The Devil’s Calculus: Mathematical Models of Civil War

In spite of the movement to turn political science into a real science, various mathematical methods that are now the staples of physics, biology, and even economics are thoroughly uncommon in political science, especially the study of civil war. This study seeks to apply such methods - specifically, ordinary differential equations (ODEs) - to model civil war based on what one might dub the capabilities school of thought, which roughly states that civil wars end only when one side’s ability to make war falls far enough to make peace truly attractive. I construct several different ODE-based models and then test them all to see which best predicts the instantaneous capabilities of both sides of the Sri Lankan civil war in the period from 1990 to 1994 given parameters and initial conditions. The model that the tests declare most accurate gives very accurate predictions of state military capabilities and reasonable short term predictions of cumulative deaths. Analysis of the model reveals the scale of the importance of rebel finances to the sustainability of insurgency, most notably that the number of troops required to put down the Tamil Tigers is reduced by nearly a full order of magnitude when Tiger foreign funding is stopped. The study thus demonstrates that accurate foresight may come of relatively simple dynamical models, and implies the great potential of advanced and currently unconventional non-statistical mathematical methods in political science.

Tumor heterogeneity dictates sensitivity to gefitinib (Iressa(TM)/ZD1839) in solid tumor models

The epidermal growth factor receptor (EGFR) and its ligands are overexpressed in many human tumors, including bladder and pancreas, correlating with a more aggressive tumor phenotype and poor patient prognosis. We initiated the present study to characterize the heterogeneity of gefitinib responsiveness in a panel of human bladder and pancreatic cancer cell lines in order to identify the biological characteristics of EGFR-dependent proliferation that could be used to prospectively identify drug-sensitive tumors. A second objective was to elucidate how to best exploit these results by utilizing gefitinib in combination therapy. To these ends, we examined the effects of the EGFR antagonist gefitinib on proliferation and apoptosis in a panel of 18 human bladder cancer cell lines and 9 human pancreatic cancer cell lines. Our data confirmed the existence of marked heterogeneity in Iressa responsiveness with less than half of the cell lines displaying significant growth inhibition by clinically relevant concentrations of the drug. Gefitinib responsiveness was found to be p27 kip1 dependent as DNA synthesis was restored following exposure to p27siRNA. Unfortunately, Iressa responsiveness was not closely linked to surface EGFR or TGF-α expression in the bladder cancer cells, however, cellular TGF-α expression correlated directly with Iressa sensitivity in the pancreatic cancer cell lines. These findings provide the potential for prospectively identifying patients with drug-sensitive tumors. ^ Further studies aimed at exploiting gefitinib-mediated cell cycle effects led us to investigate if gefitinib-mediated TRAIL sensitization correlated with increased p27kip1 accumulation. We observed that increased TRAIL sensitivity following gefitinib exposure was not dependent on p27 kip1 expression. Additional studies initiated to examine the role(s) of Akt and Erk signaling demonstrated that exposure to PI3K or MEK inhibitors significantly enhanced TRAIL-induced apoptosis at concentrations that block target phosphorylation. Furthermore, combinations of TRAIL and the PI3K or MEK inhibitors increased procaspase-8 processing above levels observed with TRAIL alone, indicating that the effects were exerted at the level of caspase-8 activation, considered the earliest step in the TRAIL pathway. ^

Years of potential life lost among Hispanics and non-Hispanics in Bexar County

While numerous studies have found similar mortality rates for Hispanics compared to non-Hispanic whites, surprisingly little is known about years of potential life lost (YPLL) differentials in mortality. The primary purpose of this paper is to quantify the effect that YPLL has on Hispanics in order to determine if YPLL differs between Hispanics and non-Hispanic whites. Using YPLL may bring attention to dissimilarities that are often obscured through traditional measures. Bexar County 2000-2004 data from the Texas Department of State Health Services, Vital Statistics Unit was analyzed for the descriptive analysis and 2003 Bexar County Multiple Cause Death data was analyzed for the regression analysis. The multiple regression models were used to examine Hispanic and non-Hispanic white differences in years of potential life lost (YPLL) before age 75 from all-causes of death. For this analysis, YPLL was regressed on ethnicity, education level and marital status for men and women. The descriptive analysis found YPLL from all-causes was greater among non-Hispanic whites than Hispanics. However, the regression analysis found Hispanics lost more year of potential from all-causes of death compared to non-Hispanic whites. This indicates that the effect of ethnicity on YPLL differs for different methods of analysis. Future research efforts should keep in mind the method of analysis when using YPLL. Understanding differences in mortality among Hispanics and non-Hispanic whites is important for targeting future health policies and research to aid in eliminating Hispanic health disparities. ^

