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The transverse strength of unidirectional carbon fiber reinforced polymer (UD-CFRP) composites is a high dimensional and nonlinear function of microstructure due to the wide scatter in mechanical properties and complex failure mechanisms, which is a challenging task to develop a general microstructure dependent strength criterion (MDSC) in theory or computation. Volume fraction and distribution of fibers are among the crucial influencing factors. A computational micromechanics and machine learning (ML) combined method is employed to uncover the transverse mechanical response of UD-CFRP composites. High-throughput finite element analyses (FEA) are performed to obtain the transverse behaviors of composites with varying fiber distribution and volume fraction under different loading states. They showed that fiber distribution has different effects on strengths in different failure modes, while the failure modes are closely related to loading states and fiber volume fractions. An ML model is then trained to characterize the relations between composite microstructure and composite strength. Then, the transverse strengths of 1000 new microstructures are predicted, which shows good agreement with FEA results, so that the MDSC of UD-CFRP is constructed by fully accounting for the influence of fiber distribution. Reliability of the method is verified by considering composites with various fiber volume fractions. [Display omitted] • High-throughput finite element analyses are performed to uncover the transverse mechanical response of UD-CFRP composites. • The transverse behaviors of composites with various fiber distribution and fiber volume fraction are presented. • The complex relations between microstructure of composites and transverse strengths are represented. • The microstructure dependent transverse strength criterion is constructed by considering the effect of fiber distribution. [ABSTRACT FROM AUTHOR]

• Unique dataset from Norway's largest dairy company. • Analysis based on domain knowledge and machine learning models. • Identification of important dairy herd features in forecasting milk deliveries. • Machine Learning models performs well in forecasting milk deliveries. • Discussion of models' property for short- and long-term forecasts. Accurate forecasts of milk deliveries to dairy are crucial to secure a stable milk supply to customers, the effective use of raw milk, and the reduction of logistics costs and greenhouse gas emissions. The primary aim of this study was to improve the prediction of the total milk supply from Norwegian dairy farmers to dairies, and to do so by utilizing a traditional time series model, as well as several modern statistical models and machine learning algorithms. The secondary aim was to explore the usefulness of dairy herd features as explanatory variables to forecast the target variable milk delivery to dairy. Several statistical learning models and machine learning algorithms were applied for forecasting, in addition to a traditional time series model and qualitative domain knowledge. Historical data of monthly milk deliveries were collected from Norwegian dairy companies, together with detailed dairy farm herd records. The herd records contain features or explanatory variables, such as the number of cows and inseminations that can influence the response variable – the monthly milk deliveries. Our results show that some of the models applied can predict monthly milk deliveries to dairies with a mean absolute prediction error of 1–2% up to 24-months ahead. While a generalized linear model performed the best up to 3-months ahead, the machine learning models performed the best up to one year ahead. Two years ahead a traditional seasonal autoregressive model performed the best. Because some of the models select the most important features to predict milk delivery, our analytical results also provide valuable information about which features are the most important to predict future milk delivery. [ABSTRACT FROM AUTHOR]

Allowable load is one of the core parameters for the design and service of composite structures (CSs). However, due to the large design parameters, cumbersome design procedure and complicated simulation model of CSs, considerable amounts of experiments and simulations are needed to determine the allowable load of CSs, which is costly and inefficient, hindering the wide application of CSs in industry. In this work, we present a general deep transfer learning approach to construct the allowable load space (ALS) of notched laminates by considering various design parameters such as geometries, materials, stacking angles, ply numbers, laminate types and load types. Initially, an ensemble deep neural network (Ensemble) is trained, which can predict the allowable loads of notched laminate with known design parameters well. Here the Ensemble guided by ensemble learning is trained to reduce the errors generated by manually setting. Then, the pre-trained Ensemble is transferred to new design parameters by fine-tuning it with scarce new samples. Finally, the ALS of notch laminates is constructed by integrating the pre-trained and fine-tuned models, covering both known and diverse new design parameters. Our approach can easily be extended to other CSs. [Display omitted] [ABSTRACT FROM AUTHOR]

