Non-Isothermal Crystallization Kinetics of Short Glass Fiber Reinforced Poly (Ether Ether Ketone) Composites

Due to its excellent chemical and temperature resistances, short glass fiber reinforced poly (ether ether ketone) composite (SGF/PEEK) is a promising material for application in automotive lightweight. Processing conditions, such as cooling rate, need to be well controlled to obtain the optimal crystallite morphology of PEEK composites. Thus, in this paper, the non-isothermal crystallization kinetics and melting behavior of SGF/PEEK were investigated by differential scanning calorimetry (DSC) at different cooling rates, and the crystallite sizes were evaluated by the X-ray diffraction technique (XRD). Crystallization kinetics models and effective activation energies were evaluated to determine the crystallization parameters of the composites. The results suggest that a lower cooling rate enlarges the size of crystallites and enhances the uniformity of size distribution. The addition of glass fibers improves the nucleation rate owing to heterogeneous nucleation while decreasing the growth rate due to retarded movement of the polymer chain. The combined Avrami-Ozawa equation was shown to describe accurately the non-isothermal crystallization. The absolute value of the crystallization activation energy for SGF/PEEK is lower than that of pure PEEK

Optimal Combination of Glycan-Based Serum Diagnostic Markers Which Maximize AUC

Recently a new high-throughput biomarker discovery platform based on printed glycan arrays (PGA) has emerged. PGAs are similar to DNA arrays but contain deposits of various carbohy-drate structures (glycans) instead of spotted DNAs. PGA-based biomarker discovery for the early detection, diagnosis and prognosis of human malignancies is based on the response of the immune system as measured by the level of binding of anti-glycan antibodies from human serum to the glycans on the ar-ray. Since the PGA offer a multitude of markers which can have moderate individual diagnostic power they can be combined in order to achieve maximal classification precision assessed by the popular performance measure area under the ROC curve (AUC). This paper presents an empirical analysis of several combination approaches including those that are specifically designed to maximize the AUC and those that are not, such as Fisher Linear Discriminant, Support Vector Machines and Gen-eralized Linear Model. The analysis is performed on real-life PGA data from three pilot studies involving malignant mesothe-lioma, lung cancer and ovarian cancer

Knowledge Amalgamation for Object Detection with Transformers

Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, most of these approaches are tailored for convolutional neural networks (CNNs). However, there is a tendency that transformers, with a completely different architecture, are starting to challenge the domination of CNNs in many computer vision tasks. Nevertheless, directly applying the previous KA methods to transformers leads to severe performance degradation. In this work, we explore a more effective KA scheme for transformer-based object detection models. Specifically, considering the architecture characteristics of transformers, we propose to dissolve the KA into two aspects: sequence-level amalgamation (SA) and task-level amalgamation (TA). In particular, a hint is generated within the sequence-level amalgamation by concatenating teacher sequences instead of redundantly aggregating them to a fixed-size one as previous KA works. Besides, the student learns heterogeneous detection tasks through soft targets with efficiency in the task-level amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that the sequence-level amalgamation significantly boosts the performance of students, while the previous methods impair the students. Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

MTA1 promotes the invasion and migration of non-small cell lung cancer cells by downregulating miR-125b

BACKGROUND: The metastasis-associated gene 1 (MTA1) has been identified as one critical regulator of tumor metastasis. Previously, we identified miR-125b as a downregualted miRNA in non-small cell lung cancer (NSCLC) cell line upon MTA1 depletion. However, the role of miR-125b and MTA1 in the regulation of NSCLC metastasis remains unclear. METHODS: Stable MTA1 knockdown NSCLC cell lines 95D and SPC-A-1 were established by transfection with MTA1 shRNA. The effects of MTA1 depletion on the expression of miR-125b and cell migration and invasion were examined by real-time PCR, wound healing and matrigel invasion assay. RESULTS: MTA1 knockdown led to the upregulation of miR-125b level in NSCLC cells. Furthermore, MTA1 knockdown reduced while miR-125b inhibitor enhanced cell migration and invasion of NSCLC cells. Notably, miR-125b inhibitor antagonized MTA1 siRNA induced inhibition of cell migration and invasion. CONCLUSION: MTA1 and miR-125b have antagonistic effects on the migration and invasion of NSCLC cells. The newly identified MTA1-miR-125b axis will help further elucidate the molecular mechanism of NSCLC progression and suggest that ectopic expression of miR-125b is a potentially new therapeutic regimen against NSCLC metastasis

Systematic Reconstruction of Molecular Cascades Regulating GP Development Using Single-Cell RNA-Seq

Summary: The growth plate (GP) comprising sequentially differentiated cell layers is a critical structure for bone elongation and regeneration. Although several key regulators in GP development have been identified using genetic perturbation, systematic understanding is still limited. Here, we used single-cell RNA-sequencing (RNA-seq) to determine the gene expression profiles of 217 single cells from GPs and developed a bioinformatics pipeline named Sinova to de novo reconstruct physiological GP development in both temporal and spatial high resolution. Our unsupervised model not only confirmed prior knowledge, but also enabled the systematic discovery of genes, potential signal pathways, and surface markers CD9/CD200 to precisely depict development. Sinova further identified the effective combination of transcriptional factors (TFs) that regulates GP maturation, and the result was validated using an in vitro EGFP-Col10a screening system. Our case systematically reconstructed molecular cascades in GP development through single-cell profiling, and the bioinformatics pipeline is applicable to other developmental processes. Video Abstract: : Li et al. have developed an unsupervised clustering approach called Sinova to analyze single-cell RNA-seq data. By using this pipeline to analyze single-cell RNA-seq data from developing GPs in mice, they have generated a spatial and temporal map of the GP and identified molecular networks involved in GP development

Oxygen Vacancies Unfold the Catalytic Potential of NiFe-Layered Double Hydroxides by Promoting Their Electronic Transport for Oxygen Evolution Reaction

Oxygen vacancies (Ov) engineering has demonstrated tremendous power to expedite electrocatalytic kinetics for oxygen evolution reaction (OER). The mechanism is elusive, and most of them were attributed to the decoration or creation of active sites. Here, we report the critical role of superficial Ov in enhancing the electronic transport, thereby unfolding the catalytic potential of NiFe-layered double hydroxides for OER. We reveal that the superficial Ov engineering barely regulates the intrinsic catalytic activities but lowers the charge transport resistances by more than one order of magnitude. Loading-dependent electrochemical analysis suggests that the superficial Ov engineering intensively modulates the utilization rate of electronically accessible active sites for OER catalysis. By correlating catalytic activities to charging capacitances of CΦ (related to the absorption of reaction intermediates), we unveil a linear dependence, which indicates switchable catalysis on electronically accessible active sites. Based on the unified experimental and theoretical analysis of the electronic structures, we propose that the superficial Ov imposes electron donation to the conductive band of NiFeOOH, thereby enabling the regulation of electronic transport to switch on/off OER catalysis. The switch effect holds fundamental and technical implications for understanding and designing efficient electrocatalysts