Twitter (United States)

PURPOSE: Standards for reporting exist for many types of quantitative research, but currently none exist for the broad spectrum of qualitative research. The purpose of the present study was to formulate and define standards for reporting qualitative research while preserving the requisite flexibility to accommodate various paradigms, approaches, and methods. METHOD: The authors identified guidelines, reporting standards, and critical appraisal criteria for qualitative research by searching PubMed, Web of Science, and Google through July 2013; reviewing the reference lists of retrieved sources; and contacting experts. Specifically, two authors reviewed a sample of sources to generate an initial set of items that were potentially important in reporting qualitative research. Through an iterative process of reviewing sources, modifying the set of items, and coding all sources for items, the authors prepared a near-final list of items and descriptions and sent this list to five external reviewers for feedback. The final items and descriptions included in the reporting standards reflect this feedback. RESULTS: The Standards for Reporting Qualitative Research (SRQR) consists of 21 items. The authors define and explain key elements of each item and provide examples from recently published articles to illustrate ways in which the standards can be met. CONCLUSIONS: The SRQR aims to improve the transparency of all aspects of qualitative research by providing clear standards for reporting qualitative research. These standards will assist authors during manuscript preparation, editors and reviewers in evaluating a manuscript for potential publication, and readers when critically appraising, applying, and synthesizing study findings.

Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

Abstract In this paper we report research results investigating microblogging as a form of electronic word‐of‐mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19% of microblogs contain mention of a brand. Of the branding microblogs, nearly 20% contained some expression of brand sentiments. Of these, more than 50% were positive and 33% were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy.

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

Recent calls for educational reform highlight ongoing concerns about the ability of current curricula to equip aspiring health care professionals with the skills for success. Whereas a wide range of proposed solutions attempt to address apparent deficiencies in current educational models, a growing body of literature consistently points to the need to rethink the traditional in-class, lecture-based course model. One such proposal is the flipped classroom, in which content is offloaded for students to learn on their own, and class time is dedicated to engaging students in student-centered learning activities, like problem-based learning and inquiry-oriented strategies. In 2012, the authors flipped a required first-year pharmaceutics course at the University of North Carolina Eshelman School of Pharmacy. They offloaded all lectures to self-paced online videos and used class time to engage students in active learning exercises. In this article, the authors describe the philosophy and methodology used to redesign the Basic Pharmaceutics II course and outline the research they conducted to investigate the resulting outcomes. This article is intended to serve as a guide to instructors and educational programs seeking to develop, implement, and evaluate innovative and practical strategies to transform students' learning experience. As class attendance, students' learning, and the perceived value of this model all increased following participation in the flipped classroom, the authors conclude that this approach warrants careful consideration as educators aim to enhance learning, improve outcomes, and fully equip students to address 21st-century health care needs.

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder (VAE) with a generative adversarial network (GAN) we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

This paper describes the use of Storm at Twitter. Storm is a real-time fault-tolerant and distributed stream data processing system. Storm is currently being used to run various critical computations in Twitter at scale, and in real-time. This paper describes the architecture of Storm and its methods for distributed scale-out and fault-tolerance. This paper also describes how queries (aka. topologies) are executed in Storm, and presents some operational stories based on running Storm at Twitter. We also present results from an empirical evaluation demonstrating the resilience of Storm in dealing with machine failures. Storm is under active development at Twitter and we also present some potential directions for future work.

Light from certain spectrums is only visible to some. The day-to-day experiences of Black people living in the United States, may finally be seen by white people. Unfortunately, this spectrum has o...

Dask enables parallel and out-of-core computation. We couple blocked algorithms with dynamic and memory aware task scheduling to achieve a parallel and out-of-core NumPy clone. We show how this extends the effective scale of modern hardware to larger datasets and discuss how these ideas can be more broadly applied to other parallel collections.

Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution have been limited to naive or inefficient architectures. In this paper, we introduce spatio-temporal sub-pixel convolution networks that effectively exploit temporal redundancies and improve reconstruction accuracy while maintaining real-time speed. Specifically, we discuss the use of early fusion, slow fusion and 3D convolutions for the joint processing of multiple consecutive video frames. We also propose a novel joint motion compensation and video super-resolution algorithm that is orders of magnitude more efficient than competing methods, relying on a fast multi-resolution spatial transformer module that is end-to-end trainable. These contributions provide both higher accuracy and temporally more consistent videos, which we confirm qualitatively and quantitatively. Relative to single-frame models, spatio-temporal networks can either reduce the computational cost by 30% whilst maintaining the same quality or provide a 0.2dB gain for a similar computational cost. Results on publicly available datasets demonstrate that the proposed algorithms surpass current state-of-the-art performance in both accuracy and efficiency.

Callose deposition in Arabidopsis has emerged as a popular model system to quantify activity of plant immunity. However, there has been a noticeable rise in contradicting reports about the regulation of pathogen-induced callose. To address this controversy, we have examined the robustness of callose deposition under different growth conditions and in response to two different pathogen-associated molecular patterns, the flagellin epitope Flg22 and the polysaccharide chitosan. Based on a commonly used hydroponic culture system, we found that variations in growth conditions have a major impact on the plant's overall capacity to deposit callose. This environmental variability correlated with levels of hydrogen peroxide (H₂O₂) production. Depending on the growth conditions, pretreatment with abscissic acid stimulated or repressed callose deposition. Despite a similar effect of growth conditions on Flg22- and chitosan-induced callose, both responses showed differences in timing, tissue responsiveness, and colocalization with H₂O₂. Furthermore, mutant analysis revealed that Flg22- and chitosan-induced callose differ in the requirement for the NADPH oxidase RBOHD, the glucosinolate regulatory enzymes VTC1 and PEN2, and the callose synthase PMR4. Our study demonstrates that callose is a multifaceted defense response that is controlled by distinct signaling pathways, depending on the environmental conditions and the challenging pathogen-associated molecular pattern.

