Exploring the Formation Mechanism of Radical Technological Innovation: An MLP Approach

This paper identifies three stages in the radical technological innovation process, namely formation process in niches, breaking out of niches and entering regimes, and new regime formation. It then adopts Multi-level Perspective (MLP) to explore the formation process, operating mechanism, breakthrough path, and impact factors of radical technological innovation. A three-phase model, which includes formation of radical innovation, breakout of radical innovation, and new regimes construction, is proposed to analyze radical technological innovation. The model is adopted in a case study to analyze the leapfrogging development of technologies in China’s mobile communication industry. This paper enriches technological innovation theory and provides supports for policy making and guidance for industries/enterprises practices regarding technological innovation in emerging economies

Technological Innovation Research: A Structural Equation Modelling Approach

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Background: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures

First Report of a Foodborne Salmonella enterica Serovar Gloucester (4:i:l,w) ST34 Strain Harboring blaCTX–M–55 and qnrS Genes Located in IS26-Mediated Composite Transposon

Extended-spectrum β-lactamases (ESBLs) production and (fluoro)quinolone (FQ) resistance among Salmonella pose a public health threat. The objective of this study was the phenotypic and genotypic characterization of an ESBL-producing and nalidixic acid-resistant Salmonella enterica serovar Gloucester isolate (serotype 4:i:l,w) of sequence type 34 (ST34) from ready-to-eat (RTE) meat products in China. Whole-genome short and long read sequencing (HiSeq and MinION) results showed that it contained blaCTX–M–55, qnrS1, and tetB genes, with blaCTX–M–55 and qnrS1 located in chromosomal IS26-mediated composite transposon (IS26–qnrS1–IS3–Tn3–orf–blaCTX–M–55–ISEcp1–IS26). The same genetic structure was found in the chromosome of S. enterica subsp. enterica serovar Typhimurium strain and in several plasmids of Escherichia coli, indicating that the IS26-mediated composite transposon in the chromosome of S. Gloucester may originate from plasmids of E. coli and possess the ability to disseminate to Salmonella and other bacterial species. Besides, the structural unit qnrS1–IS3–Tn3–orf–blaCTX–M–55 was also observed to be linked with ISKpn19 in both the chromosomes and plasmids of various bacteria species, highlighting the contribution of the insertion sequences (IS26 and ISKpn19) to the co-dissemination of blaCTX–M–55 and qnrS1. To our knowledge, this is the first description of chromosomal blaCTX–M–55 and qnrS in S. Gloucester from RTE meat products. Our work expands the host range and provides additional evidence of the co-transfer of blaCTX–M–55 and qnrS1 among different species of Salmonella through the food chain

Scrutiny of the mechanism of β-amyloid protein captures HSV-2 to protect the brain infection

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. β-amyloid protein (Aβ) is the key protein which involved in AD. But the physiological function of Aβ is needed to be investigated. Many experimental studies have shown that Aβ could bind to glycoproteins D (gD) on the surface of the herpes virus. However the mechanism is still unclear. In the present study, we elucidate the molecular mechanism of the interaction between Aβ and gD of herpes simplex virus type 2 (HSV-2) by molecular docking and molecular dynamics simulation. Molecular dynamics simulations displayed that Aβ could stably bind to the HSV-2 gD owing to the presence of several interactions. Analysis binding free energy by molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method revealed that hot residues including Glu3, Glu11, Glu22 and Ala42 of Aβ1-42 were involved in binding with HSV-2 gD. Thus, the HSV-2 gD can be entrapped by Aβ which will be utilized for prevent and therapy of AD in future