Accurate quantification of transcriptome from RNA-Seq data by effective length normalization

We propose a novel, efficient and intuitive approach of estimating mRNA abundances from the whole transcriptome shotgun sequencing (RNA-Seq) data. Our method, NEUMA (Normalization by Expected Uniquely Mappable Area), is based on effective length normalization using uniquely mappable areas of gene and mRNA isoform models. Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. Quantitative RT–PCR based on 27 randomly selected genes in two human cell lines and computer simulation experiments demonstrated superior accuracy of NEUMA over other recently developed methods. NEUMA covers a large proportion of genes and mRNA isoforms and offers a measure of consistency (‘consistency coefficient’) for each gene between an independently measured gene-wise level and the sum of the isoform levels. NEUMA is applicable to both paired-end and single-end RNA-Seq data. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data

SEALONE (Safety and Efficacy of Coronary Computed Tomography Angiography with Low Dose in Patients Visiting Emergency Room) trial: study protocol for a randomized controlled trial

Objective Chest pain is one of the most common complaints in the emergency department (ED). Cardiac computed tomography angiography (CCTA) is a frequently used tool for the early triage of patients with low- to intermediate-risk acute chest pain. We present a study protocol for a multicenter prospective randomized controlled clinical trial testing the hypothesis that a low-dose CCTA protocol using prospective electrocardiogram (ECG)-triggering and limited-scan range can provide sufficient diagnostic safety for early triage of patients with acute chest pain. Methods The trial will include 681 younger adult (aged 20 to 55) patients visiting EDs of three academic hospitals for acute chest pain or equivalent symptoms who require further evaluation to rule out acute coronary syndrome. Participants will be randomly allocated to either low-dose or conventional CCTA protocol at a 2:1 ratio. The low-dose group will undergo CCTA with prospective ECG-triggering and restricted scan range from sub-carina to heart base. The conventional protocol group will undergo CCTA with retrospective ECG-gating covering the entire chest. Patient disposition is determined based on computed tomography findings and clinical progression and all patients are followed for a month. The primary objective is to prove that the chance of experiencing any hard event within 30 days after a negative low-dose CCTA is less than 1%. The secondary objectives are comparisons of the amount of radiation exposure, ED length of stay and overall cost. Results and Conclusion Our low-dose protocol is readily applicable to current multi-detector computed tomography devices. If this study proves its safety and efficacy, dose-reduction without purchasing of expensive newer devices would be possible

CNN-Based Imagery Analysis for Monitoring Complex Coastal Processes

Video monitoring is essential for a variety of coastal research. While monitoring can provide a wealth of information about coastal processes, extracting relevant information from in-situ beach photography remains challenging. This dissertation elucidates how the novel semantic segmentation method based on Convolutional Neural Networks (CNN) could analyze beach imagery and shows how these results can shed light on two crucial coastal processes that shape beach morphology: coastal flooding and beach recovery. In the first part of the investigation, we trained CNN to extract the water areas from each coastal image captured in Cedar Lakes, Texas, following the beach breach caused by Hurricane Harvey in 2017. We found that prediction accuracy for detecting water pixels was around 90%, proving that CNN-based image segmentation can effectively analyze short-range coastal images. It is also suggested that, with the aid of transfer learning, more than 100 training images were sufficient for training a model that provides accurate image segmentation prediction. Using the CNN, we measured the time series of water area fractions where we defined coastal flooding events. It was found that the size of coastal flooding follows an exponential distribution, whereas the inter-arrival times of flooding events may or may not follow an exponential distribution, depending on the time series scale. For the second part of the investigation, we applied several new techniques to better use high-resolution images and trained the new CNN model to track how the beach regions change over time. The model predicted images in the validation set with a mean precision of 95.1% and demonstrated that it could reliably detect sand composition changes in the images, albeit failing to predict blurry or rainy images. By applying this new model, we studied how different sand areas change over time during beach recovery. The sand pattern changed dramatically following a massive aeolian transport event, which implied the beach had converted from non-recovering to recovering states after the event. Furthermore, measurement of aeolian transport revealed that the frequency of aeolian transport is primarily determined by the wind velocity, the amount of dry sand covering the site, and the direction of the wind

Sustainable Corporate Social Media Marketing Based on Message Structural Features: Firm Size Plays a Significant Role as a Moderator

Social media has been receiving attention as a cost-effective tool to build corporate brand image and to enrich customer relationships. This phenomenon calls for more attention to developing a model that measures the impact of structural features, used in corporate social media messages. Based on communication science, this study proposes a model to measure the impact of three essential message structural features (interactivity, formality, and immediacy) in corporate social media on customers’ purchase intentions, mediated by brand attitude and corporate trust. Especially, social media platforms are believed to provide a good marketing platform for small and medium enterprises (SMEs) by providing access to huge audiences at a very low cost. The findings from this study based on a structural equation model suggest that brand attitude and corporate trust have larger impacts on purchase intention for SMEs than large firms. This implies that SMEs with little to no presence in the market should pay more attention to building corporate trust and brand attitude for their sustainable growth

Sustainable Political Social Media Marketing: Effects of Structural Features in Plain Text Messages

The success of Barack Obama’s 2008 U.S. presidential campaign led politicians and voters all over the world to pay attention to social media. Including Donald Trump for his upcoming 2020 re-election, many politicians around the world have used social media for their political campaigns. While some social media can deliver information in various forms (i.e., video, audio, and interactive content), some popular ones, such as Twitter, are still focused mostly on plain text messaging. With political marketing using simple text messages via social media, there is a need to examine ways of creating messages that ultimately help shape voters’ perception of politicians and eventually win the election. Based on communication science, this study attempts to test the limited capacity model of motivated mediated message processing by examining whether this model can be applied to the simplest form of mediated message, which is plain text. In order to do so, structural features of text messages exchanged on social media engaged in political campaigns, namely linguistic formality and network-mediated human interactivity, are manipulated in an experiment. Findings suggest that linguistic formality and human interaction in plain text messages influence perceived friendliness, truthfulness, and dependability of the message source (politicians), as well as the receivers’ (constituents’) behavioral intent to vote for the message source in an upcoming election. This implies that politicians should pay more attention on sustainable political marketing through appropriate manipulation of structural features in social media messages

Economic Analysis of Charitable Donations

This paper examines the effect of message characteristics on donation behavior using an economic model of giving. The utility of giving can come from one’s own contribution and possibly from the combined contributions of others. Donors are assumed to be constrained utility maximizers, and the message attributes affect the degree to which they react altruistically or egoistically. The model is estimated with data from an incentive-aligned study of South Korean consumers, and implications for message optimization and donor targeting are explored

Charge Trapping in a Low-Crystalline High-Mobility Conjugated Polymer and Its Effects on the Operational Stability of Organic Field-Effect Transistors

The effects of the microstructure of conjugated polymer thin films on charge trapping and operational stability of organic field-effect transistors (OFETs) are investigated. Device characteristics of OFETs based on two model conjugated polymers, poly(3-hexylthiophene) (P3HT) and a random 3-hexylthiophene-thiophene copolymer (RP33), are compared. P3HT films have high crystallinity and long-range molecular order, whereas RP33 films have low crystallinity and short-range molecular order as well as enhanced polymer backbone planarity. Experimental evidence shows that although the microstructure of the RP33 film provides efficient charge transport pathways, its high degree of structural disorder causes severe shallow trapping of charge carriers, which results in its inferior stability under bias stress. This study demonstrates that low-crystalline conjugated polymers with short-range order can provide a high charge-carrier mobility but at the same time be inappropriate for practical OFETs because of their poor intrinsic operational stability.11Nsciescopu

A Regulatory Polymorphism at Position -309 in PTPRCAP Is Associated with Susceptibility to Diffuse-type Gastric Cancer and Gene Expression1

PTPRCAP (CD45-AP) is a positive regulator of protein tyrosine phosphatase PTPRC (CD45), which activates Src family kinases implicated in tumorigenesis. Single-nucleotide polymorphism (SNP) rs869736 located at position -309 of the PTPRCAP promoter was associated with susceptibility to diffuse-type gastric cancer in the current case-control study. The minor-allele homozygote was significantly associated with a 2.5-fold increased susceptibility to diffuse-type gastric cancer (P = .0021, n = 252), but not to intestinal-type (P = .30, n = 178), versus the major-allele homozygote, when comparing unrelated Korean patients with healthy controls (n = 406). Nine other SNPs were in nearly perfect linkage disequilibrium (r2 ≥ 0.97) with this SNP, exhibiting the same association, and spread out for 26 kb on chromosome 11q13.1 covering RPS6KB2, PTPRCAP, CORO1B, and GPR152. Among the four genes, however, only PTPRCAP expression was affected by haplotypes of the 10 SNPs. Endogenous transcript levels of PTPRCAP were linearly correlated with copy numbers (0, 1, and 2) of the risk-haplotype (P = .0060) in 12 lymphoblastoid cells derived from blood samples, but those of the other three genes were not. Furthermore, the cancer-risk, minor-allele T of rs869736 increased both promoter activity and specific nuclear protein-binding affinity than the nonrisk, major-allele G in luciferase reporter and electrophoretic mobility shift assays, respectively. Accordingly, the minor allele of rs869736 in the PTPRCAP promoter is associated with increased susceptibility to diffuse-type gastric cancer by increasing PTPRCAP expression, possibly leading to activation of the oncogenic Src family kinases