A Comparison of the Epidemiology and Clinical Presentation of Seasonal Influenza A and 2009 Pandemic Influenza A (H1N1) in Guatemala

A new influenza A (H1N1) virus was first found in April 2009 and proceeded to cause a global pandemic. We compare the epidemiology and clinical presentation of seasonal influenza A (H1N1 and H3N2) and 2009 pandemic influenza A (H1N1) (pH1N1) using a prospective surveillance system for acute respiratory disease in Guatemala.Patients admitted to two public hospitals in Guatemala in 2008-2009 who met a pneumonia case definition, and ambulatory patients with influenza-like illness (ILI) at 10 ambulatory clinics were invited to participate. Data were collected through patient interview, chart abstraction and standardized physical and radiological exams. Nasopharyngeal swabs were taken from all enrolled patients for laboratory diagnosis of influenza A virus infection with real-time reverse transcription polymerase chain reaction. We identified 1,744 eligible, hospitalized pneumonia patients, enrolled 1,666 (96%) and tested samples from 1,601 (96%); 138 (9%) had influenza A virus infection. Surveillance for ILI found 899 eligible patients, enrolled 801 (89%) and tested samples from 793 (99%); influenza A virus infection was identified in 246 (31%). The age distribution of hospitalized pneumonia patients was similar between seasonal H1N1 and pH1N1 (P = 0.21); the proportion of pneumonia patients <1 year old with seasonal H1N1 (39%) and pH1N1 (37%) were similar (P = 0.42). The clinical presentation of pH1N1 and seasonal influenza A was similar for both hospitalized pneumonia and ILI patients. Although signs of severity (admission to an intensive care unit, mechanical ventilation and death) were higher among cases of pH1N1 than seasonal H1N1, none of the differences was statistically significant.Small sample sizes may limit the power of this study to find significant differences between seasonal influenza A and pH1N1. In Guatemala, influenza, whether seasonal or pH1N1, appears to cause severe disease mainly in infants; targeted vaccination of children should be considered

Regulation of Cancer Aggressive Features in Melanoma Cells by MicroRNAs

MicroRNAs (miRNAs) are small non-coding RNAs with regulatory roles, which are involved in a broad spectrum of physiological and pathological processes, including cancer. A common strategy for identification of miRNAs involved in cell transformation is to compare malignant cells to normal cells. Here we focus on identification of miRNAs that regulate the aggressive phenotype of melanoma cells. To avoid differences due to genetic background, a comparative high-throughput miRNA profiling was performed on two isogenic human melanoma cell lines that display major differences in their net proliferation, invasion and tube formation activities. This screening revealed two major cohorts of differentially expressed miRNAs. We speculated that miRNAs up-regulated in the more-aggressive cell line contribute oncogenic features, while the down-regulated miRNAs are tumor suppressive. This assumption was further tested experimentally on five candidate tumor suppressive miRNAs (miR-31, -34a, -184, -185 and -204) and on one candidate oncogenic miRNA (miR-17-5p), all of which have never been reported before in cutaneous melanoma. Remarkably, all candidate Suppressive-miRNAs inhibited net proliferation, invasion or tube formation, while miR-17-5p enhanced cell proliferation. miR-34a and miR-185 were further shown to inhibit the growth of melanoma xenografts when implanted in SCID-NOD mice. Finally, all six candidate miRNAs were detected in 15 different metastatic melanoma specimens, attesting for the physiological relevance of our findings. Collectively, these findings may prove instrumental for understanding mechanisms of disease and for development of novel therapeutic and staging technologies for melanoma

Integration of coordination mechanisms in the BITE multirobot architecture

Abstract — Recent years are seeing a renewed interest in general multi-robot architectures, capable of automating coordination. However, few architectures explore integration of multiple coordination mechanisms. Thus the question of how to best integrate coordination mechanisms is left open. This paper focuses on the micro-kernel integration approach used in BITE (Bar Ilan Teamwork Engine), a multi-robot behaviorbased architecture. This approach allows the developer to plug in coordination mechanisms (teamwork behaviors) to be used depending on the context of execution. BITE imposes constraints on the specification of taskwork behaviors, which allow BITE’s control algorithm to automatically determine sequencing and task-allocation points during task execution. At such points, teamwork behaviors (known as interaction behaviors) are triggered to automate the coordination processes. We argue that BITE’s approach is preferable to the methodology of existing architectures, and provide analysis of experiments in using BITE with Sony AIBO robots, to support our argument. I

Flexible Teamwork in Behavior-Based Robots

A key challenge in deploying teams of robots in real-world applications is to automate the control of teamwork, such that the designer can focus on the taskwork. Existing teamwork architectures seeking to address this challenge are monolithic, in that they commit to interaction protocols at the architectural level, and do not allow the designer to mix and match protocols for a given task. We present BITE, a behaviorbased teamwork architecture that automates collaboration in physical robots, in a distributed fashion. BITE separates task behaviors that control a robot&apos;s interaction with its task, from interaction behaviors that control a robot&apos;s interaction with its teammates. This distinction provides for flexibility and modularity in terms of the interactions used by teammates to collaborate effectively. It also allows BITE to synthesize and significantly extend existing teamwork architectures. BITE also incorporates key lessons learned in applying multi-agent teamwork architectures in physical robot teams. We present empirical results from experiments with teams of Sony AIBO robots executing BITE, and discuss the lessons learned

In Proceedings of the Eighth Conference on Intelligent Autonomous Systems, March, 2004

Teams of robots are increasingly deployed in real world applications. One of the key challenges in building such teams is to automate the control of teamwork, such that the designer can concentrate her efforts on the taskwork to be done. This paper presents steps towards a novel framework addressing this challenge in teams of behavior-based agents. The framework provides a rich representation that facilitates management of teamwork knowledge, and separates behaviors that govern a robot&apos;s interaction with its task from behaviors that govern a robot&apos;s interaction with its teammates

Number of Patients with Respiratory Syncytial Virus by Month, Guatemala, 2008-2009.

<p>Panel A: Hospitalized pneumonia patients. Panel B: Influenza-like illness patients.</p