Artykuły w czasopismach na temat „Bataille de la mer de Java”

Genazzano, dimanche 25 mai 2006. Après sept ans, je reviens à Genazzano. Je m'y étais rendu pour une recherche sur la Madonna del Buon Consiglio, la « Mère du Bon Conseil », dont l'image vénérée se trouve dans la basilique qui aurait été construite, selon le mythe, depuis l'apparition miraculeuse de l'icône au quinzième siècle : elle serait venue en volant miraculeusement au-dessus de la mer pour fuir la menace des envahisseurs musulmans. C'est dans cette même basilique que le vainqueur de la bataille de Lepante, Marco Antonio Colonna, déposait ses armes victorieuses au lendemain de la célèbre bataille, et que plusieurs papes, y compris Jean-Paul II, s'étaient rendus en pèlerinage.

La représentation de Lépante doit tout aux gravures et peintures italiennes qui, très vite, en fixèrent les traits. La victoire navale et son ordre de bataille constituèrent tout d'abord la difficulté. Les inventions picturales (Tintoret) permirent de renouveler ces représentations. Dès avant 1575, les schémas étaient presque fixés : mêlée sur mer et déjà interventions divines et allégoriques (Vasari). Les commanditaires institutionnels (Papauté, Sérénissime) renchérirent dans les interventions surnaturelles voire patriotiques (Véronèse). La Papauté (Grégoire XIII) investit l'iconographie d'une vocation politique et religieuse par la Sainte-Ligue et l'invocation du rôle de la Vierge du Rosaire. L'iconographie triomphale des chefs de la flotte acheva cette célébration.

Azadirachta indica A. Juss (Apocynaceae) is a large tree of the lowland tropical rain forest of Southeast Asia that occurs in Thailand, the Malay Peninsula, on the island of Java (East Java) and Lesser Sunda Islands. Its economic value was in its wood (timber), and as medicinal plant. The information on genetic diversity of the species is very limited. Hence studies were initiated and genetic diversity estimated using RAPD markers in 27 accessions of A.indica procured from three geographical regions of TN Baluran and Balai Litbang Kehutanan. Seven selected Operon primers (10 mer) generated a total of 133 consistent amplification products ranging from 132 bp to 5.6 Kb. The cluster analysis separated the 27 individuals into 2 clusters. The range of genetic dissimilarityvalue among samples was from 0.07 to 0.33, while genetic distance among populations was from 0.04 to 0.10. These values showed that A. indica from TN Baluran was not genetically diverse population. Key words: Azadirachta indica, genetic diversity, RAPD

La comparaison de la Tapisserie de Bayeux avec les sources écrites contemporaines de l’œuvre brodée met en évidence les différences importantes existant entre ces deux supports de communication. Certaines de ces différences tiennent à la nature même de ces supports : les combattants anglais et continentaux possédant le même armement, il était difficile de les différencier par l’image. Les concepteurs de la broderie ont donc fait le choix de représenter les hommes de Guillaume en chevaliers armés de la lance et les Anglais en fantassins se servant de l’épée, suggérant par là l’idée d’un affrontement entre deux forces armées de nature opposée. Mais les plus importantes différences relèvent d’une volonté idéologique ou d’un choix esthétique. Ainsi la Tapisserie de Bayeux ne fait aucune allusion à l’intervention militaire, pourtant essentielle, de Harald Hardrada, ni au transfert de l’armée normande de Dives-sur-Mer à Saint-Valery-sur-Somme. En revanche, la Tapisserie livre une richesse d’informations précieuses sur des sujets dont les sources écrites ne disent mot : édifices civils et militaires, tenues vestimentaires, conditions de vie des armées en campagne et incidents de la bataille d’Hastings.

Heat recovery is considered as the key to improve energy efficiency in the process design. An appropriate heat exchanger network (HEN) design is an effective tool to maximize heat recovery from the process streams and to minimize energy consumption. The objectives of this study were arranging optimum HEN based on the annual cost in the power industry. HEN in the Paiton Steam Power Plant, East Java, Indonesia, was designed using spreadsheet and Aspen Energy Analyzer with Peng-Robinson equation. Pinch analysis was conducted by comparing Tmin (10°C - 19°C) to obtain Maximum Energy Recovery (MER) and Heat Exchanger Area (HEA). The HEN design was optimized using grid diagram. Simulation in this study succeeded to reduce the annual cost the most effectively at ∆Tmin 16°C. This design optimized the process integration and contributed to the capital, operation, and total annual cost reduction of 14.3%. The maximum energy recovery was 286,706 kW and HEA 138.790 m2. This result is a solution for Steam Power Plant as an effort for enhancing energy efficiency and the company competitiveness.

