The Mating System of the Wild-to-Domesticated Complex of Gossypium hirsutum L. Is Mixed

The domestication syndrome of many plants includes changes in their mating systems. The evolution of the latter is shaped by ecological and genetic factors that are particular to an area. Thus, the reproductive biology of wild relatives must be studied in their natural distribution to understand the mating system of a crop species as a whole. Gossypium hirsutum (upland cotton) includes both domesticated varieties and wild populations of the same species. Most studies on mating systems describe cultivated cotton as self-pollinated, while studies on pollen dispersal report outcrossing; however, the mating system of upland cotton has not been described as mixed and little is known about its wild relatives. In this study we selected two wild metapopulations for comparison with domesticated plants and one metapopulation with evidence of recent gene flow between wild relatives and the crop to evaluate the mating system of cotton’s wild-to-domesticated complex. Using classic reproductive biology methods, our data demonstrate that upland cotton presents a mixed mating system throughout the complex. Given cotton’s capacity for outcrossing, differences caused by the domestication process in cultivated individuals can have consequences for its wild relatives. This characterization of the diversity of the wild relatives in their natural distribution, as well as their interactions with the crop, will be useful to design and implement adequate strategies for conservation and biosecurity

Patient judgments about hypertension control: The role of variability, trends, and outliers in visualized blood pressure data

Background: Uncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease. Objective: As part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data. Methods: In 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient's BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients' judgments about hypertension control and the need for a medication change. Results: Participants' judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time--Study 2), and extreme values (ie, outliers--Study 3). Conclusions: Patients' judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information

“Born to Run”? Not Necessarily: Species and Trait Bias in Persistent Free-Living Transgenic Plants

The possibility of transgenes from engineered plants ending up in unmanaged populations with undesirable consequences has been a long-term biosafety concern. Experience with traditionally improved plants reveals that most cases of such gene escape have been of little consequence, but on occasion they have led to the evolution of problematic plants or have resulted in an increased extinction risk for wild taxa. Three decades have passed since the first environmental release of transgenic plants, and more than two decades since their first commercialization. Examples of transgenes gone astray are increasingly commonplace. Transgenic individuals have been identified in more than a thousand free-living plant populations. Here I review 14 well-documented consolidated “cases” in which transgenes have found their way into free-living plant populations. Some as transient volunteers; others appear to be persistent transgenic populations. The species involved in the latter are not representative of the current commercialized transgenic crops as whole. They tend to share certain traits that are absent or rare in the transgenic crops that do not exist as persistent populations. The traits commonly occurring in species with persistent transgenic free-living populations are the following, in descending order of importance: (1) a history of occurring as non-transgenic free-living plants, (2) fruits fully or partially shattering prior to harvest, (3) have small or otherwise easily dispersed seeds, either spontaneously or by seed spillage along the supply chain from harvest to consumer, (4) ability to disperse viable pollen, especially to a kilometer or more, (5) perennial habit, and (6) the transgene's fitness effects in the recipient environment are beneficial or neutral. Based on these observations, a thought experiment posits which species might be the next to be reported to occur as free-living transgenic populations

Extinction risk of Mesoamerican crop wild relatives

Ensuring food security is one of the world's most critical issues as agricultural systems are already being impacted by global change. Crop wild relatives (CWR)—wild plants related to crops—possess genetic variability that can help adapt agriculture to a changing environment and sustainably increase crop yields to meet the food security challenge. Here we report the results of an extinction risk assessment of 224 wild relatives of some of the world's most important crops (i.e. chilli pepper, maize, common bean, avocado, cotton, potato, squash, vanilla and husk tomato) in Mesoamerica—an area of global significance as a centre of crop origin, domestication and of high CWR diversity. We show that 35% of the selected CWR taxa are threatened with extinction according to The International Union for Conservation of Nature (IUCN) Red List demonstrates that these valuable genetic resources are under high anthropogenic threat. The dominant threat processes are land use change for agriculture and farming, invasive and other problematic species (e.g. pests, genetically modified organisms) and use of biological resources, including overcollection and logging. The most significant drivers of extinction relate to smallholder agriculture—given its high incidence and ongoing shifts from traditional agriculture to modern practices (e.g. use of herbicides)—smallholder ranching and housing and urban development and introduced genetic material. There is an urgent need to increase knowledge and research around different aspects of CWR. Policies that support in situ and ex situ conservation of CWR and promote sustainable agriculture are pivotal to secure these resources for the benefit of current and future generations

Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms

In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between classes—such as in the detection of network attacks or fraud detection problems. In the following work, we propose methods to modify existing stream processing solutions—Accuracy Weighted Ensemble (AWE) and Accuracy Updated Ensemble (AUE), which have demonstrated their effectiveness in adapting to time-varying class distribution. The introduced changes are aimed at increasing their quality on binary classification of imbalanced data. The proposed modifications contain the inclusion of aggregate metrics, such as F1-score, G-mean and balanced accuracy score in calculation of the member classifiers weights, which affects their composition and final prediction. Moreover, the impact of data sampling on the algorithm’s effectiveness was also checked. Complex experiments were conducted to define the most promising modification type, as well as to compare proposed methods with existing solutions. Experimental evaluation shows an improvement in the quality of classification compared to the underlying algorithms and other solutions for processing imbalanced data streams

Experiment 1—Proportion of risky choices taken for differing levels of <i>p</i>_desirable and payoff variability.

<p>Experiment 1—Proportion of risky choices taken for differing levels of <i>p</i>_desirable and payoff variability.</p