileptic drugs in children with epilepsy require multiple visits at a clinic for blood collection. Dried blood spot sampling is an alternative way of collection, performed at home by self-collection and can save time and costs for patients and family members. The aim was to develop and validate an LC-MS/MS dried blood spot method for carbamazepine, lamotrigine, levetiracetam and valproic acid with the requirements of using standard equipment and material in a routine laboratory setting. Whatman-903 filter paper was utilized, and discs were punched into a 96 well plate with an automated puncher and barcode reading. Extraction with methanol/water solution including internal standards on an orbital shaker was followed by a vacuum centrifuge step and reconstitution in mobile phase. Bioanalytical validation was performed according to guidelines from European Medicines Agency and additional dried blood spot specific validation. Calibration curves of the four included drugs had R2 values ≥0.994. Therapeutic relevant concentrations were well within measuring ranges. Within and -between run precision had %CV:s of 2.9-10.5%. Accuracy (%bias) was between -16.5% (lower limit of quantification) to +7.4%. Blood spots in a volume range of 15-50μL with hematocrit in expected ranges for this patient group were within precision and accuracy limits. To test the method, concentrations from dried blood spot venous and capillary patient samples (n=50) were compared with plasma concentrations. Good correlations for all four drugs with R2 of >0.92 was shown. In summary, a fast method for dried blood spots based on a 96 well format was developed for four commonly prescribed antiepileptic drugs. This validated method with traceability in sample preparation by bar code reading makes it suitable for the clinical laboratory.

Automatic extraction of 1D barcodes from video scans for drone-assisted inventory management in warehousing applications

ABSTRACTThe widespread use of barcodes has significantly contributed to accurate, efficient and economic inventory management in warehouses and distribution centres. However, its efficiency has always been limited by the primary method of reading barcodes with a handheld laser scanner. Compared with this reading by line-of-sight at close proximity, vision-based barcode reading algorithms can further improve efficiency, particularly if accompanied by automated data collection platforms such as drones. This paper introduces algorithms that are able to automatically extract barcodes from video data, and verifies their feasibility and promise for inventory management in warehousing applications. Three key techniques corresponding to different recognition levels are proposed: For a known barcode region, a Harris corner detector and Hough transform-based algorithm is applied to quickly estimate the angle by which the frame area needs to rotate to orient the bars vertically for information extraction. Then, the idea of exploiting connectivity and geometry property of barcode areas is proposed to directly recognise multiple barcode regions in a single video frame to eliminate reading difficulties resulting from interactive influence of multiple juxtaposed barcodes, and to save computation time by only processing frame areas of interest for valid barcodes. In addition, a histogram difference-based fast extraction strategy is designed to further improve efficiency by reducing duplicate information processing. Finally, the performance of each technique is evaluated by analysing video data from a large logistics warehouse, demonstrating satisfactory performance in inventory management applications.

Bartender: a fast and accurate clustering algorithm to count barcode reads.

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods. Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1. sasha.levy@stonybrook.edu or song.wu@stonybrook.edu. Supplementary data are available at Bioinformatics online.

SeqDB: Biological Collection Management with Integrated DNA Sequence Tracking

Agriculture and Agri-Food Canada (AAFC) is home to a world-class taxonomy program based on Canada’s national agricultural collections for Botany, Mycology and Entomology. These collections contain valuable resources, such as type specimen for authoritative identification using approaches that include phenotyping, DNA barcoding, and whole genome sequencing. These authoritative references allow for accurate identification of the taxonomic biodiversity found in environmental samples in fields such as metagenomics. AAFC’s internally developed web application, termed SeqDB, tracks the complete workflow and provenance chain from source specimen information through DNA extractions, PCR reactions, and sequencing leading to binary DNA sequence files. In the context of Next Generation Sequencing (NGS) of environmental samples, SeqDB tracks sampling metadata, DNA extractions, and library preparation workflow leading to demultiplexed sequence files. SeqDB implements the Taxonomic Databases Working Group (TDWG) Darwin Core standard Wieczorek et al. 2012 for Biodiversity Occurrence Data, as well as the Genome Standards Consortium (GSC) Minimum Information about any (X) Sequences (MIxS) specification Yilmaz et al. 2011. When coupled with the built-in data standards validation system, this has led to the ability to search consistent metadata across multiple studies. Furthermore, the application enables tracking the physical storage of the aforementioned specimens and their derivative molecular extracts using an integrated barcode printing and reading system. All the information is presented using a graphical user interface that features intuitive molecular workflows as well as a RESTful API that facilitates integration with external applications and programmatic access of the data. The success of SeqDB has been due to the close collaboration with scientists and technicians undertaking molecular research involving the national collection, and the centralization of their data sets in an access controlled relational database implementing internationally recognized standards. We will describe the overall system, and some of our lessons learned in building it.

Rapid characterization of candidate loss of function genes in primary organoid culture.

77 Background: Genome scale sequencing efforts have identified chromosomal deletions and non-synonymous mutations as putative drivers of gastro-intestinal cancer. There is a need to rapidly validate these drivers in robust models. The culture of primary, non-transformed tissues in vitro as three-dimensional organoids that accurately recapitulate organ structure and physiology serves as an ideal model for such validation, and many other applications in biology. Methods: Mouse wild type and p53 flox/flox upper digestive tract tissue containing epithelial and mesenchymal components were cultured in an air-liquid interface and subjected to adenovirus expressing either a functional control or Cre recombinase. Resultant organoids were passaged at confluency until dysplasia was evident in histology. A lentiviral shRNA (loss-of-function) screen was then conducted, where proliferative activity was measured with barcode reads from the pooled library. Results: p53-null organoids showed histology consistent with human gastrointestinal cancer. These organoids were also able to form small tumors when injected subcutaneously into immune-deficient mice. The pooled lentiviral shRNA screen functionally validated candidate drivers. Conclusions: Gastrointestinal organoids are a suitable model for the introduction of oncogenic mutations in a tractable and replicable format. In future work we will validate loss-of function and gain-of-function screens with CRISPRi/a libraries, and replicate these screens in human gastrointestinal organoids.

Understanding and supporting histopathology slide sorting

ABSTRACTHistopathology laboratories devote considerable time and effort to sorting tissue sample slides. We observed slide sorting in a typical urban hospital to understand the existing workflow and explore how it might be supported by an interactive computer support system. We observed 8.5 hours of slide sorting activity through a video camera mounted above a laboratory workbench. Through detailed video analysis, we characterised the process, examined which activities took the most time, and explored design considerations. We found that a very large proportion (23.5%) of the slide sorting time involved managing paper documents. We suggest that an interactive computer support system could automatically detect which slides are sorted into which folders and digitally list additional slides to include with these sets; this would support the workflow of technicians, while eliminating paper management and manual barcode reading operations, leading to time savings of approximately 30%. Additional recommendations for the design of such a support system include focusing on case management (e.g. how many slides belong to each case, whether a complete case will fit within the current folder, and which slides associated with a case are still missing), supporting recovery from disruptions, and enabling a flexible rather than a highly scripted workflow.

2D Barcode Identification Technology Application in Tool Management System for Workshop

With the development of advanced automation degree industries, the requirements of network management for workshop tool become more and more urgent. A very important part in network management system is identification technology of the tool. In this paper, 2D (two-dimensional) barcode identification technology is applied in the tool information in real-time query to realize network management. The 2D barcode reading device, which is combined with the local computer decoding software, could achieve communication between production line and the server. By using 2D barcode identification technology, it could achieve network management for workshop tool, improve production efficiency and reduce the artificial error.