The MADS-box gene SVP ( SHORT VEGETATIVE PHASE ) mediates the integration of signals involved in the control of flowering time and flower development by interacting with MADS-box proteins. Although SVP -like genes have been isolated from a wide range of plant species, their orthologs are not well-characterized in Prunus mume (mei), an important ornamental and fruit plant in the East Asia. To determine the role of SVP -like genes in mei flowering, two MADS-box genes with homology to Arabidopsis SVP , PmSVP1 and PmSVP2 , have been identified and analyzed in mei and functionally characterized in Arabidopsis . Phylogenetic analyses revealed that PmSVP1 and PmSVP2 fall into different sub-groups within the SVP -like gene group, which suggests distinct functions. Furthermore, expression was generally found in vegetative tissues, and decreased during flower bud differentiation development suggesting a role for these genes in the transition from the vegetative to reproductive phase. Overexpression of PmSVP1 and PmSVP2 in Arabidopsis led to a range of floral phenotype changes including additional trichomes, leaf-like sepals and increased rosette branches, whereas only PmSVP1 expression delayed flowering through down-regulation of FLOWERINGLOCUST ( FT ) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1 ). These results suggested that SVP -like genes in mei might have distinct roles during development of flowering. Our findings will help elucidate the molecular mechanisms important for the transition from vegetative to reproductive development in mei. [ABSTRACT FROM AUTHOR]

• A method coupling FEA and machine learning is derived. • The predicted data by ANN and RF model are well consistent with the curves of exact values. • Accuracy of predicted result by ANN model is higher than that by RF model. In order to predict mechanical properties of composite laminate, a method coupling finite element analysis (FEA) and machine learning is established to analyze three examples of composite laminates, such as failure factor of Puck theory under random stress state, failure factor and critical buckling eigenvalues of open-hole laminate. By writing Abaqus script, parametric FEA models of composite laminates are built and large samples are generated. Artificial neural network (ANN) model and random forest (RF) model are set up to learn these virtual samples to predict their corresponding mechanical properties for these examples. By analyzing predicted results and FEA values of these mechanical parameters, predicted data are well consistent with the curves of FEA values and the calculated root-mean-square errors are much smaller, which proves this FEA and machine learning coupled method is effective. Even if these errors predicted by ANN model are smaller than RF model, and the learning processes of RF model take less time, these methods with ANN model and RF model are all appropriate to predict mechanical properties of composite laminates. [ABSTRACT FROM AUTHOR]

Cyclic RGDyK (cRGDyK)-conjugated pH-sensitive polymeric micelles were fabricated for targeted delivery of paclitaxel to prostate cancer cells based on pH-sensitive copolymer poly(2-ethyl-2-oxazoline)-poly( d , l -lactide) (PEOz-PLA) and cRGDyK-PEOz-PLA to enhance antitumor efficacy. The prepared micelles with an average diameter of about 28 nm exhibited rapid release behavior at endo/lysosome pH, effectively enhanced the cytotoxicity of paclitaxel to PC-3 cells by increasing the cellular uptake, which was correlated with integrin α v β 3 expression in tumor cells. The active targeting activity of the micelles was further confirmed by in vivo real time near-infrared fluorescence imaging in PC-3 tumor-bearing nude mice. Moreover, the active targeting and pH-sensitivity endowed cRGDyK-conjugated micelles with a higher antitumor effect in PC-3 xenograft-bearing nude mice compared with unmodified micelles and Taxol with negligible systemic toxicity. Therefore, these results suggested that cRGDyK-conjugated pH-sensitive polymeric micelles may be a promising delivery system for efficient delivery of anticancer drugs to treat integrin α v β 3 -rich prostate cancers. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Paroxysmal Supraventricular Tachycardia (PSVT) resulting from any cause can at times lead to diagnostic difficulty mainly when due to AVNRT and AVRT. In this study we used a non-invasive approach using esophageal ECG (ECGe) and ECG derived from the various right parasternal chest leads (RPCL) to correctly diagnose the type of PSVT as well the accessory pathways involved in AVRT. Methods and results: We studied a total of 161 patients (89 male) all having a history of palpitation. All the patients underwent routine 12 lead ECG, ECGe, RPCL and electrophysiologic study. With use of ECGe and RPCL, 71 (44.1%) were diagnosed as having AVNRT and 90 (55.9%) having AVRT with various accessory pathways compared to only 49 (30.4%) and 22 (13.7%) respectively with routine 12 lead ECG. With combined ECGe and RPCL, 80 (49.7%) showed accessory pathway in the left free wall, 4 (2.5%) showed in the right free wall, 6 (3.7%) septal accessory pathway, 60 (37.3%) in the anterior wall and 30 (18.6%) in the posterior wall. In comparison, EPS could show accessory pathways in the left free wall in 81 (50.3%), in the right free wall in 5 (3.1%), in the septum in 4 (2.5%), in the anterior wall in 53 (32.9%) and in the posterior wall in 37 (23.0%) out of the 161 patients. Conclusions: ECGe+RPCL can be used as a reliable noninvasive diagnostic tool to identify the nature of tachycardia and the pathways involved in the reentrant ring of PSVT especially in those with multiple atrioventricular accessory pathways or a combination of atrioventricular accessory pathways and dual atrioventricular nodal pathway. [Copyright &y& Elsevier]

