Accurate Size Research Articles

Photon-counting-detector CT outperforms state-of-the-art cone-beam and energy-integrated-detector CT in delineation of dental root canals

This experimental phantom study investigates current standard of care protocols in cone beam computed tomography (CBCT), energy-integrating-detector (EID) CT, and photon-counting-detector (PCD) CT regarding their potential in delineation of dental root canals. Artificial accessory canals (diameters: 1000, 600, 400, 300 and 200 μm) were drilled into three bovine teeth mounted on a bovine rib as a jaw substitute. The phantom was scanned in two dental CBCTs, two EID-CTs and a PCD-CT using standard clinical protocols. Scans from a micro-CT served as reference standard. Spatial resolution was evaluated via line profiles through the canals, whereby visibility compared to surrounding noise and compared to the ground truth were assessed. PCD-CT was able to delineate all artificial canals down to 200 μm diameter. In CBCT and EID-CT canals could only be reliably detected down to 300 μm. Also, PCD-CT showed a considerably smaller width-divergence from the ground trough with 4.4% at 1000 μm and 35.1% at 300 μm compared to CBCT (13.5 and 72.9%) and EID-CT (10.1 and 115.7%). PCD-CT provided superior resolution, accurate size measurement, and enhanced detection of small dental root canals, thereby offering improvements in diagnostic capabilities compared to CBCT and EID-CT systems.

Particle size inversion of flowing aerosol using Bayesian inference in dynamic light scattering

In dynamic light scattering (DLS) measurements of flowing aerosols, an increase in flow velocity can exacerbate the ill-conditioned nature of the inversion equation, which makes it difficult to obtain accurate particle size by classical inversion methods. We established a probability model using Bayesian inference and derived a posterior probability density function (PDF) containing the undetermined particle size distribution (PSD) parameters. After setting the initial values of the parameters, the Markov chain Monte Carlo (MCMC) algorithm was used to sample the parameters in the posterior PDF, and the sample values of the Markov chain were averaged to obtain the needed PSD parameters. The PSD inverted by the Bayesian method shows that it avoids phenomena such as peak position shift, distribution broadening, and disappearance of one peak in bimodal distributions in regularization inversion. The peak position error and distribution error of the PSD inverted by the Bayesian method no longer increase with the increase of flow velocity. In addition, through the Bayesian method, the flow velocity information contained in the intensity autocorrelation function was accurately obtained, thus achieving online retrieval of PSD and flow velocity of flowing aerosols.

Endoscopic size measurement of colorectal polyps: a systematic review of techniques.

Accurate size measurement of colorectal polyps is critical for clinical decision making and patient management. This systematic review aimed to evaluate the current techniques used for colonic polyp measurement to improve the reliability of size estimations in routine practice.A comprehensive literature search was conducted across PubMed, EMBASE, and MEDLINE to identify studies relevant to size measurement techniques published between 1980 and March 2024. The primary outcome was the accuracy of polyp sizing techniques used during colonoscopy.61 studies were included with 34 focusing on unassisted and assisted endoscopic visual estimation and 27 on computer-based tools. There was significant variability in visual size estimation among endoscopists. The most accurate techniques identified were computer-based systems, such as virtual scale endoscopes (VSE) and artificial intelligence (AI)-based systems. The least accurate techniques were visual or snare-based polyp size estimation. VSE assists endoscopists by providing an adaptive scale for real-time, direct, in vivo polyp measurements, while AI systems offer size measurements independent of the endoscopist's subjective judgment.This review highlights the need for standardized, accurate, and accessible techniques to optimize sizing accuracy during endoscopic procedures. There is no consensus on a gold standard for measuring polyps during colonoscopy. While biopsy forceps, snare, and graduated devices can improve the accuracy of visual size estimation, their clinical implementation is limited by practical, time, and cost challenges. Computer-based techniques will likely offer improved accuracy of polyp sizing in the near future.

A unified combination framework for dependent tests with applications to microbiome association studies.

