scholarly journals YeaZ: A convolutional neural network for highly accurate, label-free segmentation of yeast microscopy images

2020 ◽  
Author(s):  
Nicola Dietler ◽  
Matthias Minder ◽  
Vojislav Gligorovski ◽  
Augoustina Maria Economou ◽  
Denis Alain Henri Lucien Joly ◽  
...  
Keyword(s):  
Web Application ◽  
Large Scale ◽  
High Reliability ◽  
Model Organism ◽  
A Cell ◽  
New Biology ◽  
Stressed Cells ◽  
Time Courses ◽  
Mutant Cells ◽  
Microscopy Images

AbstractThe processing of microscopy images constitutes a bottleneck for large-scale experiments. A critical step is the establishment of cell borders (‘segmentation’), which is required for a range of applications such as growth or fluorescent reporter measurements. For the model organism budding yeast (Saccharomyces cerevisiae), a number of methods for segmentation exist. However, in experiments involving multiple cell cycles, stress, or various mutants, cells crowd or exhibit irregular visible features, which necessitate frequent manual corrections. Furthermore, budding events are visually subtle but important to detect. Convolutional neural networks (CNNs) have been successfully employed for a range of image processing applications. They require large, diverse training sets. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10’000 cells) including mutants, stressed cells, and time courses, ii) a corresponding U-Net-based CNN, iii) a Python-based graphical user interface (GUI) to efficiently use the system, and iv) a web application to test it (www.quantsysbio.com). A key feature is a cell-cell boundary test which avoids the need for additional input from fluorescent channels. A bipartite graph matching algorithm tracks cells in time with high reliability. Our network is highly accurate and outperforms existing methods on benchmark images recorded by others, suggesting it transfers well to other conditions. Furthermore, new buds are detected early with high reliability. We apply the system to detect differences in geometry between wild-type and cyclin mutant cells. Our results indicate that morphogenesis control occurs unexpectedly early in the cell cycle and is gradual, demonstrating how the efficient processing of large numbers of cells uncovers new biology. Our system can serve as a resource to the community, expanded continuously with new images. Furthermore, the techniques we develop here are likely to be useful for other organisms as well.The identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. Here, we present i) the first set of publicly available, high-quality segmented yeast images (>10’000 cells) including mutants, stressed cells, and time courses, ii) a corresponding convolutional neural network (CNN), iii) a graphical user interface and a web application (www.quantsysbio.com) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient, large-scale image processing uncovers new biology, we analyzed the geometries of ≈2200 wild-type and cyclin mutant cells and found that morphogenesis control occurs unexpectedly early and gradually.

scholarly journals A convolutional neural network segments yeast microscopy images with high accuracy

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicola Dietler ◽  
Matthias Minder ◽  
Vojislav Gligorovski ◽  
Augoustina Maria Economou ◽  
Denis Alain Henri Lucien Joly ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Web Application ◽  
Large Scale ◽  
Model Organism ◽  
Fluorescent Markers ◽  
A Cell ◽  
Stressed Cells ◽  
Time Courses ◽  
Microscopy Images

AbstractThe identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.

scholarly journals In Vivo Production of RNA Aptamers and Nanoparticles: Problems and Prospects

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1422
Author(s):  
Ousama Al Shanaa ◽  
Andrey Rumyantsev ◽  
Elena Sambuk ◽  
Marina Padkina
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Large Scale ◽  
Potential Model ◽  
Model Organism ◽  
Rna Aptamers ◽  
Early Stages ◽  
Near Future

RNA aptamers are becoming increasingly attractive due to their superior properties. This review discusses the early stages of aptamer research, the main developments in this area, and the latest technologies being developed. The review also highlights the advantages of RNA aptamers in comparison to antibodies, considering the great potential of RNA aptamers and their applications in the near future. In addition, it is shown how RNA aptamers can form endless 3-D structures, giving rise to various structural and functional possibilities. Special attention is paid to the Mango, Spinach and Broccoli fluorescent RNA aptamers, and the advantages of split RNA aptamers are discussed. The review focuses on the importance of creating a platform for the synthesis of RNA nanoparticles in vivo and examines yeast, namely Saccharomyces cerevisiae, as a potential model organism for the production of RNA nanoparticles on a large scale.

