The therapeutic potential of the humoral pattern recognition molecule PTX3 in chronic lung infection caused by Pseudomonas aeruginosa

Chronic lung infections by Pseudomonas aeruginosa strains are a major cause of morbidity and mortality in cystic fibrosis (CF) patients. Although there is no clear evidence for a primary defect in the immune system of CF patients, the host is generally unable to clear P. aeruginosa from the airways. PTX3 is a soluble pattern recognition receptor that plays nonredundant roles in the innate immune response to fungi, bacteria, and viruses. In particular, PTX3 deficiency is associated with increased susceptibility to P. aeruginosa lung infection. To address the potential therapeutic effect of PTX3 in P. aeruginosa lung infection, we established persistent and progressive infections in mice with the RP73 clinical strain RP73 isolated from a CF patient and treated them with recombinant human PTX3. The results indicated that PTX3 has a potential therapeutic effect in P. aeruginosa chronic lung infection by reducing lung colonization, proinflammatory cytokine levels (CXCL1, CXCL2, CCL2, and IL-1β), and leukocyte recruitment in the airways. In models of acute infections and in in vitro assays, the prophagocytic effect of PTX3 was maintained in C1q-deficient mice and was lost in C3- and Fc common γ-chain-deficient mice, suggesting that facilitated recognition and phagocytosis of pathogens through the interplay between complement and FcγRs are involved in the therapeutic effect mediated by PTX3. These data suggested that PTX3 is a potential therapeutic tool in chronic P. aeruginosa lung infections, such as those seen in CF patients.

Unifying energy minimization and mutual information maximization for robust 2D/3D registration of X-ray and CT images

Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Choosing the path of austerity: how parties and policy coalitions influence welfare state retrenchment in periods of fiscal consolidation

What are the conditions under which some austerity programmes rely on substantial cuts to social spending? More specifically, do the partisan complexion and the type of government condition the extent to which austerity policies imply welfare state retrenchment? This article demonstrates that large budget consolidations tend to be associated with welfare state retrenchment. The findings support a partisan and a politico-institutionalist argument: (i) in periods of fiscal consolidation, welfare state retrenchment tends to be more pronounced under left-wing governments; (ii) since welfare state retrenchment is electorally and politically risky, it also tends to be more pronounced when pursued by a broad pro-reform coalition government. Therefore, the article shows that during budget consolidations implemented by left-wing broad coalition governments, welfare state retrenchment is greatest. Using long-run multipliers from autoregressive distributed lag models on 17 OECD countries during the 1982–2009 period, substantial support is found for these expectations.

Fine particulate matter measurements in Swiss restaurants, cafés and bars: what is the effect of spatial separation between smoking and non-smoking areas?

We performed 124 measurements of particulate matter (PM(2.5)) in 95 hospitality venues such as restaurants, bars, cafés, and a disco, which had differing smoking regulations. We evaluated the impact of spatial separation between smoking and non-smoking areas on mean PM(2.5) concentration, taking relevant characteristics of the venue, such as the type of ventilation or the presence of additional PM(2.5) sources, into account. We differentiated five smoking environments: (i) completely smoke-free location, (ii) non-smoking room spatially separated from a smoking room, (iii) non-smoking area with a smoking area located in the same room, (iv) smoking area with a non-smoking area located in the same room, and (v) smoking location which could be either a room where smoking was allowed that was spatially separated from non-smoking room or a hospitality venue without smoking restriction. In these five groups, the geometric mean PM(2.5) levels were (i) 20.4, (ii) 43.9, (iii) 71.9, (iv) 110.4, and (v) 110.3 microg/m(3), respectively. This study showed that even if non-smoking and smoking areas were spatially separated into two rooms, geometric mean PM(2.5) levels in non-smoking rooms were considerably higher than in completely smoke-free hospitality venues. PRACTICAL IMPLICATIONS: PM(2.5) levels are considerably increased in the non-smoking area if smoking is allowed anywhere in the same location. Even locating the smoking area in another room resulted in a more than doubling of the PM(2.5) levels in the non-smoking room compared with venues where smoking was not allowed at all. In practice, spatial separation of rooms where smoking is allowed does not prevent exposure to environmental tobacco smoke in nearby non-smoking areas.

What sets the odds of winning and losing?

Social experience influences the outcome of conflicts such that winners are more likely to win again and losers will more likely lose again, even against different opponents. Although winner and loser effects prevail throughout the animal kingdom and crucially influence social structures, the ultimate and proximate causes for their existence remain unknown. We propose here that two hypotheses are particularly important among the potential adaptive explanations: the 'social-cue hypothesis', which assumes that victory and defeat leave traces that affect the decisions of subsequent opponents; and the 'self-assessment hypothesis', which assumes that winners and losers gain information about their own relative fighting ability in the population. We discuss potential methodologies for experimental tests of the adaptive nature of winner and loser effects.

[What is the value of determining immunoreactive GHRH in acromegaly?]

Acromegaly is usually due to autonomous, excessive secretion of growth hormone from a pituitary adenoma. One would expect growth hormone-releasing factor (GHRH) in these patients to be suppressed. In the available literature referring to acromegaly, immunoreactive GHRH levels were determined in 259 acromegalic patients. When growth hormone was measured simultaneously, no correlation was found between serum growth hormone and plasma GHRH concentrations, irrespective of whether the acromegalic patients were treated or not. A possible explanation for this finding might be the lack of a feedback regulation between plasma growth hormone and GHRH. Also, since growth hormone is secreted in a pulsatile fashion the interpretation of single growth hormone values can be difficult. IGF I, which correlates well with mean growth hormone production, may therefore represent a more valuable criterion for the assessment of activity and GHRH plasma levels in acromegalics. However, no study has yet been performed to elucidate the relationship between GHRH and IGF I in acromegaly. To examine this relationship we measured the concentration of plasma GHRH and IGF I in 18 treated patients with acromegaly (age range 32-64 years median 50.5 years; median follow-up 6.5 years, range 3 months to 33 years). All immunoreactive GHRH levels were within the limits described as normal in the literature (mean +/- SD 22.89 +/- 2.72 pg/ml, range 19-28 pg/ml). The IGFI level was 396.78 +/- 224.26 ng/ml (mean +/- SD, range 71-876 ng/ml; reference ranges, age group 25-39 years: 114-492 ng/ml; 40-54 years: 90-360 ng/ml; > 55 years: 71-290 ng/ml). We found no correlation between IGF I and GHRH concentrations (r = 0.17). We therefore conclude that measuring plasma GHRH is not useful in the evaluation of the activity or therapy of acromegaly but may be helpful in its differential diagnosis since a massive elevation of GHRH is typically associated with the ectopic GHRH syndrome, a rare cause of acromegaly.