An essential role for Ran GTPase in epithelial ovarian cancer cell survival

<p>Abstract</p> <p>Background</p> <p>We previously identified that Ran protein, a member of the Ras GTPase family, is highly expressed in high grade and high stage serous epithelial ovarian cancers, and that its overexpression is associated with a poor prognosis. Ran is known to contribute to both nucleocytoplasmic transport and cell cycle progression, but its role in ovarian cancer is not well defined.</p> <p>Results</p> <p>Using a lentivirus-based tetracycline-inducible shRNA approach, we show that downregulation of Ran expression in aggressive ovarian cancer cell lines affects cellular proliferation by inducing a caspase-3 associated apoptosis. Using a xenograft tumor assay, we demonstrate that depletion of Ran results in decreased tumorigenesis, and eventual tumor formation is associated with tumor cells that express Ran protein.</p> <p>Conclusion</p> <p>Our results suggest a role for Ran in ovarian cancer cell survival and tumorigenicity and suggest that this critical GTPase may be suitable as a therapeutic target.</p

LCE: An Augmented Combination of Bagging and Boosting in Python

lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. The package is compatible with scikit-learn, therefore it can interact with scikit-learn pipelines and model selection tools. It is distributed under the Apache 2.0 license, and its source code is available at https://github.com/LocalCascadeEnsemble/LCE

From Classification Rules to Action Recommendations

Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality

Admissible generalizations of examples as rules

International audienc

A GPS network for tropospheric tomography in the framework of the Mediterranean hydrometeorological observatory Cévennes-Vivarais (south-eastern France)

International audienceThe Mediterranean hydrometeorological observatory Cévennes-Vivarais (OHM-CV) coordinates hydrometeorological observations (radars, rain gauges, water level stations) on a regional scale in southeastern France. In the framework of OHM-CV, temporary GPS measurements have been carried out for 2 months in autumn 2002, when the heaviest rainfall are expected. These measurements increase the spatial density of the existing permanent GPS network, by adding three more receivers between the Mediterranean coast and the Cévennes-Vivarais range to monitor maritime source of water vapour flow feeding the precipitating systems over the Cévennes-Vivarais region. In addition, a local network of 18 receivers covered an area of 30 by 30 km within the field of view of the meteorological radar. These regional and local networks of permanent and temporary stations are used to monitor the precipitable water vapour (PWV) with high temporal resolution (15 min). Also, the dense local network provided data which have been inverted using tomographic techniques to obtain the 3-D field of tropospheric water vapour content. This study presents methodological tests for retrieving GPS tropospheric observations from dense networks, with the aim of assessing the uncertainties of GPS retrievals. Using optimal tropospheric GPS retrieval methods, high resolution measurements of PWV on a local scale (a few kilometres) are discussed for rain events. Finally, the results of 3-D fields of water vapour densities from GPS tomography are analysed with respect to precipitation fields derived from a meteorological radar, showing a good correlation between precipitation and water vapour depletion areas

XPM: An explainable-by-design pattern-based estrus detection approach to improve resource use in dairy farms

International audienceA powerful automatic detection of estrus, the only period when the cow is susceptible to pregnancy, is a key driver to help farmers with reproduction management and subsequently to improve milk production resource use in dairy farms. Automatic solutions to detect both types of estrus (behavioral and silent estrus) based on the combination of affordable phenotyping data (activity, body temperature) exist, but they do not provide faithful explanations to support their alerts and in ways that farmers can understand based on the behaviors they could observe in animals. In this paper, we first propose XPM, a novel pattern-based classifier to detect both types of estrus with real-world affordable sensor data (activity, body temperature) which supports its predictions with perfectly faithful explanations. Then, we show that our approach performs better than a commercial reference in estrus detection, driven by the detection of silent estrus. Finally, we present the explainability of our solution which stems from the communication to the farmers the presence and/or absence of a limited number of patterns determinant of estrus detection, therefore reducing solution mistrust and supporting farmers' decision-making

