Precise Analysis Research Articles

Cleaning as care work: conceptual musings

The COVID-19 pandemic has made many aspects of care work more visible. This article focuses on cleaning as a form of care work that has been systematically ignored and made invisible. We examine both the benefits and drawbacks of acknowledging cleaning as a specific form of care: while broadening the concept of care might risk diluting its analytical precision, incorporating cleaning into care work discussions brings attention to its significance. We argue that recognizing cleaning as care is vital because it uncovers its nurturing, life-sustaining and intimate qualities. Cleaning can, thus, be understood as a biopolitical dimension of contemporary societies.

TopoQual polishes circular consensus sequencing data and accurately predicts quality scores

BackgroundPacific Biosciences (PacBio) circular consensus sequencing (CCS), also known as high fidelity (HiFi) technology, has revolutionized modern genomics by producing long (10 + kb) and highly accurate reads. This is achieved by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently, the accuracy and quality value estimation provided by HiFi technology are more than sufficient for applications such as genome assembly and germline variant calling. However, there are limitations in the accuracy of the estimated quality scores when it comes to somatic variant calling on single reads.ResultsTo address the challenge of inaccurate quality scores for somatic variant calling, we introduce TopoQual, a novel tool designed to enhance the accuracy of base quality predictions. TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. Our results demonstrate that TopoQual corrects approximately 31.9% of errors in PacBio consensus sequences. Additionally, it validates base qualities up to q59, which corresponds to one error in 0.9 million bases. These improvements will significantly enhance the reliability of somatic variant calling using HiFi data.ConclusionTopoQual represents a significant advancement in genomics by improving the accuracy of base quality predictions for PacBio HiFi sequencing data. By correcting a substantial proportion of errors and achieving high base quality validation, TopoQual enables confident and accurate somatic variant calling. This tool not only addresses a critical limitation of current HiFi technology but also opens new possibilities for precise genomic analysis in various research and clinical applications.

Droplet-Based EPR Spectroscopy for Real-Time Monitoring of Liquid-Phase Catalytic Reactions.

In situ monitoring is essential for catalytic process design, offering real-time insights into active structures and reactive intermediates. Electron paramagnetic resonance (EPR) spectroscopy excels at probing geometric and electronic properties of paramagnetic species during reactions. Yet, state-of-the-art liquid-phase EPR methods, like flat cells, require custom resonators, consume large amounts of reagents, and are unsuited for tracking initial kinetics or use with solid catalysts. To overcome these limitations, a droplet-based microfluidics platform is introduced for real-time EPR monitoring of liquid-phase catalytic reactions. By encapsulating solid and dissolved species within nanoliter droplets, this approach enables precise control over mass transport, reduces reagent consumption, and maintains uniform residence times irrespective of acquisition duration, permitting precise analysis of each spectral component under identical conditions. The platform's compatibility with standard resonators facilitates straightforward integration into any EPR spectrometer. Its versatility is demonstrated by monitoring dynamic ligand exchange processes, key for activating homogeneous catalysts, and tracking redox and radical kinetics in ascorbic acid oxidation by Cu(II) catalysts. Importantly, this method captures both supported and dissolved transition metal species, offering comprehensive insights into catalyst deactivation via metal leaching. This microfluidic approach sets a new standard for liquid-phase in situ EPR measurements, advancing studies of homogeneous and heterogeneous catalytic systems.

New Constituents from Zanthoxylum rhoifolium.

The phytochemical study of Zanthoxylum rhoifolium apolar and medium polarity stem bark and leaf extracts afforded 29 compounds, including three new sesquiterpenes (1 - 3: ) and one new α-ionone glycoside (4: ). All compounds were characterized by means of 1D and 2D NMR and HRESIMS data. Furthermore, a precise structural analysis was performed, employing a combined density functional theory (DFT)/NMR approach to elucidate the compounds configurations. The crude extracts were then tested against a panel of gram-positive and gram-negative bacteria by broth dilution methods to determine their minimal inhibitory concentration. In these experimental conditions, no interesting antimicrobial activity was observed.

AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders

IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed.ObjectivesThis paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms.MethodologyThe proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.Anticipated resultsThe AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies.ConclusionsBy integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.

Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA’s Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy’s performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m3, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy’s public data reporting and serve as guidelines for other low-cost sensors in Thailand.

Application of Developing Artificial Intelligence (AI) Techniques to Model Pan Evaporation Trends in Slovak River Sub-Basins

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (tmin) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity.

Accessible Halitosis Diagnosis: Validating the Accuracy of Novel AI-based Compact VSC Measuring Instrument.

Halitosis presents a significant global health concern, necessitating the development of precise and efficient testing methodologies owing to the high prevalence and the associated social and psychological effects. The measurement of volatile sulfur compounds (VSCs), recognized as primary contributors to halitosis, is particularly significant. While gas chromatography (GC-MS) offers accurate measurements, its bulky and expensive nature limits widespread accessibility. Hence, this study endeavors to devise a compact yet highly accurate AI-based halitosis measurement apparatus, termed "Kunkun dental" and validate its efficacy. Specifically, we intend to compare the VSC concentrations obtained from halitosis patients' breath samples using Kunkun dental against those from conventional GC-MS to assess the criterion validity of the new testing method. The study cohort comprised 68 halitosis patients aged 20 years or older, attending the breath freshening outpatient clinic at Tokyo Medical and Dental University Hospital between October 2022 and March 2023, who consented to participate and underwent routine measurements. Participants completed an age and sex questionnaire, while VSC concentrations were determined using both GC-MS and Kunkun dental (H2S, CH3SH, (CH3)2S), enabling a comparative analysis of the results. Pearson product-moment correlation coefficients between GC-MS and Kunkun dental indicated significant correlations for all three gases: 0.719 for H2S, 0.821 for CH3 SH, and 0.637 for (CH3)2S. Moreover, sensitivity and specificity in accordance with the predefined thresholds were confirmed, and their values ranged from 0.59 to 0.86 and 0.53 to 0.77, respectively. Furthermore, grouping Kunkun dental measurements into low-, medium-, and high-concentration groups revealed significantly higher GC-MS VSC concentrations in samples with elevated Kunkun dental readings. The amalgamation of AI technology and a semiconductor gas sensor holds great promise in creating a compact and precise halitosis analyzer. This study underscores the feasibility and effectiveness of Kunkun dental as a reliable tool for halitosis assessment, affirming its utility in clinical practice.

Use of an Intramolecular Quenched Fluorescence (IQF) Cleavage Assay for Assessing Enzyme Kinetics of Gamma-Secretase in Human Skin Fibroblasts and Keratinocytes.

This study describes an intramolecular quenching assay to evaluate gamma-secretase (GS) enzyme activity in human dermal cells. The method utilizes a fluorogenic peptide substrate, mimicking a fragment of amyloid precursor protein (APP), in which a quencher suppresses the fluorescence of a fluorophore until enzymatic cleavage occurs, resulting in a measurable increase in fluorescence. This real-time, direct measurement of GS activity allows for precise kinetic analysis using Michaelis-Menten modeling to define Kd and Vmax. The assay is designed to quantify GS activity in human dermal fibroblasts and keratinocytes, enabling comparison between samples derived from hidradenitis suppurativa (HS) patients and healthy controls, as well as investigating the effects of subunit knockdown, such as nicastrin, on GS function. The method offers several advantages, including simplicity, cost-effectiveness, and adaptability for high-throughput screening for GS enzyme inhibitors.

Optimum Design of Transformers for Offshore Wind Power Generators Considering Their Behavior

This paper presents a structural optimization technique based on precise heat transfer analysis for transformers. Mechanical and thermal stresses during operation are particularly critical in confined environments, such as within wind turbine generators. To optimize the transformer’s structure, the temperature distribution is first calculated using computational fluid dynamics (CFD) based on the finite volume method. Eddy current losses, necessary for thermal analysis, are determined through electromagnetic analysis. These CFD results are then used as input conditions for structural analysis. Finally, structural optimization is performed on critical areas, primarily the radiation fins and panels. All models are analyzed in 3D, and the simulation results are validated through comparison with experimental data. Consequently, the optimized design results were validated against simulations, exhibiting a maximum deviation of 4%. These findings confirm the success of the proposed transformer design optimization.