Ferroelectric polymers have been widely explored for film capacitor applications due to their high energy storage densities that are almost an order of magnitude greater than those of the commercial capacitor dielectrics. However, the existing ferroelectric polymers are designed exclusively for room-temperature operation, and suffer from poor capacitive performance at elevated temperature, which falls short of the need in the emerging harsh-environment applications. In this paper, we report multilayered ferroelectric polymer composites exhibiting simultaneous high energy density and high discharge efficiency at elevated temperature. The multilayered composites consist of two copolymers of polyvinylidene fluoride (PVDF) with similar chemical structures but distinct dielectric properties, where one contributes to high electrical polarization and the other offers high thermal stability and suppresses charge injection. More importantly, we demonstrate that the critical issue of charge conduction at elevated temperature in ferroelectric polymers is well tackled by the established highly miscible interlayer interfaces in the multilayered structure set up between the two electrically dissimilar phases. At 70 °C, the maximum discharged energy density above 80% discharge efficiency of the multilayered composites reaches 15.5 J/cm3, far outperforming all the existing ferroelectric polymers. This work sheds light on the design of high-energy-density ferroelectric polymers for high temperature capacitive energy storage. Image 1 [ABSTRACT FROM AUTHOR]

The ZnO/rGO core/shell micro-Schottky junction was successfully prepared via a one-step facile hydrothermal method. It is found that the resulting gas sensor exhibited excellent capability to detect trace vapor of nitro-explosives, typically, the responses towards 9 ppb 2,4,6-trinitrotoluene (TNT) and 4.9 ppt hexogen (RDX) reach to 56.8% and 80%, respectively, and all the response times are less than 5 s. Most importantly, superior anti-interfering property is shown even towards interfering gases of ppm level. Furthermore, it is demonstrated that with the wrapping of rGO, the response values of the pure ZnO-based sensor for TNT and RDX detection were boosted by 5.5 and 3.4 times. The enhanced sensing performance is attributed to the micro-Schottky junction built at the interface of ZnO and rGO. The present work demonstrates that the ZnO/rGO core/shell micro-Schottky junction is an excellent candidate material for explosives detection. [ABSTRACT FROM AUTHOR]

Determining the allowable compression load (ACL) of notched composite laminates (NCL) requires consideration of buckling, intra- and inter-laminar damage, which is a costly and repetitive task due to the high dimensional and nonlinear relation of ACL to the numerous design variables. In this work, a high-throughput finite element analysis (FEA) and machine learning (ML) combined approach is proposed to predict ACL of laminates. First, the high-throughput FEA model of NCL covering all of the design variables is generated, and the corresponding critical compression loads in terms of buckling, intra- and inter-laminar damage are obtained. Then, ML models are trained, with inputs being the design variables of NCL and outputs being the critical compression loads. ACL and initial failure mode are determined by the minimum compression load of the three failure modes. Moreover, transfer learning is introduced to laminates with new design variables of new ply number, notch shape and laminate type. By fine-tuning the pre-trained ML model using scarce new data, the fine-tuned models are suitable for new laminates. Consequently, ACL and initial failure mode of notched laminates with various design variables are predicted. [ABSTRACT FROM AUTHOR]

