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In recent years, many scholars at home and abroad have carried out relevant research work on scientific research cooperation, but the social network analysis method is commonly used, and the literature on research cooperation network of local universities is relatively small and lacks a systematic theory. Taking knowledge governance as the basic perspective, we analyze the existing research on scientific research cooperation, carry out the analysis of the necessity and feasibility of establishing scientific research cooperation network in colleges and universities, and take 10 colleges and universities in Fujian Province as samples to explore the existing problems of scientific research cooperation network in local colleges and universities, so as to provide theoretical support and new perspectives for the level of scientific and technological innovation and service capability of colleges and universities to enhance the competitiveness of college education.

Here, we identified a cross talk between SET and MYND domain-containing lysine methyltransferase 2 (Smyd2) and transforming growth factor (TGF)-β-Smad3 signaling and a synergistic feedback loop between them, in which TGF-β stimulates expression of Smyd2 in a Smad3-dependent manner, and upregulation of Smyd2 regulates the transcription of TGF-β and other fibrotic marker genes through direct binding on their promoters or methylating histone H3 indirectly to regulate the transcription of those genes in fibroblasts. Thus, the Smyd2-TGF-β-Smad3-Smyd2 signaling axis plays an important role in promoting renal fibrosis, and targeting Smyd2 with its specific inhibitor should not only delay cyst growth but also ameliorate renal fibrosis in ADPKD.

Population demography can change the network structure, which further plays an important role in the spreading of infectious disease. In this paper, we study the epidemic dynamics in temporal clustered networks where the local-world structure and clustering are incorporated into the attachment mechanism of new nodes. It is found that increasing the local-world size of new nodes has little influence on the clustering coefficient but increases the degree heterogeneity of networks. Besides, when the network evolves faster, increasing the local-world size of new nodes leads to a faster initial growth rate and a larger steady density of infectious nodes, while it has small impacts on the steady density of infectious disease when the network evolves slowly. Furthermore, if the average degree is fixed, increasing the probability of triad formation p enlarges the clustering coefficient of a network, which reduces the initial growth rate and steady density of infectious nodes in the network. This work could provide a theoretical foundation for the control of infectious disease.

Despite that convolutional neural networks (CNNs) have shown high-quality reconstruction for single image dehazing, recovering natural and realistic dehazed results remains a challenging problem due to semantic confusion in the hazy scene. In this article, we show that it is possible to recover textures faithfully by incorporating semantic prior into dehazing network since objects in haze-free images tend to show certain shapes, textures, and colors. We propose a semantic-aware dehazing network (SDNet) in which the semantic prior is taken as a color constraint for dehazing, benefiting the acquisition of a reasonable scene configuration. In addition, we design a densely connected block to capture global and local information for dehazing and semantic prior estimation. To eliminate the unnatural appearance of some objects, we propose to fuse the features from shallow and deep layers adaptively. Experimental results demonstrate that our proposed model performs favorably against the state-of-the-art single image dehazing approaches.

Signal enhancement of magnetic resonance imaging (MRI) in the diseased region is dependent on the molecular structure of the MRI contrast agent. In this study, a macromolecular contrast agent, Branched-LAMA-DOTA-Cy5.5-Gd (BLDCGd), was prepared to target liver cancer. Due to the affinity of lactose to the Asialoglycoprotein receptor (ASGPR) over-expressed on the surface of liver cancer cells, lactose was selected as the targeting moiety in the contrast agent. A cathepsin B-sensitive tetrapeptide, GFLG, was used as a linkage moiety to construct a cross-linked macromolecular structure of the contrast agent, and the contrast agent could be degraded into fragments for clearance. A small-molecular-weight molecule, DOTA-Gd, and a fluorescent dye, Cy5.5, were conjugated to the macromolecular structure

Feature selection (FS), which aims to identify the most informative subset of input features, is an important approach to dimensionality reduction. In this article, a novel FS framework is proposed for both unsupervised and semisupervised scenarios. To make efficient use of data distribution to evaluate features, the framework combines data structure learning (as referred to as data distribution modeling) and FS in a unified formulation such that the data structure learning improves the results of FS and vice versa. Moreover, two types of data structures, namely the soft and hard data structures, are learned and used in the proposed FS framework. The soft data structure refers to the pairwise weights among data samples, and the hard data structure refers to the estimated labels obtained from clustering or semisupervised classification. Both of these data structures are naturally formulated as regularization terms in the proposed framework. In the optimization process, the soft and hard data structures are learned from data represented by the selected features, and then, the most informative features are reselected by referring to the data structures. In this way, the framework uses the interactions between data structure learning and FS to select the most discriminative and informative features. Following the proposed framework, a new semisupervised FS (SSFS) method is derived and studied in depth. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation, environmental evaluation, and so on. Previous image recognizers cannot fulfill this task due to three challenges: 1) localizing visually/semantically salient regions within each aerial photograph in a weakly annotated context due to the unaffordable human resources required for pixel-level annotation; 2) aerial photographs are generally with multiple informative attributes (e.g., clarity and reflectivity), and we have to encode them for better aerial photograph modeling; and 3) designing a cross-domain knowledge transferal module to enhance aerial photograph perception since multiresolution aerial photographs are taken asynchronistically and are mutually complementary. To handle the above problems, we propose to optimize aerial photograph's feature learning by leveraging the low-resolution spatial composition to enhance the deep learning of perceptual features with a high resolution. More specifically, we first extract many BING-based object patches (Cheng et al., 2014) from each aerial photograph. A weakly supervised ranking algorithm selects a few semantically salient ones by seamlessly incorporating multiple aerial photograph attributes. Toward an interpretable aerial photograph recognizer indicative to human visual perception, we construct a gaze shifting path (GSP) by linking the top-ranking object patches and, subsequently, derive the deep GSP feature. Finally, a cross-domain multilabel SVM is formulated to categorize each aerial photograph. It leverages the global feature from low-resolution counterparts to optimize the deep GSP feature from a high-resolution aerial photograph. Comparative results on our compiled million-scale aerial photograph set have demonstrated the competitiveness of our approach. Besides, the eye-tracking experiment has shown that our ranking-based GSPs are over 92% consistent with the real human gaze shifting sequences.

Plant height is an important agronomic trait with a significant impact on grain yield, as demonstrated by the positive effect of the REDUCED HEIGHT ( RHT ) dwarfing alleles ( Rht1b ) on lodging and harvest index in the “Green Revolution” wheat varieties. However, these gibberellic acid (GA)-insensitive alleles also reduce coleoptile length, biomass production, and yield potential in some environments, triggering the search for alternative GA-sensitive dwarfing genes. Here we report the identification, validation, and characterization of the gene underlying the GA-sensitive dwarfing locus RHT25 in wheat. This gene, designated as PLATZ-A1 ( TraesCS6A02G156600 ), is expressed mainly in the elongating stem and developing spike and encodes a plant-specific AT-rich sequence- and zinc-binding protein (PLATZ). Natural and induced loss-of-function mutations in PLATZ-A1 reduce plant height and its overexpression increases plant height, demonstrating that PLATZ-A1 is the causative gene of RHT25. PLATZ-A1 and RHT1 show a significant genetic interaction on plant height, and their encoded proteins interact with each other in yeast and wheat protoplasts. These results suggest that PLATZ1 can modulate the effect of DELLA on wheat plant height. We identified four natural truncation mutations and one promoter insertion in PLATZ-A1 that are more frequent in modern varieties than in landraces, suggesting positive selection during wheat breeding. These mutations can be used to fine-tune wheat plant height and, in combination with other GA-sensitive dwarfing genes, to replace the GA-insensitive Rht1b alleles and search for grain yield improvements beyond those of the Green Revolution varieties.

This work studies the influence of slice permutations on tensor recovery, which is derived from a reasonable assumption about algorithm, i.e. changing data order should not affect the effectiveness of the algorithm. However, as we will discussed in this paper, this assumption is not satisfied by tensor recovery under some cases. We call this interesting problem as Slice Permutations Variability (SPV) in tensor recovery. In this paper, we discuss SPV of several key tensor recovery problems theoretically and experimentally. The obtained results show that there is a huge gap between results by tensor recovery using tensor with different slices sequences. To overcome SPV in tensor recovery, we develop a novel tensor recovery algorithm by Minimum Hamiltonian Circle for SPV (TRSPV) which exploits a low dimensional subspace structures within data tensor more exactly. To the best of our knowledge, this is the first work to discuss and effectively solve the SPV problem in tensor recovery. The experimental results demonstrate the effectiveness of the proposed algorithm in eliminating SPV in tensor recovery.

Objective. To investigate the clinical study of acupoint massage combined with recombinant bovine basic fibroblast growth factor (rbFGF) ophthalmic gel combined with limbal stem cell transplantation on visual quality, corneal refraction, and aesthetic outcome in patients with pterygium. Methods. Sixty patients with pterygium treated in People’s Hospital of Lujiang County from March 2018 to May 2021 were randomized into the control group and the research group by the random number table method, with 30 cases in each group. The control group received rbFGF plus limbal stem cell transplantation. The research group was treated with acupoint massage additionally. Results. The total clinical effective rate in the study group was higher than that in the control group ( P < 0.05 ); after treatment, the modulation transfer function (MTF) cutoff and Strehl ratio (SR) levels in the study group were significantly higher than those in the control group, and the tear film-related objective scatter index (TF-OSI) was significantly lower than that in the control group ( P < 0.05 ); corneal horizontal curvature and corneal vertical curvature in the study group after treatment were significantly higher than those in the control group, and corneal astigmatism degree (CAD) levels were significantly lower than those in the control group ( P < 0.05 ); the levels of the break-up time (BUT) and Schirmer test (SIT) in the study group were significantly higher after treatment, compared with the control group ( P < 0.05 ); and the incidence of adverse manifestations in the study group was significantly lower than that in the control group ( P < 0.05 ). Conclusion. Acupoint massage combined with rbFGF ophthalmic gel combined with limbal stem cell transplantation is effective in clinical treatment of pterygium.

Single image haze removal, which is to recover the clear version of a hazy image, is a challenging trask in computer vision. In this paper, an additive haze model is proposed to approximate the hazy image formation process. In contrast with the traditional optical model, it regards the haze as an additive layer to a clean image. The model thus avoids estimating the medium transmission rate and the global atmospherical light. In addition, based on a critical observation that haze changes gradually and smoothly accross the image, a haze smoothness prior is proposed to constrain this model. This prior assumes that the haze layer is much smoother than the clear image. Benefiting from this prior, we can directly separate the clean image from a single hazy image. Experimental results and comparisons with synthetic images and real-world images demonstrate that the proposed method outperforms state-of-the-art single image haze removal algorithms.

The computational modeling of human visual attention has received much attention in recent decades. In advanced industrial applications, it has been demonstrated that computational visual attention models (CVAMs) can predict visual attention very similarly to human visual attention. However, it is controversial whether the driver's eye fixation location (EFL) or the predicted eye fixation location of computational visual attention models is more reliable and helpful for actual driving. To address this issue, an open database of videos taken under the most common 18 driving conditions in everyday driving has been established. In experiments using this database, expert drivers found that it was not sufficient for drivers to rely on only one of the two EFLs. Based on this finding, a hybrid EFL recommendation strategy is proposed for improving driving safety. By extracting visual characteristics from human dynamic vision, the performance of the proposed recommendation method demonstrates its potential value in these collected driving tasks. In addition, the visual comfort of driving is further addressed to enhance the safety of driving. From the results of experiments on 108 driving video clips taken of the most common 18 real driving conditions, it is confirmed that the proposed EFL recommendation achieves an experience rating of driving comfort between 88.1 and 92.7 out of 100.

