ATRAP antihydrogen experiments

2007

Antihydrogen (Hbar) was first produced at CERN in 1996. Over the past decade our ATRAP collaboration has made massive progress toward our goal of producing large numbers of cold Hbar atoms that will be captured in a magnetic gradient trap for precise comparison between the atomic spectra of matter and antimatter. The AD at CERN provides bunches of 3 × 107 low energy Pbars every 100 seconds. We capture and cool to 4 K, 0.1% of these in a cryogenic Penning trap. By stacking many bunches we are able to do experiments with 3 × 105 Pbars. ∼100 e+/sec from a 22Na radioactive source are captured and cooled in the trap, with 5 × 106 available experiments.We have developed 2 ways to make Hbar from t…

PSYCHIATRY * P61 * DIMENSIONS AND CATEGORIES OF DSM V CRITERIA IN AN INTERNATIONAL SAMPLE OF DRINKING SUBJECTS AND INPATIENT ALCOHOL-DEPENDENT INDIVI…

2011

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

2019

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural ne…

Additional file 1 of High-flow nasal cannula versus non-invasive ventilation for acute hypercapnic respiratory failure in adults: a systematic review…

2022

Additional file 1: Table S1. Embase and Medline Search Results. Table S2. Cochrane Central Search Results. Table S3. Excluded Studies. Table S4. Risk of Bias Table. Fig. S1. Forest plot of mortality—subgroup analysis by risk of bias. Fig. S2. Forest plot of mortality—subgroup analysis excluding Wang et al. Fig. S3. Forest plot of intubation—subgroup analysis by risk of bias. Fig. S4. Forest plot of intubation—subgroup analysis excluding Wang et al. Fig. S5. Forest plot of ICU Length of Stay—subgroup analysis by risk of bias. Fig. S6. Forest plot of ICU Length of Stay—subgroup analysis excluding Wang et al. Fig. S7. Forest plot of Hospital Length of Stay—subgroup analysis by risk of bias. Fi…