Proteus sp. – an opportunistic bacterial pathogen – classification, swarming growth, clinical significance and virulence factors

The genus Proteus belongs to the Enterobacteriaceae family, where it is placed in the tribe Proteeae, together with the genera Morganella and Providencia. Currently, the genus Proteus consists of five species: P. mirabilis, P. vulgaris, P. penneri, P. hauseri and P. myxofaciens, as well as three unnamed Proteus genomospecies. The most defining characteristic of Proteus bacteria is a swarming phenomenon, a multicellular differentiation process of short rods to elongated swarmer cells. It allows population of bacteria to migrate on solid surface. Proteus bacteria inhabit the environment and are also present in the intestines of humans and animals. These microorganisms under favorable conditions cause a number of infections including urinary tract infections (UTIs), wound infections, meningitis in neonates or infants and rheumatoid arthritis. Therefore, Proteus is known as a bacterial opportunistic pathogen. It causes complicated UTIs with a higher frequency, compared to other uropathogens. Proteus infections are accompanied by a formation of urinary stones, containing struvite and carbonate apatite. The virulence of Proteus rods has been related to several factors including fimbriae, flagella, enzymes (urease - hydrolyzing urea to CO2 and NH3, proteases degrading antibodies, tissue matrix proteins and proteins of the complement system), iron acqusition systems and toxins: hemolysins, Proteus toxin agglutinin (Pta), as well as an endotoxin - lipopolysaccharide (LPS). Proteus rods form biofilm, particularly on the surface of urinary catheters, which can lead to serious consequences for patients. In this review we present factors involved in the regulation of swarming phenomenon, discuss the role of particular pathogenic features of Proteus spp., and characterize biofilm formation by these bacteria

Patent Landscape of Helminth Vaccines and Related Technologies

Executive Summary This report focuses on patent landscape analysis of technologies related to vaccines targeting parasitic worms, also known as helminths. These technologies include methods of formulating vaccines, methods of producing of subunits, the composition of complete vaccines, and other technologies that have the potential to aid in a global response to this pathogen. The purpose of this patent landscape study was to search, identify, and categorize patent documents that are relevant to the development of vaccines that can efficiently promote the development of protective immunity against helminths. The search strategy used keywords which the team felt would be general enough to capture (or “recall”) the majority of patent documents which were directed toward vaccines against helminths. After extensive searching of patent literature databases, approximately 2847 publications were identified and collapsed to about 446 INPADOC families. Relevant patent families, almost half of the total relevant families (210 being total number of relevant families), were then identified and sorted into the categories of trematodes, cestodes, nematodes or nonspecific helminth. The 210 patent families that were divided into these four major categories were then further divided into sub categories relating to common fields of technology (e.g. DNA vaccine, vaccine formulations, methods to produce subunits) This sorting process increased the precision of the result set. The four major categories (cestodes, nematodes, trematodes, and non specific applications) as well as the overall data set of the 210 relevant family members were subjected to a range of analytics in order to extract as much information as possible from the dataset. First, patent landscape maps were generated to assess the accuracy of the sorting procedure and to reveal the relationships between the various technologies that are involved in creating an effective vaccine. Then, filings trends are analyzed for the overall dataset of the 210 relevant families as well as by the categories of trematodes, cestodes, and nematodes. The country of origin each member of the 210 relevant families was determined, and the range of distribution to other jurisdictions was assessed. Filings were also analyzed by year, by assignee. Finally, the various patent classification systems were mapped to find which particular classes tend to hold helminth vaccine-related technologies. Besides the keywords developed during the searches and the landscape map generation, the classifications represent an alternate way for further researchers to identify emerging helminth vaccine technologies. The analysis included creation of a map of keywords describing the relationship of the various technologies involved in the development of helminth vaccines. The map has regions corresponding to plasmids and other gene based technologies used in DNA vaccines for Japonicum Schistosoma. Important technologies listed on the map include the use of reverse genetics to create reassorted viruses targeted for the use in veterinary applications. Additionally, the map suggests that numerous subunits exist for use in vaccines targeting cestodes, trematodes, and nematodes. Another major finding was that the number of patent documents related to helminths being published has been steadily increasing in the last decade, as shown in the figure below. Until the early-1990s, there were only a few helminth vaccine related patent documents being published each year. The number of publications increased noticeably when TRIPS took effect, resulting in publication of patent applications. However, since 2006 the number of vaccine publications has exploded. In the years 2011 and 2012, about 23 references disclosing parasitic worm vaccine technologies were published each year. Thus, interest in developing new and more efficacious helminth vaccines has been growing in recent years. The origin of the vaccine-related inventions was also analyzed. The team determined the country in which the priority application was filed, which was taken as an indication of the country where the invention was made or where the inventors intended to practice the invention. By far, most of the relevant families originated with patent applications filed in the United States and China. Other prominent priority countries were the United Kingdom, Japan, Brazil, Australia and France. Countries with the most filings were also analyzed. Countries that were heavily targeted for patent filings included the United States, Australia, Canada, and New Zealand. Top assignees for these families were mostly large pharmaceutical companies, with the majority of patent families coming from Heska, followed by Merck & Co., Institute Pasteur, AusBiotech Biotechnology, and Biological Sciences Research Council. Lastly, the jurisdictions were inventors have sought protection for their vaccine technologies were determined, and the number of patent families filing in a given country is plotted on the world map shown (Fig. 25). The United States, Canada, Australia, Japan, New Zealand and France have the highest level of filings, followed by Germany, Brazil, India, United Kingdom and Spain. However, although there are a significant number of filings in Brazil, the remainder of Central and South America has only sparse filings. Of concern, with the exception of South Africa, few other African nations have a significant number of filings. In summary, the goal of this report is to provide a knowledge resource for making informed policy decisions and for creating strategic plans concerning the assembly of vaccines targeting highly prevalent helminth infections. The ITTI team has defined the current state of the art of technologies involved in the manufacture of helminth vaccines, and the important assignees, inventors, and countries have been identified. This document should aid in evaluating the current state of vaccines technologies targeting helminths and the potential outgrows of these technological fields. Furthermore, as this report illustrates, the steady increase in helminth patenting, expanded diversity of assignees and greater global filings, indicates that intellectual property protection does not inhibit the development of crucial innovations for this class of neglected diseases, but, on the contrary, appears to be a driver of accelerated research and development

Situation recognition using soft computing techniques

Includes bibliographical references.The last decades have witnessed the emergence of a large number of devices pervasively launched into our daily lives as systems producing and collecting data from a variety of information sources to provide different services to different users via a variety of applications. These include infrastructure management, business process monitoring, crisis management and many other system-monitoring activities. Being processed in real-time, these information production/collection activities raise an interest for live performance monitoring, analysis and reporting, and call for data-mining methods in the recognition, prediction, reasoning and controlling of the performance of these systems by controlling changes in the system and/or deviations from normal operation. In recent years, soft computing methods and algorithms have been applied to data mining to identify patterns and provide new insight into data. This thesis revisits the issue of situation recognition for systems producing massive datasets by assessing the relevance of using soft computing techniques for finding hidden pattern in these systems

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial