Reading Accuracy Research Articles

AI-Based Object Detection, Distance Measurement and Speaking System for Blind Stick

The sense of sight is fundamental to how humans perceive and navigate their surroundings. For visually impaired individuals, this lack of vision creates significant challenges in detecting obstacles and measuring distances, making daily navigation difficult and potentially dangerous. In response to this problem, we propose an AI-based object detection, distance measurement, and speaking system integrated with a blind stick to enhance mobility and independence for the visually impaired. This system leverages cutting-edge artificial intelligence and image processing techniques to provide real-time object detection and distance measurement. By combining ultrasonic sensors with an AI-powered object recognition module, the system accurately identifies obstacles and calculates their distance, offering directional audio feedback to the user. Designed to be compact and user-friendly, the blind stick system provides seamless assistance in both indoor and outdoor environments. The object detection process utilizes advanced algorithms like YOLO (You Only Look Once) to identify obstacles. Distance measurement is performed using ultrasonic sensors to ensure accurate proximity readings. When an obstacle is detected, the system provides immediate auditory feedback, alerting users to the direction and distance of the object. The Google Maps API enhances usability by offering location awareness and route planning, empowering users to navigate with confidence. This AI-based blind stick system aims to improve the mobility and safety of visually impaired individuals, offering a more autonomous experience in their daily lives.

Gapless assembly of complete human and plant chromosomes using only nanopore sequencing.

The combination of ultra-long (UL) Oxford Nanopore Technologies (ONT) sequencing reads with long, accurate Pacific Bioscience (PacBio) High Fidelity (HiFi) reads has enabled the completion of a human genome and spurred similar efforts to complete the genomes of many other species. However, this approach for complete, "telomere-to-telomere" genome assembly relies on multiple sequencing platforms, limiting its accessibility. ONT "Duplex" sequencing reads, where both strands of the DNA are read to improve quality, promise high per-base accuracy. To evaluate this new data type, we generated ONT Duplex data for three widely studied genomes: human HG002, Solanum lycopersicum Heinz 1706 (tomato), and Zea mays B73 (maize). For the diploid, heterozygous HG002 genome, we also used "Pore-C" chromatin contact mapping to completely phase the haplotypes. We found the accuracy of Duplex data to be similar to HiFi sequencing, but with read lengths tens of kilobases longer, and the Pore-C data to be compatible with existing diploid assembly algorithms. This combination of read length and accuracy enables the construction of a high-quality initial assembly, which can then be further resolved using the UL reads, and finally phased into chromosome-scale haplotypes with Pore-C. The resulting assemblies have a base accuracy exceeding 99.999% (Q50) and near-perfect continuity, with most chromosomes assembled as single contigs. We conclude that ONT sequencing is a viable alternative to HiFi sequencing for de novo genome assembly, and provides a multirun single-instrument solution for the reconstruction of complete genomes.

Quality Learning Environment and Community Participation: Report on a Literacy Boost Project in Indonesia

This article reports the outcomes of community participation and quality learning education resulting from the MEMBACA II project. MEMBACA (Making Early Matter through Books and Community Action) is a literacy project that targets young children in Indonesia. The MEMBACA project includes teacher training, community reading activities, and age-appropriate local language material creation to support emergent literacy skills among early-grade children. These skills include letter awareness, single-word reading of most used words, reading fluency, reading accuracy, and reading comprehension. 31 participants involved in this study, purposively selected to be interviewed through the phone. The thematic analysis found that a better learning environment was produced, a code of conduct is now available in schools, and it has helped in mainstreaming a more positive disciplining approach to teachers. This study has also identified that villages are now allocating their village fund for supporting literacy activities, although the process has experienced a setback in the year 2020 due to the pandemic. Further, while teachers’ capacities were increased due to various activities, learning environments are still troubled by poor infrastructures and a lack of community awareness. Engagement of communities and local government were mostly improved, with major problem lies in the continuity of the literacy-related activities, affected by various factors including poor advocacy and COVID-19 pandemic.

IoT-Based Control, Monitoring, and Protection System for 3-Phase Induction Motors in Electric Motorcycles

This study investigates the application of the Internet of Things (IoT) for wireless control and monitoring of a 3-phase electric motor using a smartphone. The system integrates PZEM-004T, DS18B20, and Hall Effect sensors to collect data on voltage, current, temperature, and rotational speed using the NodeMCU ESP8266 microcontroller. Measurements are displayed on an LCD and transmitted to the Blynk server for smartphone access. A comparative method evaluates the accuracy of sensor readings against standard measuring instruments. Results obtained an average percentage error of 0.5% for the R phase voltage, 0.2% for the S phase, and 0.1% for the T phase. Current measurements reveal errors of 5% for the R phase, 10.3% for the S phase, and 11.7% for the T phase. The control system’s performance varies with internet speed, with an average delay of 0.99 seconds on a 4G network and 2.51 seconds on 3G. Additionally, the study evaluates three protection mechanisms, demonstrating that the motor stops within 4.03 seconds in the event of a phase failure, while overcurrent and overheating protections activate within 8.47 seconds and 3.64 seconds, respectively. Overall, the findings affirm the viability of IoT in motor monitoring and control, emphasizing accuracy and response times under varying conditions.

A Meta-Analysis of the Moderators in the Relationship Between Morphological Awareness and Chinese Reading Accuracy

ABSTRACT Purpose This study aimed to deepen the understanding of the relationship between morphological awareness and Chinese reading accuracy by exploring the factors that influence this connection through a meta-analysis. Method We conducted a meta-analysis using data from 84 articles, comprising 104 effect sizes and a sample of 14,348 children (mean ages = 3.76 to 13.79 years). The study examined potential moderators such as script type (traditional and simplified script), age, and the type of reading accuracy measure (word and character reading), as well as the interaction between these moderators on the abovementioned relationship. Results The analysis revealed that script type was a significant moderator in the relationship. The correlation between morphological awareness and word/character reading was stronger in traditional script than in simplified script. Age also had a significant moderating effect in the relationship, with the correlation increasing significantly with age. However, neither the type of reading accuracy measure nor the interaction between measure type and script type significantly moderated the relationship between MA and Chinese reading accuracy. Conclusion The moderation by script type suggests that the differences in morpheme orthography between the two scripts can affect the relationship between morphological awareness and reading accuracy. The moderator of age indicates that the influence of morphological awareness on reading accuracy may grow stronger as children get older. The lack of a moderating effect by the type of reading accuracy measure suggests that the correlation between morphological awareness and word/character reading is consistent across these measures.

Investigation of Fiber Bragg Grating Sensor Measurability in Concrete Beams under Static Load Conditions

Recent advances in construction materials and architecture have enhanced the safety of modern structures, which now feature higher, lighter, and more unique designs. Bridges, as critical infrastructure, face increasing load demands due to rising traffic volumes, leading to accelerated structural deterioration. This necessitates more frequent inspections and maintenance, resulting in increased costs and potential operational disruptions. Real-time built-in monitoring systems are thus essential for both new and aging structures. Fiber Optic Sensor (FOS) technology, particularly Fiber Bragg Grating (FBG) sensors, offers a reliable and stable solution for long-term Structural Health Monitoring (SHM). This study evaluates the performance and applicability of FBG sensors (FBGs) in comparison to traditional strain gauges (SGs) for capturing strains, loads, deflections, and temperature in full-scale beams under static loads. Four large-scale reinforced concrete (RC) and CFRP-strengthened beams were tested. Nonlinear finite element analysis (NLFEA) using VecTor2 was employed to simulate the behavior of these beams under static loading. Data from FBGs were compared to those from collocated electrical SGs. The results demonstrated the reliability and flexibility of FBGs in monitoring structural performance. Notably, FBGs continued to provide accurate readings even after the specimens reached their maximum load, showcasing their endurance and resilience compared to conventional SGs.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

LIG-Based High-Sensitivity Multiplexed Sensing System for Simultaneous Monitoring of Metabolites and Electrolytes

With improvements in medical environments and the widespread use of smartphones, interest in wearable biosensors for continuous body monitoring is growing. We developed a wearable multiplexed bio-sensing system that non-invasively monitors body fluids and integrates with a smartphone application. The system includes sensors, readout circuits, and a microcontroller unit (MCU) for signal processing and wireless communication. Potentiometric and amperometric measurement methods were used, with calibration capabilities added to ensure accurate readings of analyte concentrations and temperature. Laser-induced graphene (LIG)-based sensors for glucose, lactate, Na+, K+, and temperature were developed for fast, cost-effective production. The LIG electrode’s 3D porous structure provided an active surface area 16 times larger than its apparent area, resulting in enhanced sensor performance. The glucose and lactate sensors exhibited high sensitivity (168.15 and 872.08 μAmM−1cm−2, respectively) and low detection limits (0.191 and 0.167 μM, respectively). The Na+ and K+ sensors demonstrated sensitivities of 65.26 and 62.19 mVdec−1, respectively, in a concentration range of 0.01–100 mM. Temperature sensors showed an average rate of resistance change per °C of 0.25%/°C, within a temperature range of 20–40 °C, providing accurate body temperature monitoring.

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building's refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

Controlled Translocation of Proteins through a Biological Nanopore for Single-Protein Fingerprint Identification.

After the successful sequencing of nucleic acids, nanopore technology has now been applied to proteins. Recently, it has been demonstrated that an electro-osmotic flow can be used to induce the transport of unraveled polypeptides across nanopores. Polypeptide translocation, however, is too fast for accurate reading its amino acid compositions. Here, we show that the introduction of hydrophobic residues into the lumen of the nanopore reduces the protein translocation speed. Importantly, the introduction of a tyrosine at the entry of the nanopore and an isoleucine at the entry of the β-barrel of the nanopore reduced the speed of translocation to ∼10 amino acids/millisecond while keeping a relatively large ionic current, a crucial component for protein identification. These nanopores showed unique features within their current signatures, which may pave the way toward protein fingerprinting using nanopores.

PH Determination of Acetic Acid-Sodium Acetate Buffer: An Application of Henderson-Hasselbalch Equation at Room Temperature

In article, we have reported the applying of Henderson-Hasselbalch equation in determination of pH of acetic acid and sodium acetate buffer solutions at about 298 Kelvin as room temperature. In preparation of these buffer solutions, we have taken 1.5 ml of glacial acetic acid into 100 ml of standard flask for making up 0.2M of acetic acid solution, and 0.64 gm of sodium acetate were dissolved into 100 ml distilled water. Now, we taken 36.2 ml prepared solution of sodium acetate into 100 ml of standard flask and added 14.8 ml of prepared acetic acid to make its volume 100 ml by using of distilled water, it is a buffer. In measuring of pH of prepared buffer solutions, we have been using a calibrated digital pen pH meter for accurate and quick reading. In observation, the pH of buffer solutions a quite fluctuation was appeared. But, it has gradually raised by adding small amount of basic 5N NaOH solution into buffer sample, and, an adjusted pH was being to 4.7 as well.

Impact of Parent-Child Paired Reading on Reading Fluency in Children with Intellectual Disabilities: A Single-Subject Study

Children with intellectual disabilities (ID) face difficulties in reading fluency, which may hinder their overall reading development. To address this challenge, this study investigated the impact of parent-child pairing reading on reading accuracy and speed in children with intellectual disabilities. Three children with ID participated in this cross-subject multi-baseline single-subject study and their parents were trained to use paired reading strategies to engage in a six week reading activity with them. The study was divided into three stages, with participants without reading guidance during the baseline and maintenance periods. During the intervention period, parents used paired reading strategies to guide their children in reading. Data were collected and analyzed using Statistical Package for the Social Sciences and interviews with parents who participated in the study were conducted to assist with assessment of the intervention results. After intervention, all three children showed better reading accuracy than the baseline period, indicating the effectiveness of the intervention. The intervention had little effect on the reading speed of the three participants, but it gradually improved during the maintenance period. Each of the parents held a positive attitude toward the effectiveness and applicability of parent-child paired reading in children with intellectual disabilities. The results of this preliminary study indicate that training and guiding parents to implement paired reading strategies can effectively improve reading fluency in children with intellectual disabilities.

