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We present 13 spectra and 31 photometric observations covering the first 150 days of SN 1991bg in NGC 4374 (M 84). Although SN 1991bg was a type Ia supernova displaying the characteristic Si II absorption at 6150 A near maximum and the Fe emission lines at late phases, it varied from the well-defined norm for SNe Ia in several important respects. The peculiarities include faster declines in the B and V light curves after maximum, a distinct color evolution, a very red B - V color near maximum, relatively faint peak luminosity, a distinct spectral evolution, and a short peak phase. The narrow peak of the luminosity and the rapid declines of the light curves suggest a smaller mass in the ejecta and larger energy losses than for most SNe Ia. The unusually red color at maximum is not a result of normal extinction, since SN 1991bg was as blue as other SNe Ia at late times and no narrow interstellar lines are observed in the spectra. The faint absolute magnitude of SN 1991bg is established beyond doubt by comparison with SN 1957B, another type Ia supernova in the same galaxy, which was 2.5 magnitudes brighter than SN 1991bg. The spectral evolution reveals minor differences near maximum compared to other well-observed SNe Ia, mainly in relative line strengths. At later phases several wavelength regions display discrepancies when compared to spectra of normal SNe Ia. Although other SNe Ia, such as SN 1986G and SN 1939B, have light curves with fast decline rates, SN 1991bg is unique, with deviations in both light curves and spectra. In particular SN 1991bg is the only SN Ia observed to date with a distinct spectrum at ~40 days past maximum. Although SN 1991bg is an extreme case, with unusual photometric and spectroscopic properties, we believe it can be understood in the context of exploding white dwarf models, and is properly grouped with type Ia. SN 1991bg demonstrates the need for detailed observations of SNe Ia as part of their use as standard candles for cosmology. While there is a well-defined prototype with homogeneous properties, unusual cases like SN 1991bg must be identified and separated to avoid misleading results.
Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.
We demonstrated previously that oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphorylcholine (ox-PAPC) and, specifically, the component lipid 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphorylcholine increase interleukin-8 (IL-8) synthesis in aortic endothelial cells. The goal of the current studies was to characterize the receptor complex mediating the increased transcription of IL-8. We demonstrate that scavenger receptor class A, types I and II, lectin-like ox-LDL receptor-1, macrophage receptor with collagenous structure, and CD36 are not responsible for the increase in IL-8. Using dominant-negative constructs and antisense oligonucleotides, we demonstrate a role for Toll-like receptor 4 (TLR4) as the ox-PAPC receptor mediating IL-8 transcription. We demonstrate that a glycosylphosphatidylinositol (GPI)-anchored protein is also necessary because phosphatidylinositol-specific phospholipase C pretreatment inhibited the effect of ox-PAPC. CD14, a GPI-anchored protein that associates with TLR4 in mediating lipopolysaccharide action, did not appear to mediate ox-PAPC action because ox-PAPC-induced IL-8 transcription was not blocked by anti-CD14 neutralizing antibodies nor was it augmented by the addition of soluble CD14 or overexpression of membrane CD14. Instead, anti-TLR4 antibodies immunoprecipitated a 37-kDa protein that also bound ox-PAPC. A protein of this same size was found in aerolysin overlays used to detect GPI-anchored proteins. Therefore, these studies suggest that ox-PAPC may initially bind to a 37-kDa GPI-anchored protein, which interacts with TLR4 to induce IL-8 transcription. We demonstrated previously that oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphorylcholine (ox-PAPC) and, specifically, the component lipid 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphorylcholine increase interleukin-8 (IL-8) synthesis in aortic endothelial cells. The goal of the current studies was to characterize the receptor complex mediating the increased transcription of IL-8. We demonstrate that scavenger receptor class A, types I and II, lectin-like ox-LDL receptor-1, macrophage receptor with collagenous structure, and CD36 are not responsible for the increase in IL-8. Using dominant-negative constructs and antisense oligonucleotides, we demonstrate a role for Toll-like receptor 4 (TLR4) as the ox-PAPC receptor mediating IL-8 transcription. We demonstrate that a glycosylphosphatidylinositol (GPI)-anchored protein is also necessary because phosphatidylinositol-specific phospholipase C pretreatment inhibited the effect of ox-PAPC. CD14, a GPI-anchored protein that associates with TLR4 in mediating lipopolysaccharide action, did not appear to mediate ox-PAPC action because ox-PAPC-induced IL-8 transcription was not blocked by anti-CD14 neutralizing antibodies nor was it augmented by the addition of soluble CD14 or overexpression of membrane CD14. Instead, anti-TLR4 antibodies immunoprecipitated a 37-kDa protein that also bound ox-PAPC. A protein of this same size was found in aerolysin overlays used to detect GPI-anchored proteins. Therefore, these studies suggest that ox-PAPC may initially bind to a 37-kDa GPI-anchored protein, which interacts with TLR4 to induce IL-8 transcription. Monocyte/endothelial interactions have been shown to play an important role in all stages of atherosclerosis (1Navab M. Berliner J.A. Watson A.D. Hama S.Y. Territo M.C. Lusis A.J. Shih D.M. Van Lenten B.J. Frank J.S. Demer L.L. Edwards P.A. Fogelman A.M. Arterioscler. Thromb. Vasc. Biol. 1996; 16: 831-842Crossref PubMed Scopus (612) Google Scholar). Our laboratory has demonstrated that phospholipid oxidation products of 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphorylcholine (PAPC) 1The abbreviations used are: PAPC, 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphorylcholine; ox-PAPC, oxidized PAPC; PEIPC, 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphorylcholine; POVPC, 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphorylcholine; MM-LDL, minimally modified low density lipoprotein; LPS, lipopolysaccharide; TNF-α, tumor necrosis factor-α; PMA, phorbol 12-myristate 13-acetate; IL-8, interleukin-8; TLR4, Toll-like receptor 4; GPI, glycosylphosphatidylinositol; HAEC, human aortic endothelial cells; MAEC, murine aortic endothelial cells; CHO, Chinese hamster ovary; MARCO, macrophage receptor with collagenous structure; SRA, scavenger receptor class A; ELISA, enzyme-linked immunosorbent assay; PI-PLC, phosphatidylinositol-specific phospholipase C; RT, reverse transcription; MCP-1, monocyte chemotactic protein-1; ANOVA, analysis of variance; LOX-1, lectin-like ox-LDL receptor-1. found in minimally modified low density lipoprotein (MM-LDL) activate this interaction (2Watson A.D. Leitinger N. Navab M. Faull K.F. Horkko S. Witztum J.L. Palinski W. Schwenke D. Salomon R.G. Sha W. Subbanagounder G. Fogelman A.M. Berliner J.A. J. Biol. Chem. 1997; 272: 13597-13607Abstract Full Text Full Text PDF PubMed Scopus (692) Google Scholar, 3Leitinger N. Watson A.D. Hama S.Y. Ivandic B. Qiao J.H. Huber J. Faull K.F. Grass D.S. Navab M. Fogelman A.M. de Beer F.C. Lusis A.J. Berliner J.A. Arterioscler. Thromb. Vasc. Biol. 1999; 19: 1291-1298Crossref PubMed Scopus (144) Google Scholar). These phospholipids have been shown to accumulate in atherosclerotic lesions of mice and rabbits. Antibodies that recognize these lipids demonstrate their presence in human lesions. Furthermore, PAPC oxidation products are increased in apoptotic cells (4Huber J. Vales A. Mitulovic G. Blumer M. Schmid R. Witztum J.L. Binder B.R. Leitinger N. Arterioscler. Thromb. Vasc. Biol. 2002; 22: 101-107Crossref PubMed Scopus (247) Google Scholar) and cells exposed to oxidative stress (5Subbanagounder G. Wong J.W. Lee H. Faull K.F. Miller E. Witztum J.L. Berliner J.A. J. Biol. Chem. 2002; 277: 7271-7281Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar). We have determined that treatment of human aortic endothelial cells (HAEC) and HeLa cells with oxidized PAPC (ox-PAPC) increased the synthesis of interleukin-8 (IL-8), a chemokine involved in monocyte transmigration and retention in the vessel wall. We have also determined that isomers of 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphorylcholine (PEIPC) and to a lesser extent 1-palmitoyl-2-oxovaleroyl-sn-glycero-3-phosphorylcholine (POVPC) are responsible for most of the effect of ox-PAPC to induce IL-8 (5Subbanagounder G. Wong J.W. Lee H. Faull K.F. Miller E. Witztum J.L. Berliner J.A. J. Biol. Chem. 2002; 277: 7271-7281Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar). The increase in IL-8 was shown to be mediated by increased transcription; importantly, native PAPC was not found to have an effect (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar). The goal of these studies was to characterize the receptor responsible for the ability of ox-PAPC to increase IL-8 transcription. Several lines of evidence suggest that the induction of IL-8 by ox-PAPC is receptor-mediated. The increase in message is rapid with accumulation being observed as early as 15 min after treatment with ox-PAPC (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar). PEIPC, the major bioactive lipid in ox-PAPC, is active at concentrations as low as 100 nm, and its effect is saturable and maximal at 1 μg/ml. We first examined the possibility that ox-PAPC and its derivatives act on a receptor belonging to the scavenger receptor family. Recent studies demonstrated that autoantibodies from apoE-deficient mice that bound to oxidized phospholipids (e.g. EO6 antibody) inhibited 60–80% of the binding of copper-oxidized LDL to scavenger receptors on mouse peritoneal macrophages (7Horkko S. Bird D.A. Miller E. Itabe H. Leitinger N. Subbanagounder G. Berliner J.A. Friedman P. Dennis E.A. Curtiss L.K. Palinski W. Witztum J.L. J. Clin. Invest. 1999; 103: 117-128Crossref PubMed Scopus (474) Google Scholar). Although these particular studies were performed using macrophages, they suggested a possible role for scavenger receptors in endothelial cells mediating the effect of ox-PAPC. The expression of several known scavenger receptors was examined in the current study. Scavenger receptor class A, types I and II (SRA I/II), is a multifunctional receptor that binds to a broad variety of ligands including oxidized LDL (8de Winther M.P. van Dijk K.W. Havekes L.M. Hofker M.H. Arterioscler. Thromb. Vasc. Biol. 2000; 20: 290-297Crossref PubMed Scopus (197) Google Scholar). Another class A receptor, macrophage receptor with collagenous structure (MARCO), binds to modified LDL as well (9Elomaa O. Kangas M. Sahlberg C. Tuukkanen J. Sormunen R. Liakka A. Thesleff I. Kraal G. Tryggvason K. Cell. 1995; 80: 603-609Abstract Full Text PDF PubMed Scopus (411) Google Scholar). CD36, a class B scavenger receptor, binds to the lipid moiety of oxidized LDL (10Nicholson A.C. Frieda S. Pearce A. Silverstein R.L. Arterioscler. Thromb. Vasc. Biol. 1995; 15: 269-275Crossref PubMed Scopus (223) Google Scholar). LOX-1, an oxidized LDL receptor belonging structurally to the C-type lectin family, was initially identified in vascular endothelial cells (11Sawamura T. Kume N. Aoyama T. Moriwaki H. Hoshikawa H. Aiba Y. Tanaka T. Miwa S. Katsura Y. Kita T. Masaki T. Nature. 1997; 386: 73-77Crossref PubMed Scopus (1175) Google Scholar). We present evidence that these receptors are not responsible for ox-PAPC-induced IL-8 synthesis. We also examined the role of Toll-like receptor 4 (TLR4) and associated proteins in mediating the action of ox-PAPC. C3H/HeJ mice have a missense mutation in the Tlr4 gene resulting in nonfunctional TLR4 (12Poltorak A. He X. Smirnova I. Liu M.Y. Huffel C.V. Du X. Birdwell D. Alejos E. Silva M. Galanos C. Freudenberg M. Ricciardi-Castagnoli P. Layton B. Beutler B. Science. 1998; 282: 2085-2088Crossref PubMed Scopus (6471) Google Scholar). These mice are resistant to atherosclerosis, and aortic endothelial cells from these mice are unresponsive to MM-LDL (13Shi W. Haberland M.E. Jien M.L. Shih D.M. Lusis A.J. Circulation. 2000; 102: 75-81Crossref PubMed Scopus (162) Google Scholar). We hypothesized a role for TLR4 additionally because of the epitope similarity of bacterial lipids, known regulators of TLR4 (14Medzhitov R. Nat. Rev. Immunol. 2001; 1: 135-145Crossref PubMed Scopus (3279) Google Scholar), and oxidized phospholipids (recognized by the antibody EO6) (7Horkko S. Bird D.A. Miller E. Itabe H. Leitinger N. Subbanagounder G. Berliner J.A. Friedman P. Dennis E.A. Curtiss L.K. Palinski W. Witztum J.L. J. Clin. Invest. 