Evaluating reproductive decisions as discrete choices under social influence.

Discrete choice, coupled with social influence, plays a significant role in evolutionary studies of human fertility, as investigators explore how and why reproductive decisions are made. We have previously proposed that the relative magnitude of social influence can be compared against the transparency of pay-off, also known as the transparency of a decision, through a heuristic diagram that maps decision-making along two axes. The horizontal axis represents the degree to which an agent makes a decision individually versus one that is socially influenced, and the vertical axis represents the degree to which there is transparency in the pay-offs and risks associated with the decision the agent makes. Having previously parametrized the functions that underlie the diagram, we detail here how our estimation methods can be applied to real-world datasets concerning sexual health and contraception.

Fitness landscapes among many options under social influence.

Cultural learning represents a novel problem in that an optimal decision depends not only on intrinsic utility of the decision/behavior but also on transparency of costs and benefits, the degree of social versus individual learning, and the relative popularity of each possible choice in a population. In terms of a fitness-landscape function, this recursive relationship means that multiple equilibria can exist. Here we use discrete-choice theory to construct a fitness-landscape function for a bi-axial decision-making map that plots the magnitude of social influence in the learning process against the costs and payoffs of decisions. Specifically, we use econometric and statistical methods to estimate not only the fitness function but also movements along the map axes. To search for these equilibria, we employ a hill-climbing algorithm that leads to the expected values of optimal decisions, which we define as peaks on the fitness landscape. We illustrate how estimation of a measure of transparency, a measure of social influence, and the associated fitness landscape can be accomplished using panel data sets.

Allowing variance may enlarge the safe operating space for exploited ecosystems.

Variable flows of food, water, or other ecosystem services complicate planning. Management strategies that decrease variability and increase predictability may therefore be preferred. However, actions to decrease variance over short timescales (2-4 y), when applied continuously, may lead to long-term ecosystem changes with adverse consequences. We investigated the effects of managing short-term variance in three well-understood models of ecosystem services: lake eutrophication, harvest of a wild population, and yield of domestic herbivores on a rangeland. In all cases, actions to decrease variance can increase the risk of crossing critical ecosystem thresholds, resulting in less desirable ecosystem states. Managing to decrease short-term variance creates ecosystem fragility by changing the boundaries of safe operating spaces, suppressing information needed for adaptive management, cancelling signals of declining resilience, and removing pressures that may build tolerance of stress. Thus, the management of variance interacts strongly and inseparably with the management of resilience. By allowing for variation, learning, and flexibility while observing change, managers can detect opportunities and problems as they develop while sustaining the capacity to deal with them.

Estimating a path through a map of decision making.

Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the "transparency" of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east-west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions.

Regime shift in fertilizer commodities indicates more turbulence ahead for food security.

Recent human population increase has been enabled by a massive expansion of global agricultural production. A key component of this "Green Revolution" has been application of inorganic fertilizers to produce and maintain high crop yields. However, the long-term sustainability of these practices is unclear given the eutrophying effects of fertilizer runoff as well as the reliance of fertilizer production on finite non-renewable resources such as mined phosphate- and potassium-bearing rocks. Indeed, recent volatility in food and agricultural commodity prices, especially phosphate fertilizer, has raised concerns about emerging constraints on fertilizer production with consequences for its affordability in the developing world. We examined 30 years of monthly prices of fertilizer commodities (phosphate rock, urea, and potassium) for comparison with three food commodities (maize, wheat, and rice) and three non-agricultural commodities (gold, nickel, and petroleum). Here we show that all commodity prices, except gold, had significant change points between 2007-2009, but the fertilizer commodities, and especially phosphate rock, showed multiple symptoms of nonlinear critical transitions. In contrast to fertilizers and to rice, maize and wheat prices did not show significant signs of nonlinear dynamics. From these results we infer a recent emergence of a scarcity price in global fertilizer markets, a result signaling a new high price regime for these essential agricultural inputs. Such a regime will challenge on-going efforts to establish global food security but may also prompt fertilizer use practices and nutrient recovery strategies that reduce eutrophication.

Authors' response. more on maps, terrains, and behaviors.

In a recent New York Times column (April 15, 2013), David Brooks discussed how the big-data agenda lacks a coherent framework of social theory ­ a deficiency that the Bentley, O'Brien, and Brock (henceforth BOB) model was meant to overcome. Or, stated less pretentiously, the model was meant as a first step in that direction ­ a map that hopefully would serve as a minimal, practical, and accessible framework that behavioral scientists could use to analyze big data. Rather than treating big data as a record of, and also a predictor of, where and when certain behaviors might take place, the BOB model is interested in what big data reveal about how decisions are being made, how collective behavior evolves from daily to decadal time scales, and how this varies across communities.

Early warning signals of ecological transitions: methods for spatial patterns.

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

Mapping collective behavior in the big-data era.

The behavioral sciences have flourished by studying how traditional and/or rational behavior has been governed throughout most of human history by relatively well-informed individual and social learning. In the online age, however, social phenomena can occur with unprecedented scale and unpredictability, and individuals have access to social connections never before possible. Similarly, behavioral scientists now have access to "big data" sets - those from Twitter and Facebook, for example - that did not exist a few years ago. Studies of human dynamics based on these data sets are novel and exciting but, if not placed in context, can foster the misconception that mass-scale online behavior is all we need to understand, for example, how humans make decisions. To overcome that misconception, we draw on the field of discrete-choice theory to create a multiscale comparative "map" that, like a principal-components representation, captures the essence of decision making along two axes: (1) an east-west dimension that represents the degree to which an agent makes a decision independently versus one that is socially influenced, and (2) a north-south dimension that represents the degree to which there is transparency in the payoffs and risks associated with the decisions agents make. We divide the map into quadrants, each of which features a signature behavioral pattern. When taken together, the map and its signatures provide an easily understood empirical framework for evaluating how modern collective behavior may be changing in the digital age, including whether behavior is becoming more individualistic, as people seek out exactly what they want, or more social, as people become more inextricably linked, even "herdlike," in their decision making. We believe the map will lead to many new testable hypotheses concerning human behavior as well as to similar applications throughout the social sciences.

Word diffusion and climate science.

As public and political debates often demonstrate, a substantial disjoint can exist between the findings of science and the impact it has on the public. Using climate-change science as a case example, we reconsider the role of scientists in the information-dissemination process, our hypothesis being that important keywords used in climate science follow "boom and bust" fashion cycles in public usage. Representing this public usage through extraordinary new data on word frequencies in books published up to the year 2008, we show that a classic two-parameter social-diffusion model closely fits the comings and goings of many keywords over generational or longer time scales. We suggest that the fashions of word usage contributes an empirical, possibly regular, correlate to the impact of climate science on society.

Early warnings of regime shift when the ecosystem structure is unknown.

Abrupt changes in dynamics of an ecosystem can sometimes be detected using monitoring data. Using nonparametric methods that assume minimal knowledge of the underlying structure, we compute separate estimates of the drift (deterministic) and diffusion (stochastic) components of a general dynamical process, as well as an indicator of the conditional variance. Theory and simulations show that nonparametric conditional variance rises prior to critical transition. Nonparametric diffusion rises also, in cases where the true diffusion function involves a critical transition (sometimes called a noise-induced transition). Thus it is possible to discriminate noise-induced transitions from other kinds of critical transitions by comparing time series for the conditional variance and the diffusion function. Monte Carlo analysis shows that the indicators generally increase prior to the transition, but uncertainties of the indicators become large as the ecosystem approaches the transition point.

Defining molecular and cellular responses after low and high linear energy transfer radiations to develop biomarkers of carcinogenic risk or therapeutic outcome.

The variability in radiosensitivity across the human population is governed in part by genetic factors. The ability to predict therapeutic response, identify individuals at greatest risk for adverse clinical responses after therapeutic radiation doses, or identify individuals at high risk for carcinogenesis from environmental or medical radiation exposures has a medical and economic impact on both the individual and society at large. As radiotherapy incorporates particles, particularly particles larger than protons, into therapy, the need for such discriminators, (i.e., biomarkers) will become ever more important. Cellular assays for survival, DNA repair, or chromatid/chromosomal analysis have been used to identify at-risk individuals, but they are not clinically applicable. Newer approaches, such as genome-wide analysis of gene expression or single nucleotide polymorphisms and small copy number variations within chromosomes, are examples of technologies being applied to the discovery process. Gene expression analysis of primary or immortalized human cells suggests that there are distinct gene expression patterns associated with radiation exposure to both low and high linear energy transfer radiations and that those most radiosensitive are discernible by their basal gene expression patterns. However, because the genetic alterations that drive radio response may be subtle and cumulative, the need for large sample sizes of specific cell or tissue types is required. A systems biology approach will ultimately be necessary. Potential biomarkers from cell lines or animal models will require validation in a human setting where possible and before being considered as a credible biomarker some understanding of the molecular mechanism is necessary.

Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data.

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called 'early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.

Cyclophilin B expression is associated with in vitro radioresistance and clinical outcome after radiotherapy.

The tools for predicting clinical outcome after radiotherapy are not yet optimal. To improve on this, we applied the COXEN informatics approach to in vitro radiation sensitivity data of transcriptionally profiled human cells and gene expression data from untreated head and neck squamous cell carcinoma (HNSCC) and bladder tumors to generate a multigene predictive model that is independent of histologic findings and reports on tumor radiosensitivity. The predictive ability of this 41-gene model was evaluated in patients with HNSCC and was found to stratify clinical outcome after radiotherapy. In contrast, this model was not useful in stratifying similar patients not treated with radiation. This led us to hypothesize that expression of some of the 41 genes contributes to tumor radioresistance and clinical recurrence. Hence, we evaluated the expression the 41 genes as a function of in vitro radioresistance in the NCI-60 cancer cell line panel and found cyclophilin B (PPIB), a peptidylprolyl isomerase and target of cyclosporine A (CsA), had the strongest direct correlation. Functional inhibition of PPIB by small interfering RNA depletion or CsA treatment leads to radiosensitization in cancer cells and reduced cellular DNA repair. Immunohistochemical evaluation of PPIB expression in patients with HNSCC was found to be associated with outcome after radiotherapy. This work demonstrates that a novel 41-gene expression model of radiation sensitivity developed in bladder cancer cell lines and human skin fibroblasts predicts clinical outcome after radiotherapy in head and neck cancer patients and identifies PPIB as a potential target for clinical radiosensitization.

Early-warning signals for critical transitions.

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

Turning back from the brink: detecting an impending regime shift in time to avert it.

Ecological regime shifts are large, abrupt, long-lasting changes in ecosystems that often have considerable impacts on human economies and societies. Avoiding unintentional regime shifts is widely regarded as desirable, but prediction of ecological regime shifts is notoriously difficult. Recent research indicates that changes in ecological time series (e.g., increased variability and autocorrelation) could potentially serve as early warning indicators of impending shifts. A critical question, however, is whether such indicators provide sufficient warning to adapt management to avert regime shifts. We examine this question using a fisheries model, with regime shifts driven by angling (amenable to rapid reduction) or shoreline development (only gradual restoration is possible). The model represents key features of a broad class of ecological regime shifts. We find that if drivers can only be manipulated gradually management action is needed substantially before a regime shift to avert it; if drivers can be rapidly altered aversive action may be delayed until a shift is underway. Large increases in the indicators only occur once a regime shift is initiated, often too late for management to avert a shift. To improve usefulness in averting regime shifts, we suggest that research focus on defining critical indicator levels rather than detecting change in the indicators. Ideally, critical indicator levels should be related to switches in ecosystem attractors; we present a new spectral density ratio indicator to this end. Averting ecological regime shifts is also dependent on developing policy processes that enable society to respond more rapidly to information about impending regime shifts.

Panaceas and diversification of environmental policy.

We consider panacea formation in the framework of adaptive learning and decision for social-ecological systems (SESs). Institutions for managing such systems must address multiple timescales of ecological change, as well as features of the social community in which the ecosystem policy problem is embedded. Response of the SES to each candidate institution must be modeled and treated as a stochastic process with unknown parameters to be estimated. A fundamental challenge is to design institutions that are not vulnerable to capture by subsets of the community that self-organize to direct the institution against the overall social interest. In a world of episodic structural change, such as SESs, adaptive learning can lock in to a single institution, model, or parameter estimate. Policy diversification, leading to escape from panacea traps, can come from monitoring indicators of episodic change on slow timescales, minimax regret decision making, active experimentation to accelerate model identification, mechanisms for broadening the set of models or institutions under consideration, and processes for discovery of new institutions and technologies for ecosystem management. It is difficult to take all of these factors into account, but the discipline that comes with the attempt to model the coupled social-ecological dynamics forces policy makers to confront all conceivable responses. This process helps induce the modesty needed to avoid panacea traps while supporting systematic effort to improve resource management in the public interest.

Radiosensitivity is predicted by DNA end-binding complex density, but not by nuclear levels of band components.

BACKGROUND AND PURPOSE: We previously determined that the density of a rapidly migrating DNA end-binding complex (termed 'band-A') predicts radiosensitivity of human normal and tumor cells. The goal of this study was first to identify the protein components of band-A and to determine if the protein levels of band-A components would correlate with band-A density and radiosensitivity. PATIENTS AND METHODS: DNA end-binding protein complex (DNA-EBC) protein components were identified by adding antibodies specific for a variety of DNA repair-associated proteins to the DNA-EBC reaction and then noting which antibodies super-shifted various DNA-EBC bands. Band-A levels were correlated with SF2 for a panel of primary human fibroblasts heterozygous for sequence-proven mutations in BRCA1 or BRCA2. The nuclear protein levels of band-A components were determined in each BRCA1 heterozygote by western hybridization. RESULTS: DNA-EBC analysis of human nuclear proteins revealed 10 identifiable bands. The density of the most rapidly migrating DNA-EBC band correlated closely with both BRCA-mutation status and radiosensitivity (r(2)=0.85). This band was absent in cells with homozygous mutations in their ataxia-telangiectasia-mutated protein (ATM) genes. This band was also completely supershifted by the addition of antibodies to ATM, Ku70, DNA ligase III, Rpa32, Rpa14, DNA ligase IV, XRCC4, WRN, BLM, RAD51 and p53. However, the intranuclear concentrations of these proteins did not correlate with either the SF2 or DNA-EBC density. Neither BRCA1 or BRCA2 could be detected in band-A. CONCLUSIONS: DNA-EBC analysis of human nuclear extracts resulted in 10 bands, at least six of which contained ATM. The density of one of the DNA-EBCs predicted the radiosensitization caused by BRCA haploinsufficiency, and this band contains Ku70, ATM, DNA ligase III, Rpa32, Rpa14, DNA ligase IV, XRCC4, WRN, BLM, RAD51 and p53 but not BRCA 1 or 2. The density of band-A was independent of the nuclear concentration of any of its known component.

Accumulation of the common mitochondrial DNA deletion induced by ionizing radiation.

Point mutations and deletions in mitochondrial DNA (mtDNA) accumulate as a result of oxidative stress, including ionizing radiation. As a result, dysfunctional mitochondria suffer from a decline in oxidative phosphorylation and increased release of superoxides and other reactive oxygen species (ROS). Through this mechanism, mitochondria have been implicated in a host of degenerative diseases. Associated with this type of damage, and serving as a marker of total mtDNA mutations and deletions, the accumulation of a specific 4977-bp deletion, known as the common deletion (Delta-mtDNA(4977)), takes place. The Delta-mtDNA(4977) has been reported to increase with age and during the progression of mitochondrial degeneration. The purpose of this study was to investigate whether ionizing radiation induces the formation of the common deletion in a variety of human cell lines and to determine if it is associated with cellular radiosensitivity. Cell lines used included eight normal human skin fibroblast lines, a radiosensitive non-transformed and an SV40 transformed ataxia telangiectasia (AT) homozygous fibroblast line, a Kearns Sayre Syndrome (KSS) line known to contain mitochondrial deletions, and five human tumor lines. The Delta-mtDNA(4977) was assessed by polymerase chain reaction (PCR). Significant levels of Delta-mtDNA(4977) accumulated 72 h after irradiation doses of 2, 5, 10 or 20 Gy in all of the normal lines with lower response in tumor cell lines, but the absolute amounts of the induced deletion were variable. There was no consistent dose-response relationship. SV40 transformed and non-transformed AT cell lines both showed significant induction of the deletion. However, the five tumor cell lines showed only a modest induction of the deletion, including the one line that was deficient in DNA damage repair. No relationship was found between sensitivity to radiation-induced deletions and sensitivity to cell killing by radiation.

Radiosensitization of human and rodent cell lines by INO-1001, a novel inhibitor of poly(ADP-ribose) polymerase.

Inhibition of poly(ADP-ribose) polymerase (PARP) by a novel, potent inhibitor, INO-1001, was examined in two rodent and one human fibroblast cell lines, after single and fractionated radiation treatments. Since PARP plays a role in the early events following DNA damage and influences the effectiveness of DNA repair, its inhibition has been proposed to constitute a drug target for the development of novel radiosensitizers. We found that INO-1001 effectively inhibited PARP activity at non-cytotoxic concentrations. Combination treatment of 10 microM INO-1001 and a single dose of radiation resulted in significant radiosensitization of all three cells lines (enhancement ratios 1.4-1.6). This radioenhancement was even greater when the drug and radiation were given as fractionated treatments (enhancement ratio 8.0). Apoptosis (as evaluated by TUNEL staining) was not enhanced by the treatments, suggesting that inhibiting PARP enzyme activity by INO-1001 enhanced radiation-induced cell killing by interfering with DNA repair mechanisms, resulting in necrotic cell death. INO-1001 therefore, appears to have potential as a potent enhancer of radiation sensitivity, without any intrinsic cytotoxicity from the drug alone.

Predicting radiosensitivity using DNA end-binding complex analysis.

Previous reports have suggested that measuring radiosensitivity of normal and tumor cells would have significant clinical relevance for the practice of radiation oncology. We hypothesized that radiosensitivity might be predicted by analyzing DNA end-binding complexes (DNA-EBCs), which form at DNA double-strand breaks, the most important cytotoxic lesion caused by radiation. To test this hypothesis, the DNA-EBC pattern of 21 primary human fibroblast cultures and 15 tumor cell lines were studied. DNA-EBC patterns were determined using a modified electrophoretic mobility shift assay and were correlated with radiosensitivity, as measured by SF2. DNA-EBC analysis identified a rapidly migrating ATM-containing band (identified as "band-A") of which the density correlated with SF2 (0.02