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Academic literature on the topic 'LST1'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "LST1"

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Roberg, Kevin J., Michelle Crotwell, Peter Espenshade, Ruth Gimeno, and Chris A. Kaiser. "LST1 Is a SEC24 Homologue Used for Selective Export of the Plasma Membrane ATPase from the Endoplasmic Reticulum." Journal of Cell Biology 145, no. 4 (May 17, 1999): 659–72. http://dx.doi.org/10.1083/jcb.145.4.659.

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In Saccharomyces cerevisiae, vesicles that carry proteins from the ER to the Golgi compartment are encapsulated by COPII coat proteins. We identified mutations in ten genes, designated LST (lethal with sec-thirteen), that were lethal in combination with the COPII mutation sec13-1. LST1 showed synthetic-lethal interactions with the complete set of COPII genes, indicating that LST1 encodes a new COPII function. LST1 codes for a protein similar in sequence to the COPII subunit Sec24p. Like Sec24p, Lst1p is a peripheral ER membrane protein that binds to the COPII subunit Sec23p. Chromosomal deletion of LST1 is not lethal, but inhibits transport of the plasma membrane proton-ATPase (Pma1p) to the cell surface, causing poor growth on media of low pH. Localization by both immunofluorescence microscopy and cell fractionation shows that the export of Pma1p from the ER is impaired in lst1Δ mutants. Transport of other proteins from the ER was not affected by lst1Δ, nor was Pma1p transport found to be particularly sensitive to other COPII defects. Together, these findings suggest that a specialized form of the COPII coat subunit, with Lst1p in place of Sec24p, is used for the efficient packaging of Pma1p into vesicles derived from the ER.
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Cui, Yixian, Smriti Parashar, Muhammad Zahoor, Patrick G. Needham, Muriel Mari, Ming Zhu, Shuliang Chen, et al. "A COPII subunit acts with an autophagy receptor to target endoplasmic reticulum for degradation." Science 365, no. 6448 (July 4, 2019): 53–60. http://dx.doi.org/10.1126/science.aau9263.

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The COPII-cargo adaptor complex Lst1-Sec23 selectively sorts proteins into vesicles that bud from the endoplasmic reticulum (ER) and traffic to the Golgi. Improperly folded proteins are prevented from exiting the ER and are degraded. ER-phagy is an autophagic degradation pathway that uses ER-resident receptors. Working in yeast, we found an unexpected role for Lst1-Sec23 in ER-phagy that was independent from its function in secretion. Up-regulation of the stress-inducible ER-phagy receptor Atg40 induced the association of Lst1-Sec23 with Atg40 at distinct ER domains to package ER into autophagosomes. Lst1-mediated ER-phagy played a vital role in maintaining cellular homeostasis by preventing the accumulation of an aggregation-prone protein in the ER. Lst1 function appears to be conserved because its mammalian homolog, SEC24C, was also required for ER-phagy.
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Wan, Jikang, Min Zhu, and Wei Ding. "Accuracy Evaluation and Parameter Analysis of Land Surface Temperature Inversion Algorithm for Landsat-8 Data." Advances in Meteorology 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/9917145.

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Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the rise of LST. R2 of the inversion results of each LST algorithm and the measured data are all greater than 0.73°C in winter and about 0.5°C in the other seasons. By analyzing the stability of various algorithms inside and outside the city, it is found that the stability of each LST inversion algorithm inside the city is better than that outside the city. For the same surface features, the inversion temperature inside the city is 3–5°C higher than that outside the city. In addition, the sensitivity of various inversion algorithms to parameters was also analyzed. The influence of atmospheric transmittance on RTE, SC, and MW inversion algorithms is in logarithmic form. The effect of emissivity on each algorithm is linear. The influence of NDVI on the algorithms is mainly through the estimation of surface emissivity parameters to affect the inversion results. The effect of ascending radiation on SC (LST4 and LST5) is linear and on RTE (LST1 and LST2) is logarithmic. The effect of downslope radiation on SC and RTE is linear. The influence of atmospheric water vapor content on SW (LST7) is nonlinear.
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Yau, Anthony C. Y., Jonatan Tuncel, Sabrina Haag, Ulrika Norin, Miranda Houtman, Leonid Padyukov, and Rikard Holmdahl. "Conserved 33-kb haplotype in the MHC class III region regulates chronic arthritis." Proceedings of the National Academy of Sciences 113, no. 26 (June 14, 2016): E3716—E3724. http://dx.doi.org/10.1073/pnas.1600567113.

