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16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Doping of organic semiconductors is crucial for the operation of organic (opto)electronic and electrochemical devices. Typically, this is achieved by adding heterogeneous dopant molecules to the polymer bulk, often resulting in poor stability and performance due to dopant sublimation or aggregation. In small-molecule donor–acceptor systems, charge transfer can yield high and stable electrical conductivities, an approach not yet explored in all-conjugated polymer systems. Here, we report ground-state electron transfer in all-polymer donor–acceptor heterojunctions. Combining low-ionization-energy polymers with high-electron-affinity counterparts yields conducting interfaces with resistivity values five to six orders of magnitude lower than the separate single-layer polymers. The large decrease in resistivity originates from two parallel quasi-two-dimensional electron and hole distributions reaching a concentration of ∼10 13  cm –2 . Furthermore, we transfer the concept to three-dimensional bulk heterojunctions, displaying exceptional thermal stability due to the absence of molecular dopants. Our findings hold promise for electro-active composites of potential use in, for example, thermoelectrics and wearable electronics. Doping through spontaneous electron transfer between donor and acceptor polymers is obtained by selecting organic semiconductors with suitable electron affinity and ionization energy, achieving high conductivity in blends and bilayer configuration.

The average treatment effect of antidepressants in major depression was found to be about 2 points on the 17-item Hamilton Depression Rating Scale, which lies below clinical relevance. Here, we searched for evidence of a relevant treatment effect heterogeneity that could justify the usage of antidepressants despite their low average treatment effect. Bayesian meta-analysis of 169 randomized, controlled trials including 58,687 patients. We considered the effect sizes log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR) to analyze the difference in variability of active and placebo response. We used Bayesian random-effects meta-analyses (REMA) for lnVR and lnCVR and fitted a random-effects meta-regression (REMR) model to estimate the treatment effect variability between antidepressants and placebo. The variability ratio was found to be very close to 1 in the best fitting models (REMR: 95% highest density interval (HDI) [0.98, 1.02], REMA: 95% HDI [1.00, 1.02]). The between-study standard deviation τ under the REMA with respect to lnVR was found to be low (95% HDI [0.00, 0.02]). Simulations showed that a large treatment effect heterogeneity is only compatible with the data if a strong correlation between placebo response and individual treatment effect is assumed. The published data from RCTs on antidepressants for the treatment of major depression is compatible with a near-constant treatment effect. Although it is impossible to rule out a substantial treatment effect heterogeneity, its existence seems rather unlikely. Since the average treatment effect of antidepressants falls short of clinical relevance, the current prescribing practice should be re-evaluated.

Most people who are regular consumers of psychoactive drugs are not drug addicts, nor will they ever become addicts. In neurobiological theories, non-addictive drug consumption is acknowledged only as a “necessary” prerequisite for addiction, but not as a stable and widespread behavior in its own right. This target article proposes a new neurobiological framework theory for non-addictive psychoactive drug consumption, introducing the concept of “drug instrumentalization.” Psychoactive drugs are consumed for their effects on mental states. Humans are able to learn that mental states can be changed on purpose by drugs, in order to facilitate other, non-drug-related behaviors. We discuss specific “instrumentalization goals” and outline neurobiological mechanisms of how major classes of psychoactive drugs change mental states and serve non-drug-related behaviors. We argue that drug instrumentalization behavior may provide a functional adaptation to modern environments based on a historical selection for learning mechanisms that allow the dynamic modification of consummatory behavior. It is assumed that in order to effectively instrumentalize psychoactive drugs, the establishment of and retrieval from a drug memory is required. Here, we propose a new classification of different drug memory subtypes and discuss how they interact during drug instrumentalization learning and retrieval. Understanding the everyday utility and the learning mechanisms of non-addictive psychotropic drug use may help to prevent abuse and the transition to drug addiction in the future.

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the “best” ones. However, if only the best results are selectively reported, this may cause over-optimism: the “best” method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the “best” method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance.

