Copper-Catalyzed Oxidative <i>ipso</i>-Annulation of Activated Alkynes with Silanes: An Approach to 3‑Silyl Azaspiro­[4,5]­trienones

A novel strategy of silylation and dearomatization of activated alkynes with silanes to synthesize azaspiro­[4,5]­trienones is developed, which could be facilely achieved through a tandem difunctionalization of alkyne, dearomatization, and oxidation and provided a facile approach to produce useful 3-silyl azaspiro­[4,5]­trienones in an efficient manner

Role of Planar Conformations in Aggregation Induced Spectral Shifts of Supermolecular Oligofluorenols in Solutions and Films: A Combined Experimental and MD/TD-DFT Study

The supramolecular approach of fluorenol polymers brings about excellent self-assembly behavior to fabricate organogels and superstructured thin films through highly directional noncovalent interactions. To understand the aggregation effects on electronic structures, the packing structures and the UV/vis absorption spectra of oligofluorenols (PFOH<i>n</i>, <i>n</i> = 1/3–8), with and without OC₈H₁₇ side chains, were studied experimentally and theoretically in crystal, amorphous solids, and solutions, respectively. For the ground state in vacuum the steric repulsion between two adjacent fluorenol units renders the PFOH oligomers twisted in a helix conformation, while the molecular aggregation favors the appearance of planar π-conjugated structures. In comparison with the crystal packing, the content of planar conformation (with the torsion angle less than 20°) is increased in amorphous solids. The hydroxyl groups in oligofluorenols facilitate the formation of hydrogen bonding networks. The red shift in absorption spectra was observed in a systematic experimental study of unsubstituted and substituted oligofluorenols with the increasing concentration both in toluene and chloroform solutions. The subsitituted oligofluorenol R-PFOH1 with only one OC₈H₁₇ side chain exhibited a shoulder peak at 430–440 nm, which is different from PFOH1 without side chain and 3R-PFO1 with three OC₈H₁₇ side chain. Time-dependent density functional theory (TDDFT) calculations, which were carried out on conformation ensembles taken from a series of molecular dynamics (MD) simulations, revealed that the increase in the content of planar π-conjugated conformations is correlated to the red shift in the absorption spectra upon increasing the solution concentrations. The aggregation-induced red-shift in absorption spectra of oligofluorenols, as well as the blue-shift for oligothiophenes, was rationalized in a unified way from the increased (and reduced) content of planar conformations in molecular aggregates

Indigenous species barcode database improves the identification of zooplankton

<div><p>Incompleteness and inaccuracy of DNA barcode databases is considered an important hindrance to the use of metabarcoding in biodiversity analysis of zooplankton at the species-level. Species barcoding by Sanger sequencing is inefficient for organisms with small body sizes, such as zooplankton. Here mitochondrial <i>cytochrome c oxidase I</i> (<i>COI</i>) fragment barcodes from 910 freshwater zooplankton specimens (87 morphospecies) were recovered by a high-throughput sequencing platform, Ion Torrent PGM. Intraspecific divergence of most zooplanktons was < 5%, except <i>Branchionus leydign</i> (Rotifer, 14.3%), <i>Trichocerca elongate</i> (Rotifer, 11.5%), <i>Lecane bulla</i> (Rotifer, 15.9%), <i>Synchaeta oblonga</i> (Rotifer, 5.95%) and <i>Schmackeria forbesi</i> (Copepod, 6.5%). Metabarcoding data of 28 environmental samples from Lake Tai were annotated by both an indigenous database and NCBI Genbank database. The indigenous database improved the taxonomic assignment of metabarcoding of zooplankton. Most zooplankton (81%) with barcode sequences in the indigenous database were identified by metabarcoding monitoring. Furthermore, the frequency and distribution of zooplankton were also consistent between metabarcoding and morphology identification. Overall, the indigenous database improved the taxonomic assignment of zooplankton.</p></div

Schematic diagram of parallel barcode recovery using a high throughput sequencing protocol.

<p>Schematic diagram of parallel barcode recovery using a high throughput sequencing protocol.</p

Comparison of zooplankton identification in water samples between metabarcoding and morphology approaches.

<p>(A) Species number. (B) Frequency detected. The R2 and p-value are indicated for each regression axis.</p

Taxonomic assignment of NGS data.

<p>(A) Numbers of zooplankton OTUs and sequences in the NGS data. (B) Distribution of sequence similarity of OTUs against database (both indigenous and NCBI Genbank database). (C) Number of OTUs annotated by indigenous database and/or NCBI Genbank database. “Local” means the OTUs annotated by the indigenous database and “NCBI” means the OTUs annotated by NCBI Genbank. (D) Comparison of NGS data annotated by indigenous database and NCBI Genbank database. Only 24 species that have barcode sequence in NCBI Genbank were showed.</p

Species identified by metabarcoding analysis.

<p>The size of red dots indicated the frequency of each species that detected by morphology method (A) Reads number of each species in metabarcoding data. (B) The internal arcs indicate the species found in morphological analysis. The middle arcs indicate the species that have barcode sequences in indigenous species database. The external arcs indicate which species were detected by metabarcoding. Abundant (detected frequency > 1/2), moderate (detected frequency > 1/3) and rare (detected frequency < 1/3).</p

Zooplankton species in the indigenous barcode database of Lake Tai.

<p>(A) A tree diagram of representative sequences for each species. Distance was measured as the number of base substitutions per site, based on the Kimura two-parameter (K2P) method. One thousand bootstrap trials were run using the neighbor-joining algorithm of the Mega 6.0 program. (B) Number of specimens of each species; red dot means that the species have barcode sequence in NCBI Genbank. (C) Intraspecific divergence based on the indigenous sequences. (D) <i>COI</i> sequences in NCBI Genbank. (E) Intraspecific divergence based on the NCBI Genbank sequences. (F) Similarity of indigenous DNA sequence against NCBI Genbank using Blastx. (G) Similarity of indigenous amino acid sequence against NCBI Genbank using Blastn. (H) Converge of indigenous DNA sequence against NCBI Genbank using Blastn.</p