Sympathetic cooling in an optically trapped mixture of alkali and spin-singlet atoms

We report on the realization of a stable mixture of ultracold lithium and ytterbium atoms confined in a far-off-resonance optical dipole trap. We observe sympathetic cooling of 6Li by 174Yb and extract the s-wave scattering length magnitude |a6Li-174Yb| = (13 \pm 3)a0 from the rate of inter-species thermalization. Using forced evaporative cooling of 174Yb, we achieve reduction of the 6Li temperature to below the Fermi temperature, purely through inter-species sympathetic cooling.Comment: 4 pages, 3 figure

Le placement d'enfant en famille d'accueil: comment est évalué le bien-fondé du maintien ou de l'arrêt d'un placement

Ce travail de recherche a pour objectif de comprendre comment les intervenants en protection de l’enfance évaluent les situations afin de statuer en faveur du maintien ou de l’arrêt d’un placement en famille nourricière. Ce travail se centre principalement sur les placements à long terme qui sont caractérisés par un accueil de longue durée au sein de la même famille nourricière

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data

Geocomputation with R

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/

Medical Students Preventing Medical Errors: A Student-Led Approach to Patient Safety in Preclinical Curriculum

Introduction: Preventable medical errors are currently the third leading cause of death in the United States following heart disease and cancer (1). Early exposure to patient safety knowledge may lead to students to deliver safer care in their clerkship and residency years. This study was designed to assess the change in knowledge from earlier exposure and education during pre-clinical years and its impact on interest and knowledge about patient safety. Methods: For the past four years, a patient safety training has been conducted for interested first and second-year medical students and responses are assessed through a pre-test, immediate post-test, 3-month post-test, and 6-month post-test. The survey assesses student knowledge on various aspects of patient safety, identifying the correct course of action in different scenarios concerning patient safety. Results:The average percentage of correct answers on patient safety knowledge-based questions was 71.4% on the pre-test training and decreased to 68.4% in the 6-month post-test . The percent of students who considered themselves to be well-versed in different aspects of patient safety was 15.2% in the pre-test training and increased to 75% in the 6-month post-test. The percent of students that plan to incorporate patient safety techniques into their future practice was 97% in the pre-test training and 100% in the 6-month post-test. The percent of students who believed that patient safety can have a large impact on health outcomes was initially 97% in the pre-test training and 98.3% in the 6-month post-test. Conclusion:Improvement in patient safety knowledge amongst students immediately after training is promising. Although the percentage of correct answers decreased over time, students exhibited more knowledge on patient safety topics immediately after training than they had prior to any patient safety training. The lack of statistically significant findings can most likely be attributed to small sample size and will likely improve with further data collection.Continued training sessions will solidify knowledge about patient safety in preclinical years and potentially in clerkship years, and will allow for students to gain confidence in their knowledge of patient safety and medical errors

Tidal modulation of buoyant flow and basal melt beneath Petermann Gletscher Ice Shelf, Greenland.

A set of collocated, in situ oceanographic and glaciological measurements from Petermann Gletscher Ice Shelf, Greenland, provides insights into the dynamics of under‐ice flow driving basal melting. At a site 16 km seaward of the grounding line within a longitudinal basal channel, two conductivity‐temperature (CT) sensors beneath the ice base and a phase‐sensitive radar on the ice surface were used to monitor the coupled ice shelf‐ocean system. A six month time series spanning August 23, 2015 to February 12, 2016 exhibited two distinct periods of ice‐ocean interactions. Between August and December, radar‐derived basal melt rates featured fortnightly peaks of ~15 m yr‐1 which preceded the arrival of cold and fresh pulses in the ocean that had high concentrations of subglacial runoff and glacial meltwater. Estimated current speeds reached 0.20‐0.40 m s‐1 during these pulses, consistent with a strengthened meltwater plume from freshwater enrichment. Such signals did not occur between December and February, when ice‐ocean interactions instead varied at principal diurnal and semidiurnal tidal frequencies, and lower melt rates and current speeds prevailed. A combination of estimated current speeds and meltwater concentrations from the two CT sensors yields estimates of subglacial runoff and glacial meltwater volume fluxes that vary between 10 and 80 m3 s‐1 during the ocean pulses. Area‐average upstream ice shelf melt rates from these fluxes are up to 170 m yr‐1, revealing that these strengthened plumes had already driven their most intense melting before arriving at the study site

Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), kNearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like Generalized Additive Models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease (Diplodia sapinea) in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and Random Forest (RF) (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data. The models developed in this study enhance the detection of Diplodia sapinea in the Basque Country compared to previous studies

Eddy transport of organic carbon and nutrients from the Chukchi Shelf : impact on the upper halocline of the western Arctic Ocean

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): C05011, doi:10.1029/2006JC003899.In September 2004 a detailed physical and chemical survey was conducted on an anticyclonic, cold-core eddy located seaward of the Chukchi Shelf in the western Arctic Ocean. The eddy had a diameter of ∼16 km and was centered at a depth of ∼160 m between the 1000 and 1500 m isobaths over the continental slope. The water in the core of the eddy (total volume of 25 km3) was of Pacific origin, and contained elevated concentrations of nutrients, organic carbon, and suspended particles. The feature, which likely formed from the boundary current along the edge of the Chukchi Shelf, provides a mechanism for transport of carbon, oxygen, and nutrients directly into the upper halocline of the Canada Basin. Nutrient concentrations in the eddy core were elevated compared to waters of similar density in the deep Canada Basin: silicate (+20 μmol L−1), nitrate (+5 μmol L−1), and phosphate (+0.4 μmol L−1). Organic carbon in the eddy core was also elevated: POC (+3.8 μmol L−1) and DOC (+11 μmol L−1). From these observations, the eddy contained 1.25 × 109 moles Si, 4.5 × 108 moles NO3 −, 5.5 × 107 moles PO3 −, 1.2 × 108 moles POC, and 1.9 × 109 moles DOC, all available for transport to the interior of the Canada Basin. This suggests that such eddies likely play a significant role in maintaining the nutrient maxima observed in the upper halocline. Assuming that shelf-to-basin eddy transport is the dominant renewal mechanism for waters of the upper halocline, remineralization of the excess organic carbon transported into the interior would consume 6.70 × 1010 moles of O2, or one half the total oxygen consumption anticipated arising from all export processes impacting the upper halocline.This work was supported by the National Science Foundation, and office of Naval Research; DH OPP-0124900, NB OPP-0124868, DK OPP 0124872, RP N00014-02-1-0317

Sex-Differential Herbivory in Androdioecious Mercurialis annua

Males of plants with separate sexes are often more prone to attack by herbivores than females. A common explanation for this pattern is that individuals with a greater male function suffer more from herbivory because they grow more quickly, drawing more heavily on resources for growth that might otherwise be allocated to defence. Here, we test this ‘faster-sex’ hypothesis in a species in which males in fact grow more slowly than hermaphrodites, the wind-pollinated annual herb Mercurialis annua. We expected greater herbivory in the faster-growing hermaphrodites. In contrast, we found that males, the slower sex, were significantly more heavily eaten by snails than hermaphrodites. Our results thus reject the faster-sex hypothesis and point to the importance of a trade-off between defence and reproduction rather than growth