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Genome-wide association studies have revealed many genetic loci associated with complex autoimmune diseases. In rheumatoid arthritis (RA), the MHC gene HLA-DRB1 is the strongest candidate predicting disease development. It has been suggested that other immune-regulating genes in the MHC contribute to the disease risk, but this contribution has been difficult to show because of the strong linkage disequilibrium within the MHC. We isolated genomic regions in the form of congenic fragments in rats to test whether there are additional susceptibility loci in the MHC. By both congenic mapping in inbred strains and SNP typing in wild rats, we identified a conserved, 33-kb large haplotype Ltab-Ncr3 in the MHC-III region, which regulates the onset, severity, and chronicity of arthritis. The Ltab-Ncr3 haplotype consists of five polymorphic immunoregulatory genes: Lta (lymphotoxin-α), Tnf, Ltb (lymphotoxin-β), Lst1 (leukocyte-specific transcript 1), and Ncr3 (natural cytotoxicity-triggering receptor 3). Significant correlation in the expression of the Ltab-Ncr3 genes suggests that interaction of these genes may be important in keeping these genes clustered together as a conserved haplotype. We studied the arthritis association and the spliceo-transcriptome of four different Ltab-Ncr3 haplotypes and showed that higher Ltb and Ncr3 expression, lower Lst1 expression, and the expression of a shorter splice variant of Lst1 correlate with reduced arthritis severity in rats. Interestingly, patients with mild RA also showed higher NCR3 expression and lower LST1 expression than patients with severe RA. These data demonstrate the importance of a conserved haplotype in the regulation of complex diseases such as arthritis.
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Schiller, Christian, Maximilian J. E. Nitschké, Alexander Seidl, Elisabeth Kremmer, and Elisabeth H. Weiss. "Rat Monoclonal Antibodies Specific for LST1 Proteins." Hybridoma 28, no. 4 (August 2009): 281–86. http://dx.doi.org/10.1089/hyb.2009.0021.

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Roberg, Kevin J., Stephen Bickel, Neil Rowley, and Chris A. Kaiser. "Control of Amino Acid Permease Sorting in the Late Secretory Pathway of Saccharomyces cerevisiae by SEC13, LST4, LST7 and LST78." Genetics 147, no. 4 (December 1, 1997): 1569–84. http://dx.doi.org/10.1093/genetics/147.4.1569.

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Abstract The SEC13 gene was originally identified by temperature-sensitive mutations that block all protein transport from the ER to the Golgi. We have found that at a permissive temperature for growth, the sec13-1 mutation selectively blocks transport of the nitrogen-regulated amino acid permease, Gaplp, from the Golgi to the plasma membrane, but does not affect the activity of constitutive permeases such as Hip1p, Can1p, or Lyp1p. Different alleles of SEC13 exhibit different relative effects on protein transport from the ER to the Golgi, or on Gap1p activity, indicating distinct requirements for SEC13 function at two different steps in the secretory pathway. Three new genes, LST4, LST7, and LSTB, were identified that are also required for amino acid permease transport from the Golgi to the cell surface. Mutations in LST4 and LST7 reduce the activity of the nitrogen-regulated permeases Gap1p and Put4p, whereas mutations in LST8 impair the activities of a broader set of amino acid permeases. The LST8 gene encodes a protein composed of WD-repeats and has a close human homologue. The LST7 gene encodes a novel protein. Together, these data indicate that SEC13, LST4, LST7, and LST8 function in the regulated delivery of Gap1p to the cell surface, perhaps as components of a post-Golgi secretory-vesicle coat.
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Rollinger-Holzinger, Ingrid, Brigitte Eibl, Marc Pauly, Ute Griesser, François Hentges, Bernhard Auer, Georg Pall, et al. "LST1: A Gene with Extensive Alternative Splicing and Immunomodulatory Function." Journal of Immunology 164, no. 6 (March 15, 2000): 3169–76. http://dx.doi.org/10.4049/jimmunol.164.6.3169.

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Schiller, Christian, Carina Nowak, Kalliope N. Diakopoulos, Ulrich H. Weidle, and Elisabeth H. Weiss. "An Upstream Open Reading Frame Regulates LST1 Expression during Monocyte Differentiation." PLoS ONE 9, no. 5 (May 9, 2014): e96245. http://dx.doi.org/10.1371/journal.pone.0096245.

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Weidle, Ulrich H., Ina Rohwedder, Fabian Birzele, Elisabeth H. Weiss, and Christian Schiller. "LST1: A multifunctional gene encoded in the MHC class III region." Immunobiology 223, no. 11 (November 2018): 699–708. http://dx.doi.org/10.1016/j.imbio.2018.07.018.

