Discovery and assembly of repeat family pseudomolecules from sparse genomic sequence data using the Assisted Automated Assembler of Repeat Families (AAARF) algorithm

<p>Abstract</p> <p>Background</p> <p>Higher eukaryotic genomes are typically large, complex and filled with both genes and multiple classes of repetitive DNA. The repetitive DNAs, primarily transposable elements, are a rapidly evolving genome component that can provide the raw material for novel selected functions and also indicate the mechanisms and history of genome evolution in any ancestral lineage. Despite their abundance, universality and significance, studies of genomic repeat content have been largely limited to analyses of the repeats in fully sequenced genomes.</p> <p>Results</p> <p>In order to facilitate a broader range of repeat analyses, the Assisted Automated Assembler of Repeat Families algorithm has been developed. This program, written in PERL and with numerous adjustable parameters, identifies sequence overlaps in small shotgun sequence datasets and walks them out to create long pseudomolecules representing the most abundant repeats in any genome. Testing of this program in maize indicated that it found and assembled all of the major repeats in one or more pseudomolecules, including coverage of the major Long Terminal Repeat retrotransposon families. Both Sanger sequence and 454 datasets were appropriate.</p> <p>Conclusion</p> <p>These results now indicate that hundreds of higher eukaryotic genomes can be efficiently characterized for the nature, abundance and evolution of their major repetitive DNA components.</p

Record statistics in random vectors and quantum chaos

The record statistics of complex random states are analytically calculated, and shown that the probability of a record intensity is a Bernoulli process. The correlation due to normalization leads to a probability distribution of the records that is non-universal but tends to the Gumbel distribution asymptotically. The quantum standard map is used to study these statistics for the effect of correlations apart from normalization. It is seen that in the mixed phase space regime the number of intensity records is a power law in the dimensionality of the state as opposed to the logarithmic growth for random states.Comment: figures redrawn, discussion adde

Family of generalized random matrix ensembles

Using the Generalized Maximium Entropy Principle based on the nonextensive q entropy a new family of random matrix ensembles is generated. This family unifies previous extensions of Random Matrix Theory and gives rise to an orthogonal invariant stable Levy ensemble with new statistical properties. Some of them are analytically derived.Comment: 13 pages and 2 figure

Identifying airborne transmission as the dominant route for the spread of COVID-19

Various mitigation measures have been implemented to fight the coronavirus disease 2019 (COVID-19) pandemic, including widely adopted social distancing and mandated face covering. However, assessing the effectiveness of those intervention practices hinges on the understanding of virus transmission, which remains uncertain. Here we show that airborne transmission is highly virulent and represents the dominant route to spread the disease. By analyzing the trend and mitigation measures in Wuhan, China, Italy, and New York City, from January 23 to May 9, 2020, we illustrate that the impacts of mitigation measures are discernable from the trends of the pandemic. Our analysis reveals that the difference with and without mandated face covering represents the determinant in shaping the pandemic trends in the three epicenters. This protective measure alone significantly reduced the number of infections, that is, by over 78,000 in Italy from April 6 to May 9 and over 66,000 in New York City from April 17 to May 9. Other mitigation measures, such as social distancing implemented in the United States, are insufficient by themselves in protecting the public. We conclude that wearing of face masks in public corresponds to the most effective means to prevent interhuman transmission, and this inexpensive practice, in conjunction with simultaneous social distancing, quarantine, and contact tracing, represents the most likely fighting opportunity to stop the COVID-19 pandemic. Our work also highlights the fact that sound science is essential in decision-making for the current and future public health pandemics

Identifying airborne transmission as the dominant route for the spread of COVID-19

Various mitigation measures have been implemented to fight the coronavirus disease 2019 (COVID-19) pandemic, including widely adopted social distancing and mandated face covering. However, assessing the effectiveness of those intervention practices hinges on the understanding of virus transmission, which remains uncertain. Here we show that airborne transmission is highly virulent and represents the dominant route to spread the disease. By analyzing the trend and mitigation measures in Wuhan, China, Italy, and New York City, from January 23 to May 9, 2020, we illustrate that the impacts of mitigation measures are discernable from the trends of the pandemic. Our analysis reveals that the difference with and without mandated face covering represents the determinant in shaping the pandemic trends in the three epicenters. This protective measure alone significantly reduced the number of infections, that is, by over 78,000 in Italy from April 6 to May 9 and over 66,000 in New York City from April 17 to May 9. Other mitigation measures, such as social distancing implemented in the United States, are insufficient by themselves in protecting the public. We conclude that wearing of face masks in public corresponds to the most effective means to prevent interhuman transmission, and this inexpensive practice, in conjunction with simultaneous social distancing, quarantine, and contact tracing, represents the most likely fighting opportunity to stop the COVID-19 pandemic. Our work also highlights the fact that sound science is essential in decision-making for the current and future public health pandemics

Radiative absorption enhancement of dust mixed with anthropogenic pollution over East Asia

