Predictors for hospitalization and outpatient visits in patients with inflammatory bowel disease: results from the Swiss Inflammatory Bowel Disease Cohort Study.

OBJECTIVES: Patients with inflammatory bowel disease (IBD) have a high resource consumption, with considerable costs for the healthcare system. In a system with sparse resources, treatment is influenced not only by clinical judgement but also by resource consumption. We aimed to determine the resource consumption of IBD patients and to identify its significant predictors. MATERIALS AND METHODS: Data from the prospective Swiss Inflammatory Bowel Disease Cohort Study were analysed for the resource consumption endpoints hospitalization and outpatient consultations at enrolment [1187 patients; 41.1% ulcerative colitis (UC), 58.9% Crohn's disease (CD)] and at 1-year follow-up (794 patients). Predictors of interest were chosen through an expert panel and a review of the relevant literature. Logistic regressions were used for binary endpoints, and negative binomial regressions and zero-inflated Poisson regressions were used for count data. RESULTS: For CD, fistula, use of biologics and disease activity were significant predictors for hospitalization days (all P-values <0.001); age, sex, steroid therapy and biologics were significant predictors for the number of outpatient visits (P=0.0368, 0.023, 0.0002, 0.0003, respectively). For UC, biologics, C-reactive protein, smoke quitters, age and sex were significantly predictive for hospitalization days (P=0.0167, 0.0003, 0.0003, 0.0076 and 0.0175 respectively); disease activity and immunosuppressive therapy predicted the number of outpatient visits (P=0.0009 and 0.0017, respectively). The results of multivariate regressions are shown in detail. CONCLUSION: Several highly significant clinical predictors for resource consumption in IBD were identified that might be considered in medical decision-making. In terms of resource consumption and its predictors, CD and UC show a different behaviour.

Automated segmentation of multiple red blood cells with digital holographic microscopy.

We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs.

6.2

Airborne particles can come from a variety of sources and contain variable chemical constituents. Some particles are formed by natural processes, such as volcanoes, erosion, sea spray, and forest fires, while other are formed by anthropogenic processes, such as industrial- and motor vehicle-related combustion, road-related wear, and mining. In general, larger particles (those greater than 2.5 μm) are formed by mechanical processes, while those less than 2.5 μm are formed by combustion processes. The chemical composition of particles is highly influenced by the source: for combustion-related particles, factors such as temperature of combustion, fuel type, and presence of oxygen or other gases can also have a large impact on PM composition. These differences can often be observed at a regional level, such as the greater sulphate-composition of PM in regions that burn coal for electricity production (which contains sulphur) versus regions that do not. Most countries maintain air monitoring networks, and studies based on the resulting data are the most common basis for epidemiology studies on the health effects of PM. Data from these monitoring stations can be used to evaluate the relationship between community-level exposure to ambient particles and health outcomes (i.e., morbidity or mortality from various causes). Respiratory and cardiovascular outcomes are the most commonly assessed, although studies have also considered other related specific outcomes such as diabetes and congenital heart disease. The data on particle characteristics is usually not very detailed and most often includes some combination of PM2.5, PM10, sulphate, and NO2. Other descriptors that are less commonly found include particle number (ultrafine particles), metal components of PM, local traffic intensity, and EC/OC. Measures of association are usually reported per 10 μg/m3 or interquartile range increase in pollutant concentration. As the exposure data are taken from regional monitoring stations, the measurements are not representative of an individual's exposure. Particle size is an important descriptor for understanding where in the human respiratory system the particles will deposit: as a general rule, smaller particles penetrate to deeper regions of the lungs. Initial studies on the health effects of particulate matter focused on mass of the particles, including either all particles (often termed total suspended particulate or TSP) or PM10 (all particles with an aerodynamic diameter less than 10 μm). More recently, studies have considered both PM10 and PM2.5, with the latter corresponding more directly to combustion-related processes. UFPs are a dominant source of particles in terms of PNC, yet are negligible in terms of mass. Very few epidemiology studies have measured the effect of UFPs on health; however, the numbers of studies on this topic are increasing. In addition to size, chemical composition is of importance when understanding the toxicity of particles. Some studies consider the composition of particles in addition to mass; however this is not common, in part due the cost and labour involved in such analyses.

Raman spectroscopy and microspectroscopy of reactive dyes on cotton fibres: analysis and detection limits

A collaborative study on Raman spectroscopy and microspectrophotometry (MSP) was carried out by members of the ENFSI (European Network of Forensic Science Institutes) European Fibres Group (EFG) on different dyed cotton fabrics. The detection limits of the two methods were tested on two cotton sets with a dye concentration ranging from 0.5 to 0.005% (w/w). This survey shows that it is possible to detect the presence of dye in fibres with concentrations below that detectable by the traditional methods of light microscopy and microspectrophotometry (MSP). The MSP detection limit for the dyes used in this study was found to be a concentration of 0.5% (w/w). At this concentration, the fibres appear colourless with light microscopy. Raman spectroscopy clearly shows a higher potential to detect concentrations of dyes as low as 0.05% for the yellow dye RY145 and 0.005% for the blue dye RB221. This detection limit was found to depend both on the chemical composition of the dye itself and on the analytical conditions, particularly the laser wavelength. Furthermore, analysis of binary mixtures of dyes showed that while the minor dye was detected at 1.5% (w/w) (30% of the total dye concentration) using microspectrophotometry, it was detected at a level as low as 0.05% (w/w) (10% of the total dye concentration) using Raman spectroscopy. This work also highlights the importance of a flexible Raman instrument equipped with several lasers at different wavelengths for the analysis of dyed fibres. The operator and the set up of the analytical conditions are also of prime importance in order to obtain high quality spectra. Changing the laser wavelength is important to detect different dyes in a mixture.

Is there an age cutoff to apply adult formulas for GFR estimation in children?

BACKGROUND: Estimation of glomerular filtration rate (eGFR) using a common formula for both adult and pediatric populations is challenging. Using inulin clearances (iGFRs), this study aims to investigate the existence of a precise age cutoff beyond which the Modification of Diet in Renal Disease (MDRD), the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), or the Cockroft-Gault (CG) formulas, can be applied with acceptable precision. Performance of the new Schwartz formula according to age is also evaluated. METHOD: We compared 503 iGFRs for 503 children aged between 33 months and 18 years to eGFRs. To define the most precise age cutoff value for each formula, a circular binary segmentation method analyzing the formulas' bias values according to the children's ages was performed. Bias was defined by the difference between iGFRs and eGFRs. To validate the identified cutoff, 30% accuracy was calculated. RESULTS: For MDRD, CKD-EPI and CG, the best age cutoff was ≥14.3, ≥14.2 and ≤10.8 years, respectively. The lowest mean bias and highest accuracy were -17.11 and 64.7% for MDRD, 27.4 and 51% for CKD-EPI, and 8.31 and 77.2% for CG. The Schwartz formula showed the best performance below the age of 10.9 years. CONCLUSION: For the MDRD and CKD-EPI formulas, the mean bias values decreased with increasing child age and these formulas were more accurate beyond an age cutoff of 14.3 and 14.2 years, respectively. For the CG and Schwartz formulas, the lowest mean bias values and the best accuracies were below an age cutoff of 10.8 and 10.9 years, respectively. Nevertheless, the accuracies of the formulas were still below the National Kidney Foundation Kidney Disease Outcomes Quality Initiative target to be validated in these age groups and, therefore, none of these formulas can be used to estimate GFR in children and adolescent populations.

The role of peptidoglycan in chlamydial cell division: towards resolving the chlamydial anomaly.

