Large-scale Sets Research Articles

Regularized Bayesian algorithms for Q-matrix inference based on saturated cognitive diagnosis modelling.

Q-matrices are crucial components of cognitive diagnosis models (CDMs), which are used to provide diagnostic information and classify examinees according to their attribute profiles. The absence of an appropriate Q-matrix that correctly reflects item-attribute relationships often limits the widespread use of CDMs. Rather than relying on expert judgment for specification and post-hoc methods for validation, there has been a notable shift towards Q-matrix estimation by adopting Bayesian methods. Nevertheless, their dependency on Markov chain Monte Carlo (MCMC) estimation requires substantial computational burdens and their exploratory tendency is unscalable to large-scale settings. As a scalable and efficient alternative, this study introduces the partially confirmatory framework within a saturated CDM, where the Q-matrix can be partially defined by experts and partially inferred from data. To address the dual needs of accuracy and efficiency, the proposed framework accommodates two estimation algorithms-an MCMC algorithm and a Variational Bayesian Expectation Maximization (VBEM) algorithm. This dual-channel approach extends the model's applicability across a variety of settings. Based on simulated and real data, the proposed framework demonstrated its robustness in Q-matrix inference.

Reviewing Open Data Semantic 3D City Models to Develop Novel 3D Reconstruction Methods

Abstract. Structured semantic 3D city models are pivotal in creating urban 3D digital twins. The wide adoption of such models has been primarily enabled by robust, model-based, and automatic 3D reconstruction methods. However, these methods impose requirements on the reconstruction, mainly restricting the solution space to several model types and relying on accurate 2D footprints. Recent research shows that deep-learning-based methods promise highly generic solution space and are footprint-free. Yet, the current training and test datasets are limited, hindering the methods’ development. In this work, we analyze the ubiquity of already existing, open 3D city model datasets and their potential to serve as a large-scale training and test set for 3D reconstruction, where 27 potential dataset collections have been identified. Our review shows that more than 215 million building models are readily available. We firmly believe that this review will facilitate further research on robust automatic 3D city model reconstruction and serve as a reference for benchmarking 3D city models.

Methodological strategies to extend the attention of the students of the sixth year EGB of the Educational Center "5 de mayo" in the teaching-learning process of the English language

This investigation delves into the efficacy of various methodological strategies in sustaining student attention within a sixth-grade English classroom in Chone City. With a class size of 62, maintaining student engagement presents a significant challenge. To address this, the study employed a bibliometric analysis to inform the research framework. A convenience sample of five English teachers with varying levels of experience was selected for in-depth interviews. Additionally, classroom observations were conducted to gather comprehensive data. The research findings illuminate the correlation between specific methodological approaches and their impact on student attention levels. By examining the perspectives of experienced educators and observing classroom dynamics, this study offers valuable insights into effective strategies for optimizing student engagement in large-scale classroom settings. The study also explores the role of interactive and participatory teaching methods, such as group discussions, multimedia presentations, and hands-on activities, in fostering a more engaging learning environment. By integrating these approaches, teachers can create a more dynamic and stimulating classroom atmosphere that caters to diverse learning styles and keeps students actively involved. Furthermore, the research highlights the importance of continuous professional development for teachers, enabling them to stay updated with the latest educational trends and techniques. This ongoing learning process empowers educators to implement innovative strategies that can effectively capture and maintain student interest. Ultimately, the findings of this study aim to provide a comprehensive understanding of the factors that contribute to student engagement and offer practical recommendations for educators seeking to enhance the learning experience in large classroom settings.

Expanding the Russian allele frequency reference via cross-laboratory data integration: insights from 7452 exome samples.

Population allele frequency is crucially important for accurate interpretation of known and novel variants in medical genetics. Recently, several large allele frequency databases, such as the Genome Aggregation Database (gnomAD), have been created to serve as a global reference for such studies. However, frequencies of many rare alleles vary dramatically between populations, and population-specific allele frequency is often more informative than the global one. Many countries and regions, including Russia, remain poorly studied from the genetic perspective. Here, we report the first successful attempt to integrate genetic information between major medical genetic laboratories in Russia. We construct RUSeq, an open, large-scale reference set of genetic variants by analyzing 7452 exome samples collected in two major Russian cities-Moscow and St. Petersburg. An ∼10-fold increase in sample size compared to previous studies allowed us to characterize extensive genetic diversity within the admixed Russian population with contributions from several major ancestral groups. We highlight 51 known pathogenic variants that are overrepresented in Russia compared to other European countries. We also identify several dozen high-impact variants that are present in healthy donors despite being annotated as pathogenic in ClinVar and falling within genes associated with autosomal dominant disorders. The constructed database of genetic variant frequencies in Russia has been made available to the medical genetics community through a variant browser available at http://ruseq.ru.

Toward effective SVM sample reduction based on fuzzy membership functions

Support vector machine (SVM) is known for its good generalization performance and wide application in various fields. Despite its success, the learning efficiency of SVM decreases significantly originating from the assumption that the number of training samples increases rapidly. Consequently, the traditional SVM with standard optimization methods faces challenges such as excessive memory requirements and slow training speed, especially for large-scale training sets. To address this issue, this paper draws inspiration from the fuzzy support vector machine (FSVM). Considering that each sample has varying contributions to the decision plane, we propose an effective SVM sample reduction method based on the fuzzy membership function (FMF). This method uses FMF to calculate the fuzzy membership of each training sample. Training samples with low fuzzy memberships are then deleted. Specifically, we propose SVM sample reduction algorithms based on class center distance, kernel target alignment, centered kernel alignment, slack factor, entropy, and bilateral weighted FMF, respectively. Comprehensive experiments on UCI and KEEL datasets demonstrate that our proposed algorithms outperform other comparative methods in terms of accuracy, F-measure, and hinge-loss measures.

Evaluating the cost of classifier discrimination choices for IoT sensor attack detection

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but it costs more to implement and operate than a traditional classifier. This research proposes an economic classifier model for deploying suitable AI-based intrusion detection classifiers in IoT environments. The model was empirically validated on the IoT-23 dataset using KNN and CNN. This study closes a gap in prior research that mostly concentrated on technical elements by incorporating economic factors into the evaluation of AI algorithms for IoT intrusion detection. This research thus evaluated the economic implications of deploying AI-based intrusion detection systems in IoT environments, considering performance metrics, implementation costs, and the cost of classifier discrimination choices. Researchers and practitioners should focus on the cost–benefit trade-offs of any artificial intelligence application for intrusion detection, recommending an economic evaluation and task fit assessment before adopting automated solutions or classifiers for IoT intrusion detection, particularly in large-scale industrial settings that involve active attacks.

Solving the storage location assignment of large-scale automated warehouse based on dynamic vortex search algorithm

This paper establishes the mathematical model for Storage Location Assignment (SLA) problem in large-scale automated warehouses by combining three objectives: efficiency, shelf stability, and stacker load balancing. Along with a novel repair strategy to handle the complex constraints of large-scale problems. Additionally, a coding method and solution approach suitable for practical application scenarios are developed. In order to solve large-scale SLA problem, an improved vortex search algorithm is proposed based on attraction operation in flow field, dimension-by-dimension dynamic radius and leadership decision-making mechanism (FDVSA). In the experimental part, the algorithm effectiveness experiment of FDVSA was first conducted using the large-scale global optimization test sets IEEE congress on evolutionary computation 2010 and 2013 (CEC2010, CEC2013). The results show that: (1) Compared with other comparison algorithms, the comprehensive average optimization rate of FDVSA in CEC2010 and CEC2013 is 88 % and 78 %, respectively. (2) The experimental results of FDVSA showed that each improvement strategy has advantages in dealing with large-scale problems. (3) The post-hoc analysis showed that there are significant differences between FDVSA and other comparison algorithms, and FDVSA is significantly better. Finally, FDVSA and other comparison algorithms are solved on three different scale and complexity of SLA cases. The results show that: (1) FDVSA has significant advantages in solving large-scale SLA problem, and the comprehensive average optimization rate is 19 %. (2) The convergence curve and boxplot showed that FDVSA has good convergence speed and solving stability. (3) The effectiveness of the repair strategy was verified by experiments in the large-scale SLA problems.

