Starkkristensen2628

Fiber-cement building products are increasingly used in construction. They are used as building and finishing material for facades, internal walls and roofs. Numerous advantages such as relatively low weight, low absorbability and relatively high strength allow to use these materials in bulky constructions and in buildings which are commonly considered tall. Safety reasons, however, point to the need to control the condition of materials used to erect such structures. It is also in line with the more and more widely implemented concept of monitoring the state of the structure and its components over their entire period of use (SHM). The article presents the results of experimental tests on flexural strength of cement-fiber boards in an air-dry state, which have been soaked in water for 24 hours and subjected to high temperature. The paper also presents a possibility to use a non-invasive method of acoustic emission and wavelet analysis for testing cement boards reinforced with cellulose fibers. Obtained results allow to track the change of mechanical parameters in boards subjected to environmental and exceptional factors. These results also confirm the applicability of the presented methods as instruments for observing the condition of the panels used.Modern next generation sequencing technologies produce huge amounts of genome-wide data that allow researchers to have a deeper understanding of genomics of organisms. Despite these huge amounts of data, our understanding of the transcriptional regulatory networks is still incomplete. Conformation dependent chromosome interaction maps technologies (Hi-C) have enabled us to detect elements in the genome which interact with each other and regulate the genes. Summarizing these interactions as a data network leads to investigation of the most important properties of the 3D genome structure such as gene co-expression networks. In this work, a Pareto-Based Multi-Objective Optimization algorithm is proposed to detect the co-expressed genomic regions in Hi-C interactions. The proposed method uses fixed sized genomic regions as the vertices of the graph. Number of read between two interacting genomic regions indicate the weight of each edge. The performance of our proposed algorithm was compared to the Multi-Objective PSO algorithm on five networks derived from cis genomic interactions in three Hi-C datasets (GM12878, CD34+ and ESCs). The experimental results show that our proposed algorithm outperforms Multi-Objective PSO technique in the identification of co-interacting genomic regions.We examined the use of bivariate mutual information (MI) and its conditional variant transfer entropy (TE) to address synchronization of perinatal uterine pressure (UP) and fetal heart rate (FHR). We used a nearest-neighbour based Kraskov entropy estimator, suitable to the non-Gaussian distributions of the UP and FHR signals. Moreover, the estimates were robust to noise by use of surrogate data testing. Estimating degree of synchronicity and UP-FHR delay length is useful since they are physiological correlates to fetal hypoxia. Mutual information of the UP-FHR discriminated normal and pathological fetuses early (160 min before delivery) and discriminated normal and metabolic acidotic fetuses slightly later (110 min before delivery), with higher mutual information for progressively pathological classes. The delay in mutual information transfer was also discriminating in the last 50 min of labour. Transfer entropy discriminated normal and pathological cases 110 min before delivery with lower TE values and longer information transfer delays in pathological cases, to our knowledge, the first report of this phenomena in the literature.Pest control is a worldwide challenge. An approach that has been developed to meet this challenge is the integrated pest management (IPM) strategy, which aims to offer environmentally sensitive solutions to pest problems, and takes into account the complex dynamics involved in the design of controlling pests. SF1670 manufacturer In this paper, we propose a discrete switching host-parasitoid model with a threshold control strategy, meanwhile, provide some qualitative analyses of the complexity of dynamic behaviors of the model that includes single and multi-parameter bifurcations and chaos. Furthermore, we do some numerical bifurcations and parameter sensibility analysis, revealing how the key control parameters and initial interaction state between the two populations affect pest control, as well as the dynamical balance between of the hosts and parasitoids. The model and analytical techniques developed in this work could be applied in other settings relevant to threshold control strategies.Objective The study aims to explore the effects of receptor of hyaluronan mediated motility (RHAMM) on the proliferation, invasion and migration of the lung adenocarcinoma (LUAD) cell line A549 and its targeted regulatory pathway. Methods Bioinformatics was used to analyze the differentially expressed genes in LUAD chips. The mRNA and protein expression level of Cdc2, CyclinB1, MMPs and epithelial-mesenchymal transition (EMT) related markers E-cadherin and Vimentin were tested by qRT-PCR and western blot in A549 cell line after silencing RHAMM. Cell proliferation, cell division cycle, migration and invasion abilities were tested in RHAMM knockdown A549 cells by flow cytometry and in vitro assays. Results Silencing RHAMM inhibited EMT, proliferation, migration and invasion of A549 cell line and induced cells to cluster at G2/M phase. In addition, after silencing RHAMM, the mRNA and protein expressions of Cdc2 and CyclinB1 were decreased while those of MMP9 were increased. Conclusion The findings suggest that RHAMM regulates cell division cycle by regulating Cdc2 and CyclinB1, and regulates extracellular matrix degradation by regulating MMP9. These targeted modulations regulate the occurrence and development of LUAD cells.Pituitary adenomas (PA) is one of the most frequent types of intracranial neoplasms. Long noncoding RNAs (lncRNAs) played important roles in the progression of human cancers, including PA. However, the roles of lncRNAs in PA remained to be further investigated. We performed analysis of GSE26966 dataset to identify differently expressed lncRNAs in PA. Co-expression network, lncRNA-RNA binding proteins network, and competing endogenous RNA networks were constructed. Moreover, we performed RT-qPCR assay to validate four key lncRNAs expression in PA. This study identified differently expressed mRNAs and lncRNAs by using GSE26966 database. Furthermore, we constructed lncRNA-mRNA co-expression, lncRNA-RBP interaction and ceRNA networks. Bioinformatics analysis showed these lncRNAs were involved in regulating mechanical stimulus, gene expression, JAK-STAT cascade, cell cycle arrest, FoxO signaling, HIF-1 signaling, Insulin signaling, Oxytocin signaling, and MAPK signaling. We also showed KCNQ1OT1, SNHG7, MEG3, and SNHG5 were down-regulated in PA.