Landscape of human organoids: Ideal model in clinics and research.

In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

Elucidating hepatocellular carcinoma progression: a novel prognostic miRNA-mRNA network and signature analysis.

There is increasing evidence that miRNAs play an important role in the prognosis of HCC. There is currently a lack of acknowledged models that accurately predict patient prognosis. The aim of this study is to create a miRNA-based model to precisely forecast a patient's prognosis and a miRNA-mRNA network to investigate the function of a targeted mRNA. TCGA miRNA dataset and survival data of HCC patients were downloaded for differential analysis. The outcomes of variance analysis were subjected to univariate and multivariate Cox regression analyses and LASSO analysis. We constructed and visualized prognosis-related models and subsequently used violin plots to probe the function of miRNAs in tumor cells. We predicted the target mRNAs added those to the String database, built PPI protein interaction networks, and screened those mRNA using Cytoscape. The hub mRNA was subjected to GO and KEGG analysis to determine its biological role. Six of them were associated with prognosis: hsa-miR-139-3p, hsa-miR-139-5p, hsa-miR-101-3p, hsa-miR-30d-5p, hsa-miR-5003-3p, and hsa-miR-6844. The prognostic model was highly predictive and consistently performs, with the C index exceeding 0.7 after 1, 3, and 5 years. The model estimated significant differences in the Kaplan-Meier plotter and the model could predict patient prognosis independently of clinical indicators. A relatively stable miRNA prognostic model for HCC patients was constructed, and the model was highly accurate in predicting patients with good stability over 5 years. The miRNA-mRNA network was constructed to explore the function of mRNA.

Technical strategy for monozygotic twin discrimination by single-nucleotide variants.

Monozygotic (MZ) twins are theoretically genetically identical. Although they are revealed to accumulate mutations after the zygote splits, discriminating between twin genomes remains a formidable challenge in the field of forensic genetics. Single-nucleotide variants (SNVs) are responsible for a substantial portion of genetic variation, thus potentially serving as promising biomarkers for the identification of MZ twins. In this study, we sequenced the whole genome of a pair of female MZ twins when they were 27 and 33 years old to approximately 30 × coverage using peripheral blood on an Illumina NovaSeq 6000 Sequencing System. Potentially discordant SNVs supported by whole-genome sequencing were validated extensively by amplicon-based targeted deep sequencing and Sanger sequencing. In total, we found nine bona fide post-twinning SNVs, all of which were identified in the younger genomes and found in the older genomes. None of the SNVs occurred within coding exons, three of which were observed in introns, supported by whole-exome sequencing results. A double-blind test was employed, and the reliability of MZ twin discrimination by discordant SNVs was endorsed. All SNVs were successfully detected when input DNA amounts decreased to 0.25 ng, and reliable detection was limited to seven SNVs below 0.075 ng input. This comprehensive analysis confirms that SNVs could serve as cost-effective biomarkers for MZ twin discrimination.

Altered intestinal microbiota enhances adenoid hypertrophy by disrupting the immune balance.

Introduction: Adenoid hypertrophy (AH) is a common upper respiratory disorder in children. Disturbances of gut microbiota have been implicated in AH. However, the interplay of alteration of gut microbiome and enlarged adenoids remains elusive. Methods: 119 AH children and 100 healthy controls were recruited, and microbiome profiling of fecal samples in participants was performed using 16S rRNA gene sequencing. Fecal microbiome transplantation (FMT) was conducted to verify the effects of gut microbiota on immune response in mice. Results: In AH individuals, only a slight decrease of diversity in bacterial community was found, while significant changes of microbial composition were observed between these two groups. Compared with HCs, decreased abundances of Akkermansia, Oscillospiraceae and Eubacterium coprostanoligenes genera and increased abundances of Bacteroides, Faecalibacterium, Ruminococcus gnavus genera were revealed in AH patients. The abundance of Bacteroides remained stable with age in AH children. Notably, a microbial marker panel of 8 OTUs were identified, which discriminated AH from HC individuals with an area under the curve (AUC) of 0.9851 in the discovery set, and verified in the geographically different validation set, achieving an AUC of 0.9782. Furthermore, transfer of mice with fecal microbiota from AH patients dramatically reduced the proportion of Treg subsets within peripheral blood and nasal-associated lymphoid tissue (NALT) and promoted the expansion of Th2 cells in NALT. Conclusion: These findings highlight the effect of the altered gut microbiota in the AH pathogenesis.

Cross-Platform Application of DNA Methylation Age Estimation Model in Eastern Chinese Han Population.

OBJECTIVES: To evaluate the forensic application value of an age estimation model based on DNA methylation in eastern Chinese Han population, and to provide a theoretical basis for exploring age estimation models suitable for different detection platforms. METHODS: According to the 6 age-related methylation sites in the published blood DNA methylation age estimation models of Chinese Han population, the DNA methylation level of 48 samples was detected by pyrosequencing and next-generation sequencing (NGS). After submitting DNA methylation levels to the age estimation model, the DNA methylation ages were predicted and compared with their real ages. RESULTS: The 6 DNA methylation sites in both detection techniques were age-related, with an R2 of 0.85 and a median absolute deviation (MAD) of 4.81 years when using pyrosequencing；with an R2 of 0.84 and MAD of 4.41 years when using NGS. CONCLUSIONS: The blood DNA methylation age estimation model can be used under pyrosequencing and multi-purpose regional methylation enrichment sequencing technology based on NGS and it can accurately estimate the age.

Epigenetic regulation of human-specific gene expression in the prefrontal cortex.

BACKGROUND: Changes in gene expression levels during brain development are thought to have played an important role in the evolution of human cognition. With the advent of high-throughput sequencing technologies, changes in brain developmental expression patterns, as well as human-specific brain gene expression, have been characterized. However, interpreting the origin of evolutionarily advanced cognition in human brains requires a deeper understanding of the regulation of gene expression, including the epigenomic context, along the primate genome. Here, we used chromatin immunoprecipitation sequencing (ChIP-seq) to measure the genome-wide profiles of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 acetylation (H3K27ac), both of which are associated with transcriptional activation in the prefrontal cortex of humans, chimpanzees, and rhesus macaques. RESULTS: We found a discrete functional association, in which H3K4me3HP gain was significantly associated with myelination assembly and signaling transmission, while H3K4me3HP loss played a vital role in synaptic activity. Moreover, H3K27acHP gain was enriched in interneuron and oligodendrocyte markers, and H3K27acHP loss was enriched in CA1 pyramidal neuron markers. Using strand-specific RNA sequencing (ssRNA-seq), we first demonstrated that approximately 7 and 2% of human-specific expressed genes were epigenetically marked by H3K4me3HP and H3K27acHP, respectively, providing robust support for causal involvement of histones in gene expression. We also revealed the co-activation role of epigenetic modification and transcription factors in human-specific transcriptome evolution. Mechanistically, histone-modifying enzymes at least partially contribute to an epigenetic disturbance among primates, especially for the H3K27ac epigenomic marker. In line with this, peaks enriched in the macaque lineage were found to be driven by upregulated acetyl enzymes. CONCLUSIONS: Our results comprehensively elucidated a causal species-specific gene-histone-enzyme landscape in the prefrontal cortex and highlighted the regulatory interaction that drove transcriptional activation.

Age Estimation Based on DNA Methylation and Its Application Prospects in Forensic Medicine.

With the improvement of DNA methylation detection techniques, studies on age-related methylation sites have found more age-specific ones across tissues, which improves the sensitivity and accuracy of age estimation. In addition, the establishment of various statistical models also provides a new direction for the age estimation of tissues from different sources. This review summarizes the related studies of age estimation based on DNA methylation from the aspects of detection technology, age-related cytosine phosphate guanine site and model selection in recent years.

Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy.

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets. Additionally, high-dimensional data analysis poses a challenge to enhancing the classification performance of the different feature selection methods. This study presents a kernel partial least squares feature selection method on the basis of the enhanced maximum weight minimum redundancy algorithm to simplify the calculation and improve the classification accuracy of high-dimensional datasets. By introducing a weight factor, the correlation between the maximum weight and the minimum redundancy in the evaluation criterion can be adjusted to develop an improved maximum weight minimum redundancy method. In this study, the proposed KPLS feature selection method considers the redundancy between the features and the feature weighting between any feature and a class label in different datasets. Moreover, the feature selection method proposed in this study has been tested regarding its classification accuracy on data containing noise and several datasets. The experimental findings achieved using different datasets explore the feasibility and effectiveness of the proposed method which can select an optimal feature subset and obtain great classification performance based on three different metrics when compared with other feature selection methods.