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D’Aloia, Alessia, Edoardo Arrigoni, Barbara Costa, Giovanna Berruti, Enzo Martegani, Elena Sacco, and Michela Ceriani. "RalGPS2 Interacts with Akt and PDK1 Promoting Tunneling Nanotubes Formation in Bladder Cancer and Kidney Cells Microenvironment." Cancers 13, no. 24 (December 16, 2021): 6330. http://dx.doi.org/10.3390/cancers13246330.

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RalGPS2 is a Ras-independent Guanine Nucleotide Exchange Factor for RalA GTPase that is involved in several cellular processes, including cytoskeletal organization. Previously, we demonstrated that RalGPS2 also plays a role in the formation of tunneling nanotubes (TNTs) in bladder cancer 5637 cells. In particular, TNTs are a novel mechanism of cell–cell communication in the tumor microenvironment, playing a central role in cancer progression and metastasis formation. However, the molecular mechanisms involved in TNTs formation still need to be fully elucidated. Here we demonstrate that mid and high-stage bladder cancer cell lines have functional TNTs, which can transfer mitochondria. Moreover, using confocal fluorescence time-lapse microscopy, we show in 5637 cells that TNTs mediate the trafficking of RalA protein and transmembrane MHC class III protein leukocyte-specific transcript 1 (LST1). Furthermore, we show that RalGPS2 is essential for nanotubes generation, and stress conditions boost its expression both in 5637 and HEK293 cell lines. Finally, we prove that RalGPS2 interacts with Akt and PDK1, in addition to LST1 and RalA, leading to the formation of a complex that promotes nanotubes formation. In conclusion, our findings suggest that in the tumor microenvironment, RalGPS2 orchestrates the assembly of multimolecular complexes that drive the formation of TNTs.
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Dissertations / Theses on the topic "LST1"

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Schiller, Christian. "Funktion und Expression der transmembranen Isoformen des HLA-Klasse-III-Gens LST1." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-126674.

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D'ALOIA, ALESSIA. "RalGPS2 interacts with LST1 and supports tunneling nanotubes formation in human bladder cancer cells." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158357.

