Analyse du temps de rétablissement des ambulances

Introduction : Le Temps de Rétablissement de l'Ambulance (TRA) représente la période entre l'arrivée de l'ambulance à l'hôpital et le moment où elle est à nouveau disponible pour la Centrale d'Engagement (CE) (centrale 144 en Suisse). Ce temps est constitué principalement de l'Ambulance Offload Delay (AOD), temps entre l'arrivée de l'ambulance à l'hôpital et le moment où les ambulanciers sont déchargés du patient, et du temps de Réhabilitation de l'Ambulance (RA), temps nécessaire aux ambulanciers pour réapprovisionner le véhicule et s'annoncer comme disponible à la CE. Objectif : Déterminer les étapes ainsi que les temps du TRA au CHUV et formuler des propositions pour l'optimiser. Méthode : Récolte manuelle prospective des temps des différentes étapes du TRA au service des urgences du CHUV. Les données ont été récoltées du lundi 21 au vendredi 25 avril 2014 et du lundi 23 au vendredi 27 juin 2014 de 08h00 à 20h00, soit 120 heures au total, et ont fait l'objet d'une analyse statistique. Résultats : Les missions primaires sans passage au déchocage (197) présentent un TRA de 25 minutes, un AOD de 13 min. et un RA de 9 min. Les missions primaires avec passage au déchocage (29) présentent un TRA de 22 minutes, un AOD de 4 min. et un RA de 8 min. Les missions secondaires (70) présentent un TRA de 24 minutes, un AOD de 20 min. et un RA de 3 min. Conclusions : L'étape la plus longue du TRA est l'AOD. L'AOD est principalement composé du tri infirmier. La mise en place d'un système de communication entre l'hôpital, les ambulances en mission et la CE pourrait permettre une meilleure transmission de l'information et de diminuer la durée du TRA

Real-Time Imaging of K atoms on Graphite: Interactions and Diffusion

Scanning tunneling microscopy (STM) at liquid helium temperature is used to image potassium adsorbed on graphite at low coverage (~0.02 monolayer). Single atoms appear as protrusions on STM topographs. A statistical analysis of the position of the atoms demonstrates repulsion between adsorbates, which is quantified by comparison with molecular dynamics simulations. This gives access to the dipole moment of a single adsorbate, found to be 10.5 Debye. Time lapse imaging shows that long range order is broken by thermally activated diffusion, with a 32 meV barrier to hopping between graphite lattice sites

A single chain analysis of doped quasi one dimensional spin 1 compounds: paramagnetic versus spin 1/2 doping

We present a numerical study of single chain models of doped spin 1 compounds. We use low energy effective one-dimensional models for both the cases of paramagnetic and spin-1/2 doping. In the case of paramagnetic doping, the effective model is equivalent to the bond disordered spin-1/2 chain model recently analyzed by means of real space renormalization group by Hyman and Yang. By means of exact diagonalizations in the XX limit, we confirm the stability of the Haldane phase for weak disorder. Above a critical amount of disorder, the effective model flows to the so called random singlet fixed point. In the case of spin-1/2 doping, we argue that the Haldane phase should be destabilized even for weak disorder. This picture is not in contradiction with existing experimental data. We also discuss the possible occurrence of (unobserved) antiferromagnetically ordered phases.Comment: 13 pages, 7 included figure

New strong sector, odd-parity processes, and the Tevatron

The color-octet isosinglet "rho" of a new strong-interaction sector is readily produced in ppbar collisions. Its odd-parity decay to an "eta" and a gluon may exceed its decay rate to dijets. At center of mass energies sufficiently greater than the colored "rho" mass, the odd-parity production of ("omega" + colored "eta") or ("rho" + colored "pion") may be comparable to ttbar production. Considering that the "omega" has a dominant odd-parity decay mode, we end up with (Z, W, or gamma) + 4 jet events with two of the jets containing b or bbar.Comment: 8 pages, uuencoded PS file, try ghostscript or direct printing rather than ghostview, PS file of paper without figure is available on WWW at http://bigmac2.physics.utoronto.ca/BHpapers.htm

Contamination detection and microbiome exploration with GRIMER

Background: Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results: We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. Conclusion: GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable meth- ods for viral host prediction are necessary to safe- guard biosecurity and biosafety alike. Novel human- infecting viruses are difficult to detect with stan- dard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next- generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology- based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host pre- diction task. We propose a new approach for con- volutional filter visualization to disentangle the in- formation content of each nucleotide from its contri- bution to the final classification decision. Nucleotide- resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy- to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.Peer Reviewe

LazyFox: Fast and parallelized overlapping community detection in large graphs

The detection of communities in graph datasets provides insight about a graph's underlying structure and is an important tool for various domains such as social sciences, marketing, traffic forecast, and drug discovery. While most existing algorithms provide fast approaches for community detection, their results usually contain strictly separated communities. However, most datasets would semantically allow for or even require overlapping communities that can only be determined at much higher computational cost. We build on an efficient algorithm, Fox, that detects such overlapping communities. Fox measures the closeness of a node to a community by approximating the count of triangles which that node forms with that community. We propose LazyFox, a multi-threaded version of the Fox algorithm, which provides even faster detection without an impact on community quality. This allows for the analyses of significantly larger and more complex datasets. LazyFox enables overlapping community detection on complex graph datasets with millions of nodes and billions of edges in days instead of weeks. As part of this work, LazyFox's implementation was published and is available as a tool under an MIT licence at https://github.com/TimGarrels/LazyFox.Comment: 17 pages, 5 figure

Probing the Weak Boson Sector in γe→Ze\gamma e\rightarrow Ze

We study possible deviations from the standard model in the reaction $\gamma e\rightarrow Ze$ at a 500 GeV $e^+e^-$ collider. As a photon source we use a laser backscattered photon beam. We investigate the most general $\gamma Z\gamma$ and $\gamma ZZ$ vertices including operators up to energy-dimension-six which are Lorentz invariant. These vertices require four extra parameters; two are CP-conserving, $h^\gamma_1$ and $h^Z_1$, and two are CP-violating, $h^\gamma_2$ and $h^Z_2$. We present analytical expressions of the helicity amplitudes for the process $\gamma e\rightarrow Ze$ for arbitrary values of anomalous couplings. Assuming Standard Model values are actually measured we present the allowed region in the ($h^\gamma_1,h^Z_1$) plane at the 90\% confidence level. We then show how the angular correlation of the $Z$ decay products can be used to extract detailed information on the anomalous (especially CP-violating) $\gamma Z\gamma$ and $\gamma ZZ$ couplings.Comment: Latex, 25 pages, 12 figures (not included). One compressed postscript file including all the figures available at ftp://ftp.kek.jp/kek/preprints/TH/TH-420/kekth420.ps.g

Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides

Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%