Academic literature on the topic 'L1000'
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Journal articles on the topic "L1000"
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Liu, Chenglin, Jing Su, Fei Yang, Kun Wei, Jinwen Ma, and Xiaobo Zhou. "Compound signature detection on LINCS L1000 big data." Molecular BioSystems 11, no. 3 (2015): 714–22. http://dx.doi.org/10.1039/c4mb00677a.
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The Library of Integrated Network-based Cellular Signatures (LINCS) L1000 big data provide gene expression profiles induced by over 10 000 compounds, shRNAs, and kinase inhibitors using the L1000 platform.
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Qiu, Yue, Tianhuan Lu, Hansaim Lim, and Lei Xie. "A Bayesian approach to accurate and robust signature detection on LINCS L1000 data." Bioinformatics 36, no. 9 (January 31, 2020): 2787–95. http://dx.doi.org/10.1093/bioinformatics/btaa064.
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Abstract Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. Availability and implementation The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. Supplementary information Supplementary data are available at Bioinformatics online.
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Zihler, Annina, Mélanie Gagnon, Christophe Chassard, Anita Hegland, Marc J. A. Stevens, Christian P. Braegger, and Christophe Lacroix. "Unexpected consequences of administering bacteriocinogenic probiotic strains for Salmonella populations, revealed by an in vitro colonic model of the child gut." Microbiology 156, no. 11 (November 1, 2010): 3342–53. http://dx.doi.org/10.1099/mic.0.042036-0.
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New biological strategies for the treatment of Salmonella infection are needed in response to the increase in antibiotic-resistant strains. Escherichia coli L1000 and Bifidobacterium thermophilum RBL67 were previously shown to produce antimicrobial proteinaceous compounds (microcin B17 and thermophilicin B67, respectively) active in vitro against a panel of Salmonella strains recently isolated from clinical cases in Switzerland. In this study, two three-stage intestinal continuous fermentation models of Salmonella colonization inoculated with immobilized faeces of a two-year-old child were implemented to study the effects of the two bacteriocinogenic strains compared with a bacteriocin-negative mutant of strain L1000 on Salmonella growth, as well as gut microbiota composition and metabolic activity. Immobilized E. coli L1000 added to the proximal colon reactor showed a low colonization, and developed preferentially in the distal colon reactor independent of the presence of genetic determinants for microcin B17 production. Surprisingly, E. coli L1000 addition strongly stimulated Salmonella growth in all three reactors. In contrast, B. thermophilum RBL67 added in a second phase stabilized at high levels in all reactors, but could not inhibit Salmonella already present at a high level (>107 c.f.u. ml−1) when the probiotic was added. Inulin added at the end of fermentation induced a strong bifidogenic effect in all three colon reactors and a significant increase of Salmonella counts in the distal colon reactor. Our data show that under the simulated child colonic conditions, the microcin B17 production phenotype does not correlate with inhibition of Salmonella but leads to a better colonization of E. coli L1000 in the distal colon reactor. We conclude that in vitro models with complex and complete gut microbiota are required to accurately assess the potential and efficacy of probiotics with respect to Salmonella colonization in the gut.
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Samaraweera, Hasara, Samadhi Nawalage, R. M. Oshani Nayanathara, Chathuri Peiris, Tharindu N. Karunaratne, Sameera R. Gunatilake, Rooban V. K. G. Thirumalai, Jilei Zhang, Xuefeng Zhang, and Todd Mlsna. "In Situ Synthesis of Zero-Valent Iron-Decorated Lignite Carbon for Aqueous Heavy Metal Remediation." Processes 10, no. 8 (August 21, 2022): 1659. http://dx.doi.org/10.3390/pr10081659.
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Lignite’s large abundance, physicochemical properties and low cost are attractive for industrial wastewater remediation. However, directly applying lignite for wastewater treatment suffers low efficiency. Here, we synthesize highly efficient zero-valent iron (ZVI)-decorated lignite carbon through the in-situ carbonization of a lignite and FeCl2 mixture for heavy metal removal. The effect of carbonization temperature on the morphology, structure and crystallite phases of ZVI-decorated lignite carbons (ZVI-LXs) was investigated. At an optimized temperature (i.e., 1000 °C), ZVI particles were found evenly distributed on the lignite matrix with the particles between 20 to 190 nm. Moreover, ZVI particles were protected by a graphene shell that was formed in situ during the carbonization. The synthesized ZVI-L1000 exhibited higher Cu2+, Pb2+ and Cd2+ stripping capacities than pristine lignite in a wide pH range of 2.2–6.3 due to the surface-deposited ZVI particles. The maximum Langmuir adsorption capacities of ZVI-L1000 for Cd2+, Pb2+ and Cu2+ were 38.3, 55.2 and 42.5 mg/g at 25 °C, respectively, which were 7.8, 4.5 and 10.6 times greater than that of pristine lignite, respectively. ZVI-L1000 also exhibited a fast metal removal speed (~15 min), which is ideal for industrial wastewater treatment. The pseudo-second-order model fits well with all three adsorptions, indicating that chemical forces control their rate-limiting adsorption steps. The reduction mechanisms of ZVI-L1000 for heavy metals include reduction, precipitation and complexation.