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RalGPS2 è uno scambiatore appartenente alla famiglia RalGPS, composto da un dominio catalitico Cdc25-like nella regione N-terminale, un motivo PxxP nella regione centrale, e un dominio di omologia alla Pleckstrina (PH) nella regione C-terminale. E’ stato precedentemente dimostrato che RalGPS2 attiva in “vivo” la GTPasi RalA, mentre la regione PH-PxxP si comporta da dominante negativo per l’attività di RalA in cellule NIH3T3 e PC12. Inoltre, se è overespresso RalGPS2 causa cambiamenti morfologici consistenti nelle cellule HEK293, suggerendo che esso possa avere effetti sul citoscheletro. Tutto ciò suggerisce un possibile ruolo di RalGPS2 nella riorganizzazione del citoscheletro anche in linee cellulari tumorali. A tal fine è stata scelta come modello la linea cellulare umana 5637 di cancro alla vescica, in cui la GTPasi RalA è iperattiva. Nel presente lavoro abbiamo dimostrato che RalGPS2 da solo è in grado attivare RalA in “vivo”, mentre la sua deplezione ne abbassa notevolmente i livelli. In più si è dimostrato che la regione PH-PxxP e il dominio PH di RalGPS2 si comportano da dominanti negativi per l’attività di RalA. Inoltre, analisi al confocale hanno rivelato una parziale ma marcata co-localizzazione tra RalA, RalGPS2, il dominio PH e la regione PH-PxxP a livello della membrana plasmatica e in sottili protrusioni di membrana. La presenza di queste protrusioni in cui si localizzava RalA ha suggerito che esse potessero essere nanotubi traforati (TNT). I TNT sono condotti intracellulari per il trasporto di vari componenti cellulari o segnali importanti per la comunicazione cellulare. Siccome i TNT sono stati precedentemente descritti come strutture costituite da actina ma non da tubulina, si è utilizzato questo criterio per caratterizzare tali protrusioni. L’analisi al microscopio confocale ha evidenziato la presenza di protrusioni ricche in actina ma povere in tubulina. Per valutare se effettivamente RalGPS2 e i suoi domini influenzino la formazione dei TNT, si è condotta un’analisi al microscopio confocale in cui si andava a caratterizzare le protrusioni formate dalle cellule. Un’analisi statistica dettagliata ha evidenziato che RalGPS2 supporta la formazione di TNT in cellule 5637. Successivamente si è cercato di analizzare il ruolo degli effettori di RalA nella formazione dei TNT. Un’analisi statistica accurata ha dimostrato che il blocco di Sec5 (subunià del complesso delle esocisti ed effettore di RalA) riduce fortemente la formazione dei TNT. Dunque sia Sec5 che RalGPS2 sembrano giocare un ruolo chiave nella genesi di queste strutture. Per confermare il ruolo di RalGPS2 nella formazione dei TNT e per valutare se esso cooperi assieme a Sec5 in tale processo abbiamo effettuato un saggio di co-immunoprecipiatazione. Tale analisi rivela la presenza di un complesso tra RalA, RalGPS2,LST1 (proteina che induce la formazione dei TNT) e Sec5. Inoltre è stato dimostrato che RalGPS2 supporta la formazione dei TNT maggiormente in condizioni di carenza di nutrienti. I risultati ottenuti ci suggeriscono l’esistenza di due pathway compresenti, ma maggiormente attivati in condizioni diverse. In questa proposta RalGPS2 interagendo con LST1 e RalA determina la formazione di un complesso che in condizioni di stress si attiva e permette l’interazione tra RalA e Sec5. L’interazione RalA-Sec5 determina l’assemblaggio di un complesso multi-proteico che controlla la formazione dei nanotubi. Al contrario in condizioni di stimolo proliferativo, sebbene il complesso RalGPS2-LST1-RalA sia comunque presente e in parte attivo è eclissato dall’attivazione di un altro pathway che ha come protagonisti i GEF della famiglia RalGDS, la GTPasi RalA e Sec5. In queste condizioni infatti i GEF RalGDS sono attivi e interagiscono con RalA attivandola. In questo stato attivo RalA interagisce a sua volta con Sec5 promuovendo l’assemblaggio del complesso delle esocisti e regolando così l’esocitosi.
RalGPS2 is a murine guanine nucleotide exchange factor belonging to RalGPS family; that contains a well conserved CDC25-like domain in the N-terminal region, a PxxP motif in central region and a PH (Pleckstrin Homology) domain in the C-terminus. It has been demonstrated that RalGPS2 can activate RalA in vivo, while the PH-PxxP domain behaves as a dominant negative for RalA activation in NIH3T3 and PC12 cells. Furthermore, when overexpressed, RalGPS2 causes considerable morphological changes in HEK293 cells, suggesting its possible role on cytoskeleton re-organization. These data suggest us a possible role of RalGPS2 and its domains in cytoskeleton re-modelling also in tumour cell lines. For this purpose it has been chosen the human bladder cancer cell line 5637, as a model. In the present work it has been shown that RalGPS2 alone is able to activate RalA in “vivo”, while its depletion significantly lowers RalA levels. Furthermore, it has been demonstrated that PH-PxxP region and PH domain of RalGPS2 behave as dominant negatives for RalA activation. Moreover, confocal analysis reveals a partial, but marked co-localization between RalA, RalGPS2, the PH domain and the PH-PxxP region at the level of plasma membrane end in thin membrane protrusions. The presence of these protrusions in which localize the GTPase RalA suggested us that these structures could be Tunneling Nanotubes (TNTs). TNTs are intercellular conduits and have been shown to enable the transport of various cellular components and signals, they are important for cellular communication between cells. Since nanotubes were initially described to contain actin but not tubulin we used this criterion to characterize the protrusions that we have observed in 5637 cells. Confocal analysis reveals presence of protrusions rich in actin but poor in tubulin. To determinate whether RalGPS2 and its domain induce formation of TNTs, it has been made a confocal analysis in which it has been characterized protrusions formed by cells. Statistical analysis reveals that RalGPS2 supports TNTs formation in 5637 cells. Later, it has been analyzed the role of RalA effectors in TNTs formation. Statistical analysis shown that lack of interaction between RalA and Sec5 (subunit of exocyst complex and RalA effector) strongly reduces nanotubes formation. Therefore, both Sec5 and RalGPS2 seem to play a key role in generation of these structures. To confirm the role of RalGPS2 in TNTs formation and to evaluate whether it cooperates with Sec5 in this process, it has been performed an co-immunoprecipitation assay. This investigation reveals the presence of a complex between RalA,RalGPS2, LST1 (protein which induces TNTs formation) and Sec5. Moreover, it has been demonstrated that RalGPS2 supports TNT formation more in conditions of nutrient deficiency. Results obtained suggest the existence of two coexisting pathways, but more activates under different conditions. In this proposal, interaction between RalGPS2, LST1 and RalA establishes formation of a complex that under stress condition is active and allows the interaction between the RalA and Sec5. RalA-Sec5 interaction determines the assembly of multi-protein complex which controls TNTs formation. On the contrary, in proliferative stimulus conditions, while RalGPS2-LST1-RalA complex is still present and partially activated, it is outclassed by the activation of a distinct pathway in which GEFs of the RalGDS family, the RalA GTPase and Sec5 play a pivotal role. In such conditions, RalGDS GEFs are activated and interact with the RalA GTPase while promoting the GDP-GTP exchange. RalA in its active state also interacts with Sec5, allowing the assembly of the exocyst complex and so regulating the exocytosis.
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Pacitto, Angela. "Towards structural and functional understanding of the Flcn/Fnip complex through its yeast orthologue Lst7/Lst4." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708934.

