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RNA-directed DNA methylation (RdDM) is a biological process in which non-coding RNA molecules direct the addition of DNA methylation to specific DNA sequences. The RdDM pathway is unique to plants, although other mechanisms of RNA-directed chromatin modification have also been described in fungi and animals. To date, the RdDM pathway is best characterized within angiosperms (flowering plants), and particularly within the model plant Arabidopsis thaliana. However, conserved RdDM pathway components and associated small RNAs (sRNAs) have also been found in other groups of plants, such as gymnosperms and ferns. The RdDM pathway closely resembles other sRNA pathways, particularly the highly conserved RNAi pathway found in fungi, plants, and animals. Both the RdDM and RNAi pathways produce sRNAs and involve conserved Argonaute, Dicer and RNA-dependent RNA polymerase proteins. RdDM has been implicated in a number of regulatory processes in plants. The DNA methylation added by RdDM is generally associated with transcriptional repression of the genetic sequences targeted by the pathway. Since DNA methylation patterns in plants are heritable, these changes can often be stably transmitted to progeny. As a result, one prominent role of RdDM is the stable, transgenerational suppression of transposable element (TE) activity. RdDM has also been linked to pathogen defense, abiotic stress responses, and the regulation of several key developmental transitions. Although the RdDM pathway has a number of important functions, RdDM-defective mutants in Arabidopsis thaliana are viable and can reproduce, which has enabled detailed genetic studies of the pathway. However, RdDM mutants can have a range of defects in different plant species, including lethality, altered reproductive phenotypes, TE upregulation and genome instability, and increased pathogen sensitivity. Overall, RdDM is an important pathway in plants that regulates a number of processes by establishing and reinforcing specific DNA methylation patterns, which can lead to transgenerational epigenetic effects on gene expression and phenotype.

Imprinted gene expression occurs during seed development in plants and is associated with differential DNA methylation of parental alleles, particularly at proximal transposable elements (TEs). Imprinting variability could contribute to observed parent-of-origin effects on seed development. We investigated intraspecific variation in imprinting, coupled with analysis of DNA methylation and small RNAs, among three Arabidopsis strains with diverse seed phenotypes. The majority of imprinted genes were parentally biased in the same manner among all strains. However, we identified several examples of allele-specific imprinting correlated with intraspecific epigenetic variation at a TE. We successfully predicted imprinting in additional strains based on methylation variability. We conclude that there is standing variation in imprinting even in recently diverged genotypes due to intraspecific epiallelic variation. Our data demonstrate that epiallelic variation and genomic imprinting intersect to produce novel gene expression patterns in seeds. When animals or plants reproduce sexually, the DNA in a sperm or pollen is combined with that in an egg cell to generate an offspring that inherits two copies of each gene, one from each parent. For a very small number of genes, the copy from one of the parents is consistently turned off. This process—called imprinting—means that the same gene can have different effects depending on if it is inherited from the mother or the father. In plants, imprinting is vital for the production of seeds and typically occurs in the endosperm: the tissue within a seed that provides nourishment to the plant embryo. One way genes can be imprinted is by adding small chemical marks—called methyl groups—on to the DNA that makes up the gene or nearby sequences. These marks can either switch on, or switch off, the expression of the gene. DNA methylation also immobilises stretches of DNA called transposable elements, stopping them from moving from one location to another in the genome. These stretches of DNA are identified and targeted for methylation by small molecules of RNA that match their DNA sequences. Genes that are imprinted in the endosperm of the model plant Arabidopsis are often associated with transposable elements, which can be methylated differently in the naturally occurring varieties, or strains, of Arabidopsis. However it is unclear how many genes are differently imprinted between these different strains. Pignatta et al. looked for differences in gene imprinting, DNA methylation and small RNA production in the seeds, embryos and endosperm tissue from three strains of Arabidopsis. They also examined seeds from crosses between these three strains. While most genes had the same imprinting pattern in all strains and crosses examined, 12 genes were imprinted differently depending on whether they were inherited from the male or female of a given strain. For example, for some genes the copy inherited from the male parent is always turned off, unless it is inherited via the pollen of one specific Arabidopsis strain. Half of this variation could be explained by a transposable element near to each gene that was methylated differently among the strains. By comparing the differentially methylated regions in the genomes of 140 Arabidopsis strains, Pignatta et al. found that differences in methylation may affect 11% of imprinted genes—and went on to confirm variable imprinting in some Arabidopsis strains based on the presence or absence of DNA methylation. Future work is needed to understand how variation in gene imprinting might affect the traits of hybrid seeds, and how it might affect the evolution of new traits in hybrid plants.

