TB174: Maine Wild Blueberries Field Winnowing Systems

The objective of this study was to determine if there were differences in berry quality between the two winnowing systems currently used in the Maine wild blueberry industry. The following experiment was performed three times during the 1997 field season.https://digitalcommons.library.umaine.edu/aes_techbulletin/1034/thumbnail.jp

Evolutionary paths to macrolide resistance in a Neisseria commensal converge on ribosomal genes through short sequence duplications

Neisseria commensals are an indisputable source of resistance for their pathogenic relatives. However, the evolutionary paths commensal species take to reduced susceptibility in this genus have been relatively underexplored. Here, we leverage in vitro selection as a powerful screen to identify the genetic adaptations that produce azithromycin resistance (� 2 μg/mL) in the Neisseria commensal, N. elongata. Across multiple lineages (n = 7/16), we find mutations that reduce susceptibility to azithromycin converge on the locus encoding the 50S ribosomal L34 protein (rpmH) and the intergenic region proximal to the 30S ribosomal S3 protein (rpsC) through short tandem duplication events. Interestingly, one of the laboratory evolved mutations in rpmH is identical (7LKRTYQ12), and two nearly identical, to those recently reported to contribute to high-level azithromycin resistance in N. gonorrhoeae. Transformations into the ancestral N. elongata lineage confirmed the causality of both rpmH and rpsC mutations. Though most lineages inheriting duplications suffered in vitro fitness costs, one variant showed no growth defect, suggesting the possibility that it may be sustained in natural populations. Ultimately, studies like this will be critical for predicting commensal alleles that could rapidly disseminate into pathogen populations via allelic exchange across recombinogenic microbial genera

2000 Wild Blueberry Project Reports

The 2000 edition of the Wild Blueberry Project Reports was prepared for the Maine Wild Blueberry Commission and the University of Maine Wild Blueberry Advisory Committee by researchers at the University of Maine, Orono. Projects in this report include: 1. Determination of Pesticide Residue Levels in Fresh and Processed Wild Blueberries 2. Factors Affecting the Microbiological Quality of IQF Blueberries 3. Effect of Processed Blueberry Products on Oxidation in Meat Based Food Systems 4. Separation of Maggot Infested Wild Blueberries in the IQF Processing Line 5. Water Use of Wild Blueberries 6. Control Tactics for Blueberry Pest Insects, 2000 7. IPM Strategies 8. Biology and Ecology of Blueberry Pest Insects 9. Survey of Stem Blight and Leaf Spot Diseases in Lowbush Blueberry Fields 10. Phosphorus/Nitrogen Fertilizer Ratio 11. Effect of Boron Application Methods on Boron Uptake in Lowbush Blueberries 12. Effect of Foliar Iron and Copper Application on Growth and Yield of Lowbush Blueberries 13. Effect of Soil pH on Nutrient Uptake 14. Effect of Nutri-Phite (tm) P+K on Growth and Yield of Lowbush Blueberry 15. Effect of Fertilizer Timing on Lowbush Blueberry Growth and Productivity 16. Assessment of Azafenidin for Weed Control in Wild Blueberries 17. Assessment of Rimsulfuron for Weed Control in Wild Blueberries 18. Assessment of Pendimethalin for Weed Control in Wild Blueberries 19. Assessment of VC1447 for Weed Control in Wild Blueberries 20. Cultural Management Using pH for Weed Control in Wild Blueberries 21. Evaluation of Sprout-Less Weeder® for Weed Control in Wild Blueberries 22. Evaluation of RoundUp Ultra® and Touchdown 5® for Weed Control in Wild Blueberries 23. Evaluation and Demonstration of Techniques for Filling in Bare Spots in Wild Blueberry Fields 24. Evaluation of Fungicides Efficacy in Wild Blueberry Fields 25. Velpar® and Sinbar/Karmex® Demonstration Plot Comparison Trial 26. Blueberry Extension Education Program in 2000 27. 2000 Hexazinone Groundwater Surve

The XMM Cluster Survey: Forecasting cosmological and cluster scaling-relation parameter constraints