1999; 103: 117-128Crossref PubMed Scopus (474) Google Scholar). We further hypothesized that, like lipopolysaccharide (LPS), the interaction of ox-PAPC with TLR4 may be enhanced by binding to a glycosylphosphatidylinositol (GPI)-anchored protein. These studies present evidence for a role of both TLR4 and a GPI-anchored protein in mediating ox-PAPC induction of IL-8 synthesis in endothelial cells. Reagents—Tissue culture media and reagents were obtained from Irvine Scientific, Inc. unless otherwise stated. Fetal bovine serum was obtained from Hyclone. PAPC was obtained from Avanti Polar Lipids, Inc. (Alabaster, AL) or Sigma. PAPC was oxidized as described previously (2Watson A.D. Leitinger N. Navab M. Faull K.F. Horkko S. Witztum J.L. Palinski W. Schwenke D. Salomon R.G. Sha W. Subbanagounder G. Fogelman A.M. Berliner J.A. J. Biol. Chem. 1997; 272: 13597-13607Abstract Full Text Full Text PDF PubMed Scopus (692) Google Scholar). PEIPC was isolated as described previously (5Subbanagounder G. Wong J.W. Lee H. Faull K.F. Miller E. Witztum J.L. Berliner J.A. J. Biol. Chem. 2002; 277: 7271-7281Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar). POVPC was prepared as described previously (2Watson A.D. Leitinger N. Navab M. Faull K.F. Horkko S. Witztum J.L. Palinski W. Schwenke D. Salomon R.G. Sha W. Subbanagounder G. Fogelman A.M. Berliner J.A. J. Biol. Chem. 1997; 272: 13597-13607Abstract Full Text Full Text PDF PubMed Scopus (692) Google Scholar, 15Leitinger N. Tyner T.R. Oslund L. Rizza C. Subbanagounder G. Lee H. Shih P.T. Mackman N. Tigyi G. Territo M.C. Berliner J.A. Vora D.K. Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 12010-12015Crossref PubMed Scopus (229) Google Scholar). Mass analysis, liquid chromatography/mass spectrometry, tandem mass spectrometric analysis, and quantitation of oxidized phospholipids were performed as described previously (5Subbanagounder G. Wong J.W. Lee H. Faull K.F. Miller E. Witztum J.L. Berliner J.A. J. Biol. Chem. 2002; 277: 7271-7281Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar). LPS from Escherichia coli O111:B4 (a natural, smooth strain) was obtained from List Biological Laboratories, Inc. Phorbol 12-myristate 13-acetate (PMA) and anti-β-coatomer protein monoclonal antibodies (catalog number G6160) were obtained from Sigma. Tumor necrosis factor-α (TNF-α), anti-human CD14 polyclonal antibodies (catalog number AB383), anti-human CD14 monoclonal antibodies (catalog number MAB3831), and a human IL-8 enzyme-linked immunosorbent assay (ELISA) kit were obtained from R & D Systems. Anti-CD36 monoclonal antibodies (clone FA6-152) were obtained from Immunotech (Westbrook, ME) (16Finnemann S.C. Silverstein R.L. J. Exp. Med. 2001; 194: 1289-1298Crossref PubMed Scopus (112) Google Scholar). CD36 peptides were obtained from Dr. S. Frieda A. Pearce (Cornell University) (17Pearce S.F. Roy P. Nicholson A.C. Hajjar D.P. Febbraio M. Silverstein R.L. J. Biol. Chem. 1998; 273: 34875-34881Abstract Full Text Full Text PDF PubMed Scopus (53) Google Scholar). Anti-human TLR4 monoclonal antibodies were obtained from eBioscience (catalog number 14-9917). Anti-Rab8 monoclonal antibodies were obtained from BD Transduction Laboratories (catalog number 610844). Anti-histone monoclonal antibodies were obtained from Chemicon International (catalog number MAB052). A murine monocyte chemotactic protein-1/JE (MCP-1/JE) ELISA kit was obtained from Pharmingen. EO6 antibodies were obtained from Dr. L. Witztum of Anti-human TLR4 polyclonal antibodies (catalog number anti-human monoclonal antibodies (catalog number anti-human polyclonal antibodies (catalog number antibodies (catalog number antibodies (catalog number and antibodies (catalog number were obtained from Inc. human CD14 was obtained from Dr. and antibodies were obtained from phospholipase C was obtained from (catalog number The IL-8 was obtained from Dr. Leitinger of (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar). and antisense to TLR4 were obtained from These the to the of The dominant-negative TLR4 a mutation to that found in C3H/HeJ mice and the human CD14 were obtained from Dr. L. assay and assay were obtained from MM-LDL was obtained by treatment of LDL with and phospholipase or by oxidation Fogelman A.M. A. Territo M.C. Berliner J.A. J. Clin. Invest. PubMed Scopus Google Scholar). J.A. Territo M.C. E. Haberland M.E. Fogelman A.M. Berliner