A global ”imaging” view on systems approaches in immunology

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review.

Intervention Research Approaches

This article reviews the process of intervention research, with particular emphasis on interventions with and for older adults. First, we consider the context of intervention research: What are the resources and constraints that shape the process of intervention and research? Second, we outline a taxonomy of interventions, reflecting different combinations of level and timing of interventions. Third, we consider the types of research designs that are most appropriate in the different contexts of applied research.

Effect of Methodological and Ecological Approaches on Heterogeneity of Nest-Site Selection of a Long-Lived Vulture

The application of scientific-based conservation measures requires that sampling methodologies in studies modelling similar ecological aspects produce comparable results making easier their interpretation. We aimed to show how the choice of different methodological and ecological approaches can affect conclusions in nest-site selection studies along different Palearctic meta-populations of an indicator species. First, a multivariate analysis of the variables affecting nest-site selection in a breeding colony of cinereous vulture (Aegypius monachus) in central Spain was performed. Then, a meta-analysis was applied to establish how methodological and habitat-type factors determine differences and similarities in the results obtained by previous studies that have modelled the forest breeding habitat of the species. Our results revealed patterns in nesting-habitat modelling by the cinereous vulture throughout its whole range: steep and south-facing slopes, great cover of large trees and distance to human activities were generally selected. The ratio and situation of the studied plots (nests/random), the use of plots vs. polygons as sampling units and the number of years of data set determined the variability explained by the model. Moreover, a greater size of the breeding colony implied that ecological and geomorphological variables at landscape level were more influential. Additionally, human activities affected in greater proportion to colonies situated in Mediterranean forests. For the first time, a meta-analysis regarding the factors determining nest-site selection heterogeneity for a single species at broad scale was achieved. It is essential to homogenize and coordinate experimental design in modelling the selection of species' ecological requirements in order to avoid that differences in results among studies would be due to methodological heterogeneity. This would optimize best conservation and management practices for habitats and species in a global context.

Native EEG and treatment effects in neuroleptic-naïve schizophrenic patients: time and frequency domain approaches

Time domain analysis of electroencephalography (EEG) can identify subsecond periods of quasi-stable brain states. These so-called microstates assumingly correspond to basic units of cognition and emotion. On the other hand, Global Field Synchronization (GFS) is a frequency domain measure to estimate functional synchronization of brain processes on a global level for each EEG frequency band [Koenig, T., Lehmann, D., Saito, N., Kuginuki, T., Kinoshita, T., Koukkou, M., 2001. Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naive patients with schizophrenia: preliminary results. Schizophr Res. 50, 55-60.]. Using these time and frequency domain analyzes, several previous studies reported shortened microstate duration in specific microstate classes and decreased GFS in theta band in drug naïve schizophrenia compared to controls. The purpose of this study was to investigate changes of these EEG parameters after drug treatment in drug naïve schizophrenia. EEG analysis was performed in 21 drug-naive patients and 21 healthy controls. 14 patients were reevaluated 2-8 weeks (mean 4.3) after the initiation of drug administration. The results extended findings of treatment effect on brain functions in schizophrenia, and imply that shortened duration of specific microstate classes seems a state marker especially in patients with later neuroleptic responsive, while lower theta GFS seems a state-related phenomenon and that higher gamma GFS is a trait like phenomenon.

Short-term Functional and Patient Outcome of the Bi-cruciate Stabilized Total Knee Replacement: Prospective Consecutive Case-series

Background: Total knee replacement is the gold standard treatment for patients suffering from advanced symptomatic knee osteoarthritis. The main goals of knee prosthetics are pain reduction and restoration of knee motion. The new prostheses on the market such as the bi-cruciate stabilized Journey knee implant, promise a reconstruction of total physiological function of the knee with physiological range of motion and therefore high patient satisfaction. Purpose: The aim of this study was to analyze the patient-based Knee Injury and Osteoarthritis Outcome Score (KOOS) outcome after total knee replacement with new physiological bi-cruciate stabilized Journey knee prosthesis. Study Design: Prospective, consecutive case-series. Patients: Ninety nine patients, who received bi-cruciate stabilized Journey total knee prosthesis between January 1st 2006 and May 31st 2012, were included in the study. A single surgeon operated all patients. There were 61.1% females and the overall average age was 68 years (range 41-83 years). Left knee was replaced in 55.6%. Methods: The patients filled in KOO’s questionnaire pre- and 1 year postoperative. Range of motion (ROM) was studied preoperatively and at 1-year follow-ups. The pre- and postoperative KOOS subscores and ROM were compared using the Wilcoxon signed rank test. Results: There are significant improvements of all KOOS subscores. Ninety percent of patients have reached the minimum clinically relevant 10 points in symptoms, 94.5% in pain, 94.5% in activities of daily living, 84.9% in sport and recreation, and 90% in knee related quality of life. Postoperative, the average passive ROM was 131° (range 110-145°) and the average active ROM 122° (range 105-135°). The highest correlation coefficients ROM and the KOOS were observed for the activity and pain subscores. Very low or no correlation was seen for the sport subscore. Conclusions: Bi-cruciate stabilized knee prosthetic offers a solid outcome 1 year postoperative based on the results measured with the KOOS evaluation questionnaire. The Patients showed a generalized improvement in all domains measured in the KOOS of minimally 35, and up to over 52 points, what can be described as statistically significant. Patients described the level of functionality close to double compared to the preoperative status.

Recent Increases in Atmospheric Concentrations of Bi, U, Cs, S and Ca from a 350-Year Mount Everest Ice Core Record

High-resolution major and trace elements (Sr, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Tb, Dy, Ho, Er, Tm, Yb, Lu, Bi, U, Tl, Al, S, Ca, Ti, V, Cr, Mn, Fe, and Co) quantified in a Mount Everest ice core ( 6518 m above sea level) spanning the period 1650-2002 AD provides the first Asian record of trace element concentrations from the pre-industrial era, and the first continuous high-resolution Asian record from which natural baseline concentrations and subsequent changes due to anthropogenic activities can be examined. Modern concentrations of most elements remain within the pre-industrial range; however, Bi, U, and Cs concentrations and their enrichment factors (EF) have increased since the similar to 1950s, and S and Ca concentrations and their EFs have increased since the late 1980s. A comparison of the Bi, U, Cs, S, and Ca data with other ice core records and production data indicates that the increase in atmospheric concentrations of trace elements is widespread, but that enrichment varies regionally. Likely sources for the recent enrichment of these elements include mining, metal smelting, oil and coal combustion, and end uses for Bi, and mining and refinement for U and Cs. The source of the synchronous enrichment of Ca and S is less certain, but may be related to land use and environmental change.

Approaches to Ensure Quality Standards for Standardized/ Simulated Patient Performance in High Stakes Objective Structured Clinical Exams (OSCEs)

Introduction To meet the quality standards for high-stakes OSCEs, it is necessary to ensure high quality standardized performance of the SPs involved.[1] One of the ways this can be assured is through the assessment of the quality of SPs` performance in training and during the assessment. There is some literature concerning validated instruments that have been used to assess SP performance in formative contexts but very little related to high stakes contexts.[2], [3], [4]. Content and structure During this workshop different approaches to quality control for SPs` performance, developed in medicine, pharmacy and nursing OSCEs, will be introduced. Participants will have the opportunity to use these approaches in simulated interactions. Advantages and disadvantages of these approaches will be discussed. Anticipated outcomes By the end of this session, participants will be able to discuss the rationale for quality control of SPs` performance in high stakes OSCEs, outline key factors in creating strategies for quality control, identify various strategies for assuring quality control, and reflect on applications to their own practice. Who should attend The workshop is designed for those interested in quality assurance of SP performance in high stakes OSCEs. Level All levels are welcome. References Adamo G. 2003. Simulated and standardized patients in OSCEs: achievements and challenges:1992-2003. Med Teach. 25(3), 262- 270. Wind LA, Van Dalen J, Muijtjens AM, Rethans JJ. Assessing simulated patients in an educational setting: the MaSP (Maastricht Assessment of Simulated Patients). Med Educ 2004, 38(1):39-44. Bouter S, van Weel-Baumgarten E, Bolhuis S. Construction and validation of the Nijmegen Evaluation of the Simulated Patient (NESP): Assessing Simulated Patients' ability to role-play and provide feedback to students. Acad Med: Journal of the Association of American Medical Colleges 2012. May W, Fisher D, Souder D: Development of an instrument to measure the quality of standardized/simulated patient verbal feedback. Med Educ 2012, 2(1).