We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating P-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing P-value combination methods, including the vanilla Cauchy combination method and other methods, the proposed combination framework is flexible and can be adapted to handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to the microbiome association studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations.

Quantifying Metagenomic Strain Associations from Microbiomes with Anpan.

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpan's methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.

High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of measuring the diameter of more than 1,800,000 HDL particles with the deep learning model YOLOv7 (you only look once) from micrographs of 183 HDL samples, including patients with dementia or normal cognition (controls). This method was shown to be more efficient and accurate than conventional image analysis software. Using this method, we found a higher abundance of small HDLs in participants with dementia compared to controls in patients with the apolipoprotein E (APOE) ε3ε4 genotype, whereas patients with the APOE ε3ε3 genotype had higher variability in the abundance of different HDL subclasses. Our results show an example of accurate individual HDL particle diameter measurement for large-scale clinical samples, which can be expanded to characterize the relationship between disease risk and other nanoparticles in the sub-20-nm diameter size range.

Advancing microplastic detection in zebrafish with micro computed tomography: A novel approach to revealing microplastic distribution in organisms.

The analysis of microplastics with current spectroscopic and pyrolytic methods is reaching its limits, especially with regard to detailed spatial distribution in biological tissues. This limitation hampers a comprehensive understanding of the effects of microplastics on organisms. Therefore, there is a pressing need to expand the analytical approaches to study microplastics in biota. In this context, the aim of this study was to test the applicability of non-destructive 3D imaging using X-ray micro-computed tomography (microCT) for the detection of microplastics in fish. Zebrafish (Danio rerio) were gavaged with polyethylene spherical microplastics (30-110 μm) and the distribution of microplastics in the gut was investigated using microCT. The results showed that the particle size distribution determined by microCT closely matched the data from conventional laser diffraction analysis. In addition, microCT was able to detect microplastics in spiked fish tissue and provide precise localization data by tracing particles of known type and shape. MicroCT offers a novel approach for tracking microplastics in organisms and enables accurate sizing without compromising the integrity of the tissue under investigation. It therefore represents a valuable addition to spectroscopic methods, which are widely used for the detection of microplastics based on their chemical composition but do not provide data on their spatial distribution.

Tests for spatial dependence and temporal heterogeneity in time-varying coefficient spatial autoregressive panel data model

Time-varying coefficient spatial autoregressive panel data model is a powerful tool to simultaneously deal with spatial dependence and temporal heterogeneity in spatial panel data analysis. However, little work has been devoted to related statistical inference issues, which largely limits the application scope of the model. In this paper, we develop two generalized-likelihood-ratio-statistic-based bootstrap tests to detect spatial dependence of the response variable and temporal heterogeneity of the regression relationship, respectively. The simulation studies show that both tests are of accurate size and satisfactory power, and are quite robust to non-normality of error distribution. A house price data set is finally analyzed to demonstrate the application of the proposed tests in detecting spatial dependence and temporal heterogeneity.

Maximizing insights from longitudinal epigenetic age data: simulations, applications, and practical guidance

BackgroundEpigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.ResultsOur simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered advanced GrimAge in individuals assigned male at birth that decelerates over time.ConclusionOur results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Chromosome-level genome assembly of the king horseshoe bat (Rhinolophus rex) provides insights into its conservation status and chromosomal evolution of Rhinolophus.

A high-quality reference genome is quite valuable in assessing the conservation status of a rare species when adequate data from other sources are unavailable. Bats comprise almost a fifth of all mammals and contribute greatly to ecosystem. However, due to the nocturnal and elusive habits, it is difficult to obtain the accurate census population size of a rare bat species and assess its conservation status. Here, we generate a chromosome-level genome assembly for the king horseshoe bat (Rhinolophus rex) and assess its conservation status by comparing the genome-wide summary statistics to other related species. The genome assembly size was 2.1 Gb (contig N50: 75.26Mb) and 99.9% of the total sequences were anchored onto 30 autosomes, X and Y chromosomes. Despite lower genome-wide heterozygosity and recent inbreeding, R. rex did not exhibit higher genetic load comparing to the other two Rhinolophus species. Historical demography analysis revealed that R. rex maintained a long term (~2 million years) stable population size (~150,000). In the future whole-genome sequencing data from more individuals will be needed to comprehensively assess the conservation status at recent timescales. We also reconstructed the ancestral karyotype of Rhinolophus as 2n=54 and found that Robertsonian fissions and fusions were the main mechanism of chromosomal rearrangements in this genus. Overall, our study shows important implications of reference-quality genomes in both conservation genomics and comparative genomics.

