Accurate Assignment Research Articles

Solu: a cloud platform for real-time genomic pathogen surveillance

BackgroundGenomic surveillance is extensively used for tracking public health outbreaks and healthcare-associated pathogens. Despite advancements in bioinformatics pipelines, there are still significant challenges in terms of infrastructure, expertise, and security when it comes to continuous surveillance. The existing pipelines often require the user to set up and manage their own infrastructure and are not designed for continuous surveillance that demands integration of new and regularly generated sequencing data with previous analyses. Additionally, academic projects often do not meet the privacy requirements of healthcare providers.ResultsWe present Solu, a cloud-based platform that integrates genomic data into a real-time, privacy-focused surveillance system.EvaluationSolu’s accuracy for taxonomy assignment, antimicrobial resistance genes, and phylogenetics was comparable to established pathogen surveillance pipelines. In some cases, Solu identified antimicrobial resistance genes that were previously undetected. Together, these findings demonstrate the efficacy of our platform.ConclusionsBy enabling reliable, user-friendly, and privacy-focused genomic surveillance, Solu has the potential to bridge the gap between cutting-edge research and practical, widespread application in healthcare settings. The platform is available for free academic use at https://platform.solugenomics.com.

Accurate predictions of chemical shifts with the rSCAN and r2SCAN mGGA exchange-correlation functionals.

We benchmark the rSCAN and r2SCAN exchange-correlation functionals by comparing the Nuclear Magnetic Resonance (NMR) magnetic shieldings predicted by Density Functional Theory (DFT) to experimentally observed chemical shifts of halide and oxide inorganic compounds. Significant improvement in accuracy is achieved compared to the Generalised Gradient Approximation (GGA) at a marginally higher computational cost. When using rSCAN or r2SCAN, the correlation coefficient between computationally predicted and experimental values approaches the theoretically expected value of -1 while reducing the deviation, allowing more accurate and reliable spectrum assignments of complex compounds in experimental investigations.

A free lunch: microhaplotype discovery in an existing amplicon panel improves parentage assignment for the highly polymorphic Pacific oyster.

Amplicon panels using genotyping by sequencing methods are now common, but have focused on characterizing SNP markers. We investigate how microhaplotype (MH) discovery within a recently developed Pacific oyster (Magallana gigas) amplicon panel could increase the statistical power for relationship assignment. Trios (offspring and two parents) from three populations in a newly established breeding program were genotyped on a 592 locus panel. After processing, 92% of retained amplicons contained polymorphic MH variants and 85% of monomorphic SNP markers contained MH variation. The increased allelic richness resulted in substantially improved power for relationship assignment with much lower estimated false positive rates. No substantive differences in assignment accuracy occurred between SNP and MH datasets, but using MHs increased the separation in log-likelihood values between true parents and highly related potential parents (aunts and uncles). A high number of Mendelian incompatibilities among trios were observed, likely due to null alleles. Further development of a MH panel, including removing loci with high rates of null alleles, would enable high-throughput genotyping by reducing panel size and therefore cost for Pacific oyster research and breeding programs.

DLBclass: A Probabilistic Molecular Classifier to Guide Clinical Investigation and Practice in DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous disease. The increasing recognition and targeting of genetically defined DLBCLs highlights the need for robust classification algorithms. We previously characterized recurrent genetic alterations in DLBCL and identified five discrete subtypes, Clusters 1-5 (C1-C5), with unique mechanisms of transformation, immune evasion, candidate treatment targets and different outcomes following standard first-line therapy. Herein, we validate the C1-C5 DLBCL taxonomy in an independent dataset and use the expanded series of 699 primary DLBCLs to develop a probabilistic molecular classifier and confirm its performance in an independent test set. Using our previously assigned cluster labels as a reference, we systematically compared multiple machine learning models and strategies for input feature dimensionality reduction with a newly developed performance metric that captured the relationship between accuracy and confidence of class assignments. The winning neural network model, DLBclass, assigned all cases in the training/validation and independent test sets with 91% and 89% accuracies, respectively. In the 75% of cases with confidence >0.7, DLBclass assignments were accurate in 97% of the training/validation set and 98% of the test set. DLBclass enables robust prospective classification of single cases for inclusion in genetically guided clinical trials or practice and represents a framework for the development of genomic-based classification methods in other cancers.

