Graphene-like Molecules Based on Tetraphenylethene Oligomers: Synthesis, Characterization, and Applications

Graphene-like molecules were prepared by oxidative cyclodehydrogenation of tetraphenylethene­(TPE) oligomers using iron­(III) chloride as the catalyst under mild conditions. All the oxidized samples can be separated effectively from the stepwise ring-closing reaction that highly related to the reaction time. For example, the model compounds obtained from the stepwise cyclization reaction show a regular red-shift in UV/vis absorption and photoluminescence (PL) spectra. This result reveals that the molecular conjugation length will extend with the stepwise ring-closing reaction going on. Interestingly, we successfully obtained a series of colorful luminogens with blue, cyan, and green emission during this stepwise and accurate ring closing process. Cyclic voltammetry measurements taken give the corresponding band gap, which supports the results obtained from optical spectroscopy. For the strong intermolecular interaction, our graphene molecules can self-assemble to form a red-colored and hexagonal fiber. Furthermore, some molecules exhibit piezochromic luminescence. The PL emission of the molecules before and after oxidation can be dramatically quenched by picric acid through the electron transfer and/or energy transfer mechanism, enabling them to function as chemosensors for explosive detection. In addition, fluorescence cell imaging studies proved their potential biological application

Additional file 1 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S1. The network architectures of iDeepS. (PNG 45 kb

Additional file 4 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S3. The difference of predictive performance using sequence + structure and only sequence. On the y-axis the performance of the full model with sequence + structure is shown. The x-axis shows the performance of the model using only sequences. The two red lines indicate the 2 times standard deviation of the difference between only using sequence and using sequence + structure. (EPS 39 kb

Additional file 2 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Figure S2. The AUCs of using DBN and k-mer features to predict RBP binding sites. (EPS 54.4 KB

Additional file 3 of Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Table S1. The AUCs of using CNNs with sequence and structure information for different hyperparameters learning rate and weight decay. (PDF 47 kb

Cymbidium nipponicum Makino

原著和名: マヤラン科名: ラン科 = Orchidaceae採集地: 鹿児島県 種子島 生姜山 (大隅 種子島 生姜山)採集日: 1970/8/4採集者: 萩庭丈壽整理番号: JH015338国立科学博物館整理番号: TNS-VS-96533

Phonon Energy Transfer in Graphene–Photoacid Hybrids

Three water-soluble pyrene derivatives, i.e., 1-pyrenesulfonic acid sodium salt (PAS), 8-hydroxy-1,3,6-pyrenetrisulfonic acid trisodium salt (HPTS), and 6,8-dihydroxy-1,3-pyrenedisulfonic acid disodium salt (DHPDS), were employed in noncovalent functionalization of graphene. The phonon coupling interaction between the HPTS and DHPDS photoacids and graphene was demonstrated by UV–vis and photoluminescence spectroscopies, and the proposed mechanism of the phonon transfer was verified by temperature-dependent absorption spectroscopy. Graphene plays the role as a modulator in these graphene/photoacid hybrid systems, which switches the equilibrium between different species of the photoacids. Current work presents the pioneering investigation of phonon coupling (phonon energy transfer) in the graphene–photoacid systems

RAIN v1

<div><b>RAIN: RNA–protein Association and Interaction Networks</b></div><div><br></div><div>RAIN integrates non-coding RNA (ncRNA) and protein interaction networks in an easily accessible web interface. </div><div>It contains three types of ncRNA associations: microRNA-target, ncRNA-protein and ncRNA-ncRNA interactions and combines them with protein-protein interaction available in the STRING database. ncRNA associations cover four model organisms and are extracted from experimental data, automatic literature mining and curated examples. For miRNAs, we further include precomputed target predictions. </div