Silicene, a two-dimensional hexagonal lattice of silicon, has been synthesized recently and exhibits fascinating electronic properties that resemble graphene. The substrate effect on the electronic ...properties of silicene is important for the practical applications of silicene. First-principles calculations were performed for silicene on two kinds of representative inert substrates, that is, hexagonal boron nitride (h-BN) monolayer and SiC(0001) surface. The silicene–substrate interaction energies range in 0.067–0.089 eV per Si atom, belonging to typical van der Waals interaction. The characteristic Dirac cone is preserved for silicene on h-BN monolayer or hydrogenated Si-terminated SiC(0001) surface. On the other hand, the silicene becomes metallic when it is placed on a hydrogenated C-terminated SiC(0001) surface. This effect was explained by the work functions for silicene and the substrates. The present results provide some guidelines for selecting proper substrates for silicene in future microelectronic devices.
As attractive analogue of graphene, boron monolayers have been theoretically predicted. However, due to electron deficiency of boron atom, synthesizing boron monolayer is very challenging in ...experiments. Using first-principles calculations, we explore stability and growth mechanism of various boron sheets on Cu(111) substrate. The monotonic decrease of formation energy of boron cluster B(N) with increasing cluster size and low diffusion barrier for a single B atom on Cu(111) surface ensure continuous growth of two-dimensional (2D) boron cluster. During growth process, hexagonal holes can easily arise at the edge of a 2D triangular boron cluster and then diffuse entad. Hence, large-scale boron monolayer with mixed hexagonal-triangular geometry can be obtained via either depositing boron atoms directly on Cu(111) surface or soft landing of small planar BN clusters. Our theoretical predictions would stimulate further experiments of synthesizing boron sheets on metal substrates and thus enrich the variety of 2D monolayer materials.
Abstract
Motivation
A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are ...expensive and time-consuming. Therefore, the computational methods to predict potential miRNA-disease associations have been paid increasing attention.
Results
In this paper, we proposed a novel computational model of Bipartite Network Projection for MiRNA-Disease Association prediction (BNPMDA) based on the known miRNA-disease associations, integrated miRNA similarity and integrated disease similarity. We firstly described the preference degree of a miRNA for its related disease and the preference degree of a disease for its related miRNA with the bias ratings. We constructed bias ratings for miRNAs and diseases by using agglomerative hierarchical clustering according to the three types of networks. Then, we implemented the bipartite network recommendation algorithm to predict the potential miRNA-disease associations by assigning transfer weights to resource allocation links between miRNAs and diseases based on the bias ratings. BNPMDA had been shown to improve the prediction accuracy in comparison with previous models according to the area under the receiver operating characteristics (ROC) curve (AUC) results of three typical cross validations. As a result, the AUCs of Global LOOCV, Local LOOCV and 5-fold cross validation obtained by implementing BNPMDA were 0.9028, 0.8380 and 0.8980 ± 0.0013, respectively. We further implemented two types of case studies on several important human complex diseases to confirm the effectiveness of BNPMDA. In conclusion, BNPMDA could effectively predict the potential miRNA-disease associations at a high accuracy level.
Availability and implementation
BNPMDA is available via http://www.escience.cn/system/file?fileId=99559.
Supplementary information
Supplementary data are available at Bioinformatics online.
We report epidemiological and clinical investigations on ten pediatric SARS-CoV-2 infection cases confirmed by real-time reverse transcription PCR assay of SARS-CoV-2 RNA. Symptoms in these cases ...were nonspecific and no children required respiratory support or intensive care. Chest X-rays lacked definite signs of pneumonia, a defining feature of the infection in adult cases. Notably, eight children persistently tested positive on rectal swabs even after nasopharyngeal testing was negative, raising the possibility of fecal-oral transmission.