Chlamydiales are obligate intracellular bacteria including some important pathogens causing trachoma, genital tract infections and pneumonia, among others. They share an atypical division mechanism, which is independent of an FtsZ homologue. However, they divide by binary fission, in a process inhibited by penicillin derivatives, causing the formation of an aberrant form of the bacteria, which is able to survive in the presence of the antibiotic. The paradox of penicillin sensitivity of chlamydial cells in the absence of detectable peptidoglycan (PG) was dubbed the chlamydial anomaly, since no PG modified by enzymes (Pbps) that are the usual target of penicillin could be detected in Chlamydiales. We review here the recent advances in this field with the first direct and indirect evidences of PG-like material in both Chlamydiaceae and Chlamydia-related bacteria. Moreover, PG biosynthesis is required for proper localization of the newly described septal proteins RodZ and NlpD. Taken together, these new results set the stage for a better understanding of the role of PG and septal proteins in the division mechanism of Chlamydiales and illuminate the long-standing chlamydial anomaly. Moreover, understanding the chlamydial division mechanism is critical for the development of new antibiotics for the treatment of chlamydial chronic infections.

Evolutionary Games in Networked Populations: Models and Experiments

Cooperation and coordination are desirable behaviors that are fundamental for the harmonious development of society. People need to rely on cooperation with other individuals in many aspects of everyday life, such as teamwork and economic exchange in anonymous markets. However, cooperation may easily fall prey to exploitation by selfish individuals who only care about short- term gain. For cooperation to evolve, specific conditions and mechanisms are required, such as kinship, direct and indirect reciprocity through repeated interactions, or external interventions such as punishment. In this dissertation we investigate the effect of the network structure of the population on the evolution of cooperation and coordination. We consider several kinds of static and dynamical network topologies, such as Baraba´si-Albert, social network models and spatial networks. We perform numerical simulations and laboratory experiments using the Prisoner's Dilemma and co- ordination games in order to contrast human behavior with theoretical results. We show by numerical simulations that even a moderate amount of random noise on the Baraba´si-Albert scale-free network links causes a significant loss of cooperation, to the point that cooperation almost vanishes altogether in the Prisoner's Dilemma when the noise rate is high enough. Moreover, when we consider fixed social-like networks we find that current models of social networks may allow cooperation to emerge and to be robust at least as much as in scale-free networks. In the framework of spatial networks, we investigate whether cooperation can evolve and be stable when agents move randomly or performing Le´vy flights in a continuous space. We also consider discrete space adopting purposeful mobility and binary birth-death process to dis- cover emergent cooperative patterns. The fundamental result is that cooperation may be enhanced when this migration is opportunistic or even when agents follow very simple heuristics. In the experimental laboratory, we investigate the issue of social coordination between indi- viduals located on networks of contacts. In contrast to simulations, we find that human players dynamics do not converge to the efficient outcome more often in a social-like network than in a random network. In another experiment, we study the behavior of people who play a pure co- ordination game in a spatial environment in which they can move around and when changing convention is costly. We find that each convention forms homogeneous clusters and is adopted by approximately half of the individuals. When we provide them with global information, i.e., the number of subjects currently adopting one of the conventions, global consensus is reached in most, but not all, cases. Our results allow us to extract the heuristics used by the participants and to build a numerical simulation model that agrees very well with the experiments. Our findings have important implications for policymakers intending to promote specific, desired behaviors in a mobile population. Furthermore, we carry out an experiment with human subjects playing the Prisoner's Dilemma game in a diluted grid where people are able to move around. In contrast to previous results on purposeful rewiring in relational networks, we find no noticeable effect of mobility in space on the level of cooperation. Clusters of cooperators form momentarily but in a few rounds they dissolve as cooperators at the boundaries stop tolerating being cheated upon. Our results highlight the difficulties that mobile agents have to establish a cooperative environment in a spatial setting without a device such as reputation or the possibility of retaliation. i.e. punishment. Finally, we test experimentally the evolution of cooperation in social networks taking into ac- count a setting where we allow people to make or break links at their will. In this work we give particular attention to whether information on an individual's actions is freely available to poten- tial partners or not. Studying the role of information is relevant as information on other people's actions is often not available for free: a recruiting firm may need to call a job candidate's refer- ences, a bank may need to find out about the credit history of a new client, etc. We find that people cooperate almost fully when information on their actions is freely available to their potential part- ners. Cooperation is less likely, however, if people have to pay about half of what they gain from cooperating with a cooperator. Cooperation declines even further if people have to pay a cost that is almost equivalent to the gain from cooperating with a cooperator. Thus, costly information on potential neighbors' actions can undermine the incentive to cooperate in dynamical networks.

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions. Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition. Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

Incidence and Risk Factors of Abdominal Complications After Lung Transplantation.

BACKGROUND: Due to the underlying diseases and the need for immunosuppression, patients after lung transplantation are particularly at risk for gastrointestinal (GI) complications that may negatively influence long-term outcome. The present study assessed the incidences and impact of GI complications after lung transplantation and aimed to identify risk factors. METHODS: Retrospective analysis of all 227 consecutively performed single- and double-lung transplantations at the University hospitals of Lausanne and Geneva was performed between January 1993 and December 2010. Logistic regressions were used to test the effect of potentially influencing variables on the binary outcomes overall, severe, and surgery-requiring complications, followed by a multiple logistic regression model. RESULTS: Final analysis included 205 patients for the purpose of the present study, and 22 patients were excluded due to re-transplantation, multiorgan transplantation, or incomplete datasets. GI complications were observed in 127 patients (62 %). Gastro-esophageal reflux disease was the most commonly observed complication (22.9 %), followed by inflammatory or infectious colitis (20.5 %) and gastroparesis (10.7 %). Major GI complications (Dindo/Clavien III-V) were observed in 83 (40.5 %) patients and were fatal in 4 patients (2.0 %). Multivariate analysis identified double-lung transplantation (p = 0.012) and early (1993-1998) transplantation period (p = 0.008) as independent risk factors for developing major GI complications. Forty-three (21 %) patients required surgery such as colectomy, cholecystectomy, and fundoplication in 6.8, 6.3, and 3.9 % of the patients, respectively. Multivariate analysis identified Charlson comorbidity index of ≥3 as an independent risk factor for developing GI complications requiring surgery (p = 0.015). CONCLUSION: GI complications after lung transplantation are common. Outcome was rather encouraging in the setting of our transplant center.

'Freakish Fat', 'Wretched Black': Female abject beings in contemporary Colombian media and culture

This paper explores the construction of female abject beings in Colombian contemporary media and culture comparing a character in the 2010 telenovela Chepe Fortuna named Venezuela, and the cultural representation of Piedad Córdoba. I argue that the construction of these two characters as abject beings is coherent with the dominant discourse of Alvaro Uribe's national project, which relied on a strong nationalist rhetoric based on binary oppositions of the type "we/other." In this context both Chepe Fortuna's Venezuela and Piedad Córdoba are constructed as "other." While Venezuela's abjection is partly effected on the basis of her being fat and black, Córdoba's is on the basis of her being a left-wing politician, and mediated through her being a black female. These two instances evidence an approach to femaleness that goes hand-in-hand with particular understandings of female subjectivity within current post-feminist paradigms.

Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

Subjective perceptions as prognostic factors of time to fitness for work during a 4-year period after inpatient rehabilitation for orthopaedic trauma.

INTRODUCTION: Time to fitness for work (TFW) was measured as the number of days that were paid as compensation for work disability during the 4 years after discharge from the rehabilitation clinic in a population of patients hospitalised for rehabilitation after orthopaedic trauma. The aim of this study was to test whether some psychological variables can be used as potential early prognostic factors of TFW. MATERIAL AND METHODS: A Cox proportional hazards model was used to estimate the associations between predictive variables and TFW. Predictors were global health, pain at hospitalisation and pain decrease during the stay (all continuous and standardised by subtracting the mean and dividing by two standard deviations), perceived severity of the trauma and expectation of a positive evolution (both binary variables). RESULTS: Full data were available for 807 inpatients (660 men, 147 women). TFW was positively associated with better perceived health (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.13-1.19), pain decrease (HR 1.46, 95% CI 1.30-1.64) and expectation of a positive evolution (HR 1.50, 95% CI 1.32-1.70) and negatively associated with pain at hospitalisation (HR 0.67, 95% CI 0.59-0.76) and high perceived severity (HR 0.72, 95% CI 0.61-0.85). DISCUSSION: The present results provide some evidence that work disability during a four-year period after rehabilitation may be predicted by prerehabilitation perceptions of general health, pain, injury severity, as well as positive expectation of evolution.

