Online Joint Topology Identification and Signal Estimation with Inexact Proximal Online Gradient Descent

Identifying the topology that underlies a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model based topologies capture dependencies among time series, and are often inferred from observed spatio-temporal data. When the data are affected by noise and/or missing samples, the tasks of topology identification and signal recovery (reconstruction) have to be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. To overcome these challenges, this paper proposes two online algorithms to estimate the VAR model-based topologies. The proposed algorithms have constant complexity per iteration, which makes them interesting for big data scenarios. They also enjoy complementary merits in terms of complexity and performance. A performance guarantee is derived for one of the algorithms in the form of a dynamic regret bound. Numerical tests are also presented, showcasing the ability of the proposed algorithms to track the time-varying topologies with missing data in an online fashion.Comment: 14 pages including supplementary material, 2 figures, submitted to IEEE Transactions on Signal Processin

Social structure and the maintenance of biodiversity

Traditional ecological models assume well-mixed populations, where all members are equally likely to interact with one another. These models have been used successfully to explain competitive interactions; however, positive interactions such as intraspecific cooperation and interspecific facilitation cannot readily be captured. Previous work has highlighted the importance of spatial structure in explaining these behaviors as well as its role in maintaining biodiversity. These spatial structures have frequently been modeled using lattices, where all organisms have an equal number of interactions. Although these models capture the spatiality of interactions, natural populations are unlikely to follow such rigid patterns. There has been little work investigating the dynamics of populations with levels of social interactions that occur between these two extremes. In this work, we investigate the dynamics of a 3-strategy nontransitive system in populations with different social structures. We first describe how extending the neighborhood of interactions in traditional lattice models diminishes a population’s ability to maintain diversity. Populations are then moved to graphs where interactions are limited to cells within a defined distance of each other in Cartesian space. This method allows for a more fine-grained examination of the effects that increasing interactions have on maintaining diversity. Finally, we examine small world topologies and find that the introduction of random edges into the graph quickly disrupts the maintenance of diversity

Coevolution Drives the Emergence of Complex Traits and Promotes Evolvability

The evolution of complex organismal traits is obvious as a historical fact, but the underlying causes—including the role of natural selection—are contested. Gould argued that a random walk from a necessarily simple beginning would produce the appearance of increasing complexity over time. Others contend that selection, including coevolutionary arms races, can systematically push organisms toward more complex traits. Methodological challenges have largely precluded experimental tests of these hypotheses. Using the Avida platform for digital evolution, we show that coevolution of hosts and parasites greatly increases organismal complexity relative to that otherwise achieved. As parasites evolve to counter the rise of resistant hosts, parasite populations retain a genetic record of past coevolutionary states. As a consequence, hosts differentially escape by performing progressively more complex functions. We show that coevolution's unique feedback between host and parasite frequencies is a key process in the evolution of complexity. Strikingly, the hosts evolve genomes that are also more phenotypically evolvable, similar to the phenomenon of contingency loci observed in bacterial pathogens. Because coevolution is ubiquitous in nature, our results support a general model whereby antagonistic interactions and natural selection together favor both increased complexity and evolvability

Contribution of Integrated Crop Livestock Systems to Climate Smart Agriculture in Argentina

Integrated crop-livestock system (ICLS) is a useful practice to enhance soil organic carbon (SOC) compared to continuous cropping systems (CC). However, robust data from different regions around the world remain to be collected. So, our objectives were to (i) compare SOC and its physical fractions in ICLS and CC, and (ii) evaluate the use of δ13C to identify the source of C of SOC in these systems in the Pampas region of Argentina. For that, we compared two farms, an ICLS and a CC having the same soil type and landscape position. The ICLS farm produces alfalfa grazed alternatively with soybean and corn, and the CC farm produces the latter two crops in a continuous sequence. Soil samples (0–5, 5–20, 20–40, and 40–60 cm) were collected and analyzed for SOC, its physical fractions, and their isotopic signature (δ13C). Soils under ICLS showed an increment of 50% of SOC stock compared to CC in the first 60 cm. This increase was related to 100–2000 µm fractions of SOC. The shift in δ13C signature is more in ICLS than in CC, suggesting that rotation with C3 legumes contributed to C sequestration and, therefore, climate-smart agriculture. The combination of on-farm research and isotopic technique can help to study deeply the effect of real farm practices on soil carbon derived from pasture.EEA San LuisFil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: de Dios Herrero, Juan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; ArgentinaFil: Sager, Ricardo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: Guzmán, María Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Luis; ArgentinaFil: Zaman, Mohammad. International Atomic Energy Agency. Soil and Water Management & Crop Nutrition Section; Austri