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Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (January 10, 2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.
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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability and implementation Slinky is freely available as an R package at http://bioconductor.org/packages/slinky
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McDermott, Matthew B. A., Jennifer Wang, Wen-Ning Zhao, Steven D. Sheridan, Peter Szolovits, Isaac Kohane, Stephen J. Haggarty, and Roy H. Perlis. "Deep Learning Benchmarks on L1000 Gene Expression Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (November 1, 2020): 1846–57. http://dx.doi.org/10.1109/tcbb.2019.2910061.
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Lin, Kequan, Lu Li, Yifei Dai, Huili Wang, Shuaishuai Teng, Xilinqiqige Bao, Zhi John Lu, and Dong Wang. "A comprehensive evaluation of connectivity methods for L1000 data." Briefings in Bioinformatics 21, no. 6 (November 27, 2019): 2194–205. http://dx.doi.org/10.1093/bib/bbz129.
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Abstract The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).
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Suter, Robert, Anna Jermakowicz, Vasileios Stathias, Luz Ruiz, Matthew D'Antuono, Simon Kaeppeli, Grace Baker, et al. "EPCO-14. ISOSCELES: AN INTEGRATIVE FRAMEWORK FOR THE PREDICTION OF TREATMENT RESISTANT GLIOBLASTOMA CELLS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii118. http://dx.doi.org/10.1093/neuonc/noac209.449.
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Abstract Glioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Two of the most significant issues preventing the development of effective GBM treatments are inter- and intra-tumor heterogeneity. To address these issues, we developed a novel platform termed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures). ISOSCELES integrates single-cell gene expression data in individual GBM tumors with perturbation-response data derived from the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 dataset to predict sensitive and resistant tumor cell populations. Importantly, we analyzed the predictive power of ISOSCELES in an in vivo xenograft model and demonstrated that ISOSCELES reveals the GBM cell identities primed for lineage expansion during treatment with the aurora kinase inhibitor alisertib. These studies suggest that ISOSCELES can be used to identify sensitive and resistant cell populations to targeted therapies in GBM, which can inform treatment decisions in ongoing and future clinical trials.
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Wen, Huaming, Ryan A. Gallo, Xiaosheng Huang, Jiamin Cai, Shaoyi Mei, Ammad Ahmad Farooqi, Jun Zhao, and Wensi Tao. "Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs for Fuchs Endothelial Corneal Dystrophy." Journal of Ophthalmology 2021 (June 28, 2021): 1–8. http://dx.doi.org/10.1155/2021/5580595.
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Purpose. Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD. Methods. A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD. Results. The RNA-seq dataset of the corneal endothelial cells from FECD patients revealed the differential gene expression signatures of FECD. Many of the differential expressed genes are related to canonical pathways of the FECD pathogenesis, such as extracellular matrix reorganization and immunological response. The expression levels of genes VSIG2, IL18, and ITGB8 were significantly increased in FECD compared with control. Meanwhile, the expression levels of CNGA3, SMOX, and CERS1 were significantly lower in the FECD than in control. We employed LINCS L1000 Characteristic Direction Signature Search Engine (L1000-CDS2) to investigate pathway-based molecular treatment. L1000-CDS2 predicted that small molecule drugs such as histone deacetylase (HDAC) inhibitors might be a potential candidate to reverse the pathological gene expression signature in FECD. Conclusions. Based on differential gene expression signatures, several candidate drugs have been identified to reverse the disease phenotypes in FECD. Gene expression signature with LINCS small molecule prediction software can discover novel preclinical drug candidates for FECD.
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Wang, Zichen, Neil R. Clark, and Avi Ma’ayan. "Drug-induced adverse events prediction with the LINCS L1000 data." Bioinformatics 32, no. 15 (April 1, 2016): 2338–45. http://dx.doi.org/10.1093/bioinformatics/btw168.
Full textDissertations / Theses on the topic "L1000"
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McDermott, Matthew B. A. (Matthew Brian Andrew). "Deep learning benchmarks on L1000 gene expression data." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121738.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-62).
Gene expression data holds the potential to offer deep, physiological insights about the dynamic state of a cell beyond the static coding of the genome alone. I believe that realizing this potential requires specialized machine learning methods capable of using underlying biological structure, but the development of such models is hampered by the lack of an empirical methodological foundation, including published benchmarks and well characterized baselines. In this work, we lay that foundation by profiling a battery of classifiers against newly defined biologically motivated classification tasks on multiple L1000 gene expression datasets. In addition, on our smallest dataset, a privately produced L1000 corpus, we profile per-subject generalizability to provide a novel assessment of performance that is lost in many typical analyses. We compare traditional classifiers, including feed-forward artificial neural networks (FF-ANNs), linear methods, random forests, decision trees, and K nearest neighbor classifiers, as well as graph convolutional neural networks (GCNNs), which augment learning via prior biological domain knowledge. We find GCNNs offer performance improvements given sufficient data, excelling at all tasks on our largest dataset. On smaller datasets, FF-ANNs offer greatest performance. Linear models significantly underperform on all dataset scales, but offer the best per-subject generalizability. Ultimately, these results suggest that structured models such as GCNNs can represent a new direction of focus for the field as our scale of data continues to increase.
by Matthew B. A. McDermott.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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White, Shana. "Application and Development of Novel Methods for Pathway Analysis and Visualization of the LINCS L1000 Dataset." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623241379918016.
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Mekedem, Meriem. "Théorie et applications de modulaire analyse de réponse." Thesis, Université de Montpellier (2022-….), 2022. http://www.theses.fr/2022UMONT002.
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L'inférence des réseaux de régulation est une tâche importante de la biologie des systèmes. Elle permet de transformer des ensembles de données génomiques en connaissances biologiques de haut niveau. Elle consiste en l'ingénierie inverse des réseaux de régulation des gènes à partir des données OMIC, telles que les données RNAseq ou les données protéomiques basées sur la spectrométrie de masse, par le biais de méthodes computationnelles. L'ingénierie inverse des réseaux biologiques à partir d'un système robuste et d'un grand ensemble de données reste le principal défi de la modélisation contemporaine des réseaux. De nombreux efforts ont été faits pour établir des méthodes mais jusqu'à présent aucun gagnant clair n'a émergé. Cette thèse se concentre sur la technique de modélisation mathématique appelée Modular Response Analysis (MRA).J'ai structuré cette thèse en cinq chapitres avec une liste bibliographique de 307 citations.Dans le chapitre 1, je présente les bases de la modélisation mathématique en biologie des systèmes. Je commence par une définition de la biologie des systèmes et de la modélisation mathématique correspondante. Ensuite, je présente les caractéristiques typiques des systèmes biologiques et les modèles correspondants. Cela fournit suffisamment d'informations pour comprendre cette thèse.Dans le chapitre 2, je donne un aperçu général du domaine de l'inférence de réseau. Je me concentre sur les bases et j'essaie de classer les différentes méthodes en fonction de leurs hypothèses et de leur sémantique. Bien entendu, il est impossible de rendre justice à un domaine de recherche aussi riche et étendu dans cette revue.Dans le chapitre 3, je me concentre sur MRA. Cela est dû à sa capacité à traiter des structures biologiques importantes telles que les boucles de rétroaction et la diaphonie, ainsi que les poids de force de connectivité, d'une manière non discrète, ne nécessitant que des quantités gérables de données expérimentales. Je discute d'abord de l'origine et du développement de la théorie de MRA. Ensuite, je présente une amélioration de MRA à l'aide de l'algèbre des blocs linéaires et sa mise en œuvre parallèle. Enfin, je passe brièvement en revue les limites de MRA.Dans le chapitre 4, je présente une application de MRA à un système biologique très stable, tel que le cycle de l'acide tricarboxylique (TCA), qui peut conduire à des équations algébriques linéaires mal conditionnées lorsque des expériences de perturbation induisent de très petits changements dans les données observées. À cette fin, une régularisation de Tikhonov sera mise en œuvre, qui est considérée comme l'une des approches les plus populaires pour résoudre les problèmes discrets mal posés avec des données contaminées par des erreurs.Dans le chapitre 5, je tente d'évaluer l'applicabilité de MRA pour résoudre ce problème de manière pratique. À cette fin, j'ai utilisé des ensembles de données de taille moyenne (>50) et de grande taille (>500). Le premier ensemble de données - de taille moyenne - rapporte l'expression transcriptionnelle de 55 kinases et 6 non-kinases dans 11 conditions expérimentales. Dans chaque condition, les niveaux de transcription de l'ensemble des 61 gènes ont été obtenus par séquençage de l'ARN superficiel, y compris les cellules de type sauvage et les cellules avec des KOs individuels de chaque gène. Le deuxième ensemble de données - de grande taille - a été généré par la nouvelle génération de la carte de connectivité (CMap) en utilisant son nouvel essai L1000. Comme les ensembles de données L1000 sont plus complexes que l'ensemble de données de taille moyenne (ensemble de données sur les kinases) qui est un écran relativement classique, j'introduis, avant de présenter l'article, quelques informations sur la façon dont il a été construitRegulatory network inference is an important task of systems biology. It enables the transformation of genomics datasets into high level biological knowledge. It consists of the reverse engineering of gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. Reverse engineering biological networks from robust system and large data set is still the major challenge of contemporary network modelling. Many efforts have been establish methods but so far no clear winner has emerged. This thesis focuses on the mathematical modelling technique called Modular Response Analysis (MRA)I have structured this thesis in five chapters with a bibliographic list of 307 citations.In Chapter 1, I present the basics of mathematical modelling in systems biology. I start with a definition of systems biology and the corresponding mathematical modelling. Then I present the typical characteristics of biological systems and the corresponding models. This provides sufficient information to understand this thesis.In Chapter 2, I give a general overview of the field of network inference. I focus on the basics and try to classify the different methods according to their assumptions and semantics. Of course, it is impossible to do justice to such a rich and extensive field of research in this review.In Chapter 3, I focus on MRA. This is due to its ability to handle important biological structures such as feedback loops and crosstalk, as well as connectivity force weights, in a non-discrete manner, requiring only manageable amounts of experimental data. I first discuss the origin and development of the MRA theory. Then, I present an improvement of MRA using linear block algebra and its parallel implementation. Finally, I briefly review the limitations of MRA.In Chapter 4, I present an application of MRA to a very stable biological system, such as the tricarboxylic acid (TCA) cycle, which can lead to ill-conditioned linear algebraic equations when perturbation experiments induce very small changes in the observed data. To this end, a Tikhonov regularisation will be implemented, which is considered one of the most popular approaches to solve ill-posed discrete problems with error-contaminated data (Hochstenbach and Reichel, 2010).In Chapter 5, I attempt to evaluate the applicability of MRA to solve this problem in a practical way. For this purpose, I used medium (>50) and large (>500) datasets. The first - medium-sized - dataset reports the transcriptional expression of 55 kinases and 6 non-kinases in 11 experimental conditions. In each condition, transcript levels of all 61 genes were obtained by surface RNA sequencing, including wild-type cells and cells with individual KOs of each gene. The second - large - dataset was generated by the next generation connectivity map (CMap) using its new L1000 assay. As the L1000 datasets are more complex than the medium-sized dataset (kinase dataset) which is a relatively classical screen, I introduce, before presenting the paper, some information on how it was constructed
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Mahi, Naim. "Connectivity Analysis of Single-cell RNA-seq Derived Transcriptional Signatures." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748441148963.
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Petreski, Marjan. "Monetary-regime switch from exchange-rate targeting to inflation targeting : with reference to developing economies." Thesis, Staffordshire University, 2011. http://eprints.staffs.ac.uk/1921/.
