Mass Media and the Contagion of Fear: The Case of Ebola in America

Background

In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.

Methodology

We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.

Conclusions

We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.     

Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification

High dimensional data increase the dimension of space and consequently the computational complexity and result in lower generalization. From these types of classification problems microarray data classification can be mentioned. Microarrays contain genetic and biological data which can be used to diagnose diseases including various types of cancers and tumors. Having intractable dimensions, dimension reduction process is necessary on these data. The main goal of this paper is to provide a method for dimension reduction and classification of genetic data sets. The proposed approach includes different stages. In the first stage, several feature ranking methods are fused for enhancing the robustness and stability of feature selection process. Wrapper method is combined with the proposed hybrid ranking method to embed the interaction between genes. Afterwards, the classification process is applied using support vector machine. Before feeding the data to the SVM classifier the problem of imbalance classes of data in the training phase should be overcame. The experimental results of the proposed approach on five microarray databases show that the robustness metric of the feature selection process is in the interval of [0.70, 0.88]. Also the classification accuracy is in the range of [91%, 96%].     

Internalization of Flax Rust Avirulence Proteins into Flax and Tobacco Cells Can Occur in the Absence of the Pathogen

Translocation of pathogen effector proteins into the host cell cytoplasm is a key determinant for the pathogenicity of many bacterial and oomycete plant pathogens. A number of secreted fungal avirulence (Avr) proteins are also inferred to be delivered into host cells, based on their intracellular recognition by host resistance proteins, including those of flax rust (Melampsora lini). Here, we show by immunolocalization that the flax rust AvrM protein is secreted from haustoria during infection and accumulates in the haustorial wall. Five days after inoculation, the AvrM protein was also detected within the cytoplasm of a proportion of plant cells containing haustoria, confirming its delivery into host cells during infection. Transient expression of secreted AvrL567 and AvrM proteins fused to cerulean fluorescent protein in tobacco (Nicotiana tabacum) and flax cells resulted in intracellular accumulation of the fusion proteins. The rust Avr protein signal peptides were functional in plants and efficiently directed fused cerulean into the secretory pathway. Thus, these secreted effectors are internalized into the plant cell cytosol in the absence of the pathogen, suggesting that they do not require a pathogen-encoded transport mechanism. Uptake of these proteins is dependent on signals in their N-terminal regions, but the primary sequence features of these uptake regions are not conserved between different rust effectors.     

Optimizing a Proteomics Platform for Urine Biomarker Discovery

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.With ongoing advances in mass spectrometry (MS) and proteomics technology, proteomics analysis is progressively occupying a central position in biomarker discovery platforms. Biofluids such as urine and blood are the preferred media for proteomics analysis because of their ease of collection and extensive history of use in clinical laboratory practice. Urine, in particular, is an information-rich fluid that can be collected non-invasively and in large quantities. Many urine proteins are produced or shed in the kidney and urogenital tract (1), making urine a promising proximal source of biomarkers for diseases affecting these structures.However, proteomics-based biomarker discovery in urine faces multiple challenges. Urine proteomics is complicated by low urine protein concentration, variations in pH, and high concentrations of salts and urea or other urine components that interfere with sample processing. The urine proteome can also change with individual variables such as hydration, diurnal change, diet, and physical activity as well as variation in sample collection, processing, and storage. In addition, urine proteomics shares the usual challenges of biomarker discovery in other biofluids such as throughput, cost, and the need for a reproducible and quantitative work flow.Isotopic or isobaric labeling methods to reduce variation, increase throughput, and enable quantitative analysis have been developed to address some of these challenges. One such method, isobaric tags for relative and absolute quantitation (iTRAQ)¹ (2), combines relative and absolute peptide quantification with multiplexing ability to enable an increased throughput as well as simultaneous comparison of up to eight samples within one experimental run. Variations induced by urine sample processing have been systematically evaluated for proteomics analyses using two-dimensional gel electrophoresis (3–6), differential gel electrophoresis (7), and liquid chromatography-coupled mass spectrometry (LC-MS) (5, 8, 9). However, no systematic analyses of urine sample collection and processing have been reported for iTRAQ.Before utilizing iTRAQ-based quantitative proteomics for urine biomarker discovery, we evaluated the impact of variation in several processing steps (addition of protease inhibitors, the starting protein quantities, quantity of the iTRAQ label, protein extraction methods, and depletion of abundant proteins) on iTRAQ protein identification and quantitation. Applying this optimized biomarker discovery protocol to small quantities of long frozen urine samples from the Pima longitudinal study of diabetic nephropathy, we observed patterns suggestive of segregation of cases and controls by iTRAQ spectra. We also observed trends toward differential expression in several proteins that had been identified as putative biomarkers in previous studies. However, given the small sample size, none of these proteins retained statistical significance after multiple testing correction.     

