Analysis of transverse thermal conductivity for mesophase pitch-based carbon fibers and the through-thickness heat conduction of their composites.

A quantitative analysis method for the transverse thermal conductivity (TC) of carbon fiber is developed, which consists of three steps including TC and morphology characterization of unidirectional composite laminate, fiber contour extraction, and finite element inverse analysis. Two different pitch-based carbon fibers with folded-radial and onion-skin microstructure are characterized, and the influences of fiber volume fraction and microstructure on the heat conduction of their composites are investigated. The equivalent transverse TCs of TC-HC-800 and PCF-1 carbon fibers are measured to be 9.27 and 2.87 W m-1 K-1, respectively. The through-thickness TC of unidirectional composite exhibits rapid growth with the increase in fiber volume fraction. The finite element analysis reveals that more continuous heat conduction paths are formed with the increase in fiber volume fraction. Benefited from the bigger graphitization degree, larger cross-sectional area, and bigger aspect ratio, TC-HC-800 unidirectional composite shows higher through-thickness TC than PCF-1 composite at the same fiber volume fraction.

Is Magnetic Resonance Imaging (MRI) Still a Gold Standard to Detect Breast Cancer: A Meta-analysis.

PURPOSE: Breast cancer is fatal if it is not diagnosed and treated promptly; consequently, early and precise diagnosis is essential. In comparison to mammography and sonography, the sensitivity of MRI to cellular changes and its ability to differentiate benign from malignant tumors make it the preferred imaging technique. Consequently, the present meta-analysis assessed the effectiveness of different imaging modalities for breast cancer detection and evaluated the diagnostic accuracy of MRI. METHODS: Pertinent articles were searched in PubMed, MEDLINE, and Central databases using the appropriate keywords as per the PRISMA guidelines. Retrospective and prospective studies were included according to the predefined PICOS criteria. A meta-analysis was performed using RevMan and MedCalc software, and statistical parameters, such as odds ratio, sensitivity, specificity, likelihood ratios, and accuracy, were calculated. Publication bias was evaluated using Egger's and Begg's tests, and diagnostic performance was assessed using Youden's and Bland-Altman's plots. RESULTS: Fourteen clinical trials with 4666 breast cancer patients with perineural spread were included. The included studies used MRI for the detection of breast cancer lesions according to the BI-RADS® (Breast Imaging Reporting and Data System) guidelines and stated that it has high sensitivity and diagnostic accuracy. Similarly, the present meta-analysis found a high sensitivity of 86.12 % and a high diagnostic accuracy of 91.2%. Other than this, we obtained a specificity of 65%, a positive likelihood ratio of 2.7, and a negative likelihood ratio of 0.22. The pooled odds ratio (OR) was reported to be 1.87 (95% CI 1.42-2.46), and the pooled risk ratio value was 1.19 (95% CI 1.11-1.28). CONCLUSION: Present meta-analysis strongly recommends MRI as an effective imaging method for the detection of breast cancer.

Temperature Dependence of Electrical Resistance in Carbon Nanotube Composite Film during Curing Process.

Carbon nanotube (CNT) film possesses excellent mechanical and piezoresistivity, which may act as a sensor for process monitoring and reinforcement of the final composite. This paper prepared CNT/epoxy composite film via the solution dipping method and investigated the electrical resistance variation (ΔR/R0) of CNT/epoxy composite film during the curing process. The temperature dependence of electrical resistance was found to be closely related to resin rheological properties, thermal expansion, and curing shrinkage. The results show that two opposing effects on electrical resistivity occur at the initial heating stage, including thermal expansion and condensation caused by the wetting tension of the liquid resin. The lower resin content causes more apparent secondary impregnation and electrical resistivity change. When the resin viscosity remains steady during the heating stage, the electrical resistance increases with an increase in temperature due to thermal expansion. Approaching gel time, the electrical resistance drops due to the crosslink shrinkage of epoxy resin. The internal stress caused by curing shrinkage at the high-temperature platform results in an increase in electrical resistance. The temperature coefficient of resistance becomes larger with an increase in resin content. At the isothermal stage, an increase in ΔR/R0 value becomes less obvious with a decrease in resin content, and ΔR/R0 even shows a decreasing tendency.

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.

Influence of Electrical Heating Metal Mesh and Power Density on Resistance Welding of Carbon Fiber/PEEK Composite.

An experimental investigation on the resistance welding of carbon-fiber-reinforced polyetheretherketone (PEEK) composite laminate using three types of stainless steel (SS) meshes with different sizes and electrical resistances as heating elements is reported. The objective of this study is to determine the influence of the metal mesh on the welding process and performance at different power densities ranging from 29 to 82 kW/m2. Resistance welding equipment is used to monitor the temperature and displacement along the thickness of the laminate. The results show that the power density determines the welding time and heat concentration. A large power density results in a short welding time, but also increases the temperature gradient at the joining interface (almost 50 °C) and causes an obvious deformation of a contraction of more than 0.1 mm along the thickness of the laminate. A SS mesh with low resistance has a strong welding capability, i.e., a high welding efficiency under low power density. A lap shear strength of approximately 35 MPa can be obtained with the appropriate power density. The shear strength is affected by the bonding between the metal mesh and polymer, the metal mesh load bearing, and the metal mesh size.

Memory related predictive compensation control of Piezoelectric actuators based on global linearization Koopman theory.

Piezoelectric actuators (PEAs) are widely applied in precision positioning. However, the nonlinear characteristics such as hysteresis and creep limit the ultra-precision applications. This paper proposes a linear model predictive control (MPC) scheme for compensating the nonlinearity of PEA. Firstly, a global linearization predictor is constructed based on Koopman theory to represent the hysteresis behavior of PEA. The high-precision predictor is implemented by a novel memory related neural network (NN), and the prediction error reaches only 0.002 µm. Then the tracking control problem is transformed into a linear MPC optimization problem, thereby avoids the sophisticated nonconvex optimization problem. In practice, the constrained MPC problem is rewritten into a dense form, and solved by quadratic programming technique. Finally, the validity of the proposed scheme is demonstrated by experiments. The short-term steady-state error of the proposed scheme is 0.002 µm, which is far less than that of the inversion method; the long-term steady-state performance also indicates its effectiveness in compensating creep. Further, the excellent frequency-dependent results show that the proposed scheme is superior to the existing control method. Especially, the computational efficiency can be improved by 20%. The proposed predictor and control method are of great significance for the tracking control of PEA.

A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

Freestanding Graphene Fabric Film for Flexible Infrared Camouflage.

Graphene films, fabricated by chemical vapor deposition (CVD) method, have exhibited superiorities in high crystallinity, thickness controllability, and large-scale uniformity. However, most synthesized graphene films are substrate-dependent, and usually fragile for practical application. Herein, a freestanding graphene film is prepared based on the CVD route. By using the etchable fabric substrate, a large-scale papyraceous freestanding graphene fabric film (FS-GFF) is obtained. The electrical conductivity of FS-GFF can be modulated from 50 to 2800 Ω sq-1 by tailoring the graphene layer thickness. Moreover, the FS-GFF can be further attached to various shaped objects by a simple rewetting manipulation with negligible changes of electric conductivity. Based on the advanced fabric structure, excellent electrical property, and high infrared emissivity, the FS-GFF is thus assembled into a flexible device with tunable infrared emissivity, which can achieve the adaptive camouflage ability in complicated backgrounds. This work provides an infusive insight into the fabrication of large-scale freestanding graphene fabric films, while promoting the exploration on the flexible infrared camouflage textiles.

Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding.

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.

Compensating nonlinear temperature dependence of ultrasonic motor.

This article aims at realizing the linear parameter-varying (LPV) controller synthesis to compensate temperature dependence for the ultrasonic motor (USM). Initially, based on the improved optimal frequency tracking scheme, the compact LPV model is investigated to approximate the nonlinear temperature dependence. With the aid of the simulation tool, the accuracy of the proposed LPV model is proven. The LPV controller can be an appropriate choice to ensure the stability of passive nonlinear system. In view of the very strictly passivity (VSP) theorem, the VSP LPV controller is constructed as negative feedback. A set of well-designed experimental setup employed the Shinsei USR60 type USM is built afterwards, and the controller implemented by the host is applied to verify the control effect. Compared with the non-model-based controller, the USM with the proposed controller displays better performance, such as more stable output rotational speed. The feasible model in this paper is of great significance to USM. Particularly, the proposed modeling and control methodology are beneficial to the existing optimum frequency tracking technology for the USM.

MiR-454-3p Promotes Oxaliplatin Resistance by Targeting PTEN in Colorectal Cancer.

Colorectal cancer is one of the most common malignancies worldwide. Oxaliplatin is the first-line chemotherapeutic agent for the treatment of advanced colorectal cancer. However, acquired resistance to oxaliplatin limits its therapeutic efficacy, and the underlying mechanism remains largely unclear. In this study, we compared the expression of a panel of microRNAs (miRNAs) between oxaliplatin-sensitive and -resistant HCT-116 colorectal cancer cells. We found that miR-454-3p was significantly up-regulated in oxaliplatin-resistant cells and was the most differently expressed miRNA. Interestingly, we observed that inhibition of miR-454-3p resensitized resistant cells to oxaliplatin and enhanced oxaliplatin-induced cellular apoptosis. Moreover, we determined that miR-454-3p promoted oxaliplatin resistance through targeting PTEN and activating the AKT signaling pathway. In vivo study revealed that overexpression of miR-454-3p decreased the sensitivity of HCT-116 xenograft tumors to oxaliplatin treatment in a mouse model. Clinically, overexpression of miR-454-3p was associated with decreased responsiveness to oxaliplatin-based chemotherapy, as well as a short progression-free survival. Taken together, our study indicated that the expression of miR-454-3p could be used to predict oxaliplatin sensitivity, and targeting miR-454-3p could overcome oxaliplatin resistance in colorectal cancer.

The Fabrication and Properties of a Bendable High-Temperature Resistance Conductive Pitch-Based Carbon/CNT Film Nanocomposite.

This paper fabricates a carbon nanotube (CNT ) film-reinforced mesophase pitch-based carbon (CNTF/MPC) nanocomposite by using a hot-pressing carbonization method. During the carbonization, the stacked aromatic layers tended to rearrange into amorphous carbon, and subsequently generated crystalline carbon in the matrix. The continuous entangled CNT networks were efficiently densified by the carbon matrix though optimized external pressure to obtain the high-performance nanocomposites. The CNTF/MPC@1300 displayed a stable electrical conductivity up to 841 S/cm at RT-150 °C. Its thermal conductivity in the thickness direction was 1.89 W/m∙K, an order of magnitude higher than that of CNT film. Moreover, CNTF/MPC@1300 showed a mass retention of 99.3% at 1000 °C. Its tensile strength was 2.6 times the CNT film and the tensile modulus was two orders of magnitude higher. Though the CNTF/MPC nanocomposites exhibited brittle tensile failure mode, they resisted cyclic bending without damage. The results demonstrate that the CNTF/MPC nanocomposite has potential application in multi-functional temperature resistance aerospace structures.

MultiGuideScan: a multi-processing tool for designing CRISPR guide RNA libraries.

SUMMARY: The recent advance in genome engineering technologies based on CRISPR/Cas9 system is enabling people to systematically understand genomic functions. A short RNA string (the CRISPR guide RNA) can guide the Cas9 endonuclease to specific locations in complex genomes to cut DNA double-strands. The CRISPR guide RNA is essential for gene editing systems. Recently, the GuideScan software is developed to design CRISPR guide RNA libraries, which can be used for genome editing of coding and non-coding genomic regions effectively. However, GuideScan is a serial program and computationally expensive for designing CRISPR guide RNA libraries from large genomes. Here, we present an efficient guide RNA library designing tool (MultiGuideScan) by implementing multiple processes of GuideScan. MultiGuideScan speeds up the guide RNA library designing about 9-12 times on a 32-process mode comparing to GuideScan. MultiGuideScan makes it possible to design guide RNA libraries from large genomes. AVAILABILITY AND IMPLEMENTATION: MULTIGUIDESCAN IS AVAILABLE AT GITHUB: https://github.com/bioinfomaticsCSU/MultiGuideScan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mechanical and electrical enhancement of super-aligned carbon nanotube film by organic and inorganic doping.

