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Plant cells contain three genetically distinct DNA compartments-nuclear, chloroplast, and mitochondrial-and biologically meaningful sequence sharing among them is expected because organellar DNA can move into the nucleus and, in some cases, between organelles. This complicates alignment-free k-mer analysis, the interpretation of compartment-specific sequence vocabularies, and the evaluation of missing-sequence signals such as minimal absent words (MAWs). A reproducible workflow that treats these compartments explicitly is therefore useful both for plant genome analysis and for read-backed quality control. A fully scripted workflow was applied to a telomere-to-telomere-scale Arabidopsis thaliana nuclear reference (Col-CEN v1.2) and a chromosome-level Oryza sativa ssp. japonica reference (GCF_034140825.1), together with chloroplast, mitochondrial, and raw-read datasets. Distinct canonical k-mer types were counted for k = 11-31 and decomposed into mutually exclusive set-algebra categories. The same framework was then used for read-backed validation and for MAW/nullomer testing under Markov models with false-discovery-rate (FDR) control. The two plant species showed markedly different sharing landscapes. In Arabidopsis, chloroplast k-mers became largely compartment-specific by k = 31 (85.6% of the chloroplast set), whereas mitochondrial k-mers remained dominated by nuclear overlap (93.4% of the mitochondrial set at k = 31). In rice, chloroplast k-mers remained strongly shared with the nuclear genome across the entire range (88.6% of the chloroplast set at k = 31), whereas mitochondrial-nuclear sharing remained high but lower than that in Arabidopsis (77.5% at k = 31). Raw-read validation showed that assembly-derived k-mers were almost completely supported by reads in both species (> 99.7% at k = 31 and KMC3 -ci = 1), whereas the read-only fraction collapsed sharply as the minimum-occurrence threshold (-ci) increased from 1 to 10. Because one raw-read dataset was analyzed per species, these contrasts are best interpreted as species-specific workflow case studies rather than as a direct sequencing-platform benchmark. MAW lists derived from chloroplast, mitochondrial, nuclear, and raw-read datasets contained many candidates, but all FDR-adjusted runs returned zero significant nullomers, consistent with the corresponding sequence-based null models rather than with biologically exceptional absence. This workflow provides a reproducible framework for quantifying compartment-specific and shared plant sequence vocabularies, validating those vocabularies against raw reads, and testing absent-word candidates under explicit null models. The two-species application shows that chloroplast and mitochondrial compartments need not behave similarly and that raw-read-only vocabularies are highly sensitive to low-support k-mers. Together, these analyses provide a robust starting framework for plant compartment-aware k-mer interpretation, while locus-level explanations for shared or absent words remain a downstream task for follow-up analyses.

A primary goal of research on associative recognition has been to identify the conditions that facilitate the formation of unitized representations of stimuli to be remembered, thereby enhancing familiarity-based associative recognition in recent years. In the present study, we sought to examine whether self-generation, as an active and internally driven form of relational processing, can support familiarity-based associative recognition by facilitating the formation of unitized representations. A total of 24 participants were recruited for this experiment. Participants studied triplets of Chinese single-character words. All words within a triplet formed a compound word with a fourth missing word. The missing word had to be produced before it was presented in the self-generation condition and was immediately presented in the read-only condition. In the test phase, participants had to discriminate between intact triplets that were repeated from the study phase and rearranged triplets, in which one or two words were exchanged with words from another studied triplet. The behavioral results revealed significantly higher Pr in the self-generation condition than in the read condition. The ERP results revealed a greater FN400 effect elicited in the self-generation condition than in the read condition. These findings demonstrate that self-generation enhances familiarity-based associative recognition to a significantly greater extent than the read-only condition.

