The role of alliance in the relationship between therapist competence and outcome in brief psychodynamic psychotherapy.

Therapist competence is a key variable for psychotherapy research. Empirically, the relationship between competence and therapeutic outcome has shown contradictory results and needs to be clarified, especially with regard to possible variables influencing this relationship. A total of 78 outpatients were treated by 15 therapists in a very brief 4-session format, based on psychoanalytic theory. Data were analyzed by means of a nested design using hierarchical linear modeling. No direct link between therapist competence and outcome has been found, however, results corroborated the importance of alliance patterns as moderator in the relationship between therapist competence and outcome. Only in dyads with alliance change over the course of treatment was it clear that competence is positively related to outcome. These findings are discussed with regard to the importance for outcome of therapist competence and alliance construction processes.

Safety of combined heat and moisture exchanger filters in long-term mechanical ventilation.

STUDY OBJECTIVE: To evaluate the safety of a combined heat and moisture exchanger filter (HMEF) for the conditioning of inspired gas in long-term mechanical ventilation (MV). DESIGN: Randomized controlled trial. SETTING: Medical ICU in a large teaching hospital. PATIENTS: One hundred fifteen consecutive patients who required > or = 48 h of MV. INTERVENTIONS: Patients were randomized at intubation time (day 1) to receive inspired gas conditioned either by a water-bath humidifier heated at 32 degrees C (HWBH) or by an HMEF (Hygroster; DAR; Mirandola, Italy). MEASUREMENTS AND MAIN RESULTS: The two study groups were comparable in terms of primary pathologic condition at the time of hospital admission, disease severity as measured by the Simplified Acute Physiology Score, and ICU mortality. They did not differ with respect to ventilator days per patient (mean +/- SD: HMEF, 7.6 +/- 6.5; HWBH, 7.8 +/- 5.8), incidence of endotracheal tube obstruction (HMEF, 0/59; HWBH, 1/56), and incidence of hypothermic episodes (HMEF, five; HWBH, two). In 41 patients receiving MV for > or = 5 days, the morphologic integrity of respiratory epithelium was evaluated on day 1 and day 5, using a cytologic examination of tracheal aspirate smears. The state of ciliated epithelium was scored on a scale from 0 (poorest integrity) to 1,200 (maximum integrity), according to a well-described method. In both patient groups, the scores slightly but significantly decreased from day 1 to day 5 (mean +/- SD: HWBH, from 787 +/- 104 to 745 +/- 88; HMEF, from 813 +/- 79 to 739 +/- 62; p < 0.01 for both groups); there were no statistically significant differences between groups. CONCLUSIONS: These data indicate acceptable safety of HMEFs of the type used in the present study for long-term mechanical ventilation.

Bacterial bioassay for rapid and accurate analysis of arsenic in highly variable groundwater samples.

In this study, we report the first ever large-scale environmental validation of a microbial reporter-based test to measure arsenic concentrations in natural water resources. A bioluminescence-producing arsenic-inducible bacterium based on Escherichia coli was used as the reporter organism. Specific protocols were developed with the goal to avoid the negative influence of iron in groundwater on arsenic availability to the bioreporter cells. A total of 194 groundwater samples were collected in the Red River and Mekong River Delta regions of Vietnam and were analyzed both by atomic absorption spectroscopy (AAS) and by the arsenic bioreporter protocol. The bacterial cells performed well at and above arsenic concentrations in groundwater of 7 microg/L, with an almost linearly proportional increase of the bioluminescence signal between 10 and 100 microg As/L (r2 = 0.997). Comparisons between AAS and arsenic bioreporter determinations gave an overall average of 8.0% false negative and 2.4% false positive identifications for the bioreporter prediction at the WHO recommended acceptable arsenic concentration of 10 microg/L, which is far betterthan the performance of chemical field test kits. Because of the ease of the measurement protocol and the low application cost, the microbiological arsenic test has a great potential in large screening campaigns in Asia and in other areas suffering from arsenic pollution in groundwater resources.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

The Foundational Significance of Leggett's Non-local Hidden-Variable Theories

Laudisa (Found. Phys. 38:1110-1132, 2008) claims that experimental research on the class of non-local hidden-variable theories introduced by Leggett is misguided, because these theories are irrelevant for the foundations of quantum mechanics. I show that Laudisa's arguments fail to establish the pessimistic conclusion he draws from them. In particular, it is not the case that Leggett-inspired research is based on a mistaken understanding of Bell's theorem, nor that previous no-hidden-variable theorems already exclude Leggett's models. Finally, I argue that the framework of Bohmian mechanics brings out the importance of Leggett tests, rather than proving their irrelevance, as Laudisa supposes.

Genetic basis and variable phenotypic expression of Kallmann syndrome: towards a unifying theory.

Idiopathic hypogonadotropic hypogonadism (IHH) is defined by absent or incomplete puberty and characterised biochemically by low levels of sex steroids, with low or inappropriately normal gonadotropin hormones. IHH is frequently accompanied by non-reproductive abnormalities, most commonly anosmia, which is present in 50-60% of cases and defines Kallmann syndrome. The understanding of IHH has undergone rapid evolution, both in respect of genetics and breadth of phenotype. Once considered in monogenic Mendelian terms, it is now more coherently understood as a complex genetic condition. Oligogenic and complex genetic-environmental interactions have now been identified, with physiological and environmental factors interacting in genetically susceptible individuals to alter the clinical course and phenotype. These potentially link IHH to ancient evolutionary pressures on the ancestral human genome.

Drug-induced linear IgA bullous dermatosis probably induced by furosemide.

Linear IgA bullous dermatosis (LABD) is an autoimmune disease, characterized by linear deposition of IgA along the basement membrane zone. Drug-induced LABD is rare but increasing in frequency. A new case of drug-induced LABD associated with the administration of furosemide is described.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

A comparison of three methods of Mendelian randomization when the genetic instrument, the risk factor and the outcome are all binary.

The method of instrumental variable (referred to as Mendelian randomization when the instrument is a genetic variant) has been initially developed to infer on a causal effect of a risk factor on some outcome of interest in a linear model. Adapting this method to nonlinear models, however, is known to be problematic. In this paper, we consider the simple case when the genetic instrument, the risk factor, and the outcome are all binary. We compare via simulations the usual two-stages estimate of a causal odds-ratio and its adjusted version with a recently proposed estimate in the context of a clinical trial with noncompliance. In contrast to the former two, we confirm that the latter is (under some conditions) a valid estimate of a causal odds-ratio defined in the subpopulation of compliers, and we propose its use in the context of Mendelian randomization. By analogy with a clinical trial with noncompliance, compliers are those individuals for whom the presence/absence of the risk factor X is determined by the presence/absence of the genetic variant Z (i.e., for whom we would observe X = Z whatever the alleles randomly received at conception). We also recall and illustrate the huge variability of instrumental variable estimates when the instrument is weak (i.e., with a low percentage of compliers, as is typically the case with genetic instruments for which this proportion is frequently smaller than 10%) where the inter-quartile range of our simulated estimates was up to 18 times higher compared to a conventional (e.g., intention-to-treat) approach. We thus conclude that the need to find stronger instruments is probably as important as the need to develop a methodology allowing to consistently estimate a causal odds-ratio.

Linear relations between writhe and minimal crossing number in Conway families of ideal knots and links

We showed earlier how to predict the writhe of any rational knot or link in its ideal geometric configuration, or equivalently the average of the 3D writhe over statistical ensembles of random configurations of a given knot or link (Cerf and Stasiak 2000 Proc. Natl Acad. Sci. USA 97 3795). There is no general relation between the minimal crossing number of a knot and the writhe of its ideal geometric configuration. However, within individual families of knots linear relations between minimal crossing number and writhe were observed (Katritch et al 1996 Nature 384 142). Here we present a method that allows us to express the writhe as a linear function of the minimal crossing number within Conway families of knots and links in their ideal configuration. The slope of the lines and the shift between any two lines with the same

Influence of weather, rank, and home advantage on football outcomes in the gulf region.

