New Concepts in Pacemaker Syndrome

After implantation of a permanent pacemaker, patients may experience severe symptoms of dyspnea, palpitations, malaise, and syncope resulting from pacemaker syndrome. Although pacemaker syndrome is most often ascribed to the loss of atrioventricular (A-V) synchrony, more recent data may also implicate left ventricular dysynchrony caused by right ventricular pacing. Previous studies have not shown reductions in mortality or stroke with rate-modulated dual-chamber (DDDR) pacing as compared to ventricular-based (VVI) pacing. The benefits in A-V sequential pacing with the DDDR mode are likely mitigated by the interventricular (V-V) dysynchrony imposed by the high percentage of ventricular pacing commonly seen in the DDDR mode. Programming DDDR pacemakers to encourage intrinsic A-V conduction and reduce right ventricular pacing will likely decrease heart failure and pacemaker syndrome. Studies are currently ongoing to address these questions

Iterative image reconstruction in transcranial photoacoustic tomography based on the elastic wave equation

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT of the brain is to compensate for aberrations and attenuation in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method can be employed that can model the heterogeneous elastic properties of the medium. In this study, an optimization-based image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward-adjoint operator pair based on a finite-difference time-domain discretization of the elastic wave equation is utilized to compute penalized least squares estimates of the initial pressure distribution. Computer-simulation and experimental studies are conducted to investigate the robustness of the reconstruction method to model mismatch and its ability to effectively resolve cortical and superficial brain structures

Evolution of corporate reputation during an evolving controversy

Purpose: The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments. Design/methodology/approach: Using a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction. Findings: The findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users. Research limitations/implications: Even with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis. Practical implications: First, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages. Originality/value: This study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis

Plant Species Rather Than Climate Greatly Alters the Temporal Pattern of Litter Chemical Composition During Long-Term Decomposition

A feedback between decomposition and litter chemical composition occurs with decomposition altering composition that in turn influences the decomposition rate. Elucidating the temporal pattern of chemical composition is vital to understand this feedback, but the effects of plant species and climate on chemical changes remain poorly understood, especially over multiple years. In a 10-year decomposition experiment with litter of four species (Acer saccharum, Drypetes glauca, Pinus resinosa, and Thuja plicata) from four sites that range from the arctic to tropics, we determined the abundance of 11 litter chemical constituents that were grouped into waxes, carbohydrates, lignin/tannins, and proteins/peptides using advanced 13C solid-state NMR techniques. Decomposition generally led to an enrichment of waxes and a depletion of carbohydrates, whereas the changes of other chemical constituents were inconsistent. Inconsistent convergence in chemical compositions during decomposition was observed among different litter species across a range of site conditions, whereas one litter species converged under different climate conditions. Our data clearly demonstrate that plant species rather than climate greatly alters the temporal pattern of litter chemical composition, suggesting the decomposition-chemistry feedback varies among different plant species