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- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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- 2019
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Journal article (peer-reviewed)
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4. |
- Ariyawansa, Hiran A., et al.
(author)
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Fungal diversity notes 111–252—taxonomic and phylogenetic contributions to fungal taxa
- 2015
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In: Fungal diversity. - : Springer Science and Business Media LLC. - 1560-2745 .- 1878-9129. ; 75, s. 27-274
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Journal article (peer-reviewed)abstract
- This paper is a compilation of notes on 142 fungal taxa, including five new families, 20 new genera, and 100 new species, representing a wide taxonomic and geographic range. The new families, Ascocylindricaceae, Caryosporaceae and Wicklowiaceae (Ascomycota) are introduced based on their distinct lineages and unique morphology. The new Dothideomycete genera Pseudomassariosphaeria (Amniculicolaceae), Heracleicola, Neodidymella and P s e u d o m i c ros p h a e r i o p s i s ( D id y m e l l a c e a e ) , P s e u d o p i t h o m y c e s ( D i d y m o s p h a e r i a c e a e ) , Brunneoclavispora, Neolophiostoma and Sulcosporium (Halotthiaceae), Lophiohelichrysum (Lophiostomataceae), G a l l i i c o l a , Popul o c re s c e n t i a a nd Va g i c o l a (Phaeosphaeriaceae), Ascocylindrica (Ascocylindricaceae), E l o n g a t o p e d i c e l l a t a ( R o u s s o e l l a c e a e ) , Pseudoasteromassaria (Latoruaceae) and Pseudomonodictys (Macrodiplodiopsidaceae) are introduced. The newly described species of Dothideomycetes (Ascomycota) are Pseudomassariosphaeria bromicola (Amniculicolaceae), Flammeascoma lignicola (Anteagloniaceae), Ascocylindrica marina (Ascocylindricaceae) , Lembosia xyliae (Asterinaceae), Diplodia crataegicola and Diplodia galiicola ( B o t r yosphae r i a cea e ) , Caryospor a aquat i c a (Caryosporaceae), Heracleicola premilcurensis and Neodi dymell a thai landi cum (Didymellaceae) , Pseudopithomyces palmicola (Didymosphaeriaceae), Floricola viticola (Floricolaceae), Brunneoclavispora bambusae, Neolophiostoma pigmentatum and Sulcosporium thailandica (Halotthiaceae), Pseudoasteromassaria fagi (Latoruaceae), Keissleriella dactylidicola (Lentitheciaceae), Lophiohelichrysum helichrysi (Lophiostomataceae), Aquasubmersa japonica (Lophiotremataceae) , Pseudomonodictys tectonae (Macrodiplodiopsidaceae), Microthyrium buxicola and Tumidispora shoreae (Microthyriaceae), Alloleptosphaeria clematidis, Allophaeosphaer i a c y t i s i , Allophaeosphae r i a subcylindrospora, Dematiopleospora luzulae, Entodesmium artemisiae, Galiicola pseudophaeosphaeria, Loratospora(Basidiomycota) are introduced together with a new genus Neoantrodiella (Neoantrodiellaceae), here based on both morphology coupled with molecular data. In the class Agaricomycetes, Agaricus pseudolangei, Agaricus haematinus, Agaricus atrodiscus and Agaricus exilissimus (Agaricaceae) , Amanita m e l l e i a l b a , Amanita pseudosychnopyramis and Amanita subparvipantherina (Amanitaceae), Entoloma calabrum, Cora barbulata, Dictyonema gomezianum and Inocybe granulosa (Inocybaceae), Xerocomellus sarnarii (Boletaceae), Cantharellus eucalyptorum, Cantharellus nigrescens, Cantharellus tricolor and Cantharellus variabilicolor (Cantharellaceae), Cortinarius alboamarescens, Cortinarius brunneoalbus, Cortinarius ochroamarus, Cortinarius putorius and Cortinarius seidlii (Cortinariaceae), Hymenochaete micropora and Hymenochaete subporioides (Hymenochaetaceae), Xylodon ramicida (Schizoporaceae), Colospora andalasii (Polyporaceae), Russula guangxiensis and Russula hakkae (Russulaceae), Tremella dirinariae, Tremella graphidis and Tremella pyrenulae (Tremellaceae) are introduced. Four new combinations Neoantrodiella gypsea, Neoantrodiella thujae (Neoantrodiellaceae), Punctulariopsis cremeoalbida, Punctulariopsis efibulata (Punctulariaceae) are also introduced here for the division Basidiomycota. Furthermore Absidia caatinguensis, Absidia koreana and Gongronella koreana (Cunninghamellaceae), Mortierella pisiformis and Mortierella formosana (Mortierellaceae) are newly introduced in the Zygomycota, while Neocallimastix cameroonii and Piromyces irregularis (Neocallimastigaceae) ar e i n t roduced i n the Neocallimastigomycota. Reference specimens or changes in classification and notes are provided for Alternaria ethzedia, Cucurbitaria ephedricola, Austropleospora, Austropleospora archidendri, Byssosphaeria rhodomphala, Lophiostoma caulium, Pseudopithomyces maydicus, Massariosphaeria, Neomassariosphaeria and Pestalotiopsis montellica.
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5. |
- Aguado, D. S., et al.
(author)
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The Fifteenth Data Release of the Sloan Digital Sky Surveys : First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library
- 2019
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In: Astrophysical Journal Supplement Series. - : Institute of Physics Publishing (IOPP). - 0067-0049 .- 1538-4365. ; 240:2
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Journal article (peer-reviewed)abstract
- Twenty years have passed since first light for the Sloan Digital Sky Survey (SDSS). Here, we release data taken by the fourth phase of SDSS (SDSS-IV) across its first three years of operation (2014 July-2017 July). This is the third data release for SDSS-IV, and the 15th from SDSS (Data Release Fifteen; DR15). New data come from MaNGA-we release 4824 data cubes, as well as the first stellar spectra in the MaNGA Stellar Library (MaStar), the first set of survey-supported analysis products (e.g., stellar and gas kinematics, emission-line and other maps) from the MaNGA Data Analysis Pipeline, and a new data visualization and access tool we call "Marvin." The next data release, DR16, will include new data from both APOGEE-2 and eBOSS; those surveys release no new data here, but we document updates and corrections to their data processing pipelines. The release is cumulative; it also includes the most recent reductions and calibrations of all data taken by SDSS since first light. In this paper, we describe the location and format of the data and tools and cite technical references describing how it was obtained and processed. The SDSS website (www.sdss.org) has also been updated, providing links to data downloads, tutorials, and examples of data use. Although SDSS-IV will continue to collect astronomical data until 2020, and will be followed by SDSS-V (2020-2025), we end this paper by describing plans to ensure the sustainability of the SDSS data archive for many years beyond the collection of data.
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6. |
- Jayasiri, Subashini C., et al.
(author)
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The Faces of Fungi database: fungal names linked with morphology, phylogeny and human impacts
- 2015
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In: Fungal diversity. - : Springer Science and Business Media LLC. - 1560-2745 .- 1878-9129. ; 74:1, s. 3-18
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Journal article (peer-reviewed)abstract
- Taxonomic names are key links between various databases that store information on different organisms. Several global fungal nomenclural and taxonomic databases (notably Index Fungorum, Species Fungorum and MycoBank) can be sourced to find taxonomic details about fungi, while DNA sequence data can be sourced from NCBI, EBI and UNITE databases. Although the sequence data may be linked to a name, the quality of the metadata is variable and generally there is no corresponding link to images, descriptions or herbarium material. There is generally no way to establish the accuracy of the names in these genomic databases, other than whether the submission is from a reputable source. To tackle this problem, a new database (FacesofFungi), accessible at www.facesoffungi.org (FoF) has been established. This fungal database allows deposition of taxonomic data, phenotypic details and other useful data, which will enhance our current taxonomic understanding and ultimately enable mycologists to gain better and updated insights into the current fungal classification system. In addition, the database will also allow access to comprehensive metadata including descriptions of voucher and type specimens. This database is user-friendly, providing links and easy access between taxonomic ranks, with the classification system based primarily on molecular data (from the literature and via updated web-based phylogenetic trees), and to a lesser extent on morphological data when molecular data are unavailable. In FoF species are not only linked to the closest phylogenetic representatives, but also relevant data is provided, wherever available, on various applied aspects, such as ecological, industrial, quarantine and chemical uses. The data include the three main fungal groups (Ascomycota, Basidiomycota, Basal fungi) and fungus-like organisms. The FoF webpage is an output funded by the Mushroom Research Foundation which is an NGO with seven directors with mycological expertise. The webpage has 76 curators, and with the help of these specialists, FoF will provide an updated natural classification of the fungi, with illustrated accounts of species linked to molecular data. The present paper introduces the FoF database to the scientific community and briefly reviews some of the problems associated with classification and identification of the main fungal groups. The structure and use of the database is then explained. We would like to invite all mycologists to contribute to these web pages.
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7. |
- Kristanl, Matej, et al.
(author)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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In: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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8. |
- Feigin, Valery L., et al.
