Enantioselective synthesis of chiral α-alkynylated thiazolidones by tandem S-addition/acetalization of alkynyl imines.

A SPINOL-derived chiral phosphoric acid catalyzed asymmetric formal [2 + 3]-annulation of in situ generated alkynyl imines and 1,4-dithiane-2,5-diol has been developed to afford enantiopure α-alkynylated thiazolidones with up to 72% yield and 98.5 : 1.5 er. This tandem annulation involved a tandem S-addition of alkynyl imines/intramolecular acetalization, followed by PDC-mediated oxidation. The α-alkynylated thiazolidones could facilely afford the corresponding chiral α-alkynylated or α-alkenylated cyclic sulfoxides via further elaboration.

Optical Selection Rule of Excitons in Gapped Chiral Fermion Systems.

We show that the exciton optical selection rule in gapped chiral fermion systems is governed by their winding number w, a topological quantity of the Bloch bands. Specifically, in a C_{N}-invariant chiral fermion system, the angular momentum of bright exciton states is given by w±1+nN with n being an integer. We demonstrate our theory by proposing two chiral fermion systems capable of hosting dark s-like excitons: gapped surface states of a topological crystalline insulator with C_{4} rotational symmetry and biased 3R-stacked MoS_{2} bilayers. In the latter case, we show that gating can be used to tune the s-like excitons from bright to dark by changing the winding number. Our theory thus provides a pathway to electrical control of optical transitions in two-dimensional material.

Cu-Catalyzed Multicomponent Reaction of Styrenes, Perfluoroalkyl Halide, Alcohol, and tert-Butyl Hydroperoxide: One-Pot Synthesis of (Z)-ß-Alkoxyperfluoroalkenone.

An efficient synthesis of Z-perfluoroalkyl-substituted enones by a multicomponent reaction strategy has been described. A variety of elusive perfluoroalkylated enones are furnished under mild reaction conditions in good yields with unique chemo- and stereoselectivity. A sequence of radical-mediated Kornblum-DeLaMare reaction, Michael addition, and HF elimination is proposed for the mechanism.

Berry Phase Modification to the Energy Spectrum of Excitons.

By quantizing the semiclassical motion of excitons, we show that the Berry curvature can cause an energy splitting between exciton states with opposite angular momentum. This splitting is determined by the Berry curvature flux through the k-space area spanned by the relative motion of the electron-hole pair in the exciton wave function. Using the gapped two-dimensional Dirac equation as a model, we show that this splitting can be understood as an effective spin-orbit coupling effect. In addition, there is also an energy shift caused by other "relativistic" terms. Our result reveals the limitation of the venerable hydrogenic model of excitons, and it highlights the importance of the Berry curvature in the effective mass approximation.

Gate-tunable circular phonon dichroism effect in bilayer graphene.

Circular phonon dichroism effect has been proposed in two-dimensional materials; however, the lack of tunability hinders the exploration of the effect. Here, we investigate the role of dual-gating-induced inversion symmetry breaking in the circular phonon dichroism effect in bilayer graphene. We find that the introduction of inversion symmetry breaking modifies the response in the layer-symmetric and layer-antisymmetric channels, and results in the occurrence of phonon dichroism in the cross-channel. In the layer representation, the inversion symmetry breaking breaks the equality of intralayer circular phonon dichroism and enhances the interlayer response. Our results suggest that layer degree of freedom provides possibilities to tune phonon dynamics, which paves a way toward different physics and applications of two-dimensional acoustoelectronics and layertronics.

A computational framework for predicting novel drug indications using graph convolutional network with contrastive learning.

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin. The prediction model's code is available at https://github.com/yuxunluo9/DrIGCL.

Theoretical studies of magneto-optical Kerr and Faraday effects in two-dimensional second-order topological insulators.

Optical approaches are useful for studying the electronic and spin structure of materials. Here, based on the tight-binding model and linear response theory, we investigate the magneto-optical Kerr and Faraday effects in two-dimensional second-order topological insulators (SOTI) with external magnetization. We find that orbital-dependent Zeeman term induces band crossings for SOTI phase, which are absent for trivial phase. In the weak-magnetization regime, these crossings give rise to giant jumps (peaks) of Kerr and Faraday angles (ellipticity) for SOTI phase. In the strong-magnetization regime, we find that two nearly flat bands are formed at the high-symmetry point of Brillouin zone of SOTI phase. These flat bands give rise to two successive giant jumps (peaks) of Kerr and Faraday angles (ellipticity). These phenomena provide new possibilities to characterize and detect the two-dimensional SOTI phase.

Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs.

Combinatorial drug therapy is a promising approach for treating complex diseases by combining drugs with synergistic effects. However, predicting effective drug combinations is challenging due to the complexity of biological systems and the limited understanding of pathophysiological mechanisms and drug targets. In this paper, we proposed a computational framework called VGAETF (Variational Graph Autoencoder Tensor Decomposition), which leveraged multi-relational graph to model complex relationships between entities in biological systems and predicted disease-related synergistic drug combinations in an end-to-end manner. In the computational experiments, VGAETF achieved high performances (AUROC [the area under receiver operating characteristic] = 0.9767, AUPR [the area under precision-recall] = 0.9660), outperforming other compared methods. Moreover, case studies further demonstrated the effectiveness of VGAETF in identifying potential disease-related synergistic drug combinations.

Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training.

The DNA-protein binding plays a pivotal role in regulating gene expression and evolution, and computational identification of DNA-protein has drawn more and more attention in bioinformatics. Recently, variants of BERT are also used to capture the semantic information of DNA sequences for predicting DNA-protein bindings. In this study, we leverage a task-specific pre-training strategy on BERT using large-scale multi-source DNA-protein binding data and present TFBert. TFBert treats DNA sequences as natural sentences and k-mer nucleotides as words. It can effectively extract upstream and downstream nucleotide context information by pre-training the 690 unlabeled ChIP-seq datasets. Experiments show that the pre-trained model can achieve promising performance on every single dataset in the 690 ChIP-seq datasets after simple fine tuning, especially on small datasets. The average AUC is 94.7%, outperforming existing popular methods. In conclusion, this study provides a variant of BERT based on pre-training and achieved state-of-the-art results in predicting DNA-protein bindings. We believe that TFBert can provide insights into other biological sequence classification problems.

Machine learning in the development of targeting microRNAs in human disease.

A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

Enantioselective Phosphine-Catalyzed Trimerization of γ-Aryl-3-butynoates via Isomerization/[3 + 2] Cyclization/Michael Addition Cascade.

We disclose an l-isoleucine-derived amide phosphine-catalyzed trimerization of γ-aryl-3-butynoates, which undergo an isomerization to allenoate, [3 + 2] cyclization, and Michael addition cascade. Exocyclopentene derivatives bearing an all-carbon quaternary stereocenter were constructed stereospecifically and enantioselectively. A wide variety of γ-aryl-3-butynoates could be employed to deliver optically pure cyclopentene derivatives in moderate to good yields with ee values of ≥95% and in most cases ≥98%.

Direct asymmetric N-propargylation of indoles and carbazoles catalyzed by lithium SPINOL phosphate.

Catalytic asymmetric functionalization of the N-H groups of indoles and carbazoles constitutes an important but less developed class of reactions. Herein, we describe a propargylation protocol involving the use of a lithium SPINOL phosphate as the chiral catalyst and our recently developed C-alkynyl N,O-acetals as propargylating reagents. The direct asymmetric N-propargylation of indoles and carbazoles provides hitherto inaccessible N-functionalized products. Notably, the efficiency of the system allows reactions to be run at a very low catalyst loading (as low as 0.1 mol%). Mechanistic information about the titled reaction is also disclosed. This study represents an advance in the direct asymmetric functionalization of the N-H bonds of indoles and carbazoles, and additionally expands on the application of chiral alkali metal salts of chiral phosphoric acids in asymmetric catalysis.

Multiple hot-carrier collection in photo-excited graphene Moiré superlattices.

In conventional light-harvesting devices, the absorption of a single photon only excites one electron, which sets the standard limit of power-conversion efficiency, such as the Shockley-Queisser limit. In principle, generating and harnessing multiple carriers per absorbed photon can improve efficiency and possibly overcome this limit. We report the observation of multiple hot-carrier collection in graphene/boron-nitride Moiré superlattice structures. A record-high zero-bias photoresponsivity of 0.3 A/W (equivalently, an external quantum efficiency exceeding 50%) is achieved using graphene's photo-Nernst effect, which demonstrates a collection of at least five carriers per absorbed photon. We reveal that this effect arises from the enhanced Nernst coefficient through Lifshtiz transition at low-energy Van Hove singularities, which is an emergent phenomenon due to the formation of Moiré minibands. Our observation points to a new means for extremely efficient and flexible optoelectronics based on van der Waals heterostructures.