Molecular mechanics simulations offer a computational approach to study the behavior of biomolecules at atomic detail, but such simulations are limited in size and timescale by the available computing resources. State-of-the-art graphics processing units (GPUs) can perform over 500 billion arithmetic operations per second, a tremendous computational resource that can now be utilized for general purpose computing as a result of recent advances in GPU hardware and software architecture. In this article, an overview of recent advances in programmable GPUs is presented, with an emphasis on their application to molecular mechanics simulations and the programming techniques required to obtain optimal performance in these cases. We demonstrate the use of GPUs for the calculation of long-range electrostatics and nonbonded forces for molecular dynamics simulations, where GPU-based calculations are typically 10-100 times faster than heavily optimized CPU-based implementations. The application of GPU acceleration to biomolecular simulation is also demonstrated through the use of GPU-accelerated Coulomb-based ion placement and calculation of time-averaged potentials from molecular dynamics trajectories. A novel approximation to Coulomb potential calculation, the multilevel summation method, is introduced and compared with direct Coulomb summation. In light of the performance obtained for this set of calculations, future applications of graphics processors to molecular dynamics simulations are discussed.

The ATLS Subcommittee, American College of Surgeons’ Committee on Trauma, and the International ATLS working group Author Information

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

Abstract An updated version of the semi‐discretization method is presented for periodic systems with a single discrete time delay. The delayed term is approximated as a weighted sum of two neighbouring discrete delayed state values and the transition matrix over a single period is determined. Stability charts are constructed for the damped and delayed Mathieu equation for different time‐period/time‐delay ratios. The convergence of the method is investigated by examples. Stability charts are constructed for 1 and 2 degree of freedom milling models. The codes of the algorithm are also attached in the appendix. Copyright © 2004 John Wiley & Sons, Ltd.

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic.

PURPOSE: Harassment and discrimination include a wide range of behaviors that medical trainees perceive as being humiliating, hostile, or abusive. To understand the significance of such mistreatment and to explore potential preventive strategies, the authors conducted a systematic review and meta-analysis to examine the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. METHOD: In 2011, the authors identified relevant studies by searching MEDLINE and EMBASE, scanning reference lists of relevant studies, and contacting experts. They included studies that reported the prevalence, risk factors, and sources of harassment and discrimination among medical trainees. Two reviewers independently screened all articles and abstracted study and participant characteristics and study results. The authors assessed the methodological quality in individual studies using the Newcastle-Ottawa Scale. They also conducted a meta-analysis. RESULTS: The authors included 57 cross-sectional and 2 cohort studies in their review. The meta-analysis of 51 studies demonstrated that 59.4% of medical trainees had experienced at least one form of harassment or discrimination during their training (95% confidence interval [CI]: 52.0%-66.7%). Verbal harassment was the most commonly cited form of harassment (prevalence: 63.0%; 95% CI: 54.8%-71.2%). Consultants were the most commonly cited source of harassment and discrimination, followed by patients or patients' families (34.4% and 21.9%, respectively). CONCLUSIONS: This review demonstrates the surprisingly high prevalence of harassment and discrimination among medical trainees that has not declined over time. The authors recommend both drafting policies and promoting cultural change within academic institutions to prevent future abuse.

Despite its growing prominence in news coverage and public discourse, there is still considerable ambiguity regarding when and how fact-checking affects beliefs. Informed by theories of motivated reasoning and message design, a meta-analytic review was undertaken to examine the effectiveness of fact-checking in correcting political misinformation (k = 30,N = 20,963). Fact-checking has a significantly positive overall influence on political beliefs (d = 0.29), but the effects gradually weaken when using “truth scales,” refuting only parts of a claim, and fact-checking campaign-related statements. Likewise, the ability to correct political misinformation with fact-checking is substantially attenuated by participants’ preexisting beliefs, ideology, and knowledge. The study concludes with a discussion of the fact-checking literature in light of current gaps and future opportunities.

Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.

PURPOSE: Consensus group methods, such as the Delphi method and nominal group technique (NGT), are used to synthesize expert opinions when evidence is lacking. Despite their extensive use, these methods are inconsistently applied. Their use in medical education research has not been well studied. The authors set out to describe the use of consensus methods in medical education research and to assess the reporting quality of these methods and results. METHOD: Using scoping review methods, the authors searched the Medline, Embase, PsycInfo, PubMed, Scopus, and ERIC databases for 2009-2016. Full-text articles that focused on medical education and the keywords Delphi, RAND, NGT, or other consensus group methods were included. A standardized extraction form was used to collect article demographic data and features reflecting methodological rigor. RESULTS: Of the articles reviewed, 257 met the inclusion criteria. The Modified Delphi (105/257; 40.8%), Delphi (91/257; 35.4%), and NGT (23/257; 8.9%) methods were most often used. The most common study purpose was curriculum development or reform (68/257; 26.5%), assessment tool development (55/257; 21.4%), and defining competencies (43/257; 16.7%). The reporting quality varied, with 70.0% (180/257) of articles reporting a literature review, 27.2% (70/257) reporting what background information was provided to participants, 66.1% (170/257) describing the number of participants, 40.1% (103/257) reporting if private decisions were collected, 37.7% (97/257) reporting if formal feedback of group ratings was shared, and 43.2% (111/257) defining consensus a priori. CONCLUSIONS: Consensus methods are poorly standardized and inconsistently used in medical education research. Improved criteria for reporting are needed.