Proteomic Difference Analysis of Whole Blood and Bloodstains.

OBJECTIVES: To study the changes of protein levels in peripheral blood after it dried. METHODS: The proteins from whole blood and bloodstains were detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and normalized by the label-free quantification (LFQ) method. The differential proteins were analyzed by using R 4.2.1 software, limma and edgeR package. The analysis of biological function, signaling pathway and subcellular localization for the differential proteins was then performed. RESULTS: A total of 623 and 596 proteins were detected in whole blood and bloodstains, respectively, of which 31 were statistically significant in the quantitative results, including 10 up-regulated and 21 down-regulated proteins in bloodstains. CONCLUSIONS: The protein abundances in whole blood and bloodstains are highly correlated, and the variation of protein abundances may be related to the changes of endogenous and structural proteins in cells. The application of proteomics technology can assist the screening and identification of protein biomarkers, thereby introducing new biomarkers for forensic research.

Research on the Attenuation Characteristics of High-Frequency Elastic Waves in Rock-Like Material.

In order to study the frequency-dependent attenuation characteristics of high-frequency elastic waves in rock-like materials, we conducted high-frequency elastic wave attenuation experiments on marble, granite, and red sandstone rods, and investigated the frequency dependence of the attenuation coefficient of high-frequency elastic waves and the frequency dependence of the attenuation of specific frequency components in elastic waves. The results show that, for the whole waveform packet of the elastic wave signal, the attenuation coefficient and the elastic wave frequency have an approximate power relationship, with the exponents of this power function being 0.408, 0.420, and 0.384 for marble, granite, and red sandstone, respectively, which are close to 1/2 the exponent value obtained theoretically by the Kelvin-Voigt viscoelastic model. However, when the specific frequency components are tracked during the elastic wave propagation, the exponents of the power relationship between the attenuation coefficient and frequency are 0.982, 1.523, and 0.860 for marble, granite, and red sandstone, respectively, which indicate that the relationship between the attenuation coefficient and frequency is rock-type dependent. Through the analysis of rock microstructure, we demonstrate that this rock-type-dependent relationship is mainly caused by the scattering attenuation component due to the small wavelength of the high-frequency elastic wave. Therefore, the scattering attenuation component may need to be considered when the Kelvin-Voigt model is used to describe high-frequency elastic wave attenuation in rock-like materials. The results of this research are of good help for further understanding the attenuation characteristics of high-frequency elastic waves in rock-like materials.

Efficiency Analysis of Uncle-Nephew Relationship Identification by Increasing STR Markers and Adding Reference Samples.

OBJECTIVES: To estimate the system efficiency of uncle-nephew relationship identification by increasing STR markers and adding reference samples based on the test results of simulated data and real samples, so as to provide references for selecting the appropriate number of STRs and reference samples for uncle-nephew relationship identification. METHODS: Five common models of uncle-nephew relationship identification were constructed by adding different reference samples. In each model, the likelihood ratio (LR) for 10 000 pairs of uncle-nephew relationships and 10 000 pairs of unrelated individuals were simulated by detecting 19, 39 or 55 STRs, and the system efficiency at different thresholds was simulated. The samples of the Han population in Zhejiang were collected, and 55 autosomal STRs were obtained by using SiFaSTRTM 23plex kit, Goldeneye® DNA ID 22NC kit and AGCU 21+1 PCR amplification kit. When 19, 39 and 55 STRs were detected, the LR of each model and system efficiency under different thresholds were calculated and compared with the simulation results. RESULTS: Under the same detection system, the calculated results of simulated data and corresponding true samples were basically consistent. In the same model, there was a positive correlation between the system efficiency of uncle-nephew relationship identification and the number of STRs detected. Moreover, the system efficiency of introducing relatives was higher than identifying only two individuals. The order of preference for the introduction of relatives was the full sibling (or mother) of the uncle and the full sibling (or mother) of the nephew. CONCLUSIONS: The system efficiency of uncle-nephew relationship identification could be improved by increasing the number of STRs and introducing known relatives, which would provide the basis for selecting the most appropriate detection system and reference individuals in actual cases.