Failure assessment is one of the fundamental tasks for optimization of composite pressure vessels (CPVs). However, the extensive design space of composites usually leads to costly and repetitive work of failure assessment that hinders the design and optimization of CPVs. In this work, a combined FEM simulation and machine learning approach is used to predict the failure factors R of CPVs for further optimization. CPVs with various design parameters are automatically generated and analyzed in ABAQUS to obtain failure factors under various external loadings. Then, an assembled deep neural network (DNN) namely Multi-DNN is trained to map relations between design parameters and R of CPVs. Further, transfer learning (TL) is introduced to improve the extensibility of our approach by fine-tuning the pre-trained Multi-DNN using scarce data of new design space. Such a TL-Multi-DNN model can precisely predict R of CPVs with new design parameters. Additionally, combining the trained models and genetic algorithm, the optimization of CPVs is carried out with high efficiency and low computational cost. As a result, R of CPV with various given and new design parameters can be estimated directly by the trained models and the optimal layer sequence of CPV can be obtained efficiently. [ABSTRACT FROM AUTHOR]

Abstract: Bi3.15Nd0.85Ti3O12 (BNT) ferroelectric thin films were grown on Pt/Ti/SiO2/Si substrates by a sol–gel method. The samples were exposed to different dosage of ionizing radiation. Distortions in the crystalline structure, changes in the surface roughness and decrease in the remnant polarization were observed after radiation. For films subjected to 100Mrad radiation, decrease in the leakage current up to four orders of magnitude was also observed, which can be explained by the increased grain boundary barrier height. Our results suggest that ionizing radiation could be an effective tool in modifying the properties of ferroelectric thin film for use in non-volatile memory devices. [Copyright &y& Elsevier]

Persistent infection with human papillomavirus (HPV), particularly type 16, is causally associated with cervical cancer and its precursors. The role of miRNAs in HPV16 persistence currently remains unclear. Preliminary analysis of miRNA profile demonstrated that HPV16 infection caused a striking downregulation of miR-34a. Through bioinformatics analysis and dual-luciferase assay with site-directed mutagenesis strategy, NLRC5, a negative regulator of NF-κB signaling, was identified to be a novel interactor of miR-34a. Transfection of miR-34a mimic strikingly downregulated NLRC5 in the HPV16-positive cervical cells, which might result in the nuclear accumulation of NF-κB p65. However, transfection of miR-34a inhibitor exhibited an opposite effect. The antagonistic expressions of NLRC5 and miR-34a were also observed in keratinocytes harboring HPV16 genome as well as in human cervical samples with persistent infection of HPV16. Our data uncover a previously unknown connection among HPV16 persistence, miR-34a and its interactor NLRC5. [ABSTRACT FROM AUTHOR]

Exosomes have been intensively studied in autoimmune diseases, and circulating exosomes and microvesicles have also been explored in autoimmune thyroiditis (AITD). However, the role of thyroid cell-derived exosomes in immune responses is unclear. We showed that IFN-γ-treated Nthy-ori 3-1 cell-derived exosomes (IFN-γ-Exo) harbored TPO, HSP60 and MHC-II and activated dendritic cells (DCs) in vitro. Compared with Exo-targeted DCs (DC Exo), IFN-γ-Exo-targeted DCs (DC IFN-γ-Exo) promoted the expression and release of proinflammatory cytokines, such as IFN-γ, IL-17A and IL-22, from CD4+ T lymphocytes and inhibited the expression and release of anti-inflammatory cytokines, such as IL-4, IL-10 and TGF-β1; however, IFN-γ-Exo did not have this effect compared with Nthy-ori 3-1 cell-derived exosomes (Exo). DC IFN-γ-Exo stimulates the expression and release of cytokines from CD4+ T lymphocytes more efficiently than IFN-γ-Exo. Thus, DC IFN-γ-Exo may effectively induce CD4+ T lymphocyte-mediated immune responses and play a role in the occurrence and development of AITD. • IFN-γ-treated thyroid cell-derived exosomes (IFN-γ-Exo) express TPO, HSP60, MHC-II. • IFN-γ-Exo activate dendritic cells (DCs). • IFN-γ-Exo need to activate DCs before triggering CD4+ T lymphocyte differentiation. [ABSTRACT FROM AUTHOR]