Design of Level Sensor Prototype for Fuel Tank in Base Transceiver Station Facilities

A monitoring system for fuel availability at Base Transceiver Stations (BTS) is an absolute necessity. The fuel availability in the generator set must be guaranteed because it ensures the energy supply and continuous operation. This research purpose is to create a robust and effective sensor that can be monitored in real-time so that industry players can estimate the consumption of fuel at the BTS in a certain period. The novelty of this research is that instead of looking at the amount of fuel consumption/reduction like the previous research, this study focuses on how much diesel availability is in the generator tank. So, calculating fuel requirements and predicting actual consumption are done directly. The sensor is a reed-switch oil level sensor and is connected to ESP32 as the microcontroller. It's installed horizontally and uses an optimum distance between sensors of 2.5 mm to improve the sensor reading's accuracy. The test results of converting voltage values into ADC signals on sensor readings produced a determination coefficient of 0.9987, which showed the reliability of the sensor-level design. The sensor precision test results produced an RSD value of 0.17% and the accuracy test produced an error range of -3% to 2% with an average error value of 1.59%. These results indicate that the designed level sensor has good precision and accuracy. Through this research, the provider can predict and plan their fuel consumption needs for BTS power supply and increase the Domestic Component Level in the sensor industry.

Training rapid automatized naming in children with developmental Dyslexia

ABSTRACT The recommended rehabilitation procedures for Developmental Dyslexia (DD) are not well defined, and there is currently a large debate on which therapeutic approaches are shown to be more useful and effective. Among the trainings focused on general dysfunctional cognitive processes associated with a reading disorder, recent studies suggested the efficacy of trainings on Rapid Automatized Naming (RAN) compared to others. The present study was aimed at confirming the effectiveness of RAN training (RANt) to improve the reading performances of children with DD (n = 32) compared to children on a waiting list (WL, n = 25) and to children in different treatment groups, one following a text reading training (RT, n = 26) and the other combining RAN and text reading exercises (RANt+RT, n = 20), through an online platform that allows intensive and self-adaptive activities. Results confirmed the efficacy of RANt in improving reading speed and accuracy compared to the WL group (r2 ranging from small (.16) to medium (.48)) and found the absence of differences with the other active control groups. The single-subject level analysis confirmed the results, a high inter-subject variability in treatment response and pre-post differences were found. Further studies could consider such variability in the functional profile of the DD subjects, but RANt was confirmed to be a valid tool for improving decoding skills.

Can AI generate diagnostic reports for radiologist approval on CXR images? Amulti-reader and multi-case observer performance study.

Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming. To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study. Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports. Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P < 0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P < 0.001). This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.

Executive Functioning in Different Types of Reading Disabilities.

Students with reading disabilities form a heterogeneous group: some struggle with accurate and fast reading (dysfluent readers), others with comprehension (poor comprehenders), and some face challenges in both areas (poor readers). Research has indicated a link between executive functioning skills and reading performance; yet, further studies are necessary to fully understand the executive profiles in various types of reading disabilities. The goal of this study was to examine differences in executive functioning among three types of reading disabilities, comparing their performance with that of children without difficulties in either skill (typical readers). Ninety-one students from schools in Portugal participated in the study. The results reveal specific deficits in naming speed and cognitive flexibility in poor readers and dysfluent readers compared to the other groups. Additionally, poor readers exhibited significantly slower processing speed and lower working memory. However, no significant differences were observed in planning. Discriminant function analysis results indicated that the examined executive functions are better at discriminating groups with fluency deficits than those with comprehension difficulties. In conclusion, these results suggest distinct deficit patterns in executive functioning skills across different types of reading disabilities. Taking into account these findings is crucial for effective assessment and intervention with these children.

