Comparison between steady-state and dynamic I-V measurements from a single-cell thermionic fuel element

1997 ◽  
Author(s):  
Bernard Wernsman
Keyword(s):  
Steady State ◽  
Fuel Element ◽  
Single Cell

A novel mouse model based on intersectional genetics enables unambiguous in vivo discrimination between plasmacytoid and other dendritic cells and their comparative characterization

2022 ◽  
Author(s):  
Michael Valente ◽  
Nils Collinet ◽  
Thien-Phong Vu Manh ◽  
Karima Naciri ◽  
Gilles Bessou ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
Steady State ◽  
Mouse Model ◽  
Single Cell ◽  
Ex Vivo ◽  
High Specificity ◽  
Type I ◽  
Specific Expression ◽  
Physiological Functions

Plasmacytoid dendritic cells (pDC) were identified about 20 years ago, based on their unique ability to rapidly produce copious amounts of all subsets of type I and type III interferon (IFN-I/III) upon virus sensing, while being refractory to infection. Yet, the identity and physiological functions of pDC are still a matter of debate, in a large part due to their lack of specific expression of any single cell surface marker or gene that would allow to track them in tissues and to target them in vivo with high specificity and penetrance. Indeed, recent studies showed that previous methods that were used to identify or deplete pDC also targeted other cell types, including pDC-like cells and transitional DC (tDC) that were proposed to be responsible for all the antigen presentation ability previously attributed to steady state pDC. Hence, improving our understanding of the nature and in vivo choreography of pDC physiological functions requires the development of novel tools to unambiguously identify and track these cells, including in comparison to pDC-like cells and tDC. Here, we report successful generation of a pDC-reporter mouse model, by using an intersectional genetic strategy based on the unique co-expression of Siglech and Pacsin1 in pDC. This pDC-Tomato mouse strain allows specific ex vivo and in situ detection of pDC. Breeding them with Zbtb46GFP mice allowed side-by-side purification and transcriptional profiling by single cell RNA sequencing of bona fide pDC, pDC-like cells and tDC, in comparison to type 1 and 2 conventional DC (cDC1 and cDC2), both at steady state and during a viral infection, revealing diverging activation patterns of pDC-like cells and tDC. Finally, by breeding pDC-Tomato mice with Ifnb1EYFP mice, we determined the choreography of pDC recruitment to the micro-anatomical sites of viral replication in the spleen, with initially similar but later divergent behaviors of the pDC that engaged or not into IFN-I production. Our novel pDC-Tomato mouse model, and newly identified gene modules specific to combinations of DC types and activations states, will constitute valuable resources for a deeper understanding of the functional division of labor between DC types and its molecular regulation at homeostasis and during viral infections.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

2019 ◽  
Vol 17 (06) ◽  
pp. 1950035
Author(s):  
Huiqing Wang ◽  
Yuanyuan Lian ◽  
Chun Li ◽  
Yue Ma ◽  
Zhiliang Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Knockout ◽  
Cell Heterogeneity ◽  
State Information ◽  
Gene Regulatory ◽  
Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

Single cell Thermionic Fuel Element Test Rig high power operations and system upgrades

1995 ◽  
Author(s):  
Nick J. Roh ◽  
Dan Bruns ◽  
Carl F. Frisch ◽  
Victor N. Androsov ◽  
John C. Logothetis ◽  
...  
Keyword(s):  
Fuel Element ◽  
Single Cell ◽  
High Power ◽  
Test Rig ◽  
Power Operations ◽  
System Upgrades

scholarly journals ANALYSIS ON THE PERFORMANCE OF THE BANDUNG CONVERSION FUEL-PLATE TRIGA REACTOR IN STEADY STATE WITH CONSTANT COOLANT FLOW RATE

2020 ◽  
Vol 22 (2) ◽  
pp. 41
Author(s):  
Endiah Puji Hastuti ◽  
Sudjatmi K. Alfa ◽  
Sudarmono Sudarmono
Keyword(s):  
Steady State ◽  
Fuel Element ◽  
Flow Rate ◽  
Flow Instability ◽  
Safety Margin ◽  
Nucleate Boiling ◽  
Coolant Flow ◽  
Coolant Flow Rate ◽  
Steady State Condition ◽  
Triga Reactor

Bandung TRIGA2000 Reactor, a General Atomic (GA)-made research reactor used for training, research andiIsotope production, has been upgraded to operate at power of 2000 kW using TRIGA fuel rod type. Recently, the TRIGA reactor fuel element producers are going to discontinue the production of TRIGA fuel element. To overcome the unavailability of TRIGA fuel element, BATAN planned to modify TRIGA2000 fuel type from rod-type to U3Si2-Al plate-type fuel with 19.75% enrichment, similar to the domestically fabricated one used in RSG-GAS. The carried out design emphasized on the determination of operation condition limits for setting the reactor protection system in accordance to the reactor safety calculation results. The conceptual design of the innovative fuel plate TRIGA reactor cooling system is expected to remove heat generated by fuels with nominal power of 1 MW up to 2 MW. The design is developed through modelling and safety analysis using COOLOD-N2 validated code. The safety margin is set to its flow instability at transient condition of the fuel plate, which is ≥ 2.38; departure from nucleate boiling ratio ≥1.50; and no onset of nucleate boiling, ΔTONB ≥ 0oC. The primary coolant flow rate accommodating the existing Bandung TRIGA reactor capability is as high as 50 kg/s. The analysis results show that at power of 1 MW, the reactor can safely operate, while at power of 2 MW the safety margin is exceeded. In other words, the plate TRIGA reactor that employs forced convection mode operates safely at 1 MW with excess power 120% of its nominal power.Keywords: 1 MW, Thermalhydraulic design, Steady state condition, TRIGA plate, Constant flowrate

scholarly journals Transcriptional and Functional Description of Stem Cell Heterogeneity in Native and Transplantation Hematopoiesis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3844-3844
Author(s):  
Alejo E Rodriguez-Fraticelli ◽  
Caleb Weinreb ◽  
Allon Moshe Klein ◽  
Fernando Camargo
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Steady State ◽  
Hematopoietic Stem Cells ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Significant Proportion ◽  
Cell Heterogeneity ◽  
Hematopoietic Stem

Abstract The hematopoietic system follows a hierarchical organization, with multipotent long-term repopulating hematopoietic stem cells (LT-HSCs) occupying the top tier. This paradigm, developed mostly through cell transplantation assays, has recently been contested by a series of studies performed under native conditions, without transplantation. Application of systems-level single cell methods in this setting has revealed a heterogeneity of cell states within progenitors and stem cells, prompting a reevaluation of the theories of hematopoietic lineage fate decisions. We have previously described that hematopoietic stem cell fates are clonally heterogeneous under steady state and uncovered that a fraction of LT-HSCs contributes to a significant proportion of the megakaryocytic cell lineage under steady state, while rarely generating other types of progeny in unperturbed conditions. To elucidate the molecular underpinnings of this functional lineage-output heterogeneity, we developed a technique to barcode hematopoietic cells at the RNA level in order to simultaneously capture the lineage relationships and transcriptional states of HSCs. Using a droplet-based massive single cell RNAseq platform, we analyzed thousands of engrafted hematopoietic stem cells together with a sufficiently significant representation of downstream progenitor cells to measure HSC output. Inspection of the resulting "stem cell state-fate maps" revealed a variety of stem cell behaviors, including single cell quiescence, asymmetric and symmetric divisions, and clonal expansion. We also connected these behaviors with some of the previously observed heterogeneity in stem cell outcomes, including lineage bias, lineage output and clonal competition. Importantly, clustering of expression profiles revealed significant differences in the transcriptional programs related with some of these behaviors, which illuminate the molecular machineries that operate at the stem cell level to define this heterogeneity. Thus, our work has identified potential novel mediators for stem cell heterogeneity, which we are functionally analyzing in further detail to understand their molecular mechanisms. Disclosures No relevant conflicts of interest to declare.

Sign in / Sign up

Export Citation Format

Share Document