Advancing 19F NMR Prediction of Metal-Fluoride Complexes in Solution: Insights from Ab Initio Molecular Dynamics.

19F NMR parameters are versatile probes for studying metal-fluoride complexes. Quantum chemical calculations of 19F NMR chemical shifts enhance the accuracy and validity of the resonance signal assignments in complex spectra. However, the treatment of solvation effects in these calculations remains challenging. In this study, we establish a successful computational protocol using ab initio molecular dynamics simulations for the accurate prediction of 19F NMR chemical shifts in square-planar nickel-fluoride complexes. In particular, we have studied in detail the trans-[NiF(2,3,4,5-C6F4I)(PEt3)2] complex in a benzene solution. Our computations revealed that accounting for the dynamic conformational flexibility of the complex, including intramolecular interactions, is crucial for obtaining reliable 19F NMR chemical shifts. Overall, this study advances the understanding of employing state-of-the-art quantum chemistry methods to accurately model 19F NMR chemical shifts of nickel-fluoride complexes, emphasizing the importance of addressing solvation effects in such calculations.

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented Generation.

Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Traditional HPO tools, such as Doc2HPO and ClinPhen, employ concept recognition to automate phenotype extraction but struggle with incomplete phenotype assignment, often requiring intensive manual review. While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and "hallucinations," making them less reliable without further refinement. We present RAG-HPO, a Python-based tool that leverages Retrieval-Augmented Generation (RAG) to elevate LLM accuracy in HPO term assignment, bypassing the limitations of baseline models while avoiding the time and resource intensive process of fine-tuning. RAG-HPO integrates a dynamic vector database, allowing real-time retrieval and contextual matching. The high-dimensional vector database utilized by RAG-HPO includes >54,000 phenotypic phrases mapped to HPO IDs, derived from the HPO database and supplemented with additional validated phrases. The RAG-HPO workflow uses an LLM to first extract phenotypic phrases that are then matched via semantic similarity to entries within a vector database before providing best term matches back to the LLM as context for final HPO term assignment. A benchmarking dataset of 120 published case reports with 1,792 manually-assigned HPO terms was developed, and the performance of RAG-HPO measured against existing published tools Doc2HPO, ClinPhen, and FastHPOCR. In evaluations, RAG-HPO, powered by Llama-3 70B and applied to a set of 120 case reports, achieved a mean precision of 0.84, recall of 0.78, and an F1 score of 0.80-significantly surpassing conventional tools (p<0.00001). False positive HPO term identification occurred for 15.8% (256/1,624) of terms, of which only 2.7% (7/256) represented hallucinations, and 33.6% (86/256) unrelated terms; the remainder of false positives (63.7%, 163/256) were relative terms of the target term. RAG-HPO is a user-friendly, adaptable tool designed for secure evaluation of clinical text and outperforms standard HPO-matching tools in precision, recall, and F1. Its enhanced precision and recall represent a substantial advancement in phenotypic analysis, accelerating the identification of genetic mechanisms underlying rare diseases and driving progress in genetic research and clinical genomics.

Clinical Applicability of 2-Field High-Resolution and Extended HLA-Allele Typing in Deceased Donor Kidney Allocation.

HLA-compatibility remains an important triage test for deceased donor kidney allocation. Low-intermediate resolution donor HLA-typing is typically available at allocation, but its accuracy in assigning pre-transplant donor-specific anti-HLA antibody (DSA) and HLA mismatches compared to 2-field high-resolution typing is poorly characterised. Consecutive deceased donor/recipient pairs from a single centre between 2016 and 2020 were included. Majority of donor typing at HLA-ABDRB1 loci were performed at low-intermediate resolution, with 2-field high-resolution NGS typing across extended loci performed by NGS-technique post-transplantation. We compared the two typing methods for (1) accuracy of pre-transplant DSA assignment; (2) misassignment of HLA-antigen/allele mismatches and performance of each model for acute rejection and (3) proportion of recipients who developed de novo DSA (dnDSA) when matched at antigen but mismatched at allele level. Of 179 deceased donor/recipient pairs, 157 donors had low-intermediate resolution typing and 22 with high-resolution ONT typing. Sixty-two recipients (35%) had potential pre-transplant DSAs, with incorrect assignment of allele-specific Class I and II actual DSAs in 31% and 53% of cases, respectively. NGS typing identified 59 (33%) additional HLA-DRB1 allele mismatches. ONT typing accurately assigned pre-transplant DSAs and allele mismatches in all cases. Seven (4%) recipients with antigen/allele level discordance developed dnDSAs, majority HLA-DQ antibodies. Two-field high-resolution donor HLA typing may provide a more accurate transplant immunological risk assessment and identify those at risk of developing dnDSA to matched HLA antigen.

Design of Near-Infrared Emissive Molecular Switches Based on Stilbene-Appended Cyclometalated Bimetallic Ru(II)-Terpyridine Complexes.

In the present study, we have synthesized and thoroughly characterized two Ru(II) dimers with compositions [(ttpy)Ru(tpvpt')Ru(ttpy)](ClO4)3 and [(ttpy)Ru(t'pvpvpt')Ru(ttpy)](ClO4)2 incorporating phenylene-vinylene-substituted terpyridine bridging ligands capable of coordinating in both an NNN- and cyclometalated NNC-fashion. The complexes display strong absorption across the entire UV-vis spectral domain and exhibit luminescence in the NIR region (820-850 nm). The N atoms in the outer coordination sphere were employed for alteration of the photoredox behaviors of the complexes via acid-base equilibria. Decoordination of the Ru-C bonds in the presence of acid, followed by recoordination in the presence of base and heat, is also possible. Additionally, the phenylene-vinylene units in the bridging ligands facilitate trans → cis and trans-trans → trans-cis isomerization under visible light irradiation. The reverse isomerization (cis → trans and trans-cis → trans-trans) is also achieved upon UV light irradiation. Thus, "on-off" and "off-on" emission switching is facilitated through a judicious choice of external stimuli like acid, base, temperature, or light of particular wavelengths. Interestingly, the rate of photoswitching is significantly faster in the presence of acid compared to that in the absence of acid. DFT and TD-DFT calculations were also conducted to clearly visualize the electronic environment around the complex backbone and also for accurate assignment of the absorption and emission bands.

Mapping of Health System Performance Indicators to the WHO HSPA Framework.

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland's high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.

Potassium Alloy Reference Electrodes for Potassium-Ion Batteries

Potassium-ion batteries (KIBs) represent a promising complementary technology to lithium-ion batteries due to the availability and low cost of potassium. KIBs could also be produced with graphite anodes and Prussian blue analogue (PBA) cathodes, reducing the demand for rare, costly elements necessary in LIBs.1 However, accurate characterization of KIB electrode materials is currently impeded by a lack of suitable reference electrodes. A reference electrode should have a stable rest potential, should be chemically inert, and should operate within the electrochemical stability window of KIB electrolytes.Many studies currently use potassium metal as a reference electrode out of necessity, which does not satisfy any of the above criteria. The potential of potassium metal has been shown to be irreproducible and it can drift over time,2 which we demonstrated may be caused by the high concentrations of sodium and other impurities present in commercially available potassium metal.3 Potassium metal pre-treatment has therefore been necessary to achieve satisfactory reliability of rest potential.2,3 Potassium metal is also highly reactive, decomposing electrolyte on contact and forming resistive solid electrolyte interphase (SEI) layers. Electrolyte decomposition products formed at potassium metal electrodes have further been observed to travel across cells and deposit on the other electrode as a result of crosstalk.4 The development of a stable reference electrode for KIBs is therefore of critical importance.Here, we explore the K-In and K-Bi binary alloy systems as potential reference electrode materials and synthesize promising two-phase alloys. These are metallic, and hence electronically conductive, and the two-phase nature should provide a stable potential, independent of random composition fluctuations. The In-KIn4 system displays a rest potential of 0.64 V vs. K+/K but takes tens of hours to stabilize, indicating a kinetic limitation. On the other hand, the Bi-KBi2 system displays a rest potential of 1.07 V vs. K+/K, greatly reducing the driving force for electrolyte reduction, and stabilizes within a few hours.5 We therefore demonstrate the merits and use of Bi-KBi2 as a stable K-ion reference electrode.5 Its implementation in three-electrode cells would enable the accurate assignment of electrochemical behaviour to the correct electrode, facilitating the development of KIB electrode materials.

Development of SNP Panels from Low‐Coverage Whole Genome Sequencing (lcWGS) to Support Indigenous Fisheries for Three Salmonid Species in Northern Canada

ABSTRACTSingle nucleotide polymorphism (SNP) panels are powerful tools for assessing the genetic population structure and dispersal of fishes and can enhance management practices for commercial, recreational and subsistence mixed‐stock fisheries. Arctic Char (Salvelinus alpinus), Brook Trout (Salvelinus fontinalis) and Lake Whitefish (Coregonus clupeaformis) are among the most harvested and consumed fish species in Northern Indigenous communities in Canada, contributing significantly to food security, culture, tradition and economy. However, genetic resources supporting Indigenous fisheries have not been widely accessible to northern communities (e.g. Inuit, Cree, Dene). Here, we developed Genotyping‐in‐Thousands by sequencing (GT‐seq) panels for population assignment and mixed‐stock analyses of three salmonids, to support fisheries stewardship or co‐management in Northern Canada. Using low‐coverage Whole Genome Sequencing data from 418 individuals across source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest Territories), James Bay (Québec) and Mistassini Lake (Québec), we developed a bioinformatic SNP filtering workflow to select informative SNP markers from genotype likelihoods. These markers were then used to design GT‐seq panels, thus enabling high‐throughput genotyping for these species. The three GT‐seq panels yielded an average of 413 autosomal loci and were validated using 525 individuals with an average assignment accuracy of 83%. Thus, these GT‐seq panels are powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed‐stock fisheries across multiple regions. Interweaving genomic data derived from these tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in Indigenous communities, contributing to food security programmes and the economy in Northern Canada.

A minimum cost and maximum fairness-driven multi-objective optimization consensus model for large-scale group decision-making

The rise of social media and e-democracy has driven a paradigm shift in decision-making, notably reflected in the changing ways of public participation and policymaking within decision-making processes. The increased focus on fairness and efficiency not only complicates the consensus-building process among diverse interests and perspectives, but also significantly adds to the complexity of decision-making and its implementation. In this context, balancing the interests of all parties while ensuring fairness and improving decision-making effectiveness becomes crucial. To address these challenges, this study develops a multi-objective optimization consensus framework for large-scale group decision-making (LSGDM) that integrates the interests of decision-makers (DMs) and a moderator, providing a more comprehensive tool. Specifically, this study first designs a DM weight determination method based on structural hole theory within fuzzy social networks. The proposed DM weight determination method effectively leverages the flexibility of fuzzy social networks and the comprehensiveness of structural hole theory to enhance the accuracy and reliability of weight assignment. Building on this, a novel clustering method based on the maximum group consensus level is developed, taking into account the varying importance of different DMs. Furthermore, a minimum cost and maximum fairness-driven multi-objective optimization LSGDM consensus model, referred to as MCMF-MO-LSGDM, is explored in this study. Finally, the utility and superiority of the constructed model are confirmed through comparative analysis and simulation experiments against existing related works.

Clarifying the decision-making mystery: drivers of professional skepticism, ego depletion and overconfidence in independent auditors’ quality of judgment

PurposeThis study aims to investigate the influence of professional skepticism (PS) on the quality of judgment and decision-making (QJDM) among Iranian Certified Public Accountants (CPAs) while considering the moderating effects of ego depletion (ED) and overconfidence (OC).Design/methodology/approachNonprobabilistic sampling was used to collect data through questionnaires and direct engagement with 950 Iranian CPA members, resulting in 300 completed responses for analysis.FindingsThe study confirms that PS significantly positively impacts QJDM among independent auditors. Therefore, a high level of trait PS could improve QJDM. In addition, a majority of auditors experience ED and suffer from OC bias due to their extensive knowledge, experience and self-efficacy. ED and OC play negative moderating roles in attenuating the effect of PS on QJDM.Research limitations/implicationsThe study emphasizes that integrating PS into auditors’ codes of ethics and improving audit work systems can significantly enhance the quality of auditing practices. Furthermore, addressing existence of OC and ED among auditors will further benefit the audit process. Implementing these measures will lead to more accurate assignment and distribution of audit work among independent auditors, ultimately resulting in more reliable and objective auditing judgment and making decisions.Originality/valueThis study not only approves the vital role of PS, ED and OC in the QJDM of independent auditors but also contributes to the existing QJDM literature in auditing.

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data.

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

ChatGPT vs Gemini: Comparative Accuracy and Efficiency in CAD-RADS Score Assignment from Radiology Reports.

This study aimed to evaluate the accuracy and efficiency of ChatGPT-3.5, ChatGPT-4o, Google Gemini, and Google Gemini Advanced in generating CAD-RADS scores based on radiology reports. This retrospective study analyzed 100 consecutive coronary computed tomography angiography reports performed between March 15, 2024, and April 1, 2024, at a single tertiary center. Each report containing a radiologist-assigned CAD-RADS score was processed using four large language models (LLMs) without fine-tuning. The findings section of each report was input into the LLMs, and the models were tasked with generating CAD-RADS scores. The accuracy of LLM-generated scores was compared to the radiologist's score. Additionally, the time taken by each model to complete the task was recorded. Statistical analyses included Mann-Whitney U test and interobserver agreement using unweighted Cohen's Kappa and Krippendorff's Alpha. ChatGPT-4o demonstrated the highest accuracy, correctly assigning CAD-RADS scores in 87% of cases (κ = 0.838, α = 0.886), followed by Gemini Advanced with 82.6% accuracy (κ = 0.784, α = 0.897). ChatGPT-3.5, although the fastest (median time = 5s), was the least accurate (50.5% accuracy, κ = 0.401, α = 0.787). Gemini exhibited a higher failure rate (12%) compared to the other models, with Gemini Advanced slightly improving upon its predecessor. ChatGPT-4o outperformed other LLMs in both accuracy and agreement with radiologist-assigned CAD-RADS scores, though ChatGPT-3.5 was significantly faster. Despite their potential, current publicly available LLMs require further refinement before being deployed for clinical decision-making in CAD-RADS scoring.

Detection of low-level fentanyl concentrations in mixtures of cocaine, MDMA, methamphetamine, and caffeine via surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0-339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm-1) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0-0.4 mM, medium = 0.4-14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of "blind" fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.

Refining dual RNA-seq mapping: sequential and combined approaches in host-parasitic plant dynamics.

Transcriptional profiling in host plant-parasitic plant interactions is challenging due to the tight interface between host and parasitic plants and the percentage of homologous sequences shared. Dual RNA-seq offers a solution by enabling in silico separation of mixed transcripts from the interface region. However, it has to deal with issues related to multiple mapping and cross-mapping of reads in host and parasite genomes, particularly as evolutionary divergence decreases. In this paper, we evaluated the feasibility of this technique by simulating interactions between parasitic and host plants and refining the mapping process. More specifically, we merged host plant with parasitic plant transcriptomes and compared two alignment approaches: sequential mapping of reads to the two separate reference genomes and combined mapping of reads to a single concatenated genome. We considered Cuscuta campestris as parasitic plant and two host plants of interest such as Arabidopsis thaliana and Solanum lycopersicum. Both tested approaches achieved a mapping rate of ~90%, with only about 1% of cross-mapping reads. This suggests the effectiveness of the method in accurately separating mixed transcripts in silico. The combined approach proved slightly more accurate and less time consuming than the sequential approach. The evolutionary distance between parasitic and host plants did not significantly impact the accuracy of read assignment to their respective genomes since enough polymorphisms were present to ensure reliable differentiation. This study demonstrates the reliability of dual RNA-seq for studying host-parasite interactions within the same taxonomic kingdom, paving the way for further research into the key genes involved in plant parasitism.

Second Language Sentence Stress Assignment: Self‐ and Other‐Assessment

AbstractResearch on second language (L2) pronunciation self‐assessment reports a general misalignment between self‐ and other‐assessment. This has been attributed to the object of self‐assessment, the self‐assessment task, the measures to which self‐assessment is compared, and speakers’ characteristics. Here, we examined self‐assessment of a discrete phonological feature—sentence stress—by L2 English speakers as compared to the assessment of first language English listeners through a timed, forced‐choice judgment task with low‐pass filtered stimuli, which contained only suprasegmental cues. Additionally, we explored how individual differences among speakers predict self‐assessment. Speakers generally overestimated their accuracy in sentence stress assignment in a pattern resembling the Dunning‐Kruger effect despite the controlled nature of the task. Speakers with larger vocabulary size judged their sentence stress assignment as correct more often and showed greater overconfidence and miscalibration. Finally, the assessments of speakers with a background in applied linguistics and/or language teaching were more aligned with listeners’ assessments.

Enhancing SARS-CoV-2 Lineage Surveillance through the Integration of a Simple and Direct qPCR-Based Protocol Adaptation with Established Machine Learning Algorithms.

Emerging and evolving Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages, adapted to changing epidemiological conditions, present unprecedented challenges to global public health systems. Here, we introduce an adapted analytical approach that complements genomic sequencing, applying a cost-effective quantitative polymerase chain reaction (qPCR)-based assay. Viral RNA samples from SARS-CoV-2 positive cases detected by diagnostic laboratories or public health network units in Ceará, Brazil, were tracked for genomic surveillance and analyzed by using paired-end sequencing combined with integrative genomic analysis. Validation of a key structural variation was conducted with gel electrophoresis for the presence of a specific open reading frame 7a(ORF7a) gene deletion within the "BE.9" lineages tracked. The analytical innovation of our method is the optimization of a simple intercalating dye-based qPCR assay through repositioning primers from the ARTIC v4.1 amplicon panel to detect large molecular patterns. This assay distinguishes between "BE.9" and "non-BE.9" lineages, particularly BQ.1, without the need for expensive probes or sequencing. The protocol was validated against lineage predictions from next-generation sequencing (NGS) using 525 paired samples, achieving 93.3% sensitivity, 95.1% specificity, and 92.4% agreement, as measured by Cohen's Kappa coefficient. Machine learning (ML) models were trained using the melting curves from intercalating dye-based qPCR of 1724 samples, enabling highly accurate lineage assignment. Among them, the support vector machine (SVM) model had the best performance and after fine-tuning showed ∼96.52% (333/345) accuracy in comparison to the test data set. Our integrated approach provides an adapted analytical method that is both cost-effective and scalable, suitable for rapid assessment of emerging variants, especially in resource-limited settings. In this work, the protocol is applied to improve the monitoring of SARS-CoV-2 sublineages but can be extended to track any key molecular signature, including large insertions and deletions (indels) commonly observed in pathogenic agent subtypes. By offering a complement to traditional sequencing methods and utilizing easily trainable machine learning algorithms, our methodology contributes to enhanced molecular surveillance strategies and supports global efforts in pandemic control.

Exploring the Potential of Morphometrics to Inform the Conservation of Iguana delicatissima

Invasive alien species severely impact native and endemic species, disproportionately affecting insular species like Caribbean reptiles. The Lesser Antillean iguana, Iguana delicatissima, is rapidly declining due to hybridization with non-native green iguanas (NNGIs). With numerous NNGI populations present in the region, the last I. delicatisisma-inhabited islands remain highly susceptible to invasion, whereupon the rapid in situ differentiation of NNGIs is essential. However, currently only few scale and coloration characters allow the in situ identification of NNGIs. Here, we assessed the differentiating power of an additional 20 meristic and length-dependent characteristics between the I. delicatissima population on St. Eustatius and the NNGI population on St. Maarten, the main source of NNGIs arriving on St. Eustatius. Of these, 13 significantly differed between I. delicatissima and NNGIs, with a ≥94% assignment accuracy in multi-variate predictive models. We recommend that these characteristics be assessed and validated, especially for hybrid individuals, allowing the identification of NNGIs even in populations where hybridization is ongoing. Our work demonstrates the value of investing in baseline morphometric datasets for I. delicatissima and other native iguanas to aid in the rapid in situ detection of NNGIs for removal.