3D Bridged Carbon Nanoring/Graphene Hybrid Paper as a High-Performance Lateral Heat Spreader.

Graphene paper (GP) has attracted great attention as a heat dissipation material due to its unique thermal transfer property exceeding the limit of graphite. However, the relatively poor thermal transfer properties in the normal direction of GP restricts its wider applications in thermal management. In this work, a 3D bridged carbon nanoring (CNR)/graphene hybrid paper is constructed by the intercalation of polymer carbon source and metal catalyst particles, and the subsequent in situ growth of CNRs in the confined intergallery spaces between graphene sheets through thermal annealing. Further investigation demonstrates that the CNRs are covalently bonded to the graphene sheets and highly improve the thermal transport in the normal direction of the CNR/graphene hybrid paper. This full-carbon architecture shows excellent heat dissipation ability and is much more efficient in removing hot spots than the reduced GP without CNR bridges. This highly thermally conductive CNR/graphene hybrid paper can be easily integrated into next generation commercial high-power electronics and stretchable/foldable devices as high-performance lateral heat spreader materials. This full-carbon architecture also has a great potential in acting as electrodes in supercapacitors or hydrogen storage devices due to the high surface area.

[Acute hypotensive episodes prediction based on non-linear chaotic analysis].

In intensive care units (ICU) , the occurrence of acute hypotensive episodes (AHE) is the key problem for the clinical research and it is meaningful for clinical care if we can use appropriate computational technologies to predict the AHE. In this study, based on the records of patients in ICU from the MIMIC II clinical data, the chaos signal analysis method was applied to the time series of mean artery pressure, and then the patient's Lyapunov exponent curve was drawn ultimately. The research showed that a curve mutation appeared before AHE symptoms took place. This is powerful and clear basis for AHE determination. It is also expected that this study may offer a reference to research of AHE theory and clinical application.

MicroRNA-497 inhibition of ovarian cancer cell migration and invasion through targeting of SMAD specific E3 ubiquitin protein ligase 1.

Ovarian cancer is the leading cause of death from gynecological malignancies worldwide. Understanding the molecular mechanism underlying ovarian cancer progression facilitates the development of promising strategy for ovarian cancer therapy. Previously, we observed frequent down-regulation of miR-497 expression in ovarian cancer tissues. In this study, we investigated the role of miR-497 in ovarian cancer metastasis. We found that endogenous miR-497 expression was down-regulated in the more aggressive ovarian cancer cell lines compared with the less aggressive cells. Exogenous expression of miR-497 suppressed ovarian cancer cell migration and invasion, whereas reduction of endogenous miR-497 expression induced tumor cell migration and invasion. Mechanistic investigations confirmed pro-metastatic factor SMURF1 as a direct target of miR-497 through which miR-497 ablated tumor cell migration and invasion. Further studies revealed that lower levels of miR-497 expression were associated with shorter overall survival as well as increased SMURF1 expression in ovarian cancer patients. Our results indicate that down-regulation of miR-497 in ovarian cancer may facilitate tumor metastasis. Restoration of miR-497 expression may be a promising strategy for ovarian cancer therapy.

Engineering a DNA-cleaving DNAzyme and PCR into a simple sensor for zinc ion detection.

The development of a simple sensor (9NL27-Zn) based on DNAzyme and PCR and aimed at the detection of low concentrations of zinc (II) ions is described. A specific Zn(II)-dependent DNAzyme (9NL27) with DNA-cleaving activity was employed. In the presence of zinc (II), the DNAzyme hydrolyzed DNA substrate into two pieces (5' and 3' fragments), forming 3'-terminal hydroxyl in the 5' fragment and 5'-phosphate in the 3' fragments. Subsequently, the 5' fragment left the DNAzyme and bound a short DNA template. The 5' fragment was used as a primer and extended a single-stranded full-length template by Taq polymerase. Finally, this full-length template was amplified by PCR. The amplified products had a quantitative relationship with Zn(II) concentration. Under our experimental conditions, the DNA sensor showed sensitivity (10 nM) and high specificity for zinc ion detection. After improvement of the DNA sensor, the detection limit can reach 1 nM. The simple DNA sensor may become a DNA model for the detection of trace amounts of other targets.

Nano-engineering thermal transport performance of carbon nanotube networks with polymer intercalation: a molecular dynamics study.

