Activin B Promotes Epithelial Wound Healing In Vivo through RhoA-JNK Signaling Pathway

Background: Activin B has been reported to promote the proliferation and migration of keratinocytes in vitro via the RhoA-JNK signaling pathway, whereas its in vivo role and mechanism in wound healing process has not yet been elucidated. Principal Findings: In this study, we explored the potential mechanism by which activin B induces epithelial wound healing in mice. Recombinant lentiviral plasmids, with RhoA (N19) and RhoA (L63) were used to infect wounded KM mice. The wound healing process was monitored after different treatments. Activin B-induced cell proliferation on the wounded skin was visualized by electron microscopy and analyzed by 59-bromodeoxyuridine (BrdU) incorporation assay. Protein expression of p-JNK or p-cJun was determined by immunohistochemical staining and immunoblotting analysis. Activin B efficiently stimulated the proliferation of keratinocytes and hair follicle cells at the wound area and promoted wound closure. RhoA positively regulated activin B-induced wound healing by up-regulating the expression of p-JNK and p-cJun. Moreover, suppression of RhoA activation delayed activin B-induced wound healing, while JNK inhibition recapitulated phenotypes of RhoA inhibition on wound healing. Conclusion: These results demonstrate that activin B promotes epithelial wound closure in vivo through the RhoA-Rock

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

A hybrid non-invasive method for internal/external quality assessment of potatoes

Consumers purchase fruits and vegetables based on its quality, which can be defined as a degree of excellence which is the result of a combination of characteristics, attributes and properties that have significance for market acceptability. In this paper, a novel hybrid active imaging methodology for potato quality inspection that uses an optical colour camera and an infrared thermal camera is presented. The methodology employs an artificial neural network (ANN) that uses quality data composed by two descriptors as input. The ANN works as a feature classifier so that its output is the potato quality grade. The input vector contains information related to external characteristics, such as shape, weight, length and width. Internal characteristics are also accounted for in the input vector in the form of excessive sugar content. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The validation of the methodology was made through experimentation which consisted in fusing both, external and internal characteristics in the input vector to the ANN for an overall quality classification. Results using internal data as obtained from an infrared camera and fused with optical external parameters demonstrated the feasibility of the method since the prediction accuracy increased during potato grading