Analytical performance of OncoPrism-HNSCC, an RNA-based assay to inform immune checkpoint inhibitor treatment decisions for recurrent/metastatic head and neck squamous cell carcinoma

BackgroundWhile immune checkpoint inhibitor (ICI) therapies can significantly improve outcomes for patients with recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC), only about 15–20% benefit from such treatments. Clinical tests that guide the use of ICIs are therefore critically needed. OncoPrism-HNSCC was developed to address this need. The assay combines next generation RNA sequencing-based immunomodulatory gene expression signatures with machine learning algorithms to generate an OncoPrism score that classifies patients as having low, medium, or high likelihood of disease control in response to ICI treatment. Also, OncoPrism-HNSCC leverages the same FFPE patient tumor RNA used for ICI response prediction to identify rare cases where oncogenic rearrangements in NTRK1/2/3 or ALK genes may occur, and which may indicate the use of potentially highly effective targeted therapies. The clinical performance of OncoPrism-HNSCC has been validated. Here, we report its analytical performance in the presence of potentially confounding sources of variation.MethodsThe assay’s analytical sensitivity was assessed by varying RNA input quantity and quality, observing the effect on ICI response prediction scores. Analytical specificity was tested by spiking increasing percentages of genomic DNA into input RNA. Intra-assay and inter-assay precision were evaluated, and the analytical sensitivity, specificity, and precision of gene fusion detection were assessed. Concordance with orthogonal methods of gene fusion detection was tested on 67 FFPE clinical samples.ResultsVarying RNA inputs as low as four-fold below the nominal input amount had little effect on ICI response prediction scores. RNA quality levels below the test threshold had no significant effect. Genomic DNA spike-ins up to 30% had only a small effect on scores. The pooled standard deviation for multiple operators, reagent lots, batches, and sequencers yielded an overall variance represented by just 0.87% of the score range of the test (0–100). NTRK and ALK gene fusion detection was 100% concordant with orthogonal methods.ConclusionsRobust and reliable analytical performance of the OncoPrism-HNSCC assay supports its clinical use, even in the presence of variation typically encountered in the laboratory setting.

Inhalable Mucociliary-On-Chip System Revealing Pulmonary Clearance Dynamics in Nanodrug Delivery.

The development of a inhaled nanodrug delivery assessment platform is crucial for advancing treatments for chronic lung diseases. Traditional in vitro models and commercial aerosol systems fail to accurately simulate the complex human respiratory patterns and mucosal barriers. To address this, we have developed the breathing mucociliary-on-a-chip (BMC) platform, which replicates mucociliary clearance and respiratory dynamics in vitro. This platform allows for precise analysis of drug deposition and penetration, providing critical insights into how liposomes and other nanocarriers interact with lung tissues under various airflow conditions. Our results reveal that liposomes penetrate deeper into the cellular layer under high shear stress, with both static and dynamic airflows distinctly affecting their drug release rates. The BMC platform integrates dynamic inhalation systems with mucociliary clearance functionality, enabling a comprehensive evaluation of drug delivery efficacy. This approach highlights the importance of airflow dynamics in optimizing inhalable nanodrug delivery systems, improving nanocarrier design, and tailoring drug dosages and release strategies. The BMC platform represents a significant advancement in the field of inhaled nanodrug delivery, offering a more accurate and reliable method for assessing the performance of therapies. By providing a detailed understanding of drug interactions with lung tissues, this platform supports the development of personalized inhaled therapies and offers promising strategies for treating pulmonary diseases and advancing nanodrug development.

Fault Arc Detection Method Based on Improved ShuffleNet V2 Network

Fault arcs exhibit randomness, with current waveforms closely mirroring those of standard nonlinear load operations, posing challenges for traditional series fault arc detection methods. This study presents an improved detection approach using a lightweight convolutional neural network model, ShuffleNet V2. Current data from household loads were collected and preprocessed to establish a comprehensive database, leveraging one-dimensional convolution and channel attention mechanisms for precise analysis. Experimental results demonstrate a high fault arc detection accuracy of 97.8%, supporting real-time detection on the Jetson Nano embedded platform, with an efficient detection cycle time of 15.65 ms per sample. The proposed approach outperforms existing methods in both accuracy and speed, providing a robust foundation for developing advanced fault arc circuit breakers.

CoGraphNet for enhanced text classification using word-sentence heterogeneous graph representations and improved interpretability

Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues. To overcome information loss, we construct separate heterogeneous graphs for words and sentences, capturing multi-tiered contextual information. We enhance interpretability by incorporating positional bias weights, improving model clarity. CoGraphNet provides precise analysis, highlighting important words or sentences. We achieve enhanced contextual comprehension and accuracy through novel graph structures and the SwiGLU activation function. Experiments on Ohsumed, MR, R52, and 20NG datasets confirm CoGraphNet’s effectiveness in complex classification tasks, demonstrating its superiority.

Sector-Field–Inductively Coupled Plasma–Mass Spectrometry (SF-ICP-MS) for the Determination of Toxic Metals in Microplastics from Seawater and Beach Sediments from the Barents and White Seas

The sector-field–inductively coupled plasma–mass spectrometry (SF-ICP-MS) technique was applied for the quantification of toxic metals in microplastic particles (MPs) from seawater of six coastal sites from the Barents and White Seas following a microwave-assistant leaching with 30% HNO3. The mean repeatability of the method (relative standard deviation) was 1–4%, and intermediate precision averaged 4–7% for all metals. The dependence of metal content on the MPs size was also of immense consideration. The developed method can be recommended as a standard tool for accurate and precise microplastic analysis with important geochemical and environmental implications.

Can a plant biologist fix a thermostat?

SummaryThe shift to reductionist biology at the dawn of the genome era yielded a ‘parts list’ of plant genes and a nascent understanding of complex biological processes. Today, with the genomics era in full swing, advances in high‐definition genomics enabled precise temporal and spatial analyses of biological systems down to the single‐cell level. These insights, coupled with artificial intelligence‐driven in silico design, are propelling the development of the first synthetic plants. By integrating reductionist and systems approaches, researchers are not only reimagining plants as sources of food, fiber, and fuel but also as ‘environmental thermostats’ capable of mitigating the impacts of a changing climate.

Protic Ionic Liquid as an Alternative Electrolyte for Simultaneous Electrochemical Determination of Caffeine and Nicotine in Environmental and Food Samples

Caffeine (CAF) and nicotine (NIC) are emerging contaminants and are among the most consumed substances in the world, making it crucial to monitor these contaminants. In this work, an alternative electrolyte, 2-hydroxyethylammonium acetate (2HEAA) was used for the development of an electroanalytical method for determination and quantification of CAF and NIC, simultaneously, using differential pulse voltammetry (DPV). The system with the 2HEAA electrolyte was characterized by cyclic voltammetry and electrochemical impedance spectroscopy, and the DPV parameters were optimized for the best conditions. The method was validated from a calibration curve obtained which showed limit of detection (LOD) of 0.82 and 6.26 μmol L-1 and limit of quantification (LOQ) of 2.73 and 20.8 μmol L-1 for CAF and NIC, respectively. In the precision analyses, values lower than 10% of relative standard deviation were obtained. In the presence of concomitant inorganic and organic species, the system proved to be selective in the determination of analytes. The method was used to determine the analytes in fortified samples (river water, synthetic urine and commercial milk), obtaining recoveries between 87.25 and 111.40%. The 2HEAA demonstrated high efficiency as an alternative electrolyte with good signal-to-noise ratio, increased analytical sensitivity of the method, in addition to presenting low cost and fast electrolyte preparation.

Analytical Validation of the Labcorp Plasma Complete Test, a Cell-free DNA Comprehensive Genomic Profiling Tool for Precision Oncology.

