IMPORTANCE The definition and nature of autism have been highly debated, as exemplified by several revisions of the DSM (DSM-III, DSM-IIIR, DSM-IV, and DSM-5) criteria. There has recently been a move from a categorical view toward a spectrum-based view. These changes have been accompanied by a steady increase in the prevalence of the condition. Changes in the definition of autism that may increase heterogeneity could affect the results of autism research; specifically, a broadening of the population with autism could result in decreasing effect sizes of group comparison studies. OBJECTIVE To examine the correlation between publication year and effect size of autism-control group comparisons across several domains of published autism neurocognitive research. DATA SOURCES This meta-analysis investigated 11 meta-analyses obtained through a systematic search of PubMed for meta-analyses published from January 1, 1966, through January 27, 2019, using the search string autism AND (meta-analysis OR meta-analytic). The last search was conducted on January 27, 2019. STUDY SELECTION Meta-analyses were included if they tested the significance of group differences between individuals with autism and control individuals on a neurocognitive construct. Meta-analyses were only included if the tested group difference was significant and included data with a span of at least 15 years. DATA EXTRACTION AND SYNTHESIS Data were extracted and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline using fixed-effects models. MAIN OUTCOMES AND MEASURES Estimated slope of the correlation between publication year and effect size, controlling for differences in methods, sample size, and study quality. RESULTS The 11 meta-analyses included data from a total of 27 723 individuals. Demographic data such as sex and age were not available for the entire data set. Seven different psychological and neurologic constructs were analyzed based on data from these meta-analyses. Downward temporal trends for effect size were found for all constructs (slopes:-0.067 to-0.003), with the trend being significant in 5 of 7 cases: emotion recognition (slope:-0.028 [95% CI,-0.048 to-0.007]), theory of mind (-0.045 [95% CI,-0.066 to-0.024]), planning (-0.067 [95% CI,-0.125 to-0.009]), P3b amplitude (-0.048 [95% CI,-0.093 to-0.004]), and brain size (-0.047 [95% CI,-0.077 to-0.016]). In contrast, 3 analogous constructs in schizophrenia, a condition that is also heterogeneous but with no reported increase in prevalence, did not show a similar trend. CONCLUSIONS AND RELEVANCE The findings suggest that differences between individuals with autism and those without the diagnosis have decreased over time and that possible changes in the definition of autism from a narrowly defined and homogenous population toward an inclusive and heterogeneous population may reduce our capacity to build mechanistic models of the condition.

Fungi secrete an array of carbohydrate-active enzymes (CAZymes), reflecting their specialized habitat-related substrate utilization. Despite its importance for fitness, enzyme secretome composition is not used in fungal classification, since an overarching relationship between CAZyme profiles and fungal phylogeny/taxonomy has not been established. For 465 Ascomycota and Basidiomycota genomes, we predicted CAZyme-secretomes, using a new peptide-based annotation method, Conserved-Unique-Peptide-Patterns, enabling functional prediction directly from sequence. We categorized each enzyme according to CAZy-family and predicted molecular function, hereby obtaining a list of “EC-Function;CAZy-Family” observations. These “Function;Family”-based secretome profiles were compared, using a Yule-dissimilarity scoring algorithm, giving equal consideration to the presence and absence of individual observations. Assessment of “Function;Family” enzyme profile relatedness (EPR) across 465 genomes partitioned Ascomycota from Basidiomycota placing Aspergillus and Penicillium among the Ascomycota. Analogously, we calculated CAZyme “Function;Family” profile-similarities among 95 Aspergillus and Penicillium species to form an alignment-free, EPR-based dendrogram. This revealed a stunning congruence between EPR categorization and phylogenetic/taxonomic grouping of the Aspergilli and Penicillia. Our analysis suggests EPR grouping of fungi to be defined both by “shared presence“ and “shared absence” of CAZyme “Function;Family” observations. This finding indicates that CAZymes-secretome evolution is an integral part of fungal speciation, supporting integration of cladogenesis and anagenesis.

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .