A modular magnetic anastomotic device for minimally invasive digestive anastomosis: proof of concept and preliminary data in the pig model.

BACKGROUND: The aim of our study was to assess the feasibility of minimally invasive digestive anastomosis using a modular flexible magnetic anastomotic device made up of a set of two flexible chains of magnetic elements. The assembly possesses a non-deployed linear configuration which allows it to be introduced through a dedicated small-sized applicator into the bowel where it takes the deployed form. A centering suture allows the mating between the two parts to be controlled in order to include the viscerotomy between the two magnetic rings and the connected viscera. METHODS AND PROCEDURES: Eight pigs were involved in a 2-week survival experimental study. In five colorectal anastomoses, the proximal device was inserted by a percutaneous endoscopic technique, and the colon was divided below the magnet. The distal magnet was delivered transanally to connect with the proximal magnet. In three jejunojejunostomies, the first magnetic chain was injected in its linear configuration through a small enterotomy. Once delivered, the device self-assembled into a ring shape. A second magnet was injected more distally through the same port. The centering sutures were tied together extracorporeally and, using a knot pusher, magnets were connected. Ex vivo strain testing to determine the compression force delivered by the magnetic device, burst pressure of the anastomosis, and histology were performed. RESULTS: Mean operative time including endoscopy was 69.2 ± 21.9 min, and average time to full patency was 5 days for colorectal anastomosis. Operative times for jejunojejunostomies were 125, 80, and 35 min, respectively. The postoperative period was uneventful. Burst pressure of all anastomoses was ≥ 110 mmHg. Mean strain force to detach the devices was 6.1 ± 0.98 and 12.88 ± 1.34 N in colorectal and jejunojejunal connections, respectively. Pathology showed a mild-to-moderate inflammation score. CONCLUSIONS: The modular magnetic system showed enormous potential to create minimally invasive digestive anastomoses, and may represent an alternative to stapled anastomoses, being easy to deliver, effective, and low cost.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

OpenFluDB, a database for human and animal influenza virus.

Although research on influenza lasted for more than 100 years, it is still one of the most prominent diseases causing half a million human deaths every year. With the recent observation of new highly pathogenic H5N1 and H7N7 strains, and the appearance of the influenza pandemic caused by the H1N1 swine-like lineage, a collaborative effort to share observations on the evolution of this virus in both animals and humans has been established. The OpenFlu database (OpenFluDB) is a part of this collaborative effort. It contains genomic and protein sequences, as well as epidemiological data from more than 27,000 isolates. The isolate annotations include virus type, host, geographical location and experimentally tested antiviral resistance. Putative enhanced pathogenicity as well as human adaptation propensity are computed from protein sequences. Each virus isolate can be associated with the laboratories that collected, sequenced and submitted it. Several analysis tools including multiple sequence alignment, phylogenetic analysis and sequence similarity maps enable rapid and efficient mining. The contents of OpenFluDB are supplied by direct user submission, as well as by a daily automatic procedure importing data from public repositories. Additionally, a simple mechanism facilitates the export of OpenFluDB records to GenBank. This resource has been successfully used to rapidly and widely distribute the sequences collected during the recent human swine flu outbreak and also as an exchange platform during the vaccine selection procedure. Database URL: http://openflu.vital-it.ch.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data

Maximum entropy modeling (Maxent) is a widely used algorithm for predicting species distributions across space and time. Properly assessing the uncertainty in such predictions is non-trivial and requires validation with independent datasets. Notably, model complexity (number of model parameters) remains a major concern in relation to overfitting and, hence, transferability of Maxent models. An emerging approach is to validate the cross-temporal transferability of model predictions using paleoecological data. In this study, we assess the effect of model complexity on the performance of Maxent projections across time using two European plant species (Alnus giutinosa (L.) Gaertn. and Corylus avellana L) with an extensive late Quaternary fossil record in Spain as a study case. We fit 110 models with different levels of complexity under present time and tested model performance using AUC (area under the receiver operating characteristic curve) and AlCc (corrected Akaike Information Criterion) through the standard procedure of randomly partitioning current occurrence data. We then compared these results to an independent validation by projecting the models to mid-Holocene (6000 years before present) climatic conditions in Spain to assess their ability to predict fossil pollen presence-absence and abundance. We find that calibrating Maxent models with default settings result in the generation of overly complex models. While model performance increased with model complexity when predicting current distributions, it was higher with intermediate complexity when predicting mid-Holocene distributions. Hence, models of intermediate complexity resulted in the best trade-off to predict species distributions across time. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. Consequently, species-specific model tuning should be used to find the best modeling settings to control for complexity, notably with paleoecological data to independently validate model projections. For cross-temporal projections of species distributions for which paleoecological data is not available, models of intermediate complexity should be selected.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.

