Organocatalysis has proven to be one of the most rapidly developing and competitive research areas in asymmetric catalysis since 2000, and has become a third branch besides biocatalysis and ...transition metal catalysis. In this feature article, recent progress from our research group on asymmetric organocatalysis, focusing on fine-tunable amine-thiourea catalysis, is described. Design of novel bifunctional amine-thiourea organocatalysts based upon the synergistic activation strategy
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multiple hydrogen bonds and their applications in asymmetric C-C, C-N, and C-S bond-forming reactions under mild conditions are discussed in detail. The most attractive feature of the newly designed fine-tunable amine-thiourea catalysts is the incorporation of multiple hydrogen bonding donors and stereogenic centers.
Recent progress in asymmetric organocatalysis, focusing on fine-tunable amine-thiourea catalysis, is described. Design of novel bifunctional amine-thiourea organocatalysts bearing multiple hydrogen-bonding donors and their applications in asymmetric C-C, C-N, and C-S bond-forming reactions under mild conditions are discussed.
The rapid rise in the applications of carbon fibre reinforced polymer matrix composites (CFRPs) is creating a waste recycling challenge. The use of high-performance thermoset polymers as the matrix ...makes the recovery of the fibres and the resins extremely difficult. Implementation of a circular economy that can eliminate waste and re-use resources warrants the use of efficient processes to recycle end-of-life CFRP components and manufacturing wastes. To this end, herein we present a critical review of the current technologies for recovering carbon fibres and/or the polymers and re-manufacturing CFRPs. New research opportunities in developing new biodegradable thermosets and thermoplastic matrices are also outlined together with more radical recycling strategies for the future.
With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and ...thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.
Since the first microRNA (miRNA) was discovered, a lot of studies have confirmed the associations between miRNAs and human complex diseases. Besides, obtaining and taking advantage of association ...information between miRNAs and diseases play an increasingly important role in improving the treatment level for complex diseases. However, due to the high cost of traditional experimental methods, many researchers have proposed different computational methods to predict potential associations between miRNAs and diseases. In this work, we developed a computational model of Random Forest for miRNA-disease association (RFMDA) prediction based on machine learning. The training sample set for RFMDA was constructed according to the human microRNA disease database (HMDD) version (v.)2.0, and the feature vectors to represent miRNA-disease samples were defined by integrating miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. The Random Forest algorithm was first employed to infer miRNA-disease associations. In addition, a filter-based method was implemented to select robust features from the miRNA-disease feature set, which could efficiently distinguish related miRNA-disease pairs from unrelated miRNA-disease pairs. RFMDA achieved areas under the curve (AUCs) of 0.8891, 0.8323, and 0.8818 ± 0.0014 under global leave-one-out cross-validation, local leave-one-out cross-validation, and 5-fold cross-validation, respectively, which were higher than many previous computational models. To further evaluate the accuracy of RFMDA, we carried out three types of case studies for four human complex diseases. As a result, 43 (esophageal neoplasms), 46 (lymphoma), 47 (lung neoplasms), and 48 (breast neoplasms) of the top 50 predicted disease-related miRNAs were verified by experiments in different kinds of case studies. The results of cross-validation and case studies indicated that RFMDA is a reliable model for predicting miRNA-disease associations.
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Linear copolymer hosts bearing a number of pillar5arene dangling side chains are synthesized for the facile construction of highly emissive supramolecular polymer networks (SPNs) upon noncovalently ...cross‐linking with a series of tetraphenyethylene (TPE)‐based tetratopic guests terminated with different functional groups through supramolecular host–guest interactions. An extremely high fluorescence quantum yield (98.22%) of the SPNs materials is obtained in tetrahydrofuran (THF) by fine‐tuning the parameters, and meanwhile supramolecular light‐harvesting systems based on spherical supramolecular nanoparticles are constructed by interweaving 9,10‐distyrylanthracene (DSA) and TPE‐based guest molecules of aggregation‐induced emission (AIE) with the copolymer hosts in the mixed solvent of THF/H2O. The present study not only illustrates the restriction of the intramolecular rotations (RIR)‐ruled emission enhancement mechanism regulated particularly by macrocyclic arene‐containing copolymer hosts, but also suggests a new self‐assembly approach to construct high‐performance light‐harvesting materials.
Supramolecular polymer networks and supramolecular nanoparticles based on copolymer hosts bearing a number of pillar5arene dangling side chains and tetraphenyethylene‐based tetratopic guests are fabricated, incorporating high fluorescence quantum yield, tunable emission wavelength, and stable microstructures. This facile strategy suggests a new self‐assembly approach to construct high‐performance light‐harvesting materials.
Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of ...biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
Bi5Ti3FeO15 (BTF) has recently attracted considerable interest as a typical multiferroic oxide, wherein ferroelectric and magnetic orders coexist. The ferroelectric order of BTF implies its ...piezoelectricity, because a ferroelectric must be a piezoelectric. However, no extensive studies have been carried out on the piezoelectric properties of BTF. Considering its high ferroelectric‐paraelectric phase transition temperature (Tc ~ 761°C), it is necessary to analyze the piezoelectricity and thermal stabilities of BTF, a promising high‐temperature piezoelectric material. In this study, lightly manganese‐modified BTF polycrystalline oxides are fabricated by substituting manganese ions into Fe3+ sites via the conventional solid‐state reaction method. X‐ray diffraction and Raman spectroscopy analyses reveal that the resultant manganese‐modified BTF has an Aurivillius‐type structure with m = 4, and that the substitutions of Fe by Mn lead to a distortion of BO6. The temperature‐dependent dielectric properties and direct‐current (DC) resistivity measurements indicate that the Mn ions can significantly reduce the dielectric loss tanδ and increase the DC resistivity. The piezoelectricity of BTF is confirmed by piezoelectric constant d33 measurements; it exhibits a piezoelectric constant d33 of 7 pC/N. Remarkably, BTF with 4 mol% of Mn (BTF‐4Mn) exhibits a large d33 of 23 pC/N, three times that of unmodified BTF, whereas the Curie temperature Tc is almost unchanged, ~765°C. The increased piezoelectric performance can be attributed to the crystal lattice distortion, decreased dielectric loss tanδ, and increased DC resistivity. Additionally, BTF‐4Mn exhibits good thermal stabilities of the electromechanical coupling characteristics, which demonstrates that manganese‐modified BTF oxides are promising materials for the use in high‐temperature piezoelectric sensors.
We describe cooperative bimetallic catalysis that enables regio‐/stereodivergent asymmetric α‐allylations of aldimine esters. By employing Et3B as the key activator, racemic allylic alcohols can be ...directly ionized to form Pd or Ir‐π‐allyl species in the presence of achiral Pd or chiral Ir complexes, respectively. The less or more substituted allylic termini of the metal‐π‐allyl species are amenable to nucleophilic attack by the chiral Cu‐azomethine ylide, the formation of which is simultaneously facilitated by Et3B, affording α‐quaternary α‐amino acids with high regioselectivity and excellent stereoselectivity. The use of readily available allylic alcohols as electrophilic precursors represents an improvement from an environmental and atom/step economy perspective. Computational mechanistic studies reveal the crucial role of the Et3B additive and the origins of stereo‐ and regioselectivities by analyzing steric effects, dispersion interactions, and frontier orbital population.
The regio‐ and stereodivergent asymmetric α‐allylation of aldimine esters with racemic allylic alcohols was enabled by cooperative catalysis. The Et3B additive plays a significant role in ionizing allylic alcohols to form electrophilic metal‐π‐allyl species and simultaneously promotes the formation of nucleophilic Cu‐ylides. Computational mechanistic studies revealed the role of the Et3B additive and the origins of stereo‐ and regioselectivities.
In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to ...2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcomes, subjects, participants, hardware, and so on. Furthermore, this study reveals that personalized/adaptive learning has always been an attractive topic in this field, and personalized data sources, for example, students’ preferences, learning achievements, profiles, and learning logs have become the main parameters for supporting personalized/adaptive learning. In addition, we found that the majority of the studies on personalized/adaptive learning still only supported traditional computers or devices, while only a few studies have been conducted on wearable devices, smartphones and tablet computers. In other words, personalized/adaptive learning has a significant number of potential applications on the above smart devices with the rapid development of artificial intelligence, virtual reality, cloud computing and wearable computing. Through the in-depth analysis of the trends and developments in the various dimensions of personalized/adaptive learning, the future research directions, issues and challenges are discussed in our paper.
•A systematic review of adaptive/personalized learning from 2007 to 2017 is conducted.•A comprehensive coding scheme is developed based on the constructivism.•Research issues like learning supports, learning outcomes and so on are addressed.•The results in all categories of the coding scheme are discussed.•Future trends and applications of adaptive/personalized learning are analyzed.
Abstract
MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM–miRNA associations in ...human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule–MiRNA Association prediction (EKRRSMMA) to uncover potential SM–miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM–miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2′-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM–miRNA associations.
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