Young and older adults are often victimized by various forms of scams and fraud. However, little research has been conducted on how a scam prevention intervention may impact young and older adults' accuracy and confidence in determining the legitimacy of emails. The present study investigated young and older adults' accuracy and confidence, as well as potential contributing factors to scam susceptibility, before and after engaging with one of three intervention activities: control (no intervention), read-only (read common scam qualities), or interactive (interactively learned scam qualities in emails). Young and older adult participants were randomly assigned to one of the three conditions. Before and after the intervention, participants evaluated a series of legitimate and scam emails, indicating whether each email was legitimate or a scam. Participants also rated their confidence in their accuracy, the personal relevance of each email, and their curiosity about engaging further with the email. Both age groups showed no difference in sensitivity when determining the legitimacy of the emails before and after the interventions. Importantly, both age groups in the read-only and interactive conditions showed a bias toward labeling emails as scams, indicating a generally cautious approach. Although the interventions did not improve detection sensitivity, participants were more cautious when evaluating emails. This tendency may help reduce vulnerability to scams and fraud and suggests that interventions can encourage greater caution when evaluating suspicious emails across age groups.

In this study, we comparatively assessed short-read (Illumina), long-read (Oxford Nanopore Technologies, ONT), and hybrid (Illumina + ONT) sequencing strategies for bacterial genome analysis using the aquaculture-derived isolate 160P. Genomic DNA was extracted and sequenced on Illumina paired-end and ONT long-read platforms, and de novo assemblies were generated using SPAdes, Canu, Flye, and Unicycler under short-read-only, long-read-only, and hybrid workflows, followed by evaluation with QUAST assembly metrics. Among the tested approaches, the hybrid Unicycler assembly provided the highest contiguity, yielding seven contigs and a dominant 4.55 Mb contig consistent with near-complete chromosomal representation. Downstream analyses included functional genome annotation and in silico screening of antimicrobial resistance determinants (CARD), virulence-associated genes (VFDB), and secondary metabolite biosynthetic gene clusters (antiSMASH). Comparative genomic relatedness based on Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (dDDH) indicated that 160P is most closely related to Aeromonas sobria CECT 4245T yet falls below commonly applied species-level thresholds, supporting its placement as a genomically distinct lineage warranting further taxonomic investigation. Collectively, these findings underscore the value of hybrid sequencing for improving assembly continuity, enhancing annotation completeness, and strengthening taxonomic resolution in bacterial pathogen genomics.

Experience plays a role in belief development. We present a method to evaluate the experiential basis of a belief and investigate whether belief-change interventions are more effective if the qualities of an intervention experience more closely match the experience that might have led to the belief. Psychology department research pool participants (total N = 1102) were in either a read-only or experience-based intervention for three beliefs: that they can detect stares from unseen others, that pyramids have remarkable powers of preservation, and that pyramids produce concentration benefits for people meditating under them. Stare detection and pyramid effects on concentration were diagnosed as experience-based beliefs and were both more strongly affected by experience-based interventions. Pyramid preservation power did not have the properties of an experience-based belief, and intervention type had no effect on that belief. Potential improvements in evaluating experience and implications for more consequential belief change research are discussed.

Contemporary language learning applications such as Duolingo and Rosetta Stone often introduce vocabulary through guessing-with-feedback exercises in which learners match words and pictures. We investigated whether that process might yield a pretesting effect-that is, the phenomenon where guessing with correct answer feedback (pretesting) enhances memory. Across four experiments, adult online learners engaged in multiple-choice pretesting to learn Spanish word translations shown in word-image (Experiments 1-2) or image-word (Experiments 3-4) format. Relative to a read-only condition, pretesting yielded statistically significant performance improvements on subsequent cued recall (Cohen's d = 0.18-0.40) and, in most cases, multiple-choice tests (d = 0.25-0.67), regardless of whether test formats were separately presented or intermixed. Participants also reported preferring pretesting over reading for learning second-language vocabulary, especially for word-image learning. Together, these findings extend the pretesting effect to visual and verbal materials, offering theoretical insights and substantiating word-image and image-word guessing-based approaches of language learning.

The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected >96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data.

The pretesting effect suggests that attempting and failing to guess unknown information can improve memory compared to errorless study. A relevant question concerns the optimal timing for providing corrective feedback and administering the final test. This study explored two variables: (1) the timing of feedback after unsuccessful pretest attempts, either immediately or following a delay of 24 hours (Experiment 1) or 48 hours (Experiment 2); and (2) the timing of the final test after feedback, either immediately or after a 24-hour delay (Experiment 1). Recall accuracy was evaluated across these conditions and compared to an errorless (read-only) learning condition. The results showed that pretesting consistently yielded higher recall accuracy than the read-only condition. Immediate feedback was more effective than delayed feedback, and performance on the immediate test was superior to that of the delayed test. More importantly, the pretesting effect persisted even with delays in feedback and final testing. This flexibility in timing suggests practical applications, particularly in educational settings where immediate feedback or testing may not always be feasible.