Taking personalized medicine seriously: Biomarker approaches in phase IIb/III studies in major depression and schizophrenia

The success rate in the development of psychopharmacological compounds is insufficient. Two main reasons for failure have been frequently identified: 1) treating the wrong patients and 2) using the wrong dose. This is potentially based on the known heterogeneity among patients, both on a syndromal and a biological level. A focus on personalized medicine through better characterization with biomarkers has been successful in other therapeutic areas. Nevertheless, obstacles toward this goal that exist are 1) the perception of a lack of validation, 2) the perception of an expensive and complicated enterprise, and 3) the perception of regulatory hurdles. The authors tackle these concerns and focus on the utilization of biomarkers as predictive markers for treatment outcome. The authors primarily cover examples from the areas of major depression and schizophrenia. Methodologies covered include salivary and plasma collection of neuroendocrine, metabolic, and inflammatory markers, which identified subgroups of patients in the Netherlands Study of Depression and Anxiety. A battery of vegetative markers, including sleep-electroencephalography parameters, heart rate variability, and bedside functional tests, can be utilized to characterize the activity of a functional system that is related to treatment refractoriness in depression (e.g., the renin-angiotensin-aldosterone system). Actigraphy and skin conductance can be utilized to classify patients with schizophrenia and provide objective readouts for vegetative activation as a functional marker of target engagement. Genetic markers, related to folate metabolism, or folate itself, has prognostic value for the treatment response in patients with schizophrenia. Already, several biomarkers are routinely collected in standard clinical trials (e.g., blood pressure and plasma electrolytes), and appear to be differentiating factors for treatment outcome. Given the availability of a wide variety of markers, the further development and integration of such markers into clinical research is both required and feasible in order to meet the benefit of personalized medicine. This article is based on proceedings from the "Taking Personalized Medicine Seriously-Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia" session, which was held during the 10th Annual Scientific Meeting of the International Society for Clinical Trials Meeting (ISCTM) in Washington, DC, February 18 to 20, 2014.

Effects of two different memory training approaches in very preterm-born children

Background: Little research has been conducted to assess the effect of using memory training with school-aged children who were born very preterm. This study aimed to determine whether two types of memory training approaches resulted in an improvement of trained functions and/or a generalization of the training effect to non-trained cognitive domains. Methods: Sixty-eight children born very preterm (7¬-12 years) were randomly allocated to a group undertaking memory strategy training (n=23), working memory training (n=22), or a waiting control group (n=23). Neuropsychological assessment was performed before and immediately after the training or waiting period, and at a six-month follow-up. Results: In both training groups, significant improvement of different memory domains occurred immediately after training (near transfer). Improvement of non-trained arithmetic performance was observed after strategy training (far transfer). At a six-month follow-up assessment, children in both training groups demonstrated better working memory, and their parents rated their memory functions to be better than controls. Performance level before the training was negatively associated with the training gain. Conclusions: These results highlight the importance of cognitive interventions, in particular the teaching of memory strategies, in very preterm-born children at early school age to strengthen cognitive performance and prevent problems at school.

Best practices and innovative capacity development approaches in ARD programmes of ERA-ARD consortium members: Draft compilation of National Contribution

In the framework of the European Research Area for Agricultural Research for Development (ERA-ARD) project, a survey of innovative approaches in capacity development (CD) was undertaken. All Consortium members were asked to describe innovative approaches and best practices of CD mechanisms within their ARD programmes. A tabular overview of all the programmes or mechanisms can be found on page 4. Abstracts of the programmes or mechanisms are compiled in alphabetic order of the consortium members in this document. The intention of this catalogue of mechanisms is to give an overview of different approaches and practices and not to provide a comprehensive mapping of all the ongoing CD activities of the Consortium members. Thus, the programmes described represent only a fraction of all the ongoing CD programmes on the national level.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Effects of two different memory training approaches in very pretermborn children