Performing numerical simulations of a scaled-down experimental setup of a floating offshore wind turbine allows for a two-way validation between the two approaches. Furthermore, additional insight in the physical behavior of the system can be obtained by tuning numerical results on experimental measurements, and exploring the results. Numerical simulations of a 10MW floating offshore wind turbine model in a wave basin are performed at model scale using computational fluid dynamics. The performance-scaled turbine is designed to match the Froude-scaled thrust force in a Froude-scaled wind field. Initial results show a discrepancy in calculated turbine thrust and torque results compared with experimental results. Two main challenges are identified: (1) selecting a numerical approach appropriate for the low-Reynolds number flow, and (2) accurately modelling the environmental conditions in the basin. In this paper, a numerical sensitivity study is carried out by varying systematically the turbulence models and inflow conditions. A standard k − ε turbulence model is used, as well as a more extensive γ – Reθ turbulence transition model. Different inflow conditions are set up to model the turbulent jet wind field in the wave basin. It is found that the k-ε turbulence model is unsuitable to match the model test results, while satisfying results are obtained using the γ – Reθ model. Furthermore, it is seen that the turbulent jet inflow is represented well by both a vertical power law profile and a radial profile fitted to wind field measurements, while uniform and tabulated inflow conditions are a poor representation of the experimental conditions. It is concluded that effects from surface roughness, the Reynolds number, the inflow velocity and turbulence distribution must be included in the numerical evaluation.

Cognitive impairment associated with schizophrenia (CIAS) is regarded as a core symptom of the illness, and there is still no effective treatment. Light plays an important role in regulation of cognitive functions. However, whether light treatment (LT) can improve CIAS remains unknown. The current study investigated the efficacy of LT on CIAS and explored the underlying molecular mechanisms in a CIAS model. The CIAS and control group were sub-chronically injected with MK-801 and saline respectively, and the LT/CIAS group were CIAS mice exposed to LT (3,000 Lux, 2 hr/day, 3 weeks). Results showed that the performance of LT/CIAS mice significantly improved in the novel object recognition test, novel location recognition test and Morris water maze compared with the CIAS group. And the behavioral improvement effects of LT could last over 4 weeks after LT was terminated. Golgi-cox staining revealed that the dendritic spine density and morphological complexity of hippocampal CA1 pyramidal neurons were increased after 3 weeks LT. Further research found that higher expression of brain-derived neurotrophic factor (BDNF), accompanied by elevated cAMP response element-binding phosphorylation (p-CREB) level in the hippocampus of LT/CIAS group compared to the CIAS group. Moreover, LT elevated phosphorylated extracellular signal-regulated kinase (p-ERK) level in the hippocampus of LT/CIAS group compared to the CIAS group. Taken together, long term LT ameliorated sub-chronic MK-801-induced cognitive deficits in mice, and the altered dendritic spines density and morphology of CA1 pyramidal neurons were rescued in LT/CIAS mice group, which might associate with up-regulating BDNF/p-CREB/p-ERK signaling pathway in LT/CIAS mice.

Video deblurring, which aims at restoring the sharp video from blurry video, is drawing increasing attention in the field of computer vision. In this paper, a method called Multi-Attention Convolutional Neural Network (MACNN) consisting of the temporal-spatial attention module, the frame channel attention module, and the feature extraction-reconstruction module is proposed. First, we use the temporal-spatial attention module and the frame channel attention module to capture features with temporal and spatial information existing across neighboring frames. Then, these captured features are fused and reconstructed to restore the sharp frame. Last but not least, we train MACNN together with a content loss and a perceptual loss in an end-to-end manner to recover realistic video details. Both quantitative and qualitative evaluation results on standard benchmarks demonstrate the proposed MACNN is superior to the state-of-the-art methods in terms of accuracy, efficiency, and visual effect.

Herein, we report a distinctive photoredox/copper dual-catalyzed esterification of benzylic C-H bonds through the combination of photoredox-mediated hydrogen atom transfer and Cu(II)-mediated radical-polar crossover. This methodology demonstrates a high functional group tolerance (40 examples) and moderate to good yields with structurally diverse benzylic C-H substrates. Notably, stoichiometric amounts of carboxylic acids are used as coupling partners, which allows the synthesis of structurally diverse benzylic esters and the late-stage functionalization of pharmaceuticals.

Single image dehazing, which aims at restoring a haze-free image from its correspondingly unconstrained hazy scene, is highly challenging and has gained immense popularity in recent years. However, the images generated using existing haze-removal methods often contain haze, artifacts, and color distortions, which severely degrade the visual quality of the final images. To this end, we propose a network combining multi-scale hierarchical feature fusion and mixed convolution attention to progressively and adaptively enhance the dehazing performance. The haze levels and image structure information are accurately estimated by fusing multi-scale hierarchical features, thus the model restores images with less remaining haze. The proposed attention mechanism is capable of reducing feature redundancy, learning compact internal representations, highlighting task-relevant features and further helping the model to estimate images with sharper textural details and more vivid colors. Therefore, with the application of multi-scale features extracted from both diverse layers and filters, the dehazing performance is significantly improved. Furthermore, a deep semantic loss function is proposed to highlight more semantic information in deep features. The experimental results show that the proposed method outperforms state-of-the-art haze removal algorithms.

Chimonanthus salicifolius (CS), the leaves of Chimonanthus salicifolius S. Y. Hu., is an effective tea to prevent and treat hypertension in China.

Establishing reliable correspondences by a deep neural network is an important task in computer vision, and it generally requires permutation-equivariant architecture and rich contextual information. In this paper, we design a Permutation-Equivariant Split Attention Network (called PESA-Net), to gather rich contextual information for the feature matching task. Specifically, we propose a novel “Split–Squeeze–Excitation–Union” (SSEU) module. The SSEU module not only generates multiple paths to exploit the geometrical context of putative correspondences from different aspects, but also adaptively captures channel-wise global information by explicitly modeling the interdependencies between the channels of features. In addition, we further construct a block by fusing the SSEU module, Multi-Layer Perceptron and some normalizations. The proposed PESA-Net is able to effectively infer the probabilities of correspondences being inliers or outliers and simultaneously recover the relative pose by essential matrix. Experimental results demonstrate that the proposed PESA-Net relative surpasses state-of-the-art approaches for pose estimation and outlier rejection on both outdoor scenes and indoor scenes (i.e., YFCC100M and SUN3D). Source codes: https://github.com/x-gb/PESA-Net .

Ionically conductive double network hydrogel is proposed and used as strain sensors to monitor various motions of human body.

This study examined exosomal hepatitis B virus (HBV)-DNA levels in chronic HBV infection (CHB). Patients were grouped according to the European Association for the Study of the Liver classification (1: HBV-DNA-positive CHB, normal alanine aminotransferase [ALT]; 2: HBV-DNA-positive CHB, elevated ALT; 3: HBV-DNA-negative HBeAb-positive CHB, normal ALT; 4: HBV-DNA-positive HBeAg-negative HBeAb-positive CHB, elevated ALT; 5: HBV-DNA-negative, HBcAb-positive; 6: HBV-negative, normal ALT). Exosomes were isolated, comparative analysis of exosomes and serum HBV-DNA. The HBV-DNA content was lower in exosomes than in serum for groups 1, 2, and 4 (all P < 0.05). In the groups negative for serum HBV-DNA (groups 3 and 5), the exosomal HBV-DNA levels were higher than the serum HBV-DNA levels (all P < 0.05). The exosomal and serum HBV-DNA levels were correlated in groups 2 (R 2 = 0.84) and 4 (R 2 = 0.98). The exosomal HBV-DNA levels were correlated with total bilirubin (R 2 = 0.94), direct bilirubin (R 2 = 0.82), and indirect bilirubin (R 2 = 0.81) in group 5 (all P < 0.05). In patients with CHB and negative for serum HBV-DNA, exosomal HBV-DNA was detectable and could be used to monitor the treatment effects. Exosomal HBV-DNA could be used in patients with a high suspicion of HBV infection but negative for serum HBV-DNA.

Many studies have been conducted on single lane detection, but multi-lane detection is rarely addressed. The latter is more advantageous for applications such as autonomous navigation, unmanned vehicles, departure warning, and cruise control. In this paper, we propose a novel and robust multiple lane detection algorithm based on the road structure information, which contains five complementary constraints: length constraint, parallel constraint, distribution constraint, pair constraint and uniform width constraint. All the five constraints are incorporated into a Hough transform (HT) based unified framework to select lane candidates. Nearly 99% of the false alarm candidates in HT space can be removed. Moreover, a dynamic programming strategy is proposed to find the most rational solutions among the remaining candidates. This strategy can effectively deal with combination complexity and interferences introduced by multi-lane detection. Experimental results on the benchmark dataset and other collected data demonstrate that the proposed method can outperform the state-of-the-art approaches in both accuracy and efficiency.