Evaluating the feasibility and economics of hydrogen storage in large-scale renewable deployment for decarbonization

Renewable energy (RE) is pivotal for achieving a net-zero future, with energy storage systems essential for maximizing its utility. This study introduces a modeling framework that simulates large-scale RE deployment in Taiwan, emphasizing hydrogen as a primary storage medium. Utilizing hourly time steps, the model assesses various energy generation and storage configurations to demonstrate the technical feasibility of meeting Taiwan's 2050 net-zero target, which calls for a 60 % RE share primarily through solar and wind resources. However, achieving this target and the ambitious goal of 100 % RE penetration requires substantial enhancements in both generation capacity and storage solutions. The research evaluates the economic impacts by analyzing the levelized cost of energy (LCOE), revealing that optimal configurations, such as a wind capacity of 30 GW with a 5 % minimum capacity factor constraint on electrolyzers, significantly reduce LCOE to $0.176/kWh, underscoring the cost-effectiveness of hydrogen storage compared to battery alternatives in large-scale settings. The study underscores the necessity for ongoing investigations into replacing thermal power plants and maintaining grid stability amidst high RE penetration. To fully harness Taiwan's RE potential and contribute to global sustainability, effective deployment of hydrogen storage will require robust stakeholder support, continual technological advancements, and enabling policies. This framework, while tailored to Taiwan's power system, offers insights applicable to various global contexts.

Turbulent flame acceleration and deflagration-to-detonation transitions in ethane–air mixture

The deflagration-to-detonation transition (DDT) poses significant risks in the oil, gas, and nuclear industries, capable of causing catastrophic explosions and extensive damage. This study addresses a critical knowledge gap in understanding the DDT of ethane–air mixtures on a large scale, amid increasing industrial utilization and production of ethane. A novel computational framework is introduced, utilizing the finite-volume code named Morris Garages, which incorporates reactive compressible Navier–Stokes equations, adaptive mesh refinement, and correlations of turbulent burning velocities. This model integrates the most recent data on laminar and turbulent burning velocities for premixed ethane–air mixtures, simulating flame acceleration and DDT within a two-dimensional large-scale setting, measuring 21 m in length and 3 m in height, with obstacles mimicking pipe congestion. Two mixture scenarios, lean and near-stoichiometric, are analyzed to evaluate the effects of equivalence ratios on flame propagation and DDT. The simulations, validated against large-scale experimental data from Shell, show reasonable agreement and provide critical insights into the onset conditions of DDT, such as temperature, pressure, flame speed, and turbulent kinetic energy. Furthermore, the ξ–ε detonation peninsula diagram is utilized to explore autoignition and detonation behaviors in ethane–air mixtures.

Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.

In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model's "precision," "recall," and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets. Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default.

An optimal Bayesian strategy for comparing Wiener–Hunt deconvolution models in the absence of ground truth

Abstract This paper considers the quantitative comparison of several alternative models to perform deconvolution in situations where there is no ground truth data available. With applications to very large data sets in mind, we focus on linear deconvolution models based on a Wiener filter. Although comparatively simple, such models are widely prevalent in large scale setting such as high-resolution image restoration because they provide an excellent trade-off between accuracy and computational effort. However, in order to deliver accurate solutions, the models need to be properly calibrated in order to capture the covariance structure of the unknown quantity of interest and of the measurement error. This calibration often requires onerous controlled experiments and extensive expert supervision, as well as regular recalibration procedures. This paper adopts an unsupervised Bayesian statistical approach to model assessment that allows comparing alternative models by using only the observed data, without the need for ground truth data or controlled experiments. Accordingly, the models are quantitatively compared based on their posterior probabilities given the data, which are derived from the marginal likelihoods or evidences of the models. The computation of these evidences is highly non-trivial and this paper consider three different strategies to address this difficulty—a Chib approach, Laplace approximations, and a truncated harmonic expectation—all of which efficiently implemented by using a Gibbs sampling algorithm specialised for this class of models. In addition to enabling unsupervised model selection, the output of the Gibbs sampler can also be used to automatically estimate unknown model parameters such as the variance of the measurement error and the power of the unknown quantity of interest. The proposed strategies are demonstrated on a range of image deconvolution problems, where they are used to compare different modelling choices for the instrument’s point spread function and covariance matrices for the unknown image and for the measurement error.

Applying MALDI-TOF MS to resolve morphologic and genetic similarities between two Dermacentor tick species of public health importance

Hard ticks (Acari: Ixodidae) have been historically identified by morphological methods which require highly specialized expertise and more recently by DNA-based molecular assays that involve high costs. Although both approaches provide complementary data for tick identification, each method has limitations which restrict their use on large-scale settings such as regional or national tick surveillance programs. To overcome those obstacles, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been introduced as a cost-efficient method for the identification of various organisms, as it balances performance, speed, and high data output. Here we describe the use of this technology to validate the distinction of two closely related Dermacentor tick species based on the development of the first nationwide MALDI-TOF MS reference database described to date. The dataset obtained from this protein-based approach confirms that tick specimens collected from United States regions west of the Rocky Mountains and identified previously as Dermacentor variabilis are the recently described species, Dermacentor similis. Therefore, we propose that this integrative taxonomic tool can facilitate vector and vector-borne pathogen surveillance programs in the United States and elsewhere.

Enhancing Moisture Tolerance of Sulfide Solid Electrolytes through Nano-Level Treatment Utilizing Lewis Acid Additives

Sulfide solid-state electrolytes (SSSEs), particularly the argyrodite Li6PS5Cl, are considered a promising solid electrolyte for all-solid-state batteries due to their excellent ductility and remarkable ionic conductivity. However, a significant challenge in the large-scale application of SSSEs is their lack of stability when exposed to humid air. To address these challenges, a prominent solution involves treating SSSE particles with Lewis acid additives at the nanoscale level method is employed. Some of the several Lewis acid additives considered in this study are aluminum acetylacetonate, tetrafluoro-1,4-benzoquinone, and tetracyano-ethylene. This treatment process is dispersing the SSSE particles in solution mixtures containing Lewis acid additives. Importantly, this method is designed to have minimal impact on the ionic conductivity of the SSSE. A series of investigations are conducted using various characterization techniques, including X-ray diffraction (XRD), electrochemical impedance spectroscopy (EIS), in-situ Raman spectroscopy, solid-state nuclear magnetic resonance (NMR), and synchrotron-based X-ray Absorption Near-Edge Structure (XANES). These employed techniques are essential for analyzing the structural and chemical changes in SSSE particles treated with Lewis acid additives, leading to key findings. The key finding of the study is that the SSSE particles demonstrated outstanding atmospheric stability when treated with Lewis acid additives. This stability is attributed to the establishment of a Lewis acid-base interaction between the SSSE and the additives. This interaction effectively shields the SSSE from moisture in humid air, preventing structural deterioration in the bulk region. In other words, the Lewis acid additives create a protective layer that maintains the integrity of the SSSE, even in the presence of atmospheric moisture. Moreover, the Lewis acid additives treated SSSE samples demonstrated enhanced chemical stability in the ASSB system after being exposed to moisture as compared to the pristine SSSE. Highlighting the use of Lewis acid additives to enhance the moisture stability of SSSEs as a promising approach, this improvement in stability is crucial for the practical application of SSSEs in large-scale settings where exposure to humid conditions is inevitable. The study suggests that this method could pave the way for more reliable and durable all-solid-state batteries by addressing a critical limitation of SSSEs.

Efficient Direct Recycling of Spent Cathode Materials in Lithium-Ion Batteries

The demand for lithium-ion batteries (LIBs) has surged in recent years, driven by the rapid growth of electric vehicles (EVs) and emerging energy storage technologies. Yet, this surge in demand has also resulted from a significant increase in the number of spent batteries. Given the potential for environment pollution wastage, it is absolutely crucial to prioritize the recovery and recycling of these used lithium-ion batteries.The common techniques employed in large-scale industrial settings for battery recycling are pyrometallurgy and hydrometallurgy. These are pivotal multi-step processes, particularly suitable for materials containing Cobalt (Co) and Nickel (Ni), such as NMC (LiNixMnyCozO2). Aside from NMC, there has been a surge in the adoption of LFP (LiFePO4) as a cathode material target for lithium-ion batteries in electric vehicles worldwide over the past few decades. However, these established industrial recycling methods are not applicable to LFP due to the comparatively lower value of iron. This necessitates the pursuit of direct recycling approaches.In order to accomplish this goal, direct recycling stands out as the most suitable technique for regenerating spent batteries1. Unlike processes involving solution-based or high-temperature steps that entail potentially wasteful steps, thisapproach aims to reduce energy consumption and costs, all while maintaining environmental integrity through a judicious utilization of resources, materials, and energy. Consequently, our attention is directed towards this specific LFP material, prompting us to detail a direct recycling procedure for spent LFP cathodes via a chemical lithiation process at room temperature in a solution containing lithium iodide (LiI) and various solvents2 (Fig. 1).

A Self-Training-Based System for Die Defect Classification

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms’ reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method’s consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios.

Modernizing the NCI60 Cell Line Screen for Phenotypic Drug Discovery in the 21st Century.

Over the past three decades, high-throughput phenotypic cancer cell line screens have revealed unanticipated small-molecule activities and illuminated connections between tumor genotypes and anticancer efficacy. Founded in 1984, the National Cancer Institute's "NCI60" screen laid the conceptual groundwork for the contemporary landscape of phenotypic drug discovery. NCI60 first operated as a primary bioactivity screen, but molecular characterization of the NCI60 cell line panel and development of a small-molecule sensitivity pattern recognition algorithm (called "COMPARE") have enabled subsequent studies into drug mechanisms of action and biomarker identification. In this issue of Cancer Research, Kunkel and colleagues report an updated version of the NCI60 screen, dubbed "HTS384" NCI60, that better aligns with current cell proliferation assay standards and has higher throughput. Changes include the use of a 384-well plate format, automated laboratory equipment, 3 days of compound exposure, and a CellTiter-Glo luminescent endpoint. To confirm that data from the HTS384 and classic NCI60 screen are comparable, the authors tested a library of 1,003 anticancer agents using both protocols and applied COMPARE to analyze patterns of cell line sensitivities. More than three dozen groups of targeted therapies showed high comparability between screens. Modernization of NCI60, and closer integration with other large-scale pharmacogenomic screens and molecular feature sets, will help this public screening service remain pertinent for cancer drug discovery efforts for years to come. See related article by Kunkel et al., p. 2403.

The missing piece – New insights to comprehend enigmatic Miocene palaeoceanography of the Mediterranean Sea using the Nd isotopic record

The evolution of the Mediterranean-Paratethys marine system during the Miocene represents one of the crucial milestones in the Earth's oceans history. It fundamentally affected the climate evolution not just in Europe and the adjacent continents, but also on a global scale. The progressive transition of the Mediterranean-Paratethys system from a latitudinal marine domain connecting two main Earth's oceans to a modern-day enclosed setting happened mainly from the Burdigalian (∼16–18 Ma) to Serravallian period (∼13.8–12.6 Ma). It triggered a complex interplay of palaeoceanographic events such as overturns in water circulation regimes that affected the large-scale palaeoclimatic settings. Here, we present 3D palaeoceanographic models from the late Burdigalian to the Serravallian that are based on a comprehensive 143Nd/144Nd dataset containing 59 individual measurements. Our results imply that the first indications of separation between the western and eastern parts of the Mediterranean occurred during the late Langhian (∼16–13.8 Ma) and were linked to the pronounced circulation overturn. The modern-day setting with the characteristic εNd signatures for the Western and Eastern Mediterranean developed during the Serravallian. Altogether, our set of models demonstrates the dynamics of the two-step closure of the Indian-Atlantic gateway and the palaeoceanographic evolution of this area during the middle Miocene.

Large Language Model Instruction Following: A Survey of Progresses and Challenges

Abstract Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning to follow task instructions, that is, instruction following. Despite its impressive progress, there are some unsolved research equations that the community struggles with. This survey tries to summarize and provide insights to the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How should we model instructions? (iii) What are popular instruction following datasets and evaluation metrics? (iv) What factors influence and explain the instructions’ performance? (v) What challenges remain in instruction following? To our knowledge, this is the first comprehensive survey about instruction following.

YOLOv8-EMSC: A lightweight fire recognition algorithm for large spaces

Stringent fire prevention requirements are imperative in expansive environments. Fire detection in diverse large-scale settings typically relies on sensor-based or AI-driven target detection methods. Traditional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety requirements of large-scale structures. Many existing target detection algorithms are characterized by substantial model sizes. Some detection terminals in large structures face challenges deploying these models due to constrained computational resources. To address this issue, we propose a lightweight model, YOLOv8-EMSC, derived from YOLOv8n. The incorporation of C2f_EMSC, replacing the C2f module, significantly reduces the model parameters in the enhanced YOLOv8-EMSC model compared to YOLOv8n, thereby enhancing model inference speed. Extensive testing and validation using a custom-built large-scale infrared fire dataset demonstrates a 9.6 % reduction in parameters compared to the baseline model for YOLOv8-EMSC, achieving an average precision of 95.6 %, surpassing both the baseline and mainstream models and significantly enhancing fire detection accuracy in expansive environments.

Distribution of sequence types and antimicrobial resistance of clinical Pseudomonas aeruginosa isolates from dogs and cats visiting a veterinary teaching hospital in Thailand

BackgroundPseudomonas aeruginosa is an important opportunistic pathogen in dogs and cats and is resistant to several antimicrobial drugs; however, data on the clonal distribution of P. aeruginosa in veterinary hospital are limited. This study aimed to investigate the clonal dissemination and antimicrobial resistance of clinical P. aeruginosa in a veterinary teaching hospital in Thailand within a 1-year period. Minimum inhibitory concentration determination and whole genome sequencing were used for antimicrobial susceptibility analysis and genetic determination, respectively.ResultsForty-nine P. aeruginosa were isolated mostly from the skin, urinary tract, and ear canal of 39 dogs and 10 cats. These isolates belonged to 39 sequence types (STs) that included 9 strains of high-risk clones of ST235 (n = 2), ST244 (n = 2), ST274 (n = 2), ST277 (n = 1), ST308 (n = 1), and ST357 (n = 1). Overall antimicrobial resistance rate was low (< 25%), and no colistin-resistant strains were found. Two carbapenem-resistant strains belonging to ST235 and ST3405 were identified.ConclusionsClinical P. aeruginosa in dogs and cats represent STs diversity. High-risk clones and carbapenem-resistant strains are a public health concern. Nevertheless, this study was limited by a small number of isolates. Continuous monitoring is needed, particularly in large-scale settings with high numbers of P. aeruginosa, to restrict bacterial transfer from companion animal to humans in a veterinary hospital.