(author)
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Global, regional, and national burden of stroke and its risk factors, 1990-2019 : a systematic analysis for the Global Burden of Disease Study 2019
- 2021
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In: Lancet Neurology. - : Elsevier. - 1474-4422 .- 1474-4465. ; 20:10, s. 795-820
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Journal article (peer-reviewed)abstract
- Background Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels. Methods We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level. Findings In 2019, there were 12.2 million (95% UI 11.0-13.6) incident cases of stroke, 101 million (93.2-111) prevalent cases of stroke, 143 million (133-153) DALYs due to stroke, and 6.55 million (6.00-7.02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11.6% [10.8-12.2] of total deaths) and the third-leading cause of death and disability combined (5.7% [5.1-6.2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70.0% (67.0-73.0), prevalent strokes increased by 85.0% (83.0-88.0), deaths from stroke increased by 43.0% (31.0-55.0), and DALYs due to stroke increased by 32.0% (22.0-42.0). During the same period, age-standardised rates of stroke incidence decreased by 17.0% (15.0-18.0), mortality decreased by 36.0% (31.0-42.0), prevalence decreased by 6.0% (5.0-7.0), and DALYs decreased by 36.0% (31.0-42.0). However, among people younger than 70 years, prevalence rates increased by 22.0% (21.0-24.0) and incidence rates increased by 15.0% (12.0-18.0). In 2019, the age-standardised stroke-related mortality rate was 3.6 (3.5-3.8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3.7 (3.5-3.9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62.4% of all incident strokes in 2019 (7.63 million [6.57-8.96]), while intracerebral haemorrhage constituted 27.9% (3.41 million [2.97-3.91]) and subarachnoid haemorrhage constituted 9.7% (1.18 million [1.01-1.39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79.6 million [67.7-90.8] DALYs or 55.5% [48.2-62.0] of total stroke DALYs), high body-mass index (34.9 million [22.3-48.6] DALYs or 24.3% [15.7-33.2]), high fasting plasma glucose (28.9 million [19.8-41.5] DALYs or 20.2% [13.8-29.1]), ambient particulate matter pollution (28.7 million [23.4-33.4] DALYs or 20.1% [16.6-23.0]), and smoking (25.3 million [22.6-28.2] DALYs or 17.6% [16.4-19.0]). Interpretation The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.
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9. |
- Li, Jian Feng, et al.
(author)
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Transcriptional landscape of B cell precursor acute lymphoblastic leukemia based on an international study of 1,223 cases
- 2018
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424. ; 115:50, s. 11711-11720
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Journal article (peer-reviewed)abstract
- Most B cell precursor acute lymphoblastic leukemia (BCP ALL) can be classified into known major genetic subtypes, while a substantial proportion of BCP ALL remains poorly characterized in relation to its underlying genomic abnormalities. We therefore initiated a large-scale international study to reanalyze and delineate the transcriptome landscape of 1,223 BCP ALL cases using RNA sequencing. Fourteen BCP ALL gene expression subgroups (G1 to G14) were identified. Apart from extending eight previously described subgroups (G1 to G8 associated with MEF2D fusions, TCF3–PBX1 fusions, ETV6–RUNX1–positive/ETV6–RUNX1–like, DUX4 fusions, ZNF384 fusions, BCR–ABL1/Ph–like, high hyperdiploidy, and KMT2A fusions), we defined six additional gene expression subgroups: G9 was associated with both PAX5 and CRLF2 fusions; G10 and G11 with mutations in PAX5 (p.P80R) and IKZF1 (p.N159Y), respectively; G12 with IGH–CEBPE fusion and mutations in ZEB2 (p.H1038R); and G13 and G14 with TCF3/4–HLF and NUTM1 fusions, respectively. In pediatric BCP ALL, subgroups G2 to G5 and G7 (51 to 65/67 chromosomes) were associated with low-risk, G7 (with ≤50 chromosomes) and G9 were intermediate-risk, whereas G1, G6, and G8 were defined as high-risk subgroups. In adult BCP ALL, G1, G2, G6, and G8 were associated with high risk, while G4, G5, and G7 had relatively favorable outcomes. This large-scale transcriptome sequence analysis of BCP ALL revealed distinct molecular subgroups that reflect discrete pathways of BCP ALL, informing disease classification and prognostic stratification. The combined results strongly advocate that RNA sequencing be introduced into the clinical diagnostic workup of BCP ALL. four decades, most of the recurring chromosomal abnormalities, including aneuploidy, chromosomal rearrangements/gene fusions (e.g., ETV6–RUNX1, BCR–ABL1, and TCF3–PBX1), and rearrangements of KMT2A (previously MLL), were identified by.
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10. |
- Wei, Ting, et al.
(author)
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Developed and developing world responsibilities for historical climate change and CO2 mitigation
- 2012
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 109:32, s. 12911-12915
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Journal article (peer-reviewed)abstract
- At the United Nations Framework Convention on Climate Change Conference in Cancun, in November 2010, the Heads of State reached an agreement on the aim of limiting the global temperature rise to 2 degrees C relative to preindustrial levels. They recognized that long-term future warming is primarily constrained by cumulative anthropogenic greenhouse gas emissions, that deep cuts in global emissions are required, and that action based on equity must be taken to meet this objective. However, negotiations on emission reduction among countries are increasingly fraught with difficulty, partly because of arguments about the responsibility for the ongoing temperature rise. Simulations with two earth-system models (NCAR/CESM and BNU-ESM) demonstrate that developed countries had contributed about 60-80%, developing countries about 20-40%, to the global temperature rise, upper ocean warming, and sea-ice reduction by 2005. Enacting pledges made at Cancun with continuation to 2100 leads to a reduction in global temperature rise relative to business as usual with a 1/3-2/3 (CESM 33-67%, BNU-ESM 35-65%) contribution from developed and developing countries, respectively. To prevent a temperature rise by 2 degrees C or more in 2100, it is necessary to fill the gap with more ambitious mitigation efforts.
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11. |
- Yu, Wenjin, et al.
(author)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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In: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Journal article (peer-reviewed)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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