Ecology of Infectious Disease: Forging an Alliance

The Ecology of Infectious Diseases (EID) program is a joint National Science Foundation–National Institutes of Health initiative to produce predictive understanding of disease dynamics, with a focus on diseases with an environmental component. The interdisciplinary research projects funded by this program take advantage of the wide range of theoretical and methodological advances developed over the past 30 years. The challenge for disease ecology is to unravel these systems, discover how complex they truly are, and to determine if they can be predicted and controlled using targeted environmental, public health, or medical interventions. Between 1999 and 2005, a total of 42 research awards were made under the EID program. EID projects have had affects on policy in two areas: adoption of novel interventions on a local scale and use of models by government agencies for the purpose of allocating public health resources. The past 6 years have been an exciting time for the field of disease ecology; we expect the coming years to be even more exciting and productive. As US federal government employees writing an article as part of our official duties, copyright of all publications is retained by the US government. The views expressed here by Samuel M. Scheiner and Joshua P. Rosenthal are those of the authors and do not represent official views or policies of the National Science Foundation, the National Institutes of Health, or the United States Government.     

How ecological indicators construction reveals social changes—The case of lakes and rivers in France

Social and scientific factors are deeply enmeshed in each other within the development and the use of ecological indicators (EI). Yet low research has assessed which factors contribute to selecting ecological indicators on the long-term. This article proposes to study the historical construction of EI by examining ecological, political, and social background of specific places where EI were developed, in France on lakes and rivers. Our major findings in France were that ecological indicators were never optimised for the present market or political arena. Instead EI development was typically recycling previous tools that were elaborated for other purposes by environmentally committed outsiders, without regular funding. We found that regular funding for monitoring an EI was only provided when it matched an institution's goal. Beyond the geographically limited relevance of the case studies, these results therefore improve the theoretical framework we deploy when constructing or relying on indicators.     

An efficient code searching for sequence homology and DNA duplication

This paper presents a very simple and efficient algorithm that searches for sequence homology and gene duplication. The code finds the best alignment of two, short or long, sequences without having to specify how many unmatched bases are allowed to be looped out. Following Needleman & Wunsch (1970), Sellers (1974), Sankoff (1972) and Smith, Waterman & Fitch (1981) gap constraints are incorporated into the program and may be employed. The availability of such a fast computer code enables detection of homologous genes which may fulfill a different function at present, but have arisen from a common gene-ancestor. The code is extremely fast and runs in O(n3/2) units of time. It was applied to the phi X174 sequence.     

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

Implantable microdevices are gaining significant attention for several biomedical applications^1-4. Such devices have been made from a range of materials, each offering its own advantages and shortcomings^5,6. Most prominently, due to the microscale device dimensions, a high modulus is required to facilitate implantation into living tissue. Conversely, the stiffness of the device should match the surrounding tissue to minimize induced local strain^7-9. Therefore, we recently developed a new class of bio-inspired materials to meet these requirements by responding to environmental stimuli with a change in mechanical properties^10-14. Specifically, our poly(vinyl acetate)-based nanocomposite (PVAc-NC) displays a reduction in stiffness when exposed to water and elevated temperatures (e.g. body temperature). Unfortunately, few methods exist to quantify the stiffness of materials in vivo¹⁵, and mechanical testing outside of the physiological environment often requires large samples inappropriate for implantation. Further, stimuli-responsive materials may quickly recover their initial stiffness after explantation. Therefore, we have developed a method by which the mechanical properties of implanted microsamples can be measured ex vivo, with simulated physiological conditions maintained using moisture and temperature control^13,16,17.To this end, a custom microtensile tester was designed to accommodate microscale samples^13,17 with widely-varying Young''s moduli (range of 10 MPa to 5 GPa). As our interests are in the application of PVAc-NC as a biologically-adaptable neural probe substrate, a tool capable of mechanical characterization of samples at the microscale was necessary. This tool was adapted to provide humidity and temperature control, which minimized sample drying and cooling¹⁷. As a result, the mechanical characteristics of the explanted sample closely reflect those of the sample just prior to explantation.The overall goal of this method is to quantitatively assess the in vivo mechanical properties, specifically the Young''s modulus, of stimuli-responsive, mechanically-adaptive polymer-based materials. This is accomplished by first establishing the environmental conditions that will minimize a change in sample mechanical properties after explantation without contributing to a reduction in stiffness independent of that resulting from implantation. Samples are then prepared for implantation, handling, and testing (Figure 1A). Each sample is implanted into the cerebral cortex of rats, which is represented here as an explanted rat brain, for a specified duration (Figure 1B). At this point, the sample is explanted and immediately loaded into the microtensile tester, and then subjected to tensile testing (Figure 1C). Subsequent data analysis provides insight into the mechanical behavior of these innovative materials in the environment of the cerebral cortex.     

Data science techniques in biomolecular force field development

Recent advances in data science are impacting the development of classical force fields. Here we review some ideas and techniques from data science that have been used in force field development, including database construction, atom typing, and machine learning potentials. We highlight how new tools such as active learning and automatic differentiation are facilitating the generation of target data and the direct fitting with macroscopic observables. Philosophical changes on how force field models should be built and used are also discussed. It's inspiring that more accurate biomolecular force fields can be developed with the aid of data science techniques.     

Chlamydia trachomatis,Chlamydial Heat Shock Protein 60 and Anti-Chlamydial Antibodies in Women with Epithelial Ovarian Tumors

OBJECTIVE: Chlamydia trachomatis (C. trachomatis) infection has been suggested to promote epithelial ovarian cancer (EOC) development. This study sought to explore the presence of C. trachomatis DNA and chlamydial heat shock protein 60 (chsp60) in ovarian tissue, as well as anti-chlamydial IgG antibodies in plasma, in relation to subtypes of EOC. METHODS: This cross-sectional cohort consisted of 69 women who underwent surgery due to suspected ovarian pathology. Ovarian tissue and corresponding blood samples were collected at the time of diagnosis. In ovarian tumor tissue, p53, p16, Ki67 and chsp60 were analyzed immunohistochemically, and PCR was used to detect C. trachomatis DNA. Plasma C. trachomatis IgG and cHSP60 IgG were analyzed with a commercial MIF-test and ELISA, respectively. RESULTS: Eight out of 69 women had C. trachomatis DNA in their ovarian tissue, all were invasive ovarian cancer cases (16.7% of invasive EOC). The prevalence of the chsp60 protein, C. trachomatis IgG and cHSP60 IgG in HGSC, compared to other ovarian tumors, was 56.0% vs. 37.2% P = .13, 15.4% vs. 9.3% P = .46 and 63.6% vs. 45.5% P = .33 respectively. None of the markers of C. trachomatis infection were associated with p53, p16 or Ki67. CONCLUSIONS: C. trachomatis was detected in invasive ovarian cancer, supporting a possible role in carcinogenesis of EOC. However, there were no statistically significant associations of chsp60 in ovarian tissue, or plasma anti-chlamydial IgG antibodies, with any of the subtypes of ovarian tumors.     

An Alternative to the Traditional Cold Pressor Test: The Cold Pressor Arm Wrap

Recently research on the relationship between stress and cognition, emotion, and behavior has greatly increased. These advances have yielded insights into important questions ranging from the nature of stress'' influence on addiction¹ to the role of stress in neural changes associated with alterations in decision-making^2,3. As topics being examined by the field evolve, however, so too must the methodologies involved. In this article a practical and effective alternative to a classic stress induction technique, the cold pressor test (CPT), is presented: the cold pressor arm wrap (CPAW). CPT typically involves immersion of a participant''s dominant hand in ice-cold water for a period of time⁴. The technique is associated with robust activation of the sympatho-adrenomedullary (SAM) axis (and release of catecholamines; e.g. adrenaline and noradrenaline) and mild-to-moderate activation of the hypothalamic-pituitary-adrenal (HPA) axis with associated glucocorticoid (e.g. cortisol) release. While CPT has been used in a wide range of studies, it can be impractical to apply in some research environments. For example use of water during, rather than prior to, magnetic resonance imaging (MRI) has the potential to damage sensitive and expensive equipment or interfere with acquisition of MRI signal. The CPAW is a practical and effective alternative to the traditional CPT. Composed of a versatile list of inexpensive and easily acquired components, CPAW makes use of MRI-safe gelpacs cooled to a temperature similar to CPT rather than actual water. Importantly CPAW is associated with levels of SAM and HPA activation comparable to CPT, and can easily be applied in a variety of research contexts. While it is important to maintain specific safety protocols when using the technique, these are easy to implement if planned for. Creation and use of the CPAW will be discussed.     

Validation and Calibration of Physical Activity Monitors in Children

Objective: This study was designed to validate accelerometer-based activity monitors against energy expenditure (EE) in children; to compare monitor placement sites; to field-test the monitors; and to establish sedentary, light, moderate, and vigorous threshold counts. Research Methods and Procedures: Computer Science and Applications Actigraph (CSA) and Mini-Mitter Actiwatch (MM) monitors, on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children, 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AEE) computed as EE − RMR was regressed against counts to derive threshold counts. Results: The mean correlations between EE or AEE and counts were slightly higher for MM-hip (r = 0.78 ± 0.06) and MM-leg (r = 0.80 ± 0.05) than CSA-hip (r = 0.66 ± 0.08) and CSA-leg (r = 0.73 ± 0.07). CSA and MM performed similarly on the hip (inter-instrument r = 0.88) and on the lower leg (inter-instrument r = 0.89). Threshold counts for the CSA-hip were <800, <3200, <8200, and ≥8200 for sedentary, light, moderate, and vigorous categories, respectively. For the MM-hip, the threshold counts were <100, <900, <2200, and ≥2200, respectively. Discussion: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.     

Measuring complexity to infer changes in the dynamics of ecological systems under stress

Despite advances in our mechanistic understanding of ecological processes, the inherent complexity of real-world ecosystems still limits our ability in predicting ecological dynamics especially in the face of on-going environmental stress. Developing a model is frequently challenged by structure uncertainty, unknown parameters, and limited data for exploring out-of-sample predictions. One way to address this challenge is to look for patterns in the data themselves in order to infer the underlying processes of an ecological system rather than to build system-specific models. For example, it has been recently suggested that statistical changes in ecological dynamics can be used to infer changes in the stability of ecosystems as they approach tipping points. For computer scientists such inference is similar to the notion of a Turing machine: a computational device that could execute a program (the process) to produce the observed data (the pattern). Here, we make use of such basic computational ideas introduced by Alan Turing to recognize changing patterns in ecological dynamics in ecosystems under stress. To do this, we use the concept of Kolmogorov algorithmic complexity that is a measure of randomness. In particular, we estimate an approximation to Kolmogorov complexity based on the Block Decomposition Method (BDM). We apply BDM to identify changes in complexity in simulated time-series and spatial datasets from ecosystems that experience different types of ecological transitions. We find that in all cases, K_BDM complexity decreased before all ecological transitions both in time-series and spatial datasets. These trends indicate that loss of stability in the ecological models we explored is characterized by loss of complexity and the emergence of a regular and computable underlying structure. Our results suggest that Kolmogorov complexity may serve as tool for revealing changes in the dynamics of ecosystems close to ecological transitions.