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.

Differential Expression with the Bioconductor Project

A basic, yet challenging task in the analysis of microarray gene expression data is the identification of changes in gene expression that are associated with particular biological conditions. We discuss different approaches to this task and illustrate how they can be applied using software from the Bioconductor Project. A central problem is the high dimensionality of gene expression space, which prohibits a comprehensive statistical analysis without focusing on particular aspects of the joint distribution of the genes expression levels. Possible strategies are to do univariate gene-by-gene analysis, and to perform data-driven nonspecific filtering of genes before the actual statistical analysis. However, more focused strategies that make use of biologically relevant knowledge are more likely to increase our understanding of the data.

MODELING DIFFERENTIATED TREATMENT EFFECTS FOR MULTIPLE OUTCOMES DATA

Multiple outcomes data are commonly used to characterize treatment effects in medical research, for instance, multiple symptoms to characterize potential remission of a psychiatric disorder. Often either a global, i.e. symptom-invariant, treatment effect is evaluated. Such a treatment effect may over generalize the effect across the outcomes. On the other hand individual treatment effects, varying across all outcomes, are complicated to interpret, and their estimation may lose precision relative to a global summary. An effective compromise to summarize the treatment effect may be through patterns of the treatment effects, i.e. "differentiated effects." In this paper we propose a two-category model to differentiate treatment effects into two groups. A model fitting algorithm and simulation study are presented, and several methods are developed to analyze heterogeneity presenting in the treatment effects. The method is illustrated using an analysis of schizophrenia symptom data.

The spatiotemporal pattern of auditory cortical responses during verbal hallucinations

Functional magnetic resonance imaging (fMRI) studies can provide insight into the neural correlates of hallucinations. Commonly, such studies require self-reports about the timing of the hallucination events. While many studies have found activity in higher-order sensory cortical areas, only a few have demonstrated activity of the primary auditory cortex during auditory verbal hallucinations. In this case, using self-reports as a model of brain activity may not be sensitive enough to capture all neurophysiological signals related to hallucinations. We used spatial independent component analysis (sICA) to extract the activity patterns associated with auditory verbal hallucinations in six schizophrenia patients. SICA decomposes the functional data set into a set of spatial maps without the use of any input function. The resulting activity patterns from auditory and sensorimotor components were further analyzed in a single-subject fashion using a visualization tool that allows for easy inspection of the variability of regional brain responses. We found bilateral auditory cortex activity, including Heschl's gyrus, during hallucinations of one patient, and unilateral auditory cortex activity in two more patients. The associated time courses showed a large variability in the shape, amplitude, and time of onset relative to the self-reports. However, the average of the time courses during hallucinations showed a clear association with this clinical phenomenon. We suggest that detection of this activity may be facilitated by examining hallucination epochs of sufficient length, in combination with a data-driven approach.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.

A disease model for wheezing disorders in preschool children based on clinicians' perceptions

BACKGROUND: Wheezing disorders in childhood vary widely in clinical presentation and disease course. During the last years, several ways to classify wheezing children into different disease phenotypes have been proposed and are increasingly used for clinical guidance, but validation of these hypothetical entities is difficult. METHODOLOGY/PRINCIPAL FINDINGS: The aim of this study was to develop a testable disease model which reflects the full spectrum of wheezing illness in preschool children. We performed a qualitative study among a panel of 7 experienced clinicians from 4 European countries working in primary, secondary and tertiary paediatric care. In a series of questionnaire surveys and structured discussions, we found a general consensus that preschool wheezing disorders consist of several phenotypes, with a great heterogeneity of specific disease concepts between clinicians. Initially, 24 disease entities were described among the 7 physicians. In structured discussions, these could be narrowed down to three entities which were linked to proposed mechanisms: a) allergic wheeze, b) non-allergic wheeze due to structural airway narrowing and c) non-allergic wheeze due to increased immune response to viral infections. This disease model will serve to create an artificial dataset that allows the validation of data-driven multidimensional methods, such as cluster analysis, which have been proposed for identification of wheezing phenotypes in children. CONCLUSIONS/SIGNIFICANCE: While there appears to be wide agreement among clinicians that wheezing disorders consist of several diseases, there is less agreement regarding their number and nature. A great diversity of disease concepts exist but a unified phenotype classification reflecting underlying disease mechanisms is lacking. We propose a disease model which may help guide future research so that proposed mechanisms are measured at the right time and their role in disease heterogeneity can be studied.

