Charles B. Ward

Live attenuated influenza virus vaccines by computer-aided rational design

Despite existing vaccines and enormous efforts in biomedical research, influenza annually claims 250,000–500,000 lives worldwide, motivating the search for new, more effective vaccines that can be rapidly designed and easily produced. We applied the previously described synthetic attenuated virus engineering (SAVE) approach to influenza virus strain A/PR/8/34 to rationally design live attenuated influenza virus vaccine candidates through genome-scale changes in codon-pair bias. As attenuation is based on many hundreds of nucleotide changes across the viral genome, reversion of the attenuated variant to a virulent form is unlikely. Immunization of mice by a single intranasal exposure to codon pair–deoptimized virus conferred protection against subsequent challenge with wild-type (WT) influenza virus. The method can be applied rapidly to any emerging influenza virus in its entirety, an advantage that is especially relevant when dealing with seasonal epidemics and pandemic threats, such as H5N1- or 2009-H1N1 influenza.

Large-scale recoding of an arbovirus genome to rebalance its insect versus mammalian preference

Significance Arboviruses (arthropod-borne viruses), a large group of RNA viruses, replicate in insects that transmit them to mammals, their second host. Insects and mammals have evolved different protein encoding strategies (codon pair bias); hence, arboviruses must delicately balance their encodings between two phyla. Using dengue virus (DENV), the most important human arbovirus pathogen, as a model, we have, by computer design and chemical synthesis, undone this balance in codon pair bias in favor of insects. Recoded DENVs grow well in insect cells but are highly attenuated in mammalian cells and in suckling mice. This unique approach offers a previously unidentified possibility to rapidly develop new vaccine candidates against DENV and perhaps against many different human arboviruses. The protein synthesis machineries of two distinct phyla of the Animal kingdom, insects of Arthropoda and mammals of Chordata, have different preferences for how to best encode proteins. Nevertheless, arboviruses (arthropod-borne viruses) are capable of infecting both mammals and insects just like arboviruses that use insect vectors to infect plants. These organisms have evolved carefully balanced genomes that can efficiently use the translational machineries of different phyla, even if the phyla belong to different kingdoms. Using dengue virus as an example, we have undone the genome encoding balance and specifically shifted the encoding preference away from mammals. These mammalian-attenuated viruses grow to high titers in insect cells but low titers in mammalian cells, have dramatically increased LD50s in newborn mice, and induce high levels of protective antibodies. Recoded arboviruses with a bias toward phylum-specific expression could form the basis of a new generation of live attenuated vaccine candidates.

Only 15 Minutes? The Social Stratification of Fame in Printed Media

Contemporary scholarship has conceptualized modern fame as an open system in which people continually move in and out of celebrity status. This model stands in stark contrast to the traditional notion in the sociology of stratification that depicts stable hierarchies sustained through classic forces such as social structure and cumulative advantage. We investigate the mobility of fame using a unique data source containing daily records of references to person names in a large corpus of English-language media sources. These data reveal that only at the bottom of the public attention hierarchy do names exhibit fast turnover; at upper tiers, stable coverage persists around a fixed level and rank for decades. Fame exhibits strong continuity even in entertainment, on television, and on blogs, where it has been thought to be most ephemeral. We conclude that once a person’s name is decoupled from the initial event that lent it momentary attention, self-reinforcing processes, career structures, and commemorative practices perpetuate fame.

Name-ethnicity classification from open sources

The problem of ethnicity identification from names has a variety of important applications, including biomedical research, demographic studies, and marketing. Here we report on the development of an ethnicity classifier where all training data is extracted from public, non-confidential (and hence somewhat unreliable) sources. Our classifier uses hidden Markov models (HMMs) and decision trees to classify names into 13 cultural/ethnic groups with individual group accuracy comparable accuracy to earlier binary (e.g., Spanish/non-Spanish) classifiers. We have applied this classifier to over 20 million names from a large-scale news corpus, identifying interesting temporal and spatial trends on the representation of particular cultural/ethnic groups.

Identification of two functionally redundant RNA elements in the coding sequence of poliovirus using computer-generated design

Genomes of RNA viruses contain multiple functional RNA elements required for translation or RNA replication. We use unique approaches to identify functional RNA elements in the coding sequence of poliovirus (PV), a plus strand RNA virus. The general method is to recode large segments of the genome using synonymous codons, such that protein sequences, codon use, and codon pair bias are conserved but the nucleic acid sequence is changed. Such recoding does not affect the growth of PV unless it destroys the sequence/structure of a functional RNA element. Using genetic analyses and a method called “signal location search,” we detected two unique functionally redundant RNA elements (α and β), each about 75 nt long and separated by 150 nt, in the 3′-terminal coding sequence of RNA polymerase, 3Dpol. The presence of wild type (WT) α or β was sufficient for the optimal growth of PV, but the alteration of both segments in the same virus yielded very low titers and tiny plaques. The nucleotide sequences and predicted RNA structures of α and β have no apparent resemblance to each other. In α, we narrowed down the functional domain to a 48-nt-long, highly conserved segment. The primary determinant of function in β is a stable and highly conserved hairpin. Reporter constructs showed that the α- and β-segments are required for RNA replication. Recoding offers a unique and effective method to search for unknown functional RNA elements in coding sequences of RNA viruses, particularly if the signals are redundant in function.

Who's Bigger?: Where Historical Figures Really Rank

Is Hitler bigger than Napoleon? Washington bigger than Lincoln? Picasso bigger than Einstein? Quantitative analysts are rapidly finding homes in social and cultural domains, from finance to politics. What about history? In this fascinating book, Steve Skiena and Charles Ward bring quantitative analysis to bear on ranking and comparing historical reputations. They evaluate each person by aggregating the traces of millions of opinions, just as Google ranks webpages. The book includes a technical discussion for readers interested in the details of the methods, but no mathematical or computational background is necessary to understand the rankings or conclusions. Did you know: - Got a spare billion dollars, and want to be remembered forever? Your best investment is to get a university named after you. - Women remain significantly underrepresented in the historical record compared to men and have long required substantially greater achievement levels to get equally noted for posterity. - The long-term prominence of Elvis Presley rivals that of the most famous classical composers. Roll over Beethoven, and tell Tchaikovsky the news! Along the way, the authors present the rankings of more than one thousand of historys most significant people in science, politics, entertainment, and all areas of human endeavor. Anyone interested in history or biography can see where their favorite figures place in the grand scheme of things. While revisiting old historical friends and making new ones, you will come to understand the forces that shape historical recognition in a whole new light.

Access: news and blog analysis for the social sciences

The social sciences strive to understand the political, social, and cultural world around us, but have been impaired by limited access to the quantitative data sources enjoyed by the hard sciences. Careful analysis of Web document streams holds enormous potential to solve longstanding problems in a variety of social science disciplines through massive data analysis. This paper introduces the TextMap Access system, which provides ready access to a wealth of interesting statistics on millions of people, places, and things across a number of interesting web corpora. Powered by a flexible and scalable distributed statistics computation framework using Hadoop, continually updated corpora include newspapers, blogs, patent records, legal documents, and scientific abstracts; well over a terabyte of raw text and growing daily. The Lydia Textmap Access system, available through http://www.textmap.com/access, provides instant access for students and scholars through a convenient web user-interface. We describe the architecture of the TextMap Access system, and its impact on current research in political science, sociology, and business/marketing.

Computationally designed adeno-associated virus (AAV) Rep 78 is efficiently maintained within an adenovirus vector

Adeno-associated virus (AAV) is a single-stranded parvovirus retaining the unique capacity for site-specific integration into a transcriptionally silent region of the human genome, a characteristic requiring the functional properties of the Rep 78/68 polypeptide in conjunction with AAV terminal repeat integrating elements. Previous strategies designed to assemble these genetic elements into adenoviral (Ad) backbones have been limited by the general intolerability of AAV Rep sequences, prompting us to computationally reengineer the Rep gene by using synonymous codon pair recoding. Rep mutants generated by using de novo genome synthesis maintained the polypeptide sequence and endonuclease properties of Rep 78, while dramatically enhancing Ad replication and viral titer yields, characteristics indistinguishable from adenovirus lacking coexpressed Rep. Parallel approaches using domain swaps encompassing WT and recoded genomic segments, coupled with iterative computational algorithms, collectively established that 3′ cis-acting Rep genetic elements (and not the Rep 78 polypeptide) retain dominant-acting sequences inhibiting Ad replication. These data provide insights into the molecular relationships of AAV Rep and Ad replication, while expanding the applicability of synonymous codon pair reengineering as a strategy to effect phenotypic endpoints.

Complexity results on labeled shortest path problems from wireless routing metrics

Metrics to assess the cost of paths through networks are critical to ensuring the efficiency of network routing. This is particularly true in multi-radio multi-hop wireless networks. Effective metrics for these networks must measure the cost of a wireless path based not only on traditional measures such as throughput, but also on the distribution of wireless channels used. In this paper, we argue that routing metrics over such networks may be viewed as a class of existing shortest path problems, the formal language constrained path problems. On this basis, we describe labeled path problems corresponding to two multi-radio wireless routing metrics: Weighted Cumulative Expected Transmission Time (WCETT), developed by Draves et al., and Metric for Interference and Channel-switch (MIC), developed by Yang et al. For the first, we give a concise proof that calculating shortest WCETT paths is strongly NP-Complete for a variety of graph classes. We also show that the existing heuristic given by Draves et al. is an approximator. For the second, we show that calculating loop-free (simple) shortest MIC paths is NP-Complete, and additionally show that the optimization version of the problem is NPO PB-Complete. This result implies that shortest simple MIC paths are only poorly approximable in the worst case. Furthermore, we demonstrate how the polynomial-time algorithm for shortest MIC paths is derivable from an existing language constrained shortest path algorithm. We use this as a basis to exhibit the general utility of viewing multi-channel wireless routing metrics as labeled graph problems, and discuss how a class of related polynomial-time computable metrics are derivable from this algorithm.

Empath: A framework for evaluating entity-level sentiment analysis

Sentiment analysis is the fundamental component in text-driven monitoring or forecasting systems, where the general sentiment towards real-world entities (e.g., people, products, organizations) are analyzed based on the sentiment signals embedded in a myriad of web text available today. Building such systems involves several practically important problems, from data cleansing (e.g., boilerplate removal, web-spam detection), and sentiment analysis at individual mention-level (e.g., phrase, sentence-, document-level) to the aggregation of sentiment for each entity-level (e.g., person, company) analysis. Most previous research in sentiment analysis however, has focused only on individual mention-level analysis, and there has been relatively less work that copes with other practically important problems for enabling a large-scale sentiment monitoring system. In this paper, we propose Empath, a new framework for evaluating entity-level sentiment analysis. Empath leverages objective measurements of entities in various domains such as people, companies, countries, movies, and sports, to facilitate entity-level sentiment analysis and tracking. We demonstrate the utility of Empath for the evaluation of a large-scale sentiment system by applying it to various lexicons using Lydia, our own large scale text-analytics tool, over a corpus consisting of more than a terabyte of newspaper data. We expect that Empath will encourage research that encompasses end-to-end pipelines to enable a large-scale text-driven monitoring and forecasting systems.