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The objective of this thesis is to investigate whether a switch from exchange-rate targeting (de-facto a fixed exchange rate) to inflation targeting will facilitate a more appropriate monetary policy and a more stable macroeconomic environment in developing economies. To achieve this objective, the thesis starts by developing the argument that the exchange-rate peg, as a nominal variable, might be unimportant for affecting long-run growth performance, but detrimental to short-run output stability, particularly in times of real shocks. By using a dynamic system-GMM panel estimator, the research finds that the exchange-rate regime is not significant in explaining growth, either overall or in developing countries. Next, the Hausman-Taylor panel method is used to investigate whether the exchange-rate regime is important in determining output volatility. To overcome the spurious-regression problem arising from the potentially persistent rolling-standard-deviation based measure of output volatility, a new measure is defined; namely, the difference between the potential and the actual output, which might arise from either economic policies or external disturbances. The empirical evidence suggests that, for the overall sample and for the developing countries, a terms-of-trade shock larger than 7 percentage points under a fixed, and larger than 9 percentage points under limited-flexible and flexible exchange-rate regimes, will give higher output volatility compared to a float. These findings are in line with the expectation that pegs provide early gains in terms of inflation stabilization, but longer pegs begin to develop into a threat for output stabilization in the aftermath of an aggregate-supply shock and as the economy becomes more financially integrated. Given these findings, the thesis suggests the exchange rate be made flexible and a new nominal anchor established. The thesis argues that the direct targeting of inflation is a rational choice in the aftermath of peg exit. To investigate whether monetary-policy responses change and produce a more stable macroeconomic environment under regime switching from exchange-rate targeting to inflation targeting, allowing for the possibility of an endogenous switch, the thesis adopts the framework of a fairly classical Taylor rule, augmented by the exchange rate. Two modelling approaches are used to undertake the empirical research: a panel switching regression; and a Markov-switching VAR. Results from both suggest that inflation targeting represented a real switch in developing countries and is characterized by a more stable economic environment, by more independent monetary-policy conduct, by policy geared to strict observation of inflation and by marginal consideration of the real fluctuations of the economy.
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Hlivnjak, Sandra. "Current account sustainability : the case of Bosnia and Herzegovina." Thesis, Staffordshire University, 2010. http://eprints.staffs.ac.uk/1867/.
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This research investigates whether the persistent current account deficit in Bosnia and Herzegovina (BH) is sustainable. Initially current account sustainability is investigated by using the concept of a stationary condition and the mean reversion proposition. It is argued that stationarity of the current account presents a minimum requirement for current account sustainability assessment based on less strict intertemporal solvency conditions. It was found that four out of the five Western Balkan countries investigated have a stationary current account to GDP ratio and therefore met this minimum requirement for sustainability. In order to develop an empirical model to assess current account sustainability in BH, next the Fundamental Equilibrium Exchange Rate is estimated. The conclusion drawn from this analysis was that BH‟s high and persistent current account deficit was not caused by exchange rate misalignment, thus there is no need to adjust the peg. The main reason behind the BH current account deficit is its trade deficit. In the absence of previous analyses of trade deficit sustainability in the WB the next question assessed was whether forming an free trade agreement is a helpful policy for BH utilising an ex post empirical analysis. The analysis of the new Central European Free Trade Agreement concentrates upon three effects: on trade flows using gravity equations; on Bosnia and Herzegovina‟s trade potential and on future deficit sustainability in BH. It was found that although BH trade flows were affected by the CEFTA agreement, the net effect was to contribute to a further widening of the trade deficit in BH. Given the finding that BH‟s current account deficit cannot be attributed to (real) exchange rate misalignment the main conclusion is that current account sustainability analysis must be based on understanding the reasons why BH has a persistent trade deficit. The main reason behind BH current account deficit is its trade deficit. The main factor underlying trade deficit in BH is strong demand for imported goods and also BH‟s supply side weaknesses. Policy-makers need to create an environment for the private sector to develop. Hence both micro and macroeconomic conditions would have to be considered by BH policy-makers in order for this country to improve its export competitiveness and its trade position, which could reduce high BH current account deficit.
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Stojcic, Nebojsa. "Competitiveness, restructuring and firm behaviour in transition : the case Of Croatia." Thesis, Staffordshire University, 2011. http://eprints.staffs.ac.uk/1894/.
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The ability of nations to grow and to provide their citizens with a better standard of living depends on the competitiveness of their firms. During the transition from a centrally-planned to a market economy firms had to face the challenging task of restructuring in order to become more competitive. It was expected that through changes in their behaviour they would be able to replace once dominant price-driven competitive profiles with quality-based profiles which can generate higher value added and can lead to higher rates of growth. The aim of this thesis is to investigate competitiveness of firms and industries in Central and East European Countries (CEECs) in general and Croatia in particular. We argue that the competitiveness of firms and industries is a dynamic process closely related to their restructuring activities, characteristics and environment. With that in mind we apply dynamic panel methodology and dynamic shift-and-share analysis to two large firm and industry level datasets for the period 2000-2007, the most recent year for which data was available to us. We compare the behaviour of Croatian firms with that of their rivals from several advanced CEECs, assess the competitive profile of Croatian exporters and examine the competitiveness of Croatian industries on the EU15 market. Our findings indicate that in an advanced stage of transition the behaviour of firms in CEECs and Croatia was typical of price competitive firms with improvements in labour productivity and cost efficiency being their most important forms of restructuring. Furthermore, we identified several agglomeration externalities and government policy measures such as free trade zones as factors which can facilitate the ability of Croatian firms to compete on international markets. We have also demonstrated that Croatian trade with EU15 is mainly of the vertical intra-industry type. Finally, stronger capital and innovation intensity in combination with higher pressure of imports have positive effects on the relative quality of exports from Croatian industries to the EU15 market. Based on these findings we have developed a set of recommendations for Croatian policy makers and managers which we hope can stimulate the innovativeness of firms and industries and increase their ability to compete through quality.