The development of affective computing and medical electronic technologies has led to the emergence of Artificial Intelligence (AI)-based methods for the early detection of depression. However, previous studies have often overlooked the necessity for the AI-assisted diagnosis system to be wearable and accessible in practical scenarios for depression recognition. In this work, we present an on-board executable multi-feature transfer-enhanced fusion model for our custom-designed wearable three-lead Electroencephalogram (EEG) sensor, based on EEG data collected from 73 depressed patients and 108 healthy controls. Experimental results show that the proposed model exhibits low-computational complexity (65.0 K parameters), promising Floating-Point Operations (FLOPs) performance (25.6 M), real-time processing (1.5 s/execution), and low power consumption (320.8 mW). Furthermore, it requires only 202.0 KB of Random Access Memory (RAM) and 279.6 KB of Read-Only Memory (ROM) when deployed on the EEG sensor. Despite its low computational and spatial complexity, the model achieves a notable classification accuracy of 95.2%, specificity of 94.0%, and sensitivity of 96.9% under independent test conditions. These results underscore the potential of deploying the model on the wearable three-lead EEG sensor for assisting in the diagnosis of depression.

Safety-netting advice (SNA) can help in the management of acutely ill children. To assess the effectiveness of different SNA methods on antibiotic prescription and consumption in acutely ill children. Systematic review and network meta-analysis of randomised controlled trials, cluster randomised trials, non-randomised studies of interventions, and controlled before-after studies in ambulatory care in high-income countries. MEDLINE, Embase, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials were searched (22 January 2024). Risk of bias (RoB) was assessed with Cochrane's RoB 2 tool, the Revised Cochrane Tool for Cluster-Randomised Trials, and the Risk Of Bias In Non-randomised Studies - of Interventions tool. Certainty of evidence was assessed using the Confidence in Network Meta-Analysis approach. Sensitivity analyses and network meta-regression were performed. In total, 30 studies (20 interventions) were included. Compared with usual care, paper SNA may reduce: antibiotic prescribing (odds ratio [OR] 0.66, 95% confidence interval [CI] = 0.53 to 0.82, I 2 = 92%, very low certainty, three studies, 35 988 participants), especially when combined with oral SNA (OR 0.40, 95% CI = 0.08 to 2.00, P-score = 0.86); antibiotic consumption (OR 0.39, 95% CI = 0.27 to 0.58, low RoB, one study, 509 participants); and return visits (OR 0.74, 95% CI = 0.63 to 0.87). Compared with usual care, video SNA, read-only websites, oral SNA, and web-based SNA (in descending order of effectiveness) may increase parental knowledge (ORs 2.33-4.52), while paper SNA may not (ORs 1.18-1.62). Similarly, compared with usual care, video SNA and web-based modules may improve parental satisfaction (ORs 1.94-4.08), while paper SNA may not (OR 1.85, 95% CI = 0.48 to 7.08). Paper SNA (with oral SNA) may reduce antibiotic use and return visits. Video, oral, and online SNA may improve parental knowledge, whereas video SNA and web-based modules may increase parental satisfaction.

ABSTRACTDespite literature showing that errorful generation with corrective feedback enhances retention better than mere studying, it is unclear if this benefit depends on the composition of the learning list (pure error generation/read versus mixed). Here, we investigated whether the mnemonic advantage and metamnemonic evaluation of errorful generation generalise beyond mixed-list designs. Experiment 1 used a free-recall test, while Experiments 2 and 3 used a cued-recall test, with Experiment 3 also including a judgment of learning (JOL) assessment. Only when memory was tested via free recall did the benefit of errorful generation depend on experimental design, with the effect being most robust in mixed lists. Replicating past research, we too found that despite a clear mnemonic benefit for error generation in cued-recall tests, participants predicted better memory following read-only trials, and that this effect was not contingent on list composition. At the practical level, these findings demonstrate instances in which errorful generation is beneficial for memory and learning. At the theoretical level, the results fit nicely within the item-order framework in accounting for commonly observed design effects in free recall.

