Quantitative patterns in drone wars

Attacks by drones (i.e., unmanned combat air vehicles) continue to generate heated political and ethical debates. Here we examine the quantitative nature of drone attacks, focusing on how their intensity and frequency compare with that of other forms of human conflict. Instead of the power-law distribution found recently for insurgent and terrorist attacks, the severity of attacks is more akin to lognormal and exponential distributions, suggesting that the dynamics underlying drone attacks lie beyond these other forms of human conflict. We find that the pattern in the timing of attacks is consistent with one side having almost complete control, an important if expected result. We show that these novel features can be reproduced and understood using a generative mathematical model in which resource allocation to the dominant side is regulated through a feedback loop.Comment: 5 pages, 3 figure

Social media usage patterns during natural hazards

Natural hazards are becoming increasingly expensive as climate change and development are exposing communities to greater risks. Preparation and recovery are critical for climate change resilience, and social media are being used more and more to communicate before, during, and after disasters. While there is a growing body of research aimed at understanding how people use social media surrounding disaster events, most existing work has focused on a single disaster case study. In the present study, we analyze five of the costliest disasters in the last decade in the United States (Hurricanes Irene and Sandy, two sets of tornado outbreaks, and flooding in Louisiana) through the lens of Twitter. In particular, we explore the frequency of both generic and specific food-security related terms, and quantify the relationship between network size and Twitter activity during disasters. We find differences in tweet volume for keywords depending on disaster type, with people using Twitter more frequently in preparation for Hurricanes, and for real-time or recovery information for tornado and flooding events. Further, we find that people share a host of general disaster and specific preparation and recovery terms during these events. Finally, we find that among all account types, individuals with “average” sized networks are most likely to share information during these disasters, and in most cases, do so more frequently than normal. This suggests that around disasters, an ideal form of social contagion is being engaged in which average people rather than outsized influentials are key to communication. These results provide important context for the type of disaster information and target audiences that may be most useful for disaster communication during varying extreme events

Comparative genomics of Australian isolates of the wheat stem rust pathogen Puccinia graminis f. sp. tritici reveals extensive polymorphism in candidate effector genes

The wheat stem rust fungus Puccinia graminis f. sp. tritici (Pgt) is one of the most destructive pathogens of wheat. In this study, a draft genome was built for a founder Australian Pgt isolate of pathotype (pt.) 21-0 (collected in 1954) by next generation DNA sequencing. A combination of reference-based assembly using the genome of the previously sequenced American Pgt isolate CDL 75-36-700-3 (p7a) and de novo assembly were performed resulting in a 92 Mbp reference genome for Pgt isolate 21-0. Approximately 13 Mbp of de novo assembled sequence in this genome is not present in the p7a reference assembly. This novel sequence is not specific to 21-0 as it is also present in three other Pgt rust isolates of independent origin. The new reference genome was subsequently used to build a pan-genome based on five Australian Pgt isolates. Transcriptomes from germinated urediniospores and haustoria were separately assembled for pt. 21-0 and comparison of gene expression profiles showed differential expression in ∼10% of the genes each in germinated spores and haustoria. A total of 1,924 secreted proteins were predicted from the 21-0 transcriptome, of which 520 were classified as haustorial secreted proteins (HSPs). Comparison of 21-0 with two presumed clonal field derivatives of this lineage (collected in 1982 and 1984) that had evolved virulence on four additional resistance genes (Sr5, Sr11, Sr27, SrSatu) identified mutations in 25 HSP effector candidates. Some of these mutations could explain their novel virulence phenotypes.Authors wish to thank the Two Blades Foundation for financial support. Part of this work was supported through access to facilities managed by Bioplatforms Australia and funded by the Australian Government National Collaborative Research Infrastructure Strategy and Education Investment Fund Super Science Initiative

Association of backfat thickness with postheparin lipoprotein lipase activity and very low density lipoprotein-subfractions in growing pigs

Sixteen pigs from 2 distinct genetic lines (LGAH and VFIL) obtained after eight generations of divergent selection for high (H) and low (L) lean tissue growth rate with ad-libitum feeding (LGA) and voluntary feed intake (VFI), respectively, were used in this study. The objectives of this investigation were to establish appropriate working conditions for the postheparin plasma lipoprotein lipase (LPL) assay and to study relationships between fat deposition and plasma lipids, very low density lipoprotein (VLDL) lipids, VLDL-subfractions and postheparin plasma LPL activity in growing pigs. Four preliminary experiments were performed to determine the appropriate working conditions for the postheparin plasma LPL assays. Postheparin plasma preincubated with SDS (20-50 mM) at 26°C for 45 minutes inhibited hepatic lipase activity. A total of 2 μl VLDL/assay produced maximum stimulation of LPL activity. Postheparin plasma protein and increasing incubation time contributed an optimum response. LGAH pigs had a significantly higher proportion subfraction 2 than VFIL pigs. No differences were observed in postheparin plasma LPL activity and backfat thickness for two lines of pigs. There were positive correlations between backfat thickness and proportion of subfractions 2 and postheparin plasma LPL activity but the results were not statistically significant. Backfat thickness was not statistically correlated with proportion of subfraction 2 and postheparin plasma LPL activity in a multiple regression analysis. It is believed that the apolipoprotein E, which is present in higher quantities in VLDL-subfraction 2 plays an important role for clearing VLDL triacylglycerol into adipose tissue. LPL activity of pigs can be measured by using postheparin plasma technique. If the relationships of backfat thickness and VLDL-subfraction 2 and postheparin plasma LPL activity can be established, it suggests that these parameters could be used as indicators in selection programmes. Further experiments need to be conducted by using larger sample size and different breed of pigs with greater differences in backfat thicknesses to confirm these trends

Simon\u27s fundamental rich-get-richer model entails a dominant first-mover advantage

Herbert Simon\u27s classic rich-get-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon\u27s model does not produce a simple power-law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group\u27s size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon\u27s analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. We present an example of citation counts for a specific field that demonstrates a first-mover advantage consistent with our revised view of the rich-get-richer mechanism. Our findings call for a reexamination of preceding work invoking Simon\u27s model and provide an expanded understanding going forward

Changing the Game: Using Integrative Genomics to Probe Virulence Mechanisms of the Stem Rust Pathogen Puccinia graminis f. sp. tritici

The recent resurgence of wheat stem rust caused by new virulent races of Puccinia graminis f. sp. tritici (Pgt) poses a threat to food security. These concerns have catalyzed an extensive global effort toward controlling this disease. Substantial research and breeding programs target the identification and introduction of new stem rust resistance (Sr) genes in cultivars for genetic protection against the disease. Such resistance genes typically encode immune receptor proteins that recognize specific components of the pathogen, known as avirulence (Avr) proteins. A significant drawback to deploying cultivars with single Sr genes is that they are often overcome by evolution of the pathogen to escape recognition through alterations in Avr genes. Thus, a key element in achieving durable rust control is the deployment of multiple effective Sr genes in combination, either through conventional breeding or transgenic approaches, to minimize the risk of resistance breakdown. In this situation, evolution of pathogen virulence would require changes in multiple Avr genes in order to bypass recognition. However, choosing the optimal Sr gene combinations to deploy is a challenge that requires detailed knowledge of the pathogen Avr genes with which they interact and the virulence phenotypes of Pgt existing in nature. Identifying specific Avr genes from Pgt will provide screening tools to enhance pathogen virulence monitoring, assess heterozygosity and propensity for mutation in pathogen populations, and confirm individual Sr gene functions in crop varieties carrying multiple effective resistance genes. Toward this goal, much progress has been made in assembling a high quality reference genome sequence for Pgt, as well as a Pan-genome encompassing variation between multiple field isolates with diverse virulence spectra. In turn this has allowed prediction of Pgt effector gene candidates based on known features of Avr genes in other plant pathogens, including the related flax rust fungus. Upregulation of gene expression in haustoria and evidence for diversifying selection are two useful parameters to identify candidate Avr genes. Recently, we have also applied machine learning approaches to agnostically predict candidate effectors. Here, we review progress in stem rust pathogenomics and approaches currently underway to identify Avr genes recognized by wheat Sr genes

Human language reveals a universal positivity bias

Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i ) the words of natural human language possess a universal positivity bias, (ii ) the estimated emotional content of words is consistent between languages under translation, and (iii ) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts

Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics

We demonstrate that the concerns expressed by Garcia et al. are misplaced, due to (1) a misreading of our findings in [1]; (2) a widespread failure to examine and present words in support of asserted summary quantities based on word usage frequencies; and (3) a range of misconceptions about word usage frequency, word rank, and expert-constructed word lists. In particular, we show that the English component of our study compares well statistically with two related surveys, that no survey design influence is apparent, and that estimates of measurement error do not explain the positivity biases reported in our work and that of others. We further demonstrate that for the frequency dependence of positivity---of which we explored the nuances in great detail in [1]---Garcia et al. did not perform a reanalysis of our data---they instead carried out an analysis of a different, statistically improper data set and introduced a nonlinearity before performing linear regression.Comment: 5 pages, 2 figures, 1 table. Expanded version of reply appearing in PNAS 201