Privacy-friendly Business Models for Location-Based Mobile Services

This paper presents a theoretical model to analyze the privacy issues around location based mobile business models. We report the results of an exploratory field experiment in Switzerland that assessed the factors driving user payoff in mobile business. We found that (1) the personal data disclosed has a negative effect on user payoff; (2) the amount of personalization available has a direct and positive effect, as well as a moderating effect on user payoff; (3) the amount of control over user's personal data has a direct and positive effect, as well as a moderating effect on user payoff. The results suggest that privacy protection could be the main value proposition in the B2C mobile market. From our theoretical model we derive a set of guidelines to design a privacy-friendly business model pattern for third-party services. We discuss four examples to show the mobile platform can play a key role in the implementation of these new business models.

Modelling the consequences of a reduction in alcohol consumption among patients with alcohol dependence based on real-life observational data.

BACKGROUND: Most available pharmacotherapies for alcohol-dependent patients target abstinence; however, reduced alcohol consumption may be a more realistic goal. Using randomized clinical trial (RCT) data, a previous microsimulation model evaluated the clinical relevance of reduced consumption in terms of avoided alcohol-attributable events. Using real-life observational data, the current analysis aimed to adapt the model and confirm previous findings about the clinical relevance of reduced alcohol consumption. METHODS: Based on the prospective observational CONTROL study, evaluating daily alcohol consumption among alcohol-dependent patients, the model predicted the probability of drinking any alcohol during a given day. Predicted daily alcohol consumption was simulated in a hypothetical sample of 200,000 patients observed over a year. Individual total alcohol consumption (TAC) and number of heavy drinking days (HDD) were derived. Using published risk equations, probabilities of alcohol-attributable adverse health events (e.g., hospitalizations or death) corresponding to simulated consumptions were computed, and aggregated for categories of patients defined by HDDs and TAC (expressed per 100,000 patient-years). Sensitivity analyses tested model robustness. RESULTS: Shifting from >220 HDDs per year to 120-140 HDDs and shifting from 36,000-39,000 g TAC per year (120-130 g/day) to 15,000-18,000 g TAC per year (50-60 g/day) impacted substantially on the incidence of events (14,588 and 6148 events avoided per 100,000 patient-years, respectively). Results were robust to sensitivity analyses. CONCLUSIONS: This study corroborates the previous microsimulation modeling approach and, using real-life data, confirms RCT-based findings that reduced alcohol consumption is a relevant objective for consideration in alcohol dependence management to improve public health.

Developmental critical period in gentic redox dysregulation: animal and human studies in schizophrenia

Converging evidence favors an abnormal susceptibility to oxidative stress in schizophrenia. Decreased levels of glutathione (GSH), the major cellular antioxidant and redox regulator, was observed in cerebrospinal-fluid and prefrontal cortex of patients. Importantly, abnormal GSH synthesis of genetic origin was observed: Two case-control studies showed an association with a GAG trinucleotide repeat (TNR) polymorphism in the GSH key synthesizing enzyme glutamate-cysteine-ligase (GCL) catalytic subunit (GCLC) gene. The most common TNR genotype 7/7 was more frequent in controls, whereas the rarest TNR genotype 8/8 was three times more frequent in patients. The disease associated genotypes (35% of patients) correlated with decreased GCLC protein, GCL activity and GSH content. Similar GSH system anomalies were observed in early psychosis patients. Such redox dysregulation combined with environmental stressors at specific developmental stages could underlie structural and functional connectivity anomalies. In pharmacological and knock-out (KO) models, GSH deficit induces anomalies analogous to those reported in patients. (a) morphology: spine density and GABA-parvalbumine immunoreactivity (PV-I) were decreased in anterior cingulate cortex. KO mice showed delayed cortical PV-I at PD10. This effect is exacerbated in mice with increased DA from PD5-10. KO mice exhibit cortical impairment in myelin and perineuronal net known to modulate PV connectivity. (b) physiology: In cultured neurons, NMDA response are depressed by D2 activation. In hippocampus, NMDA-dependent synaptic plasticity is impaired and kainate induced g-oscillations are reduced in parallel to PV-I. (c) cognition: low GSH models show increased sensitivity to stress, hyperactivity, abnormal object recognition, olfactory integration and social behavior. In a clinical study, GSH precursor N-acetyl cysteine (NAC) as add on therapy, improves the negative symptoms and decreases the side effects of antipsychotics. In an auditory oddball paradigm, NAC improves the mismatched negativity, an evoked potential related to pre-attention and to NMDA receptors function. In summary, clinical and experimental evidence converge to demonstrate that a genetically induced dysregulation of GSH synthesis combined with environmental insults in early development represent a major risk factor contributing to the development of schizophrenia Conclusion Based on these data, we proposed a model for PSIP1 promoter activity involving a complex interplay between yet undefined regulatory elements to modulate gene expression.

Microvascular pathology in late-life depression.

Since the era of Gaupp who introduced the concept of atheroscletic depressive disorder, the concept of late-life depression has been correlated with cerebrovascular comorbidities, microvascular lesions, frontal cortical and subcortical gray and white matter hyperintensities. The predominant neuropsychological deficits concern the domains of planning, organization and abstraction, with executive dysfunction being the predominant finding. MRI studies reveal a higher prevalence of white matter lesions in elderly patients with depression. Molecular mechanisms underlying the disease still remain unclear. Hyperhomocysteinemia has been associated with depression through its toxicity to neurons and blood vessels. Endothelial dysfunction is another possible mechanism referring to the loss of vasodilatation capacity. Inflammatory phenomena, such as increased peripheral leucocytes, elevated CRP and cytokine levels, could play a role in endothelial dysfunction. In this review we will briefly combine findings from neurobiological, epidemiological, structural and post-mortem data. A more complex model in late-life depression combining different modalities could be an elucidating approach to the disease's etiopathogeny in the future.

Statistical discrimination of steroid profiles in doping control with support vector machines.

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society. To screen for misuse with these compounds, drug testing laboratories monitor the urinary concentrations of endogenous steroid metabolites and their ratios, which constitute the steroid profile and compare them with reference ranges to detect unnaturally high values. However, the interpretation of the steroid profile is difficult due to large inter-individual variances, various confounding factors and different endogenous steroids marketed that influence the steroid profile in various ways. A support vector machine (SVM) algorithm was developed to statistically evaluate urinary steroid profiles composed of an extended range of steroid profile metabolites. This model makes the interpretation of the analytical data in the quest for deviating steroid profiles feasible and shows its versatility towards different kinds of misused endogenous steroids. The SVM model outperforms the current biomarkers with respect to detection sensitivity and accuracy, particularly when it is coupled to individual data as stored in the Athlete Biological Passport.

Activity-dependent regulation of energy metabolism by astrocytes: an update.

Astrocytes play a critical role in the regulation of brain metabolic responses to activity. One detailed mechanism proposed to describe the role of astrocytes in some of these responses has come to be known as the astrocyte-neuron lactate shuttle hypothesis (ANLSH). Although controversial, the original concept of a coupling mechanism between neuronal activity and glucose utilization that involves an activation of aerobic glycolysis in astrocytes and lactate consumption by neurons provides a heuristically valid framework for experimental studies. In this context, it is necessary to provide a survey of recent developments and data pertaining to this model. Thus, here, we review very recent experimental evidence as well as theoretical arguments strongly supporting the original model and in some cases extending it. Aspects revisited include the existence of glutamate-induced glycolysis in astrocytes in vitro, ex vivo, and in vivo, lactate as a preferential oxidative substrate for neurons, and the notion of net lactate transfer between astrocytes and neurons in vivo. Inclusion of a role for glycogen in the ANLSH is discussed in the light of a possible extension of the astrocyte-neuron lactate shuttle (ANLS) concept rather than as a competing hypothesis. New perspectives offered by the application of this concept include a better understanding of the basis of signals used in functional brain imaging, a role for neuron-glia metabolic interactions in glucose sensing and diabetes, as well as novel strategies to develop therapies against neurodegenerative diseases based upon improving astrocyte-neuron coupled energetics.

Etude des liens entre l'alexithymie, l'expérience dépressive et la dépendance interpersonnelle chez des sujets addictifs [Relationships between alexithymia, depression and interpersonal dependency in addictive subjects].

In the scientific literature, the term of addiction is currently used to describe a whole range of phenomena characterized by an irresistible urge to engage in a series of behaviors carried out in a repetitive and persistent manner despite accruing adverse somatic, psychological and social consequences for the individual. It has been suggested that subjects presenting such behaviors would share specific features of personality which support the appearance or are associated with these addictive behaviors. Dimensions such as alexithymia and depression have been particularly well investigated. The aim of this study was to explore the hypothesis of a specific psychopathological model relating alexithymia and depression in different addictive disorders such as alcoholism, drug addiction or eating disorders. Alexithymic and depressive dimensions were explored and analyzed through the statistical tool of path analysis in a large clinical sample of addicted patients and controls. The results of this statistical method, which tests unidirectional causal relationships between a certain number of observed variables, showed a good adjustment between the observed data and the ideal model, and support the hypothesis that a depressive dimension can facilitate the development of dependence in vulnerable alexithymic subjects. These results can have clinical implications in the treatment of addictive disorders.

Modifications neurocomportementales secondaires à la sclérose en plaques [Neurobehavioral changes occurring during multiple sclerosis]

Behavioral changes occurring in patients affected by multiple sclerosis (MSI are often neglected by physicians but are actually part of the clinical spectrum of the disease. In addition, they are known to be responsible for a decline in the quality of life of MS patients. Recently, there has been a growing interest to investigate changes in the emotional experience of MS patients and their decision making, showing that the ability to take advantageous decisions was altered in MS. This paper reviews existing data on this topic.