Silicene, a silicon analogue of graphene, has attracted increasing attention during the past few years. As early as in 1994, the possibility of stage corrugation in the Si analogs of graphite had ...already been theoretically explored. But there were very few studies on silicene until 2009, when silicene with a low buckled structure was confirmed to be dynamically stable by ab initio calculations. In spite of the low buckled geometry, silicene shares most of the outstanding electronic properties of planar graphene (e.g., the "Dirac cone", high Fermi velocity and carrier mobility). Compared with graphene, silicene has several prominent advantages: (1) a much stronger spin-orbit coupling, which may lead to a realization of quantum spin Hall effect in the experimentally accessible temperature, (2) a better tunability of the band gap, which is necessary for an effective field effect transistor (FET) operating at room temperature, (3) an easier valley polarization and more suitability for valleytronics study. From 2012, monolayer silicene sheets of different superstructures were successfully synthesized on various substrates, including Ag(111), Ir(111), ZrB2(0001), ZrC(111) and MoS2 surfaces. Multilayer silicene sheets have also been grown on Ag(111) surface. The experimental successes have stimulated many efforts to explore the intrinsic properties as well as potential device applications of silicene, including quantum spin Hall effect, quantum anomalous Hall effect, quantum valley Hall effect, superconductivity, band engineering, magnetism, thermoelectric effect, gas sensor, tunneling FET, spin filter, and spin FET, etc. Recently, a silicene FET has been fabricated, which shows the expected ambipolar Dirac charge transport and paves the way towards silicene-based nanoelectronics. This comprehensive review covers all the important theoretical and experimental advances on silicene to date, from the basic theory of intrinsic properties, experimental synthesis and characterization, modulation of physical properties by modifications, and finally to device explorations.
•A slow release fertilizer was developed using starch superabsorbent polymer (SAP).•Smaller grid and larger fractal gel of SAP contributed to slowing fertilizer release.•Fertilizer coated by potato ...starch SAP exhibited steady slow release behaviors.
To enhance the effectiveness of fertilizers, a novel double-coated slow-release fertilizer was developed using ethyl cellulose (EC) as inner coating and starch-based superabsorbent polymer (starch-SAP) as outer coating. For starch-SAPs synthesized by a twin-roll mixer using starches from three botanical origins, a reduced grid size and an increased fractal gel size on nano-scale (i.e., increased stretch of 3D network) contributed to increasing the water absorbing capacity with a reduced absorbing rate and thus improving the slow-release property of fertilizer. The fertilizer particles coated with starch-SAP displayed well slow-release behaviors. In soil, compared to urea particles without and with EC coating, the particles further coated with starch-SAP showed reduced nitrogen release rate, and in particular, those with potato starch-SAP coating exhibited a steady release behavior for a period longer than 96h. Therefore, this work has demonstrated the potential of this new slow-release fertilizer system for improving the effectiveness of fertilizers.
Land surface phenology (LSP) has been widely used as the “footprint” of urbanization and global climate change. Shifts of LSP have cascading effects on food production, carbon sequestration, water ...consumption, biodiversity, and public health. Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing phenological metrics, e.g. start of season (SOS) and end of season (EOS), between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of SOS and EOS within the urban ecosystem. To fill this knowledge gap, we utilized daily two-band enhanced vegetation index, daily meteorological record, and annual land cover dataset to investigate the respective impacts of urbanization and climate change on temporal shifts of LSP between the post- and the pre-urbanization periods over 196 large cities in the northern mid-latitudes. We found 51% of the cities experienced an advanced SOS with an average of −6.39 ± 5.82 days after urbanization has occurred, while the remaining 49% of the cities had a delayed SOS with an average of 7.56 ± 5.63 days. We also found a later EOS at 53% of the cities and an earlier EOS at 47% of the cities with an average of 8.43 ± 7.59 and −5.57 ± 4.99 days between the post- and pre-urbanization periods, respectively. Multiple linear regression analysis indicates that climate variables (i.e. temperature, precipitation, and insolation) play dominant roles in regulating the temporal shifts of LSP. Furthermore, the earlier SOS and later EOS were significantly correlated with the amplitude of urbanization (i.e. increase of impervious surface area) in cities after controlling for effects of climate factors. These patterns were generally consistent across eight climate zones. Our findings provide critical information in modeling natural and anthropogenic effects on urban ecosystem, with important benefits for urban sustainability and biodiversity conservation.
•Post - and pre-urbanization periods were identified for 196 large cities.•Half of the cities experienced earlier SOS and delayed EOS after urbanization.•Climate change played a dominant role in regulating temporal shifts of phenology.•Urbanization can either amplified or off-set climate effects on phenology.•Influences of urbanization and climate change vary by climate background.
The ability of chemicals to enter the blood–brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been ...developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.
This paper reviews the development of single-polymer or self-reinforced composites (SRCs), including the fundamental sciences such as design principles and mechanisms, as well as their preparation ...techniques and potential application areas. The advantages of such SRC systems include the ability to achieve excellent interfaces between components, their pure chemical functionality, and their higher value as recyclable products due to their relative homogeneity compared to composites composed of different classes of components. Single-polymer composites are particularly important in biomaterials applications, since any additives composed of different chemicals could affect biocompatibility and biodegradation. Various techniques used to design and produce SRCs have been investigated and developed, such as hot compaction, overheating, solution, partial dissolving, cool drawing, physical treatment and chemical modification.
Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, ...namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).
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