COVID-19 deep classification network based on convolution and deconvolution local enhancement

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.

Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data

Background

Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.

Methods

Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.

Results

It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.

Conclusions

Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01314-w.

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

BACKGROUND

Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community.

RESULT

We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project.

CONCLUSIONS

The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

The effect of micro-mechanical signatures of constituent phases in modern dental restorative materials on their macro-mechanical property: A statistical nanoindentation approach

This study utilized a statistical nanoindentation analysis technique (SNT) to measure the amount of organic and inorganic constituents of twenty different brands of dental resin-based composites (RBCs) and tested whether their macro-property such as flexural modulus could be approximated by the proportions of constituents' micromechanical signatures using various rules of mixtures. The probability density function (PDF) of constitutive moduli per RBC brand were measured for three groups, comprised of different indent arrays and inter-indent spacings. SNT was then applied to deconvolute each PDF, from which the effective filler (μ^F) and matrix (μ^M) moduli and filler (V^F) and matrix (V^M) volume fractions per RBC brand were computed. V^F and V^M values obtained via SNT were strongly correlated with V^F and V^M obtained via Thermogravimetric Analysis and Archimedes method. The "observed" flexural modulus (E_c^FS) measured under macro-experiment were well associated with "predicted" effective modulus (E_c^Eff) measured under nano-experiment, thereby establishing that global modulus was strongly affected by the constituents' micromechanics. However, the "predicted" E_c^Eff were proportionally higher than the "observed" E_c^FS. V^F was a confounder to E_c^FS and E_c^Eff, whereby the influence of V^F on both modular ratios (E_c^FS/μ^M and E_c^Eff/μ^M) was best modeled by an exponential regression.

Deconvolution of sarcoma methylomes reveals varying degrees of immune cell infiltrates with association to genomic aberrations

BACKGROUND

Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments.

RESULTS

To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas.

CONCLUSIONS

In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.

SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development.

Improved Ultrasound Microvessel Imaging Using Deconvolution with Total Variation Regularization

Singular value decomposition-based clutter filters can robustly reject tissue clutter, allowing for detection of slow blood flow in imaging microvasculature. However, to identify microvessels, high ultrasound frequency must be used to increase the spatial resolution at the expense of shorter depth of penetration. Deconvolution using Tikhonov regularization is an imaging processing method widely used to improve spatial resolution. The ringing artifact of Tikhonov regularization, though, can produce image artifacts such as non-existent microvessels, which degrade image quality. Therefore, a deconvolution method using total variation is proposed in this study to improve spatial resolution and mitigate the ringing artifact. Performance of the proposed method was evaluated using chicken embryo brain, ex ovo chicken embryo chorioallantoic membrane and tumor data. Results revealed that the reconstructed power Doppler (PD) images are substantially improved in spatial resolution compared with original PD images: the full width half-maximum (FWHM) of the cross-sectional profile of a microvessel was improved from 132 to 83 µm. Two neighboring microvessels that were 154 µm apart were better separated using the proposed method than conventional PD imaging. Additionally, 223 FWHMs measured from the cross-sectional profiles of 223 vessels were used to determine the improvement in FWHM with the proposed method statistically. The mean ± standard deviation of the FWHM without and with the proposed method was 233.19 ± 85.08 and 172.31 ± 75.11 μm, respectively; the maximum FWHM without and with the proposed method was 693.01 and 668.69 μm; and the minimum FWHM without and with the proposed method was 73.92 and 45.74 μm. There were statistically significant differences between FWHMs with and without the proposed method according to the rank-sum test, p < 0.0001. The contrast-to-noise ratio improved from 1.06 to 4.03 dB with use of the proposed method. We also compared the proposed method with Tikhonov regularization using ex ovo chicken embryo chorioallantoic membrane data. We found that the proposed method outperformed Tikhonov regularization as false microvessels appeared using the Tikhonov regularization but not with the proposed method. These results indicate that the proposed method is capable of providing more robust PD images with higher spatial resolution and higher contrast-to-noise ratio.