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Golem, Silvia. "The determinants of the size of government in developed market economies." Thesis, Staffordshire University, 2010. http://eprints.staffs.ac.uk/1876/.
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This research aims at developing and extending the theoretical and empirical literature on the extent of government sector involvement in the economy. It is primarily concerned to analyse the causes of the generally increasing size of the government sector in developed market economies. Despite the importance of this topic in the field of political economy, the literature review suggests that there is no single core theory of the size of government in the economy, only various fragmented theoretical explanations. In an attempt to bridge this analytical gap in the existing knowledge, this research offers a simple integrative theoretical framework. Within that framework, this research gathers and empirically tests the most relevant theories in this field. To that end, it makes use of data for developed market economies in the period from 1970 to 2008. The obtained results indicate that national income, a country‟s degree of modernisation, trade and financial openness, relative prices of government and private goods and government sector employment play an important role in explaining the size of government in developed market economies. In addition, this research contributes to the existing empirical literature by examining the evolution of long, historical time-series of government expenditures for the four developed market economies for which this data is available (the US, the UK, Italy and Sweden). Contrary to conventional wisdom, statistical examination of the data suggests that the major change in the underlying growth rate of government expenditures occurred around the turn of the 20th century. By contributing to a better understanding of the long-run determinants of the size of government in the economy, this research offers a basis for relevant policy proposals and also informs debate on the appropriate size and role of the public sector in a mixed economy.
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Kotorri, Mrika. "An investigation into economic migration with special reference to Kosova." Thesis, Staffordshire University, 2011. http://eprints.staffs.ac.uk/1985/.
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The literature on the economics of migration is immense and it provides three different conceptual frameworks to model the decision to emigrate. In addition to differences among these three models, there are inconsistencies between previous studies within each approach in terms of theoretical rationales for inclusion of variables and their main empirical results. Recently, the literature has focussed more on the effects of migration from the perspective of the home countries, with efforts towards seeking a consensus on the appropriate model of migration decision-making left aside. This thesis is an attempt to fill this gap in the literature. Given the importance of social relations and the system of values, the household is arguably the most appropriate decision-making unit among KS- Albanian households. This thesis tests the applicability of a household perspective in modelling Kosovan migration behaviour. A theoretical framework is outlined where the household as the decision-making unit is modelled as maximising the sum of total expected present value of utilities from current and future household consumption at home and abroad. This theoretical framework is transformed into an empirical proposition that investigates the determinants of whether households plan to send at least one member abroad using a Kosova data set of 2007. This does not cover the second stage of decision making, of which household member(s) will be send. The empirical results are broadly in line with the theoretical expectations of this conceptual framework. The results from the propensity to emigrate suggest that the attitudinal variable, which is unique to this thesis and controls for whether the household head perceives that household income has decreased, is an important determinant. This household perspective is developed to consider the decision on the duration of emigration. The empirical results provide fairly broad support for the theoretical expectations of the model. Additionally, the results indicate that, in addition to economic factors, the prevailing political situation may be important in determining the probability of return conditional on migration duration. Given the major political change in Kosova in 2008, the model developed is further tested by considering its stability over time. The empirical results suggest that the model structure has remained stable over the period of investigation. A further examination based on data from the Albanian LSMS 2008 suggests that the household approach may have greater applicability to migration behaviour in that country. In summary, notwithstanding the countries were chosen to favour the household approach, the results obtained provide broad support for the extended formulation of the household approach.
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Trajkova, Natasha. "Instability and volatility of economic growth under transition : an application of exogenous growth theory." Thesis, Staffordshire University, 2013. http://eprints.staffs.ac.uk/2031/.