Based on polymer perfusion behaviour inside carbon nanotube (CNT) networks, the thermal transport performances of the CNT networks with various extents of polymer intercalation are studied by dividing them into two parts: thermal transport at the tube contact interfaces of CNT junctions and along the tube axis. The thermal transport performance at the tube contact interfaces of CNT junctions is similar to that in the transverse direction of graphene layers. Hence, to obtain a fundamental understanding of thermal transport performance at the tube contact interfaces, thermal conductance along the z-axis direction of graphene layers with and without polymer intercalation is investigated using a non-equilibrium molecular dynamics (MD) simulation method. Thermal conductivity along the tube axis direction of the polymer wrapped CNT is also calculated using the same method. The simulation results show that a low extent of polymer aggregation at the tube contact interfaces can significantly improve the interfacial thermal conductance. However, when the polymer content at the tube contact interfaces exceeds a critical fraction, the interfacial thermal conductance is decreased. The results also indicate that the polymer molecules wrapping around the CNT walls have a strong negative influence on the bulk thermal conductivity of the CNT along its axis direction.

Investigation of the nanostructure and nanomechanical properties of the interphase in carbon fiber reinforced polyamide-6 composite.

Carbon fiber reinforced polyamide-6 (CF/PA-6) composites have been widely applied in automobile, aerospace, and biomedical industries for their high mechanical properties, high thermal resistance and recyclability. On the purpose of finding ways to improve the interfacial properties, the investigation of the nanostructure and nanomechanical properties of the interphase in CF/PA-6 composites were essential. In this study, MD simulation was carried out to show the interfacial formation and nanostructure of the CF/PA-6 composite model directly at the atomic level and compute the radial distribution function, interfacial energy, total energy. Then the nanomechanical properties of the CF/PA-6 composite, such as interfacial thickness, interfacial modules, interfacial adhesion, were investigated by AFM PF-QNM model. The changes of the radical distribution function and energies over the MD simulation time indicated that the PA-6 chains adsorbed and then regularly folded on the CF surface, displaying the interfacial crystallization of the CF/PA-6 composite model. What stood out in the AFM PF-QNM tests were the abrupt decreasing of the interfacial modulus and the sharp increasing of the interfacial adhesion from those of the carbon fiber to those of the PA-6. The average interfacial thickness of the CF/PA-6 composite was 72 nm. Consistent with the simulation results, the interfacial properties were distinct from the properties of the carbon fiber and PA-6, owning to the adsorption and orderly folding of the PA-6 chains on the CF surface and the changes of the RDF and energies.

View-Aware Collaborative Learning for Survival Prediction and Subgroup Identification.

Advances of high throughput experimental methods have led to the availability of more diverse omic datasets in clinical analysis applications. Different types of omic data reveal different cellular aspects and contribute to the understanding of disease progression from these aspects. While survival prediction and subgroup identification are two important research problems in clinical analysis, their performance can be further boosted by taking advantages of multiple omics data through multi-view learning. However, these two tasks are generally studied separately, and the possibility that they could reinforce each other by collaborative learning has not been adequately considered. In light of this, we propose a View-aware Collaborative Learning (VaCoL) method to jointly boost the performance of survival prediction and subgroup identification by integration of multiple omics data. Specifically, survival analysis and affinity learning, which respectively perform survival prediction and subgroup identification, are integrated into a unified optimization framework to learn the two tasks in a collaborative way. In addition, by considering the diversity of different types of data, we make use of the log-rank test statistic to evaluate the importance of different views. As a result, the proposed approach can adaptively learn the optimal weight for each view during training. Empirical results on several real datasets show that our method is able to significantly improve the performance of survival prediction and subgroup identification. A detailed model analysis study is also provided to show the effectiveness of the proposed collaborative learning and view-weight learning approaches.

Self-Guided Partial Graph Propagation for Incomplete Multiview Clustering.

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

Review on Heat Generation of Rubber Composites.

Rubber composites are extensively used in industrial applications for their exceptional elasticity. The fatigue temperature rise occurs during operation, resulting in a serious decline in performance. Reducing heat generation of the composites during cyclic loading will help to avoid substantial overheating that most likely results in the degradation of materials. Herein, we discuss the two main reasons for heat generation, including viscoelasticity and friction. Influencing factors of heat generation are highlighted, including the Payne effect, Mullins effect, interface interaction, crosslink density, bond rubber content, and fillers. Besides, theoretical models to predict the temperature rise are also analyzed. This work provides a promising way to achieve advanced rubber composites with high performance in the future.

Can Linear Conjugated Polymers Form Stable Helical Structures on the Carbon Nanotubes?

The formation mechanism of ordered helical structures of conjugated polymers wrapping onto single-walled carbon nanotubes (SWCNTs) has been full of controversy in recent decades. A formation mechanism is proposed for the linear conjugated polymers wrapping around SWCNTs that the formation of helical structures is dependent on the orientation competition between backbone segments and side groups via transmission electron microscopy observations and molecular dynamics simulations. Results show that the conjugated polymers cannot always form stable helical structures, even if they have the capability to form a stable helix. In fact, only part of polymer segments presents a stable helix on the SWCNTs for the internal rotation in polymer deformations. Furthermore, a design framework is proposed to choose specific conjugated homopolymers and copolymers which can form helical structures on the SWCNTs.