We forecast the constraints on the values of sigma_8, Omega_m, and cluster scaling relation parameters which we expect to obtain from the XMM Cluster Survey (XCS). We assume a flat Lambda-CDM Universe and perform a Monte Carlo Markov Chain analysis of the evolution of the number density of galaxy clusters that takes into account a detailed simulated selection function. Comparing our current observed number of clusters shows good agreement with predictions. We determine the expected degradation of the constraints as a result of self-calibrating the luminosity-temperature relation (with scatter), including temperature measurement errors, and relying on photometric methods for the estimation of galaxy cluster redshifts. We examine the effects of systematic errors in scaling relation and measurement error assumptions. Using only (T,z) self-calibration, we expect to measure Omega_m to +-0.03 (and Omega_Lambda to the same accuracy assuming flatness), and sigma_8 to +-0.05, also constraining the normalization and slope of the luminosity-temperature relation to +-6 and +-13 per cent (at 1sigma) respectively in the process. Self-calibration fails to jointly constrain the scatter and redshift evolution of the luminosity-temperature relation significantly. Additional archival and/or follow-up data will improve on this. We do not expect measurement errors or imperfect knowledge of their distribution to degrade constraints significantly. Scaling-relation systematics can easily lead to cosmological constraints 2sigma or more away from the fiducial model. Our treatment is the first exact treatment to this level of detail, and introduces a new `smoothed ML' estimate of expected constraints.Comment: 28 pages, 17 figures. Revised version, as accepted for publication in MNRAS. High-resolution figures available at http://xcs-home.org (under "Publications"

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.Swiss National Science Foundation (contract number PBEL2118737)United States. Army Research Office. Multidisciplinary University Research Initiative (MURI SWARMS project W911NF-05-1-0219)National Science Foundation (U.S.) (NSF IIS-0426838)Intel Corporation (EFRI 0735953 Intel)Massachusetts Institute of Technology (UROP program)Massachusetts Institute of Technology (MSRP program

HMGA1 Induces Intestinal Polyposis in Transgenic Mice and Drives Tumor Progression and Stem Cell Properties in Colon Cancer Cells

Although metastatic colon cancer is a leading cause of cancer death worldwide, the molecular mechanisms that enable colon cancer cells to metastasize remain unclear. Emerging evidence suggests that metastatic cells develop by usurping transcriptional networks from embryonic stem (ES) cells to facilitate an epithelial-mesenchymal transition (EMT), invasion, and metastatic progression. Previous studies identified HMGA1 as a key transcription factor enriched in ES cells, colon cancer, and other aggressive tumors, although its role in these settings is poorly understood.To determine how HMGA1 functions in metastatic colon cancer, we manipulated HMGA1 expression in transgenic mice and colon cancer cells. We discovered that HMGA1 drives proliferative changes, aberrant crypt formation, and intestinal polyposis in transgenic mice. In colon cancer cell lines from poorly differentiated, metastatic tumors, knock-down of HMGA1 blocks anchorage-independent cell growth, migration, invasion, xenograft tumorigenesis and three-dimensional colonosphere formation. Inhibiting HMGA1 expression blocks tumorigenesis at limiting dilutions, consistent with depletion of tumor-initiator cells in the knock-down cells. Knock-down of HMGA1 also inhibits metastatic progression to the liver in vivo. In metastatic colon cancer cells, HMGA1 induces expression of Twist1, a gene involved in embryogenesis, EMT, and tumor progression, while HMGA1 represses E-cadherin, a gene that is down-regulated during EMT and metastatic progression. In addition, HMGA1 is among the most enriched genes in colon cancer compared to normal mucosa.Our findings demonstrate for the first time that HMGA1 drives proliferative changes and polyp formation in the intestines of transgenic mice and induces metastatic progression and stem-like properties in colon cancer cells. These findings indicate that HMGA1 is a key regulator, both in metastatic progression and in the maintenance of a stem-like state. Our results also suggest that HMGA1 or downstream pathways could be rational therapeutic targets in metastatic, poorly differentiated colon cancer

Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

BRCA mutational status shapes the stromal microenvironment of pancreatic cancer linking clusterin expression in cancer associated fibroblasts with HSF1 signaling

Tumors initiate by mutations in cancer cells, and progress through interactions of the cancer cells with non-malignant cells of the tumor microenvironment. Major players in the tumor microenvironment are cancer-associated fibroblasts (CAFs), which support tumor malignancy, and comprise up to 90% of the tumor mass in pancreatic cancer. CAFs are transcriptionally rewired by cancer cells. Whether this rewiring is differentially affected by different mutations in cancer cells is largely unknown. Here we address this question by dissecting the stromal landscape of BRCA-mutated and BRCA Wild-type pancreatic ductal adenocarcinoma. We comprehensively analyze pancreatic cancer samples from 42 patients, revealing different CAF subtype compositions in germline BRCA-mutated vs. BRCA Wild-type tumors. In particular, we detect an increase in a subset of immune-regulatory clusterin-positive CAFs in BRCA-mutated tumors. Using cancer organoids and mouse models we show that this process is mediated through activation of heat-shock factor 1, the transcriptional regulator of clusterin. Our findings unravel a dimension of stromal heterogeneity influenced by germline mutations in cancer cells, with direct implications for clinical research