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Article ; Online: A new design proposal and thermal analysis of subassemblies inside a satellite

Sorin Drăghici

Scientific Bulletin of Naval Academy, Vol XXII, Iss 1, Pp 297-

2019 Volume 301

Abstract: In this study, the authors aim to validate the use of CFRP composites in a subassembly of a satellite as a replacement for currently used aluminum. Considering the high costs of sending a mass unit of payload into orbit, the mass should be minimized. ... ...

Abstract	In this study, the authors aim to validate the use of CFRP composites in a subassembly of a satellite as a replacement for currently used aluminum. Considering the high costs of sending a mass unit of payload into orbit, the mass should be minimized. Therefore, the application of composite materials in electronics housing structures was proposed. For this, a new design is proposed and a comparative thermal analysis of the two boxes is made. The present study focusses on a subassembly’s surfaces and materials of a satellite structure subject to thermal variations in order to numerically validate the new design. A satellite electronic housing was subject to a three-dimensional finite element analysis. The satellite’s thermal loading in an orbit of sun-tracking mode is partially transferred to the electronic housing inside. A model is developed and the temperature distribution in the satellite subassembly predicted using empirical available data. Based on the numerical results conclusion and discussions are drawn.
Keywords	transient heat transfer ; carbon fiber-reinforced plastics ; satellites ; Naval Science ; V
Language	English
Publishing date	2019-06-01T00:00:00Z
Publisher	Naval Academy Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.

Nguyen, Hung / Nguyen, Ha / Tran, Duc / Draghici, Sorin / Nguyen, Tin

Nucleic acids research

2024

Abstract: Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many ...

Abstract	Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
Language	English
Publishing date	2024-04-15
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkae267
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Article: Editorial: Advancement in Gene Set Analysis: Gaining Insight From High-Throughput Data.

Maleki, Farhad / Draghici, Sorin / Menezes, Renee / Kusalik, Anthony

Frontiers in genetics

2022 Volume 13, Page(s) 928724

Language	English
Publishing date	2022-05-26
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2022.928724
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Article ; Online: Discovery of Novel Transketolase Epitopes and the Development of IgG-Based Tuberculosis Serodiagnostics.

Talreja, Jaya / Peng, Changya / Nguyen, Tuan-Minh / Draghici, Sorin / Samavati, Lobelia

Microbiology spectrum

2023 Volume 11, Issue 1, Page(s) e0337722

Abstract: Despite advances in rapid molecular techniques for tuberculosis (TB) diagnostics, there is an unmet need for a point-of-care, nonsputum-based test. Previously, through a T7 phage antigen display platform and immunoscreening, we identified that the serum ... ...

Abstract	Despite advances in rapid molecular techniques for tuberculosis (TB) diagnostics, there is an unmet need for a point-of-care, nonsputum-based test. Previously, through a T7 phage antigen display platform and immunoscreening, we identified that the serum IgGs of active TB patients differentially bind to several antigen-clones and that this immunoreactivity discriminates TB from other respiratory diseases. One of these high-performance clones has some homology to the transketolase of Mycobacterium tuberculosis (
MeSH term(s)	Humans ; Transketolase ; Epitopes ; Tuberculosis ; Mycobacterium tuberculosis ; Latent Tuberculosis/diagnosis ; Antigens, Bacterial ; Immunoglobulin G ; Sarcoidosis
Chemical Substances	Transketolase (EC 2.2.1.1) ; Epitopes ; Antigens, Bacterial ; Immunoglobulin G
Language	English
Publishing date	2023-01-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.03377-22
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A novel approach for predicting upstream regulators (PURE) that affect gene expression.

Nguyen, Tuan-Minh / Craig, Douglas B / Tran, Duc / Nguyen, Tin / Draghici, Sorin

Scientific reports

2023 Volume 13, Issue 1, Page(s) 18571

Abstract: External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely ... ...

Abstract	External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.
MeSH term(s)	Animals ; Humans ; Mice ; Rats ; Phenotype ; Gene Expression
Language	English
Publishing date	2023-10-30
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-41374-0
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Article ; Online: Autoantibodies against cytoskeletons and lysosomal trafficking discriminate sarcoidosis from healthy controls, tuberculosis and lung cancers.

Hanoudi, Samer Najeeb / Talwar, Harvinder / Draghici, Sorin / Samavati, Lobelia

Molecular biomedicine

2022 Volume 3, Issue 1, Page(s) 3

Abstract: Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from tissues ... ...

Abstract	Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from tissues of sarcoidosis patients. Using a high throughput method, we constructed a microarray platform from this cDNA library containing large numbers of sarcoidosis clones. After selective biopanning, 1070 sarcoidosis-specifc clones were arrayed and immunoscreend with 152 sera from patients with sarcoidosis and other pulmonary diseases. To identify the sarcoidosis classifiers two statistical approaches were conducted: First, we identified significant biomarkers between sarcoidosis and healthy controls, and second identified markers comparing sarcoidosis to all other groups. At the threshold of an False Discovery Rate (FDR) < 0.01, we identified 14 clones in the first approach and 12 clones in the second approach discriminating sarcoidosis from other groups. We used the classifiers to build a naïve Bayes model on the training-set and validated it on an independent test-set. The first approach yielded an AUC of 0.947 using 14 significant clones with a sensitivity of 0.93 and specificity of 0.88, whereas the AUC of the second option was 0.92 with a sensitivity of 0.96 and specificity of 0.83. These results suggest robust classifier performance. Furthermore, we characterized the informative phage clones by sequencing and homology searches. Large numbers of classifier-clones were peptides involved in cellular trafficking and cytoskeletons. These results show that sarcoidosis is associated with a specific pattern of immunoreactivity that can discriminate it from other diseases.
Language	English
Publishing date	2022-01-20
Publishing country	Singapore
Document type	Journal Article
ISSN	2662-8651
ISSN (online)	2662-8651
DOI	10.1186/s43556-021-00064-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide.

Ahsan, Sidra / Drăghici, Sorin

Current protocols in bioinformatics

2017 Volume 57, Page(s) 7.15.1–7.15.30

Abstract: iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high-throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, ... ...

Abstract	iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high-throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, Gene Ontology terms, disease processes, predicted microRNAs, and putative mechanisms based on the given differential expression signature. By using a novel analytical approach called Impact Analysis, iPathwayGuide considers the role, position, and relationships of each gene within a pathway, which results in a significant reduction in false positives, as well as a better ability to identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes. It is a Web-based, user-friendly, interactive tool that does not require prior training in bioinformatics. The protocols in this unit describe how to use iPathwayGuide to analyze a single contrast between two phenotypes (any number of samples), and provide guidance on how to interpret the results obtained from iPathwayGuide. Even though iPathwayGuide has powerful meta-analysis capabilities, these are not covered in this unit. © 2017 by John Wiley & Sons, Inc.
MeSH term(s)	Computational Biology/methods ; Gene Expression Profiling/methods ; Gene Expression Regulation ; Gene Ontology ; Humans ; MicroRNAs/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2017-06-27
Publishing country	United States
Document type	Journal Article
ISSN	1934-340X
ISSN (online)	1934-340X
DOI	10.1002/cpbi.24
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A novel approach for predicting upstream regulators (PURE) that affect gene expression

Tuan-Minh Nguyen / Douglas B. Craig / Duc Tran / Tin Nguyen / Sorin Draghici

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 12

Abstract: Abstract External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely ...

Abstract	Abstract External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Autoantibodies against cytoskeletons and lysosomal trafficking discriminate sarcoidosis from healthy controls, tuberculosis and lung cancers

Samer Najeeb Hanoudi / Harvinder Talwar / Sorin Draghici / Lobelia Samavati

Molecular Biomedicine, Vol 3, Iss 1, Pp 1-

2022 Volume 14

Abstract: Abstract Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from ...

Abstract	Abstract Sarcoidosis is a systemic granulomatous disease of unknown etiology. Hypergammaglobulinemia and the presence of autoantibodies in sarcoidosis suggest active humoral immunity to unknown antigen(s). We developed a complex cDNA library derived from tissues of sarcoidosis patients. Using a high throughput method, we constructed a microarray platform from this cDNA library containing large numbers of sarcoidosis clones. After selective biopanning, 1070 sarcoidosis-specifc clones were arrayed and immunoscreend with 152 sera from patients with sarcoidosis and other pulmonary diseases. To identify the sarcoidosis classifiers two statistical approaches were conducted: First, we identified significant biomarkers between sarcoidosis and healthy controls, and second identified markers comparing sarcoidosis to all other groups. At the threshold of an False Discovery Rate (FDR) < 0.01, we identified 14 clones in the first approach and 12 clones in the second approach discriminating sarcoidosis from other groups. We used the classifiers to build a naïve Bayes model on the training-set and validated it on an independent test-set. The first approach yielded an AUC of 0.947 using 14 significant clones with a sensitivity of 0.93 and specificity of 0.88, whereas the AUC of the second option was 0.92 with a sensitivity of 0.96 and specificity of 0.83. These results suggest robust classifier performance. Furthermore, we characterized the informative phage clones by sequencing and homology searches. Large numbers of classifier-clones were peptides involved in cellular trafficking and cytoskeletons. These results show that sarcoidosis is associated with a specific pattern of immunoreactivity that can discriminate it from other diseases.
Keywords	T7phage library ; Sarcoidosis ; Tuberculosis ; Microarray ; Immunoscreening ; Medicine ; R
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Springer
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Identification of cell types from single cell data using stable clustering.

Peyvandipour, Azam / Shafi, Adib / Saberian, Nafiseh / Draghici, Sorin

Scientific reports

2020 Volume 10, Issue 1, Page(s) 12349

Abstract: Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be ... ...

Abstract	Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.
MeSH term(s)	Algorithms ; Cluster Analysis ; Computer Simulation ; High-Throughput Nucleotide Sequencing ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome
Language	English
Publishing date	2020-07-23
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-66848-3
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