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

scholarly journals Meeting Highlights: Genome Sequencing and Biology 2001

2001 ◽  
Vol 2 (4) ◽  
pp. 243-251
Author(s):  
Jo Wixon
Keyword(s):  
Comparative Genomics ◽  
Functional Genomics ◽  
Genome Sequencing ◽  
Large Scale ◽  
Model Organism

We bring you a report from the CSHL Genome Sequencing and Biology Meeting, which has a long and prestigious history. This year there were sessions on large-scale sequencing and analysis, polymorphisms (covering discovery and technologies and mapping and analysis), comparative genomics of mammalian and model organism genomes, functional genomics and bioinformatics.

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

scholarly journals Trajectory Planning in UAV Emergency Networks with Potential Underlaying D2D Communication Based on K-means

2021 ◽  
Author(s):  
Shuo Zhang ◽  
Shuo Shi ◽  
Tianming Feng ◽  
Xuemai Gu
Keyword(s):  
Trajectory Planning ◽  
Communication Systems ◽  
High Speed ◽  
Large Scale ◽  
High Reliability ◽  
Base Station ◽  
Planning Problem ◽  
Communication Signal ◽  
User Coverage ◽  
Trajectory Length

Abstract Unmanned aerial vehicles (UAVs) have been widely used in communication systems due to excellent maneuverability and mobility. The ultra-high speed, ultra-low latency, and ultra-high reliability of 5th generation wireless systems (5G) have further promoted vigorous development of UAVs. Compared with traditional means of communication, UAV can provide services for ground terminal without time and space constraints, so it is often used as air base station (BS). Especially in emergency communications and rescue, it provides temporary communication signal coverage service for disaster areas. In the face of large-scale and scattered user coverage tasks, UAV's trajectory is an important factor affecting its energy consumption and communication performance. In this paper, we consider a UAV emergency communication network where UAV aims to achieve complete coverage of potential underlying D2D users (DUs). The trajectory planning problem is transformed into the deployment and connection problem of stop points (SPs). Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed. Due to the non-convexity of sum throughput optimization, we present a sub-optimal solution by using the successive convex approximation (SCA) method. In order to balance the relationship between trajectory length and sum throughput, we propose a joint evaluation index which is used as an objective function to further optimize trajectory. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

scholarly journals Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning

2019 ◽  
Author(s):  
Vanessa E. Gray ◽  
Katherine Sitko ◽  
Floriane Z. Ngako Kameni ◽  
Miriam Williamson ◽  
Jason J. Stephany ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Amino Acid ◽  
Alanine Scanning Mutagenesis ◽  
Aggregation Assay ◽  
Disease Etiology ◽  
Charged Amino Acids ◽  
Molecular Determinants ◽  
Aβ Aggregation ◽  
A Cell

AbstractDespite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ~75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.

scholarly journals Covidom, a Telesurveillance Solution for Home Monitoring Patients With COVID-19 (Preprint)

2020 ◽  
Author(s):  
Youri Yordanov ◽  
Agnes Dechartres ◽  
Xavier Lescure ◽  
Caroline Apra ◽  
Pascaline Villie ◽  
...  
Keyword(s):  
Health Care Professionals ◽  
Web Application ◽  
Large Scale ◽  
Home Monitoring ◽  
Control Center ◽  
Regional Control ◽  
Global Pandemic ◽  
Number Of Patients ◽  
Paris Area ◽  
Innovative Solution

UNSTRUCTURED In a matter of months, COVID-19 has escalated from a cluster of cases in Wuhan, China, to a global pandemic. As the number of patients with COVID-19 grew, solutions for the home monitoring of infected patients became critical. This viewpoint presents a telesurveillance solution—Covidom—deployed in the greater Paris area to monitor patients with COVID-19 in their homes. The system was rapidly developed and is being used on a large scale with more than 65,000 registered patients to date. The Covidom solution combines an easy-to-use and free web application for patients (through which patients fill out short questionnaires on their health status) with a regional control center that monitors and manages alerts (triggered by questionnaire responses) from patients whose health may be deteriorating. This innovative solution could alleviate the burden of health care professionals and systems while allowing for rapid response when patients trigger an alert.

scholarly journals Colony size measurement of the yeast gene deletion strains for functional genomics

2021 ◽  
Author(s):  
Negar Memarian ◽  
Matthew Jessulat ◽  
Javad Alirezaie ◽  
Nadereh Mir-Rashed ◽  
Jianhua Xu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Colony Size ◽  
Large Scale ◽  
Model Organism ◽  
Antifungal Compound ◽  
Image Analysis System ◽  
Yeast Saccharomyces Cerevisiae ◽  
Inspection Method ◽  
Manual Inspection ◽  
Growth Differences

Background Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences. Results Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research. Conclusion GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations.

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