Assessing the improved discriminatory power of a new biomarker in prognostic models

Although the area under the receiver operating characteristic (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new biomarker in the model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this dissertation, we have extended the NRI and IDI to survival analysis settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies were conducted to compare the performance of the time-dependent NRI and IDI with Pencina’s NRI and IDI. For illustration, we have applied the proposed method to a breast cancer study.^ Key words: Prognostic model, Discrimination, Time-dependent NRI and IDI ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Comparing the quality of three models of postabortion care in public hospitals in Mexico City

artículo publicado en la revista Int Fam Plan Perspect. 2003 Sep;29(3):112-20

Gravity potential spherical harmonic series

Time variable gravity fields, reflecting variations of mass distribution in the system Earth is one of the key parameters to understand the changing Earth. Mass variations are caused either by redistribution of mass in, on or above the Earth's surface or by geophysical processes in the Earth's interior. The first set of observations of monthly variations of the Earth gravity field was provided by the US/German GRACE satellite mission beginning in 2002. This mission is still providing valuable information to the science community. However, as GRACE has outlived its expected lifetime, the geoscience community is currently seeking successor missions in order to maintain the long time series of climate change that was begun by GRACE. Several studies on science requirements and technical feasibility have been conducted in the recent years. These studies required a realistic model of the time variable gravity field in order to perform simulation studies on sensitivity of satellites and their instrumentation. This was the primary reason for the European Space Agency (ESA) to initiate a study on ''Monitoring and Modelling individual Sources of Mass Distribution and Transport in the Earth System by Means of Satellites''. The goal of this interdisciplinary study was to create as realistic as possible simulated time variable gravity fields based on coupled geophysical models, which could be used in the simulation processes in a controlled environment. For this purpose global atmosphere, ocean, continental hydrology and ice models were used. The coupling was performed by using consistent forcing throughout the models and by including water flow between the different domains of the Earth system. In addition gravity field changes due to solid Earth processes like continuous glacial isostatic adjustment (GIA) and a sudden earthquake with co-seismic and post-seismic signals were modelled. All individual model results were combined and converted to gravity field spherical harmonic series, which is the quantity commonly used to describe the Earth's global gravity field. The result of this study is a twelve-year time-series of 6-hourly time variable gravity field spherical harmonics up to degree and order 180 corresponding to a global spatial resolution of 1 degree in latitude and longitude. In this paper, we outline the input data sets and the process of combining these data sets into a coherent model of temporal gravity field changes. The resulting time series was used in some follow-on studies and is available to anybody interested.

(Table 1) Stable carbon and nitrogen isotopic ratios of potential prey species of bearded seals (Erignathus barbatus), Svalbard

Changing patterns of sea-ice distribution and extent have measurable effects on polar marine systems. Beyond the obvious impacts of key-habitat loss, it is unclear how such changes will influence ice-associated marine mammals in part because of the logistical difficulties of studying foraging behaviour or other aspects of the ecology of large, mobile animals at sea during the polar winter. This study investigated the diet of pregnant bearded seals (Erignathus barbatus) during three spring breeding periods (2005, 2006 and 2007) with markedly contrasting ice conditions in Svalbard using stable isotopes (d13C and d15N) measured in whiskers collected from their newborn pups. The d15N values in the whiskers of individual seals ranged from 11.95 to 17.45 per mil, spanning almost 2 full trophic levels. Some seals were clearly dietary specialists, despite the species being characterised overall as a generalist predator. This may buffer bearded seal populations from the changes in prey distributions lower in the marine food web which seems to accompany continued changes in temperature and ice cover. Comparisons with isotopic signatures of known prey, suggested that benthic gastropods and decapods were the most common prey. Bayesian isotopic mixing models indicated that diet varied considerably among years. In the year with most fast-ice (2005), the seals had the greatest proportion of pelagic fish and lowest benthic invertebrate content, and during the year with the least ice (2006), the seals ate more benthic invertebrates and less pelagic fish. This suggests that the seals fed further offshore in years with greater ice cover, but moved in to the fjords when ice-cover was minimal, giving them access to different types of prey. Long-term trends of sea ice decline, earlier ice melt, and increased water temperatures in the Arctic are likely to have ecosystem-wide effects, including impacts on the forage bases of pagophilic seals.

Three species distribution models for the marine diatom Fragilariopsis kerguelensis of the Southern Ocean, links to NetCDF files

Fragilariopsis kerguelensis, a dominant diatom species throughout the Antarctic Circumpolar Current, is coined to be one of the main drivers of the biological silicate pump. Here, we study the distribution of this important species and expected consequences of climate change upon it, using correlative species distribution modeling and publicly available presence-only data. As experience with SDM is scarce for marine phytoplankton, this also serves as a pilot study for this organism group. We used the maximum entropy method to calculate distribution models for the diatom F. kerguelensis based on yearly and monthly environmental data (sea surface temperature, salinity, nitrate and silicate concentrations). Observation data were harvested from GBIF and the Global Diatom Database, and for further analyses also from the Hustedt Diatom Collection (BRM). The models were projected on current yearly and seasonal environmental data to study current distribution and its seasonality. Furthermore, we projected the seasonal model on future environmental data obtained from climate models for the year 2100. Projected on current yearly averaged environmental data, all models showed similar distribution patterns for F. kerguelensis. The monthly model showed seasonality, for example, a shift of the southern distribution boundary toward the north in the winter. Projections on future scenarios resulted in a moderately to negligibly shrinking distribution area and a change in seasonality. We found a substantial bias in the publicly available observation datasets, which could be reduced by additional observation records we obtained from the Hustedt Diatom Collection. Present-day distribution patterns inferred from the models coincided well with background knowledge and previous reports about F. kerguelensis distribution, showing that maximum entropy-based distribution models are suitable to map distribution patterns for oceanic planktonic organisms. Our scenario projections indicate moderate effects of climate change upon the biogeography of F. kerguelensis.

Potential impact of DOM accumulation on fCO2 and carbonate ion computations in ocean acidification experiments

Culture and mesocosm experiments are often carried out under high initial nutrient concentrations, yielding high biomass concentrations that in turn often lead to a substantial build-up of DOM. In such experiments, DOM can reach concentrations much higher than typically observed in the open ocean. To the extent that DOM includes organic acids and bases, it will contribute to the alkalinity of the seawater contained in the experimental device. Our analysis suggests that whenever substantial amounts of DOM are produced during the experiment, standard computer programmes used to compute CO2 fugacity can underestimate true fCO2 significantly when the computation is based on AT and CT. Unless the effect of DOM-alkalinity can be accounted for, this might lead to significant errors in the interpretation of the system under consideration with respect to the experimentally applied CO2 perturbation. Errors in the inferred fCO2 can misguide the development of parameterisations used in simulations with global carbon cycle models in future CO2-scenarios. Over determination of the CO2-system in experimental ocean acidification studies is proposed to safeguard against possibly large errors in estimated fCO2.

Age models, ostracods and trace elements of five gravity cores from the NW Black Sea

New Mg/Ca, Sr/Ca, and published stable oxygen isotope and 87Sr/86Sr data obtained on ostracods from gravity cores located on the northwestern Black Sea slope were used to infer changes in the Black Sea hydrology and water chemistry for the period between 30 to 8 ka B.P. (calibrated radiocarbon years). The period prior to 16.5 ka B.P. was characterized by stable conditions in all records until a distinct drop in d18O values combined with a sharp increase in 87Sr/86Sr occurred between 16.5 and 14.8 ka B.P. This event is attributed to an increased runoff from the northern drainage area of the Black Sea between Heinrich Event 1 and the onset of the Bølling warm period. While the Mg/Ca and Sr/Ca records remained rather unaffected by this inflow; they show an abrupt rise with the onset of the Bølling/Allerød warm period. This rise was caused by calcite precipitation in the surface water, which led to a sudden increase of the Sr/Ca and Mg/Ca ratios of the Black Sea water. The stable oxygen isotopes also start to increase around 15 ka B.P., although in a more gradual manner, due to isotopically enriched meteoric precipitation. While Sr/Ca remains constant during the following interval of the Younger Dryas cold period, a decrease in the Mg/Ca ratio implies that the intermediate water masses of the Black Sea temporarily cooled by 1-2°C during the Younger Dryas. The 87Sr/86Sr values drop after the cessation of the water inflow at 15 ka B.P. to a lower level until the Younger Dryas, where they reach values similar to those observed during the Last Glacial Maximum. This might point to a potential outflow to the Mediterranean Sea via the Sea of Marmara during this period. The inflow of Mediterranean water started around 9.3 ka B.P., which is clearly detectable in the abruptly increasing Mg/Ca, Sr/Ca, and 87Sr/86Sr values. The accompanying increase in the d18O record is less pronounced and would fit to an inflow lasting ~100 a.

Geochemistry and kinetic models od Cretaceous samples

The source rock potential of Cretaceous organic rich whole rock samples from deep sea drilling project (DSDP) wells offshore southwestern Africa was investigated using bulk and quantitative pyrolysis techniques. The sample material was taken from organic rich intervals of Aptian, Albian and Turonian aged core samples from DSDP site 364 offshore Angola, DSDP well 530A north of the Walvis Ridge offshore Namibia, and DSDP well 361 offshore South Africa. The analytical program included TOC, Rock-Eval, pyrolysis GC, bulk kinetics and micro-scale sealed vessel pyrolysis (MSSV) experiments. The results were used to determine differences in the source rock petroleum type organofacies, petroleum composition, gas/oil ratio (GOR) and pressure-volume-temperature (PVT) behavior of hydrocarbons generated from these black shales for petroleum system modeling purposes. The investigated Aptian and Albian organic rich shales proved to contain excellent quality marine kerogens. The highest source rock potential was identified in sapropelic shales in DSDP well 364, containing very homogeneous Type II and organic sulfur rich Type IIS kerogen. They generate P-N-A low wax oils and low GOR sulfur rich oils, whereas Type III kerogen rich silty sandstones of DSDP well 361 show a potential for gas/condensate generation. Bulk kinetic experiments on these samples indicate that the organic sulfur contents influence kerogen transformation rates, Type IIS kerogen being the least stable. South of the Walvis Ridge, the Turonian contains predominantly a Type III kerogen. North of the Walvis Ridge, the Turonian black shales contain Type II kerogen and have the potential to generate P-N-A low and high wax oils, the latter with a high GOR at high maturity. Our results provide the first compositional kinetic description of Cretaceous organic rich black shales, and demonstrate the excellent source rock potential, especially of the Aptian-aged source rock, that has been recognized in a number of the South Atlantic offshore basins.

Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models

BACKGROUND Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

Using ecological niche models to support tree species selection for forest restoration planning in largely deforested regions

Species selection for forest restoration is often supported by expert knowledge on local distribution patterns of native tree species. This approach is not applicable to largely deforested regions unless enough data on pre-human tree species distribution is available. In such regions, ecological niche models may provide essential information to support species selection in the framework of forest restoration planning. In this study we used ecological niche models to predict habitat suitability for native tree species in "Tierra de Campos" region, an almost totally deforested area of the Duero Basin (Spain). Previously available models provide habitat suitability predictions for dominant native tree species, but including non-dominant tree species in the forest restoration planning may be desirable to promote biodiversity, specially in largely deforested areas were near seed sources are not expected. We used the Forest Map of Spain as species occurrence data source to maximize the number of modeled tree species. Penalized logistic regression was used to train models using climate and lithological predictors. Using model predictions a set of tools were developed to support species selection in forest restoration planning. Model predictions were used to build ordered lists of suitable species for each cell of the study area. The suitable species lists were summarized drawing maps that showed the two most suitable species for each cell. Additionally, potential distribution maps of the suitable species for the study area were drawn. For a scenario with two dominant species, the models predicted a mixed forest (Quercus ilex and a coniferous tree species) for almost one half of the study area. According to the models, 22 non-dominant native tree species are suitable for the study area, with up to six suitable species per cell. The model predictions pointed to Crataegus monogyna, Juniperus communis, J.oxycedrus and J.phoenicea as the most suitable non-dominant native tree species in the study area. Our results encourage further use of ecological niche models for forest restoration planning in largely deforested regions.

Potential to attract drivers out of their cars in dense urban areas.

Purpose Sustainable mobility urban policies intend reducing car use and increasing walking, cycling and public transport. However, this transfer from private car to these more sustainable modes is only a real alternative where distances are small and the public transport supply competitive enough. This paper proposes a methodology to calculate the number of trips that can be transferred from private car to other modes in city centres. Method The method starts analyzing which kind of trips cannot change its mode (purposes, conditions, safety , etc.), and then setting a process to determine under which conditions trips made by car between given O-D pairs can be transferable. Then, the application of demand models allow to determine which trips fulfil the transferability conditions. The process test the possibility of transfer in a sequential way: firs to walking, then cycling and finally to public transport. Results The methodology is tested through its application to the city of Madrid (Spain), with the result of only some 18% of the trips currently made by car could be made by other modes, under the same conditions of trip time, and without affecting their characteristics. Out of these trips, 75% could be made by public transport, 15% cycling and 10% on foot. The possible mode to be transferred depends on the location: city centre areas are more favourable for walking and cycling while city skirts could attract more PT trips. Conclusions The proposed method has demonstrated its validity to determine the potential of transferring trips out of cars to more sustainable modes. Al the same time it is clear that, even in areas with favourable conditions for walking, cycling and PT trips, the potential of transfer is limited because cars fulfil more properly special requirements of some trips and tours.