The reconstruction of cosmic ray-induced air showers from measurements of radio waves constitutes a major challenge. In this work, we focus on recovering the full three-dimensional electromagnetic field from two recorded signal traces of an antenna station covering two horizontal polarization directions. The simulated field is folded by a direction and frequency-dependent characteristic antenna response pattern, resulting in voltage signal traces as a function of time. Both signal traces are contaminated by simulated background noise. We use conditional Invertible Neural Networks (cINNs) to learn posterior distributions, from which the most likely electromagnetic field given a measured signal trace can be inferred. To improve robustness, we extend the method with an autoencoder by reducing the parameter phase space and decoupling the cINN from specific data shapes. Thereby, each signal trace is condensed into a small number of abstract parameters in the latent space on which the cINN operates. The presented method shows promising results and can be transferred to other unfolding problems where the recovery of the pre-measurement state is of interest.

We assessed the potential of Machine Learning (ML) for mapping crop growth in three flood irrigated fields. Results generated from ML algorithms were compared to the output generated by the ISODATA algorithm. Affinity Propagation (AP) identifies the number of clusters by considering all data points as potential exemplars and iteratively refine the set, while Gaussian Mixture Model (GMM) algorithm treats the data as a mixture of several Gaussian distributions, allowing for flexible cluster shapes. In contrast, ISODATA, a statistical clustering method, requires an analyst to specify the number of output clusters followed by iterative splitting and merging of clusters based on variance and distance criteria. We acquired Landsat derived NDVI images for three flood-irrigated fields over a span of four years. These images were collected at the start of the growing season to ensure consistency. Initially we clustered the pixels in these images for each field using AP and determine the number of clusters. Next, we applied GMM to identify and define the clusters. Finally, we plotted the mean value of all the pixels in each cluster for every year and assigned the clusters into six thematic classes: the first three classes for consistent growth (good, average, or poor) across all four years, and the other three for mixed growth patterns (e.g., good in three years and average in one). Output maps generated from these methods were compared using IoU scores. ML methods had greater efficiency in terms of replicating the steps for other fields, whereas ISODATA requires analyst intervention and interpretation.

Conditional Invertible Neural Networks (cINNs) provide a new technique for the inference of free model parameters by enabling the creation of posterior distributions. With these distributions, the mean parameter values, their uncertainties and the correlations between the parameters can be estimated. In this contribution, we summarize the functionality of cINNs, which are based on normalizing flows, and present the application of this new method to a scenario from astroparticle physics. We show that it is possible to constrain properties of the currently unknown sources of ultra-high-energy cosmic rays and compare the posterior distributions obtained with the network to those acquired using the classic Markov Chain Monte Carlo method.

'Difficult' oil could be a gas More than half of the world's oil inventory consists of biodegraded heavy oil and tar sand deposits. Recovery of oil from these sources is complicated and expensive. Recent findings suggest that anaerobic bacteria may cause this hydrocarbon degradation, but the actual degradation pathway occurring in oil reservoirs remains obscure. Using a combination of laboratory oil degradation experiments and analysis of oilfield samples, it is now shown that the dominant process of subsurface biodegradation is methanogenesis, involving anaerobic degradation of oil hydrocarbons to produce methane. This suggests an alternative way of exploiting these 'difficult' oilfields: by accelerating the natural hydrocarbon degradation process, it may be possible to recover energy as methane, rather than conventionally as oil. Laboratory experiments in microcosms monitoring the hydrocarbon composition of degraded oils are used with carbon isotopic compositions of gas and oil samples taken at wellheads and a Rayleigh isotope fractionation box model to elucidate the mechanisms of hydrocarbon degradation in reservoirs. The data imply a common methanogenic biodegradation mechanism in subsurface degraded oil reservoirs resulting in consistent patterns of hydrocarbon alteration. Biodegradation of crude oil in subsurface petroleum reservoirs has adversely affected the majority of the world’s oil, making recovery and refining of that oil more costly 1 . The prevalent occurrence of biodegradation in shallow subsurface petroleum reservoirs 2 , 3 has been attributed to aerobic bacterial hydrocarbon degradation stimulated by surface recharge of oxygen-bearing meteoric waters 2 . This hypothesis is empirically supported by the likelihood of encountering biodegraded oils at higher levels of degradation in reservoirs near the surface 4 , 5 . More recent findings, however, suggest that anaerobic degradation processes dominate subsurface sedimentary environments 6 , despite slow reaction kinetics and uncertainty as to the actual degradation pathways occurring in oil reservoirs. Here we use laboratory experiments in microcosms monitoring the hydrocarbon composition of degraded oils and generated gases, together with the carbon isotopic compositions of gas and oil samples taken at wellheads and a Rayleigh isotope fractionation box model, to elucidate the probable mechanisms of hydrocarbon degradation in reservoirs. We find that crude-oil hydrocarbon degradation under methanogenic conditions in the laboratory mimics the characteristic sequential removal of compound classes seen in reservoir-degraded petroleum. The initial preferential removal of n -alkanes generates close to stoichiometric amounts of methane, principally by hydrogenotrophic methanogenesis. Our data imply a common methanogenic biodegradation mechanism in subsurface degraded oil reservoirs, resulting in consistent patterns of hydrocarbon alteration, and the common association of dry gas with severely degraded oils observed worldwide. Energy recovery from oilfields in the form of methane, based on accelerating natural methanogenic biodegradation, may offer a route to economic production of difficult-to-recover energy from oilfields.