Study of the Effects on Mn, Pb, and Zn Solidification in Soil by a Mixed Curing Agent of Modified Diatomite.

In order to improve the application scale of diatomite in the remediation of heavy metal-contaminated soil in non-ferrous metal mining areas, the preparation of the modified diatomite-combined curing agent and its stabilizing effect on manganese (Mn), lead (Pb), and zinc (Zn) were systematically studied in non-ferrous metal tailing soil in this paper. The results showed that compared with that in natural diatomite (DE), the contents of available Mn in soil treated by acid- and alkali-modified diatomite samples (C-D and Na-D) were 18.82 and 25.93% lower, respectively, and the content of available Zn in Na-D was significantly lower, 6.71%, than that in DE. Further research showed that modified diatomite combined with quicklime (CaO) and hydroxyapatite (HAP) could significantly improve the solidification effect of soil heavy metals. Compared with that in single modified diatomite, the contents of available Mn, Pb, and Zn in the mixed curing agent-treated soil decreased by 23.59-46.32, 5.88-47.93, and 5.37-10.68%, respectively. The final pot test showed that the mixed curing agent of modified diatomite had no significant effect on the growth of plants, but it could reduce the Mn, Pb, and Zn accumulation in the upper and lower parts of plants, which is because the acid-soluble and reducible heavy metals in soil transform into an oxidizable and residual state, which reduces the mobility of heavy metals.

Improving the system power of complex kinship analysis by combining multiple systems.

Complex kinship analysis is a critical issue in forensic genetics. To address this issue, 55 STRs and 94 SNPs collected from four commercial forensic typing kits [three kits were based on a capillary electrophoresis (CE) platform and one was based on a next-generation sequencing (NGS) platform] were employed to test the system power for 2nd-degree and 3rd-degree kinship analysis. To measure the kinship index in related individuals, likelihood ratios (LRs) were calculated based on length and sequence polymorphism information (LRlength and LRsequence, respectively) from simulation as well as true pedigree samples. LRs calculated based on sequence information are generally higher than those based on length information. The sensitivity, specificity, and effectiveness to distinguish the 2nd- and 3rd-degree kinship were estimated from four marker sets with different numbers of markers. As expected, system power for kinship analysis improved by increasing the number of markers and using LRsequence, instead of LRlength. Furthermore, the system power based on 55 STRs from the CE platform is equal to the 40 STRs and 94 SNPs from one CE kit and the kit based on NGS platform for both 2nd-degree and 3rd-degree kinship analysis. For discrimination of 2nd-degree kinship, the system effectiveness is 86.63% with an error ratio < 0.01 using the 55 STRs from the CE platform. Using sequence information from the 55 STRs and 94 SNPs, the system effectiveness is 94.43%, with an error ratio < 0.001 for 2nd-degree kinship analysis and 64.34% with an error ratio < 0.05 for 3rd-degree kinship analysis, indicating that these markers are powerful for 2nd-degree kinship analysis and can be used for 3rd-degree kinship analysis.

Investigation on Rupture Initiation and Propagation of Traffic Tunnel under Seismic Excitation Based on Acoustic Emission Technology.

Traffic tunnels are important engineering structures in transportation, and their stability is critical to traffic safety. In particular, when these tunnels are in an earthquake-prone area, the rupture process under seismic excitation needs to be studied in depth for safer tunnel design. In this paper, based on a construction project on the Nairobi-Malaba railway in East Africa, a laboratory shaking table test with 24 working cases of seismic excitation on a mountain tunnel is designed, and the acoustic emission (AE) technique is employed to investigate the tunnel rupture process. The results show that the high frequency components between 20 and 30 kHz of AE signals are the tunnel rupturing signals under the seismic excitation under such conditions. The tunnel vault and the arch foot are prone to rupture during the seismic excitation, and the initial rupture in the arch foot and vault of the tunnel occur under the horizontal and vertical Kobe wave seismic excitation, respectively, with a maximum acceleration of 0.4 g. After the rupture initiation, the tunnel arch foot continues to rupture in the subsequent working cases regardless of whether the excitation direction is horizontal or vertical, while the tunnel vault does not rupture continuously with the implementation of the subsequent excitations. Moreover, the Kobe seismic wave has a higher degree of damage potential to underground structures than the El seismic wave.