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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.

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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio files, the conventionalLSTM network yielded an accuracy rate of 64.8 % while the grid LSTM network yielded anaccuracy rate of 65.2 %. Statistically, there was no difference in the accuracy rate betweenthe models. In addition, the conventional LSTM network took 2 % longer to train. However,this difference in training time is considered to be of little significance when tralnslating it toabsolute time. Thus, it can be concluded that the one-dimensional grid LSTM model performsjust as well as the conventional one.
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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.

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Borrello, Maria Teresa. "Reversible and irreversible LSD1 inhibitors." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59682/.

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Environmental factors and lifestyle can alter the way our genes are expressed influencing a network of chemical switches within our cells collectively known as the Epigenome. Among the epigenetic mechanisms orchestrating the gene expression, methylation is of foremost importance and probably fair to say, still incompletely decoded. Dysregulations of histone methylation patterns lead to the repression or activation of signalling pathways that often promote the genesis and progression of disease states. Lysine specific demethylase 1 (LSD1) oxidatively removes methyl groups from histone H3 and its aberrant activity has been correlated with the development of a broad range of pathologies. Therefore, specific inhibitors of LSD1 have potential in pharmacological applications. Research into LSD1 and its functions in normal and abnormal cells has been hindered by the lack of a specific and potent suppressor. The development of a selective inhibitor could not only foster the understanding of the biological roles of LSD1 but also represent a breakthrough for the design of novel drugs for a range of burdensome diseases. Here we investigate on reversible and irreversible inhibitors of LSD1, with the hope of broadening the current knowledge on this epigenetic target. By analysing the LSD1 interaction with the transcription factor Snail-1, we generated a series of small peptides as potential reversible inhibitors. The synthetic peptides were then evaluated in cellular assays. In search of novel non-covalent LSD1 blockers, we next explored Phage Display technology. Thereafter, we targeted LSD1 covalently by synthesising multiple structural analogues of the clinically used antidepressant TCP (Parnate®), which is a known irreversible suppressor of LSD1 activity. We evaluated their ability of inhibiting LSD1 in a cell-free assay and the compounds showing enzymatic inhibition were tested as potential anti-proliferative and differentiating agents in leukaemia cell lines. Finally, we generated activity-based probes to fluorescently label LSD1 for biological applications.
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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.

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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean position of the thunderstorms using an LSTM network. A random search is then carried out to identify optimal parameters for the LSTM model. It is shown that the trajectories predicted by the LSTM are much closer to the true trajectories than what a linear model predicts. This is especially true for predictions of more than 1 hour. Scores commonly used to measure forecast accuracy are applied to compare the LSTM and linear model. It is found that the LSTM significantly improves forecast accuracy compared to the linear model.
Åskväder är både farliga och livsviktiga bärare av vatten för stora delar av världen. Det är dock svårt att förutsäga åskcellernas banor, främst i tropiska områden. Detta beror till större delen på deras mindre storlek och kortare livslängd. Detta examensarbete undersöker hur väl ett neuralt nätverk, bestående av long short-term memory-lager (LSTM) kan förutsäga åskväders banor baserat på flera års blixtnedlslagsdata. Först klustras datan, och viktiga karaktärsdrag hämtas ut från den. Dessa används för att förutspå åskvädrens genomsnittliga position med hjälp av ett LSTMnätverk. En slumpmässig sökning genomförs sedan för att identifiera optimala parametrar för LSTM-modellen. Det fastslås att de banor som förutspås av LSTM-modellen är mycket närmare de sanna banorna, än de som förutspås av en linjär modell. Detta gäller i synnerhet för förutsägelser mer än 1 timme framåt. Värden som är vanliga för att bedöma prognosers träffsäkerhet beräknas för att jämföra LSTM-modellen och den linjära. Det visas att LSTM-modellen klart förbättrar förutsägelsernas träffsäkerhet jämfört med den linjära modellen.
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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.

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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. This is important since the handling training data can enormously influence on the final prediction accuracy of the network. Finally, a number of changes to improve these methods are suggested. These include ways to raise accuracy, reduce network overhead, and to improve the consistency of results. This work shows that augmenting an LSTM prefetcher with a simple stream prefetcher leads to moderate improvements in prediction accuracy. This could be a way to start reducing the size of neural networks so they are usable in real hardware.
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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.

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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substituting active performance measurements with a machine-learning algorithm, you reduce the overhead created by active performance measurements Previous research has revolved around applying traditional machine learning algorithms to wireless sensor network features such as packet statistics and topological information of the network to predict round-trip time. The purpose of this thesis is to use a  more advanced deep learning algorithm namely Long short-term memory (LSTM) to try and exploit time dependencies in the data Three different datasets containing network statistics are used in three different experiments. In every experiment, LSTM models with different configurations are created, and their predictioncapabilities are compared to traditional neural networks with equivalent configurations. In all experiments, both the LSTM model and its corresponding equivalent neural network model produced similar results, meaning that a time dependency in the data could not be proven.
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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.

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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas och evalueras samtidigt. Agenterna utvecklades med hjälp av en genetisk algoritm. Resultaten visade att LSTM var minst lika bra som feedforward men båda metoderna blev slagna av Machados metoder. Toppoängen i varje spel jämfördes med Granfelts arbete som har varit en utgångspunkt för detta arbete.
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Books on the topic "LST1"

1

Finanzen, Austria Bundesministerium für. Lohnsteuerrichtlinien 1999: LStR 1999. 2nd ed. Wien: P. Dorner, 1999.

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Oklahoma. Dept. of Libraries. LSTA 5-year plan, 2003-2007. Oklahoma City, OK: Oklahoma Dept. of Libraries, 2002.

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Humphreys, Ran. My days aboard LST 748. Lynn Haven, Fla: HCR Publications, 2001.

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Gene, Harter, ed. The saga of LST 224. Jackson, Tenn: Main Street Publishing, 2003.

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D'Orta, Marcello. [In Afrika lst immer August]. [Tehran?]: [S.n.], 2000.

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Rose, Lewis, ed. Memories of the USS LST 694. [S.l: s.n., 1985.

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Lee, S. RELAP5 assessment using LSTF test data SB-CL-18. Washington, DC: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1993.

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Library, Maine State, Library Development Solutions (Firm), and Institute of Museum and Library Services (U.S.), eds. Maine shares: An evaluation of Maine's Five Year LSTA Plan. [Augusta, Me.]: Maine State Library, 2002.

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Bertung, Birgit. Kierkegaard, kristendom og konsekvens: Søren Kierkegaard lst logisk. [Copenhagen]: C.A. Reitzels forlag, 1994.

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Association, United States LST, ed. Large slow target: A history of the LST. Toledo, Ohio: U.S. LST Association, 1986.

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Book chapters on the topic "LST1"

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Korstanje, Joos. "LSTM RNNs." In Advanced Forecasting with Python, 243–51. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_18.

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Eelbode, Tom, Pieter Sinonquel, Raf Bisschops, and Frederik Maes. "Convolutional LSTM." In Computer-Aided Analysis of Gastrointestinal Videos, 121–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64340-9_14.

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Wang, Ximin, Luyi Huang, Junlan Zhu, Wenbo He, Zhaopeng Qin, and Ming Yuan. "LSTM-Exploit: Intelligent Penetration Based on LSTM Tool." In Advances in Artificial Intelligence and Security, 84–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_8.

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Adenuga, Olukorede Tijani, Khumbulani Mpofu, and Ragosebo Kgaugelo Modise. "Application of ARIMA-LSTM for Manufacturing Decarbonization Using 4IR Concepts." In Lecture Notes in Mechanical Engineering, 115–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_12.

