CryoEM ice images and labels used for VirtualIce
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ABSTRACT:
SUBMITTER: Alex J Noble
PROVIDER: EMPIAR-12287 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:BackgroundDe novo protein modeling approaches utilize 3-dimensional (3D) images derived from electron cryomicroscopy (CryoEM) experiments. The skeleton connecting two secondary structures such as α-helices represent the loop in the 3D image. The accuracy of the skeleton and of the detected secondary structures are critical in De novo modeling. It is important to measure the length along the skeleton accurately since the length can be used as a constraint in modeling the protein.ResultsWe have developed a novel computational geometric approach to derive a simplified curve in order to estimate the loop length along the skeleton. The method was tested using fifty simulated density images of helix-loop-helix segments of atomic structures and eighteen experimentally derived density data from Electron Microscopy Data Bank (EMDB). The test using simulated density maps shows that it is possible to estimate within 0.5 Å of the expected length for 48 of the 50 cases. The experiments, involving eighteen experimentally derived CryoEM images, show that twelve cases have error within 2 Å.ConclusionsThe tests using both simulated and experimentally derived images show that it is possible for our proposed method to estimate the loop length along the skeleton if the secondary structure elements, such as α-helices, can be detected accurately, and there is a continuous skeleton linking the α-helices.
| S-EPMC3953143 | biostudies-literature
Project description:High-content screening (HCS) provides an excellent tool to understand the mechanism of action of drugs on disease-relevant model systems. Careful selection of fluorescent labels (FLs) is crucial for successful HCS assay development. HCS assays typically comprise (a) FLs containing biological information of interest, and (b) additional structural FLs enabling instance segmentation for downstream analysis. However, the limited number of available fluorescence microscopy imaging channels restricts the degree to which these FLs can be experimentally multiplexed. In this article, we present a segmentation workflow that overcomes the dependency on structural FLs for image segmentation, typically freeing two fluorescence microscopy channels for biologically relevant FLs. It consists in extracting structural information encoded within readouts that are primarily biological, by fine-tuning pre-trained state-of-the-art generalist cell segmentation models for different combinations of individual FLs, and aggregating the respective segmentation results together. Using annotated datasets that we provide, we confirm our methodology offers improvements in performance and robustness across several segmentation aggregation strategies and image acquisition methods, over different cell lines and various FLs. It thus enables the biological information content of HCS assays to be maximized without compromising the robustness and accuracy of computational single-cell profiling.
| S-EPMC10951928 | biostudies-literature
Project description:Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.
| S-EPMC6309178 | biostudies-literature
Project description:Health claims on food labels are used by food manufacturers to inform consumers about the health effects of a product, and such claims can have notable effects on consumer preferences. According to regulatory definitions, health claims can be either worded or presented as images, but it is not clear under which conditions an image on a food label should be considered a health claim. This question has important practical implications, as the use of health claims is strictly regulated. The objective of this study was to determine how commonly images of the heart are used on food labels, and to investigate consumers' perceptions of products labelled with heart images, using different degrees of health relationships. Both a food supply study (N = 10,573 foods) and experiments with consumers (N = 1000) were performed in Slovenia. The use of heart imagery on food products was very common (9%). The consumer study was conducted using a web panel. Structure of the study population was comparable with Slovenian adult population (18-65 years), according to gender and age. The questionnaire was split into conjoint analysis with constructed elements, a choice-based task with real-life elements and a consumers' association task. The experiments showed that a heart image as part of the brand name itself-without an additional (worded) health claim-did not cause most consumers to relate it to health. However, consumers tended to strongly relate an image of the heart as part of a brand with health benefits, where the image was accompanied by a worded health claim or if the heart image was designed specifically to imply health benefits. We can conclude that the use of heart images was very common on food products, but references to health were less common. Without a health-related context, heart images could not be considered as a health claim.
| S-EPMC8002900 | biostudies-literature
Project description:Biomedical research is inseparable from the analysis of various histopathological images, and hematoxylin-eosin (HE)-stained images are one of the most basic and widely used types. However, at present, machine learning based approaches of the analysis of this kind of images are highly relied on manual labeling of images for training. Fully automated processing of HE-stained images remains a challenging task due to the high degree of color intensity, size and shape uncertainty of the stained cells. For this problem, we propose a fully automatic pixel-wise semantic segmentation method based on pseudo-labels, which concerns to significantly reduce the manual cell sketching and labeling work before machine learning, and guarantees the accuracy of segmentation. First, we collect reliable training samples in a unsupervised manner based on K-means clustering results; second, we use full mixup strategy to enhance the training images and to obtain the U-Net model for the nuclei segmentation from the background. The experimental results based on the meningioma pathology image dataset show that the proposed method has good performance and the pathological features obtained statistically based on the segmentation results can be used to assist in the clinical grading of meningiomas. Compared with other machine learning strategies, it can provide a reliable reference for clinical research more effectively.
| S-EPMC8815980 | biostudies-literature
Project description:Cryo-electron microscopy is an experimental technique that is able to produce 3D gray-scale images of protein molecules. In contrast to other experimental techniques, cryo-electron microscopy is capable of visualizing large molecular complexes such as viruses and ribosomes. At medium resolution, the positions of the atoms are not visible and the process cannot proceed. The medium-resolution images produced by cryo-electron microscopy are used to derive the atomic structure of the proteins in de novo modeling. The skeletons of the 3D gray-scale images are used to interpret important information that is helpful in de novo modeling. Unfortunately, not all features of the image can be captured using a single segmentation. In this paper, we present a segmentation-free approach to extract the gray-scale curve-like skeletons. The approach relies on a novel representation of the 3D image, where the image is modeled as a graph and a set of volume trees. A test containing 36 synthesized maps and one authentic map shows that our approach can improve the performance of the two tested tools used in de novo modeling. The improvements were 62 and 13 percent for Gorgon and DP-TOSS, respectively.