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Abstract Immunoglobulin heavy variable (IGHV) gene replacement is a RAG-mediated recombination event that modifies a rearranged IGHV-IGHD-IGHJ sequence by replacing the original IGHV with another IGHV. As the excision of the original IGHV occurs at a cryptic heptamer near the end of FR3 of the original IGHV, a trace (“footprint”) remains embedded in the V-D (“N1”) junction of the heavy complementarity-determining region 3 (HCDR3) sequence of the new rearrangement. Since IGHV-replaced antibody sequences are over-represented in several autoantibody-associated syndromes, it is assumed that the process is designed to eliminate unwanted antibody specificities, particularly those which are autoreactive. Because CLL antibodies/B cell receptors (BCRs) are often autoreactive, we sought to determine if IGHV replacement plays a significant role in the formation of these antibodies. To this end, we analyzed IGHV-IGHD-IGHJ rearrangements in a CLL sample set of 6,121 sequences and in another set of 3,326 sequences from normal B lymphocytes culled from the GenBank Database. Sequences without positional information for their IGHV match were eliminated, resulting in 6023 CLL and 3202 normal sequences. From among these, we looked for the presence of IGHV footprints in the N1 and N2 junctional regions. Sequences were processed by IMGT to determine the particular IGHV used as well as junctional N1, N2 sequences. The resulting junctional regions were analyzed using the Java-based VH Replacement Footprint Analyzer (VHRFA) program [Huang, L et al (2013) PLOS ONE] to identify footprints (≥5-mer) in the N1 and N2 regions. Various CLL subgroups based on ZAP-70 expression, CD38 expression, IGHV mutation status, BCR stereotypy, and genomic aberrations were also examined for differences in the presence of footprints. Comparison of CLL with normal sequences in the N1 regions revealed a slight yet significant difference in footprint frequency (12.62% versus 11.06%, respectively; P = 0.03). Comparison of IGHV-mutated (M) versus unmutated (U) subgroups of CLL also disclosed a significant difference in frequencies (M: 13.58%, U: 11.51%, P = 0.02). Using footprints in the N2 regions as an internal control, we found the frequency of ≥5-mer replacement footprint motifs to be significantly higher in N1 over N2 (P = 1.1E-7), supporting the validity of this approach. Other comparisons led to a significant enrichment of footprint frequencies, such as 13.71% for the ZAP-70-negative group (P = 0.029), 13.36% for the CD38-negative group (P = 0.013), and 14.70% for the 13q-deletion group (P = 0.016) compared with the normal group. Note, an overall comparison of stereotyped versus non-stereotyped cases revealed highly statistically significant differences in N1 footprint frequencies (9.4% for the 2084 cases having defined subsets vs 14.32% for the 3,939 cases without defined subsets; P = 2.7E-8). Focusing on stereotyped cases that exhibited evidence for IGHV replacement, we found significantly different footprint frequencies in the N1 region among different stereotyped subsets, even when restricting the comparison to subsets utilizing the same IGHV and belonging to the same mutational category (U-CLL or M-CLL). Relevant examples include differential frequencies among U-CLL, IGHV1-69-expressing subsets #3, #5, #6, #7, and #59 (0 - 15.5%; P = 0.01) and M-CLL, IGHV4-34-expressing subsets #4, #16, #29, and #201 (0 - 40.9%; P = 0.005). In summary, CLL had an elevated IGHV replacement rate compared to normal controls. In addition, M-CLL, ZAP-70-neg, CD38-neg and 13q del CLL subgroups were also elevated above normal controls. The most dramatic differences in IGHV replacement frequency were found between CLL non-stereotyped and stereotyped CLL cases, with an intriguing difference within stereotyped subgroups exhibiting the same IGHV where IGHV replacement varied by as much as 40-fold. Because higher levels of IGHV replacement are found in ZAP-70-neg, CD38-neg, 13q del CLL, and non-stereotyped cases that in general are less aggressive, the presence of IGHV replacement may pinpoint CLL clones whose precursor B cells had edited their BCRs away from autoreactivity and therefore antigen-drive. This conclusion suggests a differential role for autoantigen drive in certain stereotyped subsets: CLL subsets not exhibiting IGHV replacement appear to be more susceptible to antigen drive than those that do. Disclosures: No relevant conflicts of interest to declare.

Abstract Motivation The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise. Results Here, we present a novel approach to reliably identify transcription factor-binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in the genome, we are able to identify binding motifs in ChIP-Seq data that yield no results in traditional pipelines. Availability and implementation NoPeak is published under the GNU General Public License and available as a standalone console-based Java application at https://github.com/menzel/nopeak. Supplementary information Supplementary data are available at Bioinformatics online.