Comparative evaluation of accuracy of three different electronic apex locators in the presence of various endodonticirrigants: An in vitro study

AIM AND OBJECTIVES. To evaluate and compare the accuracy of Root ZX, Raypex6, and I Root and electronic apex locators in the presence of NaOCl (3%), Citric acid (10%), MTAD and Green tea extract as endodontic irrigants.MATERIAL AND METHOD. Sixty single rooted human permanent teeth were used for the study. Access cavities were prepared and the teeth were decoronated. Teeth were divided as follows: Group I-Root ZX apex locator (n=20), IA – NaOCl 3% endodontic irrigant (n=5), IB – Citric Acid 10% endodontic irrigant (n=5), IC – MTAD endodontic irrigant (n=5), ID- Green Tea extract endodontic irrigant (n=5). Group II: Raypex 6 apex locator (n=20). IIA – NaOCl 3% endodontic irrigant (n=5), IIB – Citric acid 10% endodontic irrigant (n=5). IIC – MTAD endodontic irrigant (n = 5), IID – Green tea extract endodontic irrigant (n=5). Group III-i-ROOT apex locator (n=20). IIIA – NaOCl 3% endodontic irrigant (n=5), IIIB – Citric Acid 10% endodontic irrigant (n=5), IIIC – MTAD endodonticirrigant (n=5), IIID – Green tea extract endodontic irrigant (n=5). The actual working length was determined using a 15 K file and the working model was prepared using Alginate in a rectangular box. The teeth were embedded into the working model and the electronic measurements were made with each electronic apex locator. The data was collected, analyzed and subjected to statistical analysis.RESULTS. No significant difference was found when Raypex 6 apex locator was used along with all the endodontic irrigants except green tea extract. Root ZXshowed statistically significant difference with green tea extract and MTAD. I-root showed statistically significant difference with all the irrigants except green tea extract.CONCLUSION. Citric acid 10% was consistently accurate when assessed with all the three apex locators, whereas Green tea extract demonstrated the least accurate readings with all apex locators.

What Quality Suffices for Nanopore Metabarcoding? Reconsidering Methodology and Ectomycorrhizae in Decaying Fagus sylvatica Bark as Case Study.

Nanopore raw read accuracy has improved to over 99%, making it a potential tool for metabarcoding. For broad adoption, guidelines on quality filtering are needed to ensure reliable taxonomic unit recovery. This study aims to provide those guidelines for a fungal metabarcoding context and to apply them to a case study of ectomycorrhizae in the decaying bark of Fagus sylvatica. We introduce the eNano pipeline to test two standard metabarcoding approaches: (1) Reference-based mapping leveraging UNITE's species hypothesis system (SH approach); (2) Constructing 98% OTUs (OTU approach). Our results demonstrate that both approaches are effective with Nanopore data. When using a reference database, we recommend strict mapping criteria rather than Phred-based filtering. Leveraging the SH-system further enhances reproducibility and facilitates cross-study communication. For the 98% OTUs, filtering reads at ≥Q25 is recommended. Our case study reveals that the decay gradient is a primary determinant of community composition and that specific mycorrhizal fungi colonize decaying bark. Complementing our metabarcoding results with root tip morphotypification, we identify Laccaria amethystina and Tomentella sublilacina as key ectomycorrhizae of saplings on decaying logs. These findings demonstrate that Nanopore sequencing can provide valuable ecological insights and support its broader use in fungal metabarcoding as read quality continues to improve.

Uncovering echoic mechanisms in verbal irony comprehension

Abstract Whereas some studies suggest that ironic praise necessitates a longer processing time than ironic criticism, others posit that the two are processed at comparable speeds. We hypothesize that the presence of an echoic antecedent within the preceding context may at least partially account for these conflicting findings. To investigate this matter, we analyzed reading times and accuracy stemming from two types of contexts: echoic and non-echoic. Our results demonstrate that ironic criticism was judged to be more ironic in both echoic and non-echoic contexts, while ironic praise was rated as more ironic in an echoic context than in a non-echoic context. Additionally, echoing contexts facilitate the comprehension of ironic criticism, but cause ironic praise to be processed more slowly. There was also an observed asymmetry between the two forms of irony. Ironic criticism demonstrated high accuracy and was rated as more ironic than ironic praise. Furthermore, ironic criticism was read faster in an echoic context, whereas performance was similar in a non-echoic context for both types. These findings suggest that echoing context affects ironic criticism and ironic praise differently, implying that distinct mechanisms may be at work in understanding irony in echoic and non-echoic contexts.

High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform.

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.