No-reference quality assessment for image-based assessment of economically important tropical woods.
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ABSTRACT:
SUBMITTER: Rajagopal H
PROVIDER: S-EPMC7236984 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Salvia splendens Ker-Gawler, scarlet or tropical sage, is a tender herbaceous perennial widely introduced and seen in public gardens all over the world. With few molecular resources, breeding is still restricted to traditional phenotypic selection, and the genetic mechanisms underlying phenotypic variation remain unknown. Hence, a high-quality reference genome will be very valuable for marker-assisted breeding, genome editing, and molecular genetics. We generated 66 Gb and 37 Gb of raw DNA sequences, respectively, from whole-genome sequencing of a largely homozygous scarlet sage inbred line using Pacific Biosciences (PacBio) single-molecule real-time and Illumina HiSeq sequencing platforms. The PacBio de novo assembly yielded a final genome with a scaffold N50 size of 3.12 Mb and a total length of 808 Mb. The repetitive sequences identified accounted for 57.52% of the genome sequence, and 54,008 protein-coding genes were predicted collectively with ab initio and homology-based gene prediction from the masked genome. The divergence time between S. splendens and Salvia miltiorrhiza was estimated at 28.21 million years ago (Mya). Moreover, 3,797 species-specific genes and 1,187 expanded gene families were identified for the scarlet sage genome. We provide the first genome sequence and gene annotation for the scarlet sage. The availability of these resources will be of great importance for further breeding strategies, genome editing, and comparative genomics among related species.
| S-EPMC6030905 | biostudies-literature
Project description:PurposeTo develop and characterize an algorithm that mimics human expert visual assessment to quantitatively determine the quality of three-dimensional (3D) whole-heart MR images.Materials and methodsIn this study, 3D whole-heart cardiac MRI scans from 424 participants (average age, 57 years ± 18 [standard deviation]; 66.5% men) were used to generate an image quality assessment algorithm. A deep convolutional neural network for image quality assessment (IQ-DCNN) was designed, trained, optimized, and cross-validated on a clinical database of 324 (training set) scans. On a separate test set (100 scans), two hypotheses were tested: (a) that the algorithm can assess image quality in concordance with human expert assessment as assessed by human-machine correlation and intra- and interobserver agreement and (b) that the IQ-DCNN algorithm may be used to monitor a compressed sensing reconstruction process where image quality progressively improves. Weighted κ values, agreement and disagreement counts, and Krippendorff α reliability coefficients were reported.ResultsRegression performance of the IQ-DCNN was within the range of human intra- and interobserver agreement and in very good agreement with the human expert (R 2 = 0.78, κ = 0.67). The image quality assessment during compressed sensing reconstruction correlated with the cost function at each iteration and was successfully applied to rank the results in very good agreement with the human expert.ConclusionThe proposed IQ-DCNN was trained to mimic expert visual image quality assessment of 3D whole-heart MR images. The results from the IQ-DCNN were in good agreement with human expert reading, and the network was capable of automatically comparing different reconstructed volumes.Supplemental material is available for this article.© RSNA, 2020.
| S-EPMC8082371 | biostudies-literature
Project description:BACKGROUND: Faba bean (Vicia faba L.) is among the earliest domesticated crops from the Near East. Today this legume is a key protein feed and food worldwide and continues to serve an important role in culinary traditions throughout Middle East, Mediterranean region, China and Ethiopia. Adapted to a wide range of soil types, the main faba bean breeding objectives are to improve yield, resistance to biotic and abiotic stresses, seed quality and other agronomic traits. Genomic approaches aimed at enhancing faba bean breeding programs require high-quality genetic linkage maps to facilitate quantitative trait locus analysis and gene tagging for use in a marker-assisted selection. The objective of this study was to construct a reference consensus map in faba bean by joining the information from the most relevant maps reported so far in this crop. RESULTS: A combination of two approaches, increasing the number of anchor loci in diverse mapping populations and joining the corresponding genetic maps, was used to develop a reference consensus map in faba bean. The map was constructed from three main recombinant inbreed populations derived from four parental lines, incorporates 729 markers and is based on 69 common loci. It spans 4,602 cM with a range from 323 to 1041 loci in six main linkage groups or chromosomes, and an average marker density of one locus every 6 cM. Locus order is generally well maintained between the consensus map and the individual maps. CONCLUSION: We have constructed a reliable and fairly dense consensus genetic linkage map that will serve as a basis for genomic approaches in faba bean research and breeding. The core map contains a larger number of markers than any previous individual map, covers existing gaps and achieves a wider coverage of the large faba bean genome as a whole. This tool can be used as a reference resource for studies in different genetic backgrounds, and provides a framework for transferring genetic information when using different marker technologies. Combined with syntenic approaches, the consensus map will increase marker density in selected genomic regions and will be useful for future faba bean molecular breeding applications.
| S-EPMC3880837 | biostudies-other
Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history traitM-bM-^@M-^Ys stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress. We used a one-color microarray design where each sample was hybridized to a single array
2014-02-01 | E-GEOD-52393 | biostudies-arrayexpress
Project description:This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history trait’s stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress.
2014-02-01 | GSE52393 | GEO
Project description:Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.
| S-EPMC5856621 | biostudies-literature
Project description:Microsatellite (or simple sequence repeat, SSR) markers are highly informative DNA markers often used in conservation genetic research. Next-generation sequencing enables efficient development of large numbers of SSR markers at lower costs. Boswellia papyrifera is an economically important tree species used for frankincense production, an aromatic resinous gum exudate from bark. It grows in dry tropical forests in Africa and is threatened by a lack of rejuvenation. To help guide conservation efforts for this endangered species, we conducted an analysis of its genomic DNA sequences using Illumina paired-end sequencing. The genome size was estimated at 705 Mb per haploid genome. The reads contained one microsatellite repeat per 5.7 kb. Based on a subset of these repeats, we developed 46 polymorphic SSR markers that amplified 2-12 alleles in 10 genotypes. This set included 30 trinucleotide repeat markers, four tetranucleotide repeat markers, six pentanucleotide markers and six hexanucleotide repeat markers. Several markers were cross-transferable to Boswellia pirrotae and B. popoviana. In addition, retrotransposons were identified, the reads were assembled and several contigs were identified with similarity to genes of the terpene and terpenoid backbone synthesis pathways, which form the major constituents of the bark resin.
| S-EPMC4433549 | biostudies-literature
Project description:In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.
| S-EPMC7595386 | biostudies-literature
Project description:Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study.
| S-EPMC5666910 | biostudies-literature
Project description:PurposeThe assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs).MethodsWe developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations.ResultsExperimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier.ConclusionsOur study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps.Translational relevanceThis study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.
| S-EPMC10996994 | biostudies-literature