Presentasjon frå Wikipedia-akademiet 2015 I foredraget ønsker vi å se på Wikipedias nytteverdi som språkressurs: Denne er ved første øyekast åpenbar. Wikipedia representerer en av de største samlingene av fri tekst som er tilgjengelig på både bokmål og nynorsk (hhv. 405.000 og 119.000 artikler). I tillegg er artiklene lenket sammen og knyttet til kategorier. Wikipedia er med andre ord ingen hvilken som helst tekstsamling - den nevnte organiseringen av tekstene gir oss muligheten til å utvikle nye og frie digitale resurser. For eksempel kan sammenlenkingen mellom artikler på bokmål og nynorsk være utgangspunkt for en fritt tilgjengelig parallellordbok/-termbank, noe som ikke finnes i dag: Et søk på "kjennelse" på bokmål, vil gi tilslag på "orskurd" på nynorsk – artiklene er koplet sammen i Wikimedias "langlinks"-tabell. Materialet som ligger i Wikipedia kan også brukes for å lage en synonymordbok. Et utgangspunkt kan her for eksempel være omdirigeringsdatabasen som leder om søk: Et søk på "øretelefoner" eller "høretelefoner" leder brukeren til artikkelen "hodetelefoner". Til slutt kan også kategoriseringen som er gjort i Wikipedia danne grunnlag for spesialordbøker og språkvitenskapelige undersøkelser. I foredraget skal vi se nærmere på sistnevnte, altså på kategoriseringen som er gjort i Wikipedia, og sammenhengen mellom ordbruk og bestemte temaområder, og hvordan temaområdene som representerer semantiske klasser kan vise frem betydningsforskjeller mellom ord. For å oppnå dette, har vi som en liten pilotstudie lastet ned siste dump av bokmålsversjonen til Wikipedia og konvertert markup til ren tekst med Java-verktøyet gwtwiki.[1] Vi ble da sittende igjen med et tekstmateriale på i underkant av 80 millioner ord. Samtidig ekstraherte vi alle kategoriene de ulike sidene er knyttet til fra Wikimedia-databasen: Siden mange av disse kategoriene er svært spesifikke, valgte vi å konsentrere oss om alle ettordskategorier som hverken inneholder spesialtegn (for eksempel bindestrekbegreper) eller tall og som har vært brukt i mer enn 60 artikler. Alle stubber ble også utelatt. Slik endte vi opp med ca. 700 kategorier (av totalt ca. 180.000 oppføringer i kategoridatabasen) der kategorien «menn» var den mest brukte, etterfulgt av «kvinner». Vi talte så frekvensene for hver ordform opp på hver kategori slik at vi fikk frekvensene for enkeltord fordelt på hver av kategoriene og en stor «restkatgori». Ved hjelp av et statistisk mål, såkalt PMI («Pointwise Mutual Information»), har vi til slutt regnet oss frem til hvilke av kategoriene ordene har størst affinitet til. PMI beregnes ut fra frekvensdata ved å se på den forventede forekomsten av ordet i en bestemt kategori sammen med den faktiske, slik at ord som klumper seg sammen under et bestemt emne vil gi høy score, mens ord som fordeler seg jevnt over ulike emner gir lavere score. Første stikkprøver vi har gjort, viser at opptellingen gir interessante resultater. Et søk på "melk" gir høy PMI på kategoriene "drikkestell" og "melkestell", altså melk både som drikk og som landbruksprodukt. Aller mest knyttes melk til ost, og i begge kategorier er forbindelsen til husdyrhold tydelig. Det er også mulig å gå den andre veien, altså fra emne til enkeltbegrep: For eksempel får vi i kategorien "kaffe" relevante oppslag som "nespresso", "starbucks", "arabica", "robusta", "latte", "kjeldsberg" som alle på sin måte er med på å prege kaffekulturen i Norge i 2015. Selv om vi ikke har hatt muligheten til å gå dypere inn i materialet ennå, tyder mye på at metoden er godt anvendbar på Wikipedia-materialet. I foredraget ønsker vi ved siden av presentasjon av relevante funn også å trekke inn mer tekstmateriale: Vi vil gjøre det samme vi her har gjort for bokmålsversjonen av Wikipedia for nynorskutgaven. Et annet mål er å sammenlikne ordfordelingen i Wikipedia med tekster fra en enda større ressurs, nemlig alle norske bøker utgitt frem til og med år 2000. Her kommer vi til å bruke Dewey-systemet som utgangspunkt for opptelling. Bøker som er katalogisert i norske bibliotek fra og med 1950-tallet har for det meste en slik kategorisering. Undersøkelsen har en klar relevans for Wikipedia-samfunnet da den blant annet sier noe om hvor god kategoriseringen på nåværende tidspunkt er og hvor den kan forbedres. Den er også med på å understreke betydningen av slik kategorisering: Wikipedia-ressursene er av stor interesse for oversettere som er opptatt av nyanser i språket og er også svært nyttig i oppbyggingen av termbaser, tesauruser og søkemotorer der for eksempel disambiguering av søketermer står sentralt. Fotnoter: [1] Fremgangsmåten vi brukte, er beskrevet her: http://trulymadlywordly.blogspot.no/2011/03/creating-text-corpus-from-wikipedia.html [sist besøkt: 28.01.2015].