Exploring spatial reasoning performances of CNN on linear layout dataset

Abstract Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions and often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study further clarifies CNNs’ capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space.

Identification of Brain Tumors Using SVM Classifier

A vital non-invasive method that is frequently employed in the medical sector for the examination, diagnosis, and management of anomalies in brain tissue is magnetic resonance imaging, or MRI. Effective treatment of brain cancers depends on early identification, which also greatly enhances patient outcomes. However, it is a difficult and time-consuming task to accurately detect and segment tumors from MRI slices. The suggested system uses cutting-edge methods to automate the tumor classification and segmentation procedure in order to overcome this difficulty. The technique finds aberrant tissue in MRI slices by using a Support Vector Machine (SVM) classifier for tumor localization. The tumor's borders are then drawn using segmentation techniques, which enables an accurate tumor size measurement. In order to train an artificial neural network (ANN) to identify the type of tumor present, these segmented regions undergo additional processing to extract important properties.

Characterization of biogenic selenium nanoparticles in hypersaline media by single particle inductively coupled plasma mass spectrometry: Haloferax mediterranei case

BackgroundSingle particle inductively coupled plasma mass spectrometry (spICP-MS) is extensively employed for the characterization of biogenic selenium nanoparticles (SeNPs) produced by mesophilic microorganisms. Nevertheless, because halophilic microorganisms are also well-known to produce SeNPs, further research efforts are required to investigate spICP-MS applicability for characterizing such nanomaterials in hypersaline media. The goal of this work is to develop a methodology for characterizing SeNPs in hypersaline media by spICP-MS. To this end, plasma operating conditions, non-spectral interferences and calibration strategies were investigated. The proposed method was employed to investigate the capabilities of the halophilic archaea Haloferax mediterranei to produce SeNPs. ResultsBy the appropriate selection of experimental conditions, SeNPs can be accurately analyzed in hypersaline media by spICP-MS. Unlike previous works in the literature, no differences in ionic signal were observed between SeNPs and dissolved Se and, hence, there is no need to apply any empirical corrector factor for obtaining accurate particle size distributions. Non-spectral interferences are mitigated by diluting the sample at least 1:103 which allows the use of water standards. Size (30 nm) and particle (7 × 105 particles mL−1) detection limits were low enough to characterize biogenic SeNPs produced by halophilic microorganisms. The use of the optimized methodology reveals that Haloferax mediterranei can produce SeNPs when it is exposed to selenite up to 1 mM, but no formation is produced for selenate exposure. Depending on incubation parameters (selenite concentration and time), the particle median diameter ranged from 80 to 100 nm, whereas particle concentration varied from 0.8 to 1.9 × 1013 particles mL−1. SignificanceThis represents the first methodology for characterizing biogenic SeNPs in hypersaline media by spICP-MS with accuracy and precision using non-matrix matched standards. It opens the opportunity to investigate the capabilities of halophilic microorganisms (e.g., H. mediterranei) to produce Se-based nanomaterials.

