Diagnostic and Prognostic Impact of pc-ASPECTS Applied to Perfusion CT in the Basilar Artery International Cooperation Study.

BACKGROUND AND PURPOSE: The posterior circulation Acute Stroke Prognosis Early CT Score (pc-APECTS) applied to CT angiography source images (CTA-SI) predicts the functional outcome of patients in the Basilar Artery International Cooperation Study (BASICS). We assessed the diagnostic and prognostic impact of pc-ASPECTS applied to perfusion CT (CTP) in the BASICS registry population. METHODS: We applied pc-ASPECTS to CTA-SI and cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) parameter maps of BASICS patients with CTA and CTP studies performed. Hypoattenuation on CTA-SI, relative reduction in CBV or CBF, or relative increase in MTT were rated as abnormal. RESULTS: CTA and CTP were available in 27/592 BASICS patients (4.6%). The proportion of patients with any perfusion abnormality was highest for MTT (93%; 95% confidence interval [CI], 76%-99%), compared with 78% (58%-91%) for CTA-SI and CBF, and 46% (27%-67%) for CBV (P < .001). All 3 patients with a CBV pc-ASPECTS < 8 compared to 6/23 patients with a CBV pc-ASPECTS ≥ 8 had died at 1 month (RR 3.8; 95% CI, 1.9-7.6). CONCLUSION: CTP was performed in a minority of the BASICS registry population. Perfusion disturbances in the posterior circulation were most pronounced on MTT parameter maps. CBV pc-ASPECTS < 8 may indicate patients with high case fatality.

Can the administration be trusted? An analysis of the concept of trust, applied to the public sector

In the first part of this paper, we present the various academic debates and, where applicable, questions that remain open in the literature, particularly regarding the nature of trust, the distinction between trust and trustworthiness, its role in specific relationships and its relationship to control. We then propose a way of demarcating and operationalizing the concepts of trust and trustworthiness. In the second part, on the basis of the conceptual clarifications we present, we put forward a number of "anchor points" regarding how trust is apprehended in the public sector with regard to the various relations hips that can be studied. Schematically, we distinguish between two types of relations hips in the conceptual approach to trust: on one hand, the trust that citizens, or third parties, place in the State or in various public sector authorities or entities, and on the other hand, trust within the State or the public sector, between its various authorities, entities, and actors. While studies have traditionally focused on citizens' trust in their institutions, the findings, limitations and problems observed in public - sector coordination following the reforms associated with New Public Management have also elicited growing interest in the study of trust in the relationships between the various actors within the public sector. Both the theoretical debates we present and our propositions have been extracted and adapted from an empirical comparative study of coordination between various Swiss public - service organizations and their politico - administrative authority. Using the analysis model developed for this specific relationship, between various actors within the public service, and in the light of theoretical elements on which development of this model was based, we propose some avenues for further study - questions that remain open - regarding the consideration and understanding of citizens' trust in the public sector.

RNA enrichment method for quantitative transcriptional analysis of pathogens in vivo applied to the fungus Candida albicans.

UNLABELLED: In vivo transcriptional analyses of microbial pathogens are often hampered by low proportions of pathogen biomass in host organs, hindering the coverage of full pathogen transcriptome. We aimed to address the transcriptome profiles of Candida albicans, the most prevalent fungal pathogen in systemically infected immunocompromised patients, during systemic infection in different hosts. We developed a strategy for high-resolution quantitative analysis of the C. albicans transcriptome directly from early and late stages of systemic infection in two different host models, mouse and the insect Galleria mellonella. Our results show that transcriptome sequencing (RNA-seq) libraries were enriched for fungal transcripts up to 1,600-fold using biotinylated bait probes to capture C. albicans sequences. This enrichment biased the read counts of only ~3% of the genes, which can be identified and removed based on a priori criteria. This allowed an unprecedented resolution of C. albicans transcriptome in vivo, with detection of over 86% of its genes. The transcriptional response of the fungus was surprisingly similar during infection of the two hosts and at the two time points, although some host- and time point-specific genes could be identified. Genes that were highly induced during infection were involved, for instance, in stress response, adhesion, iron acquisition, and biofilm formation. Of the in vivo-regulated genes, 10% are still of unknown function, and their future study will be of great interest. The fungal RNA enrichment procedure used here will help a better characterization of the C. albicans response in infected hosts and may be applied to other microbial pathogens. IMPORTANCE: Understanding the mechanisms utilized by pathogens to infect and cause disease in their hosts is crucial for rational drug development. Transcriptomic studies may help investigations of these mechanisms by determining which genes are expressed specifically during infection. This task has been difficult so far, since the proportion of microbial biomass in infected tissues is often extremely low, thus limiting the depth of sequencing and comprehensive transcriptome analysis. Here, we adapted a technology to capture and enrich C. albicans RNA, which was next used for deep RNA sequencing directly from infected tissues from two different host organisms. The high-resolution transcriptome revealed a large number of genes that were so far unknown to participate in infection, which will likely constitute a focus of study in the future. More importantly, this method may be adapted to perform transcript profiling of any other microbes during host infection or colonization.

Rockfall detection from LIDAR point clouds: A clustering approach using R

The analysis of rockfall characteristics and spatial distribution is fundamental to understand and model the main factors that predispose to failure. In our study we analysed LiDAR point clouds aiming to: (1) detect and characterise single rockfalls; (2) investigate their spatial distribution. To this end, different cluster algorithms were applied: 1a) Nearest Neighbour Clutter Removal (NNCR) in combination with the Expectation?Maximization (EM) in order to separate feature points from clutter; 1b) a density based algorithm (DBSCAN) was applied to isolate the single clusters (i.e. the rockfall events); 2) finally we computed the Ripley's K-function to investigate the global spatial pattern of the extracted rockfalls. The method allowed proper identification and characterization of more than 600 rockfalls occurred on a cliff located in Puigcercos (Catalonia, Spain) during a time span of six months. The spatial distribution of these events proved that rockfall were clustered distributed at a welldefined distance-range. Computations were carried out using R free software for statistical computing and graphics. The understanding of the spatial distribution of precursory rockfalls may shed light on the forecasting of future failures.

Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.