Klineebsen0417

The Pure Dew (Cypress Hydrolat), which could be extracted from the waste material after the extracting essential oil from Taiwan cypress, has a good bactericidal effect. However, due to the high cost on quality control and concentration measurement of the Pure Dew, its application was restricted. This research tries to find suitable spectral frequencies through which the absorbance detected by the spectrometer could be used as the index of the pure dew concentration. This study used Gas Chromatography-Mass Spectrophotometer (GC-MS) to analyze the composition of Taiwan cypress hydrolat. After obtaining the composition, the raw liquor of cypress hydrolat was diluted to 100, 50, 25 and 0% v/v with pure water. The test samples were then tested by a simple spectrophotometer. After the spectrographic detection of absorbance using a simple spectrophotometer, it is confirmed that the spectrum of wavelength between 205-350 nm is the most representative. The absorptance and the pure dew concentration was roughly in linear relation which suggested that a simple spectrophotometer can be used to develop a low-cost and high.This study aimed to identify potential circular RNA (circRNA), microRNA (miRNA) and mRNA biomarkers as well as their underlying regulatory mechanisms in papillary thyroid carcinoma (PTC). Three microarray datasets from the Gene Expression Omnibus database as well as expression data and clinical phenotype from The Cancer Genome Atlas (TCGA) were downloaded, followed by differential expression, functional enrichment, protein-protein interaction (PPI), and module analyses. The support vector machine (SVM)-recursive feature elimination (RFE) algorithm was used to screen the key circRNAs. Finally, the mRNA-miRNA-circRNA regulatory network and competitive endogenous RNA (ceRNA) network were constructed. The prognostic value and clinical correlations of key mRNAs were investigated using TCGA dataset, and their expression was validated using the UALCAN database. A total of 1039 mRNAs, 18 miRNAs and 137 circRNAs were differentially expressed in patients with PTC. A total of 37 key circRNAs were obtained using the SVM-RFE algorithm, whereas 46 key mRNAs were obtained from significant modules in the PPI network. A total of 11 circRNA-miRNA pairs and 40 miRNA-mRNA pairs were predicted. Based on these interaction pairs, 46 circRNA-miRNA-mRNA regulatory pairs were integrated, of which 8 regulatory pairs in line with the ceRNA hypothesis were obtained, including two circRNAs (circ_0004053 and circ_0028198), three miRNAs (miR-199a-5p, miR-199b-5p, and miR-7-5p), and five mRNAs, namely APOA2, CCL20, LPAR5, MFGE8, and TIMP1. Survival analysis showed that LPAR5 expression was associated with patient survival. APOA2 expression showed significant differences between metastatic and non-metastatic tumors, whereas CCL20, LPAR5, MFGE8 and TIMP1 showed significant differences between metastatic and non-metastatic lymph nodes. Overall, we identified several potential targets and regulatory mechanisms involved in PTC. APOA2, CCL20, LPAR5, MFGE8, and TIMP1 may be correlated with PTC metastasis.Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explorIntracellular transport by microtubule-based molecular motors is marked by qualitatively different behaviors. It is a long-standing and still-open challenge to accurately quantify the various individual-cargo behaviors and how they are affected by the presence or absence of particular motor families. In this work we introduce a protocol for analyzing change points in cargo trajectories that can be faithfully projected along the length of a (mostly) straight microtubule. Our protocol consists of automated identification of velocity change points, estimation of velocities during the behavior segments, and extrapolation to motor-specific velocity distributions. Using simulated data we show that our method compares favorably with existing methods. We then apply the technique to data sets in which quantum dots are transported by Kinesin-1, by Dynein-Dynactin-BicD2 (DDB), and by Kinesin-1/DDB pairs. In the end, we identify pausing behavior that is consistent with some tug-of-war model predictions, but also demonstrate that the simultaneous presence of antagonistic motors can lead to long processive runs that could contribute favorably to population-wide transport.Proportion of cancerous cells in a tumor sample, known as "tumor purity", is a major source of confounding factor in cancer data analyses. Lots of computational methods are available for estimating tumor purity from different types of genomics data or based on different platforms, which makes it difficult to compare and integrate the estimated results. To rectify the deviation caused by tumor purity effect, a number of methods for downstream