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AbstractIncreasing climate change concerns call for the manufacturing sector to decarbonize its process by introducing a mitigation strategy. Energy efficiency concepts within the manufacturing process value chain are proportional to the emission reductions, prompting decision makers to require predictive tools to execute decarbonization solutions. Accurate forecasting requires techniques with a strong capability for predicting automotive component manufacturing energy consumption and carbon emission data. In this paper we introduce a hybrid autoregressive moving average (ARIMA)-long short-term memory network (LSTM) model for energy consumption forecasting and prediction of carbon emission within the manufacturing facility using the 4IR concept. The method could capture linear features (ARIMA) and LSTM captures the long dependencies in the data from the nonlinear time series data patterns, Root means square error (RMSE) is used for data analysis comparing the performance of ARIMA which is 448.89 as a single model with ARIMA-LSTM hybrid model as actual (trained) and predicted (test) 59.52 and 58.41 respectively. The results depicted RMSE values of ARIMA-LSTM being extremely smaller than ARIMA, which proves that hybrid ARIMA-LSTM is more suitable for prediction than ARIMA.
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Manaswi, Navin Kumar. "RNN and LSTM." In Deep Learning with Applications Using Python, 115–26. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3516-4_9.

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Adam, Kazybek, Kamilya Smagulova, and Alex Pappachen James. "Memristive LSTM Architectures." In Modeling and Optimization in Science and Technologies, 155–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14524-8_12.

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Ninagawa, Chuzo. "LSTM AI Modeling." In AI Time Series Control System Modelling, 67–90. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4594-6_4.

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Zheng, Lin, Chaowei Qi, and Shibo Zhao. "Multivariate Passenger Flow Forecast Based on ACLB Model." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 104–13. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_12.

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AbstractWith the rapid increase in urban population, urban traffic problems are becoming severe. Passenger flow forecasting is critical to improving the ability of urban buses to meet the travel needs of urban residents and alleviating urban traffic pressure. However, the factors affecting passenger flow have complex non-linear characteristics, which creates a bottleneck in passenger flow prediction. Deep learning models CNN, LSTM, BISTM and the gradually emerging attention mechanism are the key points to solve the above problems. Based on summarizing the characteristics of various models, this paper proposes a multivariate prediction model ACLB to extract the nonlinear spatio-temporal characteristics of passenger flow data. We compare the performance of ACLB model with CNN, LSTM, BILSTM, CNN-LSTM, FCN-ALSTM through experiments. ACLB performance is better than other models.
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Huynh, Manh, and Gita Alaghband. "Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM." In Advances in Visual Computing, 244–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_19.

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Grósz, Tamás, and Mikko Kurimo. "LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells." In Text, Speech, and Dialogue, 382–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_32.

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Conference papers on the topic "LST1"

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Di Pierro, Federico, L. Arrabito, A. Baquero Larriva, A. Berti, J. Bregeon, D. Depaoli, D. Dominis Prester, et al. "Monte Carlo Studies of Combined MAGIC and LST1 Observations." In 36th International Cosmic Ray Conference. Trieste, Italy: Sissa Medialab, 2019. http://dx.doi.org/10.22323/1.358.0659.

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Xing, Bowen, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, and Heyan Huang. "Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/738.

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Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.
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Chen, Zhenzhong, and Wanjie Sun. "Scanpath Prediction for Visual Attention using IOR-ROI LSTM." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/89.

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Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.
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Li, Haifang, Yingce Xia, and Wensheng Zhang. "Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/331.

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Policy evaluation with linear function approximation is an important problem in reinforcement learning. When facing high-dimensional feature spaces, such a problem becomes extremely hard considering the computation efficiency and quality of approximations. We propose a new algorithm, LSTD(lambda)-RP, which leverages random projection techniques and takes eligibility traces into consideration to tackle the above two challenges. We carry out theoretical analysis of LSTD(lambda)-RP, and provide meaningful upper bounds of the estimation error, approximation error and total generalization error. These results demonstrate that LSTD(lambda)-RP can benefit from random projection and eligibility traces strategies, and LSTD(lambda)-RP can achieve better performances than prior LSTD-RP and LSTD(lambda) algorithms.
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Bian, Song, Michihiro Shintani, Masayuki Hiromoto, and Takashi Sato. "LSTA." In DAC '17: The 54th Annual Design Automation Conference 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3061639.3062280.

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Zhu, Yu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. "What to Do Next: Modeling User Behaviors by Time-LSTM." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/504.

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Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.
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Hu, Weifei, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, and Jiancheng Chen. "A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16500.

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Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.
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Wang, Fuyong, Yun Zai, Jiuyu Zhao, and Siyi Fang. "Field Application of Deep Learning for Flow Rate Prediction with Downhole Temperature and Pressure." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21364-ms.