Background: Little research has been conducted to assess the effect of using memory training with school aged children who were born very preterm. This study aimed to determine whether two types of memory training approaches resulted in an improvement of trained functions and/or a generalization of the training effect to non-trained cognitive domains. Methods: Sixty-eight children born very preterm (7-12 years) were randomly allocated to a group undertaking memory strategy training (n=23), working memory training (n=22), or a waiting control group (n=23). Neuropsychological assessment was performed before and immediately after the training or waiting period, and at a six-month follow-up. Results: In both training groups, significant improvement of different memory domains occurred immediately after training (near transfer). Improvement of non-trained arithmetic performance was observed after strategy training (far transfer). At a six-month follow-up assessment, children in both training groups demonstrated better working memory, and their parents rated their memory functions to be better than controls. Performance level before the training was negatively associated with the training gain. Conclusions: These results highlight the importance of cognitive interventions, in particular the teaching of memory strategies, in very preterm-born children at early school age to strengthen cognitive performance and prevent problems at school.

Dimensional approaches to schizophrenia: A comparison of the Bern Psychopathology scale and the five-factor model of the Positive and Negative Syndrome Scale

The aim was to examine to what extent the dimensions of the BPS map the five factors derived from the PANSS in order to explore the level of agreement of these alternative dimensional approaches in patients with schizophrenia. 149 inpatients with schizophrenia spectrum disorders were recruited. Psychopathological symptoms were assessed with the Bern Psychopathology Scale (BPS) and the Positive and Negative Syndrome Scale (PANSS). Linear regression analyses were conducted to explore the association between the factors and the items of the BPS. The robustness of patterns was evaluated. An understandable overlap of both approaches was found for positive and negative symptoms and excitement. The PANSS positive factor was associated with symptoms of the affect domain in terms of both inhibition and disinhibition, the PANSS negative factor with symptoms of all three domains of the BPS as an inhibition and the PANSS excitement factor with an inhibition of the affect domain and a disinhibition of the language and motor domains. The results show that here is only a partial overlap between the system-specific approach of the BPS and the five-factor PANSS model. A longitudinal assessment of psychopathological symptoms would therefore be of interest.

Assessing and applying evidence-based interventions at the community level in India as a model policy to reduce neonatal mortality rates in Nigeria

Background. The United Nations' Millennium Development Goal (MDG) 4 aims for a two-thirds reduction in death rates for children under the age of five by 2015. The greatest risk of death is in the first week of life, yet most of these deaths can be prevented by such simple interventions as improved hygiene, exclusive breastfeeding, and thermal care. The percentage of deaths in Nigeria that occur in the first month of life make up 28% of all deaths under five years, a statistic that has remained unchanged despite various child health policies. This paper will address the challenges of reducing the neonatal mortality rate in Nigeria by examining the literature regarding efficacy of home-based, newborn care interventions and policies that have been implemented successfully in India. ^ Methods. I compared similarities and differences between India and Nigeria using qualitative descriptions and available quantitative data of various health indicators. The analysis included identifying policy-related factors and community approaches contributing to India's newborn survival rates. Databases and reference lists of articles were searched for randomized controlled trials of community health worker interventions shown to reduce neonatal mortality rates. ^ Results. While it appears that Nigeria spends more money than India on health per capita ($136 vs. $132, respectively) and as percent GDP (5.8% vs. 4.2%, respectively), it still lags behind India in its neonatal, infant, and under five mortality rates (40 vs. 32 deaths/1000 live births, 88 vs. 48 deaths/1000 live births, 143 vs. 63 deaths/1000 live births, respectively). Both countries have comparably low numbers of healthcare providers. Unlike their counterparts in Nigeria, Indian community health workers receive training on how to deliver postnatal care in the home setting and are monetarily compensated. Gender-related power differences still play a role in the societal structure of both countries. A search of randomized controlled trials of home-based newborn care strategies yielded three relevant articles. Community health workers trained to educate mothers and provide a preventive package of interventions involving clean cord care, thermal care, breastfeeding promotion, and danger sign recognition during multiple postnatal visits in rural India, Bangladesh, and Pakistan reduced neonatal mortality rates by 54%, 34%, and 15–20%, respectively. ^ Conclusion. Access to advanced technology is not necessary to reduce neonatal mortality rates in resource-limited countries. To address the urgency of neonatal mortality, countries with weak health systems need to start at the community level and invest in cost-effective, evidence-based newborn care interventions that utilize available human resources. While more randomized controlled studies are urgently needed, the current available evidence of models of postnatal care provision demonstrates that home-based care and health education provided by community health workers can reduce neonatal mortality rates in the immediate future.^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^