Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (mu_B > 500 MeV), effects of chiral symmetry, and the equation-of-state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2022, in the context of the worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal

A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS

Recently, the Korean government has announced a plan to develop a satellite-based navigation system called the Korean Positioning System (KPS). When designing a new Radio Navigation Satellite Service (RNSS) signal, the use of the S-band has emerged as an alternative to avoiding signal congestion in the L-bands, and South Korea is considering using the S-band with the L-bands. Therefore, this study proposed possible S-band signal candidates and evaluated their performance, such as the radio frequency (RF) compatibility, spectral efficiency, ranging performance, and receiver complexity. Several figures-of-merit (FoMs) were introduced for quantitative performance evaluation for each candidate. Each FoM was calculated using an analytical equation by considering the signal design parameters, such as the center frequency, modulation scheme, and chip rate. The results showed that the outstanding candidate signal was different depending on the signal performance of interest and the reception environments. Therefore, we discuss and summarize the signal performance analysis results considering the whole FoMs together. Under the assumptions given in this paper, the binary phase shift keying (BPSK)(1), sine-phased binary offset carrier (BOCs)(5,2), and BPSK signals were superior for the spectral efficiency, ranging performance, and receiver complexity, respectively

Functional Characterization of Sequence Motifs in the Transit Peptide of Arabidopsis Small Subunit of Rubisco

The transit peptides of nuclear-encoded chloroplast proteins are necessary and sufficient for targeting and import of proteins into chloroplasts. However, the sequence information encoded by transit peptides is not fully understood. In this study, we investigated sequence motifs in the transit peptide of the small subunit of the Rubisco complex by examining the ability of various mutant transit peptides to target green fluorescent protein reporter proteins to chloroplasts in Arabidopsis (Arabidopsis thaliana) leaf protoplasts. We divided the transit peptide into eight blocks (T1 through T8), each consisting of eight or 10 amino acids, and generated mutants that had alanine (Ala) substitutions or deletions, of one or two T blocks in the transit peptide. In addition, we generated mutants that had the original sequence partially restored in single- or double-T-block Ala (A) substitution mutants. Analysis of chloroplast import of these mutants revealed several interesting observations. Single-T-block mutations did not noticeably affect targeting efficiency, except in T1 and T4 mutations. However, double-T mutants, T2A/T4A, T3A/T6A, T3A/T7A, T4A/T6A, and T4A/T7A, caused a 50% to 100% loss in targeting ability. T3A/T6A and T4A/T6A mutants produced only precursor proteins, whereas T2A/T4A and T4A/T7A mutants produced only a 37-kD protein. Detailed analyses revealed that sequence motifs ML in T1, LKSSA in T3, FP and RK in T4, CMQVW in T6, and KKFET in T7 play important roles in chloroplast targeting. In T1, the hydrophobicity of ML is important for targeting. LKSSA in T3 is functionally equivalent to CMQVW in T6 and KKFET in T7. Furthermore, subcellular fractionation revealed that Ala substitution in T1, T3, and T6 produced soluble precursors, whereas Ala substitution in T4 and T7 produced intermediates that were tightly associated with membranes. These results demonstrate that the transit peptide contains multiple motifs and that some of them act in concert or synergistically

Design and Implementation of a Smart IoT Based Building and Town Disaster Management System in Smart City Infrastructure

Recently, fire accidents in buildings have become bigger around the world, and it has become necessary to build an efficient building disaster management system suitable for fires in a Smart City. As building fires increase the number of casualties and property damage, it is necessary to take appropriate action accordingly. There has been an increasing effort to develop such disaster management systems worldwide by applying information communication technology (ICT), and many studies have been conducted in practice. In this paper, an augmented reality (AR)-based Smart Building and Town Disaster Management System is suggested in order to acquire visibility and to grasp occupants in case of fire disasters in buildings. This system provides visualization information and optimal guide for quick initial response by utilizing smart element AR-based disaster management service through linkage of physical virtual domain in the building. Additionally, we show a scenario flow chart of the fire extinguishment process according to the time from the ignition stage to the extinguishment stage in the building. Finally, we introduce the related sensors, the actuators, and a small test-bed for AR-based disaster management service. This test-bed was designed for interlocking and interoperability test of the system between the sensors and the actuators. It is expected that the proposed system can provide a quick and safe rescue guideline to the occupants and rescuers in the building where fire is generated and in regions of poor visibility

