Probabilistic Treatment Research Articles

Microbiological characteristics of community-acquired peritonitis at Souro Sanou Teaching Hospital of Bobo Dioulasso.

The purpose of this study was to describe the microbiological characteristics of community-acquired peritonitis at the Souro Sanou teaching hospital of Bobo Dioulasso, Burkina Faso. This 10-month prospective study, from July 2012 through May 2013 included all cases of community-acquired peritonitis who underwent surgery at our hospital and had a pus sample taken at that time. Bacteriological analyses were performed according to the hospital laboratory's protocol. The study included 72 patients (45 men and 27 women with a sex-ratio of 1.6). Their mean age was 27.3 years (range: 14 months to 67 years). The analysis of 72 samples of pus enabled confirmation of 39 cases and led to the identification of 53 bacterial strains. The most common families were enterobacteriaceae in 58% and Gram-positive cocci in 36%. The most common enterobacteria species was Escherichia coli (47%) followed by Streptococcus species (22%). The microbiological confirmation of peritonitis was most frequent in the 20-30 year-old age group. These bacteria appeared most susceptible to gentamycin, ceftriaxone, and ciprofloxacin, while we observed resistance to amoxicillin and the combination of clavulanic acid-amoxicillin. These findings suggest that the best probabilistic antibiotic treatment for the peritonitis in our context would combine a third-generation cephalosporin with aminoglycosides, with imidazole to cover potential anaerobic bacteria.

Elaboration of a clinical and paraclinical score to estimate the probability of herpes simplex virus encephalitis in patients with febrile, acute neurologic impairment.

Herpes simplex virus (HSV) encephalitis is associated with a high risk of mortality and sequelae, and early diagnosis and treatment in the emergency department are necessary. However, most patients present with non-specific febrile, acute neurologic impairment; this may lead clinicians to overlook the diagnosis of HSV encephalitis. We aimed to identify which data collected in the first hours in a medical setting were associated with the diagnosis of HSV encephalitis. We conducted a multicenter retrospective case-control study in four French public hospitals from 2007 to 2013. The cases were the adult patients who received a confirmed diagnosis of HSV encephalitis. The controls were all the patients who attended the emergency department of Grenoble hospital with a febrile acute neurologic impairment, without HSV detection by polymerase chain reaction (PCR) in the cerebrospinal fluid (CSF), in 2012 and 2013. A multivariable logistic model was elaborated to estimate factors significantly associated with HSV encephalitis. Finally, an HSV probability score was derived from the logistic model. We identified 36 cases and 103 controls. Factors independently associated with HSV encephalitis were the absence of past neurological history (odds ratio [OR] 6.25 [95 % confidence interval (CI): 2.22-16.7]), the occurrence of seizure (OR 8.09 [95 % CI: 2.73-23.94]), a systolic blood pressure ≥140mmHg (OR 5.11 [95 % CI: 1.77-14.77]), and a C-reactive protein <10mg/L (OR 9.27 [95 % CI: 2.98-28.88]). An HSV probability score was calculated summing the value attributed to each independent factor. HSV encephalitis diagnosis may benefit from the use of this score based upon some easily accessible data. However, diagnostic evocation and probabilistic treatment must remain the rule.

Bayesian analysis of rare events

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

A Probabilistic Crack Band Model for Quasibrittle Fracture

This paper presents a new crack band model (CBM) for probabilistic analysis of quasibrittle fracture. The model is anchored by a probabilistic treatment of damage initiation, localization, and propagation. This model regularizes the energy dissipation of a single material element for the transition between damage initiation and localization. Meanwhile, the model also takes into account the probabilistic onset of damage localization inside the finite element (FE) for the case where the element size is larger than the crack band width. The random location of the localization band is related to the random material strength, whose statistics is described by a finite weakest link model. The present model is applied to simulate the probability distributions of the nominal strength of different quasibrittle structures. It is shown that for quasibrittle structures direct application of the conventional CBM for stochastic FE simulations would lead to mesh-sensitive results. To mitigate such mesh dependence, it is essential to incorporate the strain localization mechanism into the formulation of the sampling distribution functions of material constitutive parameters.

Historical Prospective: Boltzmann’s versus Planck’s State Counting—Why Boltzmann Did Not Arrive at Planck’s Distribution Law

Why does Planck (1900), referring to Boltzmann’s 1877 probabilistic treatment, obtain his quantum distribution function while Boltzmann did not? To answer this question, both treatments are compared on the basis of Boltzmann’s 1868 three-level scheme (configuration—occupation—occupancy). Some calculations by Planck (1900, 1901, and 1913) and Einstein (1907) are also sketched. For obtaining a quantum distribution, it is crucial to stick with a discrete energy spectrum and to make the limit transitions to infinity at the right place. For correct state counting, the concept of interchangeability of particles is superior to that of indistinguishability.

Seismic response analysis of an interacting curved bridge–train system under frequent earthquakes

SummaryThis paper establishes a scheme for the seismic analysis of interacting vehicle–bridge systems. The focus is on (horizontally) curved continuous railway bridges and frequent earthquakes. Main features of the proposed scheme are (i) the treatment of the dynamics in all three dimensions (3D), employing an additional rotating system of reference to describe the dynamics of the vehicles and a realistic 3D bridge model; (ii) the simulation of the creep interaction forces generated by the rolling contact between the wheel and the rail; and (iii) the integration of the proposed scheme with powerful commercial finite element software, during the pre‐processing and post‐processing phases of the analysis. The study brings forward the dynamics of a realistic vehicle–bridge (interacting) system during seismic shaking. For the (vehicle–bridge) case examined, the results verify the favorable damping effect the running vehicles have on the vibration of the deck. By contrast, the study stresses the adverse influence of the earthquake‐induced bridge vibration on the riding comfort but, more importantly, on the safety of the running vehicles. In this context, the paper unveils also a vehicle–bridge–earthquake timing problem, behind the most critical vehicle response, and underlines the need for a probabilistic treatment. Among the 20 sets of historic records examined, the most crucial for the safety of the vehicles are near‐fault ground motions. Finally, the study shows that even frequent earthquakes, of moderate intensity, can threaten the safety of vehicles running on bridges during the ground motion excitation, in accordance with recorded accidents. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Bilateral One-Stage Revision of Infected Total Hip Arthroplasties: Report of Two Cases and Management of Antibiotic Therapy.

Recommendations for the management of chronic and bilateral total hip arthroplasty (THA) infection are lacking. However, this type of infection involves medical problems concerning the management of the antibiotic therapy. We report two cases of such infections operated as one-stage revision. For each case, both hips were infected with the same bacteria (Staphylococcus caprae for one patient and methicillin-sensitive Staphylococcus aureus for the other). The probabilistic antibiotic treatment started during the first side (after harvesting intraoperative samples) did not prevent the culture of the bacteriologic harvested during the intervention of the second side. Cultures were positive for the same bacteria for both sides in the two cases presented herein. After results of intraoperative cultures, patients received culture-guided antibiotic therapy for three months and were considered cured at the end of a two-year follow-up. Our results suggest one-stage bilateral change of infected THA is a viable option and that early intraoperative antibiotic, started during the first-side exchange, does not jeopardize microbiological documentation of the second side. This work brings indirect arguments, in favor of the use of prophylactic antibiotics during revision of infected THA.

Quantum cognition and bounded rationality

We consider several puzzles of bounded rationality. These include the Allais- and Ellsberg paradox, the disjunction effect, and related puzzles. We argue that the present account of quantum cognition—taking quantum probabilities rather than classical probabilities—can give a more systematic description of these puzzles than the alternate treatments in the traditional frameworks of bounded rationality. Unfortunately, the quantum probabilistic treatment does not always provide a deeper understanding and a true explanation of these puzzles. One reason is that quantum approaches introduce additional parameters which possibly can be fitted to empirical data but which do not necessarily explain them. Hence, the phenomenological research has to be augmented by responding to deeper foundational issues. In this article, we make the general distinction between foundational and phenomenological research programs, explaining the foundational issue of quantum cognition from the perspective of operational realism. This framework is motivated by assuming partial Boolean algebras (describing particular perspectives). They are combined into a uniform system (i.e. orthomodular lattice) via a mechanism preventing the simultaneous realization of perspectives. Gleason’s theorem then automatically leads to a distinction between probabilities that are defined by pure states and probabilities arising from the statistical mixture of pure states. This formal distinction relates to the conceptual distinction between risk and ignorance. Another outcome identifies quantum aspects in dynamic macro-systems using the framework of symbolic dynamics. Finally, we discuss several ideas that are useful for justifying complementarity in cognitive systems.

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

Evaluation of antimicrobial susceptibility of Escherichia coli strains isolated in Rabat University Hospital (Morocco).

BackgroundEscherichia coli (E. coli) is the most commonly isolated bacteria in human pathology. In Morocco the data concerning the nature and the rates of antibiotic resistance of E. coli in both hospitals and city environment remains relatively poor and needs further investigations.MethodsDuring a 16 months period, E. coli isolates were collected from different culture specimens received in the Bacteriology Department of the Military teaching Hospital Mohammed-V-Rabat for routine diagnostic purposes. E. coli isolates were identified and their antimicrobial susceptibility pattern was determined.ResultsA total of 1369 E. coli isolates comprising 33 % (1369/4110) of culture-positive samples were consecutively collected. Isolates of E. coli were, in 40.5 % (554/1369) of cases from hospitalized patients and in 59.5 % (815/1369) of cases from outpatients. Urine isolates represented 82 % (1123/1369) of the cases. High rates of resistance were found for amoxicillin (42.5 %), cefalotin (30.4 %), norfoloxacin (29.9 %) and sulfamethoxazole (37.7 %). The detection rate of ESBL was 6.1 % (85/1369). In hospitalized patients 11.9 % of the isolates of E. coli (66/554) had an ESBL phenotype while in outpatients cases only 2.3 % of isolates of E. coli (19/815) had this phenotype.ConclusionsOur findings suggest that more judicious use of antibiotics is needed especially in probabilistic treatment. The emergence of ESBL in the Moroccan cities is an indicator of the severity of this problem that is not limited to health care facilities.

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter. Biological significanceQPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories.This article is part of a Special Issue entitled: Computational Proteomics.

An Improved Estimation of Multiple-Point Fault Probabilities if the Faults Have Different Periodic Latencies

Fault tree analysis (FTA), reliability block diagrams (RBD) and event tree analysis (ETA) are established methods for assessing potential risks of hazardous events, in particular when resulting from coincidental events. Combining the Boolean algebra, probability theory and reliability data, they allow quantitative estimation of intrinsic risks from technical equipment like machinery control, aerospace systems or vehicle functions, among many others.&#x0D; The quantitative reliability theory was mainly developed between the 1960s and the 1980s. At that time, simplifications and approximations for the mathematical formulae were needed to achieve calculation results within acceptable time, regarding restricted computer resources.&#x0D; Our investigation revealed that some of these simplifications and approximations, often assumed as precise calculations in secondary literature, can lead to wrong results in quantitative risk assessment. When faults are combined, and individual latency periods exist, the currently established approximations may lead to results which are too optimistic in comparison with a precise probabilistic approach.&#x0D; This publication proposes a new approximation for the computation of the related probabilities. The approach provides an upper-bound estimation. Using the developed formulae, the under-estimation of multipleevent probabilities can be avoided.&#x0D; In addition, certain vagueness and over-simplification in the probabilistic treatment of events with latency periods can be eliminated. Examples of related shortcomings in the literature can be found, down to the early roots of reliability theory.

TU-AB-BRB-03: Coverage-Based Treatment Planning to Accommodate Organ Deformable Motions and Contouring Uncertainties for Prostate Treatment

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: 1. To understand robust-planning as a clinical alternative to using margin-based planning. 2. To understand conceptual differences between uncertainty and predictable motion. 3. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. 4. To understand the major contributing factors to target and normal tissue coverage probability. 5. To understand the similarities and differences of various robust planning techniques 6. To understand the benefits and limitations of robust planning techniques

TU‐AB‐BRB‐00: New Methods to Ensure Target Coverage

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV‐to‐CTV and CTV‐to‐PTV margins during the planning process to design a PTV‐conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. &gt;95%) target coverage probability (CP) in spite of inherent inter‐ and intra‐fractional positional variations, tissue motions, and initial contouring uncertainties.Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage‐based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP‐evaluation for the underlying CTVs and OARs to design an optimal treated volume.This symposium will describe CP‐evaluation methods as well as various robust planning techniques including use of probability‐weighted dose distributions, probability‐weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter‐fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra‐fractional organ motion. Clinical examples will be used to inter‐compare robust and margin‐based planning, highlighting advantages of robust‐plans in terms of target and normal tissue coverage. Robust‐planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning.Learning Objectives: To understand robust‐planning as a clinical alternative to using margin‐based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques

TU‐AB‐BRB‐01: Coverage Evaluation and Probabilistic Treatment Planning as a Margin Alternative

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV‐to‐CTV and CTV‐to‐PTV margins during the planning process to design a PTV‐conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. &gt;95%) target coverage probability (CP) in spite of inherent inter‐ and intra‐fractional positional variations, tissue motions, and initial contouring uncertainties.Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage‐based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP‐evaluation for the underlying CTVs and OARs to design an optimal treated volume.This symposium will describe CP‐evaluation methods as well as various robust planning techniques including use of probability‐weighted dose distributions, probability‐weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter‐fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra‐fractional organ motion. Clinical examples will be used to inter‐compare robust and margin‐based planning, highlighting advantages of robust‐plans in terms of target and normal tissue coverage. Robust‐planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning.Learning Objectives: To understand robust‐planning as a clinical alternative to using margin‐based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques

TU-AB-BRB-02: Stochastic Programming Methods for Handling Uncertainty and Motion in IMRT Planning

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. The treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: 1. To understand robust-planning as a clinical alternative to using margin-based planning. 2. To understand conceptual differences between uncertainty and predictable motion. 3. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. 4. To understand the major contributing factors to target and normal tissue coverage probability. 5. To understand the similarities and differences of various robust planning techniques 6. To understand the benefits and limitations of robust planning techniques

The role of climate models in adaptation decision-making: the case of the UK climate projections 2009

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to its probabilistic treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale.

Occupancy-elevation grid: an alternative approach for robotic mapping and navigation

SUMMARYThis paper proposes an alternative environment mapping method for accurate robotic navigation based on 3D information. Typical techniques for 3D mapping using occupancy grid require intensive computational workloads in order to both build and store the map. This work introduces an Occupancy-Elevation Grid (OEG) mapping technique, which is a discrete mapping approach where each cell represents the occupancy probability, the height of the terrain and its variance. This representation allows a mobile robot to know with an accurate degree of certainty whether a place in the environment is occupied by an obstacle and the height of such obstacle. Thus, based on its hardware characteristics, it can make calculations to decide if it is possible to traverse that specific place. In general, the map representation introduced can be used in conjunction with any kind of distance sensor. In this work, we use laser range data and stereo system data with a probabilistic treatment. The resulting maps allow the execution of tasks as decision making for autonomous navigation, exploration, localization and path planning, considering the existence and the height of the obstacles. Experiments carried out with real data demonstrate that the proposed approach yields useful maps for autonomous navigation.

Policy Evaluation with Temporal Differences: A Survey and Comparison (Extended Abstract)

Value functions are an essential tool for solving sequential decision making problems such as Markov decision processes (MDPs). Computing the value function for a given policy (policy evaluation) is not only important for determining the quality of the policy but also a key step in prominent policy-iteration-type algorithms. In common settings where a model of the Markov decision process is not available or too complex to handle directly, an approximation of the value function is usually estimated from samples of the process. Linearly parameterized estimates are often preferred due to their simplicity and strong stability guarantees. Since the late 1980s, research on policy evaluation in these scenarios has been dominated by temporal-difference (TD) methods because of their data-efficiency. However, several core issues have only been tackled recently, including stability guarantees for off-policy estimation where the samples are not generated by the policy to evaluate. Together with improving sample efficiency and probabilistic treatment of uncertainty in the value estimates, these efforts have lead to numerous new temporal-difference algorithms. These methods are scattered over the literature and usually only compared to most similar approaches. The article therefore aims at presenting the state of the art of policy evaluation with temporal differences and linearly parameterized value functions in discounted MDPs as well as a more comprehensive comparison of these approaches.We put the algorithms in a unified framework of function optimization, with focus on surrogate cost functions and optimization strategies, to identify similarities and differences between the methods. In addition, important extensions of the base methods such as off-policy estimation and eligibility traces for better bias-variance trade-off, as well as regularization in high dimensional feature spaces, are discussed.

Profil et sensibilité aux antibiotiques de 1879 bactéries urinaires pathogènes isolées chez l’enfant (2012–2013)

We present recent data on the bacteriological profile and antibiotic susceptibility of uropathogenic bacteria isolated in children and newborns in our region over the past 2 years. A retrospective study on the positive urine cultures from pediatric and neonatal populations during 2012-2013. Bacteria were identified using conventional methods. Susceptibility testing was performed and interpreted as recommended by the committee of the susceptibility of the French Society of Microbiology (CA-SFM). We collected 1879 non-redundant bacteria with more than 73% Escherichia coli. Children and infants (mean age, 32 months [range, 1 month to 14 years]) accounted for 84% of the bacteria collected and newborns (mean age, 12 days [range, 1 day to 1 month]) 16%. A female predominance was observed in the pediatric population (M:F sex ratio, 3.2), whereas for the neonatal population, the proportions were almost identical in both sexes (M:F sex ratio, 1.1). Most of the positive urine cultures (n=1234) were from the community. Hospitalized patients (n=636) were divided into pediatric (60%) and neonatal units (40%). Five bacterial genera dominated the bacteriological profile: E. coli, Klebsiella sp., Proteus sp., Enterobacter sp., and Enterococcus. The susceptibility of the main BUP antibiotics used for treatment of frequent UTI showed the effectiveness of furadoine, imipenem, fosfomycin, and colistin. Amoxicillin kept constant activity against Enterococcus and Streptococcus agalactiae. The rates of resistance of Enterobacteriaceae to beta-lactam antibiotics were high, especially in the neonatal population. The production of extended-spectrum beta-lactamase (ESBL) was noted in 12.8% of pediatric Enterobacteria vs. 22.6% of the neonatal strains. For community Enterobacteriaceae, the activity of beta-lactam antibiotics was limited with 11.2% resistance to third-generation cephalosporins (C3G), including 8.6% ESBL production. The impact of widespread use of beta-lactam antibiotics in neonatal and pediatric environments is felt. Colistin, imipenem, and fosfomycin are the most frequently used antibiotics active against bacteria responsible for neonatal and pediatric UTI; however, they cannot be used as probabilistic treatment. Nitrofurans seem to be active antibiotics on UTI, but they present limits in their use in neonatal and pediatric populations. Their indication in case of pyelonephritis should be discussed.