Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model.

MOTIVATION: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. RESULTS: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Neurally adjusted ventilatory assist (NAVA) improves patient-ventilator interaction during non-invasive ventilation delivered by face mask.

PURPOSE: To determine if, compared to pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces patient-ventilator asynchrony in intensive care patients undergoing noninvasive ventilation with an oronasal face mask. METHODS: In this prospective interventional study we compared patient-ventilator synchrony between PS (with ventilator settings determined by the clinician) and NAVA (with the level set so as to obtain the same maximal airway pressure as in PS). Two 20-min recordings of airway pressure, flow and electrical activity of the diaphragm during PS and NAVA were acquired in a randomized order. Trigger delay (T(d)), the patient's neural inspiratory time (T(in)), ventilator pressurization duration (T(iv)), inspiratory time in excess (T(iex)), number of asynchrony events per minute and asynchrony index (AI) were determined. RESULTS: The study included 13 patients, six with COPD, and two with mixed pulmonary disease. T(d) was reduced with NAVA: median 35 ms (IQR 31-53 ms) versus 181 ms (122-208 ms); p = 0.0002. NAVA reduced both premature and delayed cyclings in the majority of patients, but not the median T(iex) value. The total number of asynchrony events tended to be reduced with NAVA: 1.0 events/min (0.5-3.1 events/min) versus 4.4 events/min (0.9-12.1 events/min); p = 0.08. AI was lower with NAVA: 4.9 % (2.5-10.5 %) versus 15.8 % (5.5-49.6 %); p = 0.03. During NAVA, there were no ineffective efforts, or late or premature cyclings. PaO(2) and PaCO(2) were not different between ventilatory modes. CONCLUSION: Compared to PS, NAVA improved patient ventilator synchrony during noninvasive ventilation by reducing T(d) and AI. Moreover, with NAVA, ineffective efforts, and late and premature cyclings were absent.

Early tailored assertive community case management for hard-to-engage adolescents suffering from psychiatric disorders: an exploratory pilot study.

AIM: The study aims to evaluate the effects of assertive community treatment (ACT) on the mental health and overall functioning of adolescents suffering from severe psychiatric disorders and who refuse any traditional child psychiatric care. There are a few studies evaluating the effects of ACT on a population of adolescents with psychiatric disorders. This short report highlights the impact of an ACT programme tailored to the needs of these patients, not only as an alternative to hospitalization, but also as a new form of intervention for patients that are difficult to engage. METHODS: The effect of ACT on 35 adolescents using the Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA) as a measuring tool in pre- and post-intervention was evaluated. RESULTS: The results show that the intervention was associated with a significant improvement on the HoNOSCA overall score, with the following items showing significant amelioration: hyperactivity/focus problems, non-organic somatic symptoms, emotional symptoms, scholastic/language skills, peer relationships, family relationships and school attendance. CONCLUSION: ACT appears as a feasible intervention for hard-to-engage adolescents suffering from psychiatric disorders. The intervention seems to improve their mental health and functioning. This pilot study may serve as a basis to prepare a controlled study that will also take the costs of the intervention into account.

Adaptive diversity in heterogeneous environments for populations regulated by a mixture of soft and hard selection

The stable co-existence of two haploid genotypes or two species is studied in a spatially heterogeneous environment submitted to a mixture of soft selection (within-patch regulation) and hard selection (outside-patch regulation) and where two kinds of resource are available. This is analysed both at an ecological time-scale (short term) and at an evolutionary time-scale (long term). At an ecological scale, we show that co-existence is very unlikely if the two competitors are symmetrical specialists exploiting different resources. In this case, the most favourable conditions are met when the two resources are equally available, a situation that should favour generalists at an evolutionary scale. Alternatively, low within-patch density dependence (soft selection) enhances the co-existence between two slightly different specialists of the most available resource. This results from the opposing forces that are acting in hard and soft regulation modes. In the case of unbalanced accessibility to the two resources, hard selection favours the most specialized genotype, whereas soft selection strongly favours the less specialized one. Our results suggest that competition for different resources may be difficult to demonstrate in the wild even when it is a key factor in the maintenance of adaptive diversity. At an evolutionary scale, a monomorphic invasive evolutionarily stable strategy (ESS) always exists. When a linear trade-off exists between survival in one habitat versus that in another, this ESS lies between an absolute adjustment of survival to niche size (for mainly soft-regulated populations) and absolute survival (specialization) in a single niche (for mainly hard-regulated populations). This suggests that environments in agreement with the assumptions of such models should lead to an absence of adaptive variation in the long term.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Health care workers' influenza vaccination: motivations and mandatory mask policy

BACKGROUND: Vaccination of health care workers (HCW) against seasonal influenza (SI) is recommended but vaccination rate rarely reach >30%. Vaccination coverage against 2009 pandemic influenza (PI) was 52% in our hospital, whilst a new policy requiring unvaccinated HCW to wear a mask during patient care duties was enforced. AIMS: To investigate the determinants of this higher vaccination acceptance for PI and to look for an association with the new mask-wearing policy. METHODS: A retrospective cohort study, involving HCW of three critical departments of a 1023-bed, tertiary-care university hospital in Switzerland. Self-reported 2009-10 SI and 2009 PI vaccination statuses, reasons and demographic data were collected through a literature-based questionnaire. Descriptive statistics, uni- and multivariate analyses were then performed. RESULTS: There were 472 respondents with a response rate of 54%. Self-reported vaccination acceptance was 64% for PI and 53% for SI. PI vaccination acceptance was associated with being vaccinated against SI (OR 9.5; 95% CI 5.5-16.4), being a physician (OR 7.7; 95% CI 3.1-19.1) and feeling uncomfortable wearing a mask (OR 1.7; 95% CI 1.0-2.8). Main motives for refusing vaccination were: preference for wearing a surgical mask (80% for PI, not applicable for SI) and concerns about vaccine safety (64%, 50%) and efficacy (44%, 35%). CONCLUSIONS: The new mask-wearing policy was a motivation for vaccination but also offered an alternative to non-compliant HCW. Concerns about vaccine safety and efficiency and self-interest of health care workers are still main determinants for influenza vaccination acceptance. Better incentives are needed to encourage vaccination amongst non-physician HCW.

"A Hard Battle to Fight": Natural Theology and the Dismal Science, 1820-1850

William Whewell's and Richard Jones's criticism of Jeremy Bentham's and David Ricardo's "dismal" views on the relation of theory and evidence in political economy was motivated by the former's views on the structuring role of natural theology for questions of method and evidence in the sciences, including political economy. In comparison, natural theology was for Richard Whately as structuring on these issues as it was for the Cambridge men. Whately's view on natural theology, however, conformed with the Ricardian predilection for theory over facts. The differences between the Cambridge men and Whately became manifest after (or better: during) the publication of Jones's book on rent in 1831 and led to a somewhat acerbic exchange of views on the role of definitions in science and the use of history for establishing scientific evidence. As far as political economy was concerned, Whately's stance carried the day in Victorian England.