To help guide treatment decisions and clinical trial matching, tumor genomic profiling is an essential precision oncology tool. Liquid biopsy, a complementary approach to tissue testing, can assess tumor-specific DNA alterations circulating in the blood. Labcorp Plasma Complete is a next-generation sequencing, cell-free DNA comprehensive genomic profiling test that identifies clinically relevant somatic variants across 521 genes in advanced and metastatic solid cancers. Over 800 unique sequencing libraries across 27 cancer types were evaluated to establish analytical sensitivity, specificity, accuracy, and precision, reproducibility, and repeatability (PRR). Sensitivity was verified for each variant type, with a median variant allele frequency (VAF) of 1.25% and 1.27% for panel-wide SNVs and indels (sequence variants), respectively, with <1% VAF sensitivity observed for clinically actionable variants, 1.72-fold for copy number amplifications (CNAs), 0.48% fusion read fraction for translocations, and 0.47% sequence mutation VAF for MSI-High. Analytical specificity was 99.9999% for SNVs and 100% for all other variant types. PRR resulted in 94.9% average positive agreement (APA) and 99.9% average negative agreement (ANA) for sequence variants and 100% APA and ANA for CNAs, translocations, and MSI. Orthogonal assays were utilized to assess accuracy demonstrating an aggregate analytical concordance of 97.4% positive percent agreement and >99.99997% negative percent agreement for all variants. Overall, the test demonstrates high sensitivity, specificity, accuracy, and robustness to enable informed clinical decision-making.

Leveraging supported liquid extraction and automated workflow for high efficiency and high quality clinical LC-MS/MS method for quantification of urinary 5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid (VMA) and homovanillic acid (HVA).

5-hydroxyindoleacetic acid (5-HIAA), vanillylmandelic acid (VMA) and homovanillic acid (HVA) are neurotransmitter metabolites used for the diagnosis and monitoring of neuroendocrine tumors. This study's objective was to apply automation to improve efficiency and quality of a liquid chromatography tandem mass spectrometry (LC-MS/MS) procedure for quantification of 5-HIAA, VMA and HVA in human urine. 5-HIAA, VMA and HVA in urine samples were extracted with automated supported liquid extraction (SLE) and quantified by LC-MS/MS utilizing scheduled multiple reaction monitoring (MRM). An in-house developed middleware was used to automate data analysis and reporting. The average recovery for 5-HIAA, VMA and HVA in different types of urine matrix varied between 92.5% and 99.0%, while the average matrix factor varied between 98.6% and 103.4%. The results of other validation studies: stability, sensitivity, analytical measurement range, selectivity, accuracy, precision, dilution verification, carryover, and method comparison all met the performance criteria. Automated SLE is a suitable sample preparation technique for quantification of 5-HIAA, VMA and HVA in human urine. Workflow automation described in this study resulted in 54% of overall time savings and 80% of hands-on time savings compared to all manual approach.

Proximity Labelling to Quantify Kv7.4 and Dynein Protein Interaction in Freshly Isolated Rat Vascular Smooth Muscle Cells.

Understanding protein-protein interactions is crucial for unravelling subcellular protein distribution, contributing to our understanding of cellular organisation. Moreover, interaction studies can reveal insights into the mechanisms that cover protein trafficking within cells. Although various techniques such as Förster resonance energy transfer (FRET), co-immunoprecipitation, and fluorescence microscopy are commonly employed to detect protein interactions, their limitations have led to more advanced techniques such as the in situ proximity ligation assay (PLA) for spatial co-localisation analysis. The PLA technique, specifically employed in fixed cells and tissues, utilises species-specific secondary PLA probes linked to DNA oligonucleotides. When proteins are within 40 nm of each other, the DNA oligonucleotides on the probes interact, facilitating circular DNA formation through ligation. Rolling-circle amplification then produces DNA circles linked to the PLA probe. Fluorescently labelled oligonucleotides hybridise to the circles, generating detectable signals for precise co-localisation analysis. We employed PLA to examine the co-localisation of dynein with the Kv7.4 channel protein in isolated vascular smooth muscle cells from rat mesenteric arteries. This method enabled us to investigate whether Kv7.4 channels interact with dynein, thereby providing evidence of their retrograde transport by the microtubule network. Our findings illustrate that PLA is a valuable tool for studying potential novel protein interactions with dynein, and the quantifiable approach offers insights into whether these interactions are changed in disease.