Inspired by the chemical and physical doping methods on traditional composites, bismaleimide (BMI) resin and graphene oxide (GO) are selected for doping modification of carbon nanotube (CNT) film in this paper. Based on the diverse enhancement effects of CNT film, the mechanisms and characteristics of resin crosslink and inorganic doping are compared. Due to the crosslinking network of resin, BMI is more beneficial for cooperative deformation and mechanical enhancement, while GO doping shows more advantages in improving electrical performance because of the numerous functional groups on the surface, and good intrinsic properties. With the appropriate doping method and optimized process conditions, the tensile property and electrical conductivity of CNT film can be improved by over 150% and 200% (e.g., tensile strength and modulus of 2990 MPa and 149 GPa, and electrical conductivity of 38 700 S m-1).

Identification and Compensation of Dynamic Interaction in a Non-Contact Dual-Stage Actuator System.

Dynamic interaction seriously limits the overall performance of a Dual-Stage Actuator (DSA) system. This paper aims to identify and compensate for the dynamic interaction in a non-contact DSA system. The effects of the interaction in the non-contact DSA system are initially classified as non-contact position-dependent disturbance forces (PDDFs) and velocity-dependent disturbance forces (VDDFs). The PDDFs in the three degrees of freedom (DoFs) motion space between the two stages of the DSA system are directly identified in the time domain, and VDDFs are indirectly identified in the form of damping values in frequency domains. The feedforward networks of the force are subsequently applied to compensate the PDDFs and VDDFs, which are indexed with relative displacement and velocity, respectively. Experiments are finally conducted to investigate the effectiveness of compensation, which infers that the final positioning error in the time domain can be reduced from 260 nm to 130 nm with PDDFs and VDDFs compensation. The gain of the interaction transfer is decreased in the frequency range of up to 45 Hz with PDDFs and VDDFs compensation. With this method, some weak dynamic interaction can be completely compensated for by the force feedforward compensation, and the positioning accuracy of non-contact DSA systems can be greatly improved.

Anchor Link Prediction across Attributed Networks via Network Embedding.

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.

Piezoresistivity of resin-impregnated carbon nanotube film at high temperatures.

This paper presents the development of a continuous carbon nanotube (CNT) composite film sensor with a strain detecting range of 0%-2% for structural composites. The strain-dependent resistance responses of continuous CNT film and its resin-impregnated composite films were investigated at temperatures as high as 200 °C. The results manifest that impregnation with resin leads to a much larger gauge factor than pristine film. Both the pristine and composite films show an increase in resistivity with increasing temperature. For different composite films, the ordering of gauge factors is consistent with that of the matrix moduli. This indicates that a resin matrix with higher modulus and strong interactions between CNTs/CNT bundles and the resin matrix are beneficial for enhancing the piezoresistive effect. The CNT/PAA composite film has a gauge factor of 4.3 at 150 °C, an order of magnitude higher than the metal foil sensor. Therefore, the CNT composite films have great potential for simultaneous application for reinforcement and as strain sensor to realise a multifunctional composite.

Inhibition of HHIP Promoter Methylation Suppresses Human Gastric Cancer Cell Proliferation and Migration.