Design and Development of a Quad mode Circular Millimeter-Wave Patch Antenna for Breast Tumour Size Detection and Localization

In this study, we present the development of an antenna based on millimeter-wave specifically designed for performance in breast tumour detection and localization. A quad – mode circular microstrip antenna is engineered to function at millimeter-wave frequencies of 30 GHz and 40 GHz, focusing on accurate tumor size & location. At 30 GHz and 40 GHz, facilitating effective penetration through breast tissue and enabling precise tumor localization. The differential dielectric characteristics of malignant and normal breast tissues provide the basis for the detection of breast cancers using mm-wave imaging. Using quarter mode circular patch antennas, this method sends and receives pulses that travel through the breast at various points. The tumor's location and size are then determined by receiving the waves from the breast. Due to their strong scattering properties, malignant tumors can be identified by their transfer characteristics in the reflected signals they receive. The study also looks at how the tumor's size and location in relation to the antenna affect detection efficiency. These results suggest that mm wave planar antennas are useful for locating and detecting tiny breast cancers, offering a viable method for breast cancer detection.

Human milk fat globule size distributions: Comparison between laser diffraction and 3D confocal laser scanning microscopy

Milk fat globules (MFGs) in human milk provide energy to breastfed infants and support infant development. Accurate measurements of MFG size distributions are important to better understand MFG function and origin, as well as the influence of MFG size on milk composition analysis methods. Nevertheless, commonly used laser diffraction systems have never been thoroughly validated for size distribution measurements in human milk. Here, we introduce a new method for determining the size distribution of milk fat globules in human milk, using 3D confocal laser scanning microscopy (CLSM) in combination with fluorescent labeling of MFGs. We validate and compare 3D CLSM to laser diffraction (Mastersizer 2000, Malvern Panalytical), using polystyrene microsphere size standards. Next, we apply both methods to evaluate MFG size distributions in human milk. We show that 3D CLSM can be used to obtain more accurate size distributions between 500 nm and 10 μm compared to laser diffraction. Importantly, MFG size distributions obtained with 3D CLSM contain no secondary population around 1 μm, in contrast to laser diffraction measurements. This suggests that the bimodal MFG distribution obtained by laser diffraction can be an artifact of the built-in fitting algorithm, instead of an actual feature of human milk. This work demonstrates that care should be taken when interpreting size distributions of MFGs measured with laser diffraction and that 3D CLSM is an accurate alternative for measuring size distributions in lactation and dairy research.

Ultra-high pressure slalom chromatography: Application to the characterization of large DNA and RNA samples relevant in cell and gene therapy

Slalom chromatography (SC), initially co-discovered by Boyes and Kasai in the late 1980s, has recently re-emerged as a breakthrough technique to rapidly analyze DNA samples. With the development of cutting-edge ultra-high pressure liquid chromatography (UHPLC) systems and columns, SC now offers enhanced resolution and sensitivity for analyzing large DNA samples. By revisiting the fundamentals of the SC retention mechanism (non-equilibrium separation mode) and considering the physicochemical properties of DNA biopolymers (contour length, extension under shear flow, relaxation time), we provide analytical chemists with a general strategy and framework for selecting the most relevant applications in the expanding field of cell and gene therapy. We then present proof-of-concept data demonstrating the rapid separation (under 2 min) of plasmid digest samples containing linear double-stranded (ds) DNA macromolecules starting from 2 kbp to 25 kbp, as well as the accurate size determination (±6%) of linear dsDNAs. Additionally, we show rapid baseline separation and quantification of extensible linear dsDNAs, along with the more rigid plasmid dsDNA (supercoiled/circular/nicked circular). We also quantify dsRNA impurities present in vitro transcription (IVT) media used for producing new mRNA therapeutics and assess dsRNA structural heterogeneity (conformational isomers). These findings aim to support in a near future application chemists in addressing emerging bioanalytical challenges in cell and gene therapy by offering advanced SC columns and methods.

Study on the rigorous accuracy of any point on a linear element

In order to obtain the rigorous accuracy of any point on the straight line element, based on existing research, this article considers the error of the straight line element and the error of any point to its end point of the straight line element, attempts to discuss the accuracy calculation of any point on the straight line element in three different situations, and through case analysis, compares the position covariance matrix of any point on the straight line element in three situations and draws its error ellipse using MATLAB programming. Finally, different accuracy sizes and different error ellipses are obtained. This research has certain guiding significance and role for error calculation in precision engineering measurement, such as large bridges or large through projects.