Protein pocket and ligand shape comparison and its application in virtual screening.

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

Classification supervisée multi-étiquette en actes de dialogue: analyse discriminante et transformations de Schoenberg

Abstract This work studies the multi-label classification of turns in simple English Wikipedia talk pages into dialog acts. The treated dataset was created and multi-labeled by (Ferschke et al., 2012). The first part analyses dependences between labels, in order to examine the annotation coherence and to determine a classification method. Then, a multi-label classification is computed, after transforming the problem into binary relevance. Regarding features, whereas (Ferschke et al., 2012) use features such as uni-, bi-, and trigrams, time distance between turns or the indentation level of the turn, other features are considered here: lemmas, part-of-speech tags and the meaning of verbs (according to WordNet). The dataset authors applied approaches such as Naive Bayes or Support Vector Machines. The present paper proposes, as an alternative, to use Schoenberg transformations which, following the example of kernel methods, transform original Euclidean distances into other Euclidean distances, in a space of high dimensionality. Résumé Ce travail étudie la classification supervisée multi-étiquette en actes de dialogue des tours de parole des contributeurs aux pages de discussion de Simple English Wikipedia (Wikipédia en anglais simple). Le jeu de données considéré a été créé et multi-étiqueté par (Ferschke et al., 2012). Une première partie analyse les relations entre les étiquettes pour examiner la cohérence des annotations et pour déterminer une méthode de classification. Ensuite, une classification supervisée multi-étiquette est effectuée, après recodage binaire des étiquettes. Concernant les variables, alors que (Ferschke et al., 2012) utilisent des caractéristiques telles que les uni-, bi- et trigrammes, le temps entre les tours de parole ou l'indentation d'un tour de parole, d'autres descripteurs sont considérés ici : les lemmes, les catégories morphosyntaxiques et le sens des verbes (selon WordNet). Les auteurs du jeu de données ont employé des approches telles que le Naive Bayes ou les Séparateurs à Vastes Marges (SVM) pour la classification. Cet article propose, de façon alternative, d'utiliser et d'étendre l'analyse discriminante linéaire aux transformations de Schoenberg qui, à l'instar des méthodes à noyau, transforment les distances euclidiennes originales en d'autres distances euclidiennes, dans un espace de haute dimensionnalité.

Predicting richness and composition in mountain insect communities at high resolution: a new test of the SESAM framework

Aim The aim of this study was to test different modelling approaches, including a new framework, for predicting the spatial distribution of richness and composition of two insect groups. Location The western Swiss Alps. Methods We compared two community modelling approaches: the classical method of stacking binary prediction obtained fromindividual species distribution models (binary stacked species distribution models, bS-SDMs), and various implementations of a recent framework (spatially explicit species assemblage modelling, SESAM) based on four steps that integrate the different drivers of the assembly process in a unique modelling procedure. We used: (1) five methods to create bS-SDM predictions; (2) two approaches for predicting species richness, by summing individual SDM probabilities or by modelling the number of species (i.e. richness) directly; and (3) five different biotic rules based either on ranking probabilities from SDMs or on community co-occurrence patterns. Combining these various options resulted in 47 implementations for each taxon. Results Species richness of the two taxonomic groups was predicted with good accuracy overall, and in most cases bS-SDM did not produce a biased prediction exceeding the actual number of species in each unit. In the prediction of community composition bS-SDM often also yielded the best evaluation score. In the case of poor performance of bS-SDM (i.e. when bS-SDM overestimated the prediction of richness) the SESAM framework improved predictions of species composition. Main conclusions Our results differed from previous findings using community-level models. First, we show that overprediction of richness by bS-SDM is not a general rule, thus highlighting the relevance of producing good individual SDMs to capture the ecological filters that are important for the assembly process. Second, we confirm the potential of SESAM when richness is overpredicted by bS-SDM; limiting the number of species for each unit and applying biotic rules (here using the ranking of SDM probabilities) can improve predictions of species composition