data analysis have been developed, including tumor sample clustering, association study and differential methylation between tumor samples. However, using these computational tools remains a daunting task for many researchers since they require non-trivial computational skills. To this end, we present Purimeth, an integrated web-based tool for estimating and accounting for tumor purity in cancer DNA methylation studies. Purimeth implements three state-of-the-art methods for tumor purity estimation from DNA methylation array data InfiniumPurify, MEpurity and PAMES. It also provides graphical interface for various analyses including differential methylation (DM), sample clustering, and purification of tumor methylomes, all with the consideration of tumor purities. In addition, Purimeth catalogs estimated tumor purities for TCGA samples from nine methods for users to visualize and explore. In conclusion, Purimeth provides an easy-operated way for researchers to explore tumor purity and implement cancer methylation data analysis. It is developed using Shiny (Version 1.6.0) and freely available at http//purimeth.comp-epi.com/.In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.Adherence to public health policies such as the non-pharmaceutical interventions implemented against COVID-19 plays a major role in reducing infections and controlling the spread of the diseases. In addition, understanding the transmission dynamics of the disease is also important in order to make and implement efficient public health policies. In this paper, we developed an SEIR-type compartmental model to assess the impact of adherence to COVID-19 non-pharmaceutical interventions and indirect transmission on the dynamics of the disease. Our model considers both direct and indirect transmission routes and stratifies the population into two groups those that adhere to COVID-19 non-pharmaceutical interventions (NPIs) and those that do not adhere to the NPIs. We compute the control reproduction number and the final epidemic size relation for our model and study the effect of different parameters of the model on these quantities. Our results show that there is a significant benefit in adhering to the COVID-19 NPIs.In this work, we study second order Crank-Nicholson difference scheme (DS) for the approximate solution of problem (1). The existence and uniqueness of the theorem on a bounded solution of Crank-Nicholson DS uniformly with respect to time step τ is proved. click here In practice, theoretical results are presented on four systems of nonlinear parabolic equations to explain how it works on one and multidimensional problems. Numerical results are provided.In this paper, in order to explore the inhibition mechanism of algicidal bacteria on algae, we constructed an aquatic amensalism model with non-selective harvesting and Allee effect. Mathematical works mainly gave some critical conditions to guarantee the existence and stability of equilibrium points, and derived some threshold conditions for saddle-node bifurcation and transcritical bifurcation. Numerical simulation works mainly revealed that non-selective harvesting played an important role in amensalism dynamic relationship. Meanwhile, we proposed some biological explanations for transcritical bifurcation and saddle-node bifurcation from the aspect of algicidal bacteria controlling algae. Finally, all these results were expected to be useful in studying dynamical behaviors of aquatic amensalism ecosystems and biological algae controlling technology.As innovative technologies emerge, there is a need to evolve the environments in which these technologies are used. The trend has shifted from considering technology as a support service towards making it the means for transforming all complex systems. Smart cities focus their development on the use of technology to transform every aspect of society and embrace the complexity of these transformations towards something leading to the well-being and safety of people inhabiting these cities. Occupational Health and Safety (OHS) is an essential aspect to be considered in the design of a smart city and its digital ecosystems, however, it remains unconsidered in most smart city's frameworks, despite the need for a specific space for smart OHS. This paper summarizes a 9-month process of generation of a value proposition for evolving the sector of OHS based on a value-map in whose creation several stakeholders have participated. They focused on identifying the products, the methods, the organizational structures and the technologies required to develop an updated, dynamic and robust prevention model focused on workers in smart and complex contexts, and to improve the organizations' capability to guarantee safety even in the most changing, digital and disruptive settings.