Selective ion transport through hydrated micropores in polymer membranes

Ion-conducting polymer membranes are essential in many separation processes and electrochemical devices, including electrodialysis1, redox flow batteries2, fuel cells3 and electrolysers4,5. Controlling ion transport and selectivity in these membranes largely hinges on the manipulation of pore size. Although membrane pore structures can be designed in the dry state6, they are redefined upon hydration owing to swelling in electrolyte solutions. Strategies to control pore hydration and a deeper understanding of pore structure evolution are vital for accurate pore size tuning. Here we report polymer membranes containing pendant groups of varying hydrophobicity, strategically positioned near charged groups to regulate their hydration capacity and pore swelling. Modulation of the hydrated micropore size (less than two nanometres) enables direct control over water and ion transport across broad length scales, as quantified by spectroscopic and computational methods. Ion selectivity improves in hydration-restrained pores created by more hydrophobic pendant groups. These highly interconnected ion transport channels, with tuned pore gate sizes, show higher ionic conductivity and orders-of-magnitude lower permeation rates of redox-active species compared with conventional membranes, enabling stable cycling of energy-dense aqueous organic redox flow batteries. This pore size tailoring approach provides a promising avenue to membranes with precisely controlled ionic and molecular transport functions.

Bubble boundary R-CNN: A multitask model for segmenting and reconstructing overlapping bubbles

When measuring the dynamic characteristics of bubbles using image-based methods, the images including complex overlapping of bubbles with irregular shape pose a huge challenge to current bubble reconstruction algorithms. Based on the Mask R-CNN architecture, a multitask model “Bubble Boundary R-CNN” is proposed for detection, segmentation and shape reconstruction of overlapping bubbles with high deformation at high gas velocities. By leveraging shared deep features from detection and segmentation, model achieves flexible bubble reconstruction and accurate size distribution extraction from high gas velocity bubble flows. To overcome the limitations of Mask R-CNN and address the challenges posed by high-deformation and overlapping bubbles, a semantic segmentation auxiliary head and the Boundary-preserving Mask Head (BMask Head) are introduced to enhance edge information and extract bubble boundary features. Additionally, an Overlapping Boundary Prediction Network (OBPN) is proposed to reconstruct boundary features that are lost due to bubble occlusion and predict the complete bubble shapes. The optimized model shows its ability to flexibly and accurately reconstruct overlapped and deformed bubbles by varying occlusion rates and circularity. The new model has also proven its reliability when applied to bubble images from a bubble column at gas velocities of up to 0.067 m3/s. In future work, more efforts are needed to further enhance the detection capability of the model and reduce missed and multiple detections at high gas velocity.

Shell Constraints on Evolutionary Body Size-Limb Size Allometry Can Explain Morphological Conservatism in the Turtle Body Plan.

Turtles are a small clade of vertebrates despite having existed since the Late Triassic. Turtles have a conservative body plan relative to other amniotes, characterized by the presence of a shell and quadrupedality. This morphology is even retained in strong ecological specialists, such as sea turtles, which are secondarily adapted to marine locomotion by strong allometric scaling in their hands. It is possible that the body plan of turtles is strongly influenced by the presence of the shell, acting as a constraint to achieving greater diversity of body forms. Here, we explore the evolutionary allometric relationships of fore- and hindlimb stylopodia (i.e., humerus and femur) with one another as well as their relationship with shell size (carapace length) to assess evidence of constraint. All turtles, including Triassic shelled stem turtles, have near-isometric relationships that do not vary strongly between clades, and evolve at slow evolutionary rates. This indeed indicates that body proportions of turtles are constrained to a narrow range of possibilities. Minor allometric deviations are seen in highly aquatic sea turtles and softshell turtles, which modified their shells by bone losses. Our allometric regressions allow accurate body size estimations for fossils. Several independent sea turtle lineages converged on maximum sizes of 2.2 m of shell length, which may be a biological maximum for the group.