J.A. Arterioscler. Thromb. PubMed Google Scholar), murine aortic endothelial cells W. Shih D.M. X. Lusis A.J. 2000; PubMed Scopus Google Scholar), and G. A. A. J. Immunol. Google Scholar) were isolated and as described HeLa cells and cells were obtained from the endothelial cells were obtained from Dr. T. Reddy aortic endothelial cells were obtained from Inc. was isolated using transcription was performed after was performed on the resulting using for human TLR4 human CD14 human human CD36 human human human 1 human human or human and in a were with were or were with were performed using anti-TLR4 monoclonal antibodies anti-CD14 monoclonal antibodies & D monoclonal antibodies or antibodies to with was to the which was in a at were performed using anti-β-coatomer protein, and and IL-8 cells were with in bovine serum for at after for in bovine were performed on the using were in culture were performed with of TLR4 or or antisense TLR4 oligonucleotides, human CD14 or with the IL-8 or was used for to the after cells were with ox-PAPC, LPS, TNF-α, or for were and for were with or were with ox-PAPC LPS or for 4 the treatment were and for IL-8. were to with and with were and in a of A was to a at for and in A and 1 was for to a and at for The were used for with anti-human TLR4 polyclonal antibodies using the protein of was determined by and of protein were by and in by in The protein was to and antibodies to were used for using and proteins were using the assay as described previously B. G. W. M. Lee H. C. M.P. Cell. Biol. 2002; 22: PubMed Scopus Google Scholar). using the are as The are of at of Scavenger in of IL-8 Although studies have been performed scavenger receptors in macrophages, has been to scavenger receptor expression in human aortic endothelial cells. Therefore, to the role of endothelial scavenger receptors in ox-PAPC action, we determined which of the known scavenger receptors are present in and oxidized phospholipids their was performed to the presence of CD36, LOX-1, and on were found to low of CD36 and of or not to be low or not present with being with of the Our were by a that expression was by in atherosclerosis H. Kume N. S. M. Moriwaki H. T. T. Masaki T. N. Kita T. Circulation. 1999; PubMed Scopus Google Scholar). of with ox-PAPC was found to increase the for CD36, for MARCO, and not we to CD36 is necessary for from CD36 mice of Dr. University) and mice were with LPS MM-LDL or native LDL in to both LPS and MM-LDL not in to native from CD36 mice in to MM-LDL at with from cells with antibodies and CD36 peptides effect on with ox-PAPC and PEIPC not These that CD36 is not for and ox-PAPC-induced and of Toll-like 4 studies by R. Y. E. A. M. M. M. J. Biol. Chem. 1999; Full Text Full Text PDF PubMed Scopus Google Scholar) demonstrated that human endothelial cells of TLR4 as determined by and protein, as determined by and S. L. R. R. Lee J. Clin. Invest. 1999; PubMed Scopus Google Scholar) found that endothelial cells of TLR4 as determined by studies have been performed that vessel endothelial expression of TLR4 were to detect and to detect protein was determined by from isolated from that they TLR4 HeLa cells and human endothelial were also found to TLR4 from the that TLR4 protein on the we determined TLR4 is involved in ox-PAPC-induced We previously demonstrated that ox-PAPC IL-8 transcription in HeLa cells and an IL-8 (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar). We used this to the role of TLR4 in the ox-PAPC induction of IL-8. HeLa cells were with a dominant-negative of an IL-8 was to detect ox-PAPC-induced The dominant-negative of TLR4 effect on IL-8 transcription; the effect of on transcription was in the effect of ox-PAPC was inhibited in cells dominant-negative TLR4 with with that TLR4 a role in ox-PAPC-induced HeLa cells were with antisense TLR4 with the was a in the of cells with the antisense with with the effect on IL-8 synthesis was of GPI-anchored in the of the LPS receptor complex GPI-anchored CD14 and we hypothesized that ox-PAPC may also bind to a GPI-anchored protein with this we with bacterial PI-PLC, which GPI-anchored proteins from the and the cells with ox-PAPC. were and for IL-8. with was found to IL-8 in to ox-PAPC and LPS that a GPI-anchored protein a role in ox-PAPC-induced did not the induction of IL-8 