Arthropod Fauna Associated with Wild and Cultivated Cranberries in Wisconsin

The cranberry (Vaccinium macrocarpon Aiton) is an evergreen, trailing shrub native to North American peatlands. It is cultivated commercially in the US and Canada, with major production centers in Wisconsin, Massachusetts, New Jersey, Washington, Québec, and British Columbia. Despite the agricultural importance of cranberry in Wisconsin, relatively little is known of its arthropod associates, particularly the arachnid fauna. Here we report preliminary data on the insect and spider communities associated with wild and cultivated cranberries in Wisconsin. We then compare the insect and spider communities of wild cranberry systems to those of cultivated cranberries, indexed by region. Approximately 7,400 arthropods were curated and identified, spanning more than 100 families, across 11 orders. The vast majority of specimens and diversity derived from wild ecosystems. In both the wild and cultivated systems, the greatest numbers of families were found among the Diptera (midges, flies) and Hymenoptera (bees, ants, wasps), but numerically, the Hymenoptera and Araneae (spiders) were dominant. Within the spider fauna, 18 new county records, as well as a new Wisconsin state record (Linyphiidae: Ceratinopsis laticeps (Em.)), were documented. While more extensive sampling will be needed to better resolve arthropod biodiversity in North American cranberry systems, our findings represent baseline data on the breadth of arthropod diversity in the Upper Midwest, USA

Arthropod Fauna Associated with Wild and Cultivated Cranberries in Wisconsin

The cranberry (Vaccinium macrocarpon Aiton) is an evergreen, trailing shrub native to North American peatlands. It is cultivated commercially in the US and Canada, with major production centers in Wisconsin, Massachusetts, New Jersey, Washington, Québec, and British Columbia. Despite the agricultural importance of cranberry in Wisconsin, relatively little is known of its arthropod associates, particularly the arachnid fauna. Here we report preliminary data on the insect and spider communities associated with wild and cultivated cranberries in Wisconsin. We then compare the insect and spider communities of wild cranberry systems to those of cultivated cranberries, indexed by region. Approximately 7,400 arthropods were curated and identified, spanning more than 100 families, across 11 orders. The vast majority of specimens and diversity derived from wild ecosystems. In both the wild and cultivated systems, the greatest numbers of families were found among the Diptera (midges, flies) and Hymenoptera (bees, ants, wasps), but numerically, the Hymenoptera and Araneae (spiders) were dominant. Within the spider fauna, 18 new county records, as well as a new Wisconsin state record (Linyphiidae: Ceratinopsis laticeps (Em.)), were documented. While more extensive sampling will be needed to better resolve arthropod biodiversity in North American cranberry systems, our findings represent baseline data on the breadth of arthropod diversity in the Upper Midwest, USA

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Building an Assessment Use Argument for sign language: the BSL Nonsense Sign Repetition Test

In this article, we adapt a concept designed to structure language testing more effectively, the Assessment Use Argument (AUA), as a framework for the development and/or use of sign language assessments for deaf children who are taught in a sign bilingual education setting. By drawing on data from a recent investigation of deaf children's nonsense sign repetition skills in British Sign Language, we demonstrate the steps of implementing the AUA in practical test design, development and use. This approach provides us with a framework which clearly states the competing values and which stakeholders hold these values. As such, it offers a useful foundation for test-designers, as well as for practitioners in sign bilingual education, for the interpretation of test scores and the consequences of their use

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology.Comment: 36 pages, 8 figures, 3 table

Meeting Report: Consensus Statement—Parkinson’s Disease and the Environment: Collaborative on Health and the Environment and Parkinson’s Action Network (CHE PAN) Conference 26–28 June 2007

BackgroundParkinson's disease (PD) is the second most common neurodegenerative disorder. People with PD, their families, scientists, health care providers, and the general public are increasingly interested in identifying environmental contributors to PD risk.MethodsIn June 2007, a multidisciplinary group of experts gathered in Sunnyvale, California, USA, to assess what is known about the contribution of environmental factors to PD.ResultsWe describe the conclusions around which they came to consensus with respect to environmental contributors to PD risk. We conclude with a brief summary of research needs.ConclusionsPD is a complex disorder, and multiple different pathogenic pathways and mechanisms can ultimately lead to PD. Within the individual there are many determinants of PD risk, and within populations, the causes of PD are heterogeneous. Although rare recognized genetic mutations are sufficient to cause PD, these account for < 10% of PD in the U.S. population, and incomplete penetrance suggests that environmental factors may be involved. Indeed, interplay among environmental factors and genetic makeup likely influences the risk of developing PD. There is a need for further understanding of how risk factors interact, and studying PD is likely to increase understanding of other neurodegenerative disorders

The Beauty Contest and Short-Term Trading

Short-termism need not breed informational price inefficiency even when gen- erating Beauty Contests. We demonstrate this claim in a two-period market with persistent liquidity trading and risk-averse, privately informed, short-term investors and find that prices reflect average expectations about fundamentals and liquidity trading. Informed investors engage in “retrospective” learning to reassess inferences (about fundamentals) made during the trading game’s early stages. This behavior introduces strategic complementarities in the use of information and can yield two stable equilibria that can be ranked in terms of liquidity, volatility, and informational efficiency. We derive implications that explain market anomalies as well as empirical regularities