Major depressive disorder (MDD) is a common and severe disease characterized by mood changes, somatic alterations, and often suicide. MDD is treated with antidepressants, but the molecular mechanism of their action is unknown. We found that widely used antidepressants such as amitriptyline and fluoxetine induce autophagy in hippocampal neurons via the slow accumulation of sphingomyelin in lysosomes and Golgi membranes and of ceramide in the endoplasmic reticulum (ER). ER ceramide stimulates phosphatase 2A and thereby the autophagy proteins Ulk, Beclin, Vps34/Phosphatidylinositol 3-kinase, p62, and Lc3B. Although treatment with amitriptyline or fluoxetine requires at least 12 days to achieve sphingomyelin accumulation and the subsequent biochemical and cellular changes, direct inhibition of sphingomyelin synthases with tricyclodecan-9-yl-xanthogenate (D609) results in rapid (within 3 days) accumulation of ceramide in the ER, activation of autophagy, and reversal of biochemical and behavioral signs of stress-induced MDD. Inhibition of Beclin blocks the antidepressive effects of amitriptyline and D609 and induces cellular and behavioral changes typical of MDD. These findings identify sphingolipid-controlled autophagy as an important target for antidepressive treatment methods and provide a rationale for the development of novel antidepressants that act within a few days.

Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause. First, we crisply articulate potential pitfalls for practitioners regarding the interpretation of compositional causes from the viewpoint of interventions and warn against attributing causal meaning to common summary statistics such as diversity indices in microbiome data analysis. We then advocate for and develop multivariate methods using statistical data transformations and regression techniques that take the special structure of the compositional sample space into account while still yielding scientifically interpretable results. In a comparative analysis on synthetic and real microbiome data we show the advantages and limitations of our proposal. We posit that our analysis provides a useful framework and guidance for valid and informative cause-effect estimation in the context of compositional data.

Prenatal sex hormones exert organizational effects. It has been suggested that prenatal sex hormones affect adult morphological parameters, such as the finger length. Especially the second-to-fourth finger length (2D:4D) ratio has been implicated to be modified when exposed to higher androgen levels in utero. Here we show in a mouse model that experimental manipulation of the prenatal androgen level, by blocking the androgen receptor with flutamide or activating the androgen receptor with dihydrotestosterone (DHT), leads to changes in the length of the fingers of all paws in males and females. In addition to that, also total paw length and the 2D:4D ratio was affected. In males treated with DHT, the 2D:4D ratio was increased, while flutamide-treatment in females led to a reduced 2D:4D ratio. We also measured other parameters, such as head size, body length and tail length and demonstrate that body morphology is affected by prenatal androgen exposure with more prominent effects in females. Another factor that is thought to be influenced by early androgens is handedness. We tested mice for handedness, but did not find a significant effect of the prenatal treatment. These findings demonstrate that prenatal androgen activity is involved in the development of body morphology and might be a useful marker for prenatal androgen exposure.

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 trac (tree-aggregation of compositional 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.

Any catalyst should be efficient and stable to be implemented in practice. This requirement is particularly valid for manganese hydrogenation catalysts. While representing a more sustainable alternative to conventional noble metal-based systems, manganese hydrogenation catalysts are prone to degrade under catalytic conditions once operation temperatures are high. Herein, we report a highly efficient Mn(I)-CNP pre-catalyst which gives rise to the excellent productivity (TOF° up to 41 000 h −1 ) and stability (TON up to 200 000) in hydrogenation catalysis. This system enables near-quantitative hydrogenation of ketones, imines, aldehydes and formate esters at the catalyst loadings as low as 5–200 p.p.m. Our analysis points to the crucial role of the catalyst activation step for the catalytic performance and stability of the system. While conventional activation employing alkoxide bases can ultimately provide catalytically competent species under hydrogen atmosphere, activation of Mn(I) pre-catalyst with hydride donor promoters, e.g. KHBEt 3 , dramatically improves catalytic performance of the system and eliminates induction times associated with slow catalyst activation. Manganese-based hydrogenation catalysts are sensitive to high temperatures and may degrade under industrially relevant conditions. Here, the authors report a highly efficient manganese pincer pre-catalyst displaying high TOF values (up to 41 000 h −1 ) and stability (TON up to 200 000) at loadings as low as 5-200 ppm.