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Domestication research has largely focused on identification of morphological and genetic differences between extant populations of crops and their wild relatives. Little attention has been paid to the potential effects of environment despite substantial known changes in climate from the time of domestication to modern day. In recent research, the exposure of teosinte (i.e., wild maize) to environments similar to the time of domestication, resulted in a plastic induction of domesticated phenotypes in teosinte. These results suggest that early agriculturalists may have selected for genetic mechanisms that cemented domestication phenotypes initially induced by a plastic response of teosinte to environment, a process known as genetic assimilation. To better understand this phenomenon and the potential role of environment in maize domestication, we examined differential gene expression in maize (Zea mays ssp. mays) and teosinte (Zea mays ssp. parviglumis) between past and present conditions. We identified a gene set of over 2000 loci showing a change in expression across environmental conditions in teosinte and invariance in maize. In fact, overall we observed both greater plasticity in gene expression and more substantial changes in co-expressionnal networks in teosinte across environments when compared to maize. While these results suggest genetic assimilation played at least some role in domestication, genes showing expression patterns consistent with assimilation are not significantly enriched for previously identified domestication candidates, indicating assimilation did not have a genome-wide effect.

Highly repetitive regions have historically posed a challenge when investigating sequence variation and content. High-throughput sequencing has enabled researchers to use whole-genome shotgun sequencing to estimate the abundance of repetitive sequence, and these methodologies have been recently applied to centromeres. Previous research has investigated variation in centromere repeats across eukaryotes, positing that the highest abundance tandem repeat in a genome is often the centromeric repeat. To test this assumption, we used shotgun sequencing and a bioinformatic pipeline to identify common tandem repeats across a number of grass species. We find that de novo assembly and subsequent abundance ranking of repeats can successfully identify tandem repeats with homology to known tandem repeats. Fluorescent in-situ hybridization shows that de novo assembly and ranking of repeats from non-model taxa identifies chromosome domains rich in tandem repeats both near pericentromeres and elsewhere in the genome.

Objective: In this study, we investigated pivotal molecular markers in human high-grade breast ductal carcinoma in situ (DCIS). Expression status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2) was measured among various subtypes (Luminal (Lum) A, LumB HER2-, LumB HER2+, HER2-enriched and triple-negative). Methods: In total, 357 DCIS cases were classified into respective subtypes, according to the 2013 St. Gallen guidelines. Each subtype was categorized into three subcategories: "Pure" (those without an invasive component), "W/invasive" (those with an invasive component), and "All" (the entire group of the given subtype). ER and PR expression were registered as intervals. Equivocal HER2 immunohistochemistry (IHC) cases (2+) were further investigated using dualcolor in situ hybridization. Results: The majority of patients (71%) were over the age of 50. We discovered no significant differences in the proportion of age between the "Pure" and "W/invasive" groups. There was no significant difference in ER/PR expression between "Pure" luminal subtypes of DCIS and "W/invasive" cases. We compared the HER2 IHC scores of "0", "1+", and "2+" among LumA and LumB HER2 subtypes and identified no statistically significant differences between "Pure" and "W/invasive" (p = 0.603). ER and PR expression = 50% cutoff value was present in > 90% of all LumA cases. The incidences of cases with ER expression at cutoff values of < 10% and = 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). The proportion of cases with PR expression < 20% showed significant differences in the various luminal subtypes. In luminal B subtypes, low PR expression (< 20%) was significantly associated with both strong HER2 expression (3+) and the presence of an invasive component (p = 0.0001 and p = 0.0365, respectively). Conclusions: ER and PR expression at = 50% cutoff values were found in more than 90% of LumA cases. Samples with ER < 10% and = 50% in LumA were significantly different compared to other luminal subtypes (p < 0.0001). Low PR expression in high-grade DCIS was strongly associated with HER2 overexpression (3+) and an invasive component (p = 0.0001 and p = 0.0365, respectively). [ABSTRACT FROM AUTHOR]

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy. [ABSTRACT FROM AUTHOR]

Survey evidence tells us that stock prices reflect the risks investors associate with long-run technological change. However, there is a shortage of models that can rationalize long-run risks. Unlike the previous literature assuming a fixed number of products, our model allows for new product varieties that appear in the form of new firms which face entry costs and delay in the entry process. The fixed variety model has a significant limitation in translating macroeconomic volatility into asset return volatility. Our model with growing varieties induces endogenous low-frequency fluctuations in productivity driving large, persistent variations in consumption growth and asset prices. It also changes the valuation of assets through the increase in the volatility of the pricing kernel (with a positive long-run component) and leads to higher excess returns. Our model is motivated by a simple recursively identified VAR model containing quarterly US data 1992Q3-2018Q4 with the following list of variables: total factor productivity, output, a measure of firm entry, and the excess return on stocks. [ABSTRACT FROM AUTHOR]