Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Vitamin C u neuropsihijatriji

Vitamins are necessary factors in human development and normal brain function. Vitamin C is a hydrosoluble compound that humans cannot produce; therefore, we are completely dependent on food intake for vitamin C. Ascorbic acid is an important antioxidative agent and is present in high concentrations in neurons and is also crucial for collagen synthesis throughout the body. Ascorbic acid has a role in modulating many essential neurotransmitters, enables neurogenesis in adult brain and protects cells against infection. While SVCT1 enables the absorption of vitamin C in the intestine, SVCT2 is primarily located in the brain. Ascorbate deficiency is classically expressed as scurvy, which is lethal if not treated. However, subclinical deficiencies are probably much more frequent. Potential fields of vitamin C therapy are in neurodegenerative, cerebrovascular and affective diseases, cancer, brain trauma and others. For example, there is some data on its positive effects in Alzheimer's disease. Various dosing regimes are used, but ascorbate is safe, even in high doses for protracted periods. Better designed studies are needed to elucidate all of the potential therapeutic roles of vitamin C.Vitamin su neophodni faktori za razvoj i normalnu funkciju mozga kod ljudi. Vitamin C je hidrosolubilno jedinjenje koje ljudski organizam ne može da sintetiše tako da smo potpuno zavisni od unosa putem hrane. Askorbinska kiselina je važno antioksidativno sredstvo i prisutna je u neuronima u visokim koncentracijama. Takođe je od ključnog značaja za sintezu kolagena u celom organizmu. Askorbinska kiselina ima ulogu u modulaciji mnogih bitnih neurotransmitera, omogućava neurogenezu u mozgu odraslog i štiti ćelije od infekcije. Dok SVCT1 omogućava apsorpciju vitamina C u crevima, SVCT2 se nalazi uglavnom u mozgu. Nedostatak askorbata klasično se ispoljava kao skorbut koji je letalan ako se ne leči, ali je supklinička deficijencija verovatno mnogo češća. Potencijalni terapijski domeni vitamina C terapije su neurodegenerativne, cerebrovaskularne i afektivne bolesti, karcinomi, traume mozga i drugi. Postoje na primer podaci o pozitivnim efektima askorbinske kiseline u Alchajmerovoj bolesti. Koriste se razni režimi doziranja, ali je askorbat pokazao bezbednost čak i u visokim dozama tokom dugih perioda. Potrebne su bolje dizajnirane studije da se razjasne sve potencijalne terapijske uloge vitamina C

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The ongoing impacts of hepatitis C - a systematic narrative review of the literature

Extent: 13p.BackgroundMany countries have developed, or are developing, national strategies aimed at reducing the harms associated with hepatitis C infection. Making these strategies relevant to the vast majority of those affected by hepatitis C requires a more complete understanding of the short and longer term impacts of infection. We used a systematic approach to scope the literature to determine what is currently known about the health and psychosocial impacts of hepatitis C along the trajectory from exposure to ongoing chronic infection, and to identify what knowledge gaps remain.MethodsPubMed, Current Contents and PsychINFO databases were searched for primary studies published in the ten years from 2000-2009 inclusive. Two searches were conducted for studies on hepatitis C in adult persons focusing on: outcomes over time (primarily cohort and other prospective designs); and the personal and psychosocial impacts of chronic infection. All retrieved studies were assessed for eligibility according to specific inclusion/exclusion criteria, data completeness and methodological coherence. Outcomes reported in 264 included studies were summarized, tabulated and synthesized.ResultsInjecting drug use (IDU) was a major risk for transmission with seroconversion occurring relatively early in injecting careers. Persistent hepatitis C viraemia, increasing age and excessive alcohol consumption independently predicted disease progression. While interferon based therapies reduced quality of life during treatment, improvements on baseline quality of life was achieved post treatment--particularly when sustained viral response was achieved. Much of the negative social impact of chronic infection was due to the association of infection with IDU and inflated assessments of transmission risks. Perceived discrimination was commonly reported in health care settings, potentially impeding health care access. Perceptions of stigma and experiences of discrimination also had direct negative impacts on wellbeing and social functioning.ConclusionsHepatitis C and its management continue to have profound and ongoing impacts on health and social well being. Biomedical studies provided prospective information on clinical aspects of infection, while the broader social and psychological studies presented comprehensive information on seminal experiences (such as diagnosis and disclosure). Increasing the focus on combined methodological approaches could enhance understanding about the health and social impacts of hepatitis C along the life course.Emma R Miller, Stephen McNally, Jack Wallace, Marisa Schlichthors

Über Estersäuren und Amidsäuren der Isophthalsäure-Reihe, ein Beitrag zur Frage der Gleichwertigkeit der Stellungen 2 und 6 am Benzolkern

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