by or We the role of CD14 in ox-PAPC action because CD14 is known to complex with TLR4 J. Biol. Chem. 1999; Full Text Full Text PDF PubMed Scopus Google Scholar). were found to CD14 from the that CD14 on the We that CD14 was to the because the for the protein, and increased with the for CD14 did not not the role of CD14 in ox-PAPC-induced IL-8 neutralizing anti-CD14 antibodies were Although these antibodies blocked IL-8 synthesis in HAEC, they did not the effect of ox-PAPC the role of CD14, were with ox-PAPC or LPS in the presence of soluble CD14. shown in the induction of IL-8 by ox-PAPC was in the presence of soluble CD14 with the effect of LPS was which TLR4 E. H. A. S. K. M. N. B. J. Clin. Invest. 2000; PubMed Scopus Google Scholar), were with a human CD14 and an IL-8 to membrane CD14 the to ox-PAPC. ox-PAPC expression of cells did not from cells with LPS expression of cells were that of soluble nor membrane CD14 an ox-PAPC the expression of of the LPS receptor was Although HeLa cells are to ox-PAPC, they not that is not for ox-PAPC-induced IL-8 synthesis in HeLa cells. that CD14 and were not necessary for ox-PAPC induction we examined the binding of ox-PAPC to proteins. an antibody which binds to oxidized phospholipids in the or bound to protein (7Horkko S. Bird D.A. Miller E. Itabe H. Leitinger N. Subbanagounder G. Berliner J.A. Friedman P. Dennis E.A. Curtiss L.K. Palinski W. Witztum J.L. J. Clin. Invest. 1999; 103: 117-128Crossref PubMed Scopus (474) Google Scholar), bound to a protein of that was immunoprecipitated from of cells with anti-TLR4 antibodies binding of EO6 to a extent in cells cells. protein was observed in the A protein of this same size was also using an aerolysin to detect GPI-anchored proteins These suggest that ox-PAPC may initially bind to a 37-kDa GPI-anchored protein which interacts with TLR4 to induce Our studies demonstrated that ox-PAPC IL-8 transcription in and HeLa cells (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar). The current studies characterize the receptor responsible for this effect of ox-PAPC. We first examined the that scavenger receptors mediate the effect of ox-PAPC. Scavenger receptors have been in macrophages in the of atherosclerosis A. Bird D.A. Dennis E.A. Friedman P. K. Horkko S. Palinski W. O. P. D. Witztum J.L. N. Y. Acad. Sci. 2001; PubMed Scopus Google Scholar, Clin. 1999; PubMed Scopus Google Scholar, S. 2001; PubMed Scopus Google Scholar), their role in endothelial cells is We demonstrate that not MARCO, or expression of these did not increase ox-PAPC low of CD36 which is by ox-PAPC treatment not to was observed in we present evidence that CD36 are to MM-LDL, of which ox-PAPC is a major bioactive component (2Watson A.D. Leitinger N. Navab M. Faull K.F. Horkko S. Witztum J.L. Palinski W. Schwenke D. Salomon R.G. Sha W. Subbanagounder G. Fogelman A.M. Berliner J.A. J. Biol. Chem. 1997; 272: 13597-13607Abstract Full Text Full Text PDF PubMed Scopus (692) Google Scholar) and that with antibodies or CD36 peptides are to ox-PAPC not that CD36, important in macrophage M. E.A. Hajjar D.P. K. Silverstein R.L. J. Clin. Invest. 2000; PubMed Scopus Google Scholar), is not necessary for endothelial by MM-LDL or ox-PAPC. we examined the presence and of TLR4, the component of the LPS receptor in ox-PAPC-induced IL-8 HAEC, HeLa and human endothelial all of which to ox-PAPC, TLR4 A and Furthermore, ox-PAPC TLR4 synthesis dominant-negative constructs and antisense for TLR4 inhibited IL-8 by ox-PAPC, that TLR4 an important role in ox-PAPC-induced IL-8 synthesis. studies demonstrate that TLR4 is by endothelial cells in atherosclerotic K. J. Circulation. 2002; PubMed Scopus Google Scholar) and an TLR4 which receptor is associated with a of atherosclerosis S. E. M. E. J. D.A. N. J. Med. 2002; PubMed Scopus Google Scholar). We also present evidence that GPI-anchored proteins are necessary for ox-PAPC-induced IL-8 synthesis. pretreatment ox-PAPC-induced IL-8 synthesis not be by the of the bioactive lipids in ox-PAPC because the and the lipids were not present and demonstrate the presence of CD14 in A and early of human endothelial cells not were previously shown to CD14 A. 2001; PubMed Scopus Google Scholar). we demonstrate that CD14 is not involved in ox-PAPC-induced IL-8 synthesis Furthermore, we