PURPOSE: The objective of this study was to investigate the effects of weather, rank, and home advantage on international football match results and scores in the Gulf Cooperation Council (GCC) region. METHODS: Football matches (n = 2008) in six GCC countries were analyzed. To determine the weather influence on the likelihood of favorable outcome and goal difference, generalized linear model with a logit link function and multiple regression analysis were performed. RESULTS: In the GCC region, home teams tend to have greater likelihood of a favorable outcome (P < 0.001) and higher goal difference (P < 0.001). Temperature difference was identified as a significant explanatory variable when used independently (P < 0.001) or after adjustment for home advantage and team ranking (P < 0.001). The likelihood of favorable outcome for GCC teams increases by 3% for every 1-unit increase in temperature difference. After inclusion of interaction with opposition, this advantage remains significant only when playing against non-GCC opponents. While home advantage increased the odds of favorable outcome (P < 0.001) and goal difference (P < 0.001) after inclusion of interaction term, the likelihood of favorable outcome for a GCC team decreased (P < 0.001) when playing against a stronger opponent. Finally, the temperature and wet bulb globe temperature approximation were found as better indicators of the effect of environmental conditions than absolute and relative humidity or heat index on match outcomes. CONCLUSIONS: In GCC region, higher temperature increased the likelihood of a favorable outcome when playing against non-GCC teams. However, international ranking should be considered because an opponent with a higher rank reduced, but did not eliminate, the likelihood of a favorable outcome.

Direct Effect of Birth Weight on Childhood Blood Pressure: a Causal Mediation Analysis

Background: Numerous studies have shown a negative association between birth weight (BW) and blood pressure (BP) later in life. To estimate the direct effect of BW on BP, it is conventional to condition on current weight (CW). However, such conditioning can induce collider stratification bias in the estimate of the direct effect. Objective: To bound the potential bias due to U, an unmeasured common cause of CW and BP, on the estimate of the (controlled) direct effect of BW on BP. Methods: Data from a school based study in Switzerland were used (N = 4,005; 2,010 B/1,995 G; mean age: 12.3 yr [range: 10.1-14.9]). Measured common causes of BW-BP (SES, smoking, body weight, and hypertension status of the mother) and CW-BP (breastfeeding and child's physical activity and diet) were identified with DAGs. Linear regression models were fitted to estimate the association between BW and BP. Sensitivity analyses were conducted to assess the potential effect of U on the association between BW and BP. U was assumed 1) to be a binary variable that affected BP by the same magnitude in low BWand in normal BW children and 2) to have a different prevalence in low BW children and in normal BW children for a given CW. Results: A small negative association was observed between BW and BP [beta: -0.3 mmHg/kg (95% CI: -0.9 to 0.3)]. The association was strengthened upon conditioning for CW [beta: -1.5 mmHg/kg (95% CI: -2.1 to -0.9)]. Upon further conditioning on common causes of BW-BP and CW-BP, the association did not change substantially [beta: -1.4 mmHg/kg (95% CI: -2.0 to -0.8)]. The negative association could be explained by U only if U was strongly associated with BP and if there was a large difference in the prevalence of U between low BWand normal BW children. Conclusion: The observed negative association between BW and BP upon adjustment for CW was not easily explained by an unmeasured common cause of CWand BP.

Linear random knots and their scaling behavior

We present here a nonbiased probabilistic method that allows us to consistently analyze knottedness of linear random walks with up to several hundred noncorrelated steps. The method consists of analyzing the spectrum of knots formed by multiple closures of the same open walk through random points on a sphere enclosing the walk. Knottedness of individual "frozen" configurations of linear chains is therefore defined by a characteristic spectrum of realizable knots. We show that in the great majority of cases this method clearly defines the dominant knot type of a walk, i.e., the strongest component of the spectrum. In such cases, direct end-to-end closure creates a knot that usually coincides with the knot type that dominates the random closure spectrum. Interestingly, in a very small proportion of linear random walks, the knot type is not clearly defined. Such walks can be considered as residing in a border zone of the configuration space of two or more knot types. We also characterize the scaling behavior of linear random knots.