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IntroductionModern understanding of the concept of genetic diversity must include the study of both nuclear and organellar DNA, which differ greatly in terms of their structure, organization, gene content and distribution. This study comprises an analysis of the genetic diversity of the smut fungusSporisorium reilianumf. sp.zeaefrom a mitochondrial perspective. MethodsWhole-genome sequencing data was generated from biological samples ofS. reilianumcollected from different geographical regions. Multiple sequence alignment and gene synteny analysis were performed to further characterize genetic diversity in the context of mitogenomic polymorphisms. ResultsMitochondria of strains collected in China contained unique sequences. The largest unique sequence stretch encompassed a portion ofcox1, a mitochondrial gene encoding one of the subunits that make up complex IV of the mitochondrial electron transport chain. This unique sequence had high percent identity to the mitogenome of the related speciesSporisorium scitamineumandUstilago bromivora. DiscussionThe results of this study hint at potential horizontal gene transfer or mitochondrial genome recombination events during the evolutionary history of basidiomycetes. Additionally, the distinct polymorphic region detected in the Chinese mitogenome provides the ideal foundation to develop a diagnostic method to discern between mitotypes and enhance knowledge on the genetic diversity of this organism. 

While molecular doping is ubiquitous in all branches of organic electronics, little is known about the spatial distribution of dopants, especially at molecular length scales. Moreover, a homogeneous distribution is often assumed when simulating transport properties of these materials, even though the distribution is expected to be inhomogeneous. In this study, electron tomography is used to determine the position of individual molybdenum dithiolene complexes and their three-dimensional distribution in a semiconducting polymer at the sub-nanometre scale. A heterogeneous distribution is observed, the characteristics of which depend on the dopant concentration. At 5 mol% of the molybdenum dithiolene complex, the majority of the dopant species are present as isolated molecules or small clusters up to five molecules. At 20 mol% dopant concentration and higher, the dopant species form larger nanoclusters with elongated shapes. Even in case of these larger clusters, each individual dopant species is still in contact with the surrounding polymer. The electrical conductivity first strongly increases with dopant concentration and then slightly decreases for the most highly doped samples, even though no large aggregates can be observed. The decreased conductivity is instead attributed to the increased energetic disorder and lower probability of electron transfer that originates from the increased size and size variation in dopant clusters. This study highlights the importance of detailed information concerning the dopant spatial distribution at the sub-nanometre scale in three dimensions within the organic semiconductor host. The information acquired using electron tomography may facilitate more accurate simulations of charge transport in doped organic semiconductors. 

Abstract Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call  (ee-ggregation of ompositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest. 

Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean. 

The mitochondrial electron transport chain consists of the classical protein complexes (I–IV) that facilitate the flow of electrons and coupled oxidative phosphorylation to produce metabolic energy. The canonical route of electron transport may diverge by the presence of alternative components to the electron transport chain. The following study comprises the bioinformatic identification and functional characterization of a putative alternative oxidase in the smut fungus Sporisorium reilianum f. sp. zeae. This alternative respiratory component has been previously identified in other eukaryotes and is essential for alternative respiration as a response to environmental and chemical stressors, as well as for developmental transitionaoxs during the life cycle of an organism. A growth inhibition assay, using specific mitochondrial inhibitors, functionally confirmed the presence of an antimycin-resistant/salicylhydroxamic acid (SHAM)-sensitive alternative oxidase in the respirasome of S. reilianum. Gene disruption experiments revealed that this enzyme is involved in the pathogenic stage of the fungus, with its absence effectively reducing overall disease incidence in infected maize plants. Furthermore, gene expression analysis revealed that alternative oxidase plays a prominent role in the teliospore developmental stage, in agreement with favoring alternative respiration during quiescent stages of an organism’s life cycle. 

Compositional data sets are ubiquitous in science, including geology, ecology, and microbiology. In microbiome research, compositional data primarily arise from high-throughput sequence-based profiling experiments. These data comprise microbial compositions in their natural habitat and are often paired with covariate measurements that characterize physicochemical habitat properties or the physiology of the host. Inferring parsimonious statistical associations between microbial compositions and habitat- or host-specific covariate data is an important step in exploratory data analysis. A standard statistical model linking compositional covariates to continuous outcomes is the linear log-contrast model. This model describes the response as a linear combination of log-ratios of the original compositions and has been extended to the high-dimensional setting via regularization. In this contribution, we propose a general convex optimization model for linear log-contrast regression which includes many previous proposals as special cases. We introduce a proximal algorithm that solves the resulting constrained optimization problem exactly with rigorous convergence guarantees. We illustrate the versatility of our approach by investigating the performance of several model instances on soil and gut microbiome data analysis tasks. 

Thermal annealing of organic semiconductors is critical for optimization of their electronic properties. The selection of the optimal annealing temperature –often done on a trial-and-error basis– is essential for achieving the most desired micro/nanostructure. While classical materials science relies on time-temperature-transformation (TTT) diagrams to predict such processing-structure relationships, this type of approach is yet to find widespread application in the field of organic electronics. In this work, we constructed a TTT diagram for crystallization of the widely studied organic semiconductor 5,11-bis(triethylsilylethynyl)anthradithiophene (TES-ADT) from its melt. Thermal analysis in the form of isothermal crystallization experiments showed distinctly different types of behaviour depending on the annealing temperature, in agreement with classical crystal nucleation and growth theory. Hence, the TTT diagram correlates with the observed variation in the number of crystal domains, the crystal coverage and film texture as well as the obtained polymorph. As a result, we are able to rationalize the influence of the annealing temperature on the charge-carrier mobility extracted from field-effect transistor (FET) measurements. Evidently, the use of TTT diagrams is a powerful tool to describe structure formation of organic semiconductors and can be used to predict processing protocols that lead to optimal device performance.