Komagatini “papirnati predjeli”: uprizorenost i materijalnost slikovnice Blue to Blue

In picturebooks, the text, the image and the material book compete or cooperate with each other to convey and perform the narrative. The incorporation of material elements into the picturebook narrative at the same time extends reading beyond a purely cognitive activity: the reader enacts and interprets the narrative by interacting physically with the architectural space of the book. In Katsumi Komagata’s Blue to Blue, the paper steps forward to take up a significant narrative role rather than retreating as the decorative backdrop or mere material support for the visual and textual elements. Not only do the qualities of the paper like texture, transparency and luminosity evoke features of the landscapes and characters in the story, but the shapes of the paper and the die-cuts also create physical depth and form characters that spring into life at the turning, poking and caressing of the reader’s hands.U slikovnicama se tekst, slika i sama knjiga kao predmet međusobno natječu ili surađuju kako bi posredovali i predstavili pripovijed. Uključivanje materijalnih sastavnica u pripovjednu slikovnicu istodobno proširuje pojam čitanja preko granice čisto kognitivne aktivnosti: čitatelj izvodi i interpretira pripovijed na taj način tako što fizički surađuje s arhitektonskim prostorom knjige. U slikovnici Blue to Blue [Plavo do plavoga] Katsumija Komagate (1994), papir preuzima važnu pripovjednu ulogu umjesto povlačenja u ulogu dekorativne pozadine ili pukoga materijalnoga oslonca vizualnih i tekstualnih sastavnica. Osim što svojstva papira, kao na primjer tekstura, prozirnost i svjetlucavost, prizivaju značajke prikazanih krajolika i likova u priči, oblici papira i prorezi u njem također stvaraju dubinu i formiraju likove koji oživljavaju kad ih okreću, guraju ili miluju čitateljeve ruke

Human herpesvirus 6 envelope components enriched in lipid rafts: evidence for virion-associated lipid rafts

In general, enveloped viruses are highly dependent on their lipid envelope for entry into host cells. Here, we demonstrated that during the course of virus maturation, a significant proportion of human herpesvirus 6 (HHV-6) envelope proteins were selectively concentrated in the detergent-resistant glycosphingolipid- and cholesterol-rich membranes (rafts) in HHV-6-infected cells. In addition, the ganglioside GM1, which is known to partition preferentially into lipid rafts, was detected in purified virions, along with viral envelope glycoproteins, gH, gL, gB, gQ1, gQ2 and gO indicating that at least one raft component was included in the viral particle during the assembly process

Identification and Typing of Human Enterovirus: A Genomic Barcode Approach

Identification and typing of human enterovirus (HEVs) are important to pathogen detection and therapy. Previous phylogeny-based typing methods are mainly based on multiple sequence alignments of specific genes in the HEVs, but the results are not stable with respect to different choices of genes. Here we report a novel method for identification and typing of HEVs based on information derived from their whole genomes. Specifically, we calculate the k-mer based barcode image for each genome, HEV or other human viruses, for a fixed k, 1<k<7, where a genome barcode is defined in terms of the k-mer frequency distribution across the whole genome for all combinations of k-mers. A phylogenetic tree is constructed using a barcode-based distance and a neighbor-joining method among a set of 443 representative non-HEV human viruses and 395 HEV sequences. The tree shows a clear separation of the HEV viruses from all the non-HEV viruses with 100% accuracy and a separation of the HEVs into four distinct clads with 93.4% consistency with a multiple sequence alignment-based phylogeny. Our detailed analyses of the HEVs having different typing results by the two methods indicate that our results are in better agreement with known information about the HEVs

Applications of Nanomaterials in Electrogenerated Chemiluminescence Biosensors

Electrogenerated chemiluminescence (also called electrochemiluminescence and abbreviated ECL) involves the generation of species at electrode surfaces that then undergo electron-transfer reactions to form excited states that emit light. ECL biosensor, combining advantages offered by the selectivity of the biological recognition elements and the sensitivity of ECL technique, is a powerful device for ultrasensitive biomolecule detection and quantification. Nanomaterials are of considerable interest in the biosensor field owing to their unique physical and chemical properties, which have led to novel biosensors that have exhibited high sensitivity and stability. Nanomaterials including nanoparticles and nanotubes, prepared from metals, semiconductor, carbon or polymeric species, have been widely investigated for their ability to enhance the efficiencies of ECL biosensors, such as taking as modification electrode materials, or as carrier of ECL labels and ECL-emitting species. Particularly useful application of nanomaterials in ECL biosensors with emphasis on the years 2004-2008 is reviewed. Remarks on application of nanomaterials in ECL biosensors are also surveyed

Characterization of Neuraminidases from the Highly Pathogenic Avian H5N1 and 2009 Pandemic H1N1 Influenza A Viruses

To study the precise role of the neuraminidase (NA), and its stalk region in particular, in the assembly, release, and entry of influenza virus, we deleted the 20-aa stalk segment from 2009 pandemic H1N1 NA (09N1) and inserted this segment, now designated 09s60, into the stalk region of a highly pathogenic avian influenza (HPAI) virus H5N1 NA (AH N1). The biological characterization of these wild-type and mutant NAs was analyzed by pseudotyped particles (pseudoparticles) system. Compared with the wild-type AH N1, the wild-type 09N1 exhibited higher NA activity and released more pseudoparticles. Deletion/insertion of the 09s60 segment did not alter this relationship. The infectivity of pseudoparticles harboring NA in combination with the hemagglutinin from HPAI H5N1 (AH H5) was decreased by insertion of 09s60 into AH N1 and was increased by deletion of 09s60 from 09N1. When isolated from the wild-type 2009H1N1 virus, 09N1 existed in the forms (in order of abundance) dimer>>tetramer>monomer, but when isolated from pseudoparticles, 09N1 existed in the forms dimer>monomer>>>tetramer. After deletion of 09s60, 09N1 existed in the forms monomer>>>dimer. AH N1 from pseudoparticles existed in the forms monomer>>dimer, but after insertion of 09s60, it existed in the forms dimer>>monomer. Deletion/insertion of 09s60 did not alter the NA glycosylation pattern of 09N1 or AH N1. The 09N1 was more sensitive than the AH N1 to the NA inhibitor oseltamivir, suggesting that the infectivity-enhancing effect of oseltamivir correlates with robust NA activity

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Machine Learning-Based Decision Support to Enhance the Seismic Resilience of Distributed Infrastructure

Seismic resilience of infrastructure has received growing attention in the past decades. Thisdissertation aims to propose a general decision support framework for the post-earthquake damage and recovery of distributed infrastructures in order to improve their resilience. A damage assessment framework is first proposed that incorporates a neural network-based pre-event assessment model, and a graph neural network-based model for dynamically updating the damage estimates in the recovery process. Next, a spatially explicit model is developed to quantify and estimate the possible recovery trajectory based on the Gaussian Process model. Finally, a deep reinforcement learning based framework is proposed to optimize the repair actions in the recovery process. The framework can provide critical insights to the recovery of distributed infrastructure and improve pre-event planning and post-earthquake recovery tasks

On the improvement of reinforcement active learning with the involvement of cross entropy to address one-shot learning problem.

As a promising research direction in recent decades, active learning allows an oracle to assign labels to typical examples for performance improvement in learning systems. Existing works mainly focus on designing criteria for screening examples of high value to be labeled in a handcrafted manner. Instead of manually developing strategies of querying the user to access labels for the desired examples, we utilized the reinforcement learning algorithm parameterized with the neural network to automatically explore query strategies in active learning when addressing stream-based one-shot classification problems. With the involvement of cross-entropy in the loss function of Q-learning, an efficient policy to decide when and where to predict or query an instance is learned through the developed framework. Compared with a former influential work, the advantages of our method are demonstrated experimentally with two image classification tasks, and it exhibited better performance, quick convergence, relatively good stability and fewer requests for labels