The particle mixing state plays a significant yet poorly quantified role in aerosol radiative forcing, especially for the mixing of dust (mineral absorbing) and anthropogenic pollution (black carbon absorbing) over East Asia. We have investigated the absorption enhancement of mixed-type aerosols over East Asia by using the Aerosol Robotic Network observations and radiative transfer model calculations. The mixed-type aerosols exhibit significantly enhanced absorbing ability than the corresponding unmixed dust and anthropogenic aerosols, as revealed in the spectral behavior of absorbing aerosol optical depth, single scattering albedo, and imaginary refractive index. The aerosol radiative efficiencies for the dust, mixed-type, and anthropogenic aerosols are −101.0, −112.9, and −98.3 Wm⁻²τ⁻¹ at the bottom of the atmosphere (BOA); −42.3, −22.5, and −39.8 Wm⁻²τ⁻¹ at the top of the atmosphere (TOA); and 58.7, 90.3, and 58.5 Wm⁻²τ⁻¹ in the atmosphere (ATM), respectively. The BOA cooling and ATM heating efficiencies of the mixed-type aerosols are significantly higher than those of the unmixed aerosol types over the East Asia region, resulting in atmospheric stabilization. In addition, the mixed-type aerosols correspond to a lower TOA cooling efficiency, indicating that the cooling effect by the corresponding individual aerosol components is partially counteracted. We conclude that the interaction between dust and anthropogenic pollution not only represents a viable aerosol formation pathway but also results in unfavorable dispersion conditions, both exacerbating the regional air pollution in East Asia. Our results highlight the necessity to accurately account for the mixing state of aerosols in atmospheric models over East Asia in order to better understand the formation mechanism for regional air pollution and to assess its impacts on human health, weather, and climate

Statistical Properties of the Final State in One-dimensional Ballistic Aggregation

We investigate the long time behaviour of the one-dimensional ballistic aggregation model that represents a sticky gas of N particles with random initial positions and velocities, moving deterministically, and forming aggregates when they collide. We obtain a closed formula for the stationary measure of the system which allows us to analyze some remarkable features of the final `fan' state. In particular, we identify universal properties which are independent of the initial position and velocity distributions of the particles. We study cluster distributions and derive exact results for extreme value statistics (because of correlations these distributions do not belong to the Gumbel-Frechet-Weibull universality classes). We also derive the energy distribution in the final state. This model generates dynamically many different scales and can be viewed as one of the simplest exactly solvable model of N-body dissipative dynamics.Comment: 19 pages, 5 figures include

Antimicrobial Resistance Profiles Diversity in Salmonella from Humans Cattle, 2004-2011

Analysis of long-term anti-microbial resistance (AMR) data is useful to understsource transmission dynamics of AMR. We analysed 5124 human clinical isolates from Washington State Department of Health, 391 cattle clinical isolates from the Washington Animal Disease Diagnostic Laboratory 1864 non-clinical isolates from foodborne disease research on dairies in the Pacific Northwest. Isolates were assigned profiles based on phenotypic resistance to 11 anti-microbials belonging to eight classes. Salmonella Typhimurium (ST), Salmonella Newport (SN) Salmonella Montevideo (SM) were the most common serovars in both humans cattle. Multinomial logistic regression showed ST SN from cattle had greater probability of resistance to multiple classes of anti-microbials than ST SN from humans (P < 0.0001). While these findings could be consistent with the belief that cattle are a source of resistant ST SN for people, occurrence of profiles unique to cattle not observed in temporally related human isolates indicates these profiles are circulating in cattle only. We used various measures to assess AMR diversity, conditional on the weighting of rare versus abundant profiles. AMR profile richness was greater in the common serovars from humans, although both source data sets were dominated by relatively few profiles. The greater profile richness in human Salmonella may be due to greater diversity of sources entering the human population compared to cattle or due to continuous evolution in the human environment. Also, AMR diversity was greater in clinical compared to non-clinical cattle Salmonella, this could be due to anti-microbial selection pressure in diseased cattle that received treatment. The use of bootstrapping techniques showed that although there were shared profiles between humans cattle, the expected observed number of profiles was different, suggesting Salmonella associated resistance from humans cattle may not be wholly derived from a common population

Consequences of the H-Theorem from Nonlinear Fokker-Planck Equations

A general type of nonlinear Fokker-Planck equation is derived directly from a master equation, by introducing generalized transition rates. The H-theorem is demonstrated for systems that follow those classes of nonlinear Fokker-Planck equations, in the presence of an external potential. For that, a relation involving terms of Fokker-Planck equations and general entropic forms is proposed. It is shown that, at equilibrium, this relation is equivalent to the maximum-entropy principle. Families of Fokker-Planck equations may be related to a single type of entropy, and so, the correspondence between well-known entropic forms and their associated Fokker-Planck equations is explored. It is shown that the Boltzmann-Gibbs entropy, apart from its connection with the standard -- linear Fokker-Planck equation -- may be also related to a family of nonlinear Fokker-Planck equations.Comment: 19 pages, no figure

A record-driven growth process

We introduce a novel stochastic growth process, the record-driven growth process, which originates from the analysis of a class of growing networks in a universal limiting regime. Nodes are added one by one to a network, each node possessing a quality. The new incoming node connects to the preexisting node with best quality, that is, with record value for the quality. The emergent structure is that of a growing network, where groups are formed around record nodes (nodes endowed with the best intrinsic qualities). Special emphasis is put on the statistics of leaders (nodes whose degrees are the largest). The asymptotic probability for a node to be a leader is equal to the Golomb-Dickman constant omega=0.624329... which arises in problems of combinatorical nature. This outcome solves the problem of the determination of the record breaking rate for the sequence of correlated inter-record intervals. The process exhibits temporal self-similarity in the late-time regime. Connections with the statistics of the cycles of random permutations, the statistical properties of randomly broken intervals, and the Kesten variable are given.Comment: 30 pages,5 figures. Minor update