In modern Chinese, the verb “至于” (zhì yú) exhibits prominent subjective language characteristics. Apart from indicating degree, the verb differs semantically from other words in the semantic field of ...“degree” by emphasizing unexpected outcomes from a subjective point of view. The subjectivity of the speaker’s perspective, emotions and expressions of “I think” and attitudes of disapproval towards the other’s behavior are conveyed. The subjectivity of the selection of sentence structure, such as hypothetical sentences, interrogative sentences and negative sentences is also observed. Furthermore, the subjectivity of the combination with modal particles is shown, where affirmative forms are often paired with the particle “吗” (ma), while negative forms are paired with the particle “吧” (ba). The subjective attitudes of behavior states are also expressed, with behavior state words exhibiting attitudes of “要” or “不要” from a subjective point of view.
To achieve high photovoltaic performance of bulk hetero-junction organic solar cells (OSCs), a range of critical factors including absorption profiles, energy level alignment, charge carrier mobility ...and miscibility of donor and acceptor materials should be carefully considered. For electron-donating materials, the deep highest occupied molecular orbital (HOMO) energy level that is beneficial for high open-circuit voltage is much appreciated. However, a new issue in charge transfer emerges when matching such a donor with an acceptor that has a shallower HOMO energy level. More to this point, the chemical strategies used to enhance the absorption coefficient of acceptors may lead to increased molecular crystallinity, and thus result in less controllable phase-separation of photoactive layer. Therefore, to realize balanced photovoltaic parameters, the donor-acceptor combinations should simultaneously address the absorption spectra, energy levels, and film morphologies. Here, we selected two non-fullerene acceptors, namely BTPT-4F and BTPTT-4F, to match with a wide-bandgap polymer donor P2F-EHp consisting of an imide-functionalized benzotriazole moiety, as these materials presented complementary absorption and well-matched energy levels. By delicately optimizing the blend film morphology, we demonstrated an unprecedented power conversion efficiency of over 16% for the device based on P2F-EHp:BTPTT-4F, suggesting the great promise of materials matching toward high-performance OSCs.
Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings ...take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman's six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings.
Phycobilisome (PBS) is the main light-harvesting antenna in cyanobacteria and red algae. How PBS transfers the light energy to photosystem II (PSII) remains to be elucidated. Here we report the in ...situ structure of the PBS–PSII supercomplex from
Porphyridium purpureum
UTEX 2757 using cryo-electron tomography and subtomogram averaging. Our work reveals the organized network of hemiellipsoidal PBS with PSII on the thylakoid membrane in the native cellular environment. In the PBS–PSII supercomplex, each PBS interacts with six PSII monomers, of which four directly bind to the PBS, and two bind indirectly. Additional three ‘connector’ proteins also contribute to the connections between PBS and PSIIs. Two PsbO subunits from adjacent PSII dimers bind with each other, which may promote stabilization of the PBS–PSII supercomplex. By analyzing the interaction interface between PBS and PSII, we reveal that α
LCM
and ApcD connect with CP43 of PSII monomer and that α
LCM
also interacts with CP47' of the neighboring PSII monomer, suggesting the multiple light energy delivery pathways. The in situ structures illustrate the coupling pattern of PBS and PSII and the arrangement of the PBS–PSII supercomplex on the thylakoid, providing the near-native 3D structural information of the various energy transfer from PBS to PSII.
Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron ...cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python.
Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In ...this research, we present an image-based wafer map defect pattern classification method. The presented method consists of two main steps: without any specific preprocessing, high-level features are extracted from convolutional neural network and then the extracted features are fed to combination of error-correcting output codes and support vector machines for wafer map defect pattern classification. To the best of our knowledge, no prior work has applied the presented method for wafer map defect pattern classification. Experimental results tested on 20,000 wafer maps show the superiority of presented method and the overall classification accuracy is up to 98.43%.
Protein-protein interaction (PPI) prediction is meaningful work for deciphering cellular behaviors. Although many kinds of data and machine learning algorithms have been used in PPI prediction, the ...performance still needs to be improved. In this paper, we propose InferSentPPI, a sentence embedding based text mining method with gene ontology (GO) information for PPI prediction. First, we design a novel weighting GO term-based protein sentence representation method to generate protein sentences including multi-semantic information in the preprocessing. Gene ontology annotation (GOA) provides the reliability of relationships between proteins and GO terms for PPI prediction. Thus, GO term-based protein sentence can help to improve the prediction performance. Then we also propose an InferSent_PN algorithm based on the protein sentences and InferSent algorithm to extract relations between proteins. In the experiments, we evaluate the effectiveness of InferSentPPI with several benchmarking datasets. The result shows our proposed method has performed better than the state-of-the-art methods for a large PPI dataset.
Display omitted
•Establishment of an object-oriented CART model for high-resolution identification of riverine wetlands.•Localization of a Pressure-State-Response model for riverine wetland ecosystem ...health assessment.•Urbanization tends to more fragmentation and less connectivity of riverine wetlands landscape.•Safeguarding existing wetlands should be paid more attention in riverine urban agglomerations.
Riverine wetland is one of the important cityscapes along rivers, featuring powerful eco-hydrological regulations in safeguarding urban security and serving its quality. Over the past several decades, intensified climate change, together with the upgraded human activities, have deeply disturbed riverine wetland worldwide and caused variations in the amount and pattern, which might lead to negative effects on wetland ecosystem health (WEH), thus threaten the sustainability of the riverine urban agglomerations. To better understand the mechanism of the above effectiveness, the Riverine Urban Agglomerations along the Yellow River in China’s Ningxia Hui Autonomous Region (RUAN) was taken as an example in the present study. First, by establishing an object-oriented remote sensing image classification system based on Classification and Regression Tree (CART), riverine wetland distribution in years of 2000, 2009 and 2018 were determined for a two-Stage (ST-I: 2000–2009, ST-II: 2009–2018) comparative research. Second, the transition matrix and landscape index were used to measure the spatiotemporal dynamics of the riverine wetland in the two stages. Third, a Pressure-State-Response (P-S-R) model, together with its index system, was constructed to comprehensively assess the WEH in there. Results revealed that: (1) Wetlands in RUAN are dominated by the artificial ones, presenting an overall increase in area during the statistical period, varying with different trends in the two stages. Conversion between the wetlands and the non-wetlands were found frequent during the urbanization, leading to remarkable changes in wetland patterns in space. (2) Rivers are important and basic in forming riverine landscape in RUAN, the natural wetlands increased during the statistical period. In general, wetland patches and diversity increased, shapes of that became homogenized. Accordingly, the aggregation of wetlands decreased and the fragmentation of that worsened. (3) Riverine wetlands in most of RUAN experienced an increase of external pressure and a deterioration of state, showing an overall degradation. Meanwhile, the WEH was determined more by the fundamental state of itself, the external disturbance seemed function less. Above findings confirmed the vulnerability of riverine wetlands during urbanization in an arid circumstance. It is worthy of strengthening the protection of existing wetlands and minimizing or eliminating the conversion, to ensure the WEH and serve the sustainability of riverine urban agglomerations.
Novel heterojunction MgFe2O4–ZnO was employed as a photocatalyst to photodegrade organic pollutants Rhodamine B (RhB). This heterojunction was prepared by two steps. First, ZnO flower-like nanotube ...bundles were synthesized by using a simple solution method at low temperature. Second, MgFe2O4 was coated on ZnO surfaces on the basis of a chemical co-precipitation method. The as-prepared samples were characterized with X-ray diffraction (XRD), UV-3150 double-beam spectrophotometer, field-emission scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), and high-resolution transmission (HRTEM). Photodegradation experiments results indicated that the obtained heterojunction MgFe2O4–ZnO exhibited higher photocatalytic activity than that of pure ZnO or MgFe2O4. Furthermore, MgFe2O4–ZnO could be separated conveniently by using an external magnetic field. The enhanced photocatalytic ability of MgFe2O4–ZnO composites could be attributed to the interconnected heterojunction of MgFe2O4 and ZnO nanoparticles.
•The flower-like MgFe2O4–ZnO tubular bundles were prepared by a simple solution method.•The flower-like MgFe2O4–ZnO tubular bundles exhibited higher photocatalytic activity than that of pure ZnO or MgFe2O4.•The enhanced photocatalytic performance of MgFe2O4–ZnO may be attributed to interconnected heterojunction of MgFe2O4 and ZnO nanoparticles.•The MgFe2O4–ZnO photocatalysts can be recycled conveniently using an external magnet.
PDF