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Abstract Well real-time flow rate is one of the most important production parameters in oilfield and accurate flow rate information is crucial for production monitoring and optimization. With the wide application of permanent downhole gauge (PDG), the high-frequency and large volume of downhole temperature and pressure make applying of deep learning technique to predict flow rate possible. Flow rate of production well is predicted with long short-term memory (LSTM) network using downhole temperature and pressure production data. The specific parameters of LSTM neural network are given, as well as the methods of data preprocessing and neural network training. The developed model has been validated with two production wells in the Volve Oilfield, North Sea. The field application demonstrates that the deep learning is applicable for flow rate prediction in oilfields. LSTM has the better performance of flow rate prediction than other five machine learning methods, including support vector machine (SVM), linear regression, tree, and Gaussian process regression. The LSTM with a dropout layer has a better performance than a standard LSTM network. The optimal numbers of LSTM layers and hidden units can be adjusted to obtain the best prediction results, but more LSTM layers and hidden units lead to more time of training and prediction, and LSTM model might be unstable and cannot converge. Compared with only downhole pressure or temperature data used as input parameters, flow rate prediction with both of downhole pressure and temperature used as input parameters has the higher prediction accuracy.
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Liao, Yabin, Biswas Poudel, Priyanshu Kumar, and Mark Sensemier. "Long Short-Term Memory Neural Networks for Predicting Dynamic Response of Structures of High Complexities." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-97025.

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Abstract This paper presents an initial investigation on the feasibility of modeling structural dynamics of complex structures using the Long Short-Time Memory (LSTM) deep learning neural networks, and predicting the structures’ vibration responses due to random excitation. LSTM networks are applied to the responses of various simulated systems subjected to random excitation loads, including mass-spring-damper systems with linear or nonlinear Duffing springs, a cantilever beam, and a tapered, cambered wing structure. Given a known force input, the dynamic response of the system is simulated in Matlab or ANSYS, which is used to train the LSTM model. In the case of mass-spring-damper and beam systems, the excellent agreement between the test and LSTM-predicted responses demonstrates the potential of the LSTM method for predicting vibration responses. In the case of cambered wing structure, the LSTM shows difficulties in dealing with responses consisting of multiple modes. Parametric studies are also performed to discover possible means for performance improvement. The studied learning parameters include the number of hidden units, the number of LSTM layers, and the size of train data. It is found that all these parameters have significant impact on the model accuracy. While it is always beneficial to have as much and there could be an optimal setting for the number of is not determined by the studies, they provide valuable directions to.
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Correia, Jaqueline B., Marcos Pivetta, Givanildo Santana do Nascimento, and Karin Becker. "Comparing ARIMA and LSTM models to predict time series in the oil industry." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/kdmile.2021.17470.

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Monitoring and forecasting oil and gas (O\&G) production is essential to extend the life of a well and increase reservoirs' productivity. Popular models for O\&G time series are ARIMA and LSTM recurrent networks, and tipically several lags are forecasted at once. LSTM models can deploy the recursive prediction strategy, which uses one prediction to make the next, or the multiple outputs (MO) strategy, which predicts a sequence of values in a single shot. This work assesses ARIMA and LSTM models for the forecasting of petroleum production time series. We use time series of pressure and gas/oil flow from actual wells with distinct properties, for which we developed predictive models considering different time horizons. For the LSTM models, we deploy both the recursive and MO strategies. Our comparison revealed the superiority of LSTM models in general, and MO-based models for longer time intervals.
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Reports on the topic "LST1"

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Phipps, G. S., S. M. Gentry, J. M. Falls, P. J. Claassen, and G. J. Alder. TAOS/LS1 development final report. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/565592.

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Williams, N. Characterization of LST Z Plane Signals. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/833108.

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Lee, S., B. D. Chung, and H. J. Kim. RELAP5 assessment using LSTF test data SB-CL-18. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10162957.

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Battaglia, Sebastiano. Targeting LSD1 Epigenetic Signature in Castration-Recurrent Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612062.

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Lin, Yiwei. BHC80 is Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada576366.

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Lin, Yiwei. BHC80 is Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada603933.

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Lin, Yiwei. BHC80 ss Critical in Suppression of Snail-LSD1 Interaction and Breast Cancer Metastasis. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada560278.

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Stickland, David P. Online Luminosity Measurement at CMS for Energy Frontier Physics after LS1. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1215468.

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Chen, Jian, Yufen Xie, Ruihao Liu, Zhigao Liu, Xiaozhou Long, Jinlong Huang, Haiwei Xiong, and Yinliang Li. The association between LSP1 rs3817198(T>C) polymorphism and breast cancer: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0127.

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Vickers, Don, and Don Larson. LST CGM Generator and Viewer Final Report CRADA No. TSB-1558-98. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1410004.

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