demonstrate that is not necessary for ox-PAPC induction of IL-8 transcription in HeLa as these cells not this The role of in ox-PAPC induction of IL-8 in endothelial cells is not antibodies a 37-kDa protein that also binds ox-PAPC a 37-kDa protein was also found in aerolysin overlays used to detect GPI-anchored proteins may the GPI-anchored protein with TLR4 that is responsible for ox-PAPC-induced IL-8 expression is not CD14, which has a mass of studies by Miller S. Binder Witztum J.L. J. Biol. Chem. Full Text Full Text PDF PubMed Scopus Google Scholar) have that MM-LDL and of macrophages by binding to CD14 and the LPS receptor complex of TLR4 and Miller found that MM-LDL binding was in macrophages of the and a in cells. were in cells with human CD14. also demonstrated that cells with human TLR4 and a cells with TLR4 that is for maximal to demonstrate that CD14 is not for ox-PAPC-induced IL-8 synthesis and that is not necessary for ox-PAPC-induced IL-8 transcription in HeLa cells. These may be to the that MM-LDL is a of possible including and oxidized ox-PAPC oxidized may to an or IL-8 transcription. are several important the of the Miller studies S. Binder Witztum J.L. J. Biol. Chem. Full Text Full Text PDF PubMed Scopus Google Scholar) and studies suggest an important role for TLR4 in the of MM-LDL and ox-PAPC (a major bioactive component of in macrophages and endothelial cells. like on ox-PAPC induction of IL-8 (6Yeh M. Leitinger N. de Martin R. Onai N. Matsushima K. Vora D.K. Berliner J.A. Reddy S.T. Arterioscler. Thromb. Vasc. Biol. 2001; 21: 1585-1591Crossref PubMed Scopus (102) Google Scholar), Miller determined that of the most well of TLR4, is not because an of did not the to both studies suggest a of TLR4 that may be important in
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 26K topics and 226K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: (i) it includes a very large number of topics that do not appear in other classifications, and (ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. Machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.
ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTConventional Solvent Oxidizes Sn(II) in Perovskite InksMakhsud I. SaidaminovMakhsud I. SaidaminovDepartment of Chemistry and Electrical & Computer Engineering, Centre for Advanced Materials and Related Technologies (CAMTEC), University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P 5C2, CanadaMore by Makhsud I. Saidaminovhttp://orcid.org/0000-0002-3850-666X, Ioannis SpanopoulosIoannis SpanopoulosDepartment of Chemistry, Northwestern University, Evanston, Illinois 60208, United StatesMore by Ioannis Spanopouloshttp://orcid.org/0000-0003-0861-1407, Jehad AbedJehad AbedDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 1A4, CanadaDepartment of Materials Science and Engineering, University of Toronto, Toronto, Ontario M5S 1A4, CanadaMore by Jehad Abed, Weijun KeWeijun KeDepartment of Chemistry, Northwestern University, Evanston, Illinois 60208, United StatesMore by Weijun Kehttp://orcid.org/0000-0003-2600-5419, Joshua WicksJoshua WicksDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 1A4, CanadaMore by Joshua Wicks, Mercouri G. Kanatzidis*Mercouri G. KanatzidisDepartment of Chemistry, Northwestern University, Evanston, Illinois 60208, United States*[email protected]More by Mercouri G. Kanatzidishttp://orcid.org/0000-0003-2037-4168, and Edward H. Sargent*Edward H. SargentDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 1A4, Canada*[email protected]More by Edward H. Sargenthttp://orcid.org/0000-0003-0396-6495Cite this: ACS Energy Lett. 2020, 5, 4, 1153–1155Publication Date (Web):March 18, 2020Publication History Received20 February 2020Accepted5 March 2020Published online18 March 2020Published inissue 10 April 2020https://pubs.acs.org/doi/10.1021/acsenergylett.0c00402https://doi.org/10.1021/acsenergylett.0c00402article-commentaryACS PublicationsCopyright © 2020 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views7149Altmetric-Citations135LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Heat treatment,Oxidation,Perovskites,Solar cells,X-ray absorption near edge spectroscopy Get e-Alerts
Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
Abstract This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.
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Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
Abstract The recent discoveries of strikingly large zero-field Hall and Nernst effects in antiferromagnets Mn 3 X ( X = Sn, Ge) have brought the study of magnetic topological states to the forefront of condensed matter research and technological innovation. These effects are considered fingerprints of Weyl nodes residing near the Fermi energy, promoting Mn 3 X ( X = Sn, Ge) as a fascinating platform to explore the elusive magnetic Weyl fermions. In this review, we provide recent updates on the insights drawn from experimental and theoretical studies of Mn 3 X ( X = Sn, Ge) by combining previous reports with our new, comprehensive set of transport measurements of high-quality Mn 3 Sn and Mn 3 Ge single crystals. In particular, we report magnetotransport signatures specific to chiral anomalies in Mn 3 Ge and planar Hall effect in Mn 3 Sn, which have not yet been found in earlier studies. The results summarized here indicate the essential role of magnetic Weyl fermions in producing the large transverse responses in the absence of magnetization.
Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.
The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.
Abstract Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.
The Human Genome Project has transformed biology through its integrated big science approach to deciphering a reference human genome sequence along with the complete sequences of key model organisms. The project exemplifies the power, necessity and success of large, integrated, cross-disciplinary efforts - so-called 'big science' - directed towards complex major objectives. In this article, we discuss the ways in which this ambitious endeavor led to the development of novel technologies and analytical tools, and how it brought the expertise of engineers, computer scientists and mathematicians together with biologists. It established an open approach to data sharing and open-source software, thereby making the data resulting from the project accessible to all. The genome sequences of microbes, plants and animals have revolutionized many fields of science, including microbiology, virology, infectious disease and plant biology. Moreover, deeper knowledge of human sequence variation has begun to alter the practice of medicine. The Human Genome Project has inspired subsequent large-scale data acquisition initiatives such as the International HapMap Project, 1000 Genomes, and The Cancer Genome Atlas, as well as the recently announced Human Brain Project and the emerging Human Proteome Project.
BACKGROUND: High levels of sedentary behaviour (SB) are associated with negative health consequences. Technology enhanced solutions such as mobile applications, activity monitors, prompting software, texts, emails and websites are being harnessed to reduce SB. The aim of this paper is to evaluate the effectiveness of such technology enhanced interventions aimed at reducing SB in healthy adults and to examine the behaviour change techniques (BCTs) used. METHODS: Five electronic databases were searched to identify randomised-controlled trials (RCTs), published up to June 2016. Interventions using computer, mobile or wearable technologies to facilitate a reduction in SB, using a measure of sedentary time as an outcome, were eligible for inclusion. Risk of bias was assessed using the Cochrane Collaboration's tool and interventions were coded using the BCT Taxonomy (v1). RESULTS: Meta-analysis of 15/17 RCTs suggested that computer, mobile and wearable technology tools resulted in a mean reduction of -41.28 min per day (min/day) of sitting time (95% CI -60.99, -21.58, I2 = 77%, n = 1402), in favour of the intervention group at end point follow-up. The pooled effects showed mean reductions at short (≤ 3 months), medium (>3 to 6 months), and long-term follow-up (>6 months) of -42.42 min/day, -37.23 min/day and -1.65 min/day, respectively. Overall, 16/17 studies were deemed as having a high or unclear risk of bias, and 1/17 was judged to be at a low risk of bias. A total of 46 BCTs (14 unique) were coded for the computer, mobile and wearable components of the interventions. The most frequently coded were "prompts and cues", "self-monitoring of behaviour", "social support (unspecified)" and "goal setting (behaviour)". CONCLUSION: Interventions using computer, mobile and wearable technologies can be effective in reducing SB. Effectiveness appeared most prominent in the short-term and lessened over time. A range of BCTs have been implemented in these interventions. Future studies need to improve reporting of BCTs within interventions and address the methodological flaws identified within the review through the use of more rigorously controlled study designs with longer-term follow-ups, objective measures of SB and the incorporation of strategies to reduce attrition. TRIAL REGISTRATION: The review protocol was registered with PROSPERO: CRD42016038187.
Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation, and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are as follows: (1) the generation of high quality images, (2) diversity of image generation, and (3) stabilizing training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state-of-the-art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress toward addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Codes related to the GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.
Abstract As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed.