Origins and rationele of centres for parents and young children together

The range of centres where parents and children come together has mushroomed in different parts of the world, as new social work practices address the emerging non-material needs of parents in changing demographic contexts. In this paper, we explore the origins and modi operandi of these centres in Belgium, France, Italy and Japan. Analysis of previous studies and policy documents reveal diverse political rationales, including addressing declining birth rates, preventing psychosocial problems and social isolation of mothers and promoting social cohesion and equality of educational opportunities. Remarkably, despite the diverse cultural and socio-political contexts and rationales, these centres also share very similar ways of functioning and provide an informal type of social support to parents with young children. As these recently emerged centres are seldom studied, further research is welcomed to explore parents' and professionals' perspectives

Genetic counselling for psychiatric disorders: accounts of psychiatric health professionals in the United Kingdom

Genetic counselling is not routinely offered for psychiatric disorders in the United Kingdom through NHS regional clinical genetics departments. However, recent genomic advances, confirming a genetic contribution to mental illness, are anticipated to increase demand for psychiatric genetic counselling. This is the first study of its kind to employ qualitative methods of research to explore accounts of psychiatric health professionals regarding the prospects for genetic counselling services within clinical psychiatry in the UK. Data were collected from 32 questionnaire participants, and 9 subsequent interviewees. Data analysis revealed that although participants had not encountered patients explicitly demanding psychiatric genetic counselling, psychiatric health professionals believe that such a service would be useful and desirable. Genomic advances may have significant implications for genetic counselling in clinical psychiatry even if these discoveries do not lead to genetic testing. Psychiatric health professionals describe clinical genetics as a skilled profession capable of combining complex risk communication with much needed psychosocial support. However, participants noted barriers to the implementation of psychiatric genetic counselling services including, but not limited to, the complexities of uncertainty in psychiatric diagnoses, patient engagement and ethical concerns regarding limited capacity

Process Simulation and Control Optimization of a Blast Furnace Using Classical Thermodynamics Combined to a Direct Search Algorithm

Several numerical approaches have been proposed in the literature to simulate the behavior of modern blast furnaces: finite volume methods, data-mining models, heat and mass balance models, and classical thermodynamic simulations. Despite this, there is actually no efficient method for evaluating quickly optimal operating parameters of a blast furnace as a function of the iron ore composition, which takes into account all potential chemical reactions that could occur in the system. In the current study, we propose a global simulation strategy of a blast furnace, the 5-unit process simulation. It is based on classical thermodynamic calculations coupled to a direct search algorithm to optimize process parameters. These parameters include the minimum required metallurgical coke consumption as well as the optimal blast chemical composition and the total charge that simultaneously satisfy the overall heat and mass balances of the system. Moreover, a Gibbs free energy function for metallurgical coke is parameterized in the current study and used to fine-tune the simulation of the blast furnace. Optimal operating conditions and predicted output stream properties calculated by the proposed thermodynamic simulation strategy are compared with reference data found in the literature and have proven the validity and high precision of this simulation

From insect to man: Photorhabdus sheds light on the emergence of human pathogenicity

Photorhabdus are highly effective insect pathogenic bacteria that exist in a mutualistic relationship with Heterorhabditid nematodes. Unlike other members of the genus, Photorhabdus asymbiotica can also infect humans. Most Photorhabdus cannot replicate above 34°C, limiting their host-range to poikilothermic invertebrates. In contrast, P. asymbiotica must necessarily be able to replicate at 37°C or above. Many well-studied mammalian pathogens use the elevated temperature of their host as a signal to regulate the necessary changes in gene expression required for infection. Here we use RNA-seq, proteomics and phenotype microarrays to examine temperature dependent differences in transcription, translation and phenotype of P. asymbiotica at 28°C versus 37°C, relevant to the insect or human hosts respectively. Our findings reveal relatively few temperature dependant differences in gene expression. There is however a striking difference in metabolism at 37°C, with a significant reduction in the range of carbon and nitrogen sources that otherwise support respiration at 28°C. We propose that the key adaptation that enables P. asymbiotica to infect humans is to aggressively acquire amino acids, peptides and other nutrients from the human host, employing a so called “nutritional virulence” strategy. This would simultaneously cripple the host immune response while providing nutrients sufficient for reproduction. This might explain the severity of ulcerated lesions observed in clinical cases of Photorhabdosis. Furthermore, while P. asymbiotica can invade mammalian cells they must also resist immediate killing by humoral immunity components in serum. We observed an increase in the production of the insect Phenol-oxidase inhibitor Rhabduscin normally deployed to inhibit the melanisation immune cascade. Crucially we demonstrated this molecule also facilitates protection against killing by the alternative human complement pathway

Multi-state Modeling of Biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future

Scalable rule-based modelling of allosteric proteins and biochemical networks

Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Une petite nécropole de La Tène ancienne à Neufchâtel-en-Bray (Seine-Maritime). Méthodologie pour une anthropologie appliquée au diagnostic archéologique et résultats

Un diagnostic archéologique a permis la mise en évidence et l’étude d’un petit pôle funéraire, situé en marge d’un habitat non identifié. Cette petite nécropole est constituée de six inhumations et d’une crémation. Bien qu’aucune organisation n’ait pu être mise en évidence, nous pouvons constater une relative concentration des fosses sépulcrales. Malgré une mauvaise conservation et une faible représentativité de l’échantillon, plusieurs observations ont pu être effectuées tant sur le mode d’inhumation des défunts que sur leurs caractéristiques biologiques. L’ensemble a été daté de la fin du 1er âge du fer au début du 2nd, grâce à un vase situliforme accompagnant l’un des individus et à trois céramiques imbriquées utilisées pour la crémation.A preliminary archaeological diagnosis led to the detection and study of a small funerary pole close to an unidentified settlement. The small necropolis includes six burial and one cremation sites. Although no structure was found, a relative concentration of sepulchral tombs was recorded. Furthermore, in spite of weathered conditions and the poor representativeness of samples, several observations were made concerning the remains of the deceased with regard to both their burial method and their biological characteristics. The overall site has been dated between the end of the First Iron Age and the beginning of the Second, on the basis of a bucket-shaped (situliform) vase lying beside one of the individuals and of three imbricated ceramics inside one another