Treatment of Lime Witches’ Broom Phytoplasma-Infected Mexican Lime with a Resistance Inducer and Study of its Effect on Systemic Resistance

Journal of Plant Growth Regulation - Destruction caused by plant diseases is terrible. Mexican lime witches' broom (MLWB) is a phytoplasma disease, which has caused considerable damage...     

New frontiers in translational research: Touchscreens,open science,and the mouse translational research accelerator platform

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).     

Preparation and Characterization of Zein/Sodium Caseinate/Xanthan Gum Complex for Encapsulation of Piperine and its In Vitro Release Study

To encapsulate piperine (Pip), as a poor water-soluble bioactive compound, zein-sodium caseinate-xanthan gum (Z-SG-XG) nanocomplex was prepared as a colloidal delivery system. The effect of different parameters involved in complexation process, including concentration of proteins, polysaccharide, and Pip on the encapsulation efficiency of Pip, particle size and stability of the nanocomplexes was investigated. Powders obtained by freeze-drying of the colloidal solution had relatively uniform particles compared to those obtained from conventional drying system and showed well redispersibility in water. At the optimal condition, a stable and homogeneous nanocomplex with a mean particle size of 145.9 ± 2.7 nm, PDI of 0.27 ± 0.01, and ζ-potential of −39.7 ± 1.3 mV was obtained. The antioxidant activity of Pip was significantly improved by encapsulation into the Z-SC-XG nanocomplex. Also, the in vitro release of Pip from the synthesized nanocomplexes in phosphate-buffer saline (PBS) solution and simulated gastrointestinal fluids (SGIF) was investigated and the release kinetic was studied as well. The Pip/Z-SG-XG nanocomplex showed a slower release in SGIF compared to the free Pip and nanoparticles without XG and SC, while its antioxidant activity was remarkable. Results suggested a possible utilization of Z-SC-XG nanocomplex for improving the water solubility, bioavailability and storage stability of Pip.

     

Conquering the cytokine storm in COVID-19-induced ARDS using placenta-derived decidua stromal cells

Acute respiratory distress syndrome (ARDS) is the most common cause of death in COVID-19 patients. The cytokine storm is the main driver of the severity and magnitude of ARDS. Placenta-derived decidua stromal cells (DSCs) have a stronger immunosuppressive effect than other sources of mesenchymal stromal cells. Safety and efficacy study included 10 patients with a median age of 50 (range 14–68) years with COVID-19-induced ARDS. DSCs were administered 1–2 times at a dose of 1 × 10⁶/kg. End points were safety and efficacy by survival, oxygenation and effects on levels of cytokines. Oxygenation levels increased from a median of 80.5% (range 69–88) to 95% (range 78–99) (p = 0.012), and pulmonary infiltrates disappeared in all patients. Levels of IL-6 decreased from a median of 69.3 (range 35.0–253.4) to 11 (range 4.0–38.3) pg/ml (p = 0.018), and CRP decreased from 69 (range 5–169) to 6 (range 2–31) mg/ml (p = 0.028). Two patients died, one of a myocardial infarction and the other of multiple organ failure, diagnosed before the DSC therapy. The other patients recovered and left the intensive care unit (ICU) within a median of 6 (range 3–12) days. DSC therapy is safe and capable of improving oxygenation, decreasing inflammatory cytokine level and clearing pulmonary infiltrates in patients with COVID-19.     

AMP-activated protein kinase is a key regulator of obesity-associated factors

An imbalance between caloric intake and energy expenditure leads to obesity. Obesity is an important risk factor for the development of several metabolic diseases including insulin resistance, metabolic syndrome, type 2 diabetes mellitus, and cardiovascular disease. So, controlling obesity could be effective in the improvement of obesity-related diseases. Various factors are involved in obesity, such as AMP-activated protein kinases (AMPK), silent information regulators, inflammatory mediators, oxidative stress parameters, gastrointestinal hormones, adipokines, angiopoietin-like proteins, and microRNAs. These factors play an important role in obesity by controlling fat metabolism, energy homeostasis, food intake, and insulin sensitivity. AMPK is a heterotrimeric serine/threonine protein kinase known as a fuel-sensing enzyme. The central role of AMPK in obesity makes it an attractive molecule to target obesity and related metabolic diseases. In this review, the critical role of AMPK in obesity and the interplay between AMPK and obesity-associated factors were elaborated.