Global gene expression profile of Orientia tsutsugamushi

Orientia tsutsugamushi, an obligate intracellular bacterium, is the causative agent of Scrub typhus. The control mechanisms for bacterial gene expression are largely unknown. Here, the global gene expression of O. tsutsugamushi within eukaryotic cells was examined using a microarray and proteomic approaches for the first time. These approaches identified 643 genes, corresponding to approximately 30% of the genes encoded in the genome. The majority of expressed genes belonged to several functional categories including protein translation, protein processing/secretion, and replication/repair. We also searched the conserved sequence blocks (CSBs) in the O. tsutsugantushi genome which is unique in that up to 40% of its genome consists of dispersed repeated sequences. Although extensive shuffling of genomic sequences was observed between two different strains, 204 CSBs, covering 48% of the genome, were identified. When combining the data of CSBs and global gene expression, the CSBs correlates well with the location of expressed genes, suggesting the functional conservation between gene expression and genomic location. Finally, we compared the gene expression of the bacteria-infected fibroblasts and macrophages using microarray analysis. Some major changes were the downregulation of genes involved in translation, protein processing and secretion, which correlated with the reduction in bacterial translation rates and growth within macrophages.Ellison DW, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0005612Merhej V, 2009, BIOL DIRECT, V4, DOI 10.1186/1745-6150-4-13Kelly DJ, 2009, CLIN INFECT DIS, V48, pS203, DOI 10.1086/596576Audia JP, 2008, APPL ENVIRON MICROB, V74, P7809, DOI 10.1128/AEM.00896-08Nakayama K, 2008, DNA RES, V15, P185, DOI 10.1093/dnares/dsn011Lee JH, 2008, J INFECT DIS, V198, P250, DOI 10.1086/589284Eraso JM, 2008, J BACTERIOL, V190, P4831, DOI 10.1128/JB.00301-08La MV, 2008, FEMS MICROBIOL REV, V32, P440, DOI 10.1111/j.1574-6976.2008.00103.xDreher-Lesnick SM, 2008, BMC MICROBIOL, V8, DOI 10.1186/1471-2180-8-61Fuxelius HH, 2008, GENOME BIOL, V9, DOI 10.1186/gb-2008-9-2-r42MIN CK, 2008, COMP FUNC GENOMICS, V623, P145Fuxelius HH, 2007, RES MICROBIOL, V158, P745, DOI 10.1016/j.resmic.2007.09.008La MV, 2007, J MICROBIOL METH, V71, P292, DOI 10.1016/j.mimet.2007.09.017Darby AC, 2007, TRENDS GENET, V23, P511, DOI 10.1016/j.tig.2007.08.002Maurer AP, 2007, PLOS PATHOG, V3, P752, DOI 10.1371/journal.ppat.0030083Cho NH, 2007, P NATL ACAD SCI USA, V104, P7981, DOI 10.1073/pnas.0611553104Wilson DN, 2007, CRIT REV BIOCHEM MOL, V42, P187, DOI 10.1080/10409230701360843Chattopadhyay S, 2007, HUM VACCINES, V3, P73Ogawa M, 2007, PROTEOMICS, V7, P1232, DOI 10.1002/pmic.200600721Simard M, 2007, ANAL BIOCHEM, V362, P142, DOI 10.1016/j.ab.2006.12.036Blanc G, 2007, PLOS GENET, V3, DOI 10.1371/journal.pgen.0030014Scherl A, 2006, BMC GENOMICS, V7, DOI 10.1186/1471-2164-7-296Jansen A, 2006, CURR OPIN MICROBIOL, V9, P138, DOI 10.1016/j.mib.2006.01.003Resch A, 2006, PROTEOMICS, V6, P1867, DOI 10.1002/pmic.200500531Nolan T, 2006, NAT PROTOC, V1, P1559, DOI 10.1038/nprot.2006.236Zhang LX, 2005, BIOTECHNIQUES, V39, P640, DOI 10.2144/000112038Guerrero G, 2005, BMC EVOL BIOL, V5, DOI 10.1186/1471-2148-5-55Carlson JH, 2005, INFECT IMMUN, V73, P6407, DOI 10.1128/IAI.73.10.6407-6418.2005Shamir R, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-232Rovery C, 2005, RES MICROBIOL, V156, P211, DOI 10.1016/j.resmic.2004.09.002Darling ACE, 2004, GENOME RES, V14, P1394, DOI 10.1101/gr.2289704Hinton JCD, 2004, CURR OPIN MICROBIOL, V7, P277, DOI 10.1016/j.mib.2004.04.009Chao CC, 2004, PROTEOMICS, V4, P1280, DOI 10.1002/pmic.200300775Kurtz S, 2004, GENOME BIOL, V5Watt G, 2003, CURR OPIN INFECT DIS, V16, P429, DOI 10.1097/01.qco.0000092814.64370.70Belland RJ, 2003, P NATL ACAD SCI USA, V100, P8478, DOI 10.1073/pnas.1331135100Mathai E, 2003, ANN NY ACAD SCI, V990, P359PARK JI, 2003, TAEHAN KAN HAKHOE CH, V9, P198Matsui T, 2002, JPN J INFECT DIS, V55, P197KELLY DJ, 2002, CLIN INFECT DIS, V34, P145Mira A, 2001, TRENDS GENET, V17, P589Pfaffl MW, 2001, NUCLEIC ACIDS RES, V29Tusher VG, 2001, P NATL ACAD SCI USA, V98, P5116Wolf YI, 2001, GENOME RES, V11, P356Seong SY, 2001, MICROBES INFECT, V3, P11, DOI 10.1016/S1286-4579(00)01352-6Cho NH, 2000, INFECT IMMUN, V68, P594, DOI 10.1128/IAI.68.2.594-602.2000Soballe B, 1999, MICROBIOL-UK, V145, P1817Andersson SGE, 1998, NATURE, V396, P133, DOI 10.1038/24094Policastro PF, 1997, J MED MICROBIOL, V46, P839Blattner FR, 1997, SCIENCE, V277, P1453Watt G, 1996, LANCET, V348, P86KAWAMURA A, 1995, TSUTSUGAMUSHI DISGENG PG, 1994, MICROBIOL IMMUNOL, V38, P703HANSON B, 1987, AM J TROP MED HYG, V36, P621NACY CA, 1979, J IMMUNOL, V123, P2544NACY CA, 1979, INFECT IMMUN, V26, P744

