Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification

Social media platforms have become accessible resources for health data analysis. However, the advanced computational techniques involved in big data text mining and analysis are challenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents

Measurement-device-independent quantum key distribution with uncharacterized qubit sources

Measurement-device-independent quantum key distribution (MDIQKD) is proposed to be secure against any possible detection attacks. The security of the original proposal relies on the assumption that the legitimate users can fully characterize the encoding systems including sources. Here, we propose a MDIQKD protocol where we allow uncharacterized encoding systems as long as qubit sources are used. A security proof of the MDIQKD protocol is presented that does not need the knowledge of the encoding states. Simulation results show that the scheme is practical

Mismatched-basis statistics enable quantum key distribution with uncharacterized qubit sources

In the postprocessing of quantum key distribution, the raw key bits from the mismatched-basis measurements, where two parties use different bases, are normally discarded. Here, we propose a postprocessing method that exploits measurement statistics from mismatched-basis cases, and prove that incorporating these statistics enables uncharacterized qubit sources to be used in the measurement-device-independent quantum key distribution protocol and the Bennett-Brassard 1984 protocol, a case which is otherwise impossible.Comment: Part of this article contains a significant improvement over arXiv:1309.381

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools

Social Media Usage and Influenza Beliefs, Risk Perceptions and Behavioral Intentions Among Students at a University in Southeastern US

Background: To document social media usage for the retrieval of health information among college students; and to understand the beliefs, risk perceptions and behavioral intentions among participants who retrieved CDC influenza information via social media. Methods: We conducted an online survey to a convenience sample of students at a university in Southeastern United States during Spring 2015. The survey was self-administered and every matriculating student received an electronic invitation to participate at least once. Results: A total of 930 students completed the online survey. Most participants (n=905, 97.3%) reported that they had used a social networking site in the previous 12 months. However, only one-third (n=317, 34.1%) reported that they used social networking sites to read CDC health information or messages. Nearly one-fifth of participants (n=172, 18.5%) reported reading CDC influenza information during the 2014-15 influenza season. Among the subset of readers of CDC influenza information during the 2014-15 influenza season (N=153), 77 (50.99%) reported that it was likely they would get the influenza vaccine in the next 12 months. Women reported stronger risk perceptions and behavioral intentions than men. Blacks/African Americans reported more negative influenza-related beliefs and weaker risk perceptions compared to Whites. Conclusions: While social media penetration is high among university students in Southeastern US, only a minority of survey participants retrieved CDC influenza information via social media. Among these individuals, about half reported that they intended to vaccinate against influenza. Further research is needed to enhance CDC social media penetration among college students

Lyme Disease and YouTube™: A Cross-Sectional Study of Video Contents

Objectives: Lyme disease is the most common tick-borne disease. People seek health information on Lyme disease from YouTubeTM videos. In this study, we investigated if the contents of Lyme disease-related YouTubeTM videos varied by their sources. Methods: Most viewed English YouTubeTM videos (n = 100) were identified and manually coded for contents and sources. Results: Within the sample, 40 videos were consumer-generated, 31 were internet-based news, 16 were professional, and 13 were TV news. Compared with consumer-generated videos, TV news videos were more likely to mention celebrities (odds ratio [OR], 10.57; 95% confidence interval [CI], 2.13–52.58), prevention of Lyme disease through wearing protective clothing (OR, 5.63; 95% CI, 1.23–25.76), and spraying insecticides (OR, 7.71; 95% CI, 1.52–39.05). Conclusion: A majority of the most popular Lyme disease-related YouTubeTM videos were not created by public health professionals. Responsible reporting and creative video-making facilitate Lyme disease education. Partnership with YouTubeTM celebrities to co-develop educational videos may be a future direction

#CDCGrandRounds and #VitalSigns : A Twitter Analysis

BACKGROUND: The CDC hosts monthly panel presentations titled 'Public Health Grand Rounds' and publishes monthly reports known as Vital Signs. Hashtags #CDCGrandRounds and #VitalSigns were used to promote them on Twitter. Objectives: This study quantified the effect of hashtag count, mention count, and URL count and attaching visual cues to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency. METHODS: Through Twitter Search Application Programming Interface, original tweets containing the hashtag #CDCGrandRounds (n = 6,966; April 21, 2011-October 25, 2016) and the hashtag #VitalSigns (n = 15,015; March 19, 2013-October 31, 2016) were retrieved respectively. Negative binomial regression models were applied to each corpus to estimate the associations between retweet frequency and three predictors (hashtag count, mention count, and URL link count). Each corpus was sub-set into cycles (#CDCGrandRounds: n = 58, #VitalSigns: n = 42). We manually coded the 30 tweets with the highest number of retweets for each cycle, whether it contained visual cues (images or videos). Univariable negative binomial regression models were applied to compute the prevalence ratio (PR) of retweet frequency for each cycle, between tweets with and without visual cues. FINDINGS: URL links increased retweet frequency in both corpora; effects of hashtag count and mention count differed between the two corpora. Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significantly different retweet frequencies between tweets with and without visual cues. Of these 29 cycles, one had a PR estimate < 1; twenty-four, PR > 1 but < 3; and four, PR > 3. Of the 42 #VitalSigns cycles, 19 were statistically significant. Of these 19 cycles, six were PR > 1 and < 3; and thirteen, PR > 3. Conclusions: The increase of retweet frequency through attaching visual cues varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Future research is needed to determine the optimal choice of visual cues to maximize the influence of public health tweets

The Aqueous Extract of Rhizome of Gastrodia elata

This study aims to investigate the neuroprotective effect of the rhizome of Gastrodia elata (GE) aqueous extract on beta-amyloid(Aβ)-induced toxicity in vivo and in vitro. Transgenic Drosophila mutants with Aβ-induced neurodegeneration in pan-neuron and ommatidia were used to determine the efficacy of GE. The antiapoptotic and antioxidative mechanisms of GE were also studied in Aβ-treated pheochromocytoma (PC12) cells. In vivo studies demonstrated that GE (5 mg/g Drosophila media)-treated Drosophila possessed a longer lifespan, better locomotor function, and less-degenerated ommatidia when compared with the Aβ-expressing control (all P<0.05). In vitro studies illustrated that GE increased the cell viability of Aβ-treated PC12 cells in dose-dependent manner, probably through attenuation of Aβ-induced oxidative and apoptotic stress. GE also significantly upregulated the enzymatic activities of catalase, superoxide dismutase, and glutathione peroxidase, leading to the decrease of reactive oxidation species production and apoptotic marker caspase-3 activity. In conclusion, our current data presented the first evidence that the aqueous extract of GE was capable of reducing the Aβ-induced neurodegeneration in Drosophila, possibly through inhibition of apoptosis and reduction of oxidative stress. GE aqueous extract could be developed as a promising herbal agent for neuroprotection and novel adjuvant therapies for Alzheimer’s disease

Drosophila Exo70 is Essential for Neurite Extension and Survival under Thermal Stress

The octomeric exocyst complex governs the final step of exocytosis in both plants and animals. Its roles, however, extend beyond exocytosis and include organelle biogenesis, ciliogenesis, cell migration, and cell growth. Exo70 is a conserved component of the exocyst whose function in Drosophila is unclear. In this study, we characterized two mutant alleles of Drosophila exo70. exo70 mutants exhibit reduced synaptic growth, locomotor activity, glutamate receptor density, and mEPSP amplitude. We found that presynaptic Exo70 is necessary for normal synaptic growth at the neuromuscular junction (NMJ). At the neuromuscular junction, exo70 genetically interacts with the small GTPase ralA to regulate synaptic growth. Loss of Exo70 leads to the blockage of JNK signaling-, activity-, and temperature-induced synaptic outgrowths. We showed that this phenotype is associated with an impairment of integral membrane protein transport to the cell surface at synaptic terminals. In octopaminergic motor neurons, Exo70 is detected in synaptic varicosities, as well as the regions of membrane extensions in response to activity stimulation. Strikingly, mild thermal stress causes severe neurite outgrowth defects and pharate adult lethality in exo70 mutants. exo70 mutants also display defective locomotor activity in response to starvation stress. These results demonstrated that Exo70 is an important regulator of induced synaptic growth and is crucial for an organism’s adaptation to environmental changes