The Comparative Pathology Workbench (CPW) is a web-browser-based visual analytics platform providing shared access to an interactive "spreadsheet" style presentation of image data and associated analysis data. The software was developed to enable pathologists and other clinical and research users to compare histopathological images of diseased and/or normal tissues between different samples of the same or different patients/species. The CPW provides a grid layout of cells in rows and columns so that images that correspond to matching data can be organized in the form of an image-enabled "spreadsheet". An individual workbench or bench can be shared with other users with read-only or full edit access as required. In addition, each bench cell or the whole bench itself has an associated discussion thread to allow collaborative analysis and consensual interpretation of the data. Here, we present the updated system based on 2 years of active use in the field that generated constructive feedback. The updates deliver new capabilities, including automated importation of entire image collections, sorting image collections, long running tasks, public benches, uploading miscellaneous image types, refining search facilities, enabling use of tags, and improving efficiency, speed, and user-friendliness.

Plasmids are extrachromosomal mobile genetic elements that often carry genes responsible for antimicrobial resistance. Plasmid epidemiology aims to track the evolution and spread of plasmids, but the field currently faces significant barriers that make practical implementation using whole genome sequence data difficult. Hybrid-assembled genomes remain the most reliable way to identify and track complete plasmids; however, most genomic surveillance data exists in the form of short-read sequencing, which lacks the resolution required to accurately resolve plasmids. Despite recent advances, long-read-only assemblies have not yet reached the consistency seen in hybrid assemblies. The ideal approach to plasmid epidemiology using whole genome sequence data would consider the limitations of sequencing technologies and the constraints of existing genomic surveillance infrastructure, in addition to the unique evolutionary biology of plasmids. Here, we present ACCIO (Assembly-based Circular Contig Identification for Outbreaks), a tool which creates a reference plasmid database and uses it to infer which plasmids, and genetically related plasmid groupings, are present in an input assembly (Illumina, Nanopore, or hybrid assembly). We validated ACCIO using an internal dataset of 303 plasmid-harboring bacterial clinical and surveillance isolates collected from a single acute tertiary care center. When highly related database plasmids were grouped together, ACCIO achieved 100% sensitivity and 92.1% positive predictive value (PPV) for detection of plasmid groups using hybrid assemblies, and comparably strong performance for Illumina (93.0% sensitivity, 86.6% PPV) and Nanopore (79.3% sensitivity, 91.4% PPV) assemblies. Evaluation on three external datasets yielded consistently high performance. Finally, when benchmarked against MOB-suite, a tool for reconstruction and typing of plasmids, ACCIO demonstrated superior performance across nearly all assembly types and plasmid grouping levels. By integrating database construction, clustering, and plasmid calling into a single workflow compatible with all major sequencing platforms, ACCIO is intended to help advance plasmid epidemiology beyond its current technological and infrastructural barriers.

Carbapenems are broad-spectrum antibiotics that are losing effectiveness against infections caused by multidrug-resistant Enterobacterales that have acquired carbapenemase genes. The New Delhi metallo-β-lactamase (bla NDM) is one of the most common carbapenemases in Canada and around the globe. These genes are frequently found on conjugative plasmids, which can disseminate through horizontal gene transfer. We applied whole-genome sequencing to characterize 1,032 bla NDM carbapenemase-producing Enterobacterales isolates collected by the Canadian Nosocomial Infection Surveillance Program from 2010 to 2023. Using a combination of short-read and long-read sequencing, we obtained 226 complete and circular bla NDM-encoding plasmids. Unlike other carbapenemases in Canada, we found that bla NDM plasmids were very diverse; there was a lack of dominant clusters identified using MOB-suite, and clustering methods were not able to accurately predict plasmid clusters for short-read-only data. The majority of bla NDM plasmids were IncF-type (69.0%, 156/226). Both bla NDM and bla OXA-48-type carbapenemase genes were found in 11.4% (118/1,032) of isolates, and we identified several instances of both carbapenemase genes co-harboured on the same plasmid replicon (n=9). Our findings highlight that plasmid transfer has not played a major role in bla NDM transmission across Canada and that long-read sequencing is essential for resolving bla NDM plasmid structure and cluster membership.

Quantum algorithms claim significant speedup over their classical counterparts for solving many problems. An important aspect of many of these algorithms is the existence of a quantum oracle, which needs to be implemented efficiently in order to realize the claimed advantages in practice. A quantum random access memory (QRAM) is a promising architecture for realizing these oracles. In this paper we develop a new design for QRAM and implement it with Clifford+T circuit. We focus on optimizing the T-count and T-depth since non-Clifford gates are the most expensive to implement fault-tolerantly in most error correction schemes. Integral to our design is a polynomial encoding of bit strings and so we refer to this design as [Formula: see text]. Compared to the previous state-of-the-art bucket brigade architecture for QRAM, we achieve an exponential improvement in T-depth, while reducing T-count and keeping the qubit-count same. Specifically, if N is the number of memory locations to be queried, then [Formula: see text] has T-depth [Formula: see text], T-count [Formula: see text] and uses O(N) logical qubits, while the bucket brigade circuit has T-depth [Formula: see text], T-count O(N) and uses O(N) qubits. Combining two [Formula: see text] we design a quantum look-up-table, [Formula: see text], that has T-depth [Formula: see text], T-count [Formula: see text] and qubit count [Formula: see text]. A quantum look-up table (qLUT) or quantum read-only memory (QROM) has restricted functionality than a QRAM. For example, it cannot write into a memory location and the circuit needs to be compiled each time the contents of the memory change. The previous state-of-the-art CSWAP architecture has T-depth [Formula: see text], T-count [Formula: see text] and qubit count [Formula: see text]. Thus we achieve a double exponential improvement in T-depth while keeping the T-count and qubit-count asymptotically same. Additionally, with our polynomial encoding of bit strings, we develop a method to optimize the Toffoli-count of circuits, specially those consisting of multi-controlled-NOT gates.

Distinctive encoding usually increases correct recognition while also producing a reduction in false recognition. In the Deese-Roediger-McDermott (DRM) illusion this phenomenon, called the mirror effect, occurs when participants focus on unique features of each of the words in the study list. In previous studies, the pleasantness rating task, used to foster distinctive encoding, generated different patterns of results. The main aim of our research is to examine under what circumstances this task can produce the mirror effect in the DRM paradigm, based on evidence from recognition accuracy and subjective retrieval experience. In Experiment 1, a standard version (word pleasantness rating on a 5-point Likert-type scale) was used for comparison with two other encoding conditions: shallow processing (vowel identification) and a read-only control. The standard task, compared to the other conditions, increased correct recognition, but did not reduce false recognition, and this result may be affected by the number of lists presented for study. Therefore, in experiment 2, to minimize the possible effect of the so-called retention size, the number of studied lists was reduced. In addition, the standard version was compared with a supposedly more item-specific version (participants rated the pleasantness of words while thinking of a single reason for this), also including the read-only control condition. In both versions of the pleasantness rating task, more correct recognition is achieved compared to the control condition, with no differences between the two versions. In the false recognition observed here, only the specific pleasantness rating task achieved a reduction relative to the control condition. On the other hand, the subjective retrieval experience accompanied correct and false recognition in the various study conditions. Although the standard pleasantness rating task has been considered to perform item-specific processing, our results challenge that claim. Furthermore, we propose a possible boundary condition of the standard task for the reduction of false recognition in the DRM paradigm.

Accurate and complete de novo genome assemblies enable variant identification and the discovery of novel genomic features and biological functions. However, de novo assemblies of large and complex genomes remain challenging. Long-read sequencing data, alone or combined with short-read data, facilitate genome assembly. However, the literature has limited comprehensive evaluations of software performance, especially for human genome assembly. We benchmarked 11 pipelines, including four long-read only assemblers and three hybrid assemblers, combined with four polishing schemes, using the HG002 human reference material sequenced with Oxford Nanopore Technologies and Illumina. The best-performing pipeline was validated with non-reference human and non-human routine laboratory samples. Software performance was assessed using QUAST, BUSCO, and Merqury metrics, alongside computational cost analyses. We found that Flye outperformed all assemblers, particularly with Ratatosk error-corrected long-reads. Polishing improved the assembly accuracy and continuity, with two rounds of Racon and Pilon yielding the best results. The assembly of data from validation samples showed comparable assembly metrics to those of the reference material. Based on the results, a complete optimal analysis pipeline for the assembly, polishing, and contig curation developed on Nextflow is provided to enable efficient parallelization and built-in dependency management to further advance the generation of high-quality and chromosome-level assemblies.