Reactivity of the binders to EGFR depended on the presence of EGFR domain II.

<p>(A) Chimeric EGFR fragments were generated with the EGFR domain II replaced with domain II of either ErbB2 (orange) or ErbB4 (dark blue). (B) The reactivity of the protein binders to the EGFR fragments containing EGFR domain II (green), the chimeric EGFR fragments with ErbB2 domain II (orange), and the chimeric EGFR fragments with ErbB4 domain II (dark blue), as assessed by phage-ELISA. Error bars represent the standard deviation of triplicate phage-ELISA experiments.</p

Binders have a higher reactivity to EGFR in the presence of ligand EGF.

<p>(A) Ligand EGF stabilizes the untethered form of EGFR to expose domain II and increase scaffold binding (domain I, blue; domain II, green; domain III, yellow; domain IV, gray; EGF ligand, pink; scaffold, red). (B) The reactivity to EGFR in the presence of EGF (pink) or the absence of EGF (black). (C) The reactivity to EGFR fragment containing domains I–IV in the presence of EGF (orange) or the absence of EGF (black) as assessed by phage-ELISA. Error bars represent the standard deviation of phage-ELISA experiments performed with eight-fold replication.</p

Scaffolds 1OZJ and 1RK9 have shapes complementary to EGFR domain II.

<p>(A) The inactivated EGFR monomer exists in equilibrium between the tethered and untethered conformations. The binding of EGF (pink) stabilizes the untethered monomers, which exposes the dimerization arm of domain II (green) and activates EGFR to form homodimers at the domain II dimeric interface (domain I, blue; domain II, green; domain III, yellow; domain IV, gray). For clarity, domain II on the right-hand EGFR in the homodimer is shown in orange. (B) EGFR activation can be blocked by binding domain II, which is exposed in the untethered conformation, with the designed scaffold (magenta), which sterically interferes with EGFR dimerization. (C) The docking conformation of 1OZJ–EGFR, which was selected from the virtual screening procedure (left), and the enlarged 1OZJ structure (magenta). (D) The docking conformation of 1RK9–EGFR, which was selected from the virtual screening procedure (left), and the enlarged 1RK9 structure (magenta). Energetically unfavorable residues that were selected for optimization are depicted in blue. (E–F) Amino acid sequences for 1OZJ and 1RK9, respectively. The blue residues represent amino acid residues that were energetically unfavorable for EGFR complex formation. (G–H) The complex formation energy of the scaffold interface residues upon the formation of the 1OZJ–EGFR and 1RK9–EGFR docking complexes, respectively. The blue bars depict the energetically unfavorable residues that were selected for optimization. The horizontal axis shows each amino acid in the scaffold interface, and the vertical axis represents the energy contribution (delta G) to the complex formation.</p

Binders generated from the 1OZJ and 1RK9 scaffolds bind to EGFR fragments I–IV and I–II.

<p>The reactivity of the protein binders (wild-type and mutant clones) against EGFR domain I–IV (dark blue) and EGFR domain I–II (light blue) as assessed by phage-ELISA. Error bars represent the standard deviation of triplicate phage-ELISA experiments.</p

Design scheme of target-specific scaffolds.

<p>(A) Synthetic antibodies can achieve extremely diverse structures through sequence randomization of the complementarity determining region (CDR). Among diverse structures, only antibodies with complementary shapes are able to recognize and bind to a particular epitope. (B) By imitating synthetic antibody generation, we devised a strategy to select target-specific scaffolds from the human proteome with shapes that are complementary to the target surface patch. (C) The flow chart shows a two-step strategy to obtain target-specific scaffolds (middle). In the first step, a virtual screening of a human protein scaffold library is conducted to determine a framework specific to the surface patch of interest. Target specific-scaffolds with shapes complementary to the surface patch of interest are selected from the scaffold library through protein docking simulations (upper right). The scaffold–target docking structures with the most favorable complex formation energies are further evaluated (left). In the second step, the scaffold interface in the selected scaffold–target model is optimized by sequence randomization and phage display using directed evolution (lower right).</p