The influence of friction on the determination of rock fracture toughness.

The accurate determination of fracture toughness (KIc) in rock is of great significance to the study of rock fracture characteristics. However, the effect of contact friction in the loading process on the test results of fracture toughness is rarely considered, and the tested KIc in previous studies is generally higher than the inherent KIc of the material. Based on the ABAQUS software, the influence of friction on the test results of KIc was investigated under different material elastic moduli, loading conditions and specimen sizes by the finite element analysis in this study. The results show that for the specimen with the notch semi-circular bend configuration, if the presence of friction is considered, the changes of load level, the span of roller support and the specimen size can significantly affect the test results of fracture toughness, except the elastic modulus of the material; if the influence of friction is not considered, there will be a large deviation in the research results of the size effect of fracture toughness in rock. As the friction coefficient increases, the estimated value of the fracture energy increases, while the fracture process zone length decreases for the specimen with an infinite size.

Evaluating the effects of whole genome amplification strategies for amplifying trace DNA using capillary electrophoresis and massive parallel sequencing.

To draw robust conclusions when trace DNA samples are detected in complex cases, it is essential to successfully recover and genotype short tandem repeats (STRs) from trace DNA. However, obtaining complete STR profiles by the conventional polymerase chain reaction-capillary electrophoresis (PCR-CE) method is generally difficult as trace DNA is often less than 100 pg. Previous studies have proven that through whole-genome amplification (WGA), the yield of DNA from trace DNA samples could be improved. In this study, we used two WGA kits, namely, REPLI-g® Single Cell kit and MALBAC® Single Cell DNA Quick-Amp Kit (hereafter referred to as REPLI and MALBAC), to amplify DNA samples with a series of dilutions (from 5.00 ng/µL to 0.391 pg/µL). Typing of STR markers in samples with and without WGA were then performed on a CE platform by the application of Goldeneye® DNA ID System 20 A kit, as well as directly calling sequences from massive parallel sequencing (MPS) for WGA samples with 1.00 ng, 125 pg and 25.0 pg as DNA inputs. Quantification results demonstrated that the yield of samples with WGA could reach the microgram level. The amplification fold was at least > 2000 and > 200 for REPLI and MALBAC, respectively. CE results showed that the number of correctly called loci was improved for trace DNA after WGA when the DNA inputs were lower than 25.0 pg for REPLI and 6.25 pg for MALBAC, respectively. WGA remarkably improved the percentage of called loci with DNA inputs lower than 50.0 pg, although poor performance in repeatability was observed. MPS results suggested that the correctly called loci calculated by MPS reads were mostly more than those calculated by CE, particularly for those of short length, implying MPS of samples after WGA is worth testing in the future. In conclusion, WGA has the potential usability for forensic trace DNA analysis at the single-cell level with good fidelity, although its repeatability requires further improvement.

Subwavelength ultrasonic imaging using a deep convolutional neural network trained on structural noise.

Subwavelength ultrasonic imaging (SUI) can detect subwavelength flaws beyond the diffraction limit, however, SUI sometimes fails to clearly reveal flaws in C-scans when the signal-to-noise ratio (SNR) is low. In this work, a convolutional neural network (CNN) that takes structural noise into account is developed for SUI to distinguish flaw echoes from structural noise. The network contains a regression CNN for learning features from the structural noise and a learnable soft thresholding layer for classification. Experiments show that the proposed method performs well for imaging subwavelength flaws at different depths and of different sizes. It achieved an F1 score of 97.69 ± 1.56% in detecting flaws as compared to the enhanced ultrasonic flaw detection method with time-dependent threshold. As an example of general application of the method, we also performed SUI on natural flaws in a spheroidal graphite cast iron specimen. The results show that the method can achieve SUI without a theoretical backscattering model and is not limited by noise distribution, multiple scattering, or complex microstructures. Furthermore, the network does not need to prepare flaw echoes for training.