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The aim of this thesis is to explore growth processes in transition economies (TEs) by analysing differences between growth patterns in the course of transition and the smooth growth paths characteristic of developed market economies. Accordingly, the thesis builds upon the neoclassical growth theory 10 the transition context to develop a modified theoretical model that conceptualizes transition as a non~linear process consisting of three distinct stages or regimes of growth: the crash or adjustment stage; the recovery stage; and the take~off stage. Namely, instead of describing transition as a movement along one steady state linear growth path, this new approach depicts transition as a process of radical adjournments or shifts between growth paths caused by big structural changes in the economy. This theoretical model is tested not only informally against the observed growth patterns under transition, but also through a series of econometric investigations: (1) Perron's procedure for testing for structural breaks in the presence of a unit root in the data series; (2) a univariate Markov Switching Model (MSM) for assessing (a) whether or not the different hypothesized regimes exist in the data an~ (b) if different growth regimes do exist, both the instability between and volatility within growth regimes; and (3), a multivariate MS VAR model estimated as a small vector autoregression that repeats the univariate MSM investigation into growth regimes but conditional on both physical and human capital variables. The empirical evidence supports the concept of non~linear growth characterised by structural changes and regime shifts. In particular, the univariate MS analysis suggests that most TEs (19 from 26) have passed through all three regimes or stages of transition, with variations across groups in terms of the recorded mean GDP growth rates and the volatility in each regime. Conversely, the multivariate analysis brings forward a somewhat different depiction. Namely, although generally confirming the idea of instability and volatility, the MS VAR analysis suggests that only an elite "few", the five most developed TEs, now EU members have managed to pass through all three stages of transition, as identified in our theoretical model. They can be regarded as having completed their journey by becoming developed market economies In contrast; all the others recorded only two distinct regimes. This result is consistent with our theoretical model in identifying three main stages or growth regimes in the transition process. Finally, the thesis appraises a new notion of transition as a process of dramatic non ~ linear changes that require correspondingly bold policies, particularly if the third regime leading to the developed market economy status is to be attained. Although this thesis does not prescribe specific policy recommendations, it does provide a particular perspective for policymaking, namely one oriented to long-run supply-side reforms.
Books on the topic "L1000"
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Reckmann, Hiltraud. Johann Sebastian Bach, Brandenburgisches Konzert Nr. 5 D-Dur BWV l1050. Altenmedingen: Junker, 1996.
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Allen, Grant. What's Bred in the Bone (Large Print Edition): L1000 Prize Novel. BiblioBazaar, 2007.
Find full textBook chapters on the topic "L1000"
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Chen, Wei, and Xiaobo Zhou. "Drug Signature Detection Based on L1000 Genomic and Proteomic Big Data." In Methods in Molecular Biology, 273–86. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9089-4_15.
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Chen, Wei, and Xiaobo Zhou. "Drug Effect Prediction by Integrating L1000 Genomic and Proteomic Big Data." In Methods in Molecular Biology, 287–97. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9089-4_16.
Full textConference papers on the topic "L1000"
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Huang, Chia-Ling, Andrew Yang, Ted Natoli, Lev Litichevskiy, Frederic Vaillancourt, Alan Rolfe, Yonghong Xiao, Aravind Subramanian, and Lihua Yu. "Abstract 2467: Heme-CMap: Generation and characterization of ~20K L1000 profiles across 11 hematologic malignant lines." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-2467.
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Huang, Chia-Ling, Andrew Yang, Ted Natoli, Lev Litichevskiy, Frederic Vaillancourt, Alan Rolfe, Yonghong Xiao, Aravind Subramanian, and Lihua Yu. "Abstract 2467: Heme-CMap: Generation and characterization of ~20K L1000 profiles across 11 hematologic malignant lines." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-2467.
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Shen, Xia-Xia, Deng-Guang Yu, Li-Min Zhu, and C. Branford-White. "Preparation and Characterization of Ultrafine Eudragit L100 fibers via Electrospinning." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5163230.
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"Antipsychotic activity of a new PTPN5 blocker (TC-2051) in mice with the Disc1-L100P mutation." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-423.
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Tanner, Matthew, Peter Stryker, and Indranil Brahma. "Assessment of the Feasibility of Biodiesel Blends for Small Commercial Engines." In ASME 2012 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icef2012-92159.