Three Generations Under One Roof? Bayesian Modeling of Radiocarbon Data from Nunalleq, Yukon-Kuskokwim Delta, Alaska

Acknowledgments. This research was funded through an Arts and Humanities Research Council grant (AH/K006029/1) awarded to Drs. Rick Knecht, Charlotta Hillerdal, and Kate Britton, and two NERC Radiocarbon Facility grants (NF/2015/1/6 and NF/2015/2/3) awarded to Drs. Rick Knecht and Paul Ledger. Véronique Forbes received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement number 703322. Excavations at Nunalleq have also benefited from the support of the local community who have made us all feel at home in Quinhagak. In particular, we wish to thank Qanirtuuq Incorporated and Warren Jones for logistical support and their consistently warm hospitality. Thanks also to Philip Ashlock who took the aerial image presented in Figure 3. We also wish to acknowledge the contribution of all of the students and researchers who have excavated at Nunalleq between 2009 and 2015. Without their hard work and dedication, in sometimes challenging conditions, this article would not have been possible. Finally, we wish to thank three anonymous reviewers and Robert Kelly for constructive criticism that has helped improved this manuscript. Permission for excavations at Nunalleq was granted by Qanirtuuq Incorporated.Peer reviewedPublisher PD

A swarm of small shield volcanoes on Syria Planum, Mars

International audienceThis study focuses on the volcanism in Syria Planum, located at the center of the Tharsis bulge at an altitude of 6 to 8 km above Mars datum. Syria Planum was previously recognized as a center for the tectonic activity of Tharsis, but not as a major locus for volcanic activity, despite its centrality over the bulge. Using high-resolution images from the high resolution stereo camera on Mars Express combined with Mars Observer Laser Altimeter data, we have characterized a volcanic system that reveals a number of very interesting aspects of Mars volcanism. We identified a swarm of tens of coalesced shallow volcanic edifices, typically 10–30 km diameter, 0.1–0.2 km high, and with slopes around 0.5°. These characteristics are similar to those of small shield volcanoes found in Iceland. In addition, an intermediate-sized volcano, which is the source of lava flows that extend over >200 km, is observed west of this shield swarm. Our study characterizes a previously unrecognized volcanic assemblage on Mars which appears to be much more developed than was documented before, in terms of morphology, inferred origin, and periodicity of eruption. The estimated lava flux of the Syria Planum volcanoes is of the same order as the lava flux of Tharsis Montes. These characteristics suggest that Syria Planum experienced a very specific style of volcanism, which we dated to the Hesperian period

Characterization of three new serous epithelial ovarian cancer cell lines

<p>Abstract</p> <p>Background</p> <p>Cell lines constitute a powerful model to study cancer, and here we describe three new epithelial ovarian cancer (EOC) cell lines derived from poorly differentiated serous solid tumors (TOV-1946, and TOV-2223G), as well as the matched ascites for one case (OV-1946).</p> <p>Methods</p> <p>In addition to growth parameters, the cell lines were characterized for anchorage independent growth, migration and invasion potential, ability to form spheroids and xenografts in SCID mice.</p> <p>Results</p> <p>While all cell lines were capable of anchorage independent growth, only the TOV-1946 and OV-1946 cell lines were able to form spheroid and produce tumors. Profiling of keratins, p53 and Her2 protein expression was assessed by immunohistochemistry and western blot analyses. Somatic <it>TP53 </it>mutations were found in all cell lines, with TOV-1946 and OV-1946 harboring the same mutation, and none harbored the commonly observed somatic mutations in <it>BRAF</it>, <it>KRAS </it>or germline BRCA1/2 mutations found to recur in the French Canadian population. Conventional cytogenetics and spectral karyotype (SKY) analyses revealed complex karyotypes often observed in ovarian disease.</p> <p>Conclusion</p> <p>This is the first report of the establishment of matched EOC cell lines derived from both solid tumor and ascites of the same patient.</p