Steganography based on responsive chromatic materials (RCMs) has attracted increasing interest in information encryption and anticounterfeiting. However, conventional RCMs suffer from immutable written states that can be read repeatedly, compromising the security. Herein, a self-erasable steganography method is developed by creating a reprogrammable solvent-responsive photonic crystal (RSPC), whose core mechanism depends on reversible Na + ion storage and release in gel networks. The RSPC is constructed by filling poly(di(ethylene glycol) ethyl ether acrylate) (PeDEGA) gels into an opaline template built by ZnS@SiO2 nanospheres. The selective infiltration and removal of NaOH aqueous solution or deionized water can manipulate the hydrophilic property of the RSPC, based on the partial hydrolysis of ester groups in PeDEGA and the subsequent conversion between acrylate sodium and acrylic acid. By controlling the solvent treatment sequence and area of the RSPC, a cycle of "write-hide-read-self-erase" has been realized for achieving the integration of self-erasability and steganography. The developed self-erasable steganography can enable written information to be read only once, which greatly enhances the security of the information. Meanwhile, this cycle also endows the RSPC with recycling ability and enhances its economy. Thus, this self-erasable steganography shows tremendous potential in the next-generation anticounterfeiting technology.

In this work, we present an optimized nanopore long-read only sequencing workflow for epidemiologic analysis of clonal outbreaks built with open-source tools. A set of unrelated clinical Pseudomonas aeruginosa isolates (n = 10) was chosen for workflow optimization, and sequencing libraries were prepared using a modified rapid barcoding strategy that incorporates temperature ramps to improve performance for high-GC content genomes. Sequencing data were used to benchmark the performance of the dorado suite (v0.9.1), including its basecaller, pre-assembly read error correction, and post-assembly polishing algorithms. All long-read assemblies and core genome multilocus sequence typing (cgMLST) were performed with Flye and pyMLST, respectively. Results were compared with a standard reference Illumina short-read approach, and discordant positions were determined at the core and whole-genome levels. Optimal performance was found with dorado sup@v5.0.0 basecalling with the inclusion of dorado error correction and dorado polish with its bacterial model. This workflow was then validated with four retrospective hospital outbreak isolate sets, including Klebsiella pneumoniae (n = 12), P. aeruginosa (n = 11), Enterococcus faecium (n = 10), and Staphylococcus aureus (n = 10). The nanopore-only assemblies obtained from the optimized pipeline demonstrated fully concordant cgMLST-based minimum spanning trees compared to the Illumina short-read reference. At the whole-genome level, high concordance was also observed, with as few as two discordant positions per genome compared to short-read assemblies. This optimized library preparation and open-source computational workflow enables nanopore-only clonality and outbreak analysis with performance comparable to that of Illumina short-read sequencing and will contribute critically to hospital infection control. For the past decade, bacterial whole-genome sequencing has been performed using high-accuracy short-read sequencing. More recently, long-read sequencing with Oxford Nanopore Technologies (ONT) instruments has emerged as a potential alternative based on multiple advantages, including lower costs, portability, and speed. However, this platform has suffered from basecall error rates that were too high for many applications in clinical microbiology, including outbreak tracing. With the release of new flow cell chemistries and basecall algorithms, the accuracy has improved dramatically, making this approach feasible for outbreak investigations. In this work, we optimize a streamlined nanopore-only workflow for epidemiologic analysis of bacterial pathogens. The workflow was validated with isolates from four previously identified clinical outbreaks with varying GC content and demonstrated fully concordant cgMLST clustering as compared to short-read references. This workflow will facilitate the broader implementation of ONT-only genomes and cgMLST analysis to assist in hospital outbreaks worldwide.