T Oligo-Primed Polymerase Chain Reaction (TOP-PCR): A Robust Method for the Amplification of Minute DNA Fragments in Body Fluids.

Body fluid DNA sequencing is a powerful noninvasive approach for the diagnosis of genetic defects, infectious agents and diseases. The success relies on the quantity and quality of the DNA samples. However, numerous clinical samples are either at low quantity or of poor quality due to various reasons. To overcome these problems, we have developed T oligo-primed polymerase chain reaction (TOP-PCR) for full-length nonselective amplification of minute quantity of DNA fragments. TOP-PCR adopts homogeneous "half adaptor" (HA), generated by annealing P oligo (carrying a phosphate group at the 5' end) and T oligo (carrying a T-tail at the 3' end), for efficient ligation to target DNA and subsequent PCR amplification primed by the T oligo alone. Using DNA samples from body fluids, we demonstrate that TOP-PCR recovers minute DNA fragments and maintains the DNA size profile, while enhancing the major molecular populations. Our results also showed that TOP-PCR is a superior method for detecting apoptosis and outperforms the method adopted by Illumina for DNA amplification

The characterization of the saddle shaped nickel(III) porphyrin radical cation: an explicative NMR model for a ferromagnetically coupled metallo-porphyrin radical

Ni(III)(OETPP˙)(Br)2 is the first Ni(III) porphyrin radical cation with structural and (1)H and (13)C paramagnetic NMR data for porphyrinate systems. Associating EPR and NMR analyses with DFT calculations as a new model is capable of clearly determining the dominant state from two controversial spin distributions in the ring to be the Ni(III) LS coupled with an a1u spin-up radical

Does Product Type Affect Electronic Word-of-Mouth Richness Effectiveness? Influences of Message Valence and Consumer Knowledge

Drawing on the information richness theory, this study attempts to address how valence of electronic word-of-mouth (eWOM), product type and consumer knowledge will yield different levels of eWOM richness. The results based on an experimental study suggest that negative eWOM has a stronger effect in producing eWOM information richness than does positive eWOM, and such effect is more pronounced for a leisure farm tour (experience goods) than for digital camera (search goods). The tendency that negative eWOM will provide richer information for the leisure farm tour is more evident for high-knowledge consumers than for low-knowledge consumers. The study’s results caution against the aggravated harm of negative eWOM incurred from the dissatisfactory experience of a leisure farm tour

A Study on the Value Destruction Caused by Incomplete Interaction Behaviors on a Live Shopping Platform

This study aims to understand the impact of incomplete interaction behaviors on value destruction in online shopping. Incomplete interaction behaviors include incomplete product interaction, incomplete information interaction and incomplete parasocial interaction. Additionally, negative inter­personal and intrapersonal relationships are used as mediators in this study. Views were collected from 522 users in Taiwan who have used livestream platforms for shopping and had negative experiences. An online questionnaire was designed and sent to livestreaming customers, and quota sampling was employed to enhance the sample representativeness. The empirical results were produced by covariance-based structural equation modeling with AMOS and show that incomplete parasocial interaction has the greatest impact on value destruction via negative interpersonal relationships. The negative intrapersonal relationships were not found to have mediation effects in this study. The practical implications for individuals who plan to join the livestream shopping industry are not only able to showcase products, but also appropriately disclose their personas to build relationships with customers and interact with those who show their emotions or views. Managing customer-to-customer interactions in the livestreaming process is equally important, as negative feelings among customers directly result in value destruction. In other words, streamers should focus on training themselves in the aspects of what product and personal information should be disclosed to the public and focus on observing and managing the feelings and emotions of their live customers as well as the consumer-to-consumer relationships

The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity

A Study of Machine Learning Models in Epidemic Surveillance: Using the Query Logs of Search Engines

Epidemics inevitably result in a large number of deaths and always cause considerable social and economic damage. Epidemic surveillance has thus become an important healthcare research issue. In 2009, Ginsberg et al. observed that the query logs of search engines can be used to estimate the status of epidemics in a timely manner. In this paper, we model epidemic surveillance as a classification problem and employ query statistics from Google to classify the status of a dengue fever epidemic. The query logs of twenty-three dengue-related keywords serve as observations for machine learning and testing, and a number of machine learning models are investigated to evaluate their surveillance performance. Evaluations based on a 5-year real world dataset demonstrate that search engine query logs can be used to construct accurate epidemic status classifiers. Moreover, the learned classifiers generally outperform conventional regression approaches. We also apply various machine learning models, including generative, discriminative, sequential, and non-sequential classification models, to demonstrate their applicability to epidemic surveillance