BACKGROUND/AIMS: Human hedgehog-interacting protein (HHIP) is a negative regulator of the hedgehog (HH) signaling pathway. It is deregulated in gastric cancer. The underlying molecular mechanism of HHIP-induced inhibition of HH signaling remains to be determined. METHODS: A lentiviral HHIP expression vector ("LV-HHIP") was established to exogenously over-express HHIP in gastric cancer cells. HHIP protein and mRNA were tested by Western blotting assay and quantitative real-time PCR assay, respectively. Cell survival was tested by the Cell Counting Kit-8 (CCK-8) assay. Cell proliferation was examined by the BrdU ELISA assay and [H3] Thymidine DNA incorporation assay. Cell invasion and migration were tested by the phagokinetic track assay and the "Transwell" assay. The bisulfite-sequencing PCR was applied to test HHIP promoter methylation. RESULTS: In the established (AGS cell line) and primary human gastric cancer cells, LV-HHIP transfection increased HHIP expression and inhibited cancer cell survival and proliferation as well as cell migration and invasion. Furthermore, LV-HHIP significantly attenuated promoter methylation of the endogenous HHIP gene in AGS cells, causing it upregulation. Inhibition of methylation by 5-aza-dc similarly induced HHIP expression in gastric cancer cells, which inhibited cancer cell proliferation and migration. CONCLUSIONS: Our results suggest that inhibition of HHIP promoter methylation can efficiently inhibit human gastric cancer cell proliferation and migration.

Overexpression of Lymphocyte Antigen 6 Complex, Locus E in Gastric Cancer Promotes Cancer Cell Growth and Metastasis.

BACKGROUND/AIMS: Lymphocyte antigen 6 complex, locus E (LY6E) is a member of the lymphostromal cell membrane Ly6 superfamily protein. The present study investigated the clinical significance and potential biological function of LY6E in gastric cancer (GC). METHODS: LY6E mRNA and protein expressions in human GC tissues and GC cells were tested. Relationship between LY6E expression and the GC patients' clinicopathologic characteristics was analyzed. LY6E was silenced by siRNA in the cultured GC cells. RESULTS: The RNA expression microarray profiling assay results demonstrated that LY6E mRNA was significantly increased in multiple human GC tumor tissues. Immunohistochemistry (IHC) staining analysis revealed that 59 of 75 (78.7%) GC specimens were LY6E positive. LY6E over-expression in human GC was correlated with the histology grade, AJCC stage, N classification, lymphatic invasion, and tumor location. Notably, functional LY6E expression was also detected in AGS and other established GC cell lines. LY6E knockdown by targeted-siRNA inhibited AGS cell survival and proliferation. Meanwhile, the LY6E siRNA induced G1-S cell cycle arrest and apoptosis in AGC cells. Additionally, AGC cell migration was also inhibited by LY6E knockdown. Expressions of tumor-suppressing proteins, including PTEN (phosphatase and tensin homolog) and E-Cadherin, were increased in LY6E-silenced AGS cells. CONCLUSION: LY6E over-expression in GC is potentially required for cancer cell survival, proliferation and migration.

Computational drug repositioning using low-rank matrix approximation and randomized algorithms.

Motivation: Computational drug repositioning is an important and efficient approach towards identifying novel treatments for diseases in drug discovery. The emergence of large-scale, heterogeneous biological and biomedical datasets has provided an unprecedented opportunity for developing computational drug repositioning methods. The drug repositioning problem can be modeled as a recommendation system that recommends novel treatments based on known drug-disease associations. The formulation under this recommendation system is matrix completion, assuming that the hidden factors contributing to drug-disease associations are highly correlated and thus the corresponding data matrix is low-rank. Under this assumption, the matrix completion algorithm fills out the unknown entries in the drug-disease matrix by constructing a low-rank matrix approximation, where new drug-disease associations having not been validated can be screened. Results: In this work, we propose a drug repositioning recommendation system (DRRS) to predict novel drug indications by integrating related data sources and validated information of drugs and diseases. Firstly, we construct a heterogeneous drug-disease interaction network by integrating drug-drug, disease-disease and drug-disease networks. The heterogeneous network is represented by a large drug-disease adjacency matrix, whose entries include drug pairs, disease pairs, known drug-disease interaction pairs and unknown drug-disease pairs. Then, we adopt a fast Singular Value Thresholding (SVT) algorithm to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. The comprehensive experimental results show that DRRS improves the prediction accuracy compared with the other state-of-the-art approaches. In addition, case studies for several selected drugs further demonstrate the practical usefulness of the proposed method. Availability and implementation: http://bioinformatics.csu.edu.cn/resources/softs/DrugRepositioning/DRRS/index.html. Contact: yaohang@cs.odu.edu or jxwang@mail.csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.