Preterm birth, infant weight gain, and childhood asthma risk: A meta-analysis of 147,000 European children

BACKGROUND Preterm birth, low birth weight, and infant catch-up growth seem associated with an increased risk of respiratory diseases in later life, but individual studies showed conflicting results. OBJECTIVES We performed an individual participant data meta-analysis for 147,252 children of 31 birth cohort studies to determine the associations of birth and infant growth characteristics with the risks of preschool wheezing (1-4 years) and school-age asthma (5-10 years). METHODS First, we performed an adjusted 1-stage random-effect meta-analysis to assess the combined associations of gestational age, birth weight, and infant weight gain with childhood asthma. Second, we performed an adjusted 2-stage random-effect meta-analysis to assess the associations of preterm birth (gestational age <37 weeks) and low birth weight (<2500 g) with childhood asthma outcomes. RESULTS Younger gestational age at birth and higher infant weight gain were independently associated with higher risks of preschool wheezing and school-age asthma (P < .05). The inverse associations of birth weight with childhood asthma were explained by gestational age at birth. Compared with term-born children with normal infant weight gain, we observed the highest risks of school-age asthma in children born preterm with high infant weight gain (odds ratio [OR], 4.47; 95% CI, 2.58-7.76). Preterm birth was positively associated with an increased risk of preschool wheezing (pooled odds ratio [pOR], 1.34; 95% CI, 1.25-1.43) and school-age asthma (pOR, 1.40; 95% CI, 1.18-1.67) independent of birth weight. Weaker effect estimates were observed for the associations of low birth weight adjusted for gestational age at birth with preschool wheezing (pOR, 1.10; 95% CI, 1.00-1.21) and school-age asthma (pOR, 1.13; 95% CI, 1.01-1.27). CONCLUSION Younger gestational age at birth and higher infant weight gain were associated with childhood asthma outcomes. The associations of lower birth weight with childhood asthma were largely explained by gestational age at birth.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

VIRTOPSY--scientific documentation, reconstruction and animation in forensic: individual and real 3D data based geo-metric approach including optical body/object surface and radiological CT/MRI scanning.

Until today, most of the documentation of forensic relevant medical findings is limited to traditional 2D photography, 2D conventional radiographs, sketches and verbal description. There are still some limitations of the classic documentation in forensic science especially if a 3D documentation is necessary. The goal of this paper is to demonstrate new 3D real data based geo-metric technology approaches. This paper present approaches to a 3D geo-metric documentation of injuries on the body surface and internal injuries in the living and deceased cases. Using modern imaging methods such as photogrammetry, optical surface and radiological CT/MRI scanning in combination it could be demonstrated that a real, full 3D data based individual documentation of the body surface and internal structures is possible in a non-invasive and non-destructive manner. Using the data merging/fusing and animation possibilities, it is possible to answer reconstructive questions of the dynamic development of patterned injuries (morphologic imprints) and to evaluate the possibility, that they are matchable or linkable to suspected injury-causing instruments. For the first time, to our knowledge, the method of optical and radiological 3D scanning was used to document the forensic relevant injuries of human body in combination with vehicle damages. By this complementary documentation approach, individual forensic real data based analysis and animation were possible linking body injuries to vehicle deformations or damages. These data allow conclusions to be drawn for automobile accident research, optimization of vehicle safety (pedestrian and passenger) and for further development of crash dummies. Real 3D data based documentation opens a new horizon for scientific reconstruction and animation by bringing added value and a real quality improvement in forensic science.

Reporting and handling missing outcome data in mental health: a systematic review of Cochrane systematic reviews and meta-analyses

OBJECTIVES The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field. METHODS Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by three Cochrane Review Groups relating to mental health were included. RESULTS One hundred ninety systematic reviews were considered. Missing outcome data were present in at least one included study in 175 systematic reviews. Of these 175 systematic reviews, 147 (84%) accounted for missing outcome data by considering a relevant primary or secondary outcome (e.g., dropout). Missing outcome data implications were reported only in 61 (35%) systematic reviews and primarily in the discussion section by commenting on the amount of the missing outcome data. One hundred forty eligible meta-analyses with missing data were scrutinized. Seventy-nine (56%) of them had studies with total dropout rate between 10 and 30%. One hundred nine (78%) meta-analyses reported to have performed intention-to-treat analysis by including trials with imputed outcome data. Sensitivity analysis for incomplete outcome data was implemented in less than 20% of the meta-analyses. CONCLUSIONS Reporting of the techniques for handling missing outcome data and their implications in the findings of the systematic reviews are suboptimal.

Direct and Indirect Measures of Capacity Utilization: A Nonparametric Analysis of U.S. Manufacturing

We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

Are Indian Firms too Small? A Nonparametric Analysis of Cost Efficiency and Industry Structure of Indian Manufacturing

In this paper we use the 2004-05 Annual Survey of Industries data to estimate the levels of cost efficiency of Indian manufacturing firms in the various states and also get state level measures of industrial organization (IO) efficiency. The empirical results show the presence of considerable cost inefficiency in a majority of the states. Further, we also find that, on average, Indian firms are too small. Consolidating them to attain the optimal scale would further enhance efficiency and lower average cost.

Labor-Use Efficiency in Indian Banking: A Branch Level Analysis

This paper uses Data Envelopment Analysis to measure labor use efficiency of individual branches of a large public sector bank with several thousand branches across India. We find considerable variation in the average levels of efficiency across the four metropolitan regions considered in this study. In this context, we introduce the concept of area or spatial efficiency for each region relative to the nation as a whole. Our findings suggest that the policies, procedures, and incentives handed down from the corporate level cannot fully neutralize the influence of the local work culture in the different regions. Most of the potential reduction in labor cost appears to be coming from possible downsizing the clerical and subordinate staff. Our analysis identifies branches that operate at very low levels of efficiency and may be gainfully merged with other branches wherever possible.

The Comal County Needs Assessment Youth Survey

A crucial link in preserving and protecting the future of our communities resides in maintaining the health and well being of our youth. While every member of the community owns an opinion regarding where to best utilize monies for prevention and intervention, the data to support such opinion is often scarce. In an effort to generate data-driven indices for community planning and action, the United Way of Comal County, Texas partnered with the University Of Texas - Houston Health Science Center, School Of Public Health to accomplish a county-specific needs assessment. A community-based participatory research emphasis utilizing the Mobilization for Action through Planning and Partnership (MAPP) format developed by the National Association of City and County Health Officials (NACCHO) was implemented to engage community members in identifying and addressing community priorities. The single greatest area of consensus and concern identified by community members was the health and well being of the youth population. Thus, a youth survey, targeting these specific areas of community concern, was designed, coordinated and administered to all 9-11th grade students in the county. 20% of the 3,698 completed surveys (72% response rate) were randomly selected for analysis. These 740 surveys were coded and scanned into an electronic survey database. Statistical analysis provided youth-reported data on the status of the multiple issues affecting the health and well being of the community's youth. These data will be reported back to the community stakeholders, as part of the larger Comal County Needs Assessment, for the purposes of community planning and action. Survey data will provide community planners with an awareness of the high risk behaviors and habit patterns amongst their youth. This knowledge will permit more effective targeting of the means for encouraging healthy behaviors and preventing the spread of disease. Further, the community-oriented, population-based nature of this effort will provide answers to questions raised by the community and will provide an effective launching pad for the development and implementation of targeted, preventive health strategies. ^

Performance tiers: Implementing comparative effectiveness analysis in the health care setting through operations research and spatial methods

This dissertation develops and tests a comparative effectiveness methodology utilizing a novel approach to the application of Data Envelopment Analysis (DEA) in health studies. The concept of performance tiers (PerT) is introduced as terminology to express a relative risk class for individuals within a peer group and the PerT calculation is implemented with operations research (DEA) and spatial algorithms. The analysis results in the discrimination of the individual data observations into a relative risk classification by the DEA-PerT methodology. The performance of two distance measures, kNN (k-nearest neighbor) and Mahalanobis, was subsequently tested to classify new entrants into the appropriate tier. The methods were applied to subject data for the 14 year old cohort in the Project HeartBeat! study.^ The concepts presented herein represent a paradigm shift in the potential for public health applications to identify and respond to individual health status. The resultant classification scheme provides descriptive, and potentially prescriptive, guidance to assess and implement treatments and strategies to improve the delivery and performance of health systems. ^