Heterogeneous Photocatalytic Dehydrogenative Cross‐Coupling and Addition Reaction with Metal‐Loaded Titanium Oxide Photocatalysts

Heterogeneous photocatalytic reactions begin with photoexcitation, followed by simultaneous reduction and oxidation processes that generate radical intermediates. These intermediates contribute to unique product selectivity due to distinct reaction mechanisms. This article presents several heterogeneous photocatalytic reactions involving metal‐loaded titanium oxide photocatalysts for organic transformations, as elucidated by our recent studies. While their productivity has not yet reached a level sufficient for practical applications, these findings of novel photocatalytic reactions demonstrate the significant potential of heterogeneous photocatalysis. Notably, since photocatalysis utilizes photoenergy to drive chemical reactions, even endergonic reactions can be promoted under mild conditions, where the photoenergy is converted into the chemical potential of the products. Various photocatalytic reactions are introduced, including dehydrogenative cross‐coupling reactions and addition reactions, along with their underlying reaction mechanisms: radical addition‐elimination, radical–radical coupling, and radical anti‐Markovnikov addition to alkene. Additionally, the roles of the metal cocatalysts are highlighted as a crucial factor influencing these unique reaction mechanisms in heterogeneous photocatalytic organic transformations

<Posters>On controling the dynamical degrees of automorphisms of complex simple abelian varieties

於 京都大学理学研究科セミナーハウス (2024年10月22日-10月25日)2024年度科学研究費補助金 基盤研究(A)(課題番号 20H00111, 代表 小木曽啓示)2024年度科学研究費補助金 基盤研究(A)(課題番号 21H04429, 代表 並河良典)Date : October 22nd - 25th, 2024Location: Kyoto University (North Campus), Science Seminar HouseJSPS KAKENHI Grant-in-Aid (A) 20H00111 (Keiji Oguiso)JSPS KAKENHI Grant-in-Aid (A) 21H04429 (Yoshinori Namikawa)Organizers: Yohsuke Matsuzawa, Yusuke Nakamura, Kazuhiko Yamak

Transformation of Hamlet in Popular Theater of British Malaya: Shaping Political Culture in Colonial Society

This study delves into the adaptation of Shakespeare's Hamlet in the popular theater of British Malaya during the early twentieth century, providing insights into the development of political culture within the colonial context. Addressing contemporary challenges in Malaysian historical research, the study extends existing research focused on Hang Tuah, a national hero whose contemporary image is thought to have been shaped by twentieth-century popular theater and film. The emphasis is on bangsawan, a form of Malayan popular theater in which Hang Tuah was introduced around 1927. Due to bangsawan's improvised nature, written records are scarce—but newspaper reviews provide valuable insights. Tracing the evolution of Hamlet, a significant bangsawan performance before 1927, the study explores the play's impact on shaping political culture in colonial society. Additionally, the research discusses the recurring theme of conflict between maintaining order and justice in both Hamlet and Hang Tuah, suggesting a shared cultural significance

Organoids and microphysiological systems for pharmaceutical research of viral respiratory infections

In the pharmaceutical research of viral respiratory infections, cell culture models have traditionally been used to evaluate the therapeutic effects of candidate compounds. Although cell lines are easy to handle and cost-effective, they do not fully replicate the characteristics of human respiratory organs. Recently, organoids and microphysiological systems (MPS) have been employed to overcome this limitation for in vitro testing of drugs against viral respiratory infections. Advanced disease modeling using organoids, self-organized three-dimensional (3D) cell culture models derived from stem cells, or MPS, models for culturing multiple cell types in a microfluidic device and capable of recapitulating a physiological 3D dynamic environment, can accurately replicate the complex functions of respiratory organs, thus making them valuable tools for elucidating the organ damages caused by viral respiratory infections and evaluating the efficacy of candidate drugs against them. Recently, a wide range of organoids and MPS have been developed to model the complex pathophysiology caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and assess therapeutic drugs. In this review, we evaluate the latest pharmaceutical research on coronavirus disease 2019 (COVID-19) that utilizes organoids and MPS and discuss future perspectives of their applications

18-year plant reproductive phenology dataset from Lambir, Borneo, including four large general flowering events

Flowering and fruiting phenology can have large impacts on a plant's reproductive success. In many plant species, these phenological events are controlled by seasonal climatic cues, resulting in one-year reproductive cycles. However, parts of SE Asian tropical forests have an aseasonal climate with irregular fluctuations. This database comprises phenology records collected from 1993 to 2011 at the community level in an aseasonal lowland dipterocarp forest of the Lambir Hills National Park in Borneo. Observations were made every two weeks at three sites: The Canopy Biology Plot (CBP) with tree towers and walkways, the Operation Raleigh Tower (ORT) area with a tree tower for tourist attraction, and the Crane Plot located between the CBP and ORT, where plants were monitored from terraces on a canopy crane installed in 2000. The dataset includes in total 172, 521 records of 450, 118, and 51 plants in CBP, Crane Plot, and ORT, respectively, representing 303 species. The number of individuals per species ranges from 1 to 21, and 64.9% are represented by only one. The plants in the censuses were mostly trees but also included lianas and epiphytes. The data have been used to study the causes and consequences of synchronized flowering and fruiting at the community level, a phenomenon unique to the region. Previous studies have shown that this synchronization is synergistically driven by cool air temperature and drought. Irregular flowering and fruiting have significant impacts on flower visitors, frugivores, forest material cycling, and plant regeneration. The dataset can also be used for comparing the phenology of the same species or group among forests and regions and exploring its association with climates. One major concern regarding tropical forests in the area is the effects of climate change on this community-wide masting regime, which could disrupt forest regeneration and ecosystem processes. The dataset could be an important source of information for conservation efforts aimed at protecting these amazingly diverse forest ecosystems. This dataset can be freely used for non-commercial purposes. Users of these data should cite this data paper in any publications resulting from its use and acknowledge the Forest Department Sarawak and Sarawak Forestry Corporation

<史料翻訳>アンリ・トラン「ドイツ国内のフランスに起源を持つ改革派諸教会への呼びかけ」

本稿は、ドイツ・マクデブルクのフランス改革派教会牧師アンリ・トラン Henri Tollin (1833-1902)による書状 „Aufruf an die reformirten Gemeinden französischen Ursprungs in Deutschland“ (1888)の全訳である

Transfer learning with pre-trained conditional generative models

Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) Source and target task label spaces overlap, (ii) Source datasets are available, and (iii) Target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation