Study of Muscle Cell Dedifferentiation after Skeletal Muscle Injury of Mice with a Cre-Lox System

Background: Dedifferentiation of muscle cells in the tissue of mammals has yet to be observed. One of the challenges facing the study of skeletal muscle cell dedifferentiation is the availability of a reliable model that can confidentially distinguish differentiated cell populations of myotubes and non-fused mononuclear cells, including stem cells that can coexist within the population of cells being studied. Methodology/Principal Findings: In the current study, we created a Cre/Lox-β-galactosidase system, which can specifically tag differentiated multinuclear myotubes and myotube-generated mononuclear cells based on the activation of the marker gene, β-galactosidase. By using this system in an adult mouse model, we found that β-galactosidase positive mononuclear cells were generated from β-galactosidase positive multinuclear myofibers upon muscle injury. We also demonstrated that these mononuclear cells can develop into a variety of different muscle cell lineages, i.e., myoblasts, satellite cells, and muscle derived stem cells. Conclusions/Significance: These novel findings demonstrated, for the first time, that cellular dedifferentiation of skeletal muscle cells actually occurs in mammalian skeletal muscle following traumatic injury in vivo. © 2011 Mu et al

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Does Neighborhood Social Capital Longitudinally Affect the Nutritional Status of School-Aged Children? Evidence from China

Previous research linking social capital to child nutritional status primarily constitutes cross-sectional studies. To investigate whether a longitudinal relationship exists, by conducting fixed-effects analyses with 16,977 repeatedly measured observations of 6193 children from the 2012, 2014, 2016, and 2018 China Family Panel Studies, this study explored the longitudinal effects of neighborhood participation, bonding trust, and bridging trust on the BMI-for-age z-score (BAZ) and BMI categories of school-aged children, differentiating between urban and rural residence. We found an increasing average BAZ, a decreasing prevalence of underweight, an increasing prevalence of overweight/obesity, and a reducing urban/rural gap in nutritional status. The levels of social capital components descended faster in the urban area. Bonding trust was predictive of a lower BAZ, a higher likelihood of being underweight, and a lower likelihood of being overweight/obese. Bridging trust was predictive of a higher BAZ. The longitudinal effects of bonding trust were significant among only the rural children. Our findings indicate that neighborhood social capital may impose causal impacts on the nutritional status of children. To effectively improve child nutritional status, a more empathetic governmental approach that promotes a more supportive distal social environment is needed

Does social capital aid in leveling the income gradient in child mental health? A structural analysis of the left-behind and not-left-behind Chinese children

Abstract Background Few prior studies have investigated the income gradient in child mental health from a socio-environmental perspective. In an age when child mental health problems in a rapidly changing social environment have become a worldwide issue, an understanding of the socio-environmental mechanisms of the income disparities in child mental health outcomes is imperative and cost-effective. Methods By conducting structural equation analyses with Chinese nationally representative survey data, this study explored the family income gradient in child depression and its potential socio-environmental pathways at the neighborhood, family and school levels, differentiating left-behind and not-left-behind children. Results We found a robust family income gradient in depressive symptoms. Neighborhood cohesion mitigated the income gradient in depressive symptoms by playing a suppression role. School social capital acted as a mediator. Neighborhood trust, neighborhood safety and family social capital played no significant impact. The mitigating and mediating roles of social capital components were significant among only the not-left-behind children. Conclusions To reduce income-related inequalities in child mental health in the long run, integrating policies that directly reduce poverty with policies that improve distal socio-environments is necessary

How Do Students Become Good Workers? Investigating the Impact of Gender and School on the Relationship between Career Decision-Making Self-Efficacy and Career Exploration

In the field of vocational psychology, career decision-making self-efficacy (CDMSE) and career exploration (CE) are considered the crucial factors for developing a sustainable career. This study investigated the relationship between CDMSE and CE among Chinese high-school students, as well as the moderating effects of gender and school. From 2019 to 2021, 24,273 students from 13 different high schools were recruited in the study (male = 15,050, female = 9223; urban schools = 12,327, rural schools = 11,946). The results showed that (i) male students scored significantly higher than female students in both CDMSE and CE, (ii) students from urban schools scored significantly higher than students from rural schools in both CDMSE and CE, (iii) CDMSE positively predicted CE, and (iv) school moderated the relationship between CDMSE and CE, with the effect of CDMSE on CE stronger among rural school students; a moderating effect of gender was not found. These findings indicate that promoting CDMSE can lead high-school students, especially rural school students, to engage more in CE to ensure sustainable career development under the protean and boundaryless career orientation

Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well