Clinical Value and Imaging Features of Bedside High-Frequency Ultrasound Imaging in the Diagnosis of Neonatal Pneumonia.

The aim is to solve the problem of the urgent need of a nonradiation, noninvasive, and simple-to-operate diagnostic method for neonatal pneumonia that can indicate the severity of the disease and dynamically monitor the outcome of the disease. The authors propose a bedside high-frequency ultrasound technique based on methods for evaluation in the detection and treatment of neonatal pneumonia. The results obtained are as follows: the sensitivity of neonatal lung ultrasound in the diagnosis of neonatal pneumonia was 96.6%, the specificity was 93.3%, the positive predictive value was 93.5%, and the negative predictive value was 96.5%. The sensitivity of chest X-ray in the diagnosis of neonatal pneumonia was 93.3%. Compared with the lung ultrasound and chest X-ray in the diagnosis of neonatal pneumonia, the two had a good correlation. The neonatal respiratory score was positively correlated with the lung ultrasound score, and the higher the lung ultrasound score, the more severe the disease. The score decreased by 35% after 3 days of treatment and 68% after 7 days of treatment, indicating that the lung high-frequency ultrasound score can be very effective in characterizing the treatment situation. It has been demonstrated that the lung ultrasound can be used as an imaging method for the diagnosis of neonatal pneumonia. The higher the lung ultrasound score, the more severe the disease, and the lung ultrasound score was positively correlated with the disease severity. With dynamic monitoring of the lung ultrasound and the gradual improvement of clinical symptoms after treatment, the lung ultrasound score gradually decreased; therefore, the lung ultrasound can be used for re-examination of neonatal pneumonia to evaluate the treatment effect and guidance.

Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction.

By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.

[The design and practice of Molecular Biology Experiment teaching from the perspective of first-class courses].

Teaching quality is directly related to the performance of universities in fostering talents. Being innovative, high-level, and challenging (IHC) is the basic goal of course reform at universities in the new era. It is essential to reform the contents and teaching mode to improve the IHC properties of the existing courses. We first designed the three-dimensional goals of Molecular Biology Experiment teaching and the contents to support these goals. Then, we pinpointed the common points shared by blended teaching and experiment course, and designed the ways of blended teaching for the course. The reformed course contents and teaching mode have enhanced its IHC properties, and achieved good teaching performance. This paper provides a reference for the reform of experiment courses in universities.

Asymmetric Graph-Guided Multitask Survival Analysis With Self-Paced Learning.

Recently, multitask learning has been successfully applied to survival analysis problems. A critical challenge in real-world survival analysis tasks is that not all instances and tasks are equally learnable. A survival analysis model can be improved when considering the complexities of instances and tasks during the model training. To this end, we propose an asymmetric graph-guided multitask learning approach with self-paced learning for survival analysis applications. The proposed model is able to improve the learning performance by identifying the complex structure among tasks and considering the complexities of training instances and tasks during the model training. Especially, by incorporating the self-paced learning strategy and asymmetric graph-guided regularization, the proposed model is able to learn the model in a progressive way from "easy" to "hard" loss function items. In addition, together with the self-paced learning function, the asymmetric graph-guided regularization allows the related knowledge transfer from one task to another in an asymmetric way. Consequently, the knowledge acquired from those earlier learned tasks can help to solve complex tasks effectively. The experimental results on both synthetic and real-world TCGA data suggest that the proposed method is indeed useful for improving survival analysis and achieves higher prediction accuracies than the previous state-of-the-art methods.

Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data.

Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC).

Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera.

An increasing amount of evidence has proven that serum metabolites can instantly reflect disease states. Therefore, sensitive and reproducible detection of serum metabolites in a high-throughput manner is urgently needed for clinical diagnosis. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a high-throughput platform for metabolite detection, but it is hindered by significant signal fluctuations because of the "sweet spot" effect of organic matrices. Here, by screening two transformation methods and four normalization techniques to reduce the significant signal fluctuations of the DHB matrix, an integrated MALDI-MS data processing approach combined with machine learning methods was established to reveal metabolic biomarkers of lung cancer. In our study, 13 distinctive features with statistically significant differences (p < 0.001) between 34 lung cancer patients and 26 healthy controls were selected as significant potential biomarkers of lung cancer. 6 out of the 13 distinctive features were identified as intact metabolites. Our results demonstrate the potential for clinical application of MALDI-MS in serum metabolomics for biomarker screening in lung cancer.

Personality first in emotion: a deep neural network based on electroencephalogram channel attention for cross-subject emotion recognition.

In recent years, more and more researchers have focused on emotion recognition methods based on electroencephalogram (EEG) signals. However, most studies only consider the spatio-temporal characteristics of EEG and the modelling based on this feature, without considering personality factors, let alone studying the potential correlation between different subjects. Considering the particularity of emotions, different individuals may have different subjective responses to the same physical stimulus. Therefore, emotion recognition methods based on EEG signals should tend to be personalized. This paper models the personalized EEG emotion recognition from the macro and micro levels. At the macro level, we use personality characteristics to classify the individuals' personalities from the perspective of 'birds of a feather flock together'. At the micro level, we employ deep learning models to extract the spatio-temporal feature information of EEG. To evaluate the effectiveness of our method, we conduct an EEG emotion recognition experiment on the ASCERTAIN dataset. Our experimental results demonstrate that the recognition accuracy of our proposed method is 72.4% and 75.9% on valence and arousal, respectively, which is 10.2% and 9.1% higher than that of no consideration of personalization.

Particle hydrodynamic simulation of thrombus formation using velocity decay factor.

BACKGROUND AND OBJECTIVE: Thrombus simulation plays an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, treatment planning, etc. Although a considerable number of methods have been developed to simulate various kinds of fluid flows, it remains a non-trivial task to effectively simulate thrombus because of its unique physiological properties in contrast to other types of fluids. To tackle this issue, this study introduces a novel method to model the formation mechanism of thrombus and its interaction with blood flow. METHODS: The proposed method for thrombus formation simulation mainly consists of three steps. First, we formulate the formation of thrombus as a particle-based model and obtain the fibrin concentration of the particles with a discretized form of the convection-diffusion-reaction equation; then, we calculate the velocity decay factor using the obtained fibrin concentration. Finally, the formation of thrombus can be simulated by applying the velocity decay factor on particles. RESULTS: We carried out extensive experiments under different settings to verify the efficacy of the proposed method. The experimental results demonstrate that our method can yield more realistic simulation of thrombus and is superior to peer method in terms of computational efficiency, maintaining the stability of the dynamic particle motion, and preventing particle penetration at the boundary. CONCLUSION: The proposed method can simulate the formation mechanism of thrombus and the interaction between blood flow and thrombus both efficiently and effectively.

Preparation of phase change material filled hybrid 2D/3D graphene structure with ultra-high thermal effusivity for effective thermal management.

Graphene-based energy storage and renewable material has increasingly attracted research interest, due to its high thermal conductivity and light weight. Researchers fill phase change material (PCM) into three-dimensional graphene foam, to obtain a composite with high energy storage capability and moderate thermal conductivity. However, this kind of composite's heat transfer mode is single and cannot maximize the advantages of graphene. Herein, a stearic acid filled graphene-foam composite (GFSAC) connected with graphene paper (GP) through gravity-assisted wetting attaching process is demonstrated in this paper.•GP is obtained by thermal reduction of graphene oxide (GO) paper. Its in-plane thermal conductivity can reach up to 938 Wm-1 K-1. By controlling the preparation process of GO paper, the in-plane thermal conductivity of GP can be adjusted.•GFSAC is consisted of GF and SA, GFSAC with different heat transfer properties can be prepared by adjusting the degree of reduction of GF.•A novel gravity-assisted wetting attaching process has been developed to prepare GP/GFSAC/GP composite, which can effectively reduce the thermal resistance between GP and GFSAC. The effective thermal effusivity of the final GP/GFSAC/GP composite reaches 18.45 J cm-3/2 m-1/2 s-1/2 K-1/2, showing an excellent thermal management capability.

Compression and Stretching of Confined Linear and Ring Polymers by Applying Force.

We use Langevin dynamics to study the deformations of linear and ring polymers in different confinements by applying compression and stretching forces on their two sides. Our results show that the compression deformations are the results of an interplay among of polymer rigidity, degree of confinement, and force applied. When the applied force is beyond the threshold required for the buckling transition, the semiflexible chain under the strong confinement firstly buckles; then comes helical deformation. However, under the same force loading, the semiflexible chain under the weaker confinement exhibits buckling instability and shrinks from the folded ends/sides until it becomes three-folded structures. This happens because the strong confinement not only strongly reduces the buckling wavelength, but also increases the critical buckling force threshold. For the weakly confined polymers, in compression process, the flexible linear polymer collapses into condensed states under a small external force, whereas the ring polymer only shows slight shrinkage, due to the excluded volume interactions of two strands in the crowded states. These results are essential for understanding the deformations of the ring biomacromolecules and polymer chains in mechanical compression or driven transport.