Evidence for a non-replicative intracellular stage of nontypable Haemophilus influenzae in epithelial cells

Nontypable Haemophilus influenzae (NTHi) is a Gram-negative, non-capsulated human bacterial pathogen, a major cause of a repertoire of respiratory infections, and intimately associated with persistent lung bacterial colonization in patients suffering from chronic obstructive pulmonary disease (COPD). Despite its medical relevance, relatively little is known about its mechanisms of pathogenicity. In this study, we found that NTHi invades the airway epithelium by a distinct mechanism, requiring microtubule assembly, lipid rafts integrity, and activation of phosphatidylinositol 3-kinase (PI3K) signalling. We found that the majority of intracellular bacteria are located inside an acidic subcellular compartment, in a metabolically active and non-proliferative state. This NTHi-containing vacuole (NTHi-CV) is endowed with late endosome features, co-localizing with LysoTracker, lamp-1, lamp-2, CD63 and Rab7. The NTHi-CV does not acquire Golgi- or autophagy-related markers. These observations were extended to immortalized and primary human airway epithelial cells. By using NTHi clinical isolates expressing different amounts of phosphocholine (PCho), a major modification of NTHi lipooligosaccharide, on their surfaces, and an isogenic lic1BC mutant strain lacking PCho, we showed that PCho is not responsible for NTHi intracellular location. In sum, this study indicates that NTHi can survive inside airway epithelial cells.

Nontypeable Haemophilus influenzae clearance by alveolar macrophages is impaired by exposure to cigarette smoke

Nontypeable Haemophilus influenzae (NTHI) is an opportunistic gram-negative pathogen that causes respiratory infections and is associated with progression of respiratory diseases. Cigarette smoke is a main risk factor for development of respiratory infections and chronic respiratory diseases. Glucocorticoids, which are anti-inflammatory drugs, are still the most common therapy for these diseases. Alveolar macrophages are professional phagocytes that reside in the lung and are responsible for clearing infections by the action of their phagolysosomal machinery and promotion of local inflammation. In this study, we dissected the interaction between NTHI and alveolar macrophages and the effect of cigarette smoke on this interaction. We showed that alveolar macrophages clear NTHI infections by adhesion, phagocytosis, and phagolysosomal processing of the pathogen. Bacterial uptake requires host actin polymerization, the integrity of plasma membrane lipid rafts, and activation of the phosphatidylinositol 3-kinase (PI3K) signaling cascade. Parallel to bacterial clearance, macrophages secrete tumor necrosis factor alpha (TNF-alpha) upon NTHI infection. In contrast, exposure to cigarette smoke extract (CSE) impaired alveolar macrophage phagocytosis, although NTHI-induced TNF-alpha secretion was not abrogated. Mechanistically, our data showed that CSE reduced PI3K signaling activation triggered by NTHI. Treatment of CSE-exposed cells with the glucocorticoid dexamethasone reduced the amount of TNF-alpha secreted upon NTHI infection but did not compensate for CSE-dependent phagocytic impairment. The deleterious effect of cigarette smoke was observed in macrophage cell lines and in human alveolar macrophages obtained from smokers and from patients with chronic obstructive pulmonary disease.

AgentSpeak+: AgentSpeak with Probabilistic Planning

AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In this paper, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when facing uncertainty.

Plan Selection for Probabilistic BDI Agents

When an agent wants to fulfill its desires about the world, the agent usually has multiple plans to choose from and these plans have different pre-conditions and additional effects in addition to achieving its goals. Therefore, for further reasoning and interaction with the world, a plan selection strategy (usually based on plan cost estimation) is mandatory for an autonomous agent. This demand becomes even more critical when uncertainty on the observation of the world is taken into account, since in this case, we consider not only the costs of different plans, but also their chances of success estimated according to the agent's beliefs. In addition, when multiple goals are considered together, different plans achieving the goals can be conflicting on their preconditions (contexts) or the required resources. Hence a plan selection strategy should be able to choose a subset of plans that fulfills the maximum number of goals while maintaining context consistency and resource-tolerance among the chosen plans. To address the above two issues, in this paper we first propose several principles that a plan selection strategy should satisfy, and then we present selection strategies that stem from the principles, depending on whether a plan cost is taken into account. In addition, we also show that our selection strategy can partially recover intention revision.

A Syntactic Approach to Revising Epistemic States with Uncertain Inputs

Revising its beliefs when receiving new information is an important ability of any intelligent system. However, in realistic settings the new input is not always certain. A compelling way of dealing with uncertain input in an agent-based setting is to treat it as unreliable input, which may strengthen or weaken the beliefs of the agent. Recent work focused on the postulates associated with this form of belief change and on finding semantical operators that satisfy these postulates. In this paper we propose a new syntactic approach for this form of belief change and show that it agrees with the semantical definition. This makes it feasible to develop complex agent systems capable of efficiently dealing with unreliable input in a semantically meaningful way. Additionally, we show that imposing restrictions on the input and the beliefs that are entailed allows us to devise a tractable approach suitable for resource-bounded agents or agents where reactiveness is of paramount importance.

Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

Can(Plan)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of Can(Plan) into Can(Plan)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent's beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of lookahead planning.

Probabilistic Planning in AgentSpeak using the POMDP framework.

AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In this paper, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when facing uncertainty.

A new look towards BAC-based array CGH through a comprehensive comparison with oligo-based array CGH

BACKGROUND: Currently, two main technologies are used for screening of DNA copy number; the BAC (Bacterial Artificial Chromosome) and the recently developed oligonucleotide-based CGH (Chromosomal Comparative Genomic Hybridization) arrays which are capable of detecting small genomic regions with amplification or deletion. The correlation as well as the discriminative power of these platforms has never been compared statistically on a significant set of human patient samples.

RESULTS: In this paper, we present an exhaustive comparison between the two CGH platforms, undertaken at two independent sites using the same batch of DNA from 19 advanced prostate cancers. The comparison was performed directly on the raw data and a significant correlation was found between the two platforms. The correlation was greatly improved when the data were averaged over large chromosomic regions using a segmentation algorithm. In addition, this analysis has enabled the development of a statistical model to discriminate BAC outliers that might indicate microevents. These microevents were validated by the oligo platform results.

CONCLUSION: This article presents a genome-wide statistical validation of the oligo array platform on a large set of patient samples and demonstrates statistically its superiority over the BAC platform for the Identification of chromosomic events. Taking advantage of a large set of human samples treated by the two technologies, a statistical model has been developed to show that the BAC platform could also detect microevents.

Substance use among sportive adolescents in the French-speaking part of Switzerland

[Contents] Introduction. Objectives. Methodology. Results. Characteristics of the sample. Substance use (Psychoactive substances, Performance-enhancing substances). Profile of sportive adolescents using substances. Mixed substance use. Other factors related to substance use. Inactivity. Conclusions. References. Annexes. Annex 1. Questionnaire. Annex 2. Sample weighting procedure. Annex 3. Sports type.

Social contexts of sports-practicing youths' hazardous drinking.

Sports-practicing youths are at an elevated risk for alcohol use and misuse. Although much attention has recently been given to depicting subgroups facing the greatest threats, little evidence exists on the contexts in which their drinking takes place. Using data from a cross-sectional study on youth sports participation and substance use in the French-speaking part of Switzerland, this study focused on the social contexts associated with hazardous drinking of 894 sports-practicing adolescents aged 16 to 20. Divided between those who had been drunk in the last month (hazardous drinkers, n = 315) and those who had not (n = 579), sports-practicing adolescents were compared on reported gatherings (sports-related, sports-unrelated, mixed) likely linked to their drinking behaviour. Mixed social contexts, followed by sports-unrelated ones, were reported as the most common context by both male and female youths who practiced sports. After controlling for several possible confounders, male hazardous drinkers were more than 3 times more likely to report sports-unrelated social contexts as the most common, compared to sport-related ones, while females were more than 7 times more likely to do so. Our findings seem to indicate that, rather than focusing only on sports-related factors, prevention of alcohol misuse among sports-practicing youths should also pay attention to the social contextualisation of their hazardous drinking.

Some go without a cigarette: characteristics of cannabis users who have never smoked tobacco.

OBJECTIVES: To estimate the prevalence of youth who use cannabis but have never been tobacco smokers and to assess the characteristics that differentiate them from those using both substances or neither substance. DESIGN: School survey. SETTING: Postmandatory schools. PARTICIPANTS: A total of 5263 students (2439 females) aged 16 to 20 years divided into cannabis-only smokers (n = 455), cannabis and tobacco smokers (n = 1703), and abstainers (n = 3105). OUTCOME MEASURES: Regular tobacco and cannabis use; and personal, family, academic, and substance use characteristics. RESULTS: Compared with those using both substances, cannabis-only youth were younger (adjusted odds ratio [AOR], 0.82) and more likely to be male (AOR, 2.19), to play sports (AOR, 1.64), to live with both parents (AOR, 1.33), to be students (AOR, 2.56), and to have good grades (AOR, 1.57) and less likely to have been drunk (AOR, 0.55), to have started using cannabis before the age of 15 years (AOR, 0.71), to have used cannabis more than once or twice in the previous month (AOR, 0.64), and to perceive their pubertal timing as early (AOR, 0.59). Compared with abstainers, they were more likely to be male (AOR, 2.10), to have a good relationship with friends (AOR, 1.62), to be sensation seeking (AOR, 1.32), and to practice sports (AOR, 1.37) and less likely to have a good relationship with their parents (AOR, 0.59). They were more likely to attend high school (AOR, 1.43), to skip class (AOR, 2.28), and to have been drunk (AOR, 2.54) or to have used illicit drugs (AOR, 2.28). CONCLUSIONS: Cannabis-only adolescents show better functioning than those who also use tobacco. Compared with abstainers, they are more socially driven and do not seem to have psychosocial problems at a higher rate.

First use of multiple substances: Identification of meaningful patterns

Context: Understanding the process through which adolescents and young adults are trying legal and illegal substances is a crucial point for the development of tailored prevention and treatment programs. However, patterns of substance first use can be very complex when multiple substances are considered, requiring reduction into a few meaningful number of categories. Data: We used data from a survey on adolescent and young adult health conducted in 2002 in Switzerland. Answers from 2212 subjects aged 19 and 20 were included. The first consumption ever of 10 substances (tobacco, cannabis, medicine to get high, sniff (volatile substances, and inhalants), ecstasy, GHB, LSD, cocaine, methadone, and heroin) was considered for a grand total of 516 different patterns. Methods: In a first step, automatic clustering was used to decrease the number of patterns to 50. Then, two groups of substance use experts, three social field workers, and three toxicologists and health professionals, were asked to reduce them into a maximum of 10 meaningful categories. Results: Classifications obtained through our methodology are of practical interest by revealing associations invisible to purely automatic algorithms. The article includes a detailed analysis of both final classifications, and a discussion on the advantages and limitations of our approach.

Female adolescents' views on a youth-friendly clinic.

OBJECTIVE: To assess satisfaction among female patients of a youth friendly clinic and to determine with which factors this was associated. METHODS: A cross-sectional survey was conducted in an adolescent clinic in Lausanne, Switzerland, between March and May 2008. All female patients who had made at least one previous visit were eligible. Three hundred and eleven patients aged 12-22 years were included. We performed bivariate analysis to compare satisfied and non-satisfied patients and constructed a log-linear model. RESULTS: Ninety-four percent of patients were satisfied. Satisfied female adolescents were significantly more likely to feel that their complaints were heard, that the caregiver understood their problems, to have no change of physician, to have received the correct treatment/help and to follow the caregiver's advice. The log-linear model highlighted four factors directly linked with patient satisfaction: outcome of care, continuity of care, adherence to treatment and the feeling of being understood. CONCLUSIONS: The main point for female adolescent patient satisfaction lies in a long term, trustworthy relationship with their caregiver. Confidentiality and accessibility were secondary for our patients.