Preparation of flat dendrimers and polycyclic aromatic hydrocarbons connected via 1,3,5-triethynylbenzene core.

Flat dendrimers, consisting of a hexavalent aromatic core and rigid ethynyl units locked in place by ether connections were developed based upon the divergent synthetic method. Alternating functional groups were adopted on each site of the hexa-substituted benzene, in order to avoid undesired cyclization pathways. The flat structures of conjugated dendrimers would allow investigation on the discotic liquid crystal properties. In addition, these ethylnyl dendrimers are expected to show directed energy and electron transfer with a highly conjugated system, and thus are effective in the preparation of photoreactive materials such as electronic sensors or light harvesting materials. Conjugated polycyclic aromatic hydrocarbons, consisting of naphthalene, anthracene, pyrene, and phenanthrene groups connected via 1,3,5-triethynylbenzene cores, were synthesized. These molecules exhibited luminescence properties and the π-complexation with a mercury trifunctional lewis acid are expected to enhance the phosphorescence in the presence of the heavy metal due to the spin-orbit coupling. Besides, owing to the presence of heavy metal atom in the Au (I) complexes linked by s-bonded triethynyltriphenylene luminophore, the phosphorescence occurs from a metal-centered emission. The conjugated organic luminophores have been developed to produce excellent quantum efficiencies, brightness, and long lifetimes

Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy

We address an important gap in detection of political bias in news articles. Previous works that perform supervised document classification can be biased towards the writing style of each news outlet, leading to overfitting and limited generalizability. Our approach overcomes this limitation by considering both the sentence-level semantics and the document-level rhetorical structure, resulting in a more robust and style-agnostic approach to detecting political bias in news articles. We introduce a novel multi-head hierarchical attention model that effectively encodes the structure of long documents through a diverse ensemble of attention heads. While journalism follows a formalized rhetorical structure, the writing style may vary by news outlet. We demonstrate that our method overcomes this domain dependency and outperforms previous approaches for robustness and accuracy. Further analysis demonstrates the ability of our model to capture the discourse structures commonly used in the journalism domain.Comment: Preprint. Under revie

Emerging respiratory infections threatening public health in the Asia-Pacific region: a position paper of the Asian Pacific Society of Respirology

In past decades, we have seen several epidemics of respiratory infections from newly emerging viruses, most of which originated in animals. These emerging infections, including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and the pandemic influenza A(H1N1) and avian influenza (AI) viruses, have seriously threatened global health and the economy. In particular, MERS-CoV and AI A(H7N9) are still causing infections in several areas, and some clustering of cases of A(H5N1) and A(H7N9) may imply future possible pandemics. Additionally, given the inappropriate use of antibiotics and international travel, the spread of carbapenem-resistant Gram-negative bacteria is also a significant concern. These infections with epidemic or pandemic potential present a persistent threat to public health and a huge burden on healthcare services in the Asia-Pacific region. Therefore, to enable efficient infection prevention and control, more effective international surveillance and collaboration systems, in the context of the ‘One Health’ approach, are necessary

Effects of a multi-herbal extract on type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>An aqueous extract of multi-hypoglycemic herbs of <it>Panax ginseng </it>C.A.Meyer, <it>Pueraria lobata, Dioscorea batatas Decaisne, Rehmannia glutinosa, Amomum cadamomum Linné, Poncirus fructus </it>and <it>Evodia officinalis </it>was investigated for its anti-diabetic effects in cell and animal models.</p> <p>Methods</p> <p>Activities of PPARγ agonist, anti-inflammation, AMPK activator and anti-ER stress were measured in cell models and in <it>db/db </it>mice (a genetic animal model for type 2 diabetes).</p> <p>Results</p> <p>While the extract stimulated PPARγ-dependent luciferase activity and activated AMPK in C2C12 cells, it inhibited TNF-α-stimulated IKKβ/NFkB signaling and attenuated ER stress in HepG2 cells. The <it>db/db </it>mice treated with the extract showed reduced fasting blood glucose and HbA_1clevels, improved postprandial glucose levels, enhanced insulin sensitivity and significantly decreased plasma free fatty acid, triglyceride and total cholesterol.</p> <p>Conclusion</p> <p>The aqueous extract of these seven hypoglycemic herbs demonstrated many therapeutic effects for the treatment of type 2 diabetes in cell and animal models.</p

BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

Dense Text-to-Image Generation with Attention Modulation

Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image region. To address this, we propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions while offering control over the scene layout. We first analyze the relationship between generated images' layouts and the pre-trained model's intermediate attention maps. Next, we develop an attention modulation method that guides objects to appear in specific regions according to layout guidance. Without requiring additional fine-tuning or datasets, we improve image generation performance given dense captions regarding both automatic and human evaluation scores. In addition, we achieve similar-quality visual results with models specifically trained with layout conditions.Comment: Accepted by ICCV2023. Code and data are available at https://github.com/naver-ai/DenseDiffusio

Eficacia antiincrustante de una formulación de pintura de reducción controlada con acetofenona

Biofouling is an inevitable problem that occurs continually on marine fishing vessels and other small crafts. The nature of the antifouling (AF) coatings used to prevent biofouling on these small vessels is of great environmental concern. Therefore, the efficacy of a non-toxic AF candidate, acetophenone, was evaluated in preliminary laboratory assays using marine bacteria, diatom and Ulva spores. At a low concentration of 100 μg cm–2 of acetophenone, spore attachment of a green fouling alga was significantly reduced (p < 0.01). Similarly, 40% acetophenone coatings significantly inhibited diatom attachment. This new non-toxic AF agent was incorporated into controlled depletion paint (CDP). Fouling coverage (%), biomass, and fouling resistance (%) were estimated. On CDP coatings made with acetophenone (40%), a significant decrease in fouling biomass was estimated (p < 0.01).El biofouling es un problema inevitable que ocurre continuamente en los buques de pesca marina y en las pequeñas embarcaciones. La naturaleza de los recubrimientos antiincrustantes (AF) usados para prevenir el bioincrustado en estos pequeños buques tiene gran preocupación ambiental. Por lo tanto, la eficacia de un candidato AF no tóxico, la acetofenona, se evaluó en ensayos preliminares de laboratorio usando bacterias marinas, diatomeas y esporas de Ulva. A una concentración baja de 100 μg cm–2 de acetofenona, la adherencia de esporas de una alga incrustante verde se redujo significativamente (p < 0.01). Del mismo modo, el revestimiento de acetofenona a un nivel del 40% inhibieró significativamente la adherencia de diatomeas. Además, esta nueva acetofenona AF no tóxica se incorporó a la pintura de reducción controlada (CDP). La cobertura de las incrustaciones (%), la biomasa y la resistencia a la incrustación (%) fueron estimadas. En recubrimientos de CDP donde se incorporó la acetofenona (40%), se estimó una disminución significativa de la biomasa incrustante (p < 0.01)

Micromechanics-Based Homogenization of the Effective Physical Properties of Composites With an Anisotropic Matrix and Interfacial Imperfections

Micromechanics-based homogenization has been employed extensively to predict the effective properties of technologically important composites. In this review article, we address its application to various physical phenomena, including elasticity, thermal and electrical conduction, electric, and magnetic polarization, as well as multi-physics phenomena governed by coupled equations such as piezoelectricity and thermoelectricity. Especially, for this special issue, we introduce several research works published recently from our research group that consider the anisotropy of the matrix and interfacial imperfections in obtaining various effective physical properties. We begin with a brief review of the concept of the Eshelby tensor with regard to the elasticity and mean-field homogenization of the effective stiffness tensor of a composite with a perfect interface between the matrix and inclusions. We then discuss the extension of the theory in two aspects. First, we discuss the mathematical analogy among steady-state equations describing the aforementioned physical phenomena and explain how the Eshelby tensor can be used to obtain various effective properties. Afterwards, we describe how the anisotropy of the matrix and interfacial imperfections, which exist in actual composites, can be accounted for. In the last section, we provide a summary and outlook considering future challenges