| S-EPMC4104753 | biostudies-literature
Project description:Newly developed direct electron detection cameras have a high image output frame rate that enables recording dose fractionated image stacks of frozen hydrated biological samples by electron cryomicroscopy (cryoEM). Such novel image acquisition schemes provide opportunities to analyze cryoEM data in ways that were previously impossible. The file size of a dose fractionated image stack is 20-60 times larger than that of a single image. Thus, efficient data acquisition and on-the-fly analysis of a large number of dose-fractionated image stacks become a serious challenge to any cryoEM data acquisition system. We have developed a computer-assisted system, named UCSFImage4, for semi-automated cryo-EM image acquisition that implements an asynchronous data acquisition scheme. This facilitates efficient acquisition, on-the-fly motion correction, and CTF analysis of dose fractionated image stacks with a total time of ∼60s/exposure. Here we report the technical details and configuration of this system.
| S-EPMC4633327 | biostudies-literature
Project description:ImportanceClinical estimation of hair density has an important role in assessing and tracking the severity and progression of alopecia, yet to the authors' knowledge, no automation currently exists for this process. While some algorithms have been developed to assess alopecia presence on a binary level, their scope has been limited by focusing on a re-creation of the Severity of Alopecia Tool (SALT) score for alopecia areata (AA). Yet hair density loss is common to all alopecia forms, and an evaluation of that loss is used in established scoring systems for androgenetic alopecia (AGA), central centrifugal cicatricial alopecia (CCCA), and many more.ObjectiveTo develop and validate a new model, HairComb, to automatically compute the percentage hair loss from images regardless of alopecia subtype.Design, setting, and participantsIn this research study to create a new algorithmic quantification system for all hair loss, computational imaging analysis and algorithm design using retrospective image data collection were performed. This was a multicenter study, where images were collected at the Children's Hospital of Philadelphia, University of Pennsylvania (Penn), and via a Penn Dermatology web interface. Images were collected from 2015 to 2021, and they were analyzed from 2019 to 2021.Main outcomes and measuresScoring systems correlation analysis was measured by linear and logarithmic regressions. Algorithm performance was evaluated using image segmentation accuracy, density probability regression error, and average percentage hair loss error for labeled images, and Pearson correlation for manual scores.ResultsThere were 404 participants aged 2 years and older that were used for designing and validating HairComb. Scoring systems correlation analysis was performed for 250 participants (70.4% female; mean age, 35.3 years): 75 AGA, 66 AA, 50 CCCA, 27 other alopecia diagnoses (frontal fibrosing alopecia, lichen planopilaris, telogen effluvium, etc), and 32 unaffected scalps without alopecia. Scoring systems showed strong correlations with underlying percentage hair loss, with coefficient of determination R2 values of 0.793 and 0.804 with respect to log of percentage hair loss. Using HairComb, 92% accuracy, 5% regression error, 7% hair loss difference, and predicted scores with errors comparable to annotators were achieved.Conclusions and relevanceIn this research study,it is shown that an algorithm quantitating percentage hair loss may be applied to all forms of alopecia. A generalizable automated assessment of hair loss would provide a way to standardize measurements of hair loss across a range of conditions.
| S-EPMC9857252 | biostudies-literature
Project description:IntroductionPictorial warning labels (PWL) that use photographs and the personal details of real people whose health has been affected by smoking (testimonial PWL) provide factual information about the consequences of tobacco use.MethodsNine hundred and twenty-four adult current smokers participated in an online experiment that tested responses to four types of warning labels: (1) non-testimonial text warning labels (currently on packs in the United States); (2) non-testimonial PWL (previously proposed by the United States Food and Drug Administration); (3) image only testimonial PWL (created for study); (4) image + personal details testimonial PWL (created for study). Participants were randomly assigned to condition and then exposed to up to five warning labels addressing different health effects. Differences between conditions were assessed using emotional responses and a set of intention measures immediately following exposure, and self-reported behavior change at 5-week follow-up.ResultsCompared to the non-testimonial text warning labels, all PWL elicited stronger emotional responses and intentions to forgo cigarettes and avoid the warning labels. Non-testimonial PWL and image + personal details testimonial PWL elicited stronger intentions to quit, whereas image only testimonial PWL generated a greater amount of quitting activity in the weeks following exposure. There were no significant differences in responses when comparing the non-testimonial PWL with both types of testimonial PWL.ConclusionsPWL that use images of real people convey factual information about the health effects of tobacco use. These testimonial PWL may be a promising alternative to the images previously proposed for use on PWL in the United States.ImplicationsIn the United States, the PWL developed by the Food and Drug Administration (FDA) in 2011 were found by the courts to be unconstitutional, in part because they were deemed to present an opinion rather than fact. Findings from this experimental study indicate that PWL that use the images and personal details of real people to convey factual information about the health effects of tobacco use may satisfy the FDA's requirement for a set of PWL that (1) have the potential to positively impact the determinants of smoking cessation behavior, (2) meet legislative requirements under the Family Smoking Prevention and Tobacco Control Act and (3) may be more acceptable to the courts than the previously proposed and now dismissed PWL that carried non-factual images.
| S-EPMC5896434 | biostudies-literature