2019) The genome-wide dynamics of purging during selfing in maize. Nature Plants, 5 (9). pp. INTRODUCTION:Darwin showed that the self-fertilization of plants leads to reduced vigor and fertility -i.e., inbreeding depression 1 . His work supported the hypothesis that selffertilization is strongly disadvantageous and also provided a rationale for the prevalence of outcrossing in nature 2,3 . He did not, however, know the genetic basis of inbreeding depression. It is now thought to be caused by increased homozygosity, which inflates the genetic load by uncovering recessive deleterious alleles and/or by eliminating heterozygosity at loci with an overdominant advantage 4,5 . The increase of homozygosity -or, alternatively, the decrease of heterozygosity (H) -is expected to occur at a regular rate; in a selfed lineage, H is expected to be halved each generation.However, the actual rate of H decline is likely to be slowed by various factors, such as interference due to linkage (linked selection), epistatic interactions 6 and selective pressure to retain heterozygosity at overdominant and associative-overdominant loci 7,8 .These factors presumably contribute to the fact that inbred lines of maize and selfing Caenorhabditis species retain some heterozygosity, even after many generations of selfing 9-12 .One way to combat the increased load caused by inbreeding is the removal, or 'purging', of recessive deleterious alleles. When purging is effective, there may be no inbreeding depression 13 . Purging is expected to occur rapidly when recessive alleles have lethal effects 14-17 but should be less efficient for non-lethal recessives 8,18 . The existence of purging is supported by experiments, theory and forward simulations 4,19,20 , but it is expected to vary across species based on features like population history, mating system, and the distribution of fitness effects. Given this variation, one metaanalysis has concluded that purging is an "inconsistent force" in the evolution of inbreeding plant populations 8 .Recently, authors have argued that genomic data provide more precise insights into inbreeding effects than previous approaches (e.g. 5,6,21 ). Here we extend that argument to the phenomenon of purging, beginning with three simple predictions. The first is that selfed offspring will exhibit a bias against the retention of putatively

Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approaches have been employed in the study of perceptual decision-making, beginning with rudimentary linking of the data sets at a qualitative, structural level, culminating in sophisticated statistical approaches with quantitative links. We outline a new approach, in which a single model is developed that jointly addresses neural and behavioral data. This approach allows for specification and testing of quantitative links between neural and behavioral aspects of the model. Estimating the model in a Bayesian framework allows both data sets to equally inform the estimation of all model parameters. The use of a hierarchical model architecture allows for a model, which accounts for and measures the variability between neurons. We demonstrate the approach by re-analysis of a classic data set containing behavioral recordings of decision-making with accompanying single-cell neural recordings. The joint model is able to School of Psychology, University of Newcastle, Callaghan, New South Wales, 2308, Australia capture most aspects of both data sets, and also supports the analysis of interesting questions about prediction, including predicting the times at which responses are made, and the corresponding neural firing rates.

Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, non-standardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly-available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the Labeled Latent Dirichlet Allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic (ROC) curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of .79, and .70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scaleable method for accurate automated coding of psychotherapy sessions that performs better than comparable discriminative methods at session-level coding and can also predict fine-grained codes.

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Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. We replicate these results using a large dataset collected at the OSU Center for Cognitive and Behavioral Brain Imaging. We also introduce a novel perspective on task and subject identity prediction: BOLD Variability (BV). Conceptually, BV is a region-specific measure based on the variance within each brain region. BV is simple to compute, interpret, and visualize. We show that both FC and BV are predictive of task and subject, even across scanning sessions separated by multiple years. Subject differences rather than task differences account for the majority of changes in BV and FC. Similar to results in FC, we show that BV is reduced during cognitive tasks relative to rest.