Calibration and disparity maps for a depth camera based on a four-lens device

We propose a model of depth camera based on a four-lens device. This device is used for validating alternate approaches for calibrating multi-view cameras and also for computing disparity or depth images. Calibration method arises from previous works, where principles of variable homography were extended for 3-D measurement. In this paper, calibration is performed between two contiguous views obtained on the same image sensor. This approach leads to propose a new approach for simplifying calibration, by using the properties of the variable homography. The second part of this paper addresses new principles for obtaining disparity images without any matching. A fast algorithm using a contour propagation algorithm is proposed without requiring structured or random pattern projection. These principles are proposed in a framework of quality control by vision, for inspection in natural illumination. By preserving scene photometry, some other standard controls, as for example calipers, shape recognition, or barcode reading, can be done conjointly with 3-D measurements. Approaches presented in this paper are evaluated. Firstly, we show that rapid calibration is relevant for devices mounted with multi lenses. Secondly, synthetic and real experimentations validate our method for computing depth images.

Inselect: Automating the Digitization of Natural History Collections.

The world’s natural history collections constitute an enormous evidence base for scientific research on the natural world. To facilitate these studies and improve access to collections, many organisations are embarking on major programmes of digitization. This requires automated approaches to mass-digitization that support rapid imaging of specimens and associated data capture, in order to process the tens of millions of specimens common to most natural history collections. In this paper we present Inselect—a modular, easy-to-use, cross-platform suite of open-source software tools that supports the semi-automated processing of specimen images generated by natural history digitization programmes. The software is made up of a Windows, Mac OS X, and Linux desktop application, together with command-line tools that are designed for unattended operation on batches of images. Blending image visualisation algorithms that automatically recognise specimens together with workflows to support post-processing tasks such as barcode reading, label transcription and metadata capture, Inselect fills a critical gap to increase the rate of specimen digitization.

Towards intraoperative microCT imaging of breast cancer resections.

147 Background: MicroCT analysis of breast cancer resections was shown by Rong et al. [Tang R et al. (2013) The Breast Journal 19(5), 485-489; Tang R et al. (2013) Breast Cancer Research and Treatment 139(2), 311-316] to attain equivalence with pathology-histology in the assessment of positive margin. However, Rong et al. noted that the 14 minute scan cycle time–7 minutes scan, 7 minutes reconstruction–was a potential limitation. Methods: This paper reports the development of microCT instrumentation and software to address this and other technical challenges. These include (a) reducing the scan-reconstruction time, (b) a small desktop footprint, (c) reconciling optimal soft tissue contrast with minimizing artefacts from inserted metal wires and large calcifications, and (d) features to aid hospital workflow such as a barcode reader, an optical camera to photograph the specimen, and process automation. Scans were done with the purpose-built SkyScan1275 table-top microCT scanner, on frozen anonymous archived breast cancer resection samples. Results: Minimum scan time retaining acceptable image quality was 3 minutes. Reconstruction time was reduced to less than a minute by PC graphics accelerators. Scan-reconstruction cycle time can thus be < 4 minutes. A small (105 x 64 cm) desktop footprint with a weight of 160 kg, requiring only a sturdy laboratory desk, has also been attained in this scanner. Artefacts from large calcifications and metal wires were reduced to non-obtrusive levels by scanning with 1mm aluminum filter and software techniques. An automated scan sequence was developed with sample holders recognized electronically in the scanner. On pressing a single button, the scanner automatically goes to the appropriate magnification and sample height, scans the sample, then reconstructs the scan with preset parameters, and finally opens the reconstructed dataset in software for interactive volume-rendered viewing. Sample barcode reading and optical photo taking can be included in this auto-scan sequence. Conclusions: These developments bring microCT nearer to realizing its potential as an intraoperative “back-table” technique in breast cancer surgery to obviate revisions and improve the surgery process and outcome.

A smartphone-based system for the automated management of point-of-care test results in hospitals.

Managing test results is an important issue in hospitals because of the increasing use of point-of-care testing (POCT). Here, we propose a smartphone-based system for automatically managing POCT test results. We developed the system to provide convenience to the medical staffs. The system recognizes the patient identification or prescription number of the test by reading barcodes and provides a countdown to indicate when the results will be ready. When the countdown in finished, a picture of the test result is transferred to the electronic medical record server using the Health Level 7 protocol. Human immunodeficiency virus (HIV) kits were selected in this research because HIV is a life-threatening infectious virus, especially for the medical staff who treat undiagnosed patients. The performance of the system was verified from a survey of the users. The performance of the system was tested at the emergency room (ER) for 10 months using commercially available POCT kits for detecting HIV. The survey showed that, in total, 80% and 0% of users reported positive or negative feedback, respectively. The staff also reported that the system reduced total processing time by approximately 32 min, in addition to reducing workload. The developed automated management system was successfully tested at an ER for 10 months. The survey results show that the system is effective and that medical staff members who used the system are satisfied with using the system at the ER.

Decoding Deformed Barcode Images with Scanpath Walkthrough

Most camera-based barcode reading systems are scanline-based and designed to work with barcodes attached on flat or smooth surfaces. These systems are likely not able to deal with barcodes on surfaces with high level of deformation, thus making it difficult to find a scanline passing through readable regions. In this paper, an approach that addresses the decoding of deformed barcode images is presented. Our method is based on constructing a scangraph that covers over the area of the barcode, and creating a scanpath that walks through the scangraph by avoiding the regions that are less readable. Barcode digit templates are then used to decode the data sampled along the scanpath. Preliminary results show that some challenging deformed barcode images can be correctly decoded with the presented technique.

A smartphone-readable barcode assay for the detection and quantitation of pesticide residues.

In this paper, we present a smartphone-readable barcode assay for the qualitative detection of methyl parathion residues, a toxic organophosphorus pesticide that is popularly used in agriculture worldwide. The detection principle is based on the irreversible inhibition of the enzymatic activity of acetylcholinesterase (AchE) by methyl parathion; AchE catalytically hydrolyzes acetylthiocholine iodine to thiocholine that in turn dissociates dithiobis-nitrobenzoate to produce a yellow product (deprotonated thio-nitrobenzoate). The yellow intensity of the product was confirmed to be inversely dependent on the concentration of the pesticide. We have designed a barcode-formatted assay chip by using a PDMS (polydimethylsiloxane) channel plate (as the reaction reservoir), situated under a printed partial barcode, to complete the whole barcode such that it can be directly read by a barcode scanning app installed on a smartphone. The app is able to qualitatively present the result of the pesticide test; the absence or a low concentration of methyl parathion results in the barcode reading as "-", identifying the test as negative for pesticides. Upon obtaining a positive result (the app reads a "+" character), the captured image can be further analyzed to quantitate the methyl parathion concentration in the sample. Besides the portability and simplicity, this mobile-app based colorimetric barcode assay compares favorably with the standard spectrophotometric method.

Implementation of Barcode Localization Technique using Morphological Operations

Barcode Localization is an extremely important task in Barcode Reading system which depends highly on imaging conditions and methods used for barcode localization. In this paper, we have presented a method for barcode localization which is based on basic morphological operations. The method introduced is implemented in MATLAB 2012 and is then examined for different types of test images such as images with skewed, blurry or multiple barcodes in an image. The method is then compared with some existing methods of literature on the basis of these test images. It has been found that the performance of the algorithm depends upon the proper choice of the switching element.

Barcode Localization using Curvelet Transform and Neural Network

Barcode localization is the main challenge in developing an imagebased barcode reading system. Bar codes are able to carry both explicit information and a database key, by encoding a series of characters or symbols. This paper deals with localization of European Article Number-13 (EAN-13) barcode in an image. A new approach for detecting and locating bar-codes is introduced here, which is based on the curvelet transform. All extracted feature by curvelet transform are applied to the neural network for training and testing. The performance of the proposed work shows efficient result.