Coevolutionary dynamics shape the structure of bacteria‐phage infection networks

Coevolution—reciprocal evolutionary change among interacting species driven by natural selection—is thought to be an important force in shaping biodiversity. This ongoing process takes place within tangled networks of species interactions. In microbial communities, evolutionary change between hosts and parasites occurs at the same time scale as ecological change. Yet, we still lack experimental evidence of the role of coevolution in driving changes in the structure of such species interaction networks. Filling this gap is important because network structure influences community persistence through indirect effects. Here, we quantified experimentally to what extent coevolutionary dynamics lead to contrasting patterns in the architecture of bacteria–phage infection networks. Specifically, we look at the tendency of these networks to be organized in a nested pattern by which the more specialist phages tend to infect only a proper subset of those bacteria infected by the most generalist phages. We found that interactions between coevolving bacteria and phages become less nested over time under fluctuating dynamics, and more nested under arms race dynamics. Moreover, when coevolution results in high average infectivity, phages and bacteria differ more from each other over time under arms race dynamics than under fluctuating dynamics. The tradeoff between the fitness benefits of evolving resistance/infectivity traits and the costs of maintaining them might explain these differences in network structure. Our study shows that the interaction pattern between bacteria and phages at the community level depends on the way coevolution unfolds.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149309/1/evo13731_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149309/2/evo13731.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149309/3/evo13731-sup-0001-TableS1.pd

Modeling the evolutionary dynamics of plasmids in spatial populations

One of the processes by which microorganisms are able to rapidly adapt to changing conditions is horizontal gene trans-fer, whereby an organism incorporates additional genetic material from sources other than its parent. These genetic elements may encode a wide variety of beneficial traits. Un-der certain conditions, many computational models capture the evolutionary dynamics of adaptive behaviors such as toxin production, quorum sensing, and biofilm formation, and have even provided new insights into otherwise unknown or misunderstood phenomena. However, such models rarely incorporate horizontal gene transfer, so they may be inca-pable of fully representing the vast repertoire of behaviors exhibited by natural populations. Although models of hori-zontal gene transfer exist, they rarely account for the spatial structure of populations, which is often critical to adaptive behaviors. In this work we develop a spatial model to examine how conjugation, one mechanism of horizontal gene transfer, can be maintained in populations. We investigate how both the costs of transfer and the benefits conferred affect evolution-ary outcomes. Further, we examine how rates of transmis-sion evolve, allowing this system to adapt to different en-vironments. Through spatial models such as these, we can gain a greater understanding of the conditions under which horizontally-acquired behaviors are evolved and are main-tained

Dynamic network identification from non-stationary vector autoregressive time series

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Low genetic polymorphism in the immunogenic sequences of Rhipicephalus microplus clade C

Rhipicephalus microplus tick highly affects the veterinary sector throughout the world. Different tick control methods have been adopted, and the identification of tick-derived highly immunogenic sequences for the development of an anti-tick vaccine has emerged as a successful alternate. This study aimed to characterize immunogenic sequences from R. microplus ticks prevalent in Pakistan. Ticks collected in the field were morphologically identified and subjected to DNA and RNA extraction. Ticks were molecularly identified based on the partial mitochondrial cytochrome C oxidase subunit (cox) sequence and screened for piroplasms (Theileria/Babesia spp.), Rickettsia spp., and Anaplasma spp. PCR-based pathogens-free R. microplus-derived cDNA was used for the amplification of full-length cysteine protease inhibitor (cystatin 2b), cathepsin L-like cysteine proteinase (cathepsin-L), glutathione S-transferase (GST), ferritin 1, 60S acidic ribosomal protein (P0), aquaporin 2, ATAQ, and R. microplus 05 antigen (Rm05Uy) coding sequences. The cox sequence revealed 100% identity with the nucleotide sequences of Pakistan’s formerly reported R. microplus, and full-length immunogenic sequences revealed maximum identities to the most similar sequences reported from India, China, Cuba, USA, Brazil, Egypt, Mexico, Israel, and Uruguay. Low nonsynonymous polymorphisms were observed in ATAQ (1.5%), cathepsin-L (0.6%), and aquaporin 2 (0.4%) sequences compared to the homologous sequences from Mexico, India, and the USA, respectively. Based on the cox sequence, R. microplus was phylogenetically assembled in clade C, which includes R. microplus from Pakistan, Myanmar, Malaysia, Thailand, Bangladesh, and India. In the phylogenetic trees, the cystatin 2b, cathepsin-L, ferritin 1, and aquaporin 2 sequences were clustered with the most similar available sequences of R. microplus, P0 with R. microplus, R. sanguineus and R. haemaphysaloides, and GST, ATAQ, and Rm05Uy with R. microplus and R. annulatus. This is the first report on the molecular characterization of clade C R. microplus-derived immunogenic sequences