Understanding psychophysiological interaction and its relations to beta series correlation

Psychophysiological interaction (PPI) was proposed 20 years ago for study of task modulated connectivity on functional MRI (fMRI) data. A few modifications have since been made, but there remain misunderstandings on the method, as well as on its relations to a similar method named beta series correlation (BSC). Here, we explain what PPI measures and its relations to BSC. We first clarify that the interpretation of a regressor in a general linear model depends on not only itself but also on how other effects are modeled. In terms of PPI, it always reflects differences in connectivity between conditions, when the physiological variable is included as a covariate. Secondly, when there are multiple conditions, we explain how PPI models calculated from direct contrast between conditions could generate identical results as contrasting separate PPIs of each condition (a.k.a. "generalized" PPI). Thirdly, we explicit the deconvolution process that is used for PPI calculation, and how is it related to the trial-by-trial modeling for BSC, and illustrate the relations between PPI and those based upon BSC. In particular, when context sensitive changes in effective connectivity are present, they manifest as changes in correlations of observed trial-by-trial activations or functional connectivity. Therefore, BSC and PPI can detect similar connectivity differences. Lastly, we report empirical analyses using PPI and BSC on fMRI data of an event-related stop signal task to illustrate our points.

Spotting isomer mixtures in forensic illicit drug casework with GC-VUV using automated coelution detection and spectral deconvolution

Analysis of isomeric mixtures is a significant analytical challenge. In the forensic field, for example, over 1000 new psychoactive substances (NPSs), comprising of many closely related and often isomeric varieties, entered the drugs-of-abuse market within the last decade. Unambiguous identification of the isomeric form requires advanced spectroscopic techniques, such as GC-Vacuum Ultraviolet Spectroscopy (GC-VUV). The continuous development of NPSs makes the appearance of a novel compound in case samples a realistic scenario. While several analytical solutions have been presented recently to confidently distinguish NPS isomers, the presence of multiple isomers in a single drug sample is typically not considered. Due to their structural similarities it is possible that a novel NPS coelutes with a known isomer and thus remains undetected. This study investigates the capabilities of VUV spectral deconvolution for peak detection and identification in incompletely resolved drug mixtures. To mimic worst case scenarios, severe coelution was deliberately induced at elevated GC temperatures. The deconvolution software was nevertheless able to correctly detect both substances, even in case of near-identical VUV spectra at almost full coelution. As a next step, spectra were subsequently removed from the reference library to simulate the scenario in which a novel substance was encountered for the first time in forensic case work. However, also in this situation the deconvolution software still detected the coelution. This work shows that a VUV library match score below 0.998 may serve as a warning that a novel substance may be present in a street sample.

stochprofML: stochastic profiling using maximum likelihood estimation in R

BACKGROUND

Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue.

RESULTS

We present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm's performance in simulation studies and present further application opportunities.

CONCLUSION

Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.

The power of modelling pulsatile profiles

The quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

Analysis of patchclamp recordings: model-free multiscale methods and software

Analysis of patchclamp recordings is often a challenging issue. We give practical guidance how such recordings can be analyzed using the model-free multiscale idealization methodology JSMURF, JULES, and HILDE. We provide an operational manual how to use the accompanying software available as an R-package and as a graphical user interface. This includes selection of the right approach and tuning of parameters. We also discuss advantages and disadvantages of model-free approaches in comparison to hidden Markov model approaches and explain how they complement each other.

Deconvolution of ultrasonic signals using a convolutional neural network

Successfully employing ultrasonic testing to distinguish a flaw in close proximity to another flaw or geometrical feature depends on the wavelength and the bandwidth of the ultrasonic transducer. This explains why the frequency is commonly increased in ultrasonic testing in order to improve the axial resolution. However, as the frequency increases, the penetration depth of the propagating ultrasonic waves is reduced due to an attendant increase in attenuation. The nondestructive testing research community is consequently very interested in finding methods that combine high penetration depth with high axial resolution. This work aims to improve the compromise between the penetration depth and the axial resolution by using a convolutional neural network to separate overlapping echoes in time traces in order to estimate the time-of-flight and amplitude. The originality of the proposed framework consists in its training of the neural network using data generated in simulations. The framework was validated experimentally to detect flat bottom holes in an aluminum block with a minimum depth corresponding to λ/4.

Thermally and optically stimulated luminescence properties of BeO dosimeter with double TL peak in the main dosimetric region

Beryllium oxide (BeO), which has a widely known single main TL peak over the main dosimetric region, is an important luminescent material widely used in personal, medical, and environmental dosimetry fields. The thermal and optically stimulated luminescence properties (TL and OSL) of the BeO dosimeter, supplied by a private company in Turkey (BeO_R) that yields a double peak in the main dosimetric region (100-300 °C; heating rate = 2 °C/s) were studied. The corresponding properties were compared to the commercially prevalent Thermalox 995 BeO (BeO_T). Multiple characterization techniques including x-ray diffraction analysis (XRD), energy dispersive x-ray spectroscopy (EDX), scanning electron microscope (SEM) image and electron spin resonance (ESR) were used to detect possible differences in TL and OSL signals. It was hypothesized that the calcination and or sintering process applied during the manufacturing process might lead to a double peak in the main dosimetric region of BeO_R TL signals. Moreover, dosimetric properties of this dosimeter such as reproducibility, dose-response, minimum detectable dose (MDD), thermal quenching, bleaching, and thermal stability in combination with annealing properties were comprehensively investigated. An analysis of the results shows that BeO_R has lower detection limits in TL than BeO_T. In contrast, BeO_T exhibited lower detection limits in the OSL signal than BeO_R. Although both dosimeters have dissimilarities in several aspects, both are appropriate for dosimetric research on health physics applications. Therefore, selecting the appropriate BeO dosimeter could be an important factor to consider when assessing accurate doses in studies. With this outline in mind and by investigating using different characterization methods, the available luminescence knowledge base of BeO dosimeters was expanded.

Casework applications of probabilistic genotyping methods for DNA mixtures that allow relationships between contributors

In both criminal cases and civil cases there is an increasing demand for the analysis of DNA mixtures involving relationships. The goal might be, for example, to identify the contributors to a DNA mixture where the unknown donors may be related, or to infer the relationship between individuals based on a DNA mixture. This paper applies a new approach to modelling and computation for DNA mixtures involving contributors with arbitrarily complex relationships to two real cases from the Spanish Forensic Police.

Reinjection flow field-flow fractionation method for nanoparticle quantitative analysis in unknown and complex samples

An analytical challenge that arises in environmental and food analysis is to quantify heterogeneous nanoparticles especially in polydisperse and complex samples. The method stated herein based on the reinjection asymmetrical flow field-flow fractionation (AF4 × AF4) coupled with inductively coupled plasma-mass spectrometer (ICP-MS) and statistical deconvolution allowed for identifying the molecular weight (Mw) and selenium abundance of the low Mw protein fractions (ca. < 132 kDa) in an unknown and complex sample (e.g., selenium-rich soybean protein isolates (Se-SPI)). A non-linear decay crossflow program was also developed to get better resolution and shorter elution time for both low and high Mw components. The concept of the reinjection method was based on the excellent ability for reducing sample complexity regarding the size fractionation, and peak reproducibility under the identical conditions of AF4 system. The standard protein mixture was used as a proof-of-principle sample. The results showed the underlying peaks predicted by the reinjection method were agreed with the separation result using the standard mixture (the relative standard deviation of peak locations < 1%), which indicated the reinjection method could provide an accurate assessment of the underlying peak number and location, and was promising to minimize the overfitting problem for statistic deconvolution. Interestingly, significant differences of Se abundance in protein fractions were observed in the low Mw range for Se-SPI, ranging from 0.28 to 1.66 cps/V with the Mw ranging from 13.75 kDa to 104.17 kDa, which indicated significant differences in the ability of binding Se for these selenium-rich proteins in Se-SPI.

Probabilistic interpretation of the Amelogenin locus

We describe a method to assign weights to genotype combinations at the Amelogenin locus. It is a typical practise in forensic laboratories that once the weight exceeds a threshold (such as 99 %), then they can be considered to be resolved enough to interpret (for example to load onto a database). We found that unless an individual is a clear major (or minor) contributor, the genotype weights do not typically exceed 99 % for any genotype. LRs have not been traditionally assigned for the Amelogenin locus and are small compared to an LR assigned for a modern day STR multiplex where, for a more discriminatory locus, the per locus LR for a resolved contributor could be in the order of tens or hundreds. The method described uses per contributor template values for a previously interpreted profile. The discrimination power is restricted, with a maximum possible LR of 2 for a fully resolved genotype, due to the limited number of alleles and hence genotypes and assuming equal proportions of genders in the population. However, it has a good power to exclude when the component is well resolved and non-concordant with a POI.

Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration

Here we present an analysis protocol for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data of the kidneys. It covers comprehensive steps to facilitate signal to contrast agent concentration mapping via T1 mapping and the calculation of renal perfusion and filtration parametric maps using model-free approaches, model free analysis using deconvolution, the Toft's model and a Bayesian approach.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Analysis of Intracellular Vesicles in B Lymphocytes: Antigen Traffic in the Spotlight

All eukaryotic cells are delimited by the plasma membrane, separating the cell from its environment. Two critical cellular pathways, the endocytic and the exocytic vesicle networks, shuttle material in and out the cell, respectively. The substantial development of cell biological imaging techniques, along with improved fluorescent probes and image analysis tools, has been instrumental in increasing our understanding of various functions and regulatory mechanisms of various intracellular vesicle subpopulations and their dynamics. Here, using B lymphocytes (B cells) as a model system, we provide a protocol for 3D analysis of the intracellular vesicle traffic in either fixed or living cells using spinning disk confocal microscopy. We also describe the usage of image deconvolution to improve the resolution, particularly important for vesicular networks in lymphocytes due to the small size of these cells. Lastly, we describe two types of quantitative analysis: vesicle distribution/clustering toward the microtubule organizing center (MTOC), and colocalization analysis with endolysosomal markers.

4D Widefield Fluorescence Imaging of Appressorium Morphogenesis by Magnaporthe oryzae

Fluorescence microscopy has become a widely used and indispensable tool for the M. oryzae research community, providing unique insight into appressorium formation and function. A common practice within the field is to acquire and present images of a number of different conidia, expressing a fluorescent fusion protein of interest, at various stages of infectious development, therein providing a representative "snapshot" of the population at a given point in time. Furthermore, these images typically show only a single focal plane through the specimen (2D) and therefore lack, often valuable, volumetric information. While this approach has its advantages, the continuous imaging of (multiple) single conidia in three dimensions (3D), and over time (4D), can provide additional insight into the spatial and temporal dynamics of fluorescent fusion proteins, and the subcellular structures and compartments they label, in living cells. Here we describe our typical workflow for the 4D live-cell imaging of appressorium morphogenesis in vitro using two-color widefield fluorescence microscopy and briefly outline some important considerations for strain construction, and downstream image processing and visualization.

Advances in Confocal Microscopy and Selected Applications

Over the last 30 years, confocal microscopy has emerged as a primary tool for biological investigation across many disciplines. The simplicity of use and widespread accessibility of confocal microscopy ensure that it will have a prominent place in biological imaging for many years to come, even with the recent advances in light sheet and field synthesis microscopy. Since these more advanced technologies still require significant expertise to effectively implement and carry through to analysis, confocal microscopy-based approaches still remain the easiest way for biologists with minimal imaging experience to address fundamental questions about how their systems are arranged through space and time. In this review, we discuss a number of advanced applications of confocal microscopy for probing the spatiotemporal dynamics of biological systems.

The analogy between DNA kinship and DNA mixture evaluation, with applications for the interpretation of likelihood ratios produced by possibly imperfect models

Two main applications of forensic DNA analysis are the investigation of possible relatedness and the investigation whether a person left DNA in a trace. Both of these are usually carried out by the calculation of likelihood ratios. In the kinship case, it is standard to let the likelihood ratio express the support in favour of the investigated relatedness versus no relatedness, and in the investigation of traces, one by default compares the hypothesis that the person of interest contributed DNA, versus that he is unrelated to any of the actual contributors. In both cases however, we can also view the probabilistic procedure as an inference of the profile of the person we look for: in other words, in both cases we carry out probabilistic genotyping. In this article we use this general analogy to develop various more specific analogies between kinship and mixture likelihood ratios. These analogies help to understand the concepts that play a role, and also to understand the importance of the statistical modeling needed for DNA mixtures. In this article, we apply our findings to consider what we can and cannot conclude from a likelihood ratio in favour of contribution to a mixed DNA profile, if that is computed by a model whose specifics are not entirely known to us, or where we do not know whether they provide a good description of the stochastic effects involved in the generation of DNA trace profiles. We show that, if unrelated individuals are adequately modeled, we can give bounds on how often LR's coming from certain types of black box models may arise, both for persons who are actual contributors and who are unrelated. In particular we show that no model, provided it satisfies basic requirements, can overestimate the evidence found for actual contributors both often and strongly.

Thermoluminescence glow-curve deconvolution using analytical expressions: A unified presentation

This study provides a unified presentation of thermoluminescence (TL) glow-curve deconvolution within the framework of the open source R package "tgcd", according to various analytical expressions that describe first-, second-, general-, and mixed-order kinetics as well as the recently developed semi-analytical expressions that derive from the one trap-one recombination center (OTOR) model that utilizes the Lambert W function or the Wright Omega function. We provide a comprehensive, flexible, convenient, and openly accessible program to analyze TL glow curves according to different models and expressions. The consistency of kinetic parameters determined using different model expressions was assessed using measured TL glow curve of CaF2:Dy. The performance of the computerized glow curve deconvolution (CGCD) method was also tested using simulated glow curves. Results revealed the benefits of comparing kinetic parameters determined from different model expressions and those obtained using experimental TL evaluation methods to assess the reliability of deconvolution results. The accuracy of the CGCD method is dependent upon both the model expressions used and the intrinsic trapping parameters of the TL material.

100 MHz bandwidth planar laser-generated ultrasound source for hydrophone calibration

High-frequency calibration of hydrophones is becoming increasingly important, both for clinical and scientific applications of ultrasound, and user safety. At present, the calibrations available routinely to the user community extend to 60 MHz. However, hydrophones that can measure beyond this are available, and ultrasonic fields often contain energy at higher frequencies, e.g., generated through nonlinear propagation of high-amplitude ultrasound used for therapeutic applications, and the increasing use of higher frequencies in imaging. Therefore, there is a need for calibrations up to at least 100 MHz, to allow ultrasonic fields to be accurately characterized, and the risk of harmful bioeffects to be properly assessed. Currently, sets of focused piezoelectric transducers are used to meet the pressure amplitude and bandwidth requirements of Primary Standard calibration facilities. However, when the frequency is high enough such that the size of the ultrasound focus becomes less than the hydrophone element's diameter, the uncertainty due to spatial averaging becomes significant, and can be as high as 20% at 100 MHz. As an alternate to piezoelectric transducers, a laser-generated ultrasound calibration source was designed, fabricated, and characterized. The source consists of an optically absorbing carbon-polymer nanocomposite excited by a large-diameter 1064 nm laser pulse of 2.6 ns duration. Peak pressure amplitudes of several Mega-Pascal were readily achievable, and the signal contained measurable frequency components up to 100 MHz. The variation in the pressure amplitudes was less than 2% from its mean over a three-hour test period. The ultrasound beam was sufficiently broad that the uncertainties due to spatial averaging were negligible.

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

Background

The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing.

Results

We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.

Conclusion

This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.