scholarly journals High-Performance Single Cell Genetic Analysis Using Microfluidic Emulsion Generator Arrays

2010 ◽  
Vol 82 (8) ◽  
pp. 3183-3190 ◽  
Author(s):  
Yong Zeng ◽  
Richard Novak ◽  
Joe Shuga ◽  
Martyn T. Smith ◽  
Richard A. Mathies
Keyword(s):  
Genetic Analysis ◽  
Single Cell ◽  
High Performance

scholarly journals Single Cell Genetic Analysis Helps Validating Cytopathological Identification of Ctcs in Patients with Ccrc

2014 ◽  
Vol 25 ◽  
pp. v46
Author(s):  
Patrizia Paterlini-Brechot ◽  
Basma Ben Njima ◽  
Paul Hofman ◽  
Veronique Hofman ◽  
Marius Ilie ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Single Cell

Testing of solid oxide cells at high current densities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
André Weber
Keyword(s):  
Single Cell ◽  
High Performance ◽  
High Efficiency ◽  
Electrochemical Impedance ◽  
Single Cells ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Current Densities ◽  
Cell Testing ◽  
The Impact

Abstract Solid Oxide Cells (SOCs) have gained an increasing interest as electrochemical energy converters due to their high efficiency, fuel flexibility and ability of reversible fuel cell/electrolysis operation. During the development process as well as in quality assurance tests, the performance of single cells and cell stacks is commonly evaluated by means of current/voltage- (CV-) characteristics. Despite of the fact that the measurement of a CV-characteristic seems to be simple compared to more complex, dynamic methods as electrochemical impedance spectroscopy or current interrupt techniques, the resulting performance strongly depends on the test setup and the chosen operating conditions. In this paper, the impact of different single cell testing environments and operating conditions on the CV-characteristic of high performance cells is discussed. The influence of cell size, contacting and current collection, contact pressure, fuel flow rate and composition on the achievable cell performance is presented and limitations arising from the test bed and testing conditions will be pointed out. As today’s high performance cells are capable of delivering current densities of several ampere per cm2 a special emphasis will be laid on single cell testing in this current range.

scholarly journals A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Mehmet Tekman ◽  
Bérénice Batut ◽  
Alexander Ostrovsky ◽  
Christophe Antoniewski ◽  
Dave Clements ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
High Performance ◽  
Learning Experience ◽  
Sequencing Analysis ◽  
Teaching Resources ◽  
Single Cell Rna Sequencing ◽  
The Galaxy ◽  
Downstream Analysis

Abstract Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

scholarly journals Single-cell genetic analysis validates cytopathological identification of circulating cancer cells in patients with clear cell renal cell carcinoma

Oncotarget ◽  
2018 ◽  
Vol 9 (28) ◽  
pp. 20058-20074 ◽  
Author(s):  
Lucile Broncy ◽  
Basma Ben Njima ◽  
Arnaud Méjean ◽  
Christophe Béroud ◽  
Khaled Ben Romdhane ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Genetic Analysis ◽  
Cell Carcinoma ◽  
Single Cell ◽  
Cancer Cells ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Circulating Cancer Cells

Single Cell Genetic Analysis Helps Validating Cytopathological Identification of CTCs in Patients with Clear Cells Renal Carcinoma

2013 ◽  
Vol 2 (1) ◽  
pp. S7
Author(s):  
Patrizia Paterlini-Brechot ◽  
Basma Ben Njima ◽  
Marius Ilie ◽  
Paul Hofman ◽  
Veronique Hofman ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Single Cell ◽  
Renal Carcinoma ◽  
Clear Cells

A 1.0V power supply, 9.3GB/s write speed, Single-Cell Self-Boost program scheme for high performance ferroelectric NAND flash SSD

2011 ◽  
Vol 58 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Kousuke Miyaji ◽  
Shinji Noda ◽  
Teruyoshi Hatanaka ◽  
Mitsue Takahashi ◽  
Shigeki Sakai ◽  
...  
Keyword(s):  
Single Cell ◽  
Power Supply ◽  
High Performance ◽  
Program Scheme ◽  
Nand Flash

scholarly journals A High-Performance 75 W Direct Ammonia Fuel Cells Stack

2021 ◽  
Author(s):  
Teng Wang ◽  
Yun Zhao ◽  
Brian P. Setzler ◽  
Reza Abbasi ◽  
Shimshon Gottesfeld ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Single Cell ◽  
High Performance ◽  
Electric Energy ◽  
Power Source ◽  
Bipolar Plates ◽  
Attractive Option ◽  
Li Ion ◽  
Spinel Cathode ◽  
First Time

Ammonia can be directly used as fuel to generate electric energy in a low-temperature direct ammonia fuel cell (DAFC), making the DAFC an attractive option for zero-emission transportation. However, with a high-performance and durable DAFC still to be demonstrated, and with the remaining need to identify a suitable first market, the introduction of this technology has been delayed so far. Here, we report a high-performance DAFC stack enabled by a hydrophobic spinel cathode, which achieves the best combination of performance and durability reported to date. Peak power density of 410 mW cm-2 and continuous operation for 80 hours at 300 mA cm-2 were achieved for the first time in 5 cm2 DAFCs, and then successfully scaled up to 50 cm2. Five such cells were assembled into a 75 W DAFC stack using graphite bipolar plates, demonstrating stack performance at the level expected from the single cell tests. The best combination of performance and durability for the single cell and, particularly, the demonstration of the world’s first DAFC bipolar stack, constitute significant milestones in the development of DAFC technology. We also performed an in-depth techno-economic analysis of a 2 kW, 10 kWh DAFC system serving as power source for drones. Based on the DAFC performance demonstrated by us to date, such system can be a competitive power source over hydrogen fuel cells and Li-ion batteries.

scholarly journals A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution

2021 ◽  
Author(s):  
Michele Bortolomeazzi ◽  
Lucia Montorsi ◽  
Damjan Temelkovski ◽  
Mohamed Reda Keddar ◽  
Amelia Acha-Sagredo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Performance ◽  
Spatial Information ◽  
Single Cell Analysis ◽  
Biological Tissues ◽  
Imaging Data ◽  
Cell Identification ◽  
Spatially Resolved ◽  
Multiplexed Imaging

ABSTRACTMultiplexed imaging technologies enable to study biological tissues at single-cell resolution while preserving spatial information. Currently, the analysis of these data is technology-specific and requires multiple tools, restricting the scalability and reproducibility of results. Here we present SIMPLI (Single-cell Identification from MultiPlexed Images), a novel, technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After processing raw images, SIMPLI performs a spatially resolved, single-cell analysis of the tissue as wells as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for the analysis of large datasets. It produces multiple outputs at each step, including tabular text files and visualisation plots. The containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. SIMPLI is available at: https://github.com/ciccalab/SIMPLI.

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