We present a neural network architecture designed to autonomously create characteristic features of high energy physics collision events from basic four-vector information. It consists of two stages, the first of which we call the Lorentz Boost Network (LBN). The LBN creates composite particles and rest frames from the combination of final state particles, and then boosts said particles into their corresponding rest frames. From these boosted particles, characteristic features are created and used by the second network stage to solve a given physics problem. We apply our model to the task of separating top-quark pair associated Higgs boson events from a t t ¯ background, and observe improved performance compared to using domain unspecific deep neural networks. We also investigate the learned combinations and boosts to gain insights into what the network is learning.

In high-energy particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo production. However, physicists performing data analyses are usually required to steer their individual workflows manually which is time-consuming and often leads to undocumented relations between particular workloads. We present a generic analysis design pattern that copes with the sophisticated demands of end-to-end HEP analyses and provides a make-like execution system. It is based on the open-source pipelining package Luigi which was developed at Spotify and enables the definition of arbitrary workloads, so-called Tasks, and the dependencies between them in a lightweight and scalable structure. Further features are multi-user support, automated dependency resolution and error handling, central scheduling, and status visualization in the web. In addition to already built-in features for remote jobs and file systems like Hadoop and HDFS, we added support for WLCG infrastructure such as LSF and CREAM job submission, as well as remote file access through the Grid File Access Library. Furthermore, we implemented automated resubmission functionality, software sandboxing, and a command line interface with auto-completion for a convenient working environment. For the implementation of a tt¯H cross section measurement, we created a generic Python interface that provides programmatic access to all external information such as datasets, physics processes, statistical models, and additional files and values. In summary, the setup enables the execution of the entire analysis in a parallelized and distributed fashion with a single command.

Osteoclasts are multinucleated cells and the principal resorptive cells of bone. Although osteoclasts are of myeloid origin 1 , the role of haematopoietic transcription factors in osteoclastogenesis has not been explored. Here we show that messenger RNA for the myeloid- and B-cell-specific transcription factor PU.1 progressively increases as marrow macrophages assume the osteoclast phenotype in vitro . The association between PU.1 and osteoclast differentiation was confirmed by demonstrating that PU.1 expression increased with the induction of osteoclastogenesis by either 1,25-dihydroxyvitamin D 3 or dexamethasone. Consistent with the participation of PU.1 in osteoclastogenesis, we found that the development of both osteoclasts and macrophages is arrested in PU.1-deficient mice. Reflecting the absence of osteoclasts, PU.1 −/− mice exhibit the classic hallmarks of osteopetrosis, a family of sclerotic bone diseases 2 . These animals were rescued by marrow transplantation, with complete restoration of osteoclast and macrophage differentiation, verifying that the PU.1 lesion is intrinsic to haematopoietic cells. The absence of both osteoclasts and macrophages in PU.1-mutant animals suggests that the transcription factor regulates the initial stages of myeloid differentiation, and that its absence represents the earliest developmental osteopetrotic mutant yet described.