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Petroleum supply and environmental issues have increased interest in renewable low polluting alternative fuels. Published test results generally indicate decreased pollution with similar power output from internal combustion engines burning alternative fuels. More specifically, diesel engines burning biodiesel derived from plant oils and animal fats, not only reduce harmful exhaust emissions, but are renewable and environmentally friendly. A literature review found little previous research with biodiesel in small commercial diesel engines. This paper presents the research that was conducted to study the effect of biodiesel/diesel fuel blends on engine performance and emissions for a Yanmar L100 EE (7.1 kW) engine. This is a standard commercial grade diesel engine used for small equipment such as generators. Independent engine dynamometer and emissions testing were performed to validate the lower emission claims and assess the feasibility of alternative fuels. A testing apparatus capable of making relevant measurements was designed, built and used to perform this study. Fuel blends used included B2, B20, B40, B60, B80, and B100 where the biodiesel component of the blend was a commercial product. An analysis of the fuel showed large percentages of linoleic acid, palmitic acid and stearic acid which is typical for a blend of soybean oil and beef tallow. Test were performed at a constant torque (95 % of the continuously rated value) and variable engine speeds. Test results included calculated values of BMEP, BSFC, thermal efficiency, air mass flow rate, air fuel ratio, corrected NOx, energy lost to exhaust, and heat rejection, and measured values of unburned hydrocarbons, carbon monoxide, and carbon dioxide. Results indicate an increase in thermal efficiency compared to standard diesel and significant reductions of unburned hydrocarbons and carbon monoxide at all engine speeds. Brake specific fuel consumption increased with increasing percent biodiesel consistent with the decreased energy content of blended fuel. Significantly, there were small but consistent reductions in corrected NOx for all blends at all speeds. We posit possible explanations for these results, which are contrary to the published results for larger engines which show an increase in NOx for biodiesel blends.
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Fisher, Brian T., Jim S. Cowart, Michael R. Weismiller, Zachary J. Huba, and Albert Epshteyn. "Effects of Amorphous Ti-Al-B Nanopowder Additives on Combustion in a Single-Cylinder Diesel Engine." In ASME 2016 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icef2016-9315.
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Energetic nanoparticles have shown promise as additives to liquid hydrocarbon fuels due to their high specific surface area, high energy content, and catalytic capability. Novel amorphous reactive mixed-metal nanopowders (RMNPs) containing Ti, A1, and B, synthesized via a sonochemical reaction, have been developed at the Naval Research Laboratory. These materials have higher energy content than commercial nano-aluminum (nano-A1), making them potentially useful as energy-boosting fuel components rather than simply catalytic additives. This work examines the combustion behavior of these RMNPs in a small, single-cylinder, 4-stroke diesel engine (Yanmar L48V). Fuel formulations included varying fractions of RMNPs, up to 4 wt. %, suspended in jet fuel JP-5. Comparative experiments also were conducted with equivalent suspensions of nano-A1 in JP-5. For each fuel formulation, with the engine operating at constant speed of 3000 RPM, load was varied across its full range. At each load, cylinder pressure data were recorded for 30 seconds (750 cycles) to enable determination of important combustion characteristics. Although differences were small, both nano-A1 and RMNPs resulted in shorter ignition delays, retarded peak pressure locations, decreased maximum rates of heat release, and increased burn durations. In addition, a similar but larger engine (Yanmar L100V) was used to examine fuel consumption and emissions for a suspension of 8 wt. % RMNPs in JP-5 (and 8 wt. % nano-A1 for comparison). The engine was connected to a genset operating at a constant speed of 3600 RPM and constant load with nominal gIMEP (gross indicated mean effective pressure) of 6.5 bar. Fuel consumption rate was determined from time required to consume 175 mL of each fuel formulation, while emissions levels were recorded once per minute during that time. Unfortunately, combustion data and visual inspection of the injector indicated that RMNPs led to significant deposits on the injector tip and in and around the orifices, which had a negative impact on both fuel consumption rate and emissions. The engine stalled after four minutes of operation with the nano-A1-laden fuel, apparently due to clogging at the bottom of the fuel reservoir. It was concluded that particle settling in the fuel reservoir and particle clogging in the fuel system and injector were significant problems for these composite liquid/powder fuels. Nevertheless, fuel consumption rate was found to be 17% lower for the nano-A1 suspension compared to baseline JP-5 for the period of time that the engine was able to operate, which is a significant achievement towards demonstrating the potential value of reactive metal powder additives in boosting the volumetric energy density of hydrocarbon fuels.
Reports on the topic "L1000"
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Corral, Leonardo, and Giulia Zane. Impact Evaluation of SU-L1009: Support to Improve the Sustainability of Electricity Services. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002952.
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