Independent and combined associations of risky single-occasion drinking and drinking volume with alcohol use disorder: Evidence from a sample of young Swiss men.

BACKGROUND: Risky single-occasion drinking (RSOD) is a prevalent and potentially harmful alcohol use pattern associated with increased alcohol use disorder (AUD). However, RSOD is commonly associated with a higher level of alcohol intake, and most studies have not controlled for drinking volume (DV). Thus, it is unclear whether the findings provide information about RSOD or DV. This study sought to investigate the independent and combined effects of RSOD and DV on AUD. METHODS: Data were collected in the longitudinal Cohort Study on Substance Use Risk Factors (C-SURF) among 5598 young Swiss male alcohol users in their early twenties. Assessment included DV, RSOD, and AUD at two time points. Generalized linear models for binomial distributions provided evidence regarding associations of DV, RSOD, and their interaction. RESULTS: DV, RSOD, and their interaction were significantly related to the number of AUD criteria. The slope of the interaction was steeper for non/rare RSOD than for frequent RSOD. CONCLUSIONS: RSOD appears to be a harmful pattern of drinking, associated with increased AUD and it moderated the relationship between DV and AUD. This study highlighted the importance of taking drinking patterns into account, for both research and public health planning, since RSO drinkers constitute a vulnerable subgroup for AUD.

Tipping the scales: Evolution of the allometric slope independent of average trait size.

The scaling of body parts is central to the expression of morphology across body sizes and to the generation of morphological diversity within and among species. Although patterns of scaling-relationship evolution have been well documented for over one hundred years, little is known regarding how selection acts to generate these patterns. In part, this is because it is unclear the extent to which the elements of log-linear scaling relationships-the intercept or mean trait size and the slope-can evolve independently. Here, using the wing-body size scaling relationship in Drosophila melanogaster as an empirical model, we use artificial selection to demonstrate that the slope of a morphological scaling relationship between an organ (the wing) and body size can evolve independently of mean organ or body size. We discuss our findings in the context of how selection likely operates on morphological scaling relationships in nature, the developmental basis for evolved changes in scaling, and the general approach of using individual-based selection experiments to study the expression and evolution of morphological scaling.

How much protein and energy are needed to equilibrate nitrogen and energy balances in ventilated critically ill children?

BACKGROUND & AIMS: Protein and energy requirements in critically ill children are currently based on insufficient data. Moreover, longitudinal measurements of both total urinary nitrogen (TUN) and resting energy expenditure (REE) are lacking. The aim of this study was to investigate how much protein and energy are needed to equilibrate nitrogen and energy balances in ventilated critically ill children on the basis of daily measurements of TUN, REE and protein and energy intakes. Comparisons were made with the guidelines of the American Society for Parenteral and Enteral Nutrition and the Dietary Reference Intakes. METHODS: Children with an expected duration of mechanical ventilation ≥72 h were prospectively recruited. TUN was measured by chemiluminescence, and REE was measured by indirect calorimetry. Generalised linear models for longitudinal data were used to study the relation between protein intake and nitrogen balance and to calculate the minimum intake of protein needed to achieve nitrogen equilibrium. A similar approach was used for energy. Results were compared to the recommended values. RESULTS: Based on 402 measurements performed in 74 children (median age: 21 months), the mean TUN was high at 0.20 (95% CI: 0.20, 0.22) g/kg/d and the REE was 55 (95% CI: 54, 57) kcal/kg/d. Nitrogen and energy balances were achieved with 1.5 (95% CI: 1.4, 1.6) g/kg/d of protein and 58 (95% CI: 53, 63) kcal/kg/d for the entire group, but there were differences among children of different ages. Children required more protein and less energy than the Dietary Reference Intakes. CONCLUSIONS: In critically ill children, TUN was elevated and REE was reduced during the entire period of mechanical ventilation. Minimum intakes of 1.5 g/kg/d of protein and 58 kcal/kg/d can equilibrate nitrogen and energy balances in children up to 4 years old. Older children require more protein.

AltitudeOmics : Resetting of Cerebrovascular CO2 Reactivity Following Acclimatization to High Altitude.

Previous studies reported enhanced cerebrovascular CO2 reactivity upon ascent to high altitude using linear models. However, there is evidence that this response may be sigmoidal in nature. Moreover, it was speculated that these changes at high altitude are mediated by alterations in acid-base buffering. Accordingly, we reanalyzed previously published data to assess middle cerebral blood flow velocity (MCAv) responses to modified rebreathing at sea level (SL), upon ascent (ALT1) and following 16 days of acclimatization (ALT16) to 5260 m in 21 lowlanders. Using sigmoid curve fitting of the MCAv responses to CO2, we found the amplitude (95 vs. 129%, SL vs. ALT1, 95% confidence intervals (CI) [77, 112], [111, 145], respectively, P = 0.024) and the slope of the sigmoid response (4.5 vs. 7.5%/mmHg, SL vs. ALT1, 95% CIs [3.1, 5.9], [6.0, 9.0], respectively, P = 0.026) to be enhanced at ALT1, which persisted with acclimatization at ALT16 (amplitude: 177, 95% CI [139, 215], P < 0.001; slope: 10.3%/mmHg, 95% CI [8.2, 12.5], P = 0.003) compared to SL. Meanwhile, the sigmoidal response midpoint was unchanged at ALT1 (SL: 36.5 mmHg; ALT1: 35.4 mmHg, 95% CIs [34.0, 39.0], [33.1, 37.7], respectively, P = 0.982), while it was reduced by ~7 mmHg at ALT16 (28.6 mmHg, 95% CI [26.4, 30.8], P = 0.001 vs. SL), indicating leftward shift of the cerebrovascular CO2 response to a lower arterial partial pressure of CO2 (PaCO2) following acclimatization to altitude. Sigmoid fitting revealed a leftward shift in the midpoint of the cerebrovascular response curve which could not be observed with linear fitting. These findings demonstrate that there is resetting of the cerebrovascular CO2 reactivity operating point to a lower PaCO2 following acclimatization to high altitude. This cerebrovascular resetting is likely the result of an altered acid-base buffer status resulting from prolonged exposure to the severe hypocapnia associated with ventilatory acclimatization to high altitude.

Towards the validation of "in silico" models and physicochemical filters to identify and characterize new chemical entities

Summary: Lipophilicity plays an important role in the determination and the comprehension of the pharmacokinetic behavior of drugs. It is usually expressed by the partition coefficient (log P) in the n-octanol/water system. The use of an additional solvent system (1,2-dichlorethane/water) is necessary to obtain complementary information, as the log Poct values alone are not sufficient to explain ail biological properties. The aim of this thesis is to develop tools allowing to predict lipophilicity of new drugs and to analyze the information yielded by those log P values. Part I presents the development of theoretical models used to predict lipophilicity. Chapter 2 shows the necessity to extend the existing solvatochromic analyses in order to predict correctly the lipophilicity of new and complex neutral compounds. In Chapter 3, solvatochromic analyses are used to develop a model for the prediction of the lipophilicity of ions. A global model was obtained allowing to estimate the lipophilicity of neutral, anionic and cationic solutes. Part II presents the detailed study of two physicochemical filters. Chapter 4 shows that the Discovery RP Amide C16 stationary phase allows to estimate lipophilicity of the neutral form of basic and acidic solutes, except of lipophilic acidic solutes. Those solutes present additional interactions with this particular stationary phase. In Chapter 5, 4 different IANI stationary phases are investigated. For neutral solutes, linear data are obtained whatever the IANI column used. For the ionized solutes, their retention is due to a balance of electrostatic and hydrophobie interactions. Thus no discrimination is observed between different series of solutes bearing the same charge, from one column to an other. Part III presents two examples illustrating the information obtained thanks to Structure-Properties Relationships (SPR). Comparing graphically lipophilicity values obtained in two different solvent systems allows to reveal the presence of intramolecular effects .such as internai H-bond (Chapter 6). SPR is used to study the partitioning of ionizable groups encountered in Medicinal Chemistry (Chapter7). Résumé La lipophilie joue un .rôle important dans la détermination et la compréhension du comportement pharmacocinétique des médicaments. Elle est généralement exprimée par le coefficient de partage (log P) d'un composé dans le système de solvants n-octanol/eau. L'utilisation d'un deuxième système de solvants (1,2-dichloroéthane/eau) s'est avérée nécessaire afin d'obtenir des informations complémentaires, les valeurs de log Poct seules n'étant pas suffisantes pour expliquer toutes les propriétés biologiques. Le but de cette thèse est de développer des outils permettant de prédire la lipophilie de nouveaux candidats médicaments et d'analyser l'information fournie par les valeurs de log P. La Partie I présente le développement de modèles théoriques utilisés pour prédire la lipophilie. Le chapitre 2 montre la nécessité de mettre à jour les analyses solvatochromiques existantes mais inadaptées à la prédiction de la lipophilie de nouveaux composés neutres. Dans le chapitre 3, la même méthodologie des analyses solvatochromiques est utilisée pour développer un modèle permettant de prédire la lipophilie des ions. Le modèle global obtenu permet la prédiction de la lipophilie de composés neutres, anioniques et cationiques. La Partie II présente l'étude approfondie de deux filtres physicochimiques. Le Chapitre 4 montre que la phase stationnaire Discovery RP Amide C16 permet la détermination de la lipophilie de la forme neutre de composés basiques et acides, à l'exception des acides très lipophiles. Ces derniers présentent des interactions supplémentaires avec cette phase stationnaire. Dans le Chapitre 5, 4 phases stationnaires IAM sont étudiées. Pour les composés neutres étudiés, des valeurs de rétention linéaires sont obtenues, quelque que soit la colonne IAM utilisée. Pour les composés ionisables, leur rétention est due à une balance entre des interactions électrostatiques et hydrophobes. Donc aucune discrimination n'est observée entre les différentes séries de composés portant la même charge d'une colonne à l'autre. La Partie III présente deux exemples illustrant les informations obtenues par l'utilisation des relations structures-propriétés. Comparer graphiquement la lipophilie mesurée dans deux différents systèmes de solvants permet de mettre en évidence la présence d'effets intramoléculaires tels que les liaisons hydrogène intramoléculaires (Chapitre 6). Cette approche des relations structures-propriétés est aussi appliquée à l'étude du partage de fonctions ionisables rencontrées en Chimie Thérapeutique (Chapitre 7) Résumé large public Pour exercer son effet thérapeutique, un médicament doit atteindre son site d'action en quantité suffisante. La quantité effective de médicament atteignant le site d'action dépend du nombre d'interactions entre le médicament et de nombreux constituants de l'organisme comme, par exemple, les enzymes du métabolisme ou les membranes biologiques. Le passage du médicament à travers ces membranes, appelé perméation, est un paramètre important à optimiser pour développer des médicaments plus puissants. La lipophilie joue un rôle clé dans la compréhension de la perméation passive des médicaments. La lipophilie est généralement exprimée par le coefficient de partage (log P) dans le système de solvants (non miscibles) n-octanol/eau. Les valeurs de log Poct seules se sont avérées insuffisantes pour expliquer la perméation à travers toutes les différentes membranes biologiques du corps humain. L'utilisation d'un système de solvants additionnel (le système 1,2-dichloroéthane/eau) a permis d'obtenir les informations complémentaires indispensables à une bonne compréhension du processus de perméation. Un grand nombre d'outils expérimentaux et théoriques sont à disposition pour étudier la lipophilie. Ce travail de thèse se focalise principalement sur le développement ou l'amélioration de certains de ces outils pour permettre leur application à un champ plus large de composés. Voici une brève description de deux de ces outils: 1)La factorisation de la lipophilie en fonction de certaines propriétés structurelles (telle que le volume) propres aux composés permet de développer des modèles théoriques utilisables pour la prédiction de la lipophilie de nouveaux composés ou médicaments. Cette approche est appliquée à l'analyse de la lipophilie de composés neutres ainsi qu'à la lipophilie de composés chargés. 2)La chromatographie liquide à haute pression sur phase inverse (RP-HPLC) est une méthode couramment utilisée pour la détermination expérimentale des valeurs de log Poct.

A random linear functional approach to efficiency bounds

This paper suggests a method for obtaining efficiency bounds in models containing either only infinite-dimensional parameters or both finite- and infinite-dimensional parameters (semiparametric models). The method is based on a theory of random linear functionals applied to the gradient of the log-likelihood functional and is illustrated by computing the lower bound for Cox's regression model

Predicting trabecular bone microdamage initiation and accumulation using a non-linear perfect damage model

Studies evaluating the mechanical behavior of the trabecular microstructure play an important role in our understanding of pathologies such as osteoporosis, and in increasing our understanding of bone fracture and bone adaptation. Understanding of such behavior in bone is important for predicting and providing early treatment of fractures. The objective of this study is to present a numerical model for studying the initiation and accumulation of trabecular bone microdamage in both the pre- and post-yield regions. A sub-region of human vertebral trabecular bone was analyzed using a uniformly loaded anatomically accurate microstructural three-dimensional finite element model. The evolution of trabecular bone microdamage was governed using a non-linear, modulus reduction, perfect damage approach derived from a generalized plasticity stress-strain law. The model introduced in this paper establishes a history of microdamage evolution in both the pre- and post-yield regions

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Symmetry-breaking in cumulative measures of shapes of polymer models.

Using numerical simulations we investigate shapes of random equilateral open and closed chains, one of the simplest models of freely fluctuating polymers in a solution. We are interested in the 3D density distribution of the modeled polymers where the polymers have been aligned with respect to their three principal axes of inertia. This type of approach was pioneered by Theodorou and Suter in 1985. While individual configurations of the modeled polymers are almost always nonsymmetric, the approach of Theodorou and Suter results in cumulative shapes that are highly symmetric. By taking advantage of asymmetries within the individual configurations, we modify the procedure of aligning independent configurations in a way that shows their asymmetry. This approach reveals, for example, that the 3D density distribution for linear polymers has a bean shape predicted theoretically by Kuhn. The symmetry-breaking approach reveals complementary information to the traditional, symmetrical, 3D density distributions originally introduced by Theodorou and Suter.

Association of alcohol consumption and HIV surrogate markers in participants of the swiss HIV cohort study.

BACKGROUND: Alcohol consumption may affect the course of HIV infection and/or antiretroviral therapy (ART). The authors investigated the association between self-reported alcohol consumption and HIV surrogate markers in both treated and untreated individuals. DESIGN: Prospective cohort study. METHODS: Over a 7-year period, the authors analyzed 2 groups of individuals in the Swiss HIV Cohort Study: (1) ART-naïve individuals remaining off ART and (2) individuals initiating first ART. For individuals initiating first ART, time-dependent Cox proportional hazards models were used to assess the association between alcohol consumption, virological failure, and ART interruption. For both groups, trajectories of log-transformed CD4 cell counts were analyzed using linear mixed models with repeated measures. RESULTS: The authors included 2982 individuals initiating first ART and 2085 ART naives. In individuals initiating first ART, 241 (8%) experienced virological failure. Alcohol consumption was not associated with virological failure. ART interruption was noted in 449 (15%) individuals and was more prevalent in severe compared with none/light health risk drinkers [hazard ratio: 2.24, 95% confidence interval: 1.42 to 3.52]. The association remained significant even after adjusting for nonadherence. The authors did not find an association between alcohol consumption and change in CD4 cell count over time in either group. CONCLUSIONS: No effect of alcohol consumption on either virological failure or CD4 cell count in both groups of ART-initiating and ART-naive individuals was found. However, severe drinkers were more likely to interrupt ART. Efforts on ART continuation should be especially implemented in individuals reporting high alcohol consumption.

Orthotropic active strain models for the numerical simulation of cardiac biomechanics

An active strain formulation for orthotropic constitutive laws arising in cardiac mechanics modeling is introduced and studied. The passive mechanical properties of the tissue are described by the Holzapfel-Ogden relation. In the active strain formulation, the Euler-Lagrange equations for minimizing the total energy are written in terms of active and passive deformation factors, where the active part is assumed to depend, at the cell level, on the electrodynamics and on the specific orientation of the cardiac cells. The well-posedness of the linear system derived from a generic Newton iteration of the original problem is analyzed and different mechanical activation functions are considered. In addition, the active strain formulation is compared with the classical active stress formulation from both numerical and modeling perspectives. Taylor-Hood and MINI finite elements are employed to discretize the mechanical problem. The results of several numerical experiments show that the proposed